Loading [Contrib]/a11y/accessibility-menu.js
Review Special Issues

Mycobacterium avium complex: Adherence as a way of life

  • Mycobacterium avium complex (MAC) organisms are waterborne, opportunistic pathogens whose source is natural waters and soils and proliferates and persists in premise plumbing, for example household and hospital plumbing. M. avium complex and other environmental mycobacteria grow slowly, not because their metabolism is slow, but because they synthesize long chain (C60–C80) fatty acids that make up its hydrophobic and impermeable outer membrane. There are costs and benefits to the presence of that lipid-rich outer membrane. One benefit is that cell-surface hydrophobicity drives M. avium complex cells to adhere to surfaces to reduce their interaction with charged ions in suspension; they are likely “biofilm pioneers”, adhering to a wide variety of surface materials. The result is that the slow-growing M. avium complex cells (1 gen/day at 37 °C) will not be washed out of any flowing system, whether a stream or plumbing in the built environment. Although the slow permeation of nutrients in M. avium complex organisms limits growth, they are also resistant to disinfectants, thus increasing their survival in water distribution systems, premise plumbing, and medical equipment. There are three components to the antimicrobial resistance of M. avium complex in biofilms: (1) innate resistance due to the hydrophobic, impermeable outer membrane, (2) residence in a matrix of extracellular polysaccharide, lipid, DNA, and protein that prevents access of antimicrobials to M. avium cells, and (3) an adaptive and transient increased resistance of biofilm-grown M. avium cells grown in biofilms. As expected M. avium in biofilms will display neutral, antagonistic, or beneficial interactions with other biofilm inhabitants. Methylobacterium spp., the common pink-pigmented, waterborne bacteria compete with M. avium for surface binding, suggested an approach to reducing M. avium biofilm formation and hence persistence in premise plumbing.

    Citation: Joseph O. Falkinham. Mycobacterium avium complex: Adherence as a way of life[J]. AIMS Microbiology, 2018, 4(3): 428-438. doi: 10.3934/microbiol.2018.3.428

    Related Papers:

    [1] Merve Temizer Ersoy, Hasan Furkan . Distinguished subspaces in topological sequence spaces theory. AIMS Mathematics, 2020, 5(4): 2858-2868. doi: 10.3934/math.2020183
    [2] Cai-Yun Li, Chun-Gang Zhu . Construction of the spacelike constant angle surface family in Minkowski 3-space. AIMS Mathematics, 2020, 5(6): 6341-6354. doi: 10.3934/math.2020408
    [3] Mahmut Karakuş . Spaces of multiplier $ \sigma $-convergent vector valued sequences and uniform $ \sigma $-summability. AIMS Mathematics, 2025, 10(3): 5095-5109. doi: 10.3934/math.2025233
    [4] Jesús Gómez-Gardeñes, Ernesto Estrada . Network bipartitioning in the anti-communicability Euclidean space. AIMS Mathematics, 2021, 6(2): 1153-1174. doi: 10.3934/math.2021070
    [5] Osman Duyar . On some new vector valued sequence spaces $ E(X, \lambda, p) $. AIMS Mathematics, 2023, 8(6): 13306-13316. doi: 10.3934/math.2023673
    [6] Faik Babadağ, Ali Atasoy . A new approach to Leonardo number sequences with the dual vector and dual angle representation. AIMS Mathematics, 2024, 9(6): 14062-14074. doi: 10.3934/math.2024684
    [7] Mingzhou Xu . On the complete moment convergence of moving average processes generated by negatively dependent random variables under sub-linear expectations. AIMS Mathematics, 2024, 9(2): 3369-3385. doi: 10.3934/math.2024165
    [8] Hsien-Chung Wu . Near fixed point theorems in near Banach spaces. AIMS Mathematics, 2023, 8(1): 1269-1303. doi: 10.3934/math.2023064
    [9] Faik Babadağ, Ali Atasoy . On hyper-dual vectors and angles with Pell, Pell-Lucas numbers. AIMS Mathematics, 2024, 9(11): 30655-30666. doi: 10.3934/math.20241480
    [10] Kemal Eren, Soley Ersoy, Mohammad Nazrul Islam Khan . Novel theorems on constant angle ruled surfaces with Sasai's interpretation. AIMS Mathematics, 2025, 10(4): 8364-8381. doi: 10.3934/math.2025385
  • Mycobacterium avium complex (MAC) organisms are waterborne, opportunistic pathogens whose source is natural waters and soils and proliferates and persists in premise plumbing, for example household and hospital plumbing. M. avium complex and other environmental mycobacteria grow slowly, not because their metabolism is slow, but because they synthesize long chain (C60–C80) fatty acids that make up its hydrophobic and impermeable outer membrane. There are costs and benefits to the presence of that lipid-rich outer membrane. One benefit is that cell-surface hydrophobicity drives M. avium complex cells to adhere to surfaces to reduce their interaction with charged ions in suspension; they are likely “biofilm pioneers”, adhering to a wide variety of surface materials. The result is that the slow-growing M. avium complex cells (1 gen/day at 37 °C) will not be washed out of any flowing system, whether a stream or plumbing in the built environment. Although the slow permeation of nutrients in M. avium complex organisms limits growth, they are also resistant to disinfectants, thus increasing their survival in water distribution systems, premise plumbing, and medical equipment. There are three components to the antimicrobial resistance of M. avium complex in biofilms: (1) innate resistance due to the hydrophobic, impermeable outer membrane, (2) residence in a matrix of extracellular polysaccharide, lipid, DNA, and protein that prevents access of antimicrobials to M. avium cells, and (3) an adaptive and transient increased resistance of biofilm-grown M. avium cells grown in biofilms. As expected M. avium in biofilms will display neutral, antagonistic, or beneficial interactions with other biofilm inhabitants. Methylobacterium spp., the common pink-pigmented, waterborne bacteria compete with M. avium for surface binding, suggested an approach to reducing M. avium biofilm formation and hence persistence in premise plumbing.


    Workplace bullying can be defined as a situation where a worker is, for a long time period, exposed to persistent negative mistreatment, consisting of frequent and constant criticism and person-related physical, verbal, or psychological violence [1],[2]. The three main characteristics of bullying, which differentiate it from some isolated episodes of conflict or workplace aggression, are that the employee becomes the target of repeated negative and undesirable social behaviors, the experience is for a lengthy period of time, and the victims perceive that they cannot easily escape from the negative situation or stop the undesirable behavior [3].

    The healthcare sector is a workplace with a high prevalence of bullying, with up to 84% of health professionals reporting that they have been bullied at work [4],[5]. Moreover, the prevalence of bullying among nurses ranges from 2%–81% [6]. The lack of leadership skills and organizational support from supervisors, the highly stressful and demanding working environment of nurses, and the limited number of measures to prevent bullying may enhance the development of bullying behavior [7][9]. The pandemic created a particularly demanding working environment characterized by very high workloads, overtime, stress, burnout, and lack of material resources. Within these working conditions, incidents of bullying increased [10].

    The phenomenon of burnout, which constitutes a response to prolonged exposure to occupational stressors, since the mid-1970s when it was first named in a study that examined the experience of people working in healthcare [11], continues to be the subject of many studies in the health sector even today. Among health professionals, nurses experience high rates of burnout. There are estimations that the prevalence of burnout worldwide is 11.23% [12], however studies in different settings and countries show that it exceeds 30% of nursing staff [13][15]. Maslach and colleagues have highlighted six organizational attributes that are related to the development of burnout in healthcare organizations: workload, control, reward, community, fairness, and values [11],[16]. Nurses working in understaffed departments, with high workloads and shortages of material resources, are more likely to develop burnout [17][19]. Studies in different settings reveal the high incidence of bullying, which is significantly associated with the development of burnout among nurses [20][22]. Also, when nurses' work organizations recognize their valuable contributions and show concern for their well-being, with nurses participating in hospital affairs and decision-making, as well as having good collegial nurse–physician relationships and organizational values for support, goals, innovation, and rules, then the likelihood of nurses experiencing burnout is reduced [23][26]. During the pandemic period, the same organizational factors were found to influence the occurrence of high rates of burnout in nursing staff [27]. However, even after the pandemic, burnout rates among nurses remain very high, showing that demanding working conditions and organizational weaknesses still exist [28].

    A particularly frequent phenomenon among nursing staff, which affects the staffing and functioning of healthcare organizations, is their turnover intention. This may involve changing positions within the organization, moving to another, or even leaving permanently from the profession and following another career path [29]. Given that turnover intention is a strong predictor of the actual turnover behavior [30],[31], it is a constant threat than could affects the staffing and functioning of healthcare organizations. Both prior to and during the pandemic, the prevalence of turnover intention of nurses remained very high, exceeding 40% [32],[33], which is higher than for other health professionals [34]. The end of the pandemic also finds a large percentage of nurses (>40%) declaring their turnover intention [35]. Several organizational factors, such as reduced organizational support and understaffing, create the conditions for nurses' turnover intention [36][39]. When nursing staff experience increased burnout, dissatisfaction, stress, fatigue, and depression, the likelihood of choosing turnover increases [13],[40][44]. In addition to the above factors, which take a physical and mental toll on nurses, there is also the burden of workplace bullying, with nurses who experience it declaring their turnover intention [45][47]. Increased turnover rates of nursing staff have multifaceted effects on their health, nursing care quality, interpersonal relationships, workload and work environment, nurses' perceptions of responsibility, and the economic burden of recruiting new staff [48].

    Until now, there are no studies in Greece measuring the relationship between workplace bullying, job burnout, and turnover intention in nursing staff. Moreover, we have not identified studies worldwide that measured the relationships between these variables post-pandemic. Thus, our aim was to investigate the influence of workplace bullying on job burnout and turnover intention in nursing staff following the pandemic. In our study, we focused only on workplace bullying that nurses experienced from superiors and colleagues, and not from other sources such as patients.

    We conducted a cross-sectional study during January–February 2024. We enrolled a convenience sample of nursing staff. Inclusion criteria were: (a) work in clinical settings, i.e., hospitals, (b) clinical experience at least for two years, and (c) good use of the Greek language. We developed an anonymous online version of our questionnaire with Google forms, which was posted in nursing groups through social media. Additionally, we performed face-to-face interviews with nurses who did not have social media accounts.

    Assuming a low effect size (f2 = 0.03) of workplace bullying on job burnout and turnover intention, the number of independent variables (one predictor [i.e., workplace bullying] and five confounders, a confidence level of 95%, and a margin of 5% error, the sample size was estimated at 436 participants.

    We used the 22-item Negative Acts Questionnaire-Revised (NAQ-R) to evaluate nurses' perceptions of their workplace bullying experience across the preceding six months [49]. The NAQ-R [49] determines the frequency that participants have faced bullying behaviors in the last six months. The NAQ-R measures perceived exposure to bullying from colleagues and superiors, and includes items such as “coworkers/superiors withholding information which affects your performance”, “being ignored or excluded from coworkers/superiors”, and “excessive monitoring of my work from coworkers/superiors”. Answers are on a 5-point Likert-type scale ranging from 1 (never) to 5 (daily). The total NAQ-R score is estimated by summing up the 22 items and ranges from 22 to 110 [49]. A higher score implies more frequent workplace bullying. The Greek version of the NAQ-R is a valid measure [50]. Cronbach's alpha was 0.963 in our study.

    We used the single-item burnout measure in this study [51]. This instrument assesses burnout on a scale from 0 (not at all burnt-out) to 10 (extremely burnt-out). Higher values indicate increased job burnout. The validated Greek version was used [52].

    We assessed nursing staff's turnover intention with the following question: “How often have you seriously considered leaving your current job?” [53]. Answers are on a 6-point Likert-type scale that ranges from 1 (never) to 6 (extremely often). Higher values indicate higher turnover intention. Values <3 indicate a low level of turnover intention; values ≥4 indicate increased turnover intention.

    For demographic and job characteristics, we included: sex (female/male), age (in years), clinical experience (in years), shift work (no/yes), and understaffed department (no/yes). Shift work referred to nurses working in the night shift as well as in the morning shift.

    The study protocol was submitted for approval from the Ethics Committee of the Department of Nursing of the National and Kapodistrian University of Athens (approval number 479, 10 January 2024). We conducted this study in accordance with the Declaration of Helsinki [54]. Individual data of nursing staff were not collected and informed consent was obtained.

    We present categorical variables as numbers and percentages. Also, we present continuous variables as the mean, standard deviation (SD), median, and range. We used the Kolmogorov-Smirnov test to detect continuous variables' distribution. The NAQ-R score [49], job burnout score, and age followed normal distribution but not clinical experience. The NAQ-R score was the independent variable; job burnout and turnover intention were the dependent variables. We considered demographic and job characteristics as potential confounders. Since the job burnout score was a continuous variable, we performed univariate and multivariable linear regression analyses. In this case, we present unadjusted and adjusted beta coefficients, 95% confidence intervals (CI), and p-values. Given that turnover intention was a dichotomous variable, we performed univariate and multivariable logistic regression analyses, and we calculated unadjusted and adjusted odds ratios (OR), 95% Cis, and p-values. By constructing multivariable regression models, we removed confounding by demographic and job variables. Statistical significance was considered at p < 0.05. We performed our analysis with the IBM SPSS 21.0 (IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. IBM Corp., Armonk, NY, USA).

    The study sample was comprised of 450 nurses. The mean age of nurses was 39.1 years (SD = 10.2), the median age was 39 years, and 86.7% were females. In our sample, 74.7% have been working in night shifts and 80.2% declared that they were working in understaffed departments. The mean years of clinical experience was 14.3 (SD = 10.3), while the median and range were 14 and 39 years, respectively. The demographic and job characteristics are shown in Table 1.

    Table 1.  Demographic and job characteristics of nurses (N = 450).
    Variables N %
    Sex
     Males 60 13.3
     Females 390 86.7
    Age (years)a 39.1 10.2
    Clinical experience (years)a 14.3 10.3
    Shift work
     No 114 25.3
     Yes 336 74.7
    Understaffed department
     No 89 19.8
     Yes 361 80.2

    Note: a mean, standard deviation.

     | Show Table
    DownLoad: CSV

    The mean score on the NAQ-R was 51.5 (SD = 20.5), ranging from 22 to 108 and a median value of 47. The mean job burnout score was 7.7 (SD = 2.0), ranging from 0 to 10 and a median value of 8, and 57.3% (n = 258) of nurses had increased turnover intention.

    After eliminating confounders, we found that increased workplace bullying (adjusted beta = 0.031, 95% CI = 0.023 to 0.039, p < 0.001) was associated with increased job burnout. Additionally, nurses who have been working in night shifts (adjusted beta = 0.694, 95% CI = 0.296 to 1.091, p = 0.001) and those who have been working in understaffed departments (adjusted beta = 0.748, 95% CI = 0.329 to 1.168, p = 0.001) experienced higher levels of job burnout. Linear regression analysis with job burnout as the dependent variable is shown in Table 2.

    Table 2.  Univariate and multivariable linear regression analysis with job burnout (continuous variable) as the dependent variable.
    Independent variables Univariate model
    Multivariable model
    Unadjusted coefficient beta (95% CI) p-value Adjusted coefficient beta (95% CI)a p-value
    Females vs. males 0.155 (-0.381 to 0.691) 0.570 0.035 (-0.450 to 0.519) 0.889
    Age (years) 0.034 (0.016 to 0.052) <0.001 0.039 (-0.002 to 0.079) 0.064
    Clinical experience 0.032 (0.014 to 0.049) <0.001 0.008 (-0.033 to 0.049) 0.715
    Shift work (yes vs. no) 0.543 (0.127 to 0.959) 0.011 0.694 (0.296 to 1.091) 0.001
    Understaffed department (yes vs. no) 1.120 (0.674 to 1.566) <0.001 0.748 (0.329 to 1.168) 0.001
    NAQ-R score 0.033 (0.025 to 0.042) <0.001 0.031 (0.023 to 0.039) <0.001

    Note: Bold p-values indicate statistically significant associations in the multivariable model. CI: confidence interval. a p-value for ANOVA < 0.001; R2 for the final multivariable model was 19.9%.

     | Show Table
    DownLoad: CSV

    Multivariable logistic regression analysis identified that increased turnover intention was more frequent among nurses who experienced higher levels of workplace bullying (adjusted odds ratio = 1.057, 95% CI = 1.043 to 1.071, p < 0.001). Moreover, we found that increased turnover intention was 1.690 times more frequent among nurses who have been working in understaffed departments (adjusted odds ratio = 1.690, 95% CI = 1.005 to 2.845, p = 0.048). Logistic regression analysis with turnover intention as the dependent variable is shown in Table 3.

    Table 3.  Univariate and multivariable logistic regression analysis with turnover intention (dichotomous variable) as the dependent variable (reference category: low level of turnover intention).
    Independent variables Univariate model
    Multivariable model
    Unadjusted odds ratio (95% CI) p-value Adjusted odds ratio (95% CI)a p-value
    Females vs. males 1.303 (0.756 to 2.246) 0.341 1.081 (0.583 to 2.002) 0.805
    Age (years) 1.002 (0.984 to 1.021) 0.823 1.026 (0.974 to 1.079) 0.335
    Clinical experience 1.001 (0.983 to 1.019) 0.944 0.988 (0.938 to 1.040) 0.645
    Shift work (yes vs. no) 1.637 (1.068 to 2.511) 0.055 1.647 (0.989 to 2.742) 0.055
    Understaffed department (yes vs. no) 2.351 (1.465 to 3.775) <0.001 1.690 (1.005 to 2.845) 0.048
    NAQ-R score 1.057 (1.043 to 1.071) <0.001 1.057 (1.043 to 1.071) <0.001

    Note: Bold p-values indicate statistically significant associations in the multivariable model. CI: confidence interval. a R2 for the final multivariable model was 28.2%.

     | Show Table
    DownLoad: CSV

    This study showed the association of workplace bullying with burnout and turnover intention of nurses. As we mentioned above in detail, we measured workplace bullying that nurses experienced by colleagues and superiors. Two other factors, shift work and understaffing, were also found to influence the occurrence of burnout and turnover intention. Violence at the workplace of nurses is a common phenomenon, as nurses are often victims of violence from patients and their relatives. Young nurses who have just entered the profession are more often victims of bullying than older and more experienced nurses [7],[55]. Despite their limited work experience, young nurses who are victims of bullying may suffer burnout and other mental health disorders, and this will ultimately lead to turnover or leaving the nursing profession entirely [56].

    Nursing staff's turnover intention is an ongoing risk to the sustainability of both the performance of health organizations and the healthcare system. Turnover intention places a financial burden on healthcare organizations. It is estimated that the cost of recruiting, training, and filling the vacancy caused by the turnover of a nurse can reach $88 thousand dollars [57],[58]. At the same time, staffing problems arise until the position is filled, with the remaining nurses having a higher workload and being burdened with more night and weekend shifts. Also, the turnover and shortage of experienced, university graduate nurses can affect the efficiency of healthcare organizations [59]. According to the findings of this study, understaffing and shift working are associated with the manifestation of burnout and turnover intention. Thus, we observe that a vicious cycle of nurses' burden is created, where they are trapped between the interaction of bullying, burnout, turnover intention, and understaffing, with victims being not only themselves but the patients (whose safety is compromised) and, consequently, the healthcare organizations. The impact of turnover intention also extends to health systems. Nursing staffing is the Achilles heel of health systems and will probably be their greatest challenge in the coming years. It was mentioned above that turnover intention is a robust predictor of the actual turnover behavior, with high percentages of nurses reporting their intention for turnover. A USA National Council of State Boards of Nursing study revealed a severe nursing personnel shortage and a crisis, where 100,000 nurses were recorded leaving the profession during the COVID-19 pandemic, with almost 900,000 nurses expressing their turnover intention until 2027 [60]. This situation is made even more ominous by estimates that by 2030 there will be significant shortages of health professionals, with nurses holding the highest proportion of shortages [61]. The incidents of bullying experienced by nurses contribute to increasing work stress and, at the same time, to the deterioration of the quality of their working life [62]. Nurses, in their attempt to protect themselves in difficult working conditions, although they remain in their position, opt for quiet quitting, which is a situation where they provide their minimum services, just enough to avoid being fired [63]. Bullying is a factor that triggers the occurrence of quiet quitting in nurses [64]. However, quiet quitting does not seem to be enough to stop turnover either, as those who choose it are more likely to experience turnover [35].

    Bullying has a direct and indirect (through burnout) effect on the quality of care. Nurses who are bullied are more prone to errors during the provision of care [65] and patients may experience adverse events [66]. Exhausted nurses due to bullying express worse personal- and ward-level patient safety opinions [67]. Patients hospitalized in units with a high rate of nurse burnout have a greater risk of suffering from a harmful event, such as a pressure ulcer, fall, medication error, or surgical wound infection, as well as expressing dissatisfaction with the care provided [68],[69]. Patient safety is often at risk, and efforts to improve patient safety by health service organization managements have been ongoing for decades [70],[71]. Therefore, bullying and burnout are an alarming issue as they undermine efforts to improve patient safety, creating conditions for errors and negligence.

    Efforts to prevent and reduce bullying should start with interventions that address attributes of the nurses' work environment that are linked with the incidence of bullying behaviors. Other interventions that can be implemented may include training activities, cognitive rehearsal, team-building, and sharing experiences of nurse managers. Training programs usually target detecting bullying behavior and perceiving the effects of this behavior, responding under stress, recognizing conflict resolution strategies, establishing a safe environment, and handling critical debates. Finally, the contribution of nurse supervisors in reducing bullying includes creating a healthy work environment with the main characteristics of collaboration, respect, effective interpersonal communication, collegiality, and mutual support between those entering the profession and senior nursing staff [72].

    Our study had several limitations. We conducted an online survey through social media to collect our data. Also, we used a convenience sample. Despite achieving the minimum requirements for sample size, we cannot generalize our results since our sample was not representative of nurses in Greece. Thus, we cannot extend our findings to all nurses or all healthcare workers in Greece. Therefore, scholars should pay attention to the generalization of our results. Further research with random and stratified samples of nurses may reduce this bias. We used valid tools to measure workplace bullying, job burnout, and turnover intention. However, these tools are self-reported and, consequently, information bias is probable. Moreover, we measured job burnout and turnover intention with scales that were comprised of only one item. Also, our measurement methods were weak since they were based on uncontrolled reporting. Thus, further studies with more valid measurements should improve our knowledge. We eliminated several confounders on the correlation among bullying and burnout and turnover intention. However, further confounders can be eliminated in future studies. Because we conducted a cross-sectional study, it is not possible to conclude a causal relationship among workplace bullying, job burnout, and turnover intention. Longitudinal studies monitoring nurses' attitudes may reduce this bias. We examined, for the first time, the effect of workplace bullying on burnout and turnover intention in a sample of nurses after the pandemic; further studies may expand this research question by examining the mediating or/and moderating effect of job satisfaction, quiet quitting, and coping strategies.

    Workplace bullying is an important form of violence with a high prevalence in healthcare settings, with nurses experiencing such behaviors very often. The impact of burnout is multifaceted and negatively affects staff and patients. The significant consequences of burnout and turnover intention on the functioning of healthcare organizations make it imperative that healthcare organization managements mobilize to ensure a healthy work environment that does not foster the development of bullying behaviors while ensuring their optimal diffusion by staff and supervisors.

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    [1] Falkinham JO (2009) Surrounded by mycobacteria: nontuberculous mycobacteria in the human environment. J Appl Microbiol 107: 356–367. doi: 10.1111/j.1365-2672.2009.04161.x
    [2] Prevots DR, Marras TK (2015) Epidemiology of human pulmonary infection with nontuberculous mycobacteria: a review. Clin Chest Med 36: 13–34. doi: 10.1016/j.ccm.2014.10.002
    [3] Marras TK, Daley CL (2002) Epidemiology of human and pulmonary infection with nontuberculous mycobacteria. Clin Chest Med 23: 553–567. doi: 10.1016/S0272-5231(02)00019-9
    [4] Prince DS, Peterson DD, Steiner RM, et al. (1989) Infection with Mycobacterium avium complex in patients without predisposing conditions. N Engl J Med 321: 863–868. doi: 10.1056/NEJM198909283211304
    [5] Griffith DE, Aksamit T, Brown-Elliott BA, et al. (2007) An official ATS/IDSA statement: Diagnosis, treatment, and prevention of nontuberculous mycobacterial diseases. Am J Resp Crit Care 175: 367–416. doi: 10.1164/rccm.200604-571ST
    [6] Wallace RJ, Zhang Y, Brown-Elliott BA, et al. (2002) Repeat positive cultures in Mycobacterium intracellulare lung disease after macrolide therapy represent new infection in patients with nodular bronchiectasis. J Infect Dis 186: 266–173. doi: 10.1086/341207
    [7] Boyle DP, Zembower TR, Qi C (2016) Relapse versus reinfection of Mycobacterium avium complex pulmonary disease: patient characteristics and macrolide susceptibility. Ann Am Thorac Soc 13: 1956–1961. doi: 10.1513/AnnalsATS.201605-344BC
    [8] Wolinsky E (1995) Mycobacterial lymphadenitis in children: a prospective study of 105 nontuberculous cases with long-term follow up. Clin Infect Dis 20: 954–963. doi: 10.1093/clinids/20.4.954
    [9] Iivanainen E, Sallantaus T, Katila MJ, et al. (1999) Mycobacteria in runoff-waters from natural and drained peatlands. J Environ Qual 28: 1226–1234.
    [10] Falkinham JO, Parker BC, Gruft H (1980) Epidemiology of infection by nontuberculous mycobacteria. I. Geographic distribution in the eastern United States. Am Rev Respir Dis 121: 931–937.
    [11] Falkinham JO, Norton CD, LeChevallier MW (2001) Factors influencing numbers of Mycobacterium avium, Mycobacterium intracellulare, and other mycobacteria in drinking water distribution systems. Appl Environ Microbiol 67: 1225–1231. doi: 10.1128/AEM.67.3.1225-1231.2001
    [12] Falkinham JO (2011) Nontuberculous mycobacteria from household plumbing of patients with nontuberculous mycobacteria disease. Emerg Infect Dis 17: 419–424. doi: 10.3201/eid1703.101510
    [13] Falkinham JO, Iseman MD, de Haas P, et al. (2008) Mycobacterium avium in a shower linked to pulmonary disease. J Water Health 6: 209–213.
    [14] Feazel LM, Baumgartner LK, Peterson KL, et al. (2009) Opportunistic pathogens enriched in showerhead biofilms. P Natl Acad Sci USA 106: 16393–16399. doi: 10.1073/pnas.0908446106
    [15] Falkinham JO (2010) Hospital water filters as a source of Mycobacterium avium complex. J Med Microbiol 59: 1198–1202. doi: 10.1099/jmm.0.022376-0
    [16] Sax H, Bloemberg G, Hasse B, et al. (2015) Prolonged outbreak of Mycobacterium chimaera infection after open-chest heart surgery. Clin Infect Dis 61: 67–75. doi: 10.1093/cid/civ198
    [17] Mullis SN, Falkinham JO (2013) Adherence and biofilm formation of Mycobacterium avium, Mycobacterium intracellulare and Mycobacterium abscessus to household plumbing materials. J Appl Microbiol 115: 908–914. doi: 10.1111/jam.12272
    [18] George KL, Parker BC, Gruft H, et al. (1980) Epidemiology of infection by nontuberculous mycobacteria: II. Growth and survival in natural waters. Am Rev Respir Dis 122: 89–94.
    [19] Taylor RM, Norton CD, LeChevallier MW, et al. (2000) Susceptibility of Mycobacterium avium, Mycobacterium intracellulare, and Mycobacterium scrofulaceum to chlorine, chloramine, chlorine dioxide, and ozone. Appl Environ Microbiol 66: 1702–1705. doi: 10.1128/AEM.66.4.1702-1705.2000
    [20] Norton CD, LeChevallier MW, Falkinham JO (2004) Survival of Mycobacterium avium in a model distribution system. Water Res 38: 1457–1466. doi: 10.1016/j.watres.2003.07.008
    [21] Lewis AH, Falkinham JO (2015) Microaerobic growth and anaerobic survival of Mycobacterium avium, Mycobacterium intracellulare and Mycobacterium scrofulaceum. Int J Mycobacteriol 4: 25–30. doi: 10.1016/j.ijmyco.2014.11.066
    [22] Brennan PJ, Nikaido H (1995) The envelope of mycobacteria. Annu Rev Biochem 64: 29–63. doi: 10.1146/annurev.bi.64.070195.000333
    [23] Jarlier V, Nikaido H (1994) Mycobacterial cell wall: structure and role in natural resistance to antibiotics. FEMS Microbiol Lett 123: 11–18. doi: 10.1111/j.1574-6968.1994.tb07194.x
    [24] Falkinham JO, George KL, Parker BC, et al. (1984) In vitro susceptibility of human and environmental isolates of Mycobacterium avium, M. intracellulare, and M. scrofulaceum to heavy metal salts and oxyanions. Antimicrob Agents Ch 25: 137–139.
    [25] Chen CI, Griebe T, Srinivasan R, et al. (1993) Effects of various metal substrata on accumulation of Pseudomonas aeruginosa biofilms and the efficacy of monochloramine as a biocide. Biofouling 7: 241–251. doi: 10.1080/08927019309386256
    [26] Ojha A, Anand M, Bhatt A, et al. (2005) GroEL1: A dedicated chaperone involved in mycolic acid biosynthesis during biofilm formation in mycobacteria. Cell 123: 861–873. doi: 10.1016/j.cell.2005.09.012
    [27] Ojha AK, Baughn AD, Sambandan D, et al. (2008) Growth of Mycobacterium tuberculosis biofilms containing free mycolic acids and harbouring drug-tolerant bacteria. Mol Microbiol 69: 164–174. doi: 10.1111/j.1365-2958.2008.06274.x
    [28] De Beer D, Srinivasan R, Stewart PS (1994) Direct measurement of chlorine penetration into biofilms during disinfection. Appl Environ Microbiol 60: 4339–4344.
    [29] Birmes FS, Wolf T, Kohl TA, et al. (2017) Mycobacterium abscessus subsp. abscessus is capable of degrading Pseudomonas aeruginosa quinolone signals. Front Microbiol 8: 339.
    [30] Van Oss CJ, Gillman CF, Neumann AW (1975) Phagocytic engulfment and cell adhesiveness as cellular phenomena, New York: Marcel Dekker.
    [31] Lequette Y, Boels G, Clarisse M, et al. (2010) Using enzymes to remove biofilms of bacteria isolates sampled in the food-industry. Biofouling 26: 421–431. doi: 10.1080/08927011003699535
    [32] Muñoz-Egea MC, García-Pedrazuela M, Mahillo-Fernandez I, et al. (2016) Effect of antibiotics and antibiofilm agents in the ultrastructure and development of biofilms developed by nonpigmented rapidly growing mycobacteria. Microb Drug Resist 22: 1–6. doi: 10.1089/mdr.2015.0124
    [33] Steed KA, Falkinham JO (2006) Effect of growth in biofilms on chlorine susceptibility of Mycobacterium avium and Mycobacterium intracellulare. Appl Environ Microbiol 72: 4007–4100. doi: 10.1128/AEM.02573-05
    [34] Falkinham JO (2007) Growth in catheter biofilms and antibiotic resistance of Mycobacterium avium. J Med Microbiol 56: 250–254. doi: 10.1099/jmm.0.46935-0
    [35] Tortoli E, Rindi L, Garcia MJ, et al. (2004) Proposal to elevate the genetic variant MAC-A, included in the Mycobacterium avium complex, to species rank as Mycobacterium chimaera sp. nov. Int J Syst Evol Micr 54: 1277–1285. doi: 10.1099/ijs.0.02777-0
    [36] Wallace RJ, Iakhiaeva E, Williams MD, et al. (2013) Absence of Mycobacterium intracellulare and presence of Mycobacterium chimaera in household water and biofilms samples in the United States with Mycobacterium avium complex respiratory disease. J Clin Microbiol 51: 1747–1752. doi: 10.1128/JCM.00186-13
    [37] Kim E, Kinney WH, Ovrutsky AR, et al. (2014) A surface with a biomimetic micropattern reduces colonization of Mycobacterium abscessus. FEMS Microbiol Lett 359: 1–6. doi: 10.1111/1574-6968.12576
    [38] Kirschner RA, Parker BC, Falkinham JO (1992) Epidemiology of infection by nontuberculous mycobacteria. X. Mycobacterium avium, M. intracellulare, and M. scrofulaceum in acid, brown-water swamps of the southeastern United States and their association with environmental variables. Am Rev Respir Dis 145: 271–275.
    [39] Carter G, Wu M, Drummond DC, et al. (2003) Characterization of biofilm formation by clinical isolates of Mycobacterium avium. J Med Microbiol 52: 747–752. doi: 10.1099/jmm.0.05224-0
    [40] Freeman R, Geier H, Weigel KM, et al. (2006) Roles for cell wall glycopeptidolipid in surface adherence and planktonic dispersal of Mycobacterium avium. Appl Environ Microbiol 72: 7554–7558. doi: 10.1128/AEM.01633-06
    [41] Martínez A, Torello S, Kolter R (1999) Sliding motility in mycobacteria. J Bacteriol 181: 7331–7338.
    [42] Kelley ST, Theisen U, Angenent LT, et al. (2004) Molecular analysis of shower curtain biofilm microbes. Appl Environ Microbiol 70: 4187–4192. doi: 10.1128/AEM.70.7.4187-4192.2004
    [43] Falkinham JO, Williams MD, Kwait R, et al. (2016) Methylobacterium spp. as an indicator for the presence or absence of Mycobacterium spp. Int J Mycobacteriol 5: 240–243.
    [44] Muńoz-Egea MC, Ji P, Pruden A, et al. (2017) Inhibition of adherence of Mycobacterium avium to plumbing surface biofilms of Methylobacterium spp. Pathogens 6: 42. doi: 10.3390/pathogens6030042
    [45] Elbein AD, Pan YT, Pastuszak I, et al. (2003) New insights on trehalose: a multifunctional molecule. Glycobiology 13: 17R–27R. doi: 10.1093/glycob/cwg047
  • This article has been cited by:

    1. Argyro Pachi, Aspasia Panagiotou, Nikolaos Soultanis, Maria Ivanidou, Maria Manta, Christos Sikaras, Ioannis Ilias, Athanasios Tselebis, Resilience, Anger, and Insomnia in Nurses after the End of the Pandemic Crisis, 2024, 5, 2673-3986, 643, 10.3390/epidemiologia5040045
    2. Christos Sikaras, Argyro Pachi, Sofia Alikanioti, Ioannis Ilias, Eleni Paraskevi Sideri, Athanasios Tselebis, Aspasia Panagiotou, Occupational Burnout and Insomnia in Relation to Psychological Resilience Among Greek Nurses in the Post-Pandemic Era, 2025, 15, 2076-328X, 126, 10.3390/bs15020126
  • Reader Comments
  • © 2018 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(6744) PDF downloads(900) Cited by(29)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog