From late 2019, whole world has been facing COVID-19 pandemic which is caused by SARS-CoV-2 virus. This virus primarily attacks the respiratory tract and enter host cell by binding with angiotensin 2 converting enzyme receptors present on alveoli of the lungs. Despite its binding in the lungs, many patients have reported gastrointestinal symptoms and indeed, RNA of the virus have been found in faecal sample of patients. This observation gave a clue of the involvement of gut-lung axis in this disease development and progression. From several studies reported in past two years, intestinal microbiome has shown to have bidirectional link with lungs i.e., gut dysbiosis increases the tendency of infection with COVID-19 and coronavirus can also cause perturbations in intestinal microbial composition. Thus, in this review we have tried to figure out the mechanisms by which disturbances in the gut composition can increase the susceptibility to COVID-19. Understanding these mechanisms can play a crucial role in decreasing the disease outcomes by manipulating the gut microbiome using prebiotics, probiotics, or combination of two. Even, faecal microbiota transplantation can also show better results, but intensive clinical trials need to be done first.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9936796 | PMC |
| http://dx.doi.org/10.1016/j.heliyon.2023.e13801 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Association of organ iron levels with type 2 diabetes mellitus and glycemic traits: A bidirectional two-sample Mendelian randomization study.
J Trace Elem Med Biol
December 2024
Department of Biochemistry, Christian Medical College, Vellore, Tamil Nadu, India; Affiliated to The Tamil Nadu Dr. MGR Medical University, Chennai, India. Electronic address:
Introduction: Observational studies have found that higher iron levels are associated with an increased risk of diabetes mellitus. Given the limitations of causal inferences from observational studies and the expensive and time-consuming nature of randomized controlled trials, Mendelian randomization analysis presents a reasonable alternative to study causal relationships. Previous MR analyses studying iron levels and diabetes have used indirect markers of iron levels, such as serum ferritin, and found conflicting results.
View Article and Find Full Text PDFToxicity on Social Media During the 2022 Mpox Public Health Emergency: Quantitative Study of Topical and Network Dynamics.
J Med Internet Res
December 2024
School of Information, University of Michigan, Ann Arbor, MI, United States.
Background: Toxicity on social media, encompassing behaviors such as harassment, bullying, hate speech, and the dissemination of misinformation, has become a pressing social concern in the digital age. Its prevalence intensifies during periods of social crises and unrest, eroding a sense of safety and community. Such toxic environments can adversely impact the mental well-being of those exposed and further deepen societal divisions and polarization.
View Article and Find Full Text PDFTumor aware recurrent inter-patient deformable image registration of computed tomography scans with lung cancer.
Med Phys
November 2024
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Background: Voxel-based analysis (VBA) for population level radiotherapy (RT) outcomes modeling requires topology preserving inter-patient deformable image registration (DIR) that preserves tumors on moving images while avoiding unrealistic deformations due to tumors occurring on fixed images.
Purpose: We developed a tumor-aware recurrent registration (TRACER) deep learning (DL) method and evaluated its suitability for VBA.
Methods: TRACER consists of encoder layers implemented with stacked 3D convolutional long short term memory network (3D-CLSTM) followed by decoder and spatial transform layers to compute dense deformation vector field (DVF).
Exploring the reversal curse and other deductive logical reasoning in BERT and GPT-based large language models.
Patterns (N Y)
September 2024
Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
The "Reversal Curse" describes the inability of autoregressive decoder large language models (LLMs) to deduce "B is A" from "A is B," assuming that B and A are distinct and can be uniquely identified from each other. This logical failure suggests limitations in using generative pretrained transformer (GPT) models for tasks like constructing knowledge graphs. Our study revealed that a bidirectional LLM, bidirectional encoder representations from transformers (BERT), does not suffer from this issue.
View Article and Find Full Text PDFBackground: Immune cells are crucial in the etiology of acute myeloid leukemia (AML). Given the genetic, epigenetic, and clonal complexities of AML, pinpointing factors linked to immunotherapy presents a formidable challenge. Moreover, investigations into the connection between immune cells and AML are still in their infancy, necessitating further studies to decode the intricate connections involved.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!