Browse Articles
Discover research articles across all indexed journals
Reproductive toxicity in male rats induced by chronic arsenic exposure involves hormonal and structural changes
Metabolic alterations associated with epileptic seizures detected by NMR spectroscopy
Suicide gene therapy targeting ewing sarcoma via an ewing-specific GGAA promoter
Sex-specific associations between muscle-fat ratio and bone density in middle-aged adults
Impact of Tregs on tumor regression in locally advanced G/GEJ cancer patients undergoing neoadjuvant chemoimmunotherapy
Oversized nanodiscs for combined structural and functional investigation of multicomponent membrane protein systems
Abstract Membrane proteins are fundamental to many crucial cellular processes but removing them from their native environment for structural and functional studies creates experimental challenges. Numerous strategies have been developed to replicate native-like membrane environments in vitro for membrane protein research, however, most studies have focused on systems for either structural or functional characterisation, not both together. Here, we apply an in-vivo split intein strategy to produce stable circularised nanodiscs for combined structural and functional analysis of respiratory complex I, using its highly hydrophobic native ubiquinone-10 substrate and an auxiliary ubiquinol oxidase from Trypanosoma brucei brucei. We successfully reconstituted Paracoccus denitrificans complex I into circularised nanodiscs, determined its cryo-EM structure at 3.1 Å resolution and conducted biophysical and biochemical analyses to demonstrate how the ‘oversized’ nanodiscs have space to accommodate both enzymes and substrates to sustain steady-state catalysis. Our work establishes a proof-of-principle for using oversized nanodiscs as an integrated platform for structural and functional interrogation of complex membrane proteins in near-native membrane environments.
A transportable laser-plasma accelerator in the MeV range
Predicting COVID-19 severity in pediatric patients using machine learning: a comparative analysis of algorithms and ensemble methods
Abstract COVID-19 has posed a significant global health challenge, affecting individuals across all age groups. While extensive research has focused on adults, pediatric patients exhibit distinct clinical characteristics that necessitate specialized predictive models for disease severity. Machine learning offers a powerful approach to analyzing complex datasets and predicting outcomes, yet its application in pediatric COVID-19 remains limited. This study evaluates the performance of machine learning algorithms in predicting disease severity among pediatrics. A retrospective analysis was conducted on a dataset of 588 pediatric with confirmed COVID-19, incorporating demographic, clinical, and laboratory variables. Various machine learning models were trained and assessed, with a SuperLearner ensemble model implemented to enhance predictive accuracy. Among the models, Random Forest exhibited the highest performance, achieving an accuracy of 90.1%, sensitivity of 90.2%, and specificity of 90.1%. The SuperLearner ensemble further improved predictive performance, demonstrating the lowest mean risk estimate. Key predictors, including oxygen saturation, respiratory parameters, and specific laboratory markers, played a crucial role in distinguishing severe from non-severe cases. These findings emphasize the potential of machine learning, particularly ensemble methods, in improving risk stratification for pediatric COVID-19. Integrating these predictive models into clinical practice could support early identification of high-risk patients and optimize clinical decision-making.
Isoform analysis of heterozygous putative splicing variants at the allele level using nanopore long-read sequencing
Abstract One of the challenges in clinical genetics for rare diseases and personalized medicine is evaluating isoform alterations arising from heterozygous putative splicing variants at the allele level. Our aim was to analyze these variants by dividing cDNA or direct RNA nanopore long reads into two alleles, referencing whole-genome sequencing data containing allele-informative single nucleotide variants and then comparing the allele-separated reads using Full-Length Alternative Isoform analysis of RNA (FLAIR), a previously published bioinformatics tool for isoform analysis. In this study, we developed an allele-separative bioinformatics pipeline and described its performance. We applied our pipeline to previously published nanopore direct RNA sequencing data, as well as 5’ cap-trapping full-length cDNA nanopore sequencing (CTR-seq) data from blood samples of three individuals. We successfully identified heterozygous splicing variants associated with significant isoform differences between alleles. Furthermore, we uncovered the effects of a novel pathogenic splicing variant in PYGM on isoforms in a compound-heterozygous case of McArdle disease using nanopore cDNA amplicon and targeted genomic sequencing. This study demonstrates the utility of nanopore long-read sequencing for isoform analysis at the allele level, providing a valuable approach to evaluating the direct consequences of heterozygous splicing variants in individuals.
Temperature dependent microstructural defects and surface charge effects on antioxidant activity of green synthesized nanoceria
CB2 and TRPV1 receptors in inflammatory state of macrophages from sickle cell anemia pediatric/young adults
Abstract Sickle Cell Disease (SCD) is a monogenic disorder characterized by the production of abnormal hemoglobin. Polymerization of HbS causes sickling of red blood cells (RBCs) evidenced by acute adverse events and persistent inflammatory state, vasculopathy and organ damage. Sickled RBCs cause an anemic condition and vaso-occlusive crisis which trigger leukocytes, endothelial cells, and platelets. Due to these events, SCD patients unveiled an elevated level of pro-inflammatory cytokines, which contribute to the ongoing inflammatory state, oxidative stress, and other severe complications. SCD patients also experience neuropathic, inflammatory, and nociceptive pain. The discovery of novel therapeutic approaches and targets to counteract and manage inflammation in SCD are needed. Our study aimed to better understand the role of macrophages in SCD inflammation by first investigating their phenotype and then studying the iron metabolism involvement in the inflammatory processes. Therefore, given the importance to find novel therapeutic approach to contain and manage inflammation in these patients, and considering the role of CB2 and TRPV1 in this process, we decided to investigate the expression of these receptors and the effects of their stimulation on inflammatory state in SCD macrophages.
Carnivorous plants can decompose the polyesters poly(ethylene terephthalate) and poly(butylene adipate terephthalate)
Developing real-time IoT-based public safety alert and emergency response systems
Land use classification using multi-year Sentinel-2 images with deep learning ensemble network
Abstract Accurate land use classification is essential for urban planning, environmental monitoring, and agricultural management. Sentinel-2 satellite imagery provides rich spatial and spectral information suitable for this purpose. This study proposes a deep learning ensemble network named IRUNet, which integrates InceptionResNetV2 with a UNet framework for multi-year Sentinel-2 imagery classification over the Katpadi region (2017–2024). Unlike prior works, IRUNet utilizes multi-scale feature fusion and incorporates Test-Time Augmentation (TTA) to enhance prediction robustness. While the data spans multiple years, each year is treated as an independent input without modeling temporal sequences. The proposed method demonstrates superior performance over UNet, ResUNet, and Attention-UNet models, achieving an accuracy of 98.21% and Dice similarity coefficient (DSC) of 88.96%. Additional metrics including precision (94.71%), recall (89.19%), F1-score, and Kappa coefficient have been reported. This research contributes a high-performance, generalizable framework for multi-year land use classification.
Cyanobacterial bloom causes expansion of isotopic niche areas and overlap in crustacean zooplankton
Abstract We aimed to study how cyanobacterial blooms affect the use of the basal resources by three groups of crustacean zooplankton (calanoid and cyclopoid copepods, Daphnia spp.). We used measurements of naturally occurring stable isotopes of carbon (δ13C) and nitrogen (δ15N) to quantify the areas of isotopic niches (sample size-corrected standard ellipse areas; SEAc) of planktonic crustaceans during the pre-bloom and cyanobacterial bloom phases. In the pre-bloom phase, SEAcs accounted for 15.0‰2 in calanoid copepods, 21.2‰2 in cyclopoid copepods and 14.4‰2 in Daphnia spp. During the cyanobacterial bloom phase, the SEAcs of studied animals increased to 37.8, 27.0 and 43.6‰2 respectively. In addition, the overlap among the niches of the crustacean groups increased during the bloom phase compared to the pre-bloom phase. The results suggest that, despite reduced diversity of basal resources during the cyanobacterial bloom, crustaceans exhibited dietary adaptability. This involved a shift toward alternative food sources.