Unlocking the genetic tapestry of autoimmune diseases: Unveiling common genes across multiple conditions.

OBJECTIVES: This study aimed to unravel the complexities of autoimmune diseases by conducting a comprehensive analysis of gene expression data across 10 conditions, including systemic lupus erythematosus (SLE), psoriasis, Sjögren's syndrome, sclerosis, immune-associated diseases, osteoarthritis, cystic fibrosis, inflammatory bowel disease (IBD), type 1 diabetes, and Guillain-Barré syndrome. METHODS: Gene expression profiles were rigorously examined to identify both upregulated and downregulated genes specific to each autoimmune disease. The study employed visual representation techniques such as heatmaps, volcano plots, and contour-MA plots to provide an intuitive understanding of the complex gene expression patterns in these conditions. RESULTS: Distinct gene expression profiles for each autoimmune condition were uncovered, with psoriasis and osteoarthritis standing out due to a multitude of both upregulated and downregulated genes, indicating intricate molecular interplays in these disorders. Notably, common upregulated and downregulated genes were identified across various autoimmune conditions, with genes like SELENBP1, MMP9, BNC1, and COL1A1 emerging as pivotal players. CONCLUSION: This research contributes valuable insights into the molecular signatures of autoimmune diseases, highlighting the unique gene expression patterns characterizing each condition. The identification of common genes shared among different autoimmune conditions, and their potential role in mitigating the risk of rare diseases in patients with more prevalent conditions, underscores the growing significance of genetics in healthcare and the promising future of personalized medicine.

Bacterial abundance and antimicrobial resistance patterns of uropathogens among pregnant women with asymptomatic bacteriuria: Association with glycemic status.

Objectives: Antimicrobial resistance (AMR), a growing global menace, poses a significant threat to maternal and fetal health. Gestational diabetes mellitus (GDM) causes double trouble in pregnancy, increasing the risk of a variety of infectious morbidities while also raising the possible association with AMR. Asymptomatic bacteriuria (ASB) is a common problem in pregnancy, but little research has been done to date explicitly examining the relationship between GDM and ASB and yielded conflicting results. Even fewer studies have specifically examined the relationship between GDM and AMR in women with ASB. Retrieving the most recent information on the disease burden, the range of causative pathogens, their patterns of AMR, and associated risk factors in pregnant women is crucial to stop the exponential rise in AMR in pregnancy and improve maternal and neonatal outcomes of infectious morbidities. Hence, this study was planned to investigate the association between glycemic status and the contemporary bacterial profile, antimicrobial resistance(AMR), and associated variables among pregnant women with ASB. Study design: This prospective, hospital-based, cross-sectional study was conducted among 320 pregnant women; divided into two groups, GDM and non-GDM. Data regarding sociodemographic and clinical characteristics were collected using a structured questionnaire. Clean-catch midstream urine samples were investigated for the presence of significant bacterial uropathogens and their AMR pattern was determined using recommended culture methods. Results: We found ASB in 46.25% of study participants with significantly higher occurrence in the GDM group. Dominant isolates were Escherichia coli followed by Klebsiella pneumoniae. AMR was noted in 51.35% and multidrug resistance(MDR) in 23.65% of isolates. Overall AMR, MDR and higher degrees of AMR were higher among uropathogens isolated from the GDM group as compared to the non GDM group, although the difference was not statistically significant. Conclusion: The high occurrence of ASB in pregnancy along with substantially high AMR in this study suggests the need for effective infection control and stewardship programmes. By defining the association of ASB and AMR with hyperglycemia, our study calls for the exploitation of this potential association in halting the pandemic of AMR and in improving the management of infectious morbidities, thus in-turn alleviating their undesired maternal and infant outcomes.

Deep Reinforcement Learning Framework for Category-Based Item Recommendation.

Deep reinforcement learning (DRL)-based recommender systems have recently come into the limelight due to their ability to optimize long-term user engagement. A significant challenge in DRL-based recommender systems is the large action space required to represent a variety of items. The large action space weakens the sampling efficiency and thereby, affects the recommendation accuracy. In this article, we propose a DRL-based method called deep hierarchical category-based recommender system (DHCRS) to handle the large action space problem. In DHCRS, categories of items are used to reconstruct the original flat action space into a two-level category-item hierarchy. DHCRS uses two deep Q -networks (DQNs): 1) a high-level DQN for selecting a category and 2) a low-level DQN to choose an item in this category for the recommendation. Hence, the action space of each DQN is significantly reduced. Furthermore, the categorization of items helps capture the users' preferences more effectively. We also propose a bidirectional category selection (BCS) technique, which explicitly considers the category-item relationships. The experiments show that DHCRS can significantly outperform state-of-the-art methods in terms of hit rate and normalized discounted cumulative gain for long-term recommendations.

Assessment of regional climatic changes in the Eastern Himalayan region: a study using multi-satellite remote sensing data sets.

In this study, an attempt has been made to capture the sensitivity of a mountainous region to elevation-dependent warming and the response of a glacier-laden surface to increasing greenhouse gases (GHGs) and aerosol concentration. Some of the changes Sikkim has undergone due to urban sprawl are as follows: an increase of ~0.7 ± 0.46 °C temperature in the past 40 years at an altitude of 5.5 km; a 2.21 km(2)/year rate of loss of glacierised area in the past 33 years; an increase in absorbed longwave radiation (6 ± 2.41 W/m(2)); an increase in heat fluxes (2 ± 0.97 W/m(2)); a decrease in albedo during the last 30 years; an increase in the concentrations of carbon dioxide (4.42%), methane (0.61%), ozone (0.67%) and black carbon column optical depth (7.19%); a decrease in carbon monoxide (2.61%) and an increase in aerosol optical depth (19.16%) during the last decade; a decrease in precipitation, water yield, discharge and groundwater; and an increase in evapotranspiration during 1971-2005. Detection of three climate signals (1976, 1997 and 2005) in the entire analysis is the quantification of the fact that the climate of Sikkim is moving away from its inter-annual variability. An increase in temperature (0.23 °C/decade) at higher altitude (~5.5 km), suppression of precipitation, decreasing water availability and rapid loss of glacierised area are the evidences of the fact that air pollution is playing a significant role in bringing about regional climatic changes in Sikkim. In this study, change detection method has been used for the first time for the estimation of change in a glacierised area of the region.