Soluble Fas ligand (sFasL, sCD95L) and its specific soluble binders, soluble Fas receptor (sFas, sCD95) and decoy receptor 3 (DcR3), have been investigated as possible clinical biomarkers in many serious diseases. The present review aimed to provide an overview of the current state of this medically promising research by extensively examining the relevant literature. The summarized results of the survey are presented after classification into six categories according to the type of targeted disease. To date, the studies have been mainly devoted to the diagnosis of disease severity states and prognosis of treatments about various types of cancers and autoimmune diseases represented by autoimmune lymphoproliferative syndrome and systemic lupus erythematosus, because these important life-threatening or intractable diseases were suggested to be most relevant to the impairment of apoptotic cell death-inducing systems, including the Fas receptor-mediated signaling system, and the mechanisms responsible for their onset. However, various more general inflammation-related diseases, including, but not limited to, other autoimmune and allergic diseases (e.g., rheumatoid arthritis and atopic asthma), infectious diseases (e.g., sepsis and chronic hepatitis), cardiovascular system-specific disorders (e.g., acute coronary syndromes and heart failure) as well as other diseases specific to the renal, hepatic, and respiratory systems, etc., have also been targeted as important fields of research. The data obtained so far demonstrated that sFas, sFasL, and DcR3 possess significant potential in the assessment of various disease states, which can contribute to the development of therapeutic interventions. Although further studies in various relevant fields are essential, it is expected that clinical translation of sFas, sFasL, and DcR3 into practical biomarkers will contribute to effective treatments of a wide variety of diseases.
Citation: Michiro Muraki. Soluble Fas ligand, soluble Fas receptor, and decoy receptor 3 as disease biomarkers for clinical applications: A review[J]. AIMS Medical Science, 2022, 9(2): 98-267. doi: 10.3934/medsci.2022009
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Abstract
Soluble Fas ligand (sFasL, sCD95L) and its specific soluble binders, soluble Fas receptor (sFas, sCD95) and decoy receptor 3 (DcR3), have been investigated as possible clinical biomarkers in many serious diseases. The present review aimed to provide an overview of the current state of this medically promising research by extensively examining the relevant literature. The summarized results of the survey are presented after classification into six categories according to the type of targeted disease. To date, the studies have been mainly devoted to the diagnosis of disease severity states and prognosis of treatments about various types of cancers and autoimmune diseases represented by autoimmune lymphoproliferative syndrome and systemic lupus erythematosus, because these important life-threatening or intractable diseases were suggested to be most relevant to the impairment of apoptotic cell death-inducing systems, including the Fas receptor-mediated signaling system, and the mechanisms responsible for their onset. However, various more general inflammation-related diseases, including, but not limited to, other autoimmune and allergic diseases (e.g., rheumatoid arthritis and atopic asthma), infectious diseases (e.g., sepsis and chronic hepatitis), cardiovascular system-specific disorders (e.g., acute coronary syndromes and heart failure) as well as other diseases specific to the renal, hepatic, and respiratory systems, etc., have also been targeted as important fields of research. The data obtained so far demonstrated that sFas, sFasL, and DcR3 possess significant potential in the assessment of various disease states, which can contribute to the development of therapeutic interventions. Although further studies in various relevant fields are essential, it is expected that clinical translation of sFas, sFasL, and DcR3 into practical biomarkers will contribute to effective treatments of a wide variety of diseases.
In meteorology, wind speed is a fundamental atmospheric quantity caused by the movement of air from high to low pressure, usually due to temperature variations. Alongside wind speed, wind direction plays a pivotal role in the analysis and prediction of weather patterns and the global climate. Wind speed and direction significantly affect factors such as evaporation rates, sea surface turbulence, and the formation of oceanic waves and storms. Moreover, these factors have substantial impacts on water quality, water levels, and various fields, including weather forecasting, climatology, renewable energy, environmental monitoring, aviation, and agriculture. Therefore, a comprehensive understanding of wind speed is important for managing potential impacts. Additionally, using wind speed data for analysis can help researchers and experts understand and address issues related to wind speed across various areas. For wind speed data, a normal distribution may not be appropriate, even though the normal distribution is one of the most widely used statistical distributions. If the data exhibits skewness, it is advisable to consider alternative distributions. Many new distributions have been developed using certain transformations from the normal distribution. One such distribution is the Birnbaum-Saunders distribution. Importantly, Mohammadi, Alavi, and McGowan [1] investigated the application of the two-parameter Birnbaum-Saunders distribution for analyzing wind speed and wind energy density at ten different stations in Canada. Their results demonstrated that the Birnbaum-Saunders distribution was especially effective at all the chosen locations. The Birnbaum-Saunders distribution was introduced by Birnbaum and Saunders [2] for the purpose of modeling the fatigue life of metals subjected to periodic stress. As a result, this distribution is sometimes referred to as the fatigue life distribution. The Birnbaum-Saunders distribution has been applied in various contexts, such as engineering, testing, medical sciences, and environmental studies. It is well known that the Birnbaum-Saunders distribution is a positive skewed one. However, some data to be analyzed may have both positive and zero values. Therefore, if zero observations follow a binomial distribution combined with the Birnbaum-Saunders distribution, the resulting distribution is the zero-inflated Birnbaum-Saunders (ZIBS) distribution, which is a new and interesting distribution. This ZIBS distribution was inspired by Aitchison [3], and several researchers have studied the combination of zero observations with other distributions to form new distributions, such as the zero-inflated lognormal distribution [4], the zero-inflated gamma distribution [5], and the zero-inflated two-parameter exponential distribution [6].
The coefficient of variation (CV) of wind speed is important for several reasons. Since the CV measures the dispersion of data relative to the mean, it is expressed as the ratio of the standard deviation to the mean. The CV assesses the variability of a dataset, regardless of the unit of measurement. Additionally, using the CV to evaluate wind speed is beneficial in various contexts. For instance, calculating the CV helps in understanding how much wind speed fluctuates compared to its average. If the CV is high, it indicates that the wind speed is highly variable, making it more difficult to predict wind conditions. In the context of wind energy, the CV can help assess the reliability of energy sources. If wind speed variability is high, it may result in inconsistent energy production, which could affect the stability of energy output from wind farms. Additionally, the coefficient of variation has been used in many fields, including life insurance, science, economics, and medicine. Importantly, many researchers have constructed confidence intervals (CIs) for the coefficient of variation, which have been applied to various distributions. For example, Vangel [7] constructed the CIs for a normal distribution coefficient of variation. Buntao and Niwitpong [8] introduced the CIs for the coefficient of variation of zero-inflated lognormal and lognormal distributions. D'Cunha and Rao [9] proposed the Bayes estimator and created CIs for the coefficient of variation of the lognormal distribution. Sangnawakij and Niwitpong [10] developed CIs for coefficients of variation in two-parameter exponential distributions. Janthasuwan, Niwitpong, and Niwitpong [11] established CIs for the coefficients of variation in the zero-inflated Birnbaum-Saunders distribution.
In the analysis and comparison of wind variability across multiple weather stations or wind directions, without needing to account for the differences in average wind speed at each station or direction, it is necessary to use the common CV. The common CV provides a single indicator representing the overall variability of wind speed, which is crucial when planning wind energy projects, designing wind turbines, or calculating the power production of wind farms that require knowledge of wind stability across different areas. Additionally, the common CV is useful in meteorological and climatological research, as it allows for the analysis of wind variability across multiple regions simultaneously. It can also assist in examining the relationship between wind variability and long-term climate changes or recurring events, such as storms or shifts in wind patterns. Therefore, the common coefficient of variation is a crucial aspect when making inferences for more than one population. This holds particularly true when collecting independent samples from various situations. Consequently, numerous researchers have investigated methods for computing the common coefficient of variation in several populations from variety distributions. For instance, Tian [12] made inferences about the coefficient of variation of a common population within a normal distribution. Then, Forkman [13] studied methods for constructing CIs and statistical tests based on McKay's approximation for the common coefficient of variation in several populations with normal distributions. Sangnawakij and Niwitpong [14] proposed the method of variance of estimate recovery to construct CIs for the common coefficient of variation for several gamma distributions. Next, Singh et al. [15] used several inverse Gaussian populations to estimate the common coefficient of variation, test the homogeneity of the coefficient of variation, and test for a specified value of the common coefficient of variation. After that, Yosboonruang, Niwitpong, and Niwitpong [16] presented methods to construct CIs for the common coefficient of variation of zero-inflated lognormal distributions, employing the method of variance estimate recovery, equal-tailed Bayesian intervals, and the fiducial generalized confidence interval. Finally, Puggard, Niwitpong, and Niwitpong [17] introduced Bayesian credible intervals, highest posterior density intervals, the method of variance estimate recovery, generalized confidence intervals, and large-sample methods to construct confidence intervals for the common coefficient of variation in several Birnbaum-Saunders distributions. Previous research has shown that no studies have investigated the estimation of the common coefficient of variation in the context of several ZIBS distributions. Therefore, the primary objective of this article is to determine the CIs for the common coefficient of variation of several ZIBS distributions. The article presents five distinct methods: the generalized confidence interval, the method of variance estimates recovery, the large sample approximation, the bootstrap confidence interval, and the fiducial generalized confidence interval.
2.
Materials and methods
Let Yij,i=1,2,…,k and j=1,2,…,mi be a random sample drawn from the ZIBS distributions. The density function of Yij is given by
where ϑi,αi, and βi are the proportion of zero, shape, and scale parameters, respectively. I is an indicator function, with I0[yij]={1;yij=0,0;otherwise, and I(0,∞)[yij]={0;yij=0,1;yij>0. This distribution is a combination of Birnbaum-Saunders and binomial distributions. Suppose that mi=mi(1)+mi(0) is the sample size, where mi(1) and mi(0) are the numbers of positive and zero values, respectively. For the expected value and variance of Yij, we have applied the concepts from Aitchison [3], which can be expressed as follows:
respectively. Hence, the coefficient of variation of Yij is defined as
θ=12+α2i√α2i(4+5α2i)+ϑi(2+α2i)21−ϑi.
The asymptotic distribution of ˆϑi is calculated by using the delta method, which is given by √mi(ˆϑi−ϑi)∼N(0,ϑi(1−ϑi)), where ˆϑi=mi(0)/mi. According to Ng, Kundu, and Balakrishnan [18], the asymptotic joint distribution of ˆαi and ˆβi is obtained as
where ˆαi={2[(−yi∑mi(1)j=1y−1ijmi(1))12−1]}12, ˆβi={−yi(∑mi(1)j=1y−1ijmi(1))−1}12, and −yi=∑mi(1)j=1yijmi(1). The estimator of θi is given by
ˆθi=12+ˆα2i√ˆα2i(4+5ˆα2i)+ˆϑi(2+ˆα2i)21−ˆϑi.
(1)
According to Janthasuwan, Niwitpong, and Niwitpong [11], the asymptotic variance of ˆθi, derived using the Taylor series in the delta method, is given by
where Ψi=(2+α2i)2(1−ϑi)[α2i(4+5α2i)+ϑi(2+α2i)2]. According to Graybill and Deal [19], the common CV of several ZIBS distributions can be written as
ˆθ=∑ki=1ˆθi/ˆV(ˆθi)∑ki=11/ˆV(ˆθi),
(3)
where ˆV(ˆθi) denotes the estimator of V(ˆθi), which is defined in Eq (2) with αi and ϑi replaced by ˆαi and ˆϑi, respectively. This can be expressed as follows:
where ˆΨi=(2+ˆα2i)2(1−ˆϑi)[ˆα2i(4+5ˆα2i)+ˆϑi(2+ˆα2i)2].
The following subsection provides detailed explanations of the methods employed for constructing confidence intervals.
2.1. Generalized confidence interval
Weerahandi [20] recommended the generalized confidence interval (GCI) method for constructing confidence intervals, which is based on the concept of a generalized pivotal quantity (GPQ). To construct the confidence interval for θ using the GCI, we get the generalized pivotal quantities for the parameters βi, αi, and ϑi. Sun [21] introduced the GPQ for the scale parameter βi, which can be derived as
Gβi(yij;Λi)={max(βi1,βi2);Λi≤0;min(βi1,βi2);Λi>0,
(4)
where Λi follows the t-distribution with mi(1)−1 degrees of freedom. βi1 and βi2 are the two solutions of the following quadratic equation:
Ω1βi2−2Ω2βi+(mi(1)−1)Ci2−1mi(1)DiΛ2i=0,
where Ω1=(mi(1)−1)A2i−1mi(1)BiΛ2i, Ω2=(mi(1)−1)AiCi−(1−AiCi)Λ2i, Ai=1mi(1)∑mi(1)j=11√Yij, Bi=∑mi(1)j=1(1√Yij−Ai)2, Ci=1mi(1)∑mi(1)j=1√Yij, and Di=∑mi(1)j=1(√Yij−Ci)2. Next, considering the GPQ for the shape parameter αi as proposed by Wang [22], the GPQ for αi is derived as
where Ei1=∑mi(1)j=1Yij, Ei2=∑mi(1)j=11Yij, and Ki follows the chi-squared distribution with mi(1) degrees of freedom. Subsequently, the GPQ for the proportion of zero ϑi was recommended by Wu and Hsieh [23], who proposed using the GPQ based on the variance stabilized transformation to construct confidence intervals. Therefore, the GPQ for ϑi is defined as
Gϑi=sin2[arcsin√ˆϑi−Wi2√mi],
(6)
where Wi=2√mi(arcsin√ˆϑi−arcsin√ϑi)∼N(0,1). Now, we can calculate the GPQs for θi and the variance of ˆθi using Eqs (5) and (6), resulting in
where GΨi=(2+G2αi)2(1−Gϑi)[G2αi(4+5G2αi)+Gϑi(2+G2αi)2]. Therefore, the GPQ for θi is the weighted average of the GPQ Gθi based on k individual samples, given by
Gθ=∑ki=1Gθi/GV(ˆθi)∑ki=11/GV(ˆθi).
(9)
Then, the (1−ρ)100% CI for the common CV of several ZIBS distributions employing the GCI method is given by
[LGCI,UGCI]=[Gθ(ρ/2),Gθ(1−ρ/2)],
(10)
where Gθ(ρ/2) and Gθ(1−ρ/2) denote the 100(ρ/2)th and 100(1−ρ/2)th percentiles of Gθ, respectively.
Algorithm 1 is used to construct the GCI for the common coefficient of variation of several ZIBS distributions.
Algorithm 1.
For g=1 to n, where n is the number of generalized computations:
1) Compute Ai,Bi,Ci,Di,Ei1, and Ei2.
2) At the p step:
a) Generate Λi∼t(mi(1)−1), and then compute Gβi(yij;Λi) from Eq (4);
b) If Gβi(yij;Λi)<0, regenerate Λi∼t(mi(1)−1);
c) Generate Ki∼χ2mi(1), and then compute Gαi(yij;Ki,Λi) from Eq (5);
d) Compute Gϑi, Gθi, and GV(ˆθi) from Eqs (6)–(8), respectively;
e) Compute Gθ from Eq (9).
End g loop.
3) Repeat step 2, a total of G times;
4) Compute LGCI and UGCI from Eq (10).
2.2. Method of variance estimates recovery
The method of variance estimates recovery (MOVER) estimates a closed-form confidence interval. Let ˆωi be an unbiased estimator of ωi. Furthermore, let [li,ui] represent the (1−ρ)100% confidence interval for ωi,i=1,2,...,k. Assume that ∑ki=1ciωi is a linear combination of the parameters ωi, where ci are constants. According to Zou, Huang, and Zhang [24], the lower and upper limits of the confidence interval for ∑ki=1ciωi are defined by
L=k∑i=1ciˆωi−√k∑i=1[ciˆωi−min(cili,ciui)]2
and
U=∑ki=1ciˆωi+√∑ki=1[ciˆωi−max(cili,ciui)]2.
Considering Eq (7), the (1−ρ)100% CI for θi based on the GPQs has become
[li,ui]=[Gθi(ρ/2),Gθi(1−ρ/2)],
(11)
where Gθi(ρ/2) and Gθi(1−ρ/2) represent the 100(ρ/2)th and 100(1−ρ/2)th percentiles of Gθi, respectively. Hence, the (1−ρ)100% CI for the common CV of several ZIBS distributions employing the MOVER method is given by
where ˆΨi=(2+ˆα2i)2(1−ˆϑi)[ˆα2i(4+5ˆα2i)+ˆϑi(2+ˆα2i)2]. The large sample (LS) estimate of the CV for the ZIBS distribution is a pooled estimate, as described in Eq (3). Accordingly, the (1−ρ)100% CI for the common CV of several ZIBS distributions employing the LS method is as follows:
[LLS,ULS]=[ˆθ−z1−ρ2√1∑ki=1ηi,ˆθ+z1−ρ2√1∑ki=1ηi],
(14)
where ηi=1ˆV(ˆθi).
Algorithm 3 is used to construct the LS for the common coefficient of variation of several ZIBS distributions.
Algorithm 3.
1) Compute ˆαi and ˆϑi;
2) Compute ˆθi and ˆV(ˆθi);
3) Compute LLS and ULS from Eq (14).
2.4. Bootstrap confidence interval
Efron [25] introduced the bootstrap method, which involves repeated resampling of existing data. According to Lemonte, Simas, and Cribari-Neto [26], the constant-bias-correcting parametric bootstrap is the most efficient method for reducing bias. As a result, we used it to estimate the confidence interval for θ. Assuming that there are D bootstrap samples available, the ˆαi series for those samples can be computed, which is shown as ˆα#i1,ˆα#i2,...,ˆα#iD. Here, ˆα#ir is a sequence of the bootstrap maximum likelihood estimation (MLE) of αir for i=1,2,...,k and r=1,2,...,D. The MLE of αir can be calculated using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton nonlinear optimization algorithm. The bias of the estimator αi is defined as
D(ˆαi,αi)=E(ˆαi)−αi,
and then the bootstrap expectation E(ˆαi) could be approximated using the mean ˆα/i=1D∑Dr=1ˆα#ir. As a result, the bootstrap bias estimate for D replications of ˆαi is derived as ˆD(ˆαi,αi)=ˆα/i−ˆαi. According to Mackinnon and Smith [27], the corrected estimate for ˆα#i is obtained by applying the bootstrap bias estimate, which is
ˆα∗i=ˆα#i−2ˆD(ˆαi,αi).
(15)
Let ˆϑ#i be observed values of ˆϑi based on bootstrap samples. In accordance with Brown, Cai, and DasGupta [28], the bootstrap estimator of ϑi is given by
ˆϑ∗i∼beta(miˆϑ#i+12,mi(1−ˆϑ#i)+12).
(16)
By using Eqs (15) and (16), the bootstrap estimators of θi and the variance of ˆθi can be written as
where ˆΨ∗i=(2+(ˆα∗i)2)2(1−ˆϑ∗i)[(ˆα∗i)2(4+5(ˆα∗i)2)+ˆϑ∗i(2+(ˆα∗i)2)2]. Now, the common θ based on k individual sample is obtained by
ˆθ∗=∑ki=1ˆθ∗i/ˆV∗(ˆθi)∑ki=11/ˆV∗(ˆθi).
(19)
Consequently, the (1−ρ)100% CI for the common CV of several ZIBS distributions employing the bootstrap confidence interval (BCI) method is provided by
[LBCI,UBCI]=[ˆθ∗(ρ/2),ˆθ∗(1−ρ/2)],
(20)
where ˆθ∗(ρ/2) and ˆθ∗(1−ρ/2) denote the 100(ρ/2)th and 100(1−ρ/2)th percentiles of ˆθ∗, respectively.
Algorithm 4 is used to construct the BCI for the common coefficient of variation of several ZIBS distributions.
Algorithm 4.
For b=1 to n :
1) At the q step:
Fa) Generate y∗ij, with replacement from yij where i=1,2,...,k and j=1,2,...,mi;
b) Compute ˆα/i and ˆD(ˆαi,αi);
c) Compute ˆα∗i from Eq (15);
d) Generate ˆϑ∗i from Eq (16);
e) Compute ˆθ∗i from Eq (17);
f) Compute ˆV∗(ˆθi) from Eq (18);
g) Compute ˆθ∗ from Eq (19).
End b loop.
2) Repeat step 1, a total of B times;
3) Compute LBCI and UBCI from Eq (20).
2.5. Fiducial generalized confidence interval
Hannig [29] and Hannig [30] introduced the concept of the generalized fiducial distribution by assuming a functional relationship Rj=Qj(δ,U) for j=1,2,...,m, where Q=(Q1,...,Qm) are the structural equations. Then, assume that U=(U1,...,Um) are independent and identically distributed samples from a uniform distribution U(0,1) and that the parameter δ∈Ξ⊆Rp is p -dimensional. Consequently, the generalized fiducial distribution is absolutely continuous with a density
ψ(δ)=J(r,δ)L(r,δ)∫ΞJ(r,δ')L(r,δ')dδ',
(21)
where L(r,δ) represents the joint likelihood function of the observed data and
where ddrQ−1(r,δ) and ddδQ−1(r,δ) are m×p and m×m Jacobian matrices, respectively. In addition, Hannig [29] deduced that if the sample r was independently and identically distributed from an absolutely continuous distribution with cumulative distribution function Fδ(r), then Q−1=(Fδ(R1),...,Fδ(Rm)). Let Zij,i=1,2,...,k,j=1,2,...,mi(1), be a random sample drawn from the Birnbaum-Saunders distribution. The likelihood function can be written as
as obtained by Li and Xu [31]. Let α#i and β#i be the generalized fiducial samples for αi and βi, respectively. According to Li and Xu [31], the adaptive rejection Metropolis sampling (ARMS) method was used to obtain the fiducial estimates of αi and βi from the generalized fiducial distribution. Thus, the calculation of α#i and β#i can be implemented using the function arms in the package dlm of R software. Additionally, Hannig [29] recommended methods for estimating the fiducial generalized pivotal quantities for binomial proportion ϑi, with simulation results indicating that the best option is the mixture distribution of two beta distributions with weight ½, which is
ϑ#i∼12beta(mi(0),mi(1)+1)+12beta(mi(0)+1,mi(1)).
(22)
Currently, the approximate fiducial generalized pivotal quantities for θi and the variance of ˆθi can be computed by
where Ψ#i=(2+(α#i)2)2(1−ϑ#i)[(α#i)2(4+5(α#i)2)+ϑ#i(2+(α#i)2)2]. As a result, the common θ based on k individual samples is calculated as
θ#=∑ki=1θ#i/V#(ˆθi)∑ki=11/V#(ˆθi).
(25)
The (1−ρ)100% confidence interval for the common CV of several ZIBS distributions employing the fiducial generalized confidence interval (FGCI) method is obtained by
[LFGCI,UFGCI]=[θ#(ρ/2),θ#(1−ρ/2)],
(26)
where θ#(ρ/2) and θ#(1−ρ/2) denote the 100(ρ/2)th and 100(1−ρ/2)th percentiles of θ#, respectively.
The algorithm 5 is used to construct the FGCI for the common coefficient of variation of several ZIBS distributions.
Algorithm 5.
For g=1 to n :
1) Generate G samples of αi and βi by using the arms function in the dlm package of R software;
2) Burn-in F samples (the number of remaining samples is G−F);
3) Thin the samples by applying sampling lag L>1, and the final number of samples is G'=(G−F)/L. Because the generated samples are not independent, we must reduce the autocorrelation by thinning them;
4) Generate ϑ#i from Eq (22);
5) Compute θ#i and V#(ˆθi) from Eqs (23) and (24), respectively;
6) Compute θ# from Eq (25);
End g loop.
7) Repeat steps 1–6, a total of G times;
8) Compute LFGCI and UFGCI from Eq (26).
3.
Simulation results and discussion
To evaluate the performance of the proposed methods, Monte Carlo simulations in R software were conducted under various scenarios using different sample sizes, proportions of zeros, and shape parameters, as shown in Table 1. The scale parameter was consistently fixed at 1.0 in all scenarios. In generating a simulation, we set the total number of replications to 1000 replicates, 3000 replications for the GCI and FGCI, and 500 replications for the BCI. The performance comparison was based on a coverage probability (CP) greater than or equal to the nominal confidence level of 0.95, as well as the narrowest average width (AW). Algorithm 6 shows the computational steps to estimate the coverage probability and average width performances of all the methods.
The simulation results for k = 3 are shown in Table 2 and Figure 1. The coverage probabilities of the confidence intervals for the GCI method are greater than the nominal confidence level of 0.95 in almost all scenarios, while the coverage probabilities for the MOVER method are close to the specified coverage probability value when proportions of zeros equal 0.13. For the BCI method, they are close to the target, especially when the sample size is large. For the LS and FGCI methods, they provide coverage probability values lower than 0.95 in all scenarios. In terms of average width, the LS and MOVER methods have narrower confidence intervals than other methods in most scenarios. However, the coverage probabilities of both confidence intervals are less than 0.95 in almost all scenarios, so they do not meet the requirements. Among the remaining methods, the GCI method has the shortest average width in all scenarios studied, while the BCI method has the widest.
Table 2.
Performance measures of the 95% confidence intervals for the common CV; k = 3.
Scenarios
Coverage probability
Average width
GCI
MOVER
LS
BCI
FGCI
GCI
MOVER
LS
BCI
FGCI
1
0.960
0.954
0.898
0.943
0.941
0.3124
0.2772
0.2826
0.3127
0.3190
2
0.956
0.931
0.866
0.934
0.937
0.4539
0.3607
0.3706
0.4235
0.4313
3
0.964
0.941
0.857
0.935
0.920
0.4097
0.3738
0.3921
0.4678
0.4459
4
0.975
0.920
0.786
0.923
0.909
0.6208
0.5442
0.5843
0.7380
0.6854
5
0.952
0.952
0.864
0.948
0.931
0.2648
0.2344
0.2354
0.2779
0.2756
6
0.968
0.947
0.818
0.933
0.928
0.4445
0.3103
0.3108
0.4040
0.4133
7
0.968
0.935
0.797
0.953
0.923
0.3517
0.3257
0.3325
0.4519
0.4129
8
0.987
0.908
0.747
0.943
0.910
0.5304
0.4918
0.5044
0.7448
0.6674
9
0.965
0.953
0.865
0.950
0.929
0.2193
0.1965
0.1938
0.2527
0.2407
10
0.967
0.907
0.758
0.943
0.926
0.4259
0.2698
0.2620
0.3955
0.3937
11
0.976
0.922
0.715
0.937
0.901
0.2917
0.2885
0.2770
0.4365
0.3813
12
0.982
0.911
0.682
0.945
0.910
0.4374
0.4473
0.4247
0.7475
0.6405
13
0.960
0.953
0.900
0.942
0.937
0.2055
0.1927
0.1977
0.2176
0.2136
14
0.956
0.926
0.886
0.938
0.917
0.2770
0.2877
0.2998
0.3452
0.3159
15
0.965
0.936
0.863
0.941
0.920
0.2647
0.2590
0.2761
0.3312
0.3012
16
0.965
0.925
0.843
0.941
0.902
0.3964
0.3780
0.4159
0.5280
0.4647
17
0.957
0.944
0.898
0.945
0.934
0.1730
0.1618
0.1647
0.1950
0.1849
18
0.953
0.906
0.811
0.939
0.902
0.2519
0.2507
0.2525
0.3395
0.3026
19
0.978
0.933
0.834
0.946
0.911
0.2259
0.2267
0.2326
0.3188
0.2774
20
0.966
0.916
0.767
0.943
0.892
0.3358
0.3371
0.3515
0.5252
0.4429
21
0.954
0.944
0.859
0.948
0.908
0.1427
0.1356
0.1344
0.1761
0.1609
22
0.956
0.905
0.717
0.956
0.922
0.2403
0.2170
0.2092
0.3444
0.3006
23
0.970
0.918
0.759
0.952
0.893
0.1872
0.1996
0.1926
0.3086
0.2570
24
0.977
0.912
0.696
0.948
0.886
0.2877
0.3083
0.2973
0.5290
0.4305
25
0.962
0.944
0.894
0.946
0.930
0.2247
0.2130
0.2172
0.2394
0.2345
26
0.969
0.960
0.914
0.950
0.941
0.2951
0.2764
0.2841
0.3215
0.3095
27
0.965
0.934
0.844
0.940
0.910
0.2898
0.2888
0.3035
0.3631
0.3295
28
0.963
0.922
0.832
0.930
0.898
0.4228
0.4224
0.4543
0.5779
0.5085
29
0.967
0.954
0.900
0.942
0.923
0.1878
0.1796
0.1804
0.2135
0.2037
30
0.968
0.942
0.838
0.938
0.916
0.2754
0.2367
0.2390
0.3086
0.2920
31
0.964
0.932
0.813
0.940
0.902
0.2438
0.2494
0.2546
0.3483
0.3037
32
0.978
0.901
0.775
0.943
0.892
0.3631
0.3816
0.3885
0.5766
0.4877
33
0.951
0.939
0.842
0.930
0.908
0.1543
0.1498
0.1478
0.1930
0.1768
34
0.969
0.907
0.790
0.953
0.922
0.2610
0.2050
0.1972
0.2997
0.2773
35
0.977
0.919
0.723
0.941
0.882
0.2022
0.2227
0.2128
0.3380
0.2830
36
0.978
0.916
0.772
0.916
0.854
0.3030
0.3462
0.3280
0.5797
0.4705
37
0.953
0.944
0.917
0.945
0.926
0.1508
0.1500
0.1537
0.1685
0.1604
38
0.951
0.955
0.926
0.946
0.916
0.1871
0.1973
0.2013
0.2248
0.2076
39
0.936
0.937
0.893
0.950
0.906
0.1917
0.2062
0.2144
0.2582
0.2271
40
0.921
0.915
0.873
0.943
0.891
0.2691
0.3055
0.3206
0.4083
0.3453
41
0.952
0.939
0.887
0.943
0.928
0.1249
0.1262
0.1270
0.1502
0.1390
42
0.947
0.943
0.876
0.955
0.926
0.1677
0.1701
0.1679
0.2150
0.1936
43
0.948
0.913
0.838
0.957
0.909
0.1601
0.1799
0.1800
0.2476
0.2091
44
0.951
0.899
0.804
0.956
0.894
0.2276
0.2749
0.2708
0.4040
0.3288
45
0.955
0.943
0.879
0.954
0.916
0.1016
0.1053
0.1034
0.1354
0.1205
46
0.954
0.900
0.810
0.959
0.902
0.1540
0.1452
0.1381
0.2101
0.1849
47
0.938
0.909
0.763
0.951
0.880
0.1309
0.1614
0.1486
0.2379
0.1937
48
0.915
0.890
0.727
0.958
0.883
0.1877
0.2503
0.2267
0.4030
0.3163
*Note: Italics indicate the most suitable average width.
Figure 1.
Comparison of the performance of the proposed method for k = 3 in terms of coverage probability with respect to (A) sample size, (B) shape parameter, (C) proportion of zero, and in terms of average width with respect to (D) sample sizes, (E) shape parameter, and (F) proportion of zero (a1 = (303), b1 = (30, 50,100), c1 = (503), d1 = (1003), e1 = (2.03), f1 = (2.53), g1 = (3.03), h1 = (0.13), i1 = (0.1, 0.3, 0.5), j1 = (0.33), k1 = (0.53)).
The simulation results for k = 5 are shown in Table 3 and Figure 2. The coverage probabilities of the LS and BCI methods are close to the nominal confidence level of 0.95 in almost all scenarios. In contrast, the MOVER and FGCI methods have values below the specified target. For the GCI method, the coverage probability meets the target when the proportions of zeros are unequal. In terms of the average width, the confidence interval of the MOVER method is the narrowest. However, this method has a coverage probability lower than 0.95 in all scenarios, thus failing to meet the criteria. The LS and BCI methods have the widest confidence intervals compared to the other methods.
Table 3.
Performance measures of the 95% confidence intervals for the common CV; k = 5.
Scenarios
Coverage probability
Average width
GCI
MOVER
LS
BCI
FGCI
GCI
MOVER
LS
BCI
FGCI
49
0.944
0.933
0.978
0.944
0.938
0.2182
0.1734
0.2820
0.2145
0.2196
50
0.954
0.935
0.942
0.954
0.951
0.2685
0.2293
0.3080
0.2834
0.2831
51
0.937
0.902
0.965
0.944
0.933
0.2862
0.2319
0.3922
0.3217
0.3080
52
0.940
0.893
0.910
0.904
0.893
0.4433
0.3356
0.5854
0.5083
0.4783
53
0.924
0.930
0.977
0.941
0.934
0.1860
0.1490
0.2356
0.1903
0.1904
54
0.951
0.920
0.910
0.946
0.930
0.2355
0.1987
0.2566
0.2657
0.2582
55
0.915
0.905
0.927
0.941
0.912
0.2485
0.2079
0.3326
0.3074
0.2841
56
0.910
0.882
0.895
0.934
0.919
0.3868
0.3079
0.5006
0.5068
0.4596
57
0.900
0.928
0.961
0.941
0.917
0.1570
0.1266
0.1946
0.1711
0.1655
58
0.959
0.914
0.813
0.935
0.920
0.2159
0.1730
0.2119
0.2573
0.2457
59
0.881
0.879
0.896
0.955
0.920
0.2094
0.1866
0.2774
0.2993
0.2633
60
0.866
0.832
0.803
0.922
0.888
0.3235
0.2896
0.4272
0.5123
0.4468
61
0.915
0.918
0.986
0.940
0.937
0.1794
0.1482
0.2542
0.1822
0.1824
62
0.959
0.935
0.933
0.931
0.913
0.2317
0.2076
0.2873
0.2584
0.2453
63
0.898
0.900
0.975
0.937
0.911
0.2318
0.1991
0.3535
0.2751
0.2559
64
0.897
0.879
0.944
0.912
0.891
0.3514
0.2837
0.5282
0.4348
0.3954
65
0.907
0.917
0.988
0.937
0.928
0.1526
0.1264
0.2120
0.1617
0.1583
66
0.950
0.923
0.909
0.946
0.921
0.2032
0.1796
0.2397
0.2467
0.2274
67
0.878
0.895
0.948
0.946
0.904
0.2016
0.1778
0.3009
0.2640
0.2367
68
0.875
0.873
0.922
0.939
0.905
0.3101
0.2663
0.4551
0.4360
0.3821
69
0.877
0.911
0.979
0.951
0.920
0.1266
0.1053
0.1743
0.1456
0.1376
70
0.966
0.888
0.827
0.945
0.908
0.1865
0.1581
0.1996
0.2466
0.2211
71
0.816
0.859
0.912
0.958
0.906
0.1664
0.1565
0.2496
0.2550
0.2195
72
0.833
0.816
0.863
0.928
0.887
0.2587
0.2442
0.3822
0.4364
0.3693
73
0.938
0.946
0.996
0.950
0.944
0.1531
0.1339
0.2344
0.1615
0.1588
74
0.959
0.937
0.954
0.940
0.932
0.1894
0.1762
0.2585
0.2225
0.2082
75
0.919
0.918
0.982
0.942
0.926
0.1967
0.1761
0.3266
0.2440
0.2228
76
0.884
0.880
0.955
0.916
0.884
0.2931
0.256
0.4900
0.3874
0.3432
77
0.928
0.920
0.996
0.944
0.923
0.1296
0.1134
0.1945
0.1429
0.1377
78
0.965
0.898
0.935
0.952
0.925
0.1656
0.1574
0.216
0.2128
0.1940
79
0.870
0.881
0.966
0.948
0.917
0.1684
0.1581
0.2772
0.2346
0.2055
80
0.875
0.835
0.932
0.932
0.894
0.2572
0.2371
0.4154
0.3859
0.3298
81
0.923
0.935
0.980
0.942
0.905
0.1074
0.0949
0.1594
0.1289
0.1197
82
0.952
0.928
0.882
0.952
0.922
0.1488
0.1371
0.1773
0.2100
0.1868
83
0.843
0.907
0.933
0.953
0.892
0.1417
0.1401
0.2284
0.2271
0.1910
84
0.844
0.866
0.896
0.945
0.882
0.2195
0.2161
0.3534
0.3878
0.3185
85
0.931
0.925
0.984
0.948
0.929
0.1346
0.1221
0.1881
0.1460
0.1415
86
0.950
0.939
0.959
0.953
0.937
0.1661
0.1606
0.2165
0.1994
0.1852
87
0.907
0.916
0.961
0.939
0.899
0.1701
0.1615
0.2626
0.2221
0.1999
88
0.883
0.895
0.954
0.922
0.891
0.2420
0.2330
0.3924
0.3520
0.3043
89
0.928
0.926
0.978
0.948
0.924
0.1123
0.1025
0.1558
0.1297
0.1228
90
0.952
0.935
0.925
0.955
0.919
0.1431
0.1379
0.1806
0.1898
0.1712
91
0.861
0.903
0.945
0.946
0.905
0.1432
0.1424
0.2215
0.2126
0.1837
92
0.862
0.864
0.903
0.938
0.894
0.2063
0.2140
0.3319
0.3503
0.2915
93
0.909
0.907
0.953
0.939
0.903
0.0919
0.0858
0.1269
0.1167
0.1064
94
0.958
0.900
0.847
0.947
0.918
0.1290
0.1225
0.1481
0.1857
0.1631
95
0.860
0.889
0.897
0.952
0.914
0.1172
0.1258
0.1831
0.2054
0.1705
96
0.819
0.832
0.847
0.937
0.881
0.1713
0.2029
0.2787
0.3511
0.2824
*Note: Italics indicate the most suitable average width.
Figure 2.
Comparison of the performance of the proposed method for k = 5 in terms of coverage probability with respect to (G) sample size, (H) shape parameter, (I) proportion of zero, and in terms of average width with respect to (J) sample sizes, (K) shape parameter, and (L) proportion of zero (a2 = (303, 502), b2 = (302, 502, 100), c2 = (30, 502, 1002), d2 = (503, 1002), e2 = (2.05), f2 = (2.55), g2 = (3.05), h2 = (0.15), i2 = (0.12, 0.32, 0.5), j2 = (0.35), k2 = (0.55)).
The simulation results for k = 10 are shown in Table 4 and Figure 3. In almost all scenarios, the LS and BCI methods have coverage probabilities greater than or close to 0.95, except when the proportions of zeros equal 0.510. Both methods have wider average widths compared to the other methods. The GCI method has coverage probabilities close to 0.95 when the sample size is large and the shape parameters are equal to 2.0. For the MOVER method, even though it has the narrowest average width, it has coverage probabilities lower than 0.95 in all scenarios.
Table 4.
Performance measures of the 95% confidence intervals for the common CV; k = 10.
Scenarios
Coverage probability
Average width
GCI
MOVER
LS
BCI
FGCI
GCI
MOVER
LS
BCI
FGCI
97
0.927
0.894
0.961
0.953
0.953
0.1535
0.1264
0.2812
0.1481
0.1525
98
0.951
0.940
0.935
0.959
0.952
0.1855
0.1628
0.2812
0.1867
0.1872
99
0.954
0.850
0.943
0.958
0.954
0.2056
0.1670
0.3932
0.2208
0.2145
100
0.929
0.789
0.854
0.902
0.891
0.3392
0.2946
0.5878
0.3486
0.3355
101
0.903
0.905
0.950
0.953
0.929
0.1336
0.1075
0.2353
0.1307
0.1336
102
0.929
0.931
0.948
0.953
0.944
0.1707
0.1443
0.2344
0.1745
0.1707
103
0.909
0.862
0.904
0.957
0.936
0.1814
0.1472
0.3341
0.2105
0.1967
104
0.888
0.803
0.826
0.907
0.896
0.3046
0.2221
0.5069
0.3499
0.3245
105
0.824
0.914
0.951
0.937
0.920
0.1122
0.0900
0.1926
0.1165
0.1148
106
0.866
0.908
0.952
0.957
0.940
0.1581
0.1275
0.1935
0.1688
0.1587
107
0.834
0.853
0.864
0.957
0.906
0.1547
0.1313
0.2802
0.2057
0.1827
108
0.836
0.791
0.764
0.931
0.870
0.2584
0.2081
0.4254
0.3564
0.3196
109
0.920
0.908
0.961
0.959
0.933
0.1333
0.1111
0.2818
0.1316
0.1339
110
0.944
0.931
0.965
0.963
0.915
0.1626
0.1540
0.2816
0.1782
0.1728
111
0.934
0.895
0.927
0.956
0.925
0.1774
0.1455
0.3911
0.1979
0.1876
112
0.934
0.874
0.856
0.920
0.889
0.2932
0.2572
0.5836
0.3132
0.2929
113
0.887
0.926
0.953
0.953
0.920
0.1160
0.0948
0.2347
0.1167
0.1172
114
0.913
0.915
0.963
0.952
0.931
0.1417
0.1362
0.2356
0.1686
0.1563
115
0.904
0.895
0.904
0.958
0.908
0.1584
0.1290
0.3312
0.1889
0.1732
116
0.892
0.865
0.840
0.917
0.871
0.2691
0.1937
0.5054
0.3140
0.2841
117
0.873
0.920
0.936
0.944
0.892
0.0983
0.0786
0.1927
0.1039
0.1007
118
0.926
0.905
0.959
0.955
0.912
0.1309
0.1214
0.1922
0.1667
0.1490
119
0.861
0.879
0.836
0.958
0.905
0.1361
0.1148
0.2756
0.1833
0.1598
120
0.875
0.788
0.747
0.910
0.830
0.2287
0.1802
0.4273
0.3159
0.2729
121
0.943
0.921
0.962
0.958
0.929
0.1039
0.0941
0.2170
0.1103
0.1090
122
0.949
0.931
0.957
0.954
0.923
0.1250
0.1221
0.2174
0.1426
0.1357
123
0.950
0.903
0.951
0.947
0.903
0.1332
0.1243
0.3034
0.1674
0.1532
124
0.958
0.900
0.917
0.932
0.883
0.1959
0.1793
0.4527
0.2642
0.2349
125
0.880
0.919
0.961
0.941
0.889
0.0877
0.0797
0.1804
0.0979
0.0944
126
0.924
0.930
0.954
0.956
0.927
0.1100
0.1071
0.1808
0.1341
0.1237
127
0.955
0.907
0.920
0.948
0.887
0.1134
0.1092
0.2552
0.1599
0.1408
128
0.950
0.859
0.865
0.943
0.882
0.1697
0.1626
0.3878
0.2634
0.2246
129
0.894
0.923
0.952
0.943
0.866
0.0723
0.0671
0.1480
0.0880
0.0819
130
0.920
0.904
0.959
0.956
0.910
0.1008
0.0943
0.1468
0.1301
0.1158
131
0.921
0.863
0.873
0.950
0.906
0.0941
0.0986
0.2125
0.1551
0.1309
132
0.924
0.823
0.789
0.934
0.862
0.1446
0.1531
0.3257
0.2649
0.2175
133
0.959
0.933
0.970
0.954
0.936
0.0836
0.0798
0.1534
0.0920
0.0888
134
0.953
0.949
0.961
0.951
0.923
0.0957
0.1005
0.1538
0.1100
0.1037
135
0.964
0.907
0.959
0.940
0.919
0.1057
0.1063
0.2146
0.1404
0.1257
136
0.964
0.883
0.943
0.930
0.895
0.1490
0.1541
0.3189
0.2215
0.1904
137
0.946
0.909
0.969
0.952
0.931
0.0696
0.0678
0.1266
0.0817
0.0769
138
0.942
0.946
0.964
0.955
0.946
0.0838
0.0886
0.1274
0.1022
0.0934
139
0.968
0.888
0.927
0.954
0.929
0.0886
0.0946
0.1798
0.1343
0.1154
140
0.957
0.859
0.904
0.927
0.895
0.1261
0.1418
0.2706
0.2205
0.1824
141
0.936
0.921
0.947
0.959
0.934
0.0568
0.0590
0.1038
0.0733
0.0666
142
0.940
0.918
0.958
0.957
0.916
0.0751
0.0782
0.1036
0.0968
0.0858
143
0.958
0.875
0.877
0.958
0.905
0.0727
0.0841
0.1486
0.1302
0.1073
144
0.962
0.831
0.858
0.940
0.907
0.1047
0.1298
0.2275
0.2218
0.1763
*Note: Italics indicate the most suitable average width.
Figure 3.
Comparison of the performance of the proposed method for k = 10 in terms of coverage probability with respect to (M) sample size, (N) shape parameter, (O) proportion of zero, and in terms of average width with respect to (P) sample sizes, (Q) shape parameter, and (R) proportion of zero (a3 = (305, 505), b3 = (305, 503, 1002), c3 = (506, 1004), d3 = (10010), e3 = (2.010), f3 = (2.510), g3 = (3.010), h3 = (0.110), i3 = (0.15, 0.33, 0.52), j3 = (0.310), k3 = (0.510)).
Figures 1–3 exhibit similar patterns, showing consistent trends. As the sample size increases, all the proposed methods tend to decrease. Similarly, as the shape parameter increases, all the proposed methods also tend to decrease. Conversely, when the proportion of zeros increases, all the proposed methods tend to increase. These observations are all based on the average width.
Algorithm 6.
For a given (m1,m2,...,mk), (α1,α2,...,αk), (ϑ1,ϑ2,...,ϑk), and β1=β2=...=βk=1,
for r=1 to M
1) Generate sample from the ZIBS distribution;
2) Compute the unbiased estimates ˆαi and ˆϑi;
3) Compute the 95% confidence intervals for θ based on the GCI, MOVER, LS, BCI, and FGCI via Algorithms 1–5, respectively;
4) If [Lr≤θ≤Ur], set Dr=1; else set Dr=0;
End r loop.
5) The coverage probability and average width for each method are obtained by CP=1M∑Mr=1Dr and AW=Ur−LrM, where Ur and Lr are the upper and lower confidence limits, respectively.
4.
An empirical application
In this study, we leverage wind speed data from all directions to construct CIs for the common coefficient of variation of several ZIBS distributions. The data were collected from January 1 to 7, 2024, from three weather stations: Chanthaburi Weather Observing Station in Chanthaburi Province, Chumphon Weather Observing Station in Chumphon Province, and Songkhla Weather Observing Station in Songkhla Province. The selection of these three stations is due to their proximity to the Gulf of Thailand, which makes them directly influenced by sea breezes and tropical storms. This results in high wind speed fluctuations and also impacts the livelihoods, economy, and environment of the surrounding communities. All data were collected by the Thai Meteorological Department and are presented in Table 5 (Thai Meteorological Department Automatic Weather System, https://www.tmd.go.th/service/tmdData). To visualize the data distribution, we plotted histograms of wind speed data from all three stations, as shown in Figure 4. Table 6 provides statistical summaries for wind speed data at each station, revealing that the coefficients of variation of the wind speed data for the Chanthaburi Weather Observing Station, Chumphon Weather Observing Station, and Songkhla Weather Observing Station are 2.6799, 2.5111, and 2.7118, respectively. When considering the entire wind speed dataset, we observe a mixture of zero values (no wind) and positive values. For the positive values, we evaluate the suitability of the data distribution using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) calculated as AIC=2ln(L)+2p and BIC=2ln(L)+2pln(o), respectively, where p is the number of parameters estimated, o is the number of observations, and L is the likelihood function. From Table 7, it is evident that the Birnbaum-Saunders distribution exhibits the lowest AIC and BIC values compared to other distributions, indicating its best fit for positive wind speed data. Additionally, to confirm that the positive wind speed data follows the Birnbaum-Saunders distribution, we plotted the cumulative distribution function (CDF) derived from the positive wind speed data and the estimated CDF from the Birnbaum-Saunders distribution. As shown in Figure 5, both graphs are similar, indicating a good fit. Therefore, the wind speed data comprises both positive and zero values and follows the ZIBS distribution. This distribution was thus used to compute the CIs for the common coefficient of variation of the wind speed data. Table 8 presents the 95% confidence intervals for the common coefficient of variation of wind speed data from the three weather observing stations using the GCI, MOVER, LS, BCI, and FGCI methods. We compared wind speed data with parameters generated from simulation using a sample size of mi = 1003, parameter αi = 2.53, and parameter ϑi = 0.53, as shown in Table 2. The simulation results indicate that the GCI and BCI methods meet the criterion of coverage probability greater than or equal to the nominal confidence level of 0.95. When considering the average width, the GCI method provides the narrowest confidence interval. The results in Table 8 show that the confidence interval for the common coefficient of variation for the wind speed data using the GCI method is [2.5001, 2.7224], with a confidence interval width of 0.2224, which is the narrowest among all methods. This leads to the conclusion that the appropriate method for the wind speed data is consistent with the simulation results.
Table 5.
Data on the wind speed (knots) from the Chanthaburi Weather Observing Station, Chumphon Weather Observing Station, and Songkhla Weather Observing Station, Thailand.
Based on the study results, it is evident that the GCI demonstrates good performance in almost all scenarios, as the coverage probability is greater than or close to the nominal confidence level of 0.95, which is consistent with the previous research by Ye, Ma, and Wang [32], Thangjai, Niwitpong, and Niwitpong [33], Janthasuwan, Niwitpong, and Niwitpong [11]. When k is large, both LS and BCI perform well. In most scenarios, MOVER and FGCI have coverage probabilities below acceptable levels, indicating that these methods may not be suitable for many situations, which aligns with the previous research by Puggard, Niwitpong, and Niwitpong [17]. Considering the average width, all proposed methods tend to decrease as the sample size and shape parameters increase, which improves their efficiency. Conversely, when the proportion of zeros increases, all proposed methods tend to decrease, leading to reduced efficiency. In our case, the simulation results showed that the MOVER method provided the narrowest confidence intervals for most scenarios and performed well with small sample sizes combined with a low proportion of zeros. However, the MOVER method yielded coverage probabilities lower than the specified confidence level in almost all scenarios. Similarly, the FGCI method achieves a coverage probability close to the specified confidence level in scenarios with a low proportion of zeros. This could be attributed to certain weaknesses that affect the fiducial generalized pivotal quantities for the proportion of zeros. Additionally, the issues with both the MOVER and FGCI methods likely arise from the upper and lower bounds for zero values used in constructing the confidence intervals and the combined effect with other parameters; this results in insufficient coverage probability. Finally, wind energy is a vital, renewable source of power, primarily generated by capturing wind speed. However, fluctuations in wind speed can introduce uncertainty. According to Lee, Fields, and Lundquist [34], understanding these variations is crucial for assessing wind resource potential.
6.
Conclusions
This article presents an estimation of the common coefficient of variation of several ZIBS distributions. The methods proposed include GCI, MOVER, LS, BCI, and FGCI. The performance of each method was evaluated through Monte Carlo simulations, comparing their coverage probabilities and average widths. The simulation results for k = 3 recommend the GCI method due to its acceptable coverage probability and narrow confidence intervals in almost all scenarios, while the BCI method is another option for situations with large sample sizes. For k = 5, we recommend the GCI method when ϑi is unequal, the LS method when ϑi is small and the sample size is large, and the BCI method when ϑi is large. For k = 10, the BCI and GCI methods are recommended: the BCI method (for small to medium sample sizes) and the GCI method for large sample sizes. Additionally, in all sample cases (k = 3, 5, and 10), the MOVER method has the narrowest confidence intervals but a coverage probability below the acceptable level in most situations, and the FGCI method has a coverage probability below the acceptable level in almost all situations. Therefore, these two methods are not recommended. Finally, all the proposed methods were applied to wind speed data in Thailand and yielded results consistent with the simulation findings. In future research, we will explore new methods for constructing confidence intervals, potentially using Bayesian and highest posterior density (HPD) approaches to enhance their effectiveness. Additionally, we will use other real-world data to conduct a more comprehensive study.
Author contributions
Usanee Janthasuwan conducted the data analysis, drafted the initial manuscript, and contributed to the writing. Suparat Niwitpong developed the research framework, designed the experiment, and reviewed the manuscript. Sa-Aat Niwitpong provided analytical methodologies, validated the final version, and obtained funding.
Use of Generative-AI tools declaration
The authors declare that they have not used Artificial Intelligence (AI) tools in the creation of this article.
Acknowledgments
The authors would like to express their sincere gratitude to the editor and reviewers for their valuable comments and suggestions, which have significantly improved the quality of the manuscript. This research was funded by the King Mongkut's University of Technology North Bangkok, contract no: KMUTNB-68-KNOW-17.
Conflict of interest
The authors declare no conflict of interest.
Acknowledgments
The author acknowledges that many additional papers that could improve this review were not included in the reference list. The author is a senior researcher retired from the National Institute of Advanced Industrial Science and Technology, Japan (AIST). This research was privately conducted after the expiration of the contract period with AIST. The author expresses thanks to Mses. Yasuko Yamazaki, Ayako Sasaki (Library Group), and Drs. Shinya Honda, Kiyonori Hirota (Biomedical Research Institute) at AIST for helpful technical support and kind advice, respectively, about retrieving some of the reference papers. The author would like to gratefully appreciate Dr. Denise Blesila (Editage) for English language editing and also Ms. Keiko Muraki for her secretarial assistance.
Conflict of interests
The author declares that there is no conflict of interest.
Pitti RM, Marsters SA, Lawrence DA, et al. (1998) Genomic amplification of a decoy receptor for Fas ligand in lung and colon cancer. Nature 396: 699-703. https://doi.org/10.1038/25387
[4]
Muraki M (2020) Sensitization to cell death induced by soluble Fas ligand and agonistic antibodies with exogenous agents: A review. AIMS Med Sci 7: 122-203. https://doi.org/10.3934/medsci.2020011
[5]
Cheng J, Zhou T, Liu C, et al. (1994) Protection from Fas-mediated apoptosis by a soluble form of the Fas molecule. Science 263: 1759-1762. https://doi.org/10.1126/science.7510905
[6]
Guégan JP, Legembre P (2018) Nonapoptotic functions of Fas/CD95 in the immune response. FEBS J 285: 809-827. https://doi.org/10.1111/febs.14292
[7]
Herrero R, Kajikawa O, Matute-Bello G, et al. (2011) The biological activity of FasL in human and mouse lungs is determined by the structure of its stalk region. J Clin Invest 121: 1174-1190. https://doi.org/10.1172/JCI43004
[8]
Malleter M, Tauzin S, Bessede A, et al. (2013) CD95L cell surface cleavage triggers a prometastatic signaling pathway in triple-negative breast cancer. Cancer Res 73: 6711-6721. https://doi.org/10.1158/0008-5472.CAN-13-1794
Audo R, Calmon-Hamaty F, Papon L, et al. (2014) Distinct effects of soluble and membrane-bound Fas ligand on fibroblast-like synoviocytes from rheumatoid arthritis patients. Arthritis Rheumatol 66: 3289-3299. https://doi.org/10.1002/art.38806
[11]
Hsieh SL, Lin WW (2017) Decoy receptor 3: an endogenous immunomodulator in cancer growth and inflammatory reactions. J Biomed Sci 24: 39. https://doi.org/10.1186/s12929-017-0347-7
[12]
Miwa K, Asano M, Horai R, et al. (1998) Caspase1-independent IL-1beta release and inflammation induced by the apoptosis inducer Fas ligand. Nat Med 4: 1287-1297. https://doi.org/10.1038/3276
[13]
Kurma K, Boizard-Moracchini A, Galli G, et al. (2021) Soluble CD95L in cancers and chronic inflammatory disorders, a new therapeutic target?. Biochim Biophys Acta Rev Cancer 1876: 188596. https://doi.org/10.1016/j.bbcan.2021.188596
[14]
Ueno T, Toi M, Tominaga T (1999) Circulating soluble Fas concentration in breast cancer patients. Clin Cancer Res 5: 3529-3533.
Konno R, Takao T, Sato S, et al. (2000) Serum soluble Fas level as a prognostic factor in patients with gynecological malignancies. Clin Cancer Res 6: 3576-3580.
Ugurel S, Rappl G, Tilgen W, et al. (2001) Increased soluble CD95 (sFas/CD95) serum level correlates with poor prognosis in melanoma patients. Clin Cancer Res 7: 1282-1286.
[20]
Hoffmann TK, Dworacki G, Tsukihiro T, et al. (2002) Spontaneous apoptosis of circulating T lymphocytes in patients with head and neck and its clinical importance. Clin Cancer Res 8: 2553-2562.
[21]
Akhmedkhanov A, Lundin E, Guller S, et al. (2003) Circulating soluble Fas levels and risk of ovarian cancer. BMC Cancer 3: 33. https://doi.org/10.1186/1471-2407-3-33
[22]
Sheen-Chen SM, Chen HS, Eng HL, et al. (2003) Circulating soluble Fas in patients with breast cancer. World J Surg 27: 10-13. https://doi.org/10.1007/s00268-002-6378-5
[23]
Wu Y, Han B, Sheng H, et al. (2003) Clinical significance of detecting elevated serum DcR3/TR6/M68 in malignant tumor patients. Int J Cancer 105: 724-732. https://doi.org/10.1002/ijc.11138
[24]
Erdoğan B, Uzaslan E, Budak F, et al. (2005) The evaluation of soluble Fas and soluble Fas ligand levels of bronchoalveolar lavage fluid in lung cancer patients. Tuberk Toraks 53: 127-131.
[25]
Nadal C, Maurel J, Gallego R, et al. (2005) Fas/Fas ligand ratio: a marker of oxaliplatin-based intrinsic and acquired resistance in advanced colorectal cancer. Clin Cancer Res 11: 4770-4774. https://doi.org/10.1158/1078-0432.CCR-04-2119
[26]
Shimizu M, Kondo M, Ito Y, et al. (2005) Soluble Fas and Fas ligand new information on metastasis and response to chemotherapy in SCLC patients. Cancer Detect Prev 29: 175-180. https://doi.org/10.1016/j.cdp.2004.09.001
[27]
Yatsuya H, Toyoshima H, Tamakoshi K, et al. (2005) Serum levels of insulin-like growth factor I, II, and binding protein 3, transforming growth factor β-1, soluble Fas ligand and superoxide dismutase activity in stomach cancer cases and their controls in the JACC study. J Epidemiol 15: S120-S125. https://doi.org/10.2188/jea.15.S120
[28]
Sonoda K, Miyamoto S, Hirakawa T, et al. (2006) Clinical significance of RCAS1 as a biomarker of uterine cancer. Gynecol Oncol 103: 924-931. https://doi.org/10.1016/j.ygyno.2006.05.047
[29]
Svatek RS, Herman MP, Lotan Y, et al. (2006) Soluble Fas-a promising novel urinary marker for the detection of recurrent superficial bladder cancer. Cancer 106: 1701-1707. https://doi.org/10.1002/cncr.21795
[30]
Baldini E, Ulisse S, Marchioni E, et al. (2007) Expression of Fas and Fas ligand in human testicular germ cell tumours. Int J Andorol 32: 123-130. https://doi.org/10.1111/j.1365-2605.2007.00823.x
[31]
Simon I, Liu Y, Krall KL, et al. (2007) Evaluation of the novel serum markers B7-H4, spondin 2, and DcR3 for diagnosis and early detection of ovarian cancer. Gynecol Oncol 106: 112-118. https://doi.org/10.1016/j.ygyno.2007.03.007
[32]
Aguilar-Lemarroy A, Romero-Ramos JE, Olimon-Andalon V, et al. (2008) Apoptosis induction in Jurkat cells and sCD95 levels in women's sera are related with the risk of developing cervical cancer. BMC Cancer 8: 99. https://doi.org/10.1186/1471-2407-8-99
[33]
Chaudhry P, Srinvasan R, Patel FD, et al. (2008) Serum soluble Fas levels and prediction of response to platinum-based chemotherapy in epithelial ovarian cancer. Int J Cancer 122: 1716-1721. https://doi.org/10.1002/ijc.23213
[34]
Connor JP, Felder M (2008) Ascites from epithelial ovarian cancer contain high levels of functional decoy receptor 3 (DcR3) and is associated with platinum resistance. Gynecol Oncol 111: 330-335. https://doi.org/10.1016/j.ygyno.2008.07.012
[35]
Kilic A, Schubert MJ, Luketich JD, et al. (2008) Use of novel autoantibody and cancer-related protein arrays for the detection of esophageal adenocarcinoma in serum. J Thorac Cardiovasc Surg 136: 199-204. https://doi.org/10.1016/j.jtcvs.2008.01.012
[36]
Macher-Goeppinger S, Aulmann S, Wagener N, et al. (2008) Decoy receptor 3 is a prognostic factor in renal cell cancer. Neoplasia 10: 1049-1056. https://doi.org/10.1593/neo.08626
[37]
Nolen BM, Marks JR, Ta'san S, et al. (2008) Serum biomarker profiles and response to neoadjuvant chemotherapy for locally advanced breast cancer. Breast Cancer Res 10: R45. https://doi.org/10.1186/bcr2096
[38]
Tamakoshi A, Nakachi K, Ito Y, et al. (2008) Soluble Fas level and cancer mortality: Findings from a nested case-control study within a large-scale prospective study. Int J Cancer 123: 1913-1916. https://doi.org/10.1002/ijc.23731
[39]
Anderson GL, McIntosh M, Wu L, et al. (2010) Assessing lead time of selected ovarian cancer biomarkers: A nested case-control study. J Natl Cancer Inst 102: 26-38. https://doi.org/10.1093/jnci/djp438
[40]
Goon PKY, Lip GYH, Stonelake PS, et al. (2009) Circulating endothelial cells and circulating progenitor cells in breast cancer: Relationship to endothelial damage/dysfunction/apoptosis, clinicopathologic factors, and the Nottingham prognostic index. Neoplasia 11: 771-779. https://doi.org/10.1593/neo.09490
[41]
Kavathia N, Jain A, Walston J, et al. (2009) Serum markers of apoptosis decrease with age and cancer stage. Aging 1: 652-663. https://doi.org/10.18632/aging.100069
[42]
Vysotskii MM, Digaeva MA, Kushlinskii NE, et al. (2009) Serum sFas, leptin, and VEGF in patients with ovarian cancer and benign tumors. Bull Exp Biol Med 148: 810-814. https://doi.org/10.1007/s10517-010-0823-5
[43]
Boroumand-Noughabi S, Sima HR, Ghaffarzadehgan K, et al. (2010) Soluble Fas might serve as a diagnostic tool for gastric adenocarcinoma. BMC Cancer 10: 275. https://doi.org/10.1186/1471-2407-10-275
[44]
Habibagahi M, Jaberipour M, Fattahi MJ, et al. (2010) High concentration of serum soluble Fas in patients with head and neck carcinoma: A comparative study before and after surgical removal of tumor. Middle East J Cancer 1: 21-26.
[45]
Nolen B, Velikokhatnaya L, Marrangoni A, et al. (2010) Serum biomarker panels for the discrimination of benign from malignant cases in patients with an adnexal mass. Gynecol Oncol 117: 440-445. https://doi.org/10.1016/j.ygyno.2010.02.005
[46]
Ulukaya E, Acilan C, Yilmaz M, et al. (2010) sFas levels increase in response to cisplatin-based chemotherapy in lung cancer patients. Cell Biochem Funct 28: 565-570. https://doi.org/10.1002/cbf.1689
[47]
Yang M, Chen G, Dang Y, et al. (2010) Significance of decoy receptor 3 in sera of hepatocellular carcinoma patients. Upsala J Med Sci 115: 232-237. https://doi.org/10.3109/03009734.2010.516410
[48]
Zekri AN, El-Din HMA, Bahnassy AA, et al. (2010) Serum levels of soluble Fas, soluble tumor necrosis factor-receptor II, interleukin-2 receptor and interleukin-8 as early predictors of carcinoma in Egyptian patients with hepatitis C virus genotype-4. Comp Hepatol 9: 1. https://doi.org/10.1186/1476-5926-9-1
[49]
Hoogwater FJH, Snoeren N, Nijkamp MW, et al. (2011) Circulating CD95-ligand as a potential prognostic marker for recurrence in patients with synchronous colorectal liver metastases. Anticancer Res 31: 4507-4512.
[50]
Fersching DMI, Nagel D, Siegele B, et al. (2012) Apoptosis-related biomarkers sFas, MIF, ICAM-1 and PAI-1 in serum of breast cancer patients undergoing neoadjuvant chemotherapy. Anticancer Res 32: 2047-2058.
[51]
Hammam O, Mahmoud O, Zahran M, et al. (2012) The role of Fas/Fas ligand system in the pathogenesis of liver cirrhosis and hepatocellular carcinoma. Hepat Mon 12: e6132. https://doi.org/10.5812/hepatmon.6132
[52]
Izbicka E, Streeper RT, Michalek JE, et al. (2012) Plasma biomarkers distinguish non-small cell lung cancer from asthma and differ in men and women. Cancer Genomics Proteomics 9: 27-35.
[53]
Eissa S, Swellam M, Abdel-Malak C, et al. (2013) The clinical relevance of urinary soluble fas (sFas) for diagnosis of bilharzial bladder cancer. Asian Biomed 7: 761-767. https://doi.org/10.5372/1905-7415.0706.238
[54]
Owonikoko TK, Hossain MS, Bhimani C, et al. (2013) Soluble Fas ligand as a biomarker of disease recurrence in differentiated thyroid cancer. Cancer 119: 1503-1511. https://doi.org/10.1002/cncr.27937
[55]
Elbedewy TA, El-Feky S, El Sheikh MA, et al. (2014) Serum levels of soluble Fas and soluble Fas ligand as markers for hepatocellular carcinoma in hepatitis C virus patients. Int J Adv Res 2: 911-919.
[56]
Xu Y, Zhang L, Sun S, et al. (2014) CC Chemokine ligand 18 and IGF-binding protein 6 as potential serum biomarkers for prostate cancer. Tohoku J Exp Med 233: 25-31. https://doi.org/10.1620/tjem.233.25
[57]
Yeh CN, Chung WH, Su SC, et al. (2014) Fas/Fas ligand mediates keratinocyte death in sunitinib-induced hand-foot skin reaction. J Invest Dermatol 134: 2768-2775. https://doi.org/10.1038/jid.2014.218
[58]
Kadam CY, Abhang SA (2015) Serum levels of soluble Fas ligand, granzyme B and cytochrome c during adjuvant chemotherapy of breast cancer. Clin Chim Acta 438: 98-102. https://doi.org/10.1016/j.cca.2014.08.012
[59]
Kouloubnis A, Sofroniadou S, Panoulas VF, et al. (2015) The role of TNF-α, Fas/Fas ligand system and NT-proBNP in the early detection of asymptomatic left ventricular dysfunction in cancer patients treated with anthracyclines. IJC Heart Vasc 6: 85-90. https://doi.org/10.1016/j.ijcha.2015.01.002
[60]
Lancrajan I, Schneider-Stock R, Naschberger E, et al. (2015) Absolute quantification of DcR3 and GDF-15 from human serum by LC-ESI MS. J Cell Mol Med 19: 1656-1671. https://doi.org/10.1111/jcmm.12540
[61]
Bamias G, Gizis M, Delladetsima I, et al. (2016) Elevated serum levels of the antiapoptotic protein decoy-receptor 3 are associated with advanced liver disease. Can J Gastroenterol Hepatol 2016: 2637010. https://doi.org/10.1155/2016/2637010
[62]
Chang SW, Wang BC, Gong XG, et al. (2016) Serum decoy receptor 3 is a diagnostic and prognostic biomarker in patients with colorectal cancer. Int J Clin Exp Pathol 9: 7488-7492.
[63]
Dressen K, Hermann N, Manekeller S, et al. (2017) Diagnostic performance of a novel multiplex immunoassay in colorectal cancer. Anticancer Res 37: 2477-2486. https://doi.org/10.21873/anticanres.11588
[64]
Hermann N, Dressen K, Schroeder L, et al. (2017) Diagnostic relevance of a novel multiplex immunoassay panel in breast cancer. Tumor Biol 2017: 1-11. https://doi.org/10.1177/1010428317711381
[65]
Johdi NA, Mazlan L, Sagap I, et al. (2017) Profiling of cytokines, chemokines and other soluble proteins as a potential biomarker in colorectal cancer and polyps. Cytokine 99: 35-42. https://doi.org/10.1016/j.cyto.2017.06.015
[66]
Li J, Xie N, Yuan J, et al. (2017) DcR3 combined with hematological traits serves as a valuable biomarker for the diagnosis of cancer metastasis. Oncotarget 8: 107612-107620. https://doi.org/10.18632/oncotarget.22544
[67]
Korczyński P, Mierzejewski M, Krenke R, et al. (2018) Cancer ratio and other new parameters for differentiation between malignant and nonmalignant pleural effusions. Pol Arch Intern Med 128: 354-361. https://doi.org/10.20452/pamw.4278
[68]
Ku SC, Ho PS, Tseng YT, et al. (2018) Benzodiazepine-associated carcinogenesis: Focus on lorazepam-associated cancer biomarker changes in overweight individuals. Psychiatry Invest 15: 900-906. https://doi.org/10.30773/pi.2018.05.02.1
[69]
Singh RK, Meena RN, Tiwary SK, et al. (2018) Fas ligand as a circulating apoptosis marker in carcinoma breast. World J Surg Res 7: 7-14.
[70]
Łaniewski P, Cui H, Roe DJ, et al. (2019) Features of the cervicovaginal microenvironment drive cancer biomarker signatures in patients across cervical carcinogenesis. Sci Rep 9: 7333. https://doi.org/10.1038/s41598-019-43849-5
[71]
Wei Y, Chen X, Yang J, et al. (2019) DcR3 promotes proliferation and invasion of pancreatic cancer via a DcR3/STAT1/IRF1 feedback loop. Am J Cancer Res 9: 2618-2633.
[72]
Akhmaltdinova L, Sirota V, Zhumaliyeva V, et al. (2020) Inflammatory serum biomarkers in colorectal cancer in Kazakhstan population. Int J Inflam 2020: 9476326. https://doi.org/10.1155/2020/9476326
[73]
Ali AS, Perren A, Lindskog C, et al. (2020) Candidate protein biomarkers in pancreatic neuroendocrine neoplasms grade 3. Sci Rep 10: 10639. https://doi.org/10.1038/s41598-020-67670-7
[74]
Chiu CT, Wang PW, Asare-Werehene M, et al. (2020) Circulating plasma gelsolin: A predictor of favorable clinical outcomes in head and neck cancer and sensitive biomarker for early disease diagnosis combined with soluble Fas ligand. Cancers 12: 1569. https://doi.org/10.3390/cancers12061569
[75]
Feng K, Liu Y, Zhao Y, et al. (2020) Efficacy and biomarker analysis of nivolumab plus gemcitabine and cisplatin in patients with unresectable or metastatic biliary tract cancers: results from a phase II study. J Immunother Cancer 8: e000367. https://doi.org/10.1136/jitc-2019-000367
[76]
Honma N, Inoue T, Tsuchiya N, et al. (2020) Prognostic value of plasminogen activator inhibitor-1 in biomarker exploration using multiplex immunoassay in patients with axitinib. Health Sci Rep 3: e197. https://doi.org/10.1002/hsr2.197
[77]
Ghonaim E, El-Haggar S, Gohar S (2021) Possible protective effect of pantoprazole against cisplatin-induced nephrotoxicity in head and neck cancer patients: a randomized controlled trial. Med Oncol 38: 108. https://doi.org/10.1007/s12032-021-01558-y
[78]
López-Jornet P, Aznar C, Ceron J (2021) Salivary biomarkers in breast cancer: a cross-sectional study. Support Care Cancer 29: 889-896. https://doi.org/10.1007/s00520-020-05561-3
[79]
Jodo S, Kobayashi S, Kayagaki N, et al. (1997) Serum levels of soluble Fas/APO-1 (CD95) and its molecular structure in patients with systemic lupus erythematosus (SLE) and other autoimmune diseases. Clin Exp Immunol 107: 89-95. https://doi.org/10.1046/j.1365-2249.1997.d01-901.x
[80]
Ohtsuka K, Hashimoto M (2000) Serum levels of soluble Fas in patients with Graves' ophthalmopathy. Br J Ophthalmol 84: 103-106. https://doi.org/10.1136/bjo.84.1.103
[81]
Wang CY, Zhong WB, Chang TC, et al. (2002) Circulating soluble Fas ligand correlates with disease activity in Graves' Hyperthyroidism. Metabolism 51: 769-773. https://doi.org/10.1053/meta.2002.32034
[82]
Boku S, Takahashi T, Watanabe K, et al. (2003) Serum soluble Fas as a seromarker of disease activity in chronic hepatitis C and the possible involvement of peripheral blood mononuclear cells in the production of serum soluble Fas. Acta Med Biol 51: 49-58.
[83]
Kiraz S, Ertenli I, Öztürk MA, et al. (2003) Increased soluble Fas suggests delayed apoptosis in familial Mediterranean fever complicated with amyloidosis. J Rheumatol 30: 313-315.
[84]
Silvestris F, Grinello D, Tucci M, et al. (2003) Enhancement of T cell apoptosis correlates with increased serum levels of soluble Fas (CD95/Apo-I) in active lupus. Lupus 12: 8-14. https://doi.org/10.1191/0961203303lu250oa
[85]
Hayashi H, Maeda M, Murakami S, et al. (2009) Soluble interleukin-2 as an indicator of immunological disturbance found in silicosis patients. Int J Immunol Pharmacol 22: 53-62. https://doi.org/10.1177/039463200902200107
[86]
Magerus-Chatinet A, Stolzenberg MC, Loffredo MS, et al. (2009) FAS-L, IL-10, and double-negative CD4−CD8− TCR α/β+ T cells are reliable markers of autoimmune lymphoproliferative syndrome (ALPS) associated with FAS loss of function. Blood 113: 3027-3030. https://doi.org/10.1182/blood-2008-09-179630
[87]
Sahebari M, Hatef MR, Rezaieyazdi Z, et al. (2010) Correlation between serum levels of soluble Fas (CD95/Apo-1) with disease activity in systemic lupus erythematosus patients in Khorasan, Iran. Arch Iran Med 13: 135-142. https://doi.org/10.1007/s00296-010-1633-9
[88]
Ikonomidis I, Tzortzis S, Lekakis J, et al. (2011) Association of soluble apoptotic markers with impaired left ventricular deformation in patients with rheumatoid arthritis. Effects of inhibition of interleukin-1 activity by anakinra. Thromb Haemost 106: 959-967. https://doi.org/10.1160/TH11-02-0117
[89]
Bamias G, Stamatelopoulos K, Zampeli E, et al. (2013) Circulating levels of TNF-like cytokine 1A correlate with the progression of atheromatous lesions in patients with rheumatoid arthritis. Clin Immunol 147: 144-150. https://doi.org/10.1016/j.clim.2013.03.002
[90]
Moreno M, Sáenz-Cuesta M, Castilló J, et al. (2013) Circulating levels of soluble apoptosis-related molecules in patients with multiple sclerosis. J Neuroimmunol 263: 152-154. https://doi.org/10.1016/j.jneuroim.2013.07.013
[91]
Oka S, Furukawa H, Shimada K, et al. (2013) Serum biomarker analysis of collagen disease patients with acute-onset diffuse interstitial lung disease. BMC Immunol 14: 9. https://doi.org/10.1186/1471-2172-14-9
[92]
Rensing-Ehl A, Janda A, Lorenz MR, et al. (2013) Sequential decisions on Fas sequencing guided by biomarkers in patients with lymphoproliferation and autoimmune cytopenia. Heamatologica 98: 1948-1955. https://doi.org/10.3324/haematol.2012.081901
[93]
Roberts CA, Ayers L, Bateman EAL, et al. (2013) Investigation of common variable immunodeficiency patients and healthy individuals using autoimmune lymphoproliferative syndrome biomarkers. Hum Immunol 74: 1531-1535. https://doi.org/10.1016/j.humimm.2013.08.266
[94]
Bollain-y-Goytia JJ, Arellano-Rodríguez M, Torres-Del-Muro FJ, et al. (2014) Soluble Fas and the -670 polymorphism of Fas in lupus nephritis. Int J Nephrol 2014: 780406. https://doi.org/10.1155/2014/780406
[95]
Chen MH, Liu PC, Chang CW, et al. (2014) Decoy receptor 3 suppresses B cell functions and has a negative correlation with disease activity in rheumatoid arthritis. Clin Exp Rheumatol 32: 715-723.
[96]
Munroe ME, Vista ES, Guthridge JM, et al. (2014) Proinflammatory adaptive cytokine and shed tumor necrosis factor receptor levels are elevated preceding systemic lupus erythematosus disease flare. Arthritis Rheumatol 66: 1888-1899. https://doi.org/10.1002/art.38573
[97]
Price S, Shaw PA, Seitz A, et al. (2014) Natural history of autoimmune lymphoproliferative syndrome associated with FAS gene mutations. Blood 123: 1989-1999. https://doi.org/10.1182/blood-2013-10-535393
[98]
Romano E, Terenzi R, Manetti M, et al. (2014) Disease activity improvement in rheumatoid arthritis treated with tumor necrosis factor-α inhibitors correlates with increased soluble Fas levels. J Rheumatol 41: 1961-1965. https://doi.org/10.3899/jrheum.131544
[99]
Liu J, Zhao Z, Zou Y, et al. (2015) The expression of death decoy receptor 3 was increased in the patients with primary Sjögren's syndrome. Clin Rheumatol 34: 879-885. https://doi.org/10.1007/s10067-014-2853-2
[100]
Zijuan X, Hui S, Ye T, et al. (2015) Serum and synovial fluid levels of tumor necrosis factor-like ligand 1A and decoy receptor 3 in rheumatoid arthritis. Cytokine 72: 185-189. https://doi.org/10.1016/j.cyto.2014.12.026
[101]
Elsaeed GSM, Elshamaa MF, Salah DM, et al. (2016) Fas-ligand and granzyme-b levels in children with nephrotic syndrome. Int J Pharma Clin Res 8: 1600-1604.
[102]
Maruyama H, Hirayama K, Nagai M, et al. (2016) Serum decoy receptor 3 levels are associated with the disease activity of MPO-ANCA-associated renal vasculitis. Clin Rheumatol 35: 2469-2476. https://doi.org/10.1007/s10067-016-3321-y
[103]
Chen MH, Kan HT, Liu CY, et al. (2017) Serum decoy receptor 3 is a biomarker for disease severity in nonatopic asthma patients. J Formos Med Assoc 116: 49-56. https://doi.org/10.1016/j.jfma.2016.01.007
[104]
Engmann J, Rüdrich U, Behrens G, et al. (2017) Increased activity and apoptosis of eosinophils in blister fluids, skin and peripheral blood of patients with bullous pemphigoid. Acta Derm Venereol 97: 464-471. https://doi.org/10.2340/00015555-2581
[105]
Ding YW, Pan SY, Xie W, et al. (2018) Elevated soluble Fas and FasL in cerebrospinal fluid and serum of patients with anti-N-methyl-D-aspartate receptor encephalitis. Front Neurol 9: 904. https://doi.org/10.3389/fneur.2018.00904
[106]
Liphaus BL, Sallum AEM, Aikawa NE, et al. (2018) Increased soluble cytoplasmic Bcl-2 protein serum levels and expression and decreased Fas expression in lymphocytes and monocytes in Juvenile dermatomyositis. J Rheumatol 45: 1577-1580. https://doi.org/10.3899/jrheum.171248
[107]
Wigren M, Svenungsson E, Mattisson IY, et al. (2018) Cardiovascular disease in systemic lupus erythematosus is associated with increased levels of biomarkers reflecting receptor-activated apoptosis. Atherosclerosis 270: 1-7. https://doi.org/10.1016/j.atherosclerosis.2018.01.022
[108]
Molnár E, Radwan N, Kovács G, et al. (2020) Key diagnostic markers for autoimmune lymphoproliferative syndrome with molecular genetic diagnosis. Blood 136: 1933-1945. https://doi.org/10.1182/blood.2020005486
[109]
Sremec J, Tomasović S, Sremec NT, et al. (2020) Elevated concentrations of soluble Fas and FasL in multiple sclerosis patients with antinuclear antibodies. J Clin Med 9: 3845. https://doi.org/10.3390/jcm9123845
[110]
Vincent FB, Kandane-Rathnayake R, Koelmeyer R, et al. (2020) Associations of serum soluble Fas and Fas ligand (FasL) with outcomes in systemic lupus erythematosus. Lupus Sci Med 7: e000375. https://doi.org/10.1136/lupus-2019-000375
[111]
Kamal A, Abdelmegeid AK, Gabr MAM, et al. (2021) Serum decoy receptor 3 (DcR3): a promising biomarker for atopic asthma in children. Immunol Res 69: 568-575. https://doi.org/10.1007/s12026-021-09218-z
[112]
Kessel C, Fail N, Grom A, et al. (2021) Definition and validation of serum biomarkers for optimal differentiation of hyperferritinaemic cytokine storm conditions in children: a retrospective cohort study. Lancet Rheumatol 3: 563-573. https://doi.org/10.1016/S2665-9913(21)00115-6
[113]
Qi Y, Xu J, Lin Z, et al. (2021) The network of pro-inflammatory factors CD147, DcR3, and IL33 in the development of Kawasaki disease. J Inflamm Res 14: 6043-6053. https://doi.org/10.2147/JIR.S338763
[114]
Su KW, Chiu CY, Tsai MH, et al. (2021) Cord blood soluble Fas ligand linked to allergic rhinitis and lung function in seven-year-old children. J Microbiol Immunol Infect . (in press). https://doi.org/10.1016/j.jmii.2021.03.016
[115]
De Freitas I, Fernández-Somoza M, Essenfeld-Sekler E, et al. (2004) Serum levels of the apoptosis-associated molecules, tumor necrosis factor-α/tumor necrosis factor type-I receptor and Fas/FasL, in sepsis. Chest 125: 2238-2246. https://doi.org/10.1378/chest.125.6.2238
[116]
Paunel-Görgülü A, Flohé S, Scholz M, et al. (2011) Increased serum soluble Fas after major trauma is associated with delayed neutrophil apoptosis and development of sepsis. Crit Care 15: R20. https://doi.org/10.1186/cc9965
[117]
Hou YQ, Xu P, Zhang M, et al. (2012) Serum decoy receptor 3, a potential new biomarker for sepsis. Clin Chim Acta 413: 744-748. https://doi.org/10.1016/j.cca.2012.01.007
[118]
Huttunen R, Syrjänen J, Vuento R, et al. (2012) Apoptosis markers soluble Fas (sFas), Fas ligand (FasL) and sFas/sFasL ratio in patients with bacteremia: A prospective cohort study. J Infect 64: 276-281. https://doi.org/10.1016/j.jinf.2011.12.006
[119]
Kim S, Mi L, Zhang L (2012) Specific elevation of DcR3 in sera of sepsis patients and its potential role as a clinically important biomarker of sepsis. Diagn Microbiol Infect Dis 73: 312-317. https://doi.org/10.1016/j.diagmicrobio.2012.04.008
[120]
Liu YJ, Shao LH, Wang Q, et al. (2014) Predictive value of decoy receptor 3 in postoperative nosocomial bacterial meningitis. Int J Mol Sci 15: 19962-19970. https://doi.org/10.3390/ijms151119962
[121]
Liu YJ, Shao LH, Zhang J, et al. (2015) The combination of decoy receptor 3 and soluble triggering receptor expressed on myeloid cells-1 for the diagnosis of nosocomial bacterial meningitis. Ann Clin Microbiol Antimicrob 14: 17. https://doi.org/10.1186/s12941-015-0078-0
[122]
Chu CM, Kao KC, Huang SH, et al. (2016) Diagnostic value of apoptosis biomarkers in severe sepsis-A pilot study. Cell Mol Biol 62: 32-37. https://doi.org/10.14715/cmb/2016.62.11.6
[123]
Carcillo JA, Halstead ES, Hall MW, et al. (2017) Three hypothetical inflammation pathobiology phenotypes and pediatric sepsis-induced multiple organ failure outcome. Pediatr Crit Care Med 18: 513-523. https://doi.org/10.1097/PCC.0000000000001122
[124]
Mikacenic C, Price BL, Harju-Baker S, et al. (2017) A two-biomarker model predicts mortality in the critically ill with sepsis. Am J Respir Crit Care Med 196: 1004-1011. https://doi.org/10.1164/rccm.201611-2307OC
[125]
Gao L, Yang B, Zhang H, et al. (2018) DcR3, a new biomarker for sepsis, correlates with infection severity and procalcitonin. Oncotarget 9: 10934-10944. https://doi.org/10.18632/oncotarget.23736
[126]
Garcia-Obregon S, Azkargorta M, Seijas I, et al. (2018) Identification of a panel of serum protein markers in early stage of sepsis and its validation in a cohort of patients. J Microbiol Immunol Infect 51: 465-472. https://doi.org/10.1016/j.jmii.2016.12.002
[127]
Hou Y, Liang D, Liu Y, et al. (2018) Up-regulation of DcR3 in microbial toxins-stimulated HUVECs involves NF-κB signaling. BMC Biochem 19: 13. https://doi.org/10.1186/s12858-018-0102-z
[128]
Zhao JJ, Lou XL, Chen HW, et al. (2018) Diagnostic value of decoy receptor 3 combined with procalcitonin and soluble urokinase-type plasminogen activator receptor for sepsis. Cell Mol Biol Lett 23: 22. https://doi.org/10.1186/s11658-018-0087-z
[129]
Thompson K, Connor J (2019) When cultures fail: Postmortem decoy receptor 3 (DcR3) as a marker of antemortem sepsis. Acad Forensic Pathol 9: 15-23. https://doi.org/10.1177/1925362119851075
[130]
Hassan MH, Yassin MM, Hassan YM, et al. (2020) Decoy receptor 3 as a biomarker for diagnosis of bacterial sepsis. Egypt J Med Microbiol 29: 153-162. https://doi.org/10.51429/EJMM29320
[131]
Medina LMP, Rath E, Jahagirdar S, et al. (2021) Discriminatory plasma biomarkers predict specific clinical phenotypes of necrotizing soft-tissue infections. J Clin Invest 131: e149523. https://doi.org/10.1172/JCI149523
Raghuraman S, Abraham P, Daniel HD, et al. (2005) Characterization of soluble FAS, FAS ligand and tumour necrosis factor-alpha in patients with chronic HCV infection. J Clin Virol 34: 63-70. https://doi.org/10.1016/j.jcv.2005.01.009
[134]
Torre F, Bellis L, Delfino A, et al. (2008) Peripheral blood serum markers for apoptosis and liver fibrosis: Are they trustworthy indicators of liver realness?. Digest Liver Dis 40: 441-445. https://doi.org/10.1016/j.dld.2007.10.027
[135]
Hou Y, Xu P, Lou X, et al. (2013) Serum decoy receptor 3 is a useful predictor for the active status of chronic hepatitis B in hepatitis B e antigen-negative patients. Tohoku J Exp Med 230: 227-232. https://doi.org/10.1620/tjem.230.227
[136]
Dong Y, Shi D, Li M, et al. (2015) Elevated serum levels of decoy receptor 3 are associated with disease severity in patients with hemorrhagic fever with renal syndrome. Intern Emerg Med 10: 567-573. https://doi.org/10.1007/s11739-015-1195-7
[137]
Lin YT, Yen CH, Chen HL, et al. (2015) The serologic decoy receptor 3 (DcR3) levels are associated with slower disease progression in HIV-1/AIDS patients. J Formos Med Assoc 114: 498-503. https://doi.org/10.1016/j.jfma.2013.01.007
[138]
Ikomey GM, Julius A, Jacobs GB, et al. (2016) Fas mediated (CD95L) peripheral T-cell apoptosis marker in monitoring HIV-1 disease progression in adults in Yaoundé, Cameroon. Int J Immunol 4: 1-5. https://doi.org/10.11648/j.iji.20160401.11
[139]
Ikomey G, Assoumou MCO, Atashili J, et al. (2016) The potentials of Fas receptors and ligands in monitoring HIV-1 disease in children in Yaoundé, Cameroon. J Int Assoc Prov AIDS Care 15: 418-422. https://doi.org/10.1177/2325957413488202
[140]
McElroy AK, Harmon JR, Flietstra TD, et al. (2016) Kinetic analysis of biomarkers in a cohort of US patients with Ebola virus disease. Clin Infect Dis 63: 460-467. https://doi.org/10.1093/cid/ciw334
[141]
Shin SY, Jeong SH, Sung PS, et al. (2016) Comparative analysis of liver injury-associated cytokines in acute hepatitis A and B. Yonsei Med J 57: 652-657. https://doi.org/10.3349/ymj.2016.57.3.652
[142]
Lou X, Hou Y, Cao H, et al. (2018) Clinical significance of decoy receptor 3 upregulation in patients with hepatitis B and liver fibrosis. Oncol Lett 16: 1147-1154. https://doi.org/10.3892/ol.2018.8762
[143]
Zhang Z, Dai X, Qi J, et al. (2018) Astragalus mongholicus (Fisch.) Bge improves peripheral Treg cell immunity imbalance in the children with viral myocarditis by reducing the levels of miR-146b and miR-155. Front Pediatr 6: 139. https://doi.org/10.3389/fped.2018.00139
[144]
Dambaya B, Nkenfou CN, Ambada G, et al. (2019) Differential expression of Fas receptors (CD95) and Fas ligands (CD95L) in HIV infected and exposed uninfected children in Cameroon versus unexposed children. Pan Afric Med J 34: 39. https://doi.org/10.11604/pamj.2019.34.39.15038
[145]
Liang DY, Sha S, Yi Q, et al. (2019) Hepatitis B X protein upregulates decoy receptor 3 expression via the PI3K/NF-κB pathway. Cell Signal 62: 109346. https://doi.org/10.1016/j.cellsig.2019.109346
[146]
Shata MTM, Abdel-hameed EA, Rouster SD, et al. (2019) HBV and HIV/HBV infected patients have distinct immune exhaustion and apoptotic serum biomarker profiles. Pathog Immun 4: 39-65. https://doi.org/10.20411/pai.v4i1.267
[147]
Mercedes R, Brown J, Minard C, et al. (2020) A liver biopsy validation pilot study of shear wave elastography, APRI, FIB-4, and novel serum biomarkers for liver fibrosis staging in children with chronic viral hepatitis. Glob Pediatr Health 6: 1-8. https://doi.org/10.1177/2333794X20938931
[148]
Abers MS, Delmonte OM, Ricotta EE, et al. (2021) An immune-based biomarker signature is associated with mortality in COVID-19 patients. JCI Insight 6: e144455. https://doi.org/10.1172/jci.insight.144455
[149]
El-Mesery M, El-Mowafy M, Youssef LF, et al. (2021) Serum soluble fibrinogen-like protein 2 represents a novel biomarker for differentiation between acute and chronic Egyptian hepatitis B virus-infected patients. J Interferon Cytokine Res 41: 52-59. https://doi.org/10.1089/jir.2020.0118
[150]
Kessel C, Vollenberg R, Masjosthusmann K, et al. (2021) Discrimination of COVID-19 from inflammation-induced cytokine storm syndromes using disease-related blood biomarkers. Arthritis Rheumatol 73: 1791-1799. https://doi.org/10.1002/art.41763
[151]
Armah HB, Wilson NO, Sarfo BY, et al. (2007) Cerebrospinal fluid and serum biomarkers of malaria mortality in Ghanaian children. Malar J 6: 147. https://doi.org/10.1186/1475-2875-6-147
[152]
Jain V, Armah HB, Tongren JE, et al. (2008) Plasma IP-10, apoptotic and angiogenic factors associated with fatal cerebral malaria in India. Malar J 7: 83. https://doi.org/10.1186/1475-2875-7-83
[153]
Wu SH, Li CT, Lin CH, et al. (2010) Soluble Fas ligand is another good diagnostic marker for tuberculous pleurisy. Diagn Microbiol Infect Dis 68: 395-400. https://doi.org/10.1016/j.diagmicrobio.2010.08.008
[154]
Shu CC, Wu MF, Hsu CL, et al. (2013) Apoptosis-associated biomarkers in tuberculosis: promising for diagnosis and prognosis prediction. BMC Infect Dis 13: 45. https://doi.org/10.1186/1471-2334-13-45
[155]
Goeijenbier M, Gasem MH, Meijers JCM, et al. (2015) Markers of endothelial cell activation and immune activation are increased in patients with severe leptospirosis and associated with disease severity. J Infect 71: 437-446. https://doi.org/10.1016/j.jinf.2015.05.016
[156]
Shu CC, Wang JY, Hsu CL, et al. (2015) Diagnostic role of inflammatory and anti-inflammatory cytokines and effector molecules of cytotoxic T lymphocytes in tuberculous pleural effusion. Respirology 20: 147-154. https://doi.org/10.1111/resp.12414
[157]
Korczynski P, Klimiuk J, Safianowska A, et al. (2019) Impact of age on the diagnostic yield of four different biomarkers of tuberculous pleural effusion. Tuberculosis 114: 24-29. https://doi.org/10.1016/j.tube.2018.11.004
[158]
Struck NS, Zimmermann M, Krumkamp R, et al. (2020) Cytokine profile distinguishes children with Plasmodium falciparum malaria from those with bacterial blood stream infections. J Infect Dis 221: 1098-1106. https://doi.org/10.1093/infdis/jiz587
[159]
Nishigaki K, Minatoguchi S, Seishima M, et al. (1997) Plasma Fas ligand, an inducer of apoptosis, and plasma soluble Fas, an inhibitor of apoptosis, in patients with chronic congestive heart failure. J Am Coll Cardiol 29: 1214-1220. https://doi.org/10.1016/S0735-1097(97)00055-7
[160]
Sliwa K, Skudickey D, Bergemann A, et al. (2000) Peripartum cardiomyopathy: Analysis of clinical outcome, left ventricular function, plasma levels of cytokines and Fas/APO-1. J Am Coll Cardiol 35: 701-705. https://doi.org/10.1016/S0735-1097(99)00624-5
[161]
Adamopoulos S, Parissis J, Karatzas D, et al. (2002) Physical training modulates proinflammatory cytokines and the soluble Fas/soluble Fas ligand system in patients with chronic heart failure. J Am Coll Cardiol 39: 653-663. https://doi.org/10.1016/S0735-1097(01)01795-8
[162]
Shimizu M, Fukuo K, Nagata S, et al. (2002) Increased plasma levels of the soluble form of Fas ligand in patients with acute myocardial infarction and unstable angina pectoris. J Am Coll Cardiol 39: 585-590. https://doi.org/10.1016/S0735-1097(01)01800-9
[163]
Blanco-Colio LM, Martín-Ventura JL, Sol JM, et al. (2004) Decreased circulating Fas ligand in patients with familial combined hyperlipidemia or carotid atherosclerosis. Normalization by atorvastatin. J Am Coll Cardiol 43: 1188-1194. https://doi.org/10.1016/j.jacc.2003.10.046
[164]
Van der Meer IM, Oei HHS, Hofman A, et al. (2006) Soluble Fas, a mediator of apoptosis, C-reactive protein, and coronary and extracoronary atherosclerosis. The Rotterdam coronary calcification study. Atherosclerosis 189: 464-469. https://doi.org/10.1016/j.atherosclerosis.2006.01.004
[165]
Blanco-Colio LM, Martín-Ventura JL, de Teresa E, et al. (2007) Increased soluble Fas plasma levels in subjects at high cardiovascular risk. Atorvastatin on inflammatory markers (AIM) study, a substudy of ACTFAST. Arterioscler Thromb Vasc Biol 27: 168-174. https://doi.org/10.1161/01.ATV.0000250616.26308.d7
[166]
Blanco-Colio LM, Martín-Ventura JL, Tuñón J, et al. (2008) Soluble Fas ligand plasma levels are associated with forearm reactive hyperemia in subjects with coronary artery disease. A novel biomarker of endothelial function?. Atherosclerosis 201: 407-412. https://doi.org/10.1016/j.atherosclerosis.2008.02.005
[167]
Boos CJ, Balakrishnan B, Blann AD, et al. (2008) The relationship of circulating endothelial cells to plasma indices of endothelial damage/dysfunction and apoptosis in acute coronary syndromes: Implications for prognosis. J Thromb Haemost 6: 1841-1850. https://doi.org/10.1111/j.1538-7836.2008.03148.x
[168]
Lanfear DE, Hasan R, Gupta RC, et al. (2009) Short term effects of milrinone on biomarkers of necrosis, apoptosis, and inflammation in patients with severe heart failure. J Trans Med 7: 67. https://doi.org/10.1186/1479-5876-7-67
[169]
Cardinal H, Brophy JM, Bogaty P, et al. (2010) Usefulness of soluble Fas levels for improving diagnostic accuracy and prognosis for acute coronary syndromes. Am J Cardiol 105: 797-803. https://doi.org/10.1016/j.amjcard.2009.10.061
[170]
Oyama JI, Maeda T, Sasaki M, et al. (2010) Green tea catechins improve human forearm vascular function and have potent anti-inflammatory and anti-apoptotic effects in smokers. Intern Med 49: 2553-2559. https://doi.org/10.2169/internalmedicine.49.4048
[171]
Cross DS, McCarty CA, Hytopoulos E, et al. (2012) Improved coronary risk assessment among intermediate risk patients using a clinical and biomarker based algorithm developed and validated in two population cohorts. Curr Med Res Opin 28: 1819-1830. https://doi.org/10.1185/03007995.2012.742878
[172]
Fertin M, Bauters A, Pinet F, et al. (2012) Circulating levels of soluble Fas ligand and left ventricular remodeling after acute myocardial infarction (from the REVE-2 study). J Cardiol 60: 93-97. https://doi.org/10.1016/j.jjcc.2012.03.001
[173]
Kinugawa T, Kato M, Yamamoto K, et al. (2012) Proinflammatory cytokine activation is linked to apoptotic mediator; soluble Fas level in patients with chronic heart failure. Int Heart J 53: 182-186. https://doi.org/10.1536/ihj.53.182
[174]
Sahinarslan A, Boyaci B, Kocaman SA, et al. (2012) The relationship of serum soluble Fas ligand (sFasL) level with the extent of coronary artery disease. Int J Angiol 21: 29-34. https://doi.org/10.1055/s-0032-1306418
[175]
Nilsson L, Szymanowski A, Swahn E, et al. (2013) Soluble TNF receptors are associated with infarct size and ventricular dysfunction in ST-elevation myocardial infarction. Plos One 8: e55477. https://doi.org/10.1371/journal.pone.0055477
[176]
Osmancik P, Teringova E, Tousek P, et al. (2013) Prognostic value of TNF-related apoptosis inducing ligand (TRAIL) in acute coronary syndrome patients. Plos One 8: e53860. https://doi.org/10.1371/journal.pone.0053860
[177]
Shah NR, Bieniarz MC, Basra SS, et al. (2013) Serum biomarkers in severe refractory cardiogenic shock. J Am Coll Cardiol HF 1: 200-206. https://doi.org/10.1016/j.jchf.2013.03.002
[178]
Szymanowski A, Li W, Lundberg A, et al. (2014) Soluble Fas ligand is associated with natural killer cell dynamics in coronary artery disease. Atherosclerosis 233: 616-622. https://doi.org/10.1016/j.atherosclerosis.2014.01.030
[179]
Taneja AK, Gaze D, Coats AJS, et al. (2014) Effects of nebivolol on biomarkers in elderly patients with heart failure. Int J Cardiol 175: 253-260. https://doi.org/10.1016/j.ijcard.2014.05.018
[180]
Chang TY, Hsu CY, Huang PH, et al. (2015) Usefulness of circulating decoy receptor 3 in predicting coronary artery disease severity and future major adverse cardiovascular events in patients with multivessel coronary artery disease. Am J Cardiol 116: 1028-1033. https://doi.org/10.1016/j.amjcard.2015.06.041
[181]
Adly AA, Ismail EA, Andrawes NG, et al. (2016) Soluble Fas/FasL ratio as a marker of vasculopathy in children and adolescents with sickle cell disease. Cytokine 79: 52-58. https://doi.org/10.1016/j.cyto.2015.12.022
[182]
Iso H, Maruyama K, Eshak ES, et al. (2017) Blood soluble Fas levels and mortality from cardiovascular disease in middle-aged Japanese: The JACC study. Atherosclerosis 260: 97-101. https://doi.org/10.1016/j.atherosclerosis.2017.03.020
[183]
Mattisson IY, Björkbacka H, Wigren M, et al. (2017) Elevated markers of death receptor-activated apoptosis are associated with increased risk for development of diabetes and cardiovascular disease. EBioMedicine 26: 187-197. https://doi.org/10.1016/j.ebiom.2017.11.023
[184]
Yan Y, Song D, Liu L, et al. (2017) The relationship of plasma decoy receptor 3 and coronary collateral circulation in patients with coronary artery disease. Life Sci 189: 84-88. https://doi.org/10.1016/j.lfs.2017.09.025
[185]
Li XY, Hou HT, Chen HX, et al. (2018) Increased circulating levels of tumor necrosis factor-like cytokine IA and decoy receptor 3 correlate with SYNTAX score in patients undergoing coronary surgery. J Int Med Res 46: 5167-5175. https://doi.org/10.1177/0300060518793787
[186]
Chen X, Wang R, Chen W, et al. (2019) Decoy receptor-3 regulates inflammation and apoptosis via PI3K/AKT signaling pathway in coronary heart disease. Exp Ther Med 17: 2614-2622. https://doi.org/10.3892/etm.2019.7222
[187]
Miyoshi T, Hosoda H, Nakai M, et al. (2019) Maternal biomarkers for fetal heart failure in fetuses with congenital heart defects or arrhythmias. Am J Obstet Gynecol 220: 104.e1-15. https://doi.org/10.1016/j.ajog.2018.09.024
[188]
Younus M, Fan W, Harrington DS, et al. (2019) Usefulness of a coronary artery disease predictive algorithm to predict global risk for cardiovascular disease and acute coronary syndrome. Am J Cardiol 123: 769-775. https://doi.org/10.1016/j.amjcard.2018.11.044
[189]
Doustkami H, Avesta L, Babapour B, et al. (2021) Correlation of serum decoy receptor 3 and interleukin-6 with severity of coronary artery diseases in male acute myocardial infarction patients. Acta Biomed 92: e2021285. https://doi.org/10.23750/abm.v92i5.9711
[190]
Masse M, Hérbert MJ, Troyanov S, et al. (2002) Soluble Fas is a marker of peripheral arterial occlusive disease in haemodialysis patients. Nephrol Dial Transplant 17: 485-491. https://doi.org/10.1093/ndt/17.3.485
[191]
El-Agroudy AE, El-Baz A (2010) Soluble Fas: a useful marker of inflammation and cardiovascular diseases in uremic patients. Clin Exp Nephrol 14: 152-157. https://doi.org/10.1007/s10157-009-0261-8
[192]
Chang EP, Lin YS, Huang SC, et al. (2012) High serum DcR3 levels are associated with the occurrence of peritonitis in patients receiving chronic peritoneal dialysis. J Chin Med Assoc 75: 644-648. https://doi.org/10.1016/j.jcma.2012.09.009
[193]
Dounousi E, Koliousi E, Papagianni A, et al. (2012) Mononuclear leukocyte apoptosis and inflammatory markers in patients with chronic kidney disease. Am J Nephrol 36: 531-536. https://doi.org/10.1159/000345352
[194]
Hung SC, Hsu TW, Lin YP, et al. (2012) Decoy receptor 3, a novel inflammatory marker, and mortality in hemodialysis patients. Clin J Am Soc Nephrol 7: 1257-1265. https://doi.org/10.2215/CJN.08410811
[195]
Bartnicki P, Rysz J, Franczyk B, et al. (2016) Impact of continuous erythropoietin receptor activator on selected biomarkers of cardiovascular disease and left ventricle structure and function in chronic kidney disease. Oxid Med Cell Longev 2016: 9879615. https://doi.org/10.1155/2016/9879615
[196]
Bhatraju PK, Robinson-Cohen C, Mikacenic C, et al. (2017) Circulating levels of soluble Fas (sCD95) are associated with risk for development of a nonresolving acute kidney injury subphenotype. Critical Care 21: 217. https://doi.org/10.1186/s13054-017-1807-x
[197]
Claro LM, Moreno-Amaral AN, Gadotti AC, et al. (2018) The impact of uremic toxicity induced inflammatory response on the cardiovascular burden in chronic kidney disease. Toxins 10: 384. https://doi.org/10.3390/toxins10100384
[198]
Raptis V, Bakogiannis C, Loutradis C, et al. (2018) Serum Fas ligand, serum myostatin and urine TGF-β1 are elevated in autosomal dominant polycystic kidney disease patients with impaired and preserved renal function. Kidney Blood Press Res 43: 744-754. https://doi.org/10.1159/000489911
[199]
Bhatraju PK, Zelnick LR, Katz R, et al. (2019) A prediction model for severe AKI in critically ill adults that incorporates clinical and biomarker data. Clin J Am Soc Nephrol 14: 506-514. https://doi.org/10.2215/CJN.04100318
[200]
Chiloff DM, de Almeida DC, Dalboni MA, et al. (2020) Soluble Fas affects erythropoiesis in vitro and acts as a potential predictor of erythropoiesis-stimulating agent therapy in patients with chronic kidney disease. Am J Physiol Renal Physiol 318: F861-F869. https://doi.org/10.1152/ajprenal.00433.2019
[201]
Abu-Rajab Tamimi TI, Elgouhari HM, Alkhouri N, et al. (2011) An apoptosis panel for nonalcoholic steatohepatitis diagnosis. J Hepatol 54: 1224-1229. https://doi.org/10.1016/j.jhep.2010.08.023
[202]
Fealy CE, Haus JM, Solomon TPJ, et al. (2012) Short-term exercise reduces markers of hepatocyte apoptosis in nonalcoholic fatty liver disease. J Appl Physiol 113: 1-6. https://doi.org/10.1152/japplphysiol.00127.2012
[203]
Page S, Birerdinc A, Estep M, et al. (2013) Knowledge-based identification of soluble biomarkers: Hepatic fibrosis in NAFLD as an example. Plos One 8: e56009. https://doi.org/10.1371/journal.pone.0056009
[204]
El Bassat H, Ziada DH, Hasby EA, et al. (2014) Apoptotic and anti-apoptotic seromarkers for assessment of disease severity of non-alcoholic steatohepatitis. Arab J Gastroenterol 15: 6-11. https://doi.org/10.1016/j.ajg.2014.01.009
[205]
Buck M, Garcia-Tsao G, Groszmann RJ, et al. (2014) Novel inflammatory biomarkers of portal pressure in compensated cirrhosis patients. Hepatology 59: 1052-1059. https://doi.org/10.1002/hep.26755
[206]
Alkhouri N, Alisi A, Okwu V, et al. (2015) Circulating soluble Fas and Fas ligand levels are elevated in children with nonalcoholic steatohepatitis. Dig Dis Sci 60: 2353-2359. https://doi.org/10.1007/s10620-015-3614-z
[207]
Ajmera V, Perito ER, Bass NM, et al. (2017) Novel plasma biomarkers associated with liver disease severity in adults with nonalcoholic fatty liver disease. Hepatology 65: 65-77. https://doi.org/10.1002/hep.28776
[208]
Perito ER, Ajmera V, Bass NM, et al. (2017) Association between cytokines and liver histology in children with nonalcoholic fatty liver disease. Hepatol Comm 1: 609-622. https://doi.org/10.1002/hep4.1068
[209]
Lin S, Wu B, Lin Y, et al. (2019) Expression and clinical significance of decoy receptor 3 in acute-on-chronic liver failure. BioMed Res Int 2019: 9145736. https://doi.org/10.1155/2019/9145736
[210]
Yasuda N, Gotoh K, Minatoguchi S, et al. (1998) An increase of soluble Fas, an inhibitor of apoptosis, associated with progression of COPD. Respir Med 92: 993-999. https://doi.org/10.1016/S0954-6111(98)90343-2
[211]
Takabatake N, Arao T, Sata M, et al. (2005) Circulating levels of soluble Fas ligand in cachexic patients with COPD are higher than those in non-cachexic patients with COPD. Intern Med 44: 1137-1143. https://doi.org/10.2169/internalmedicine.44.1137
[212]
Chen CY, Yang KY, Chen MY, et al. (2009) Decoy receptor 3 levels in peripheral blood predict outcomes of acute respiratory distress syndrome. Am J Respir Crit Care Med 180: 751-760. https://doi.org/10.1164/rccm.200902-0222OC
[213]
Carolan BJ, Hughes G, Morrow J, et al. (2014) The association of plasma biomarkers with computed tomography-assessed emphysema phenotypes. Respir Res 15: 127. https://doi.org/10.1186/s12931-014-0127-9
[214]
Stapleton RD, Suratt BT, Neff MJ, et al. (2019) Bronchoalveolar fluid and plasma inflammatory biomarkers in contemporary ARDS patients. Biomarkers 24: 352-359. https://doi.org/10.1080/1354750X.2019.1581840
[215]
Aydin H, Tekin YK, Korkmaz I, et al. (2020) Apoptosis biomarkers (APAF-1, sFas, sFas-L, and caspase-9), albumin, and fetuin-A levels in pulmonary thromboembolic patients. Disaster Emerg Med J 5: 1-6. https://doi.org/10.5603/DEMJ.a2020.0005
[216]
Ghobadi H, Hosseini N, Aslani MR (2020) Correlations between serum decoy receptors 3 and airflow limitation and quality of life in male patients with stable stage and acute exacerbation of COPD. Lung 198: 515-523. https://doi.org/10.1007/s00408-020-00348-z
[217]
Felderhoff-Mueser U, Bührer C, Groneck P, et al. (2003) Soluble Fas (CD95/Apo-1), soluble Fas ligand, and activated caspase 3 in the cerebrospinal fluid of infants with posthemorrhagic and nonhemorrhagic hydrocephalus. Pediatr Res 54: 659-664. https://doi.org/10.1203/01.PDR.0000084114.83724.65
[218]
Sival DA, Felderhoff-Müser U, Schmitz T, et al. (2008) Neonatal high pressure hydrocephalus is associated with elevation of pro-inflammatory cytokines IL-18 and IFNγ in cerebrospinal fluid. Cerebrospinal Fluid Res 5: 21. https://doi.org/10.1186/1743-8454-5-21
[219]
Craig-Schapiro R, Kuhn M, Xiong C, et al. (2011) Multiplexed immunoassay panel identifies novel CSF biomarkers for Alzheimer's disease diagnosis and prognosis. Plos One 6: e18850. https://doi.org/10.1371/journal.pone.0018850
[220]
Leifsdottir K, Mehmet H, Eksborg S, et al. (2018) Fas-ligand and interleukin-6 in the cerebrospinal fluid are early predictors of hypoxic-ischemic encephalopathy and long-term outcomes after birth asphyxia in term infants. J Neuroinflammation 15: 223. https://doi.org/10.1186/s12974-018-1253-y
[221]
Jiang W, Jin P, Wei W, et al. (2020) Apoptosis in cerebrospinal fluid as outcome predictors in severe traumatic brain injury. An observational study. Medicine 99: e20922. https://doi.org/10.1097/MD.0000000000020922
[222]
Niewczas MA, Ficociello LH, Johnson AC, et al. (2009) Serum concentrations of markers of TNFα and Fas-mediated pathways and renal function in nonproteinuric patients with type 1 diabetes. Clin J Am Soc Nephrol 4: 62-70. https://doi.org/10.2215/CJN.03010608
[223]
Hussian SK, Al-Karawi IN, Sahab KS (2013) Role of Fas/Fas ligand pathway in a sample of Iraqi diabetic foot patients. Diyala J Med 5: 95-107.
[224]
Stoynev N, Kainov K, Kirilov G, et al. (2014) Serum levels of sFas and sFasL in subjects with type 2 diabetes-the impact of arterial hypertension. Cent Eur J Med 9: 704-708. https://doi.org/10.2478/s11536-013-0318-7
[225]
Midena E, Micera A, Frizziero L, et al. (2019) Sub-threshold micropulse laser treatment reduces inflammatory biomarkers in aqueous humour of diabetic patients with macular edema. Sci Rep 9: 10034. https://doi.org/10.1038/s41598-019-46515-y
[226]
Abe R, Shimizu T, Shibaki A, et al. (2003) Toxic epidermal necrolysis and Stevens-Johnson syndrome are induced by soluble Fas ligand. Am J Pathol 162: 1515-1520. https://doi.org/10.1016/S0002-9440(10)64284-8
[227]
Stur K, Karlhofer FM, Stingl G, et al. (2007) Soluble FAS ligand: A discriminating feature between drug-induced skin eruptions and viral exanthemas. J Invest Dermatol 127: 802-807. https://doi.org/10.1038/sj.jid.5700648
[228]
Chung WH, Hung SI, Yang JY, et al. (2008) Granulysin is a key mediator for disseminated keratinocyte death in Stevens-Johnson syndrome and toxic epidermal necrolysis. Nat Med 14: 1343-1350. https://doi.org/10.1038/nm.1884
[229]
Murata J, Abe R, Shimizu H (2008) Increased soluble Fas ligand levels in patients with Stevens-Johnson syndrome and toxic epidermal necrolysis preceding skin detachment. J Allergy Clin Immunol 122: 992-1000. https://doi.org/10.1016/j.jaci.2008.06.013
[230]
Genç ŞÖ, Karakuş S, Çetin A, et al. (2019) Serum Bcl-2, caspase-9 and soluble FasL as perinatal markers in late preterm pregnancies with intrauterine growth restriction. Turk J Pediatr 61: 686-696. https://doi.org/10.24953/turkjped.2019.05.007
[231]
Yeh CC, Yang MJ, Lussier EC, et al. (2019) Low plasma levels of decoy receptor 3 (DcR3) in the third trimester of pregnancy with preeclampsia. Taiwan J Obstet Gynecol 58: 349-353. https://doi.org/10.1016/j.tjog.2019.03.011
[232]
Oyama JI, Yamamoto H, Maeda T, et al. (2012) Continuous positive airway pressure therapy improves vascular dysfunction and decreases oxidative stress in patients with the metabolic syndrome and obstructive sleep apnea syndrome. Clin Cardiol 35: 231-236. https://doi.org/10.1002/clc.21010
[233]
Chedraui P, Escobar GS, Pérez-López FR, et al. (2014) Angiogenesis, inflammation and endothelial function in postmenopausal women screened for the metabolic syndrome. Maturitas 77: 370-374. https://doi.org/10.1016/j.maturitas.2014.01.014
[234]
Alkhouri N, Kheirandish-Gozal L, Matloob A, et al. (2015) Evaluation of circulating markers of hepatic apoptosis and inflammation in obese children with and without obstructive sleep apnea. Sleep Med 16: 1031-1035. https://doi.org/10.1016/j.sleep.2015.05.002
[235]
Farey JE, Fisher OM, Levert-Mignon AJ, et al. (2017) Decreased levels of circulating cancer-associated protein biomarkers following bariatric surgery. Obes Surg 27: 578-585. https://doi.org/10.1007/s11695-016-2321-y
[236]
Goto M (2008) Elevation of soluble Fas (APO-1, CD95) ligand in natural aging and Werner syndrome. BioSci Trend 2: 124-127.
[237]
Jiang S, Moriarty-Craige SE, Li C, et al. (2008) Associations of plasma-soluble Fas ligand with aging and age-related macular degeneration. Invest Ophthalmol Vis Sci 49: 1345-1349. https://doi.org/10.1167/iovs.07-0308
[238]
Karaflou M, Kaparos G, Rizos D, et al. (2010) Estrogen plus progestin treatment: effect of different progestin components on serum markers of apoptosis in healthy postmenopausal women. Fertil Steril 94: 2399-2401. https://doi.org/10.1016/j.fertnstert.2010.04.010
[239]
Kämppä N, Mäkelä KM, Lyytikäinen LP, et al. (2013) Vascular cell adhesion molecule 1, soluble Fas and hepatocyte growth factor as predictors of mortality in nonagenarians: The vitality 90+ study. Exp Gerontrol 48: 1167-1172. https://doi.org/10.1016/j.exger.2013.07.009
[240]
Kangas R, Pöllänen E, Rippo MR, et al. (2014) Circulating miR-21, miR-146a and Fas ligand respond to postmenopausal estrogen-based hormone replacement therapy—a study with monozygotic twin pairs. Mech Ageing Dev 143–144: 1-8. https://doi.org/10.1016/j.mad.2014.11.001
[241]
Wu Q, Chen H, Fang J, et al. (2012) Elevated Fas/FasL system and endothelial cell microparticles are involved in endothelial damage in acute graft-versus-host disease: A clinical analysis. Leuk Res 36: 275-280. https://doi.org/10.1016/j.leukres.2011.08.005
[242]
Fadel FI, Elshamaa MF, Salah A, et al. (2016) Fas/Fas ligand pathways gene polymorphisms in pediatric renal allograft rejection. Transpl Immunol 37: 28-34. https://doi.org/10.1016/j.trim.2016.04.006
[243]
Meki ARAM, Hasan HA, El-Deen ZMM, et al. (2003) Dysregulation of apoptosis in scorpion envenomed children: its reflection on their outcome. Toxicon 42: 229-237. https://doi.org/10.1016/S0041-0101(03)00128-4
[244]
Tamakoshi A, Suzuki K, Lin Y, et al. (2009) Cigarette smoking and serum soluble Fas levels: Findings from the JACC study. Mutat Res 679: 79-83. https://doi.org/10.1016/j.mrgentox.2009.08.002
[245]
Muraki M (2018) Development of expression systems for the production of recombinant human Fas ligand extracellular domain derivatives using Pichia pastoris and preparation of the conjugates by site-specific chemical modifications: A review. AIMS Bioengineer 5: 39-62. https://doi.org/10.3934/bioeng.2018.1.39
[246]
Muraki M, Hirota K (2019) Site-specific biotin-group conjugate of human Fas ligand extracellular domain: preparation and characterization of cell-death-inducing activity. Curr Top Pep Prot Res 20: 17-24.
[247]
Muraki M, Honda S (2010) Efficient production of human Fas receptor extracellular domain-human IgG1 heavy chain Fc domain fusion protein using baculovirus/silkworm expression system. Prot Expr Purif 73: 209-216. https://doi.org/10.1016/j.pep.2010.05.007
[248]
Connolly K, Cho YH, Duan R, et al. (2001) In vivo inhibition of Fas ligand-mediated killing by TR6, a Fas ligand decoy receptor. J Pharmacol Exp Ther 298: 25-33.
Table 5.
Data on the wind speed (knots) from the Chanthaburi Weather Observing Station, Chumphon Weather Observing Station, and Songkhla Weather Observing Station, Thailand.