Phosgene Toxicity Clinical Manifestations and Treatment: A Systematic Review

Exposure to phosgene, a colourless poisonous gas, can lead to various health issues including eye irritation, a dryand burning throat, vomiting, coughing, the production of foamy sputum, difficulty in breathing, and chest pain. Thissystematic review aims to provide a comprehensive overview of the clinical manifestations and treatment of phosgenetoxicity by systematically analyzing available literature. The search was carried out on various scientific online databasesto include related studies based on inclusion and exclusion criteria with the use of PRISMA guidelines. The quality ofthe studies was assessed using the Mixed Methods Appraisal Tool (MMAT). Thirteen articles were included in thisstudy after the screening process. Inhalation was found to be the primary health problem of phosgene exposure withrespiratory symptoms such as coughing and dyspnea. Chest pain and pulmonary oedema were also observed in somecases. Furthermore, pulmonary crackle was the most common reported physical examination. Beyond respiratory tracthealth issues, other organs involvements such as cardiac, skin, eye, and renal were also reported in some studies. Thesymptoms can occur within minutes to hours after exposure, and the severity of symptoms depends on the amount ofinhaled phosgene. The findings showed that bronchodilators can alleviate symptoms of bronchoconstriction causedby phosgene. Oxygen therapy is essential for restoring oxygen levels and improving respiratory function in casesof hypoxemia. In severe cases, endotracheal intubation and invasive mechanical ventilation are used for artificialrespiration, along with the removal of tracheal secretions and pulmonary oedema fluid through suctioning as crucialcomponents of supportive therapy

Identification of candidate genes and proteins in aging skeletal muscle (sarcopenia) using gene expression and structural analysis

Sarcopenia is an age-related disease characterized by the loss of muscle mass and muscle function. A proper understanding of its pathogenesis and mechanisms may lead to new strategies for diagnosis and treatment of the disease. This study aims to discover the underlying genes, proteins, and pathways associated with sarcopenia in both genders. Integrated analysis of microarray datasets has been performed to identify differentially expressed genes (DEGs) between old and young skeletal muscles. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were then performed to uncover the functions of the DEGs. Moreover, a protein–protein interaction (PPI) network was constructed based on the DEGs. We have identified 41,715 DEGs, including 19 downregulated and 41,696 upregulated ones, in men. Among women, 3,015 DEGs have been found, with 2,874 of them being upregulated and 141 downregulated genes. Among the top up-regulated and downregulated genes, the ribosome biogenesis genes and genes involved in lipid storage may be closely related to aging muscles in men and women respectively. Also, the DEGs were enriched in the pathways including those of ribosome and Peroxisome proliferator-activated receptor (PPAR) in men and women, respectively. In the PPI network, Neurotrophic Receptor Tyrosine Kinase 1 (NTRK1), Cullin 3 (CUL3) and P53 have been identified as significant hub proteins in both genders. Using the integrated analysis of multiple gene expression profiles, we propose that the ribosome biogenesis genes and those involved in lipid storage would be promising markers for sarcopenia in men and women, respectively. In the reconstructed PPI network, neurotrophic factors expressed in skeletal muscle are essential for motoneuron survival and muscle fiber innervation during development. Cullin E3 ubiquitin ligase (Cul3) is an important component of the ubiquitin–proteasome system—it regulates the proteolysis. P53 is recognized as a central regulator of the cell cycle and apoptosis. These proteins, which have been identified as the most significant hubs, may be involved in aging muscle and sarcopenia

Computer Simulation Study of the Levy Flight Process

Random walk simulation of the Levy flight shows a linear relation between the mean square displacement and time. We have analyzed different aspects of this linearity. It is shown that the restriction of jump length to a maximum value (lm) affects the diffusion coefficient, even though it remains constant for lm greater than 1464. So, this factor has no effect on the linearity. In addition, it is shown that the number of samples does not affect the results. We have demonstrated that the relation between the mean square displacement and time remains linear in a continuous space, while continuous variables just reduce the diffusion coefficient. The results are also implied that the movement of a levy flight particle is similar to the case the particle moves in each time step with an average length of jumping . Finally, it is shown that the non-linear relation of the Levy flight will be satisfied if we use time average instead of ensemble average. The difference between time average and ensemble average results points that the Levy distribution may be a non-ergodic distribution.Comment: 14 pages, 7 figure

Differential expression analysis in epithelial ovarian cancer using functional genomics and integrated bioinformatics approaches

Background: Epithelial Ovarian Cancer (EOC) has remained the most frequent and leading cause of death among gynecologic malignancies, with a remarkably elevated and alarming global mortality rate and a poor prognosis. As a result of its asymptomatic characteristics in the early stages, the disease is typically diagnosed at advanced stages, with extensive dissemination. Therefore, it is of great importance to explore more reliable diagnostic and prognostic biomarkers. The present study intended to design an integrative bioinformatics approach to investigate robust differentially expressed genes (DEGs) associated with EOC progression as valuable diagnostic and prognostic biomarkers, providing inspiring insights into cancer mechanisms. Materials and methods: Three mRNA (GSE40595, GSE14407, and GSE18520) expression profiles related to EOC were retrieved from Gene Expression Omnibus (GEO) database. Significant DEGs were screened out following raw data quality assessment, preprocessing, and statistical computing. Gene ontology and pathway enrichment analyses were performed on candidate DEGs. Next, the interactions between genes were visualized by constructing their networks, and subsequently, a list of hub genes was extracted through the estimation of their connections, which were subjected to further evaluations for validation and survival analysis. Results: We identified 241 overlapping DEGs (20 up- and 221 down-regulated) between 3 mRNA datasets. After evaluating the protein-protein interactions, validation, and survival analysis, five hub genes including four up-regulated (AURKA, CD24, CDCA3, CENPF) and one down-regulated (PGR) were eventually selected as potential biomarkers of EOC. GO and Enriched pathways related to selected genes compromised cell growth, actin filament organization, enzyme inhibitor, peptidase regulator, and receptor protein kinase activities, glycosaminoglycan binding, glycine, serine, and threonine metabolism pathways, proteoglycans, and EGFR tyrosine kinase inhibitor resistance. Conclusion: In summary, these findings will shed further light on the molecular mechanisms underlying EOC. In addition, the candidate genes, related metabolites, and signaling pathways could serve as promising prognostic, diagnostic, or even potential drug targets

Controllability in cancer metabolic networks according to drug targets as driver nodes

No grant i

GCPBayes: An R package for studying Cross-Phenotype Genetic Associations with Group-level Bayesian Meta-Analysis

International audienceSeveral R packages have been developed to study cross-phenotypes associations (or pleiotropy) at the SNP-level, based on summary statistics data from genome-wide association studies (GWAS). However, none of them allow for consideration of the underlying group structure of the data. We developed an R package, entitled GCPBayes (Group level Bayesian Meta-Analysis for Studying Cross-Phenotype Genetic Associations), introduced by Baghfalaki et al. (2021), that implements continuous and Dirac spike priors for group selection, and also a Bayesian sparse group selection approach with hierarchical spike and slab priors, to select important variables at the group level and within the groups. The methods use summary statistics data from association studies or individual level data as inputs, and perform Bayesian meta-analysis approaches across multiple phenotypes to detect pleiotropy at both group-level (e.g., at the gene or pathway level) and within group (e.g., at the SNP level)

The effect of autophagy-related MicroRNAs on FIP200, ATG13 and HIF1A expression levels in breast cancer patients

Autophagy acts like a double-edged sword in either tumor promotion or suppression of breast cancer. Crosstalk between miRNAs and autophagic targets is one interesting scenario for the dual behavior of this pathway. On this basis the present study was designed to evaluate the expression pattern of certain candidate miRNAs and their targets in breast cancer patients. A total of 47 fresh breast carcinomas and matched adjacent non-neoplastic tissues were obtained. Bioinformatics analysis of putative miRNA binding sites identified miR-133, and miR-206, miR-199a/b, as regulating expressions of the FIP200, ATG-13 and HIF1a, respectively. The expression levels of candidate miRNAs and their targets were examined using quantitative Real-Time Polymerase Chain Reaction. Our results demonstrated that all four miRNAs expression levels are downregulated in breast tumor tissue compared with corresponding non-neoplastic tissue. Decreased expression of miR-133 and miR-199b showed a significant correlation with tumor grade. Moreover, a significant downregulation of miR-199b was observed in HER-2-negative patients. We found that FIP200 and ATG13 were downregulated in tumor tissues while HIF1a showed a significant upregulation. No significant association between the target genes and clinicopathological features was observed. Our data clarified a strong positive correlation between expression levels of miR-133 and FIP200 while the correlation between miR-206 and ATG13, and, miR-199a/b and HIF1a were not statistically significant. In conclusion, these results support the regulatory role of miR-133 during breast cancer development via the autophagy pathway and provide an opportunity to develop targeted therapeutics for breast cancer