The microbial contamination in food packaging has been a major concern that has paved the way to search for novel, natural anti-microbial agents, such as modified α-mangostin. In the present study, twelve synthetic analogs were obtained through semi-synthetic modification of α-mangostin by Ritter reaction, reduction by palladium-carbon (Pd-C), alkylation, and acetylation. The evaluation of the anti-microbial potential of the synthetic analogs showed higher bactericidal activity than the parent molecule. The anti-microbial studies proved that I E showed high anti-bacterial activity whereas I I showed the highest anti-fungal activity. Due to their microbicidal potential, modified α-mangostin derivatives could be utilized as active anti-microbial agents in materials for the biomedical and food industry.

Background Diabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemia. The avalanche of healthcare data is accelerating precision and personalized medicine. Artificial intelligence and algorithm-based approaches are becoming more and more vital to support clinical decision-making. These methods are able to augment health care providers by taking away some of their routine work and enabling them to focus on critical issues. However, few studies have used predictive modeling to uncover associations between comorbidities in ICU patients and diabetes. This study aimed to use Unified Medical Language System (UMLS) resources, involving machine learning and natural language processing (NLP) approaches to predict the risk of mortality. Methods We conducted a secondary analysis of Medical Information Mart for Intensive Care III (MIMIC-III) data. Different machine learning modeling and NLP approaches were applied. Domain knowledge in health care is built on the dictionaries created by experts who defined the clinical terminologies such as medications or clinical symptoms. This knowledge is valuable to identify information from text notes that assert a certain disease. Knowledge-guided models can automatically extract knowledge from clinical notes or biomedical literature that contains conceptual entities and relationships among these various concepts. Mortality classification was based on the combination of knowledge-guided features and rules. UMLS entity embedding and convolutional neural network (CNN) with word embeddings were applied. Concept Unique Identifiers (CUIs) with entity embeddings were utilized to build clinical text representations. Results The best configuration of the employed machine learning models yielded a competitive AUC of 0.97. Machine learning models along with NLP of clinical notes are promising to assist health care providers to predict the risk of mortality of critically ill patients. Conclusion UMLS resources and clinical notes are powerful and important tools to predict mortality in diabetic patients in the critical care setting. The knowledge-guided CNN model is effective (AUC = 0.97) for learning hidden features.

Mast cells are tissue-resident cells that contribute to allergic diseases, among others, due to excessive or inappropriate cellular activation and degranulation. Therapeutic approaches to modulate mast cell activation are urgently needed. Siglec-6 is an immunoreceptor tyrosine-based inhibitory motif (ITIM)-bearing receptor selectively expressed by mast cells, making it a promising target for therapeutic intervention. However, the effects of its engagement on mast cells are poorly defined. Siglec-6 expression and endocytosis on primary human mast cells and mast cell lines were assessed by flow cytometry. SIGLEC6 mRNA expression was examined by single-cell RNAseq in esophageal tissue biopsy samples. The ability of Siglec-6 engagement or co-engagement to prevent primary mast cell activation was determined based on assessments of mediator and cytokine secretion and degranulation markers. Siglec-6 was highly expressed by all mast cells examined, and the SIGLEC6 transcript was restricted to mast cells in esophageal biopsy samples. Siglec-6 endocytosis occurred with delayed kinetics relative to the related receptor Siglec-8. Co-crosslinking of Siglec-6 with FcεRIα enhanced the inhibition of mast cell activation and diminished downstream ERK1/2 and p38 phosphorylation. The selective, stable expression and potent inhibitory capacity of Siglec-6 on human mast cells are favorable for its use as a therapeutic target in mast cell-driven diseases.

The G-coupled receptors seen on the cell surface are composites with a lipid bilayer. The chemokines are kind of G-coupled receptor which majorly involved in the activation and downstream signalling of the cell. In general, many G-coupled receptors lack their 3D structures which become a hurdle in the drug designing process. In this study, comparative modelling of the CXCR3 receptor was carried out, structure evaluation was done using various tools and softwares. Additionally, molecular dynamics and docking were performed to prove the structural quality and architecture. Interestingly, the studies like toggle switch mechanism, lipid dynamics, virtual screening were carried out to find the potent antagonist for the CXCR3 receptor. During virtual screening 14,303 similar molecules were retrieved among them only four compounds have an ability to interact with a crucial amino acid residue of an antagonist. Hence, these screened compounds can serve as a drug candidate for a CXCR3 receptor, but further in vitro and in vivo studies are ought to do to prove its same efficacy.

BackgroundEosinophilic esophagitis (EoE) involves a chronic immune‐mediated response to dietary antigens. Recent work identifies T‐cell clonality in children with EoE, however, it is unknown whether this is true in adults or whether there is a restricted food‐specific T‐cell repertoire. We sought to confirm T‐cell receptor (TCR) clonality in EoE and assess for differences with specific food triggers.MethodsBulk TCR sequencing was performed on mRNA isolated from esophageal biopsies obtained from adults and children with EoE (n = 15) who had food triggers confirmed by endoscopic evaluation. Non‐EoE adult and pediatric controls (n = 10) were included. Differences in TCR clonality by disease and treatment status were assessed. Shared and similar V‐J‐CDR3s were assessed based on specific food triggers.ResultsActive EoE biopsies from children but not adults displayed decreased unique TCRα/β clonotypes and increased relative abundance of TCRs comprising >1% of the total compared to non‐EoE controls and paired inactive EoE samples. Among patients in which baseline, post diet elimination, and food trigger reintroduction samples (n = 6) were obtained, we observed ~1% of TCRs were shared only between pre‐diet elimination and trigger reintroduction. Patients with a shared EoE trigger (milk) had a greater degree of shared and similar TCRs compared to patients with differing triggers (seafood, wheat, egg, soy).ConclusionWe confirmed relative clonality in children but not adults with active EoE and identified potential food‐specific TCRs, particularly for milk‐triggered EoE. Further studies are needed to better identify the broad TCR repertoire relevant to food triggers.