Figure 1.
Study workflow. GEO, Gene Expression Omnibus; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; RRA, robust rank aggregation; TCGA, The Cancer Genome Atlas.
Citation: Khidir A.M. Hassan, MD Ali Mujtaba. Antibacterial efficacy of garlic oil nano-emulsion[J]. AIMS Agriculture and Food, 2019, 4(1): 194-205. doi: 10.3934/agrfood.2019.1.194
| [1] | Panid Borhanjoo, Priscile Kouamo, Mafuzur Rahman, Margaret Norton, Madhavi Gavini . Effect of clinical pharmacist encounters in the transitional care clinic on 30-day re-admissions: A retrospective study. AIMS Public Health, 2019, 6(3): 345-354. doi: 10.3934/publichealth.2019.3.345 |
| [2] | Calum F Leask, Andrea Gilmartin . Implementation of a neighbourhood care model in a Scottish integrated context—views from patients. AIMS Public Health, 2019, 6(2): 143-153. doi: 10.3934/publichealth.2019.2.143 |
| [3] | Val Bellman, David Thai, Anisha Chinthalapally, Nina Russell, Shazia Saleem . Inpatient violence in a psychiatric hospital in the middle of the pandemic: clinical and community health aspects. AIMS Public Health, 2022, 9(2): 342-356. doi: 10.3934/publichealth.2022024 |
| [4] | Margarida A. R. Tomás, Marisa R. Soares, Joaquim M. Oliveira-Lopes, Luís M. M. Sousa, Vânia L. D. Martins . The influence of nursing handover on nurses' mental health: A scoping review. AIMS Public Health, 2025, 12(1): 106-123. doi: 10.3934/publichealth.2025008 |
| [5] | Susanne Klawetter, Jennifer C. Greenfield, Stephanie Rachel Speer, Kyria Brown, Sunah S. Hwang . An integrative review: maternal engagement in the neonatal intensive care unit and health outcomes for U.S.-born preterm infants and their parents. AIMS Public Health, 2019, 6(2): 160-183. doi: 10.3934/publichealth.2019.2.160 |
| [6] | Katherine Karacaoglu, Calum F Leask . Staff views of a hospital at home model implemented in a Scottish care setting. AIMS Public Health, 2021, 8(3): 467-478. doi: 10.3934/publichealth.2021036 |
| [7] | Dylan C. Rowe, Zachary K. Winkelmann, Shawn M. Arent, Michelle A. Arent, Alexa J. Chandler, Nancy A. Uriegas, Toni M. Torres-McGehee . A qualitative report of the perceptions of the COVID-19 pandemic from collegiate student-athletes. AIMS Public Health, 2022, 9(2): 357-377. doi: 10.3934/publichealth.2022025 |
| [8] | Aikaterini Toska, Sofia Ralli, Evangelos C. Fradelos, Ioanna Dimitriadou, Anastasios Christakis, Viktor Vus, Maria Saridi . Evaluation of burnout levels among healthcare staff in anesthesiology departments in Greece - Is there a connection with anxiety and depression?. AIMS Public Health, 2024, 11(2): 543-556. doi: 10.3934/publichealth.2024027 |
| [9] | Carol Nash . The Health Narratives Research Group (HeNReG): A self-direction process offered to help decrease burnout in public health nurse practitioners. AIMS Public Health, 2024, 11(1): 176-208. doi: 10.3934/publichealth.2024009 |
| [10] | Thiresia Sikioti, Afroditi Zartaloudi, Despoina Pappa, Polyxeni Mangoulia, Evangelos C. Fradelos, Freideriki Eleni Kourti, Ioannis Koutelekos, Evangelos Dousis, Nikoletta Margari, Areti Stavropoulou, Eleni Evangelou, Chrysoula Dafogianni . Stress and burnout among Greek critical care nurses during the COVID-19 pandemic. AIMS Public Health, 2023, 10(4): 755-774. doi: 10.3934/publichealth.2023051 |
Head and neck squamous cell cancer (HNSCC) ranks the 6th place among all cancers globally, which occurs in the oral cavity, nasal cavity, sinus, throat and pharynx. The pathological type of HNSCC is squamous cell carcinoma (except thyroid tumor). In America, the incidence of HNSCC was 7.863% in 2006 [1], in China, the incidence of HNSCC was up to 3.268% [2]. At present, the standard treatment of HNSCC is still surgery, intensity-modulated radiotherapy and platinum-based chemotherapy. However, there is no better treatment plan when first-line therapy is ineffective. Therefore, new treatment is urgently needed for HNSCC to prolong the survival time of patient [3]. In recent years, the emergence of immunotherapy has brought new hope to cancer patients. Nivolumab and Pembrolizumab are approved by the FDA for advanced HNSCC after first-line treatment [4,5]. However, the effectiveness of immunotherapy of HNSCC is still low, in this regard, searching for novel therapeutic targets to treat HNSCC is of great necessity.
With the development of computer science and bioinformatics, network-based methods have become an effective tool for the research of pathogenic mechanism [6]. Robust Rank Aggregation (RRA) method can maximally reduce errors or biases between multiple data sets [7]. Nowadays, RRA method has been widely used in cancer research [7,8,9]. This method is better than RemoveBatchEffect method, which was widely used on analyzing GEO datasets now.
The cibersort algorithm is a general calculation method to quantify cell components from a large number of tissues expression profiles (GEPs). Combined with support vector regression and prior knowledge from the expression profile of purified leukocyte subsets, cibersort algorithm can accurately estimate the immune components of tumor tissues.
The protein-protein interaction (PPI) network exerts a vital part in cancer biology, and it is an effective method for screening cancer related Hub genes. At present, some studies have pointed out that the method based on PPI network can successfully predict the Hub gene of breast cancer [10], liver cancer [11] and gastric cancer [12].
In this study, we downloaded the mircoarray datasets GSE686 [13], GSE2379 [14], GSE6631 [15], GSE13399 [16], GSE33205 [17,18], GSE33493, GSE39376, GSE107591 [19,20] were downloaded from the GEO database (www.ncbi.nlm.nih.gov/geo/). In addition, the RRA approach was used to discover the potent DEGs in normal compared with HNSCC samples. This study discovered altogether 240 potent DEGs, among which, 105 showed up-regulation and 135 showed down-regulation. Further, GO functional annotation and KEGG analyses were conducted to explore the functions of those identified DEGs. Cibersort algorithm was used to analyze the infiltration of immune cells. Then, a PPI network was established, meanwhile, the key modules were also built. At last, we screened 5 hub genes from the whole network using cytoHubba. Hub gene survival was analyzed by the R packages. To sum up, the potent DEGs along with hub genes were discovered in this study by the combined bioinformatic approach, which might be a new and potential prognostic biomarker.
We downloaded 8 head and neck squamous cell carcinoma chips from GEO databases, which contained GSE686 [13], GSE2379 [14], GSE6631 [15], GSE13399 [16], GSE33205 [17,18], GSE33493, GSE39376, GSE107591 [19,20]. These datasets covered both human normal and tumor samples, and each dataset contained at least 20 samples. We downloaded the matrix files of all the microarray along with the platform annotation profiles, so that the names of microarray probes were easily converted into genetic symbols by the use of Perl. We identified the normal tissue from the HNSCC tissues from the 8 datasets using the R package limma function by the thresholds of P-value < 0.05 and log2-fold change (FC) > 0.5.
The RRA method is a standard approach that can minimizes bias and errors between multiple datasets. It can detect genes whose ranking is consistently better than expected under null hypothesis of uncorrelated inputs, and assign a significance score to each gene, the specific algorithmic computations are shown by Eqs (1) and (2). The underlying probability model makes the algorithm parameter independent and robust to outliers, noise and errors. Significance sores also provide a rigorous way to keep only the statistically relevant genes in the final list.
In this study, in order to integrate eight microarray datasets, we used RRA method to determine the robust DEGs. Before RRA analysis, we sequenced the up-regulated and down-regulated were sequenced from each dataset according to FC, then the sequencing results of 8 datasets were combined, and the R package RobustRankAggreg function was used to select robust DEGs according to the above thresholds.
| βk,n(x):=∑nℓ=k(nℓ)xℓ(1−x)n−ℓ | (1) |
| ρ(r)=mink=1,..,nβk,n(r) | (2) |
For exploring functions of those selected DEGs, the R package "Clusterprofiler" was applied to obtain GO enrichment results, which included biological processes (BPs), cell components (CCs), together with molecular functions (MFs). and we also used the R package to analyzed the KEGG pathway of the robust DEGs. A difference of P < 0.05 indicated statistical significance.
The cibersort algorithm [21] has been developed as the machine learning approach on the basis of linear support vector regression (SVR), as shown by Eq (3), which shows high robustness against noise. This algorithm outperforms others in terms of noise, tightly associated cell types, along with unclear mixture content. This algorithm was used in the present work for predicting the infiltrating degrees of 22 immunocyte types within HNSCC tissues. LM22 is called immune cell gene expression tag matrix. It contained 547 genes and can distinguish 22 kinds of human hematopoietic cell phenotypes. The HNSCC data together with LM22 matrix were used to be the input of the above algorithm to obtain the proportions of those 22 immunocyte types within HNSCC. As a result, the cell composition related to HNSCC response was quantified. Using P < 0.05 as the standard to screen immune cell matrix, the relative expression of immune cells in normal compared with HNSCC samples was detected by R software package. Differences in normal compared with cancer tissues were determined by principal component analysis (PCA).
| gSVM=sign(∑Nn=1αnYnZTnZ+b)=sign(∑Nn=1αnYnK(xn,x)+b) | (3) |
We uploaded the differentially expressed genes to string online database, and selected the confidence level > 0.7 as the screening criteria, and removed the free nodes to get the PPI (protein-protein interaction network), and downloaded the gene interaction files. The PPI network was visualized using Cytoscape (Version 3.6.1). In addition, the MCODE plug-in of Cytoscape was used to screen those significant modules from the as-constructed PPI network [22].
The Cytoscape plug-in CytoHubba can be used to sort those network-derived nodes based on network features. Cytohubba offers 11 topological analysis methods, such as edge penetration component, degree, maximum cluster centrality, maximum neighborhood component density, maximum neighborhood component, and six centralities (bottleneck, eccentricity, compactness, radius, intermediate degree and stress) based on the shortest path. Of these 11 approaches, the modified Maximal Clique Centrality (MCC) approach is more effective in predicting the essential proteins in PPI network.
We obtained RNA-seq and clinical data of HNSCC cases from The Cancer Genome Atlas (TCGA) database. Thereafter, hub gene survival was analyzed by survminer and survival functions of R package. A difference of P < 0.05 indicated statistical significance.
The bioinformatic approaches were utilized in the present work for identifying DEGs, and analyze the biological characteristics of these DEGs. We conducted this study according to the workflow shown in Figure 1.
The microarray data of HNSCC GSE686, GSE2379, GSE6631, GSE13399, GSE33205, GSE33493, GSE39376, GSE107591 were downloaded and analyzed by R package limma software. The distribution of DEGs is shown in the volcanic map (Figure 2). A total of 336 samples including 108 normal samples and 258 tumor samples were analyzed in our study. On the basis of the exclusion standard of log2FC > 1 and P < 0.05, there were 4 downregulated genes in GSE686. In GSE2379, there were 443 DEGs selected, among which, 238 showed up-regulation while 205 showed down-regulation. In the GSE6631 dataset, we selected 142 GSEs, among which, 53 showed up-regulation while 89 showed down-regulation. Altogether 322 DEGs were selected from the GSE13399 dataset, among which, 149 showed up-regulation while 173 showed down-regulation. There were 419 DEGs selected from the GSE33205 dataset, of them, 178 showed up-regulation while 241 showed down-regulation. Altogether 3330 DEGs were found from GSE33493, of them, 1773 showed up-regulation while 1557 showed down-regulation. Altogether 940 DEGs were selected from GSE39376, of them, 414 showed up-regulation while 526 showed down-regulation. In addition, altogether 466 DEGs were selected from GSE107591, of them, 200 showed up-regulation while 266 showed down-regulation. The volcano map for each GSE is shown in Figure 2, where the green and red dots indicate genes with down-regulation and up-regulation, separately.
We used the RRA method to integrate eight datasets. We selected altogether 240 DEGs, among which, 105 showed up-regulation while 135 showed down-regulation. According to the P-value threshold for selecting potent DEGs, the top 20 potent DEGs with up-regulation and down-regulation were distributed within the heat map.
Functions of those identified potent DEGs were analyzed by GO as well as KEGG analysis. Three function types were included in the GO analysis results: including BP, CC and MF. For BP, the robust DEGs showed enrichment in extracellular matrix (ECM) organization, skin development and extracellular organization. In the CC term, these genes were enriched in cornified envelope, collagen trimer complex as well as collagen-containing ECM. And in MF sections, the most significantly enriched terms were activity of serine-type endopeptidase, activity of serine-type peptidase, along with activity of serine hydrolase. The Figure 3 also showed the KEGG pathway enrichment analysis. Among them, IL-17 signaling pathway, cell cycle and ECM-receptor interaction are highly related to tumor growth and progress.
Using cibersort algorithm, the barplot of immune cells between normal and tumor samples in HNSCC tissues was shown in Figure 4(A). The heapmap of immune cells between normal and tumor samples in HNSCC tissues was shown in Figure 4(B). From the visualized violin plot (Figure 4C), we could find that B cells memory, B cells naïve, Macrophages M0, M1, T cells CD4 memory activated, Dendritic cells activated and Dendritic cells resting showed significant differences in normal compared with cancer tissues. PCA revealed no difference in normal compared with cancer tissues (Figure 4D).
For better exploring the associations among the potent DEGs, the String database was used to establish the PPI network. The Cytoscape was used to establish the visual PPI network when there were hidden disconnected nodes with the confidence value of > 0.7 (Figure 5A). The final network contained 125 edges along with 115 nodes, and there were 85 upregulated genes and 67 downregulated genes. We selected three key networks from the whole network through MCODE plugin (Figure 5B–D).
CytoHubba was applied for predicting and exploring the key nodes from the as-constructed PPI network. It can score each node in PPI network with topological algorithms. The genes with high scores are identified to be the Hub genes. The present work adopted ten topological algorithms ((Density of Maximum Neighhorhood Component (DMNC), Maximal Clique Centrality (MCC), Degrees, Maximum Neighborhood Component (MNC), BottleNeck, Edge Percolated Component (EPC), Closeness, EcCentricity, Betweenness and Radiality) to determine genes in the entire network. After calculation, there were 5 hub genes selected by the 10 algorithms, there were BIRC5, AURKA, UBE2C, CDC20, COL4A1.
| Gene | Full name | Synonyms | Function |
| AURKA | Aurora Kinase A | Activation of CDK1 | |
| BIRC5 | Baculoviral IAP Repeat Containing 5 | survivin | Regulation of death receptor signaling and TNF signaling |
| CDC20 | Cell Division Cycle 20 | Regulation of activated PAK-2p34 by proteasome mediated degradation; APC-CDC 20 mediated degradation of Nek2A | |
| COL4A1 | Collagen Type IV Alpha 1 Chain | Focal Adhesion; miRNA targets in ECM and membrane receptors; Overview of nanoparticle effects; Spinal Cord Injury | |
| UBE2C | Ubiquitin Conjugating Enzyme E2 C | Ubiquitin-Proteasome Dependent Proteolysis |
DownLoad:
CSV
We analyzed the relationship between 5 hub genes and the overall survival rate of patients with HNSCC using "survminer" and "survival" packages of R software. Based on the best cutoff value calculated by the "surv_cutpoint" function for all Hub genes, we classified HNSCC samples as 2 groups (namely, high or low expression group), then acquired the respective Kaplan Meier (K-M) survival curves. As a result, BIRC5, AURKA (P = 0.017), UBE2C (P = 0.015) and (P = 0.045) expression was markedly related to the HNSCC survival.
As a result of the development of bioinformatics, there are more and more studies on HNSCC biomarkers in public databases such as GEO and TCGA database. For example, Ding Y et al. [23] and Chamorro Petronacci CM et al. [24] screened potential prognosis biomarkers of HNSCC. Nevertheless, due to the fact that the DEGs in the above research were selected using one dataset only with a small sample size, the results are unstable. In our study, compared with other HNSCC researches, we conformitied eight datasets by RRA methods.
In this study, we selected altogether 240 DEGs, including 105 with up-regulation while 135 with down-regulation. As suggested by GO as well as KEGG enrichment analysis, those potent DEGs were mainly enriched to extracellular matrix organization, skin development, extracellular organization, cornified envelope, complex of collagen trimers, serine-type activity and IL-17 signaling pathway, cell cycle and ECM-receptor interaction, which were related to tumor growth and progress. Then, we performed analysis of the immune cell infiltration between the normal and tumor HNSCC samples by cibersort algorithm. Module analysis was adopted to construct a PPI network based on the STRING database. Finally, 5 hub genes were screened from the whole network by cytoHubba including BIRC5, AURKA, UBE2C, CDC20, COL4A1. And the sample survival was analyzed using the R packages according to hub gene expression.
We also examined the GO terms together with KEGG pathways within the HNSCC samples in enrichment analysis. It has been extensively suggested that, the epithelial-mesenchymal transition (EMT) indicates a metastatic process. In this process, the epithelial cells acquire migratory and invasive mesenchymal phenotypes [25]. Extracellular matrix also exerts a vital part during cancer development, which can regulate cell growth, metabolism, migration, proliferation and differentiation through integrin or other cell surface receptors. Therefore, the degradation of ECM is indispensable for the invasion and metastasis of malignant tumors [26,27]. The high IL-17B level is verified to be tightly associated with dismal prognostic outcomes for cancer patients, such as breast cancer [28,29], gastric cancer [30], colon cancer [31], lung cancer [32] and so on. The IL-17 pathway may also be involved in the development of HNSCC. DNA damage may occur during normal cell division or in the presence of external stimuli. If the repair mechanism of DNA damage is defective, genomic instability can be caused. Cell cycle disorder is an important part of genomic instability, which may promote the further malignant transformation of unstable cells, thus accelerating the occurrence and development of tumors [33,34]. In line with the analysis above, the potent DEGs were tightly related to the HNSCC pathogenic mechanism as well as progression.
An increasing number of recent articles suggest that, tumor microenvironment (TME) exerts a vital part in tumor genesis and progression [35,36]. Typically, TME mainly includes endothelial cells, mesenchymal cells, immune cells, and ECM [37]. From previous studies, we found that the B7 protein family contains seven members: CD80, CD86, ICOS-L, PD-L1, PD-L2, B7-H3 and B7-H4 [38]. All ligands of the B7 family can be detected in dendritic cells, B cells, together with macrophages. To be specific, the B7 family mainly plays a role in regulating immune response. If B7 family gene is knocked out, the mice will suffer from immune deficiency and autoimmune disease [39]. PD-L1 can lead to tumor escape in the immune system by weakening the specific response of T cells to tumor cells [40]. Based on two clinical trials of PD-1 and PD-L1 in HNSCC (KEYNOTE-012 and CheckMate-141), Nivolumab and Pembrolizumab are approved by the FDA to treat advanced HNSCC. In our study, we found that B cells, dendritic cells and macrophages were the principal infiltrating immune cells in HNSCC tissues. The immunotherapy for HNSCC needs to be further explored.
In our study, we discovered 5 hub genes according to the as-constructed PPI network. Among them, 3 key genes were screened for further exploration. AURKA is one of the mitotic serine/threonine kinase family members, which exerts a vital part in a variety of biological events, such as centrosome separation and maturation, chromosome alignment, spindle assembly, as well as G2-to-M transition [41,42]. Besides, AURKA is previously suggested to show over-expression in diverse cancers [43,44,45,46,47], such as neuroblastoma [48], gastric cancer [49,50,51] and so on. AURKA can promote tumor development by inhibiting tumor suppressor genes such as p73 [52,53] and p53 [54], activating β-catenin [55], NF-κB [56] and cap-dependent translation of oncogenes [57]. Nonetheless, it remains unknown about the function of AURKA within HNSCC. BIRC5, which is also referred to as survivin, belongs to the IAP family [58]. Its expression can be detected in different cancers, including breast cancer (BC) [59], colorectal cancer (CRC) [60], liver cancer [61] and so on. It inhibited the Caspase activity and apoptosis by inhibiting the binding of Caspase-3 and Caspase-7, thus leading to the survival of cancer cells in the process of tumorigenesis [62]. UBE2C belongs to the E2 ubiquitin-conjugating enzyme family [63], which exerts an important part in regulating the cell cycle, and this is achieved through the catalysis of polyubiquitination-induced APC/C substrate degradation [64]. Many recent studies report the abnormal expression of UBE2C in different human cancers, like hepatocellular carcinoma (HCC) [65], CRC [66,67], lung cancer [68], BC [69] and so on.
These abundant evidences showed that UBE2C played an important part in tumor genesis and development. According to our results, the high expression of AURKA, BIRC5 and UBE2C genes in HNSCC tumor showed poor survival of patients in TCGA database, suggesting that these genes are related to the prognosis of HNSCC, and they may act as therapeutic targets of HNSCC.
In conclusion, through Robust Rank Aggregation method and cibersort algorithm method, a series of robust DEFs and gene modules were identified in HNSCC. The identified genes were subjected to functional analyses, which revealed their close relationship with the occurrence and development of HNSCC. We not only screened five hub genes, but also analyzed the immune cell infiltration in HNSCC. From above discussion, we found that AURKA, BIRC5 and UBE2C may be considered as new biomarker and therapeutic targets of HNSCC. The Robust Rank Aggregation method and cibersort algorithm method can accurately predict the potential prognostic biomarker and therapeutic targets. Further investigation is warranted to explore their roles in HNSCC in further research.
This work was supported by grants from the National Natural Science Foundation of China (82073344, 81874217, 81703027, 81703028, 81672983).
The authors declare that there is no conflict of interests.
| [1] |
Adorjan B, Buchbauer G (2010) Biological properties of essential oils: An updated review. Flavour Fragrance J 25: 407–426. doi: 10.1002/ffj.2024
|
| [2] |
Alboofetileh M, Rezaei M, Hosseini H, et al. (2014) Antimicrobial activity of alginate/clay nanocomposite films enriched with essential oils against three common foodborne pathogens. Food Control 36: 1–7. doi: 10.1016/j.foodcont.2013.07.037
|
| [3] | Bilia AR, Guccione C, Isacchi B, et al. (2014) Essential oils loaded in nanosystems: A developing strategy for a successful therapeutic approach. J Evidence-Based Complementary Altern Med. |
| [4] | Pesavento G, Calonico C, Bilia AR, et al. (2015) Antibacterial activity of Oregano, Rosmarinus and Thymus essential oils against S. aureus and Listeria monocytogenes in beef meatballs. Food Control 54: 188–199. |
| [5] |
Arranz E, Jaime L, de las Hazas ML, et al. (2015) Supercritical fluid extraction as an alternative process to obtain essential oils with anti-inflammatory properties from marjoram and sweet basil. Ind Crops Prod 67: 121–129. doi: 10.1016/j.indcrop.2015.01.012
|
| [6] | Sobral MV, Xavier AL, Lima TC, et al. (2014) Antitumor activity of monoterpenes found in essential oils. Sci World J. |
| [7] |
Yen HF, Hsieh CT, Hsieh TJ, et al. (2015) In vitro anti-diabetic effect and chemical component analysis of 29 essential oils products. J Food Drug Anal 23: 124–129. doi: 10.1016/j.jfda.2014.02.004
|
| [8] |
Petropoulos S, Fernandes Â, Barros L, et al. (2018) Antimicrobial and antioxidant properties of various Greek garlic genotypes. Food Chem 245: 7–12. doi: 10.1016/j.foodchem.2017.10.078
|
| [9] |
El-Sayed HS, Chizzola R, Ramadan AA, et al. (2017) Chemical composition and antimicrobial activity of garlic essential oils evaluated in organic solvent, emulsifying, and self-microemulsifying water-based delivery systems. Food Chem 221: 196–204. doi: 10.1016/j.foodchem.2016.10.052
|
| [10] | Dixit S, Dubey AK (2017) Medicinal properties of garlic (Allium sativum L): A review. HortFlora Res Spectrum 6: 66–68. |
| [11] |
Chan JYY, Yuen ACY, Chan RYK, et al. (2013) A review of the cardiovascular benefits and antioxidant properties of allicin. Phytother Res 27: 637–646. doi: 10.1002/ptr.4796
|
| [12] |
Fahmy SA, Mamdouh W (2018) Garlic oil-loaded PLGA nanoparticles with controllable size and shape and enhanced antibacterial activities. J Applied Polym Science 135: 46133. doi: 10.1002/app.46133
|
| [13] |
Sha R, Wu Y, Cai C, et al. (2017) Preparation of a Nanoscale Garlic Oil Microemulsion and Its Antioxidant Activity. Nanosci Nanotechnol Lett 9: 123–133. doi: 10.1166/nnl.2017.2269
|
| [14] | Morsy MK, Khalaf HH, Sharoba AM, et al. (2014) Incorporation of essential oils and nanoparticles in pullulan films to control foodborne pathogens on meat and poultry products. J Food Science 79. |
| [15] |
Ragavan G, Muralidaran Y, Sridharan B, et al. (2017) Evaluation of garlic oil in nano-emulsified form: Optimization and its efficacy in high-fat diet induced dyslipidemia in Wistar rats. Food Chem Toxicol 105: 203–213. doi: 10.1016/j.fct.2017.04.019
|
| [16] |
Fornaguera C, Dols-Perez A, Caldero G, et al. (2015) PLGA nanoparticles prepared by nano-emulsion templating using low-energy methods as efficient nanocarriers for drug delivery across the blood–brain barrier. J Controlled Release 211: 134–143. doi: 10.1016/j.jconrel.2015.06.002
|
| [17] |
Swathy JS, Mishra P, Thomas J, et al. (2018) Antimicrobial potency of high-energy emulsified black pepper oil nanoemulsion against aquaculture pathogen. Aquaculture 491: 210–220. doi: 10.1016/j.aquaculture.2018.03.045
|
| [18] |
Satyal P, Craft JD, Dosoky NS, et al. (2017) The chemical compositions of the volatile oils of garlic (Allium sativum) and wild garlic (Allium vineale). Foods 6: 63. doi: 10.3390/foods6080063
|
| [19] | Ibrahim M, Kainulainen P, Aflatuni A, et al. (2012) Adams, Rp Identification of Essential Oil Components by Gas Cromatography. Alternative Control of Anthracnose During Post-Harvesting of Guavas "Paluma" Simulating Storage and Marketing, 42. |
| [20] |
Prylutskyy YI, Buchelnikov AS, Voronin DP, et al. (2013) C 60 fullerene aggregation in aqueous solution. Phys Chem Chem Phys 15: 9351–9360. doi: 10.1039/c3cp50187f
|
| [21] |
Rojas-Graü MA, Raybaudi-Massilia RM, Soliva-Fortuny RC, et al. (2007) Apple puree-alginate edible coating as carrier of antimicrobial agents to prolong shelf-life of fresh-cut apples. Postharvest Biol Technol 45: 254–264. doi: 10.1016/j.postharvbio.2007.01.017
|
| [22] |
Douiri L, Boughdad A, Assobhei O, et al (2013) Chemical composition and biological activity of Allium sativum essential oils against Callosobruchus maculatus. IOSR J Environ Sci Toxicol Food Technol 3: 30–36. doi: 10.9790/2402-0313036
|
| [23] |
Radulović NS, Miltojević AB, Stojković MB, et al (2015) New volatile sulfur-containing compounds from wild garlic (Allium ursinum L., Liliaceae). Food Res Int 78:1–10. doi: 10.1016/j.foodres.2015.11.019
|
| [24] |
Motsei ML, Lindsey KL, Van Staden J, et al. (2003) Screening of traditionally used South African plants for antifungal activity against Candida albicans. J Ethnopharmacol 86: 235–241. doi: 10.1016/S0378-8741(03)00082-5
|
| [25] | Wencui Z, Qi Z, Ying W, et al. (2015) Preparation of solid lipid nanoparticles loaded with garlic oil and evaluation of their in vitro and in vivo characteristics. Eur Rev Med Pharmacol Sci 19: 3742–3750. |
| [26] |
Wu Y, Luo Y, Wang Q (2012) Antioxidant and antimicrobial properties of essential oils encapsulated in zein nanoparticles prepared by liquid-liquid dispersion method. LWT-Food Science Technol 48: 283–290. doi: 10.1016/j.lwt.2012.03.027
|
| [27] |
Acevedo-Fani A, Salvia-Trujillo L, Rojas-Graü MA, et al. (2015) Edible films from essential-oil-loaded nanoemulsions: Physicochemical Characterization Antimicrobial Properties. Food Hydrocolloids 47: 168–177. doi: 10.1016/j.foodhyd.2015.01.032
|
| [28] |
Córdoba LJP, Sobral PJ (2017) Physical and antioxidant properties of films based on gelatin, gelatin-chitosan or gelatin-sodium caseinate blends loaded with nanoemulsified active compounds. J Food Eng 213: 47–53. doi: 10.1016/j.jfoodeng.2017.05.023
|
| [29] |
Hasssanzadeh H, Alizadeh M, Bari MR (2018) Formulation of garlic oil-in-water nanoemulsion: Antimicrobial and physicochemical aspects. IET Nanobiotechnol 12: 647–652. doi: 10.1049/iet-nbt.2017.0104
|
| [30] | Pérez-Córdoba LJ, Norton IT, Batchelor HK, et al. (2017) Physico-chemical, antimicrobial and antioxidant properties of gelatin-chitosan based films loaded with nanoemulsions encapsulating active compounds. Food Hydrocolloids 74: 544–559. |
| [31] | Johnson M, Olaleye ON, Kolawole OS (2016) Antimicrobial and Antioxidant Properties of Aqueous Garlic (Allium sativum) Extract against S. aureus and Pseudomonas aeruginosa. Br Microbiol Res J 14: 1–11. |
| [32] | Zheng HM, Li HB, Wang DW, et al. (2013) Preparation methods for monodispersed garlic oil microspheres in water using the microemulsion technique and their potential as antimicrobials. J Food Science 78. |
| 1. | Aazam Hosseinnejad, Maryam Rassouli, Simin Jahani, Nasrin Elahi, Shahram Molavynejad, Scope of Iranian community health nurses ‘services from the viewpoint of the managers and nurses: a content analysis study, 2022, 21, 1472-6955, 10.1186/s12912-022-00908-z | |
| 2. | Abtin Heidarzadeh, Mehdi Alvandi, Bita Hedayati, Mostafa Rezaei, Farshid Abedi, Zahra Hamidi Madani, Somayeh Noori Hekmat, Ideh Dadgaran, Roksana Mirkazemi, The Roles of the Nurses in Family Physician Team: A Qualitative Systematic Review, 2023, 28, 1735-9066, 361, 10.4103/ijnmr.ijnmr_244_22 |
Figures(8) / Tables(1)
Khidir A.M. Hassan, MD Ali Mujtaba. Antibacterial efficacy of garlic oil nano-emulsion[J]. AIMS Agriculture and Food, 2019, 4(1): 194-205. doi: 10.3934/agrfood.2019.1.194
| Gene | Full name | Synonyms | Function |
| AURKA | Aurora Kinase A | Activation of CDK1 | |
| BIRC5 | Baculoviral IAP Repeat Containing 5 | survivin | Regulation of death receptor signaling and TNF signaling |
| CDC20 | Cell Division Cycle 20 | Regulation of activated PAK-2p34 by proteasome mediated degradation; APC-CDC 20 mediated degradation of Nek2A | |
| COL4A1 | Collagen Type IV Alpha 1 Chain | Focal Adhesion; miRNA targets in ECM and membrane receptors; Overview of nanoparticle effects; Spinal Cord Injury | |
| UBE2C | Ubiquitin Conjugating Enzyme E2 C | Ubiquitin-Proteasome Dependent Proteolysis |
DownLoad:
CSV
| Gene | Full name | Synonyms | Function |
| AURKA | Aurora Kinase A | Activation of CDK1 | |
| BIRC5 | Baculoviral IAP Repeat Containing 5 | survivin | Regulation of death receptor signaling and TNF signaling |
| CDC20 | Cell Division Cycle 20 | Regulation of activated PAK-2p34 by proteasome mediated degradation; APC-CDC 20 mediated degradation of Nek2A | |
| COL4A1 | Collagen Type IV Alpha 1 Chain | Focal Adhesion; miRNA targets in ECM and membrane receptors; Overview of nanoparticle effects; Spinal Cord Injury | |
| UBE2C | Ubiquitin Conjugating Enzyme E2 C | Ubiquitin-Proteasome Dependent Proteolysis |
