Browse Articles

Establishing molecular biomarkers for efficient demarcation of tumor and non-tumor tissue in oral squamous cell carcinoma

Abstract Oral squamous cell carcinoma (OSCC), which accounts for 90% of head and neck cancers ( HNSC), is a prevalent cancer, especially in India, where it ranks among the top three cancers. Despite treatment advancements, OSCC incidence is rising, and recurrence remains a major concern. To improve patient prognosis, effective biomarkers for tumor demarcation are crucial. RNA isolation, library preparation, and RNA sequencing were performed on tumor and adjacent normal oral tissue samples. Transcriptomic analysis identified differentially expressed genes (DEGs) between tumor and normal tissues, which may serve as potential biomarkers. These findings were subsequently validated using RT-qPCR. The analysis revealed 704 upregulated and 1540 downregulated genes. Among these from the top 100 upregulated and downregulated genes, 15 and 9 genes respectively were also reported in the HNSC database of TCGA (The Cancer Genome Atlas). To identify potential biomarkers, the study evaluated multiple factors, including log2 fold change, average RPKM, ROC curve analysis, protein-protein interaction (PPI) network analysis and gene ontology (GO) analysis. The differential expression across various cancer stages and individual sample comparisons were also assessed. The upregulated genes MMP1 , MMP10 , MMP13 , MMP3 , ADAM12 , IL24 , and ISG15 demonstrated potential as biomarkers, with the highest log2 fold change, average RPKM, and AUC values. These significant genes could be valuable biomarkers for efficient demarcation between tumor and non-tumor tissues in oral cancer. This could lead to improved margin clearance, addressing concerns related to high recurrence rate of OSCC and ultimately enhance patient prognosis.

Integrated stress response inhibition prolongs the lifespan of a Pelizaeus-Merzbacher disease mouse model by increasing oligodendrocyte survival

Patellar shape diversity as a functional indicator of locomotor specialization in selected ruminant species

Mechanochemical ligand-controlled regiodivergent hydroarylation of alkenes via iron-catalyzed C−H activation

Abstract The iron-catalyzed hydroarylation of alkenes with indoles is a sustainable, effective synthetic transformation towards the construction of functionalized indoles - crucial motifs for various bioactive molecules and drug candidates. However, such transformations have proven challenging for unactivated alkenes, and the requirement for (super)stoichiometric amounts of reactive Grignard reagents has limited broader applications. Herein, we address these major challenges by integrating iron/ N -heterocyclic carbene-catalyzed C–H activation with mechanochemistry techniques. This approach enables mechanochemical iron-catalyzed anti -Markovnikov hydroarylations of unactivated alkenes using a bis( N -heterocyclic carbene) ligand, as well as regiodivergent hydroarylation of aryl alkenes by varying the N -heterocyclic carbene ligand. To this end, magnesium metal serves as a convenient reductant to form catalytically active iron(0) species by mechanochemistry, thereby improving the sustainability and functional group compatibility. Experimental and computational studies elucidate the possible catalytic mode of action, and a data science analysis captured the key features of the N -heterocyclic carbene ligands in controlling regiodivergent selectivity.

Butin inhibits osteoarthritis progression by modulating the Nrf2/HO-1 signaling pathway and inhibiting NF-κB signaling

A topological superconductor tuned by electronic correlations

Fusion of clinical magnet resonance images and electronic health records promotes multimodal predictions of postoperative delirium

Abstract Brain morphometry derived from clinical imaging has an underexplored potential for the multimodal prediction of postoperative delirium (POD), an acute encephalopathy that can lead to long-term adverse outcomes or death. This study conducted a comprehensive analysis of patient trajectories, integrating magnetic resonance imaging (MRI) data and electronic health records (EHRs) across two general surgical cohorts. We applied univariate test methods and linear mixed-effects models correcting for confounding. Non-linear multi-layer perceptrons (MLPs), boosted decision trees, and logistic regressions were trained on EHR data, brain morphometry measures, and their multimodal fusion to predict POD. Age-adjusted correlations identified cortical thickness of temporal gyri, as well as thalamic and brainstem volumes to be POD-relevant neuroanatomical features. MLP models demonstrated robust predictive capability, achieving notably high performances up to 86% AUROC (area under the receiver operating characteristic). Multimodal fusion yielded pronounced benefits in less critically ill patients. MLP model weights showed high predictive potential for cerebral atrophy in higher-order cortical regions, including the temporal pole, superior frontal gyrus, and the insula. These findings reveal the previously unrecognized potential of clinically derived brain morphometry in enhancing early multimodal predictions of POD. A better understanding of brain vulnerability in POD may translate into improved clinical decision making based on multimodal health care data.

Self-assembly pathways towards floppy colloidal square lattices

A case study of the application of AI to early stage drug discovery

Abstract Artificial intelligence (AI) has emerged as a powerful tool in drug discovery, offering the potential to expedite the design of novel therapeutics. This study evaluates the effectiveness of a general-purpose conversational AI, ChatGPT (GPT-4o), in performing three distinct drug discovery tasks, assessing its ability to assist in early-stage molecular ideation and design. In the first task, ChatGPT generated molecules starting from five low-affinity EGFR inhibitors (IC₅₀ values of 10–3.16 µM), which were iteratively optimized in a QSAR model to produce compounds with predicted IC₅₀ values of ~ 10–50 nM. In the second task, de novo design of EGFR inhibitors produced a molecule with a predicted IC₅₀ of 94 nM in a single attempt. In the third task, ChatGPT generated non-covalent MCL1 inhibitors, with a top candidate achieving a docking score corresponding to a 39 nM dissociation constant. Because AI-generated molecules often face synthetic feasibility challenges, we also identified readily available analogues from a chemical vendor. These analogues were evaluated using molecular docking (AutoDock Vina) and QSAR models, with several achieving a promising activity range of 10–100 nM across the three tasks. These results demonstrate that general-purpose AI models like ChatGPT can accelerate early-stage drug discovery by assisting in molecular ideation and candidate prioritization.

Elevated atmospheric CO2 decreases methylmercury production in freshwater lakes

Abstract Elevated atmospheric carbon dioxide (CO 2 ) level reshapes microbial communities in nature, yet its consequences for neurotoxic methylmercury (MeHg) production in waters remain unclear. Here, we show that elevated CO 2 levels (650 and 1000 ppm) consistently reduced net MeHg production across 45 freshwater lakes spanning 1200 longitudinal kilometers, particularly in eutrophic conditions (54–96%). Elevated CO 2 -driven shifts in carbon substrates favored hydrogenotrophic methanogens (e.g., Methanobacterium ) lacking the hgcA methylation gene over hgcA -harboring acetoclastic strains (e.g., Methanosarcina ), decreasing methanogen abundance (18–98% in hgcA copies) and activity (13–53% in net CH 4 production) and suppressing Hg methylation. Model simulations predict a 33%–74% global decline in lake MeHg production under future CO 2 scenarios, partially counteract MeHg increases associated with intensified algal blooms under warming. This overlooked pathway highlights the need to integrate interacting climate drivers to improve predictions of MeHg risks in a climate-changing future.

Enhanced feature dynamic fusion gated UNet for robust retinal vessel segmentation

Abstract This study proposes a Deep learning model, the Enhanced Feature Dynamic Fusion-Gated U-Net (EFDG-UNet), for retinal vessel segmentation. To address challenges in segmenting small vessels, handling lesion interference, and adapting to multi-scale structures, the model incorporates optimized feature fusion, dynamic selection, and global position modeling. The Feature Navigation Hub (FN-Hub) captures long-range dependencies across multiple encoder layers, improving multi-scale vessel segmentation. The Adaptive Gated Residual Block (AGRB) uses a dynamic gating mechanism to enhance feature selectivity in lesion areas and low-contrast scenarios. The Parallel Focused Attention Module (PFAM) optimizes channel and spatial information for fine-grained vessel features. Experimental validation on DRIVE, CHASE_DB1, and STARE datasets shows that EFDG-UNet achieves state-of-the-art performance, attaining an AUC of 0.9932 and F1-score of 0.8469 on CHASE_DB1, and an AUC of 0.9886 and F1-score of 0.8412 on DRIVE. The model shows improved performance in low-contrast regions and complex vessel structures compared to baseline methods.

SREBP1-mediated lipogenesis promotes dedifferentiation and senescence of vascular smooth muscle cells through epigenetic remodeling

In vitro assessment of an antimicrobial peptide against Acinetobacter baumannii persister cells

Abstract The rise of multidrug-resistant and persister cell populations of Acinetobacter baumannii ( A. baumannii ) poses a significant threat in healthcare settings, highlighting the need for novel therapeutic strategies. This study investigates a specifically designed antimicrobial peptide and its potential activity against this pathogen. Using advanced bioinformatics, a 20-amino acid antimicrobial peptide was designed and synthesized. The peptide’s efficacy was evaluated in vitro through MIC assays against A. baumannii , along with assessments of its effects on persister cells, biofilm formation, and gene expression (pmrB and lasI) using quantitative PCR. The designed peptide exhibited a potent MIC value of 64 µg/mL, reducing persister cell populations of A. baumannii by 75% within 24 h (p < 0.0001). It significantly inhibited biofilm formation, with OD reductions of up to 5.4 log at 64 µg/mL (p < 0.0001). Real-time PCR analysis revealed a 6.2-fold upregulation of pmrB and 3.7-fold upregulation of lasI after 24 h (p < 0.0001), indicating bacterial adaptive responses. The antimicrobial peptide demonstrated strong antibacterial and antibiofilm activity against A. baumannii , though with moderate cytotoxicity (8–17% reduction in cell viability). These findings suggest a promising avenue for developing novel antimicrobial strategies.

Engineering artificial biosynthetic pathway enables simultaneous production and in-situ bio-dyeing of indigoids for textiles

Seasonal rainfall and land-use impacts on microplastic characteristics in an endangered salmon stream

Aldehyde cool-flame chemistry explains a missing source of organic acids

Semantic decentralized authentication for IoT-based e-learning using Hedera Hashgraph and Knowledge Graphs

Designer RNA nanostructures co-transcribed and self-assembled inside human cell nuclei

Mapping the technological evolution of generative AI: a patent network analysis

Subcellular glycan-mannose receptor binding kinetics correlate with myeloid cell function

Abstract Extracting single-molecule lectin binding kinetics from primary cells has not been possible to date. Here, we present Glyco-PAINT-APP (Automated Processing Pipeline), an automated method that enables the extraction of subcellular glycan interaction kinetics using a Points Accumulation for Imaging in Nano-Topography (PAINT)-based approach. This approach leverages an algorithm for precise, high-throughput, subcellular analysis of glycan binding dynamics and facilitates functional correlation studies between glycoform binding patterns and immune cell polarization. Using synthetic glycans and glycosylated antigens, we demonstrate the ability of the technique to automatically correlate glycan binding parameters in subregions of dendritic cell membranes with increased uptake and cross-presentation efficiency of these antigens. Additionally, we show how the method can uncover subtle differences in MR-mediated glycan binding across various MR-expressing primary cells and cell lines. Taken together, Glyco-PAINT-APP enables insights into the cell-intrinsic heterogeneity of glycan-structure-activity relationships in myeloid immune cells.