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Reactive oxygen and nitrogen species (RONS) are generated through various endogenous and exogenous processes; however, they are neutralized by enzymatic and non-enzymatic antioxidants. An imbalance between the generation and neutralization of oxidants results in the progression to oxidative stress (OS), which in turn gives rise to various diseases, disorders and aging. The characteristics of aging include the progressive loss of function in tissues and organs. The theory of aging explains that age-related functional losses are due to accumulation of reactive oxygen species (ROS), their subsequent damages and tissue deformities. Moreover, the diseases and disorders caused by OS include cardiovascular diseases [CVDs], chronic obstructive pulmonary disease, chronic kidney disease, neurodegenerative diseases and cancer. OS, induced by ROS, is neutralized by different enzymatic and non-enzymatic antioxidants and prevents cells, tissues and organs from damage. However, prolonged OS decreases the content of antioxidant status of cells by reducing the activities of reductants and antioxidative enzymes and gives rise to different pathological conditions. Therefore, the aim of the present review is to discuss the mechanism of ROS-induced OS signaling and their age-associated complications mediated through their toxic manifestations in order to devise effective preventive and curative natural therapeutic remedies.

Internet of Things (IoT) devices for the home have made a lot of people’s lives better, but their popularity has also raised privacy and safety concerns. This study explores the application of deep learning models for anomaly detection and face recognition in IoT devices within the context of smart homes. Six models, namely, LR-XGB-CNN, LR-GBC-CNN, LR-CBC-CNN, LR-HGBC-CNN, LR-ABC-CNN, and LR-LGBM-CNN, were proposed and evaluated for their performance. The models were trained and tested on labeled datasets of sensor readings and face images, using a range of performance metrics to assess their effectiveness. Performance evaluations were conducted for each of the proposed models, revealing their strengths and areas for improvement. Comparative analysis of the models showed that the LR-HGBC-CNN model consistently outperformed the others in both anomaly detection and face recognition tasks, achieving high accuracy, precision, recall, F1 score, and AUC-ROC values. For anomaly detection, the LR-HGBC-CNN model achieved an accuracy of 94%, a precision of 91%, a recall of 96%, an F1 score of 93%, and an AUC-ROC of 0.96. In face recognition, the LR-HGBC-CNN model demonstrated an accuracy of 88%, precision of 86%, recall of 90%, F1 score of 88%, and an AUC-ROC of 0.92. The models exhibited promising capabilities in detecting anomalies, recognizing faces, and integrating these functionalities within smart home IoT devices. The study’s findings underscore the potential of deep learning approaches for enhancing security and privacy in smart homes. However, further research is warranted to evaluate the models’ generalizability, explore advanced techniques such as transfer learning and hybrid methods, investigate privacy-preserving mechanisms, and address deployment challenges.

The study aims to assess the landscape vulnerability to forest fire susceptibility of Rudraprayag district, India, using frequency ratio model. Firstly, forest-fire-affected pixels were identified by using normalized difference burning ratio and ground survey. A total of 19,834 forest fire pixels were identified; out of these, 14,876 (70%) pixels were used to generate forest fire susceptibility map and the remaining 4958 affected pixels (30%) were used to validate the susceptibility model. Twelve forest fire conditioning indicators were selected: slope angle, slope aspect, curvature, elevation, topographic wetness index, soil texture, land use/land cover, normalized difference moisture index, annual average rainfall, road buffer, distance from settlement and distance from drainage to build the forest fire susceptibility model. Receiver operating characteristic curve was used to validate the forest fire susceptibility map, and 85% prediction accuracy was found. Final landscape vulnerability to forest fire susceptibility was assessed by using overlay function in GIS environment. The result shows that 73% area of Rudraprayag district falls into low and moderate susceptibility classes and approximately 16% area falls into high and very high susceptibility classes. Landscape vulnerability analysis revealed that moderate and very high forest fire susceptibility occupies the inaccessible parts of the core forest area of the district.

Jeffrey Barrett, Carl Anderson and colleagues report the results of a large genome-wide association study of inflammatory bowel disease. They identify 25 new genome-wide significant loci, 3 of which contain integrin genes, and find that the associated variants at several of these loci are correlated with expression changes in response to immune stimulus. Genetic association studies have identified 215 risk loci for inflammatory bowel disease 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , thereby uncovering fundamental aspects of its molecular biology. We performed a genome-wide association study of 25,305 individuals and conducted a meta-analysis with published summary statistics, yielding a total sample size of 59,957 subjects. We identified 25 new susceptibility loci, 3 of which contain integrin genes that encode proteins in pathways that have been identified as important therapeutic targets in inflammatory bowel disease. The associated variants are correlated with expression changes in response to immune stimulus at two of these genes ( ITGA4 and ITGB8 ) and at previously implicated loci ( ITGAL and ICAM1 ). In all four cases, the expression-increasing allele also increases disease risk. We also identified likely causal missense variants in a gene implicated in primary immune deficiency, PLCG2 , and a negative regulator of inflammation, SLAMF8 . Our results demonstrate that new associations at common variants continue to identify genes relevant to therapeutic target identification and prioritization.

ObjectiveWe have previously described a prognostic transcriptional signature in CD8 T cells that separates patients with IBD into two phenotypically distinct subgroups, termed IBD1 and IBD2. Here we sought to develop a blood-based test that could identify these subgroups without cell separation, and thus be suitable for clinical use in Crohn’s disease (CD) and ulcerative colitis (UC).DesignPatients with active IBD were recruited before treatment. Transcriptomic analyses were performed on purified CD8 T cells and/or whole blood. Phenotype data were collected prospectively. IBD1/IBD2 patient subgroups were identified by consensus clustering of CD8 T cell transcriptomes. In a training cohort, machine learning was used to identify groups of genes (‘classifiers’) whose differential expression in whole blood recreated the IBD1/IBD2 subgroups. Genes from the best classifiers were quantitative (q)PCR optimised, and further machine learning was used to identify the optimal qPCR classifier, which was locked down for further testing. Independent validation was sought in separate cohorts of patients with CD (n=66) and UC (n=57).ResultsIn both validation cohorts, a 17-gene qPCR-based classifier stratified patients into two distinct subgroups. Irrespective of the underlying diagnosis, IBDhi patients (analogous to the poor prognosis IBD1 subgroup) experienced significantly more aggressive disease than IBDlo patients (analogous to IBD2), with earlier need for treatment escalation (hazard ratio=2.65 (CD), 3.12 (UC)) and more escalations over time (for multiple escalations within 18 months: sensitivity=72.7% (CD), 100% (UC); negative predictive value=90.9% (CD), 100% (UC)).ConclusionThis is the first validated prognostic biomarker that can predict prognosis in newly diagnosed patients with IBD and represents a step towards personalised therapy.

James Lee, Kenneth Smith and colleagues report a within-cases genome-wide association analysis for Crohn's disease to identify genetic loci specifically associated with disease severity and outcome. They find four loci associated with prognosis, none of which is associated with susceptibility to Crohn's disease. For most immune-mediated diseases, the main determinant of patient well-being is not the diagnosis itself but instead the course that the disease takes over time (prognosis) 1 , 2 , 3 . Prognosis may vary substantially between patients for reasons that are poorly understood. Familial studies support a genetic contribution to prognosis 4 , 5 , 6 , but little evidence has been found for a proposed association between prognosis and the burden of susceptibility variants 7 , 8 , 9 , 10 , 11 , 12 , 13 . To better characterize how genetic variation influences disease prognosis, we performed a within-cases genome-wide association study in two cohorts of patients with Crohn's disease. We identified four genome-wide significant loci, none of which showed any association with disease susceptibility. Conversely, the aggregated effect of all 170 disease susceptibility loci was not associated with disease prognosis. Together, these data suggest that the genetic contribution to prognosis in Crohn's disease is largely independent of the contribution to disease susceptibility and point to a biology of prognosis that could provide new therapeutic opportunities.

Background Glioblastoma (GBM) is a highly aggressive brain tumor associated with a poor patient prognosis. The survival rate remains low despite standard therapies, highlighting the urgent need for novel treatment strategies. Advanced imaging techniques, particularly magnetic resonance imaging (MRI), are crucial in assessing GBM. Disruptions in various oncogenic signaling pathways, such as Receptor Tyrosine Kinase (RTK)-Ras-Extracellular signal-regulated kinase (ERK) signaling, Phosphoinositide 3- Kinases (PI3Ks), tumor protein p53 (TP53), and Neurogenic locus notch homolog protein (NOTCH), contribute to the development of different tumor types, each exhibiting distinct morphological and phenotypic features that can be observed at a microscopic level. However, identifying genetic abnormalities for targeted therapy often requires invasive procedures, prompting exploration into non-invasive approaches like radiogenomics. This study explores the utility of radiogenomics and machine learning (ML) in predicting these oncogenic signaling pathways in GBM patients. Methods We collected post-operative MRI scans (T1w, T1c, FLAIR, T2w) from the BRATS-19 dataset, including scans from patients with both GBM and LGG, linked to genetic and clinical data via TCGA and CPTAC. Signaling pathway data was manually extracted from cBioPortal. Radiomic features were extracted from four MRI modalities using PyRadiomics. Dimensionality reduction and feature selection were applied and Data imbalance was addressed with SMOTE. Five ML models were trained to predict signaling pathways, with Grid Search optimizing hyperparameters and 5-fold cross-validation ensuring unbiased performance. Each model’s performance was evaluated using various metrics on test data. Results Our results showed a positive association between most signaling pathways and the radiomic features derived from MRI scans. The best models achieved high AUC scores, namely 0.7 for RTK-RAS, 0.8 for PI3K, 0.75 for TP53, and 0.4 for NOTCH, and therefore, demonstrated the potential of ML models in accurately predicting oncogenic signaling pathways from radiomic features, thereby informing personalized therapeutic approaches and improving patient outcomes. Conclusion We present a novel approach for the non-invasive prediction of deregulation in oncogenic signaling pathways in glioblastoma (GBM) by integrating radiogenomic data with machine learning models. This research contributes to advancing precision medicine in GBM management, highlighting the importance of integrating radiomics with genomic data to understand tumor behavior and treatment response better.

The current study was designed to investigate whether application of irradiated chitosan (ICn), a recently established plant growth promoter, can prove effective in alleviating arsenic (As) stress in peppermint, a medicinally important plant. This study investigated how foliar application of ICn alleviated As toxicity in peppermint ( Mentha piperita L.). Peppermint plants were treated with ICn (80 mg L −1 ) alone or in combination with As (10, 20, or 40 mg kg −1 of soil, as Na 2 HAsO 4 ·7H 2 O) 40 days after transplantation (DAT), and effects on the growth, photosynthesis, and antioxidants were assessed at 150 DAT as stress severely decreases plant growth, affects photosynthesis, and alters enzymatic (ascorbate peroxidase, superoxide dismutase) and non-enzymatic (glutathione) antioxidants. When applied at 40 mg kg −1 , ICn significantly decreased the content of essential oil (EO) and total phenols in peppermint by 13.8 and 16.0%, respectively, and decreased phenylalanine ammonia lyase (PAL) and deoxy-D-xylulose-5-phosphate reductoisomerase (DXR) activities by 12.8 and 14.6%, respectively. Application of ICn mitigated the disadvantageous effects caused by As toxicity in peppermint by enhancing activities of antioxidative enzymes and photosynthesis and increased accretion of secondary metabolism products (EOs and phenols). An enhancement of total phenols (increased by 17.3%) and EOs (36.4%) is endorsed to ICn-stimulated enhancement in the activities of PAL and DXR (65.9 and 28.9%, respectively) in comparison to the control. To conclude, this study demonstrated that foliar application of ICn (80 mgL −1 ) effectively promoted the growth and physiology of peppermint and eliminated As-induced toxicity to achieve high production of EO-containing crops grown in metal-contaminated soils.

Carl Anderson, Jeffrey Barrett and colleagues use whole-genome sequencing and imputation to explore the genetic architecture of inflammatory bowel disease. They identify a low-frequency missense variant in ADCY7 that doubles risk of ulcerative colitis and detect a burden of very rare, damaging missense variants in known Crohn's disease risk genes. To further resolve the genetic architecture of the inflammatory bowel diseases ulcerative colitis and Crohn's disease, we sequenced the whole genomes of 4,280 patients at low coverage and compared them to 3,652 previously sequenced population controls across 73.5 million variants. We then imputed from these sequences into new and existing genome-wide association study cohorts and tested for association at ∼12 million variants in a total of 16,432 cases and 18,843 controls. We discovered a 0.6% frequency missense variant in ADCY7 that doubles the risk of ulcerative colitis. Despite good statistical power, we did not identify any other new low-frequency risk variants and found that such variants explained little heritability. We detected a burden of very rare, damaging missense variants in known Crohn's disease risk genes, suggesting that more comprehensive sequencing studies will continue to improve understanding of the biology of complex diseases.

Niacin, an essential B-complex vitamin, used in the treatment of nonalcoholic fatty liver disease is the first perceived lipid regulating medication, inhibits and reverses hepatic steatosis and inflammation in animals and liver cell cultures. Niacin shows beneficial effects on adiposity. Niacin plays an important role in DNA repair, electron transfer, one-carbon metabolism and fatty acid synthesis in cells. Natural polysaccharides with desirable chemical modifications can be combined with vitamins for developing the supplement treatment for vitamin deficiency disorders. Modification of inulin was carried out by tosylation, amination and then conjugated with niacin. Structural elucidation of the derivatives and conjugates was carried out by FT-IR, 1H NMR and SEM. Thermal behavior was investigated by TGA and DSC techniques. The release and kinetics of niacin, from the conjugate, at different pH was studied. The release was observed to be pH dependent, showing a greater release at higher pH following Korsmeyer–Peppas kinetic model. Polysaccharide based approach was used for the preparation of stable niacin-inulin conjugates with controllable and prolonged release of niacin. These types of conjugates may be useful as vitamin delivery systems for vitamin deficiency disorders.