Effects of selective serotonin reuptake inhibitors on endocrine system (Review).

Selective serotonin reuptake inhibitors (SSRIs) are typically prescribed for treating major depressive disorder (MDD) due to their high efficacy. These drugs function by inhibiting the reuptake of serotonin [also termed 5-hydroxytryptamine (5-HT)], which raises the levels of 5-HT in the synaptic cleft, leading to prolonged activation of postsynaptic 5-HT receptors. Despite the therapeutic benefits of SSRIs, this mechanism of action also disturbs the neuroendocrine response. Hypothalamic-pituitary-adrenal (HPA) axis activity is strongly linked to both MDD and the response to antidepressants, owing to the intricate interplay within the serotonergic system, which regulates feeding, water intake, sexual drive, reproduction and circadian rhythms. The aim of the present review was to provide up-to-date evidence for the proposed effects of SSRIs, such as fluoxetine, citalopram, escitalopram, paroxetine, sertraline and fluvoxamine, on the endocrine system. For this purpose, the literature related to the effects of SSRIs on the endocrine system was searched using the PubMed database. According to the available literature, SSRIs may have an adverse effect on glucose metabolism, sexual function and fertility by dysregulating the function of the HPA axis, pancreas and gonads. Therefore, considering that SSRIs are often prescribed for extended periods, it is crucial to monitor the patient closely with particular attention to the function of the endocrine system.

Genomic Evolution Strategy in SARS-CoV-2 Lineage B: Coevolution of Cis Elements.

In the SARS-CoV-2 lineage, RNA elements essential for its viral life cycle, including genome replication and gene expression, have been identified. Still, the precise structures and functions of these RNA regions in coronaviruses remain poorly understood. This lack of knowledge points out the need for further research to better understand these crucial aspects of viral biology and, in time, prepare for future outbreaks. In this research, the in silico analysis of the cis RNA structures that act in the alpha-, beta-, gamma-, and deltacoronavirus genera has provided a detailed view of the presence and adaptation of the structures of these elements in coronaviruses. The results emphasize the importance of these cis elements in viral biology and their variability between different viral variants. Some coronavirus variants in some groups, depending on the cis element (stem-loop1 and -2; pseudoknot stem-loop1 and -2, and s2m), exhibited functional adaptation. Additionally, the conformation flexibility of the s2m element in the SARS variants was determined, suggesting a coevolution of this element in this viral group. The variability in secondary structures suggests genomic adaptations that may be related to replication processes, genetic regulation, as well as the specific pathogenicity of each variant. The results suggest that RNA structures in coronaviruses can adapt and evolve toward different viral variants, which has important implications for viral adaptation, pathogenicity, and future therapeutic strategies.

Classification and selection of the main features for the identification of toxicity in Agaricus and Lepiota with machine learning algorithms.

The occurrence of fungi is cosmopolitan, and while some mushroom species are beneficial to human health, others can be toxic and cause illness problems. This study aimed to analyze the organoleptic, ecological, and morphological characteristics of a group of fungal specimens and identify the most significant features to develop models for fungal toxicity classification using genetic algorithms and LASSO regression. The results of the study indicated that odor, spore print color, and habitat were the most significant characteristics identified by the genetic algorithm GALGO. Meanwhile, odor, gill size, stalk shape, and twelve other features were the relevant characteristics identified by LASSO regression. The importance score of the odor variable was 99.99%, gill size obtained 73.7%, stalk shape scored 39.9%, and the remaining variables did not score higher than 18%. Logistic regression, k-nearest neighbor (KNN), and XG-Boost classification algorithms were used to develop models using the features selected by both GALGO and LASSO. The models were evaluated using sensitivity, specificity, and accuracy metrics. The models with the highest AUC values were XGBoost, with a maximum value of 0.99 using the features selected by LASSO, followed by KNN with a maximum value of 0.99. The GALGO selection resulted in a maximum AUC of 0.98 in KNN and XGBoost. The models developed in this study have the potential to aid in the accurate identification of toxic fungi, which can prevent health problems caused by their consumption.

Genomic Analysis of Multidrug-Resistant Escherichia coli Strains Isolated in Tamaulipas, Mexico.

The global spread of antimicrobial resistance genes (ARGs) is a major public health concern. Mobile genetic elements (MGEs) are the main drivers of this spread by horizontal gene transfer (HGT). Escherichia coli is widespread in various environments and serves as an indicator for monitoring antimicrobial resistance (AMR). Therefore, the objective of this work was to evaluate the whole genome of multidrug-resistant E. coli strains isolated from human clinical, animal, and environmental sources. Four E. coli strains previously isolated from human urine (n = 2), retail meat (n = 1), and water from the Rio Grande River (n = 1) collected in northern Tamaulipas, Mexico, were analyzed. E. coli strains were evaluated for antimicrobial susceptibility, followed by whole genome sequencing and bioinformatic analysis. Several ARGs were detected, including blaCTX-M-15, blaOXA-1, blaTEM-1B, blaCMY-2, qnrB, catB3, sul2, and sul3. Additionally, plasmid replicons (IncFIA, IncFIB, IncFII, IncY, IncR, and Col) and intact prophages were also found. Insertion sequences (ISs) were structurally linked with resistance and virulence genes. Finally, these findings indicate that E. coli strains have a large repertoire of resistance determinants, highlighting a high pathogenic potential and the need to monitor them.

Regulation Transcriptional of Antibiotic Resistance Genes (ARGs) in Bacteria Isolated from WWTP.

The incidence of antibiotics and transcriptional regulation of ARGs in isolated bacteria from wastewater needs to be explored. By HPLC, in samples of untreated wastewater, ampicillin (49.74 ± 5.70 µg/mL), chloramphenicol (0.60 ± 0.03 µg/mL), tylosin (72.95 ± 2.03 µg/mL), and oxytetracycline (0.22 ± 0.01 µg/mL) was determined. Through metagenomic analysis identified 58 bacterial species belonging to 9 phyla and at least 14 species have shown resistance to a variety of antibiotics. Twenty-two bacterial isolates were proved to be resistant to fifteen antibiotics of new generation and used in medical research to combat infectious diseases. Fourteen strains were shown to harbor plasmids in size ranges of 2-5 Kb, 6-10 Kb and plasmids with size greater than 10 Kb. By quantitative PCR it was possible to identify genes sul, qnr, cat1, aadA1, and sat-1 gene were shown to be present in gDNA samples from treated and untreated samples of wastewater and by relative expression analysis, differential expression of cat1, ermB, act, and tetA genes was demonstrated in strains that showed identity with Escherichia coli, Bacteroides fragilis, and Salmonella thyphi, and that were stressed with different concentrations of antibiotics. The presence of ARGs in untreated water samples, as well as in bacterial isolates, was indicative that in these habitats there are microorganisms that can resist ß-lactams, aminoglycosides, tetracyclines, sulfonamides, and quinolones.

Genomic insights into Mycobacterium simiae human colonization.

Mycobacterium simiae (Karassova V, Weissfeiler J, Kraszanay E, Acta Microbiol Acad Sci Hung 12:275-82, 1965) is a slow-growing nontuberculous Mycobacterium species found in environmental niches, and recently evidenced as an opportunistic Human pathogen. We report here the genome of a clinical isolate of M. simiae (MsiGto) obtained from a patient in Guanajuato, Mexico. With a size of 6,684,413 bp, the genomic sequence of strain MsiGto is the largest of the three M. simiae genomes reported to date. Gene prediction revealed 6409 CDSs in total, including 6354 protein-coding genes and 52 RNA genes. Comparative genomic analysis identified shared features between strain MsiGto and the other two reported M. simiae genomes, as well as unique genes. Our data reveals that M. simiae MsiGto harbors virulence-related genes, such as arcD, ESAT-6, and those belonging to the antigen 85 complex and mce clusters, which may explain its successful transition to the human host. We expect the genome information of strain MsiGto will provide a better understanding of infective mechanisms and virulence of this emergent pathogen.

Insights into the 1.59-Mbp largest plasmid of Azospirillum brasilense CBG497.

The plant growth-promoting proteobacterium Azospirillum brasilense enhances growth of many economically important crops, such as wheat, maize, and rice. The sequencing and annotation of the 1.59-Mbp replicon of A. brasilense CBG497, a strain isolated from a maize rhizosphere grown on an alkaline soil in the northeast of Mexico, revealed a GC content of 68.7 % and the presence of 1,430 potential protein-encoding genes, 1,147 of them classified into clusters of orthologous groups categories, and 16 tRNA genes representing 11 tRNA species. The presence of sixty-two genes representatives of the minimal gene set and chromid core genes suggests its importance in bacterial survival. The phaAB â†’ G operon, reported as involved in the bacterial adaptation to alkaline pH in the presence of K(+), was also found on this replicon and detected in several Azospirillum strains. Phylogenetic analysis suggests that it was laterally acquired. We were not able to show its inference on the adaptation to basic pH, giving a hint about the presence of an alternative system for adaptation to alkaline pH.

Genome Sequence of Azospirillum brasilense CBG497 and Comparative Analyses of Azospirillum Core and Accessory Genomes provide Insight into Niche Adaptation.

Bacteria of the genus Azospirillum colonize roots of important cereals and grasses, and promote plant growth by several mechanisms, notably phytohormone synthesis. The genomes of several Azospirillum strains belonging to different species, isolated from various host plants and locations, were recently sequenced and published. In this study, an additional genome of an A. brasilense strain, isolated from maize grown on an alkaline soil in the northeast of Mexico, strain CBG497, was obtained. Comparative genomic analyses were performed on this new genome and three other genomes (A. brasilense Sp245, A. lipoferum 4B and Azospirillum sp. B510). The Azospirillum core genome was established and consists of 2,328 proteins, representing between 30% to 38% of the total encoded proteins within a genome. It is mainly chromosomally-encoded and contains 74% of genes of ancestral origin shared with some aquatic relatives. The non-ancestral part of the core genome is enriched in genes involved in signal transduction, in transport and in metabolism of carbohydrates and amino-acids, and in surface properties features linked to adaptation in fluctuating environments, such as soil and rhizosphere. Many genes involved in colonization of plant roots, plant-growth promotion (such as those involved in phytohormone biosynthesis), and properties involved in rhizosphere adaptation (such as catabolism of phenolic compounds, uptake of iron) are restricted to a particular strain and/or species, strongly suggesting niche-specific adaptation.