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The G protein-coupled estrogen receptor in the nucleus accumbens mediates the estrogen-induced increase in cataplexy-like behavior
CBC-derived inflammatory indices predict adverse outcomes in geriatric emergency patients with acute infectious diarrhea: a MIMIC-IV analysis
Hypoxia-preconditioned gingiva-derived mesenchymal stem cell-conditioned medium accelerates burn wound healing
Abstract Burn injuries remain a major clinical challenge due to the complex wound microenvironment and limited donor skin availability. Gingival mesenchymal stem cells (GMSCs), which are easily accessible and possess strong immunomodulatory and regenerative capacities, represent a promising therapeutic resource. Increasing attention has focused on the use of GMSC-derived conditioned medium (CM) as a cell-free alternative, particularly when its efficacy is enhanced through hypoxic preconditioning. In this study, human-derived GMSC-conditioned medium was used to examine the effects of hypoxia-preconditioned GMSC-CM (HP-GMSC-CM) on burn wound healing in vitro and in vivo. In HaCaT keratinocyte cultures, HP-GMSC-CM significantly promoted scratch wound closure and enhanced cell migration in Transwell assays, accompanied by upregulated expression of LAMC2 and COL4A1. In M1-polarized macrophages, HP-GMSC-CM markedly suppressed secretion of the pro-inflammatory cytokines TNF-α and IL-1β. In a murine burn model, treatment with HP-GMSC-CM accelerated wound closure and increased the expression of epithelial marker Pan-CK and extracellular matrix protein TNC. Mechanistic inhibition with LY294002 suggested that these therapeutic effects are mediated, at least in part, through PI3K/AKT-associated signaling . Collectively, these findings suggest that hypoxia-preconditioned GMSC-derived CM may serve as an effective cell-free therapeutic strategy for promoting burn wound repair and support its potential translational applicability despite the use of a xenogeneic experimental model.
Supervised learning-guided design and CNC fabrication of Schwarz TPMS with experimental evaluation for atmospheric water generation
Abstract Atmospheric Water Generation (AWG) offers a decentralized and sustainable solution to global water scarcity by extracting moisture from the air. This study enhances AWG performance by combining supervised learning–guideddesign optimization and experimental validation of Schwarz Triply Periodic Minimal Surface (TPMS) structures. TPMS geometries improve condensation through a combination of large surface area and enhanced thermal conduction, enabling efficient droplet shedding. Structural and thermal simulations in nTop software generated data for predicting the top surface temperature—critical for condensation—based on parameters such as lattice thickness, cell size, surface area, and solid volume. Among the supervised learning models tested (Support Vector Machine, K-Nearest Neighbor, and Decision Tree), the Decision Tree showed the best accuracy (R² = 0.9676), captuirng both linear and nonlinear relationships. Using optimized parameters, a Schwarz TPMS structure was fabricated from Aluminum 6061-T6 via multi-axis CNC milling through a layer-wise subtractive method. Experimental evaluation under thermoelectric cooling (4–7 V) showed that the TPMS outperformed a solid aluminum block at 5 V and 6 V, achieving up to 33% higher water yield, though performance declined at 7 V due to frost formation. Thermal mapping highlighted non-uniform cooling from interfacial resistance between layers. This work demonstrates a scalable approach that integrates supervised learning–guided design with subtractive manufacturing for efficient AWG systems.
TDDC-YOLO: texture–defect disentanglement for robust wood surface crack detection under domain shifts
Ultrawide Potential Window for Ammonia Electrosynthesis on a Bifunctional Metal−Organic Framework Across Broad Nitrate Concentrations
ABSTRACT Adaptability to the fluctuating supply of renewable electricity is one of the most critical challenges in realizing green ammonia electrosynthesis, especially when dealing with wastewater containing low concentration nitrate. Here, we report spatial electronic engineering of metal–organic frameworks (MOFs) that achieves an ultrawide potential window of 700 mV for nitrate reduction to ammonia (NRA) in 100 mM nitrate electrolyte and Faradaic efficiencies exceeding 90% over a broad nitrate concentration range (10−100 mM), superior to almost all reported catalysts. Coupling with the thermodynamically favorable glycerol oxidation reaction (GOR), simultaneous NRA||GOR reaches 50 mA·cm −2 at 1.16 V, 230 mV below the conventional oxygen evolution reaction (OER) based NRA||OER. Furthermore, the paired system can continuously operate at an industrial current density (∼200 mA·cm −2 ) over 100 h. The spatial electronic engineering lowers the d ‐band center and work function of the resulting bifunctional MOF and energy barrier of the rate determining step (RDS) while promoting water dissociation and suppressing hydrogen evolution. The ultrawide potential window and spontaneous nitrate adsorption equip the catalyst with excellent adaptability to renewable electricity and low‐concentration nitrate water treatment, exhibiting dual sustainability advantage.
A Multi-Strategy Adaptive Hybrid Optimization Algorithm for Benchmark Functions and Engineering Design Problems
Abstract Engineering optimization problems are increasingly complex, requiring more sophisticated approaches to locate global optima. This paper presents the Adaptive Hybrid Optimization (AHO) algorithm, which addresses the limitations of single-equation update mechanisms and conventional linear decay schedules through three structural contributions. First, the algorithm implements four distinct position-update strategies, selected uniformly at random at each agent update, ensuring continuous structural diversity throughout the optimization process. Second, a dual-leader probabilistic guidance mechanism directs each search agent using asymmetric weighting between the top two solutions. Third, a nonlinear power-changing control parameter D replaces the conventional linear decay schedule used in existing algorithms such as WOA and GWO, providing 43% more total exploration budget while maintaining faster late-phase exploitation. AHO is validated on 23 benchmark functions from the CEC2005 suite and on seven constrained engineering problems, including four space truss structures: the 10-bar Truss Design, the 25-bar Spatial Truss, the 72-bar Spatial Truss, and the 120-bar Dome Truss. Friedman ranking analysis across all 23 benchmark functions ranked AHO first among the eight algorithms tested, with an overall best rank of 1.78 and an overall mean rank of 1.84. Wilcoxon signed-rank test results confirm statistically significant improvement over five of seven competitor algorithms on more than 19 of 23 benchmark functions, with AHO achieving the best overall Friedman rank of 1.78 across all 23 functions. On the four truss optimization problems, AHO achieves the best ranking across all five performance metrics against all seven competitor algorithms under an equal budget of 50,000 structural function evaluations per run. Performance analysis demonstrates competitive convergence behaviour and solution quality compared with the selected benchmark methods.
Prevalence of poor self-care practice and associated factors among adult asthmatic patients in Africa: systematic review and meta-analysis
Abstract Poor self-care practice among adult asthmatic patients is a prevalent and serious clinical concern, as it is directly associated with significantly greater morbidity and mortality. It leads to adverse outcomes, including more frequent exacerbations, increased utilization of rescue inhalers and oral steroids, higher rates of emergency department visits, quantifiable reductions in lung function, and an overall decline in quality of life. Although several primary studies in Africa have reported highly variable prevalence rates of poor self-care practice among adult populations, no study has yet synthesized these findings. Therefore, this systematic review and meta-analysis were undertaken to estimate the pooled prevalence of poor self-care practice among adult asthmatics in Africa and to identify the key associated factors. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 guidelines. Studies conducted among adult asthmatic patients in Africa that reported asthma self-care practice levels and/or associated factors were included, while interventional studies, reviews, articles without full-text availability, or those of low methodological quality were excluded. Data were retrieved from Web of Science, CINHAL, PubMed/MEDLINE, ScienceDirect, Google Scholar, and University repositories. The quality of the studies was assessed using the Newcastle–Ottawa Scale. A random-effects model was applied to estimate the pooled prevalence of poor self-care practice. Publication bias was assessed using Egger’s test and funnel plots, and sensitivity analysis examined the impact of individual studies on the pooled estimate. Data were extracted in Microsoft Excel, and analyses were conducted in STATA 17, with significance set at p < 0.05 and a 95% confidence interval. A total of eight studies with 1779 adult asthmatics were included in this systematic review and meta-analysis. The pooled prevalence of poor self-care practice was 55.47% (95% CI = 46.10, 64.84) with 95% prediction interval (26.38%, 84.55%). Several socio-demographic, clinical, behavioral, and psychosocial factors were consistently associated with poor asthma self-care practice. Interventions to improve asthma self-care practice in Africa should adopt a holistic approach that targets high-risk groups (older adults, those with low education or comorbidities), addresses modifiable behaviors such as smoking and alcohol use, supports mental health, and strengthens social support networks.
Protective effect of rapeseed pollen extract on alcoholic liver injury in mice
Genome-wide association study of positive and negative affect reveals shared genetic architecture and a potential causal relationship with cognition
Abstract Altered affect and cognitive dysfunction are burdensome features of many neuropsychiatric conditions that are highly comorbid, remain poorly understood, and have few efficacious treatments. Exploring their genetic architecture and causal relationships may provide insight into their aetiology and comorbidity. Compared to related but distinct traits (depression, wellbeing, neuroticism), findings from genome-wide association studies (GWAS) of positive and negative affect may be informative due to these phenotypes being less heterogenous (compared to broader phenotypes of wellbeing and depression), whilst still retaining important clinical relevance as potential targets for indirect intervention (compared to neuroticism which may be less modifiable). Using data from the Lifelines Cohort Study, we conducted the first GWAS of positive and negative affect using a validated measure (N = 57,946), and four cognitive domains: working memory, reaction time, learning and memory, and executive function (N ≥ 35,729). We then assessed genetic overlap and potential causal relationships using genetic correlation and bidirectional Mendelian randomization (MR) analyses, incorporating large GWAS on related—albeit distinct—phenotypes (depression, anxiety, wellbeing, general cognitive ability [GCA]). We identified one SNP that reached genome-wide significance ( p < 5 × 10 –8 ) for reaction time, and many independent SNPs with suggestive associations for other phenotypes (N = 11–20). For most phenotypes, exploratory gene mapping indicated that SNPs with suggestive associations have higher gene expression in brain tissue compared to other tissues; however, this only met Bonferroni-corrected p -value threshold criteria for positive affect and visual learning and memory. Genetic correlations between negative and positive affect suggest that they are dissociable constructs ( r g = − 0.18). GCA has higher genetic overlap with negative affect than with positive affect ( r g = − 0.19 vs − 0.06), which could indicate that negative affect and GCA have a higher shared neural basis and/or they exhibit causal relationships. Supporting the latter, MR analyses indicated that higher GCA may reduce negative affect, depression, and anxiety, and increase wellbeing, with little impact on positive affect. Conversely, MR analyses indicated that higher risk of depression and lower wellbeing may causally reduce GCA. Together, these findings suggest that interventions that indirectly influence GCA may be valid targets to prevent negative affect, while interventions that indirectly influence depression/wellbeing may be valid targets for GCA.
Trypanosoma cruzi cell atlas as a single-cell resource for understanding parasite population heterogeneity and differentiation
Abstract Trypanosoma cruzi , the causative agent of Chagas disease, exhibits a complex life cycle with multiple hosts, stages and differentiation steps. We present a cell atlas for the T. cruzi life cycle, based on single cell transcriptomes for over 31,000 cells and population-based transcriptomics. The atlas reveals many life cycle associated genes and can be utilised to accurately annotate life cycle stages. It provides detailed insights into cell heterogeneity, including cell-specific repertoires of surface antigens in trypomastigotes, with key implications for immune responses. Enabled by single-cell resolution, we define the transcriptomic changes that occur across the epimastigote to metacyclic trypomastigote differentiation axis. Furthermore, we provide comprehensive UTR annotation, identifying previously unannotated transcripts as well as revealing alternative polyadenylation and an unanticipated complexity of reverse strand and antisense transcripts. This T. cruzi atlas provides a comprehensive resource and unlocks a range of new avenues for research on this important human pathogen.
Progression-guided spatiotemporal memory transformers for accurate and consistent longitudinal brain tumor segmentation
Abstract For monitoring the progression of the disease and the efficacy of treatment, it is essential to segment the brain tumor. The majority of the available deep learning models in use today are based on discrete time points without considering the continuity of the process, which causes irregular shapes of the tumors in the subsequent images. In this study, a model called Progression-Guided Spatiotemporal Memory Transformer (PGSMT) has been proposed, which has a unique design to overcome the constraints. A progression-aware temporal memory module, where the latent tumor representation is built up across successive MRI scans, a cross-time structural alignment mechanism, where the consistency of tumor morphology is preserved with the ability to accommodate pathological changes, and a boundary-enhanced transformer encoder, where the spatial dependencies are captured and stored for precise boundary delineation, are the three major components of the proposed framework. PGSMT learns the temporal weighting, making it possible for the network to distinguish between noise and actual progression, unlike other temporal fusion methods. When compared with the Convolutional Neural Network (CNN), hybrid CNN-Transformer, and Transformer models, the proposed PGSMT outperforms these models in the BraTS longitudinal benchmark dataset. PGSMT shows statistically significant improvements ( p < 0.05), with 88.1% Dice for the enhancing tumor, 90.2% Dice for the tumor core, and 93.0% Dice for the total tumor. while reducing inter-scan volumetric inconsistencies significantly. The relevant therapeutic interest in the dynamics of changing tumor volumes is substantiated by attention analysis. With respect to the empirical evaluation on the BraTS longitudinal data set, the results achieve 88.1% Dice for the enhancing tumor, 90.2% Dice for the tumor core, and 93.0% Dice for the total tumor, demonstrating statistically significant improvements ( p < 0.05) compared to traditional methods, while the results indicate increased stability in terms of temporal variance.
Unraveling the dynamics of multiple excited states in a single-molecule transistor
No effect of interspecific thermal tolerance on the stability of a blue mussel, Mytilus sp., hybrid zone
Abstract Arctic coastal ecosystems are undergoing rapid transformation as accelerated warming intensifies cryosphere melt and drives poleward range expansions of boreal species. In West Greenland, the blue mussels Mytilus edulis and M. trossulus form a distinct hybrid zone that may be destabilized in response to increasing temperatures and changing salinity regimes. To assess the impacts of warming and salinity stress on the hybrid zone stability, we conducted a mortality experiment. Mussels were exposed to three salinities (5, 15, or 25 PSU) for seven days before daily exposure to one of four air temperatures (5, 30, 33, or 36°C) for 1.5 hours over the course of six days, representative of conditions in the collection area. Survival was assessed daily, and we measured the expression of six stress-related genes ( hsp70, hsp90, sod, oat, p38 , and p53 ) in M. edulis , M. trossulus , and their hybrids after six simulated tidal regimes. Mortality increased significantly with increasing temperatures and decreasing salinity across all species, with >75% mortality at 36°C, but no interspecific differences in survival or gene expression were detected. Our findings suggest that atmospheric warming alone is unlikely to destabilize or shift the M. edulis–M. trossulus hybrid zone northward. Instead, other abiotic and biotic constraints, such as prolonged exposure to sub-zero temperatures, food availability, or reproductive limitations, may limit the poleward expansion of M. edulis . Our study provides the first experimental evidence of comparable thermal and salinity tolerance among co-occurring Mytilus congeners in the Arctic, highlighting the resilience of this hybrid zone under near-future climate scenarios.
Upcycling spent layered oxides into high-capacity Li-rich materials for next-generation lithium-ion batteries
An analysis of the influence of perceptual dimensions in biomimetic packaging design on generation Z consumers’ flow experiences using PLS-SEM, ANN, and fsQCA
Elasticity-gated thermal plasticity via superheating-mediated nucleation and growth in polymer networks
High-resolution GaN p-i-n alpha-particle detector with thick i-GaN layer
Alpha-particle detectors face several fundamental challenges, including the difficulty in achieving thick, high-quality depletion regions essential for efficient charge collection, high leakage currents caused by material defects, and severe energy loss in conventional electrode configurations. To address these limitations, we developed a GaN p-i-n alpha-particle detector on a free-standing substrate with a 20 μm thick intrinsic layer. A grid-shaped front electrode is introduced to minimize dead-layer energy loss. The device achieves full depletion at a low bias of −40 V and maintains leakage currents below several tens of picoampere up to −100 V. Notably, it exhibits an energy resolution of 1.5% at just −10 V, as well as a charge collection efficiency of 83.6%, which increases to 98.1% at −70 V. Theoretical modeling further reveals the underlying mechanism behind the anomalous energy resolution trend. These advances are attributed to the high crystalline quality of the thick i-GaN layer and the grid-shaped electrode design, which collectively suppress dislocation-induced leakage and dead-layer effects. This work provides a practical pathway to low-voltage, high-performance GaN alpha-particle detectors.