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Antifungal Properties of Polycephalomyces nipponicus (Ascomycetes) against Candida albicans: Potential for Novel Therapeutic Development.
Int J Med Mushrooms
December 2024
Department of Biology, Faculty of Science, Mahasarakham University, Kantarawichai District, Maha Sarakham, Thailand; Microbiology and Applied Microbiology Research Unit, Faculty of Science, Mahasarakham University, Kantarawichai District, Maha Sarakham, Thailand.
Candida albicans has the potential to turn pathogenic and cause mild to severe infections, particularly in people with weakened immune systems. Novel therapeutics are required due to its morphological alterations, biofilm development, and resistance to antifungal drugs. Polycephalomyces nipponicus, a traditional East Asian medicinal fungus, has shown potential as an antifungal agent.
View Article and Find Full Text PDFEmerging lineages A2.2.1 and A2.2.2 of human metapneumovirus (hMPV) in pediatric respiratory infections: Insights from India.
IJID Reg
March 2025
Regional Level Viral Research & Diagnostic Laboratory (RVRDL), Department of Microbiology, Jawaharlal Institute of Post-Graduate Medical Education and Research (JIPMER), Puducherry, India.
Objectives: Human metapneumovirus (hMPV) is recognized as a significant cause of acute respiratory infections among infants under 5 years of age.
Methods: Nasal swabs collected from January 2021 to June 2024 were screened to detect hMPV using reverse transcription-quantitative polymerase chain reaction. Furthermore, representative positive samples were sequenced and subjected to phylogenetic analysis.
Discrimination of leaf diseases in Maize/Soybean intercropping system based on hyperspectral imaging.
Front Plant Sci
December 2024
College of Agronomy, College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, Shandong, China.
In order to achieve precise discrimination of leaf diseases in the Maize/Soybean intercropping system, i.e. leaf spot disease, rust disease, mixed leaf diseases, this study utilized hyperspectral imaging and deep learning algorithms for the classification of diseased leaves of maize and soybean.
View Article and Find Full Text PDFAnchorFCI: harnessing genetic anchors for enhanced causal discovery of cardiometabolic disease pathways.
Front Genet
December 2024
Department of Statistics, Federal University of São Carlos (UFSCar), São Carlos, Brazil.
Introduction: Cardiometabolic diseases, a major global health concern, stem from complex interactions of lifestyle, genetics, and biochemical markers. While extensive research has revealed strong associations between various risk factors and these diseases, latent confounding and limited causal discovery methods hinder understanding of their causal relationships, essential for mechanistic insights and developing effective prevention and intervention strategies.
Methods: We introduce anchorFCI, a novel adaptation of the conservative Really Fast Causal Inference (RFCI) algorithm, designed to enhance robustness and discovery power in causal learning by strategically selecting and integrating reliable anchor variables from a set of variables known not to be caused by the variables of interest.
Deep clustering representation of spatially resolved transcriptomics data using multi-view variational graph auto-encoders with consensus clustering.
Comput Struct Biotechnol J
December 2024
School of Information Science and Engineering, Yunnan University, Kunming, 650091, Yunnan, China.
The rapid development of spatial transcriptomics (ST) technology has provided unprecedented opportunities to understand tissue relationships and functions within specific spatial contexts. Accurate identification of spatial domains is crucial for downstream spatial transcriptomics analysis. However, effectively combining gene expression data, histological images and spatial coordinate data to identify spatial domains remains a challenge.
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