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Immune correlates of risk for SARS-CoV-2 infection in children: a prospective, community-based cohort study
Chemotherapy-induced senescence promotes stroma stiffness and antioxidant adaptation to promote chemoresistance in pancreatic ductal adenocarcinoma
Abstract Chemoresistance in pancreatic ductal adenocarcinoma (PDAC) is partly driven by pathological stromal remodeling, yet the underlying mechanisms remain poorly understood. Here, we show that gemcitabine treatment induces tumor cell senescence and activates cancer-associated fibroblasts via the senescence-associated secretory phenotype, leading to progressive fibrotic matrix stiffening. This biomechanical reprogramming engages the mechanosensitive ion channel Piezo1, triggering metabolic rewiring that renders BRG1-positive tumor cells increasingly dependent on NRF2-mediated antioxidant defenses. Piezo1 signaling promotes NRF2 nuclear translocation and its chromatin-remodeling cooperation with BRG1, thereby upregulating SLC7A11-dependent antioxidant programs and suppressing ferroptosis. Notably, the combination of the senolytic agent ABT-263 with the ferroptosis inducer Erastin effectively dismantles BRG1–NRF2-driven gemcitabine resistance, alleviates stromal fibrosis, enhances T-cell infiltration, and suppresses tumor growth in vivo. This senolytic–ferroptosis approach exploits metabolic vulnerabilities in chemotherapy-aged PDAC and provides a mechanistic rationale for stroma-targeted combination therapies.
Scale-bridging interface design enables high-performance sustainable thermoelectrics
Enhanced nuclear fusion in the sub-keV energy regime
Abstract Nuclear fusion requires overcoming or traversing a repulsive Coulomb barrier of hundreds of kiloelectronvolts, rendering the probability of fusion at sub-keV energies vanishingly small. Yet in condensed matter, the electronic and structural environment of reacting nuclei can profoundly alter fusion rates. Here we demonstrate that deuterium–deuterium fusion within metallic foils exhibits a pronounced reaction yield plateau (i.e., a finite, non-vanishing yield floor) below 2 keV—in stark contrast to the expected exponential suppression at low energy. At the lowest energies measured, this corresponds to fusion yields enhanced by more than 10¹⁸ relative to bare-nucleus (unscreened) expectations. Using a dual-chamber platform that combines electrochemical deuterium loading with low-energy ion-beam bombardment, we observe this behavior in both palladium and titanium hydrides. These results reveal a previously unrecognized regime of low-energy nuclear reactions in solids, demonstrating that materials degrees of freedom can fundamentally renormalize tunneling probabilities and fusion cross-sections.
Coordination electrochemistry taming reversible hypervalent bromine redox for energetic six-electron-transfer lithium-bromine battery
Abstract Conversion-type static bromine batteries demonstrate promise in high output voltage and large capacity for rechargeable energy storage due to the inherent polyvalent reaction potential. Nevertheless, the combination of the limited two-electron 2Br - /Br 2 redox couple and the redox-inactive organic ligands presents a fundamental bottleneck for the overall specific energy. Herein, ethyl viologen dibromide is developed as an energetically active positive electrode for organic lithium-bromine batteries by efficient coordination chemistry, featuring an advanced six-electron redox mechanism triggered by both intercalation and conversion reactions. The activated redox couple of 2Br - /2Br + incubates a collaborative increase in capacity (632.8 mAh g −1 Br ) and discharge voltage (3.7 V). Besides, ethyl viologen undergoes reversible two-step intercalation and extraction of Li + ions, contributing to additional energy storage. Reciprocal spectroscopic characterizations and computational electrochemistry indicate the chemisorption effect and interhalogen confinement and detail the dynamic mass-charge transfer pathway. This work sets a paradigm worth emulating for designing high-performance halogen batteries.
A quantum-classical hybrid framework for optimal energy storage systems planning
Abstract The extensive deployment of power-electronics introduce spatial-temporal variability that can degrade voltage quality and operational reliability. Energy storage systems (ESS) can mitigate these effects through fast active and reactive power support, but their value is contingent on coordinated siting and sizing. Integrated formulations that minimize voltage deviations, reduce substation power-flow variability, and account for installation costs typically yield in large-scale mixed-integer optimization problems that are computationally burdensome for classical solvers and may yet not lead to the most optimum solution. To address these challenges, this paper proposes a two-stage hybrid quantum–classical planning framework that separates binary siting from continuous sizing and operation. In Stage I, the siting problem is reformulated as a Quadratic Unconstrained Binary Optimization model and solved via a hybrid quantum workflow. Acting as a “quantum sieve,” stochastic sampling generates a diverse set of candidate site combinations that classical single-point methods can overlook. In Stage II, selected site sets are evaluated using a classical convex solver (SOCP) to compute optimal ESS capacities and operating setpoints subject to network constraints, ensuring physical feasibility. Experiments on IonQ Forte hardware show grid-standard accuracy with industry-standard classical solvers. Although current hardware latencies limit performance in the NISQ era, the paper outlines scaling pathways and discusses key practical hurdles, including state-preparation overlap and higher-order cost couplings.
Federated graph learning with spatio-temporal dynamics for cross-border recommendation
Abstract Cross-border data sharing is strictly constrained by privacy regulations, which presents a critical challenge for recommendation systems due to the severe shortage of training data. Existing federated graph neural network methods predominantly rely on the federated averaging strategy, which struggles to handle the highly heterogeneous data encountered in scenarios characterized by user isolation and business homogeneity. To address this issue, this paper proposes FedSTAR, a privacy-preserving cross-border recommendation framework that integrates spatio-temporal dynamic modeling with federated graph neural networks. Its core innovations include the design of a dynamic sequential graph structure to capture the evolution of user preferences, the use of a multi-head attention mechanism to filter noisy neighbors in the spatial dimension, and the introduction of a personalized federated aggregation strategy to replace traditional FedAvg, thereby enabling adaptive fusion of heterogeneous multi-source data. Evaluations on three public datasets, Gowalla, Yelp 2018, and Amazon Book, demonstrate that FedSTAR achieves average improvements of 2–5 percentage points in Recall@20 and 1–3 percentage points in NDCG@20, respectively. Under the privacy constraint of exchanging only model updates, FedSTAR delivers both high accuracy and strong robustness, offering a secure and practically viable solution for cross-border recommendation.
Biomechanical stability of five fixation techniques for pediatric distal radius metaphyseal-diaphyseal junction fractures: a finite element analysis
Dynamic stability is tied to gait assistance when walking with a hip exoskeleton
First-derivative synchronous spectrofluorimetric method for simultaneous determination of dapagliflozin and sitagliptin in dosage forms and spiked human plasma
Abstract The co-administration of Dapagliflozin and Sitagliptin has attracted considerable interest in the management of type 2 diabetes mellitus due to their complementary therapeutic effects. However, their simultaneous determination is analytically challenging because of the significant overlap in their native fluorescence spectra. In this study, a selective and sensitive first-derivative synchronous spectrofluorimetric method was developed for the simultaneous determination of both drugs without prior separation. The proposed approach enabled efficient spectral resolution through zero-crossing points at 348 nm and 289 nm for dapagliflozin and sitagliptin, respectively, using a constant wavelength difference (Δλ = 30 nm). The method exhibited excellent linearity over the concentration ranges of 50–1000 ng/mL and 100–2000 ng/mL for dapagliflozin and sitagliptin, respectively, covering concentration levels relevant to their reported maximum plasma concentrations (Cmax), with low limits of detection (16.02 and 31.07 ng/mL, respectively), indicating high sensitivity. The proposed method demonstrated satisfactory accuracy (mean recoveries of 100.67% and 99.86%) and precision (%RSD < 2%). The method was successfully applied to the analysis of pharmaceutical dosage forms and spiked human plasma, showing reliable recoveries. To the best of our knowledge, this is the first validated spectrofluorimetric method for the simultaneous determination of these co-administered drugs, offering a simple, cost-effective, and efficient alternative to conventional analytical techniques.
Causal responsibility based explainable AI for vibrational spectroscopy applied to oral FTIR and oesophageal Raman diagnostics
Abstract Biomedical applications of vibrational spectroscopy increasingly use deep learning, but these models often give classifications without revealing which spectral features drive their decisions. We introduce Spec-ReX, a causal responsibility-based explainable AI method for vibrational spectroscopy, and compare it with the widely used explainability methods SHAP and Grad-CAM across synthetic spectra, biochemical mixtures, oral FTIR tissue spectra and oesophageal Raman tissue spectra. In a definitive in silico experiment with known discriminative peaks, Spec-ReX achieved the highest ground truth localisation, with an Intersection over Union of 0.10 and attribution concentration of 0.56, compared with 0.09 and 0.26 for the next best method. In an ambiguous in silico experiment, SHAP achieved the highest positive hit rate, while Spec-ReX was most robust to removal of low-importance regions, consistent with a sensitivity–specificity trade-off. In the in vitro biochemical experiment, Spec-ReX was again the most robust to removal of low-importance regions, while SHAP performed best when removing high-importance regions. In the ex vivo datasets, where no definitive attribution ground truth was available, the methods returned different and architecture-dependent attribution patterns. Overall, Spec-ReX provides sparse and specific model-causal responsibility maps for spectral classifiers. These explanations pertain to trained model behaviour rather than biological causality, and their clinical utility remains to be validated through user-centred studies.
Synthesis of Ag/TiO2 photocatalysts via direct photodeposition of silver from industrial electroplating wastewater
SSA(BBO)-optimized neural networks for remaining useful life estimation and health monitoring of lithium-ion batteries
Unsteady boundary-layer flow and radiative heat transfer over a deformable stretching cylinder with transpiration and slip: cross-validated Akbari-Ganji and finite-element solutions
“Uropathogens and antimicrobial susceptibility patterns in urosepsis patients at kafr el sheikh University hospital: a cross-sectional study”
Abstract Urosepsis is a life-threatening consequence of urinary tract infections, often linked to obstruction, indwelling devices, and healthcare exposure. Increasing antimicrobial resistance among uropathogens complicates empirical therapy and adversely affects outcomes, making local surveillance essential for appropriate management. To determine the bacterial profile and antimicrobial susceptibility patterns of urosepsis cases admitted to Kafr El-Sheikh University Hospital. This cross-sectional study included 168 patients with confirmed urosepsis admitted to the Urology Unit between February 2024 and October 2025. Urosepsis was defined by identical organisms in urine and blood cultures with systemic sepsis and a SOFA score of ≥ 2. Cultures were processed using standard microbiological methods, with identification confirmed by biochemical testing and ATR-FTIR. Antimicrobial susceptibility testing was performed using the Kirby–Bauer method according to CLSI 2024 and verified by the BD Phoenix™ system. Multidrug-resistant (MDR) and ESBL-producing organisms were identified using standard criteria. The mean age of the patients was 56.2 ± 15.6 years, and 70.8% were male. Catheter-associated infections were identified in all cases (100%), reflecting the healthcare-associated nature of this cohort, while renal abscesses were detected in 26.2%. Gram-negative organisms predominated (90.5%), with Escherichia coli being the most frequently isolated pathogen. Statistical analysis revealed no significant demographic or clinical predictors of antimicrobial resistance patterns ( p > 0.05). Gram-negative isolates showed higher susceptibility to carbapenems and fluoroquinolones. ESBL-producing isolates accounted for 24.34% and were susceptible to carbapenems. MDR isolates comprised 27.63% and showed high resistance to cephalosporins, piperacillin-tazobactam, and carbapenems. All gram-positive isolates were fully sensitive to vancomycin and teicoplanin. Catheter-associated urosepsis is mainly caused by resistant gram-negative pathogens. The high prevalence of ESBL and MDR strains underscores the need for early culture-guided therapy and continuous local antimicrobial surveillance to optimize outcomes.
Cardiovascular impact of modern warfare: STEMI patterns before and during the Israel-Hamas conflict
Regional differences in foveal avascular zone morphology in cynomolgus macaques using a normative OCTA database
Abstract Cynomolgus macaques are widely used in preclinical ophthalmic research because of their close anatomical and physiological similarity to the human eye. However, the small cohort sizes typical of laboratory studies and the limited implementation of automated image analysis have restricted the availability of robust normative datasets. In particular, comprehensive reference values for foveal avascular zone (FAZ) measurements in laboratory cynomolgus monkeys remain scarce. To address this, an automated OCT/OCTA image-processing workflow was used to quantify FAZ morphology in healthy cynomolgus monkeys, including a deep learning-based OCT segmentation step followed by analysis of the corresponding OCTA data. The study also evaluated morphological variations as well as the influence of sex and geographical origin. The overall FAZ area measured 0.451 mm (range 0.132–0.820 mm 2 ), the perimeter 2.770 mm (range 1.569–3.795), aspect-ratio 0.959 mm (range 0.622–1.388), circularity 0.725 (range 0.517–0.847), solidity 0.944 mm (range 0.816–0.991 mm). Moreover, geographical origin significantly affects FAZ area and aspect-ratio. Establishing a normative FAZ reference database is essential for the accurate interpretation of structural and vascular changes in translational ocular research and provides a quantitative framework to distinguish physiological variability from treatment-related effects, thereby supporting safety assessment and efficacy evaluation in preclinical drug development.