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Interpretable prediction of mortality in zinc phosphide poisoning using combined statistical and machine learning models: a retrospective observational study
Abstract Zinc phosphide poisoning is associated with high mortality due to rapidly evolving multi-organ dysfunction, yet reliable early risk stratification remains limited. This retrospective observational study evaluated the feasibility of combining conventional statistical modelling with interpretable machine learning (ML) for prediction of in-hospital mortality. A total of 290 adults with confirmed zinc phosphide poisoning admitted to a tertiary care hospital between January 2016 and November 2022 were included. In-hospital mortality occurred in 88 patients (30.3%). Demographic, physiological, and laboratory variables recorded within the first 48 h of hospitalization were analysed. Multivariable logistic regression was performed to identify independent predictors of mortality. Subsequently, ML models (logistic regression, Random Forest, and Extreme Gradient Boosting [XGBoost]) were developed using repeated stratified cross-validation, and model discrimination was assessed using the area under the receiver operating characteristic curve (AUROC), calibration analysis, and bootstrap-derived confidence intervals. Explainable artificial intelligence using SHapley Additive exPlanations (SHAP) was applied to the best-performing model. Serum lactate remained the only independent predictor of in-hospital mortality in the multivariable logistic regression analysis. Among the ML models, XGBoost achieved the highest numerical AUROC (0.745; 95% confidence interval, 0.680–0.807), although differences between models (DeLong’s test) were not statistically significant. SHAP analysis identified bicarbonate, lactate, potassium, creatinine, and activated partial thromboplastin time as the largest contributors to the mortality predictions generated by the optimized XGBoost model. These findings suggest that interpretable ML may complement conventional regression by providing transparent, individualized mortality prediction while preserving clinical interpretability. However, these exploratory findings require prospective multicentre validation before routine clinical implementation.
From Salinity Gradients to Sustainable Power: A Paradigm Shift in Materials, Thermodynamics, and System Design in Next‐Generation Osmotic Energy Harvesting
ABSTRACT Osmotic energy, arising from the Gibbs free energy of mixing between solutions of differing salinity, is an entropy‐driven resource that can, in principle, deliver continuous baseload power at river‐sea interfaces, desalination brine outlets, and wastewater discharges. Despite this attractive thermodynamic ceiling, practical deployment of pressure‐retarded osmosis, reverse electrodialysis, and emerging osmotic architectures has long been constrained by membrane resistance, concentration polarization, fouling, and limited operational stability. This Review traces the evolution of osmotic energy conversion through the lens of materials science, from early polymeric and ion‐exchange membranes to contemporary highly selective ion pathways. By correlating pore size, surface charge density, interfacial chemistry, and hierarchical morphology with ion selectivity, power density, and durability under realistic salinity gradients, we distil general design principles that reconcile the classical permeability selectivity trade‐off and mitigate fouling and internal resistance. We further discuss the integration of molecular dynamics and multiscale transport modelling to rationalize ion migration in confined geometries and to guide the atomic‐scale engineering of nanochannels. Finally, we assess emerging directions, including scalable fabrication routes, and data‐driven optimization of membrane and module design that define a materials‐centered roadmap for translating osmotic energy from laboratory prototypes to technologically and economically relevant blue energy infrastructures.
AI guided trust evaluation and quantum resistant certificate management for next-generation PKI security
Dual‐Site Synergistic Regulation Enabled Interface Passivation and Strain Release Toward Efficient Perovskite Solar Cells
ABSTRACT The continuous breakthroughs in photovoltaic conversion efficiency (PCE) of inverted perovskite solar cells (PSCs) demonstrate the enormous potential for commercial application. However, accumulating numerous defects at the buried interface and residual strain within the perovskite film severely constrain the further improvement in PCE and stability of the optoelectronic device. Herein, two benzyl phosphoric acids, brominated benzyl phosphate (4‐BrBPA) and methoxy‐substituted benzyl phosphate (4‐MeOBPA), both containing double binding sites were assembled between [4‐(3,6‐dimethyl‐9 H ‐carbazol‐9‐yl)butyl]phosphonic acid (Me‐4PACz) hole transport layers (HTLs) and perovskite as buried interface modifiers. More interestingly, the dipole orientation of 4‐BrBPA aligned with Me‐4PACz, which can promote interface energy level alignment, and facilitate carrier extraction and transport. In addition, the phosphate groups (─PO 3 H 2 ) group and Br atom in 4‐BrBPA can chelate with uncoordinated Pb 2+ and vacancy I − , which will effectively achieve perovskite interfacial defect passivation and strain release. Consequently, the PSCs based on 4‐BrBPA interface layer achieve champion efficiency of 26.62% (certified 26.22%). Moreover, this strategy is extended to wide‐bandgap (1.77 eV), large‐area (1 cm 2 ) PSCs, and mini‐module (11.3 cm 2 ), resulting in PCEs of 21.64%, 24.43%, and 21.08%, respectively. The optimized PSCs demonstrate excellent operational and storage stability. This work provides an effective strategy for interface modification and strain regulation.
Non-mannitol-fermenting Staphylococcus aureus strains: a new perspective on diagnostic challenge
Intranasal dopamine stabilizes prefrontal cortical CaMKIIα-positive neuronal population dynamics during object discrimination
Abstract Dopamine (DA) is a key modulator of prefrontal cortex function, yet how distinct dopaminergic states shape the temporal organization of cortical population activity during behavior remains poorly understood. We combined in vivo fiber photometry of dorsomedial prefrontal cortex (dmPFC) putative glutamatergic neurons with an object-based attention (OBAT) task in male mice to examine this question. Intranasal DA (IN-DA), which accesses the brain directly via nose-to-brain pathways, left locomotion and exploratory behavior unchanged. Although both vehicle- and IN-DA- treated mice showed a preference for the novel object, this preference reached statistical significance only in the IN-DA group. IN-DA selectively amplified calcium activity within the familiar-object zone and enhanced movement-normalized calcium spike rates. Strikingly, IN-DA reduced both the mean and variability of calcium transient intervals, reflecting a shift toward more frequent and temporally regular events during object processing. To model the network consequences of supraphysiological dopaminergic drive, we administered the psychostimulant amphetamine as a proxy for dopaminergic excess. In contrast to the stabilizing effects of IN-DA, amphetamine induced locomotor hyperactivity accompanied by a collapse of temporal structure, characterized by prolonged inter-transient intervals and markedly increased variability. Together, these findings demonstrate that dopaminergic modulation shapes not only the magnitude but also the temporal organization of dmPFC population activity. At the network level, physiological DA promotes a temporally stable prefrontal state, whereas dopaminergic excess destabilizes it. This provides population-level evidence for the inverted-U relationship between DA signaling and prefrontal function.
UHRF1-regulated aerobic glycolysis mediates TOX lactylation to induce CD8⁺ T cell exhaustion in laryngeal cancer
Pharmacokinetics, bioequivalence, and safety of two formulations of Toremifene citrate tablets in healthy Chinese participants under fasted and fed conditions
Hypoglycemic exposure and glycemic variability are associated with surgical site infection after cardiac surgery
Increasing rainwater use efficiency and seed yield of chickpea and lentil using plastic mulch
Scalable Poisson regression via divide and recombine with asymptotic theory, Monte Carlo simulation, and application to cardiovascular risk factor burden in BRFSS
The effect of RISA on push out bond strength of an epoxy resin sealer and a bioceramic sealer: an in vitro study
Abstract This study aimed to evaluate the effect of Reduced Ionic Strength Agent (RISA), used as a final irrigant, on the push-out bond strength of resin-based and bioceramic root canal sealers compared with ethylenediaminetetraacetic acid (EDTA). Sixty extracted single-rooted mandibular premolars were prepared using Reciproc R40 instruments and randomly assigned to four groups according to the final irrigation protocol and sealer type: EDTA–resin, EDTA–bioceramic, RISA–resin, and RISA–bioceramic. After obturation using the single-cone technique, one 1-mm-thick slice was obtained from the middle third of each root. Push-out bond strength was measured using a universal testing machine. Data were analyzed using two-way analysis of variance and Bonferroni-adjusted post hoc tests. Failure modes were assessed under a stereomicroscope and compared using the chi-square test. The RISA–bioceramic group exhibited the highest mean push-out bond strength, whereas the EDTA–resin group showed the lowest mean value. Two-way ANOVA revealed significant main effects of both final irrigant and sealer type on push-out bond strength ( p < 0.001). Overall, RISA resulted in higher bond strength values than EDTA, and the tested bioceramic sealer exhibited higher bond strength than the tested epoxy resin-based sealer. No significant interaction was observed between irrigation protocol and sealer type. Failure mode distributions were predominantly mixed and did not differ significantly among the groups. Within the limitations of this in vitro study, RISA resulted in higher overall push-out bond strength than EDTA. Although these findings suggest that RISA may have the potential to enhance sealer adhesion, further studies incorporating aging protocols and clinically relevant experimental conditions are needed to determine the long-term durability and clinical relevance of the observed bond-strength advantage.
Nicotinamide in a model of osteoarthritis: mechanism of analgesic action and its effect on cognition and reduction of oxidative stress in the rat heart
Correction: Pilot tests of continuous gas extraction with improved L-shaped wells from mining and goaf areas
Correction: Exploring the spatiotemporal evolution and spatial correlation network characteristics of cultivated land carbon emissions in China
Pharmaceutical payments to Australian health care professionals from 2015 to 2024
The generative creation in new media art under diffusion and ControlNet
Neuro-symbolic constraint verification for LLM-driven internet finance transaction execution
Abstract Large language model (LLM) agents cannot guarantee that their outputs satisfy the hard business rules that govern financial transactions. We present the Verifier-Actor Neuro-Symbolic Framework ( VA-NSF ), a dual-layer architecture that separates natural language understanding from formal constraint enforcement for internet finance transaction execution. A neural Actor parses financial instructions into a typed, schema-grounded abstract syntax tree (AST) through grammar-constrained decoding, and a symbolic Verifier evaluates each AST against Answer Set Programming (ASP) integrity constraints, issuing either a formal approval certificate or a structured violation report that drives targeted repair. On FinTxBench , a curated evaluation set of 8600 annotated financial instructions spanning three complexity tiers, VA-NSF attains 91.8% overall transaction accuracy (macro-averaged across the three tiers, with 94.7%, 91.2%, and 89.6% on the Simple, Compound, and Constrained tiers) at a constraint violation rate of at most 0.3%. It thereby outperforms four LLM baselines by 13 to 51 percentage points and reduces violations 30-fold, all within a median latency of 399 ms, of which ASP verification accounts for only 24 ms. Ablations confirm that each component contributes independently, and cross-domain experiments on payment processing, procurement, payroll, and inventory modules show at most 5.8 percentage points of degradation. All formal guarantees are relative to the fidelity of the encoded rule set, and the evaluation uses a curated benchmark with up to 80 rules; we therefore discuss explicitly the resulting limitations regarding noisy real-world language, rule-set scale, and human-escalation overhead.