Exploring the diversity of vaginal microbiota between healthy women and cervical cancer patients in India.

Introduction. Cervicovaginal diversity has been reported as a predictive biomarker for cervical cancer risk. We recently reported the bio-therapeutic potential of vaginal probiotics from healthy Indian women against vaginal pathogens, isolated from the invasive cervical cancer (ICC) patients.Gap Statement. The cervicovaginal microflora from cervical cancer patients has not yet been reported from Indian population.Aim. The present study aimed at comparing the cervicovaginal microbiome between healthy controls (HC) and ICC patients from the Indian population.Methodology. In total, 30 vaginal swabs (15 from HC and 15 from ICC) were subjected to 16S rRNA gene sequencing. Alpha diversity was evaluated by Shannon and Chao1 index; and beta diversity by principle coordinate analysis (PCoA) of weighted and unweighted UniFrac distances. The relative abundance of the microbial taxa was done according to linear discriminant analysis effect size (LEfSe).Results. Predominance of Staphylococcus spp. in ICC and Lactobacillus gasseri in HC groups was observed. Alpha-diversity was found to be higher in ICC as compared to HC but was statistically non-significant. LEfSe analysis revealed Bacteroides fragilis and Escherichia coli as the marker genera in ICC with a marked decrease in Lactobacillus sp. Contrarily, in HC, L. gasseri, L. iners and L. fermentum were found to be abundant.Conclusion. Differences in the vaginal microbiome between healthy and ICC women could help in the early prediction of cervical cancer risk and thus in designing prevention strategies.

Benzyl isothiocyanate as an alternative to antibiotics? a comparative in vivo study using Pseudomonas aeruginosa infection as a model.

Due to over-prescription of antibiotics, antimicrobial resistance has emerged to be a critical concern globally. Many countries have tightened the control of antibiotic usage, which, in turn, promotes the search for alternatives to antibiotics. Quite a few phytochemicals have been investigated. Benzyl isothiocyanate (BITC) is an important secondary metabolite in cruciferous species and exhibited potent antimicrobial activity under in vitro conditions. In this research, we undertook a comparative mouse model study of BITC with gentamycin sulfate (positive antibiotic control) and ceftiofur hydrochloride (negative antibiotic control) against Pseudomonas aeruginosa infection. Our results showed that BITC exhibited comparable or better antimicrobial activity and lower infiltration of mouse immune cells upon comparing to gentamycin sulfate. Furthermore, BITC did not impose any toxicity to the air pouch skin tissues. In summary, our current study suggests that BITC could be an alternative to antibiotics and deserves further in vivo and clinical trial studies.

Prediction and Diagnosis of Breast Cancer Using Machine and Modern Deep Learning Models.

Background &Objective: Carcinoma of the breast is one of the major issues causing death in women, especially in developing countries. Timely prediction, detection, diagnosis, and efficient therapies have become critical to reducing death rates. Increased use of artificial intelligence, machine, and deep learning techniques create more accurate and trustworthy models for predicting and detecting breast cancer. This study aims to examine the effectiveness of several machine and modern deep learning models for prediction and diagnosis of breast cancer. METHODS: This research compares traditional machine learning classification methods to innovative techniques that use deep learning models. Established usual classification models such as k-Nearest Neighbors (kNN), Gradient Boosting, Support Vector Machine (SVM), Neural Network, CN2 rule inducer, Naive Bayes, Stochastic Gradient Descent (SGD), and Tree, and deep learning models such as Neural Decision Forest and Multilayer Perceptron used. The investigation, which was carried out using the Orange and Python tools, evaluates their diagnostic effectiveness in breast cancer detection. The evaluation uses UCI's publicly accessible Wisconsin Diagnostic Data Set, enabling transparency and accessibility in the study approach. RESULT: The mean radius ranges from 6.981 to 28.110, while the mean texture runs from 9.71 to 39.28 in malignant and benign cases. Gradient boosting and CN2 rule inducer classifiers outperform SVM in accuracy and sensitivity, whereas SVM has the lowest accuracy and sensitivity at 88%. The CN2 rule inducer classifier achieves the greatest ROC curve score for benign and malignant breast cancer datasets, with an AUC score of 0.98%. MLP displays distinguish positive and negative classes, with a higher AUC-ROC of 0.9959. with accuracy of 96.49%, precision of 96.57%, recall of 96.49%, and an F1-Score of 96.50%. CONCLUSION: Among the most commonly used classifier models, CN2 rule and  GB performed better than other models. However, MLP from deep learning produced the greatest overall performance.

Protective effects of fecal microbiota transplantation against ischemic stroke and other neurological disorders: an update.

The bidirectional communication between the gut and brain or gut-brain axis is regulated by several gut microbes and microbial derived metabolites, such as short-chain fatty acids, trimethylamine N-oxide, and lipopolysaccharides. The Gut microbiota (GM) produce neuroactives, specifically neurotransmitters that modulates local and central neuronal brain functions. An imbalance between intestinal commensals and pathobionts leads to a disruption in the gut microbiota or dysbiosis, which affects intestinal barrier integrity and gut-immune and neuroimmune systems. Currently, fecal microbiota transplantation (FMT) is recommended for the treatment of recurrent Clostridioides difficile infection. FMT elicits its action by ameliorating inflammatory responses through the restoration of microbial composition and functionality. Thus, FMT may be a potential therapeutic option in suppressing neuroinflammation in post-stroke conditions and other neurological disorders involving the neuroimmune axis. Specifically, FMT protects against ischemic injury by decreasing IL-17, IFN-γ, Bax, and increasing Bcl-2 expression. Interestingly, FMT improves cognitive function by lowering amyloid-ß accumulation and upregulating synaptic marker (PSD-95, synapsin-1) expression in Alzheimer's disease. In Parkinson's disease, FMT was shown to inhibit the expression of TLR4 and NF-κB. In this review article, we have summarized the potential sources and methods of administration of FMT and its impact on neuroimmune and cognitive functions. We also provide a comprehensive update on the beneficial effects of FMT in various neurological disorders by undertaking a detailed interrogation of the preclinical and clinical published literature.