FORMULATION AND EVALUATION OF MATRIX TABLET OF VENLAFAXINE HCL BY USING DIRECTLY COMPRESSIBLE CO-PROCESSED EXCIPIENT

Objective: The sustained release of drug from the dosage form is useful especially for achieving controlled plasma level of the drug as well as improving bioavailability. The objective of the present work was to release matrix tablet of Venlafaxine HCl by using directly compressible co-processed Excipient. Methods: Co-processed excipient (Chitosan 88%: eudragit s-100) was prepared in the ratio of 1:1, 1:3, and 1:5 by the solvent evaporation method. The sustained release matrix tablets were prepared by using Co-processed excipient by direct compression and formulate formulations such as F1to F9. The tablets evaluated for various physical parameters. Direct compression method followed by optimization of the evaluation parameters was employed to get the final optimized formulation. Results: The developed Co-processed excipient was characterized for DSC, FTIR, SEM and XRD which confirm the absence of any chemical changes during co-processing. Co-processed excipient prepared in the ratio of 1:5 showed excellent flow properties. Among all formulations, Optimized formulation F9 showed the desired release profile 98.7% for a period of 24 h in phosphate buffer (pH 7.4). The release co-efficient values ‘n' (>0.5) indicated that the drug release followed non fickian anomalous mechanism based on formulation factors. Developed formulations were kept for stability study for three month as per ICH guidelines and found to be stable Conclusion: Developed co-processed excipient showed good drug release retarding property and could be alternate way to overcome the problems associated with single polymer alone. Venlafaxine HCl matrices could be developed with desirable release modulation for a once daily administration

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Plasma Metabolic and Lipidomic Fingerprinting of Individuals with Increased Intestinal Permeability

The dual-sugar intestinal permeability test is a commonly used test to assess changes in gut barrier function. However, it does not identify functional changes and the exact mechanism of damage caused by the increased intestinal permeability. This study aims to explore the application of untargeted metabolomics and lipidomics to identify markers of increased intestinal permeability. Fifty fasting male participants (18–50 years) attended a single visit to conduct the following procedures: assessment of anthropometric measures, assessment of gastrointestinal symptoms, intestinal permeability test, and assessment of blood samples 90 min post-administration of the intestinal permeability test. Rhamnose and lactulose were analysed using gas chromatography-mass spectrometry (GC-MS). Untargeted polar metabolites and lipidomics were assessed by liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QToF MS). There was an elevated lactulose/rhamnose ratio in 27 subjects, indicating increased permeability compared to the remaining 23 control subjects. There were no significant differences between groups in characteristics such as age, body mass index (BMI), weight, height, and waist conference. Fourteen metabolites from the targeted metabolomics data were identified as statistically significant in the plasma samples from intestinal permeability subjects. The untargeted metabolomics and lipidomics analyses yielded fifteen and fifty-one statistically significant features, respectively. Individuals with slightly elevated intestinal permeability had altered energy, nucleotide, and amino acid metabolism, in addition to increased glutamine levels. Whether these biomarkers may be used to predict the early onset of leaky gut warrants further investigation

Utilizing the food–pathogen metabolome to putatively identify biomarkers for the detection of shiga toxin-producing e. coli (STEC) from spinach

Shiga toxigenic E. coli (STEC) are an important cause of foodborne disease globally with many outbreaks linked to the consumption of contaminated foods such as leafy greens. Existing methods for STEC detection and isolation are time-consuming. Rapid methods may assist in preventing contaminated products from reaching consumers. This proof-of-concept study aimed to determine if a metabolomics approach could be used to detect STEC contamination in spinach. Using untargeted metabolic profiling, the bacterial pellets and supernatants arising from bacterial and inoculated spinach enrichments were investigated for the presence of unique metabolites that enabled categorization of three E. coli risk groups. A total of 109 and 471 metabolite features were identified in bacterial and inoculated spinach enrichments, respectively. Supervised OPLS-DA analysis demonstrated clear discrimination between bacterial enrichments containing different risk groups. Further analysis of the spinach enrichments determined that pathogen risk groups 1 and 2 could be easily discriminated from the other groups, though some clustering of risk groups 1 and 2 was observed, likely representing their genomic similarity. Biomarker discovery identified metabolites that were significantly associated with risk groups and may be appropriate targets for potential biosensor development. This study has confirmed that metabolomics can be used to identify the presence of pathogenic E. coli likely to be implicated in human disease

Unravelling the gut-lung axis: insights into microbiome interactions and Traditional Indian Medicine's perspective on optimal health

The microbiome of the human gut is a complex assemblage of microorganisms that are in a symbiotic relationship with one another and profoundly influence every aspect of human health. According to converging evidence, the human gut is a nodal point for the physiological performance matrixes of the vital organs on several axes (i.e., gut-brain, gut-lung, etc.). As a result of COVID-19, the importance of gut-lung dysbiosis (balance or imbalance) has been realised. In view of this, it is of utmost importance to develop a comprehensive understanding of the microbiome, as well as its dysbiosis. In this review, we provide an overview of the gut-lung axial microbiome and its importance in maintaining optimal health. Human populations have successfully adapted to geophysical conditions through traditional dietary practices from around the world. In this context, a section has been devoted to the traditional Indian system of medicine and its theories and practices regarding the maintenance of optimally customized gut health

Detection of Foodborne Pathogens Using Proteomics and Metabolomics-Based Approaches

Considering the short shelf-life of certain food products such as red meat, there is a need for rapid and cost-effective methods for pathogen detection. Routine pathogen testing in food laboratories mostly relies on conventional microbiological methods which involve the use of multiple selective culture media and long incubation periods, often taking up to 7 days for confirmed identifications. The current study investigated the application of omics-based approaches, proteomics using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-ToF MS) and metabolomics using gas chromatography-mass spectrometry (GC-MS), for detection of three red meat pathogens – Listeria monocytogenes, Salmonella enterica and Escherichia coli O157:H7. Species-level identification was achieved within 18 h for S. enterica and E. coli O157:H7 and 30 h for L. monocytogenes using MALDI-ToF MS analysis. For the metabolomics approach, metabolites were extracted directly from selective enrichment broth samples containing spiked meat samples (obviating the need for culturing on solid media) and data obtained using GC-MS were analyzed using chemometric methods. Putative biomarkers relating to L. monocytogenes, S. enterica and E. coli O157:H7 were observed within 24, 18, and 12 h, respectively, of inoculating meat samples. Many of the identified metabolites were sugars, fatty acids, amino acids, nucleosides and organic acids. Secondary metabolites such as cadaverine, hydroxymelatonin and 3,4-dihydroxymadelic acid were also observed. The results obtained in this study will assist in the future development of rapid diagnostic tests for these important foodborne pathogens