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Eco-Friendly approach for enhancing functionality of PET/C blended fabric with ZnO NPs
Abstract The current study explores at the viability of employing enzymatic treatments to activate fabric surfaces and enable for the long-term loading of zinc oxide nanoparticles (ZnO NPs) onto PET/C blended fabrics. The fabric was treated with pectinase degrading enzyme, manufactured by a locally identified fungus from agriculture waste.; the active strain was genetically identified as Aspergillus foetidus (NR_163668.1). Factors affecting the activation of textile material with pectinase enzyme (incubation time and concentration) were studied. Parent and pretreated textiles loaded with ZnO NPs were investigated using Scanning Electron Microscopy (SEM), Energy Dispersive X-Ray (EDX), and Fourier Transformed Infrared Spectroscopy (FT-IR). Antibacterial activity against Staphylococcus aureus, Escherichia coli, Bacillus subtilis, Pseudomonas aeruginosa and Candida albicans by using inhibition zone and shake flask methods was evaluated. UV protection efficacy of activated and ZnO NPs loaded textiles were assessed. The impact of different concentrations of the enzyme and different periods of pectinase incubation with the textile revealed that the highest activity of the partially purified enzyme (41.4 U/ml) was got at the first 15 min and an increase in the activity of pectin degrading enzyme from 20.5 U/ml by 0.8 g/l enzyme to 70.6 U/ml by the use of 4 g/l pectinase. Candida albicans and Pseudomonas aeruginosa were got 0.84 × 108 and 0.6 × 108 CFU/ml using shake flask method respectively. The pretreated and ZnO NPs loaded fabrics demonstrated exceptional and durable antibacterial activity using inhibition zone and UV protection efficiency, even after five washing cycles.
Heat shock protein family H member 1 HSPH1 expression correlates with progression and prognosis of hepatocellular carcinoma
Revolutionizing cancer treatment with Halomonas Aquamarina L-Glutaminase: insights from in vitro and computational studies
Abstract Bacterial L-glutaminase (L-GLS) has emerged as a potential therapeutic target in cancer treatment by disrupting glutamine-dependent metabolic pathways in tumor cells. This study focused on isolating and characterizing L-GLS-producing marine bacteria from Mediterranean seawater for preliminary therapeutic evaluation. Halomonas aquamarina HBIM1 was identified as the most efficient isolate through comprehensive phenotypic, genotypic, and enzymatic screening. The enzyme was successfully purified, achieving a specific activity of 748.35 U/mg with 3.39-fold purification. SDS-PAGE analysis confirmed high purity with a single 66 kDa protein band. Kinetic characterization revealed optimal activity at pH 8 and 50 °C, with strong substrate affinity (Km = 0.198 mM⁻¹). Preliminary in vitro cytotoxicity screening demonstrated selective antiproliferative effects on HepG2 liver cancer cells (IC50 = 33.98 µg/ml) compared to normal WI-38 cells (IC50 = 93.43 µg/ml), yielding a 2.75-fold selectivity index. Molecular docking analysis identified tannic acid and 6-diazo-5-oxo-L-norleucine as selective inhibitors of bacterial L-GLS, with tannic acid showing the highest binding affinity (-12.25 kcal/mol) and 5-fold selectivity over human L-GLS, suggesting potential for combination therapy strategies. These proof-of-concept findings indicate the preliminary anticancer potential of Halomonas-derived L-GLS and computational support for selective inhibitor development. However, comprehensive preclinical validation, including in vivo efficacy studies, toxicological evaluation, and pharmacological profiling, is essential to establish therapeutic viability and safety before clinical consideration.
Co3O4@mSiO2 nanocomposite supported Pd/ionic liquid as an efficient and magnetically recoverable nanocatalyst
CHASHNIt for enhancing skin disease classification using GAN augmented hybrid model with LIME and SHAP based XAI heatmaps
Abstract Correct categorization of skin diseases is vital for prompt diagnosis. However, obstacles such as imbalance of data and interpretability of deep learning models limit their use in medical settings. To overcome these setbacks, Combined Hybrid Architecture for Scalable High-performance in Neural Iterations or CHASHNIt is proposed, which is an integration of EfficientNetB7, DenseNet201, and InceptionResNetV2 to outperform current models on every ground. GAN-based data augmentation is used to create synthetic images, to ensure that all classes are equally represented. Sophisticated preprocessing methods such as normalization and feature selection improve data quality and model generalization. Explainable AI methods, i.e., SHAP and LIME, enable model decision-making transparent. A rigorous comparative analysis testifies to the excellence of CHASHNIt compared to other benchmark models with 97.8% accuracy, 98.1% precision, 97.5% recall, 97.6% F1 Score and IoU of 92.3%, which exceeds Swin Transformer, ResNet101, InceptionResNetV2, MobileNetV3, EfficientNetB7, DenseNet201, and ConvNeXt models. The model was trained and tested on a 19,500-image dataset of 23 types of skin diseases with 80:20 split for training and testing. An ablation study testifies to the synergy advantage of the hybrid approach. LIME-SHAP heatmaps confirm the model’s predictive result. CHASHNIt is an advanced automated skin disease classification framework, attaining a balance between scalability, accuracy, and explainability. Computational complexity is the sole drawback, but future developments will optimize efficiency for low-resource devices.
Predicting water quality index using stacked ensemble regression and SHAP based explainable artificial intelligence
Network toxicology reveals glyphosate mechanisms in kidney injury and cancer
Abstract Molecular mechanisms underlying glyphosate-induced nephrotoxicity and carcinogenicity were investigated through integrated network toxicology, molecular docking, and dynamics simulations. Screening identified 47 potential glyphosate targets; intersection analysis yielded 20 kidney injury and 31 kidney cancer shared targets. Protein-protein interaction networks highlighted matrix metalloproteinases (MMP9, MMP2, MMP8, MMP3) and PLG as topological hubs. Pathway enrichment revealed significant alterations in extracellular matrix reorganization and nitrogen metabolism. Molecular modeling demonstrated stable glyphosate binding within catalytic domains of MMPs (affinities: −5.03 to − 6.29 kcal/mol), with dynamics simulations confirming persistent complex formation over 100 ns. Results indicate MMP-mediated dysregulation of structural homeostasis, alongside metabolic pathway perturbation, as contributory factors in glyphosate-associated renal pathology. The prominence of MMPs across target networks and functional analyses suggests their role as molecular conduits for glyphosate toxicity.
Association between C-reactive protein-triglyceride glucose index and Future cardiovascular disease risk in a population with cardiovascular-Kidney-metabolic syndrome stage 0–3
A framework for robotic manipulation tasks based on multiple zero shot models
Differences in the relationship among grit, self-regulation, competition preparation, and sport confidence across performance levels: a multi-group analysis
Spatial perspective taking is impaired in spinocerebellar ataxias and Friedreich ataxia
Abstract Spinocerebellar ataxias (SCA) are rare neurodegenerative diseases affecting the cerebellum and its connections, leading to progressive motor disability and cognitive impairment as part of the cerebellar cognitive affective syndrome. Spatial navigation, cognitive function important for everyday movement, relies on spatial perspective taking—the ability to imagine the environment from different viewpoints. While animal and neuroimaging studies suggest a crucial role of the cerebellum in spatial navigation, research on patients with cerebellar disorders is lacking. This study aimed to investigate perspective taking in patients with SCA and Friedreich ataxia (FRDA) using two tests. The Perspective-Taking/Spatial Orientation Test (PTSOT) was administered to 30 SCA patients, 30 FRDA patients, and 34 healthy controls (HC). In addition, SCA and HC completed the Directional-approach Task and a comprehensive neuropsychological assessment. SCA patients performed significantly worse than HC on both perspective taking tests. FRDA patients performed better than SCA and differed from HC only in a subset of PTSOT measures. Perspective taking performance in SCA was associated with global cognition and multiple cognitive domains but not with cerebellar motor impairment. These findings are of potential clinical relevance, as spatial navigation deficits are known to negatively affect the mobility and independence of the affected individuals. Our findings expand the understanding of cognitive impairments in cerebellar diseases, adding spatial navigation to the spectrum of the cerebellar cognitive affective syndrome.
Dynamic fractional-order ISDR rumor propagation model incorporating refutation mechanism in complex networks
Aboveground biomass estimation using multimodal remote sensing observations and machine learning in mixed temperate forest
Origin centric and part based pose decomposition for 3D human pose estimation
Multi-kernel inception-enhanced vision transformer for plant leaf disease recognition
Abstract The timely and precise identification of diseases in plants is essential for efficient disease control and safeguarding of crops. Manual identification of diseases requires expert knowledge in the field, and finding people with domain knowledge is challenging. To overcome the challenge, computer vision-based machine learning techniques have been proposed by the researchers in recent years. Most of these solutions with the standard convolutional neural network (CNN) approaches use uniform background laboratory setup leaf images to identify the diseases. However, only a few works considered real-field images in their work. Therefore, there is a need for a robust CNN architecture that can identify the diseases in plants in both laboratory and real-field conditioned images. In this paper, we have proposed an Inception-Enhanced Vision Transformer (IEViT) architecture to identify diseases in plants. The proposed IEViT architecture extracts local as well as global features, which improves feature learning. The use of multiple filters with different kernel sizes efficiently uses computing resources to extract relevant features without the need for deeper networks. The robustness of the proposed architecture is established by hyper-parameter tuning and comparison with state-of-the-art. In the experiment, we consider five datasets with both laboratory-conditioned and real-field conditioned images. From the experimental results, we see that the proposed model outperforms state-of-the-art deep learning models with fewer parameters. The proposed model achieves an accuracy rate of 99.23% for the apple leaf dataset, 99.70% for the rice dataset, 97.02% for the ibean dataset, 76.51% for the cassava leaf dataset, and 99.41% for the plantvillage dataset.