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Microenvironments as an Explanation for the Mismatch between Photochemical Absorptivity and Reactivity
Association between protein and energy intake during acute phase and sepsis outcomes: a retrospective cohort study
The status of ownership and utilization of long-lasting insecticidal treated nets in war-torn Tigray, Ethiopia
A Quasi-Ordered Mn-Rich Cathode with Highly Reversible Oxygen Anion Redox Chemistry
Generation of transchromosomic mice harboring HLA-A/B/C and human B2M via mouse artificial chromosome and triple BAC integration
Synthesis and Properties of Poly(vinyl chloride)-<i>b</i>-polyethylene Multiblock Copolymers through the Hydrodechlorination of PVC with Silylium Ions
Histopathological-based brain tumor grading using 2D-3D multi-modal CNN-transformer combined with stacking classifiers
Abstract Reliability in diagnosing and treating brain tumors depends on the accurate grading of histopathological images. However, limited scalability, adaptability, and interpretability challenge current methods for frequently grading brain tumors to accurately capture complex spatial relationships in histopathological images. This highlights the need for new approaches to overcome these shortcomings. This paper proposes a comprehensive hybrid learning architecture for brain tumor grading. Our pipeline uses complementary feature extraction techniques to capture domain-specific knowledge related to brain tumor morphology, such as texture and intensity patterns. An efficient method of learning hierarchical patterns within the tissue is the 2D-3D hybrid convolution neural network (CNN), which extracts contextual and spatial features. A vision transformer (ViT) additionally learns global relationships between image regions by concentrating on high-level semantic representations from image patches. Finally, a stacking ensemble machine learning classifier is fed concatenated features, allowing it to take advantage of the individual model’s strengths and possibly enhance generalization. Our model’s performance is evaluated using two publicly accessible datasets: TCGA and DeepHisto. Extensive experiments with ablation studies and cross-dataset evaluation validate the model’s effectiveness, demonstrating significant gains in accuracy, precision, and specificity using cross-validation scenarios. In total, our brain tumor grading model outperforms existing methods, achieving an average accuracy, precision, and specificity of 97.1%, 97.1%, and 97.0%, respectively, on the TCGA dataset, and 95%, 94%, and 95% on DeepHisto dataset. Reported results demonstrate how the suggested architecture, which blends deep learning (DL) with domain expertise, can achieve reliable and accurate brain tumor grading.
Nucleophilic α- and β-Additions Enable Redox-Neutral Aziridination of Conjugated Hydroxamates
Action unit based micro-expression recognition framework for driver emotional state detection
Divergent Kinetic/Thermodynamic Selectivity in Palladium(II)-Mediated C–H Activation to Form 5- and 6-Membered Palladacycles
Spatial correlations between cultural ecosystem services and human activities along the Shenyang’s Hun River corridor
Influence of pyridyl nitrogen’s position and hydrogen bonding interactions on antibacterial activities investigated by in vitro and in silico
Abstract Pyridine derivatives are multifunctional materials with various biological activities viz., antibacterial, antiviral, anticancer, antihistamine, etc. and are also the building blocks in supramolecular chemistry. Schiff’s base compounds are known for their diversified applications / functionalities. A Schiff base with a pyridine moiety in its molecular structure is anticipated with potential biological activity. In order to study the effect of position of a pyridyl nitrogen on the antibacterial activity, two Schiff’s bases containing pyridine moiety viz., 3-chloro-4-fluoro-N-((pyridin-3-yl)methylene)benzenamine (3Py) and 3-chloro-4-fluoro-N-((pyridin-4-yl)methylene)benzenamine (4Py), were prepared by condensing 4-fluoro-3-chloro-aniline with 3-pyridinecarboxaldehyde and 4-pyridinecarboxaldehyde. Pyridine derivatives form a hetero synthon type intermolecular hydrogen bonding interactions with substituted benzoic acids. The lower homologues of 4-n-alkyloxybenzoic acids (nOBAs, ethyl, propyl and butyl homologues) were treated with the pyridine derivatives, 3Py and 4Py and were observed to be involved in intermolecular hydrogen bonding (HB) interactions. The pyridine derivatives (3Py and 4Py), benzoic acid derivatives and their 1:1 molar ratio complex were subjected to antibacterial activities through in vitro and in silico techniques. The antibacterial activities of the pristine compounds and their HB complexes against Gram-positive, Staphylococcus aureus and Gram-negative, Escherichia coli bacteria were studied by paper disc diffusion method. The antibacterial activity of the compounds is evaluated for their interactions with the key enzymes viz., E. coli MurB and S. aureus GyraseB by in silico technique. The pyridyl nitrogen at 4th position of the Schiff base (4Py) showed effective inhibition towards the S. aureus whereas the pyridyl nitrogen at 3rd position (3Py) of the Schiff base showed effective inhibition towards the E. coli. The increase in alkyl chain length of carboxylic acids was found to decrease the antimicrobial activity in the pristine homologues. The HB interactions between 3Py and nOBA showed different antibacterial activities compared to that between 4Py and nOBA against E. coli and S. aureus. The experimental results were supported by molecular docking, molecular dynamics simulation and ADME/T analysis. The findings are consistent with experimental data, demonstrating that Schiff base 4Py exhibits greater inhibitory efficacy against S.aureus, while 3Py shows superior activity agains E. coli, compared to their respective HB complexes.
Physiologic model of the cerebrovascular system using supply and demand between arteries and tissues
Abstract Image-based modeling heavily relies on boundary conditions to obtain realistic blood flow and pressure. For the cerebrovascular system, boundary conditions are derived using in-vivo measurements or geometry-based models such as Murray’s law, but these are constrained by the image resolution or high sensitivity to the segmented geometry. We propose a physiologic model of the cerebrovascular system based on a supply and demand relationship between arteries and tissues. Blood flow and perfusion territory are determined by associating brain tissues with nearby vessels using Voronoi tessellation. The model was evaluated for 40 healthy young individuals and two diseased patients, and was validated by comparing the estimated blood flows and perfusion territories against literature data and perfusion imaging. The estimated blood flows are within the physiologically reported values for major cerebral arteries and the predicted perfusion territories are similar to the literature and perfusion imaging. Further, the model demonstrates more robustness to segmentation uncertainties compared to Murray’s law. The proposed model is shown to estimate physiologically plausible cerebrovascular blood flow and perfusion territory in a subject-specific manner using medical image data only. It may be used to simulate blood flow more realistically by developing boundary conditions based on this model in the cerebrovascular system.
Accessing Square Planar Cobalt Nitrenoid Redox Isomers across Three Oxidation States
Thermodynamic, electrochemical and surface characterization of copper corrosion inhibition in acidic solution using rice straw extract
Abstract The use of natural inhibitors offers a sustainable and cost-effective approach to mitigating copper corrosion. In this study, rice straw extract, an agricultural byproduct, was investigated as an eco-friendly corrosion inhibitor for copper in 0.5 M H2SO4 solutions. The corrosion inhibition performance was evaluated using electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization techniques across a temperature range of 25–55 °C. The results demonstrated that the rice straw extract achieved an inhibition efficiency of approximately 91% at 25 °C with a concentration of 0.05 g/L and maintained a significant 69.1% efficiency at 55 °C, suggesting that the inhibition primarily occurs through physisorption of the extract molecules onto the copper surface. Scanning electron microscopy (SEM) analysis further confirmed the protective layer formation, while potential of zero charge (PZC) measurements indicated a negatively charged copper surface in sulfuric acid (− 13 mV). Adsorption isotherm studies revealed a strong binding constant, confirming that rice straw extract is an effective and sustainable corrosion inhibitor for copper in acidic environments.