Search Results (21,950)

Adult hematopoietic stem cells (HSCs) and bone marrow (BM) mesenchymal stem/stromal cells (MSCs) are essential for lifelong hematopoiesis, skeletal homeostasis, immune competence, and tissue regeneration. The use of electronic cigarettes (E-cigs) among women of reproductive age continues to rise, raising concerns about potential adverse developmental effects; however, the long-term consequences of maternal E-cig vaping on offspring BM stem cell function and hematopoietic homeostasis remain incompletely understood. Here, using a rat model of maternal E-cig exposure (containing nicotine) during gestation, combined with longitudinal in vivo analyses and complementary ex vivo studies of human cells, we show that prenatal E-cig exposure is associated with persistent alterations in offspring BM stem cell function and lineage commitment. Gestational E-cig exposure was associated with expansion of the CD11b/c⁺ myeloid-enriched compartment, increased CD90⁺ stromal cells, and impaired osteogenic differentiation in rat offspring. Complementary experiments using primary human cells showed that nicotine exposure was associated with reduced T-cell proliferation and impaired cytotoxic activity in a proof-of-principle co-culture assay. Mechanistically, transcriptomic profiling followed by Gene Ontology and pathway enrichment analyses identified alterations in molecular programs associated with KLF4–Notch1 signaling, mitochondrial biogenesis, inflammation, and stem cell regulation in the BM of E-cig-exposed rat offspring. Changes in CCL11, FTO, and RUNX2 were additionally associated with an inflammatory and aging-related molecular phenotype that persisted from early life into adulthood, although these findings do not establish a causal CCL11–FTO–RUNX2 signaling axis or direct cellular senescence. Collectively, our study provides a phenotypic and mechanistic framework for understanding how maternal E-cig exposure may influence long-term offspring hematopoietic, skeletal, and immune health while highlighting the need for further studies to establish causal molecular mechanisms and determine their relevance to maternal E-cig use in humans. Full article

Parkinson’s disease (PD) is characterized by progressive dopaminergic degeneration and maladaptive motor cortical plasticity. However, the cellular pathways underlying cortical surface activity in the primary motor cortex (M1) remain unclear, despite serving as a potential target for electrotherapy. We investigated the excitatory–inhibitory (E-I) balance and synaptic plasticity of superficial M1 circuits in a unilateral 6-hydroxydopamine (6-OHDA)-induced rat model of PD. Using extracellular local field potential and whole-cell patch recordings from the contralateral and ipsilateral M1 hemispheres of hemi-parkinsonian rats, we observed a significantly elevated field excitatory postsynaptic potential (fEPSP) input–output function but unchanged intrinsic neuronal excitability in the M1 superficial layer. An altered relative contribution between alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR)- and N-methyl-D-aspartate receptor (NMDAR)-mediated transmission was reflected by a significantly increased AMPA/NMDA ratio. Markedly reduced inhibitory synaptic tone was also evidenced by the decreased amplitude and frequency of spontaneous inhibitory postsynaptic currents (sIPSCs), supporting an E-I imbalance favoring excitation in PD. Furthermore, group I metabotropic glutamate receptor (mGluR_1/5)-dependent long-term depression (LTD) was abolished in the ipsilateral PD hemisphere, whereas NMDAR-dependent LTD remained intact. In summary, dopamine depletion appears to enhance network excitation and disrupt mGluR_1/5-mediated control of M1 surface circuitry. Our findings identify altered cortical surface mGluR-dependent plasticity in the hemi-parkinsonian model; however, the relationship between these electrophysiological alterations and individual motor outcomes remains to be determined. Full article

Sustainable agriculture is critical to sustainable development and climate resilience, yet evidence of its multidimensional environmental, social and economic benefits and trade-offs remains limited, especially in regions most vulnerable to climate change. This study addresses this gap in Vietnam’s Mekong Delta (MKD), where decades of intensive rice farming has bolstered rice yields at the expense of environmental quality and farmers’ health. This study presents a multi-criteria analysis (MCA) comparing organic rice (OR) and conventional rice (CR) farming systems in the MKD. We applied a weighted sum model to evaluate the environmental, social and economic performance of the two production systems. The assessment drew on quantitative and qualitative primary data collected through stakeholder engagements and field measurements from Vinh Long province between 2023 and 2025, as part of the OrganoRice project. Overall, OR farming performed cumulatively better (0.663 and 0.695) than CR farming (0.496 and 0.484). OR scores were higher across most sub-criteria, particularly “farmers’ income” and “biodiversity”. However, CR outperformed OR for “rice yield” and “farmers’ workload”. Comparable water and soil quality scores, due to the presence of pesticide residues in both systems, suggest that the environmental performance of OR farming was likely dampened due to cross-contamination from surrounding or upstream non-organic farms. The expansion of OR farming in the MKD has the potential to enhance environmental, social and economic performance of rice production in the region. However, in order to achieve the full scope of these benefits, short-term measures should aim to reduce the financial risks of conversion for farmers, ensure access to affordable organic inputs, increase farmer training, ensure reliable premium contracts for rice producers and support them in accessing organic markets. Longer-term measures must improve irrigation water management and infrastructure to minimise cross-contamination risks. Coordinated marketing campaigns are needed to develop a trusted regional brand for organic rice from the MKD to create new opportunities in international and domestic markets. Moreover, long-term monitoring of post-transition outcomes is important to capture the full impacts of conversion. As OR cultivation continues to expand across the MKD, evidence on its benefits and trade-offs is essential to guide this transition effectively. This study provides that evidence, offering a context-specific, multidimensional evaluation to inform OR policy and practice in the region. Full article

Ornamental palm pruning residues represent a locally abundant, underutilized lignocellulosic waste stream with potential as a waste-valorized epoxy reinforcement. This study investigates the flexural behavior of particulate epoxy composites reinforced with Washingtonia robusta leaf stalk residue, evaluating the influence of reinforcement granulometry on mechanical and microstructural response. Four specimen families were fabricated from a Bisphenol A/F epoxy resin cured with a cycloaliphatic amine hardener: neat resin (RS, reference) and composites reinforced with fine (RF), coarse (RG) and mixed-fraction (RM) particles at 20 vol.% loading. Flexural properties were assessed by three-point bending and fracture surfaces were characterized by SEM. The neat resin exhibited a non-monotonic, viscoelastic-dominated response with no fracture within the extended deformation range tested, whereas all reinforced systems fractured within a substantially narrower window (~8–14.5 mm). RF showed the highest observed flexural modulus (≈15.8 GPa), followed by RM (≈15.4 GPa) and RG (≈14.2 GPa). These differences were not statistically significant (one-way ANOVA, p > 0.05). Damage tolerance followed a similar descriptive trend: RG failed earliest, linked to large interfacial pull-out cavities; RF delayed fracture through crack deflection; and RM showed the most favorable overall balance, combining a modulus comparable to RF with superior crack path tortuosity. These results indicate the potential of Washingtonia robusta, particularly in mixed-granulometry form, as a candidate reinforcement for semi-structural epoxy biocomposites, pending further characterization of properties such as tensile strength, impact resistance, moisture absorption, and long-term durability. Full article

Intensive yak fattening in cold alpine regions requires essential long-term high-concentration feeding, which disrupts rumen microbial homeostasis and causes inefficient digestion of nutrients. Lactobacillus may have probiotic potential for ruminants, yet its regulation of rumen function is poorly understood under high-energy diets. During a 120-day feeding experiment, 120 male Pamir yaks were allocated to four dietary treatments, with LEG and LLG serving as the primary comparison for evaluating 0.02% Lactobacillus supplementation to explore the regulatory effects of Lactobacillus supplementation on the rumen microbiota and host metabolism of yaks during a fattening process based on a concentrate feed diet via phenotypic data (body wight and nutrient digestibility) and multi-omics analyses (rumen microbial sequencing and rumen epithelial transcriptome) in a concentrate-based rearing yak model. The results showed that Lactobacillus intervention reduced OTU (Operational Taxonomic Unit) richness during the early fattening period and subsequently promoted microbial recovery through colonization resistance, which significantly enhanced microbial diversity (p < 0.05), and restructured the microbial community structure toward efficient energy utilization under high-concentrate feeding by reducing Prevotellaceae and Ruminococcaceae abundance, increasing the Bacillota/Bacteroidota ratio (p < 0.05). Concurrently, Lactobacillus enhanced the apparent digestibility of dry matter, crude protein, and fibrous components (p < 0.05). According to the transcriptomic analysis, there was an activation of signaling pathways related to IL-18 and TNF, and up-regulation of immune-and metabolism-related genes, in addition to strengthening the rumen mucosal barrier function. Multi-omics integration supported that dietary supplementation of Lactobacillus can optimize rumen fermentation, enhance nutrient digestion, and strengthen immune defense in Pamir yaks fed high-concentrate in cold seasons. These modifications demonstrate the positive effects of the Lactobacillus supplementation strategy on yak rumen health without interfering with the high-energy intensive rearing pattern. The present research presents a scientific basis for the use of targeted probiotic strategies to improve the rumen health and efficiency of alpine yak production systems. Full article

Endometrial cancer is increasingly diagnosed in patients who have not yet completed childbearing. For carefully selected patients with grade 1 endometrioid endometrial carcinoma confined to the endometrium, fertility-sparing treatment (FST) can preserve reproductive potential but requires rigorous histologic surveillance. In eligible patients, oral progestins and/or the levonorgestrel-releasing intrauterine system (LNG-IUS) remain the mainstay of fertility-sparing treatment, with hysteroscopic lesion resection incorporated in selected cases. Obesity, polycystic ovary syndrome, abnormalities in glucose metabolism, molecular subtype, and primary or acquired progestin resistance collectively contribute to heterogeneity in treatment response and the risk of recurrence. This narrative review critically integrates current guidelines, randomized controlled trials, prospective studies, retrospective cohorts, and early exploratory evidence to evaluate the biological rationale, clinical positioning, efficacy, safety, and maturity of evidence for progestin-based therapy, metabolic interventions, combined endocrine approaches, molecularly guided strategies, and exploratory immunotherapeutic approaches. We further propose an integrated clinical pathway encompassing candidate selection, molecular assessment, response evaluation, transition to pregnancy, retreatment after recurrence, and timely conversion to definitive surgery. Importantly, this review distinguishes guideline-supported approaches from adjunctive, investigational, and exploratory strategies. Major evidence gaps include inconsistent definitions of treatment response, limited prospective molecularly stratified data, uncertain reproductive safety of emerging systemic therapies, and insufficient long-term data on pregnancy outcomes and offspring. Full article

Capsaicin is an amphiphilic organic molecule containing both a hydrophobic hydrocarbon chain and polar functional groups, giving it the potential to regulate the surface wettability and interfacial interactions of organic materials. To evaluate capsaicin as an interfacial modifier for improving the anti-icing performance of asphalt binder, capsaicin-modified binders with different dosages were prepared and systematically characterized in terms of conventional properties, high- and low-temperature rheological performance, chemical structure, surface wettability, and water-droplet freezing and melting-induced shedding behavior. Capsaicin was incorporated into the asphalt matrix mainly through physical blending and moderately increased the high-temperature stiffness. At an appropriate dosage, the low-temperature creep and stress-relaxation properties were also improved. The addition of capsaicin increased the water contact angle and reduced the total surface free energy, thereby weakening water spreading and water-asphalt interfacial interactions. At temperatures from −5 to −20 °C, all modified binders exhibited delayed complete freezing and facilitated the gravity-driven shedding of frozen droplets during melting at room temperature. Pearson correlation analysis further showed that longer freezing times and shorter ice-shedding times were closely associated with a larger water contact angle and lower surface free energy. Additional validation after long-term aging confirmed that the surface-regulation and freezing-shedding effects of capsaicin remained effective after aging. Considering both binder performance and freezing-shedding behavior, a capsaicin dosage of 12% provided the best overall balance. These findings identify capsaicin as a promising interfacial modifier for the design of anti-icing asphalt materials. Full article

Background and Objectives: Conduction disorders (CDs) and permanent pacemaker implantation (PPI) remain among the most common complications after transcatheter aortic valve implantation (TAVI). Given their potential impact on long-term outcomes, improved preprocedural identification of patients at high risk for new-onset CDs is increasingly important. We investigated whether selected preprocedural inflammatory and fibrotic biomarkers, along with echocardiographic indices of regional myocardial remodeling, were associated with new-onset CDs after TAVI. Materials and Methods: This single-center prospective observational study included 112 patients with severe aortic stenosis undergoing TAVI. Peripheral blood samples were obtained within 24 h before TAVI for measurement of inflammatory and fibrotic biomarkers, including interleukin-6 (IL-6), C-reactive protein (CRP), CRP-to-albumin ratio (CAR), procalcitonin, ferritin, lactate dehydrogenase, and transforming growth factor-β1. The primary outcome was the occurrence of new-onset CDs during the index hospitalization or within three months after TAVI. Results: New-onset CDs occurred in 58 patients (51.8%). IL-6 showed the strongest association with the outcome in univariable analysis (OR per 1-SD increase, 9.55; 95% CI 3.00–30.40; p < 0.001), remained associated after adjustment for selected clinical and procedural predictors in exploratory models (adjusted OR 10.70; 95% CI 2.43–47.07; p = 0.002), and demonstrated the highest, although moderate, discriminatory performance among the evaluated biomarkers (AUC 0.730; 95% CI 0.637–0.823). CRP and CAR were higher in patients with CDs and were significant in univariable analysis, but showed weaker and less consistent adjusted associations. Among echocardiographic markers, AB strain ratio ≥ 2 was the most consistent imaging correlate in exploratory biomarker–echocardiographic models. Conclusions: Elevated preprocedural IL-6 was the biomarker most consistently associated with new-onset CDs after TAVI. These findings suggest that higher preprocedural IL-6 levels may reflect patient-specific susceptibility to new-onset CDs after TAVI and could have potential value as an adjunctive biomarker, alongside echocardiographic assessment, for preprocedural risk evaluation, with further validation required in larger prospective studies. Full article

Managing the transition risks of carbon-intensive firms is essential for reconciling climate governance with the stable operation of the real economy; nevertheless, existing scholarship has yet to fully elucidate how climate policy uncertainty contributes to the formation of these risks. In this paper, we develop a firm-specific measure of climate policy uncertainty exposure by integrating China’s aggregate climate policy uncertainty index with climate-risk-related textual data retrieved from listed companies’ annual reports. Drawing on a panel dataset of A-share listed companies in nine carbon-intensive sectors over 2010–2023, we employ a partial-linear double/debiased machine-learning methodology to investigate how climate policy uncertainty exposure influences multidimensional firm transition risk. Our baseline estimations indicate that greater climate policy uncertainty exposure is associated with a statistically significant rise in transition risk among high-carbon firms, with the preferred model producing a coefficient estimate of 0.0243. These findings remain robust to an array of sensitivity checks and endogeneity-correction procedures. Mechanism analysis provides evidence consistent with four potential channels involving weaker intra-industry competition, lower corporate risk-taking, tighter financing constraints, and higher agency costs. Heterogeneity examinations reveal that the detrimental impact is particularly evident among larger enterprises, high-technology companies, and firms characterized by comparatively lower pollution levels. Further analysis based on conditional average treatment effects and best linear predictors reveals that media supervision and the presence of long-term institutional investors substantially reduce the extent to which climate policy uncertainty translates into firm transition risk. This study provides firm-level empirical evidence elucidating how climate policy uncertainty shapes multidimensional transition risk in the low-carbon transformation of high-carbon industries. Full article

Pathological gait classification plays an important role in objective motor function assessment, early clinical screening, and rehabilitation evaluation. However, traditional clinical gait assessment methods are highly dependent on expert experience and may fail to detect subtle gait abnormalities. Moreover, existing inertial measurement unit (IMU)-based gait recognition methods often rely on single-sensor configurations or single-scale temporal models, limiting their ability to capture complex pathological gait patterns. In this study, a convolutional neural network–bidirectional long short-term memory–temporal convolutional network (CNN-BiLSTM-TCN) multi-branch feature fusion framework was proposed for pathological gait classification using a publicly available clinical multi-inertial measurement unit dataset containing 260 subjects. The proposed model employs three parallel branches to extract local instantaneous motion variations, continuous temporal dynamics, and relatively broader temporal dependencies within the 2 s input window, respectively, followed by feature-level fusion and end-to-end joint optimization. Experimental results show that the proposed model achieves a test accuracy of 0.9818 and an F1-score of 0.9700, outperforming conventional machine learning methods, single-branch models, voting-based fusion methods, and other temporal models, including Support Vector Machine (SVM), Temporal Convolutional Network (TCN), and Convolutional Neural Network-long short-term memory (CNN-LSTM). Five repeated experiments with stratified random splits demonstrate minimal performance variation, indicating good robustness and stability. The proposed framework provides a potential approach for pathological gait screening and quantitative rehabilitation assessment. Full article

Background/Objectives: Chimeric antigen receptor (CAR)-T cell therapy has achieved substantial clinical success in haematological malignancies but has shown limited efficacy against solid tumours. Key barriers include inadequate tumour trafficking, immunosuppressive tumour microenvironments, poor selectivity and heterogeneity of antigen expression, and challenges related to safety and manufacturing. Biomaterial-based technologies have emerged as a potential strategy to address many of these limitations. This review aims to critically evaluate biomaterial approaches designed to enhance CAR-T cell therapy of solid tumours and assess their translational potential. Methods: A narrative review of recent pre-clinical translational studies was conducted, focussing on biomaterial platforms developed to improve CAR-T cell delivery, persistence, functionality, safety control, and manufacturing efficiency in solid-tumour settings. Approaches were analysed according to their mechanisms of action, therapeutic benefits, and stage of translational readiness. Results: Biomaterial strategies, including nanoparticles, injectable and implantable hydrogels, scaffolds, and hybrid delivery systems, have improved CAR-T infiltration, survival, and therapeutic efficacy in several solid-tumour models. Localised delivery of cytokines and other immunomodulatory cues enabled improved spatio-temporal control of CAR-T activation, reducing systemic toxicity, and increasing persistence. Additional applications include amplified ex vivo CAR-T expansion and support for non-viral or in vivo CAR-T generation. However, increased material complexity was frequently associated with challenges in scalability, regulatory approval, and long-term safety. Conclusions: Biomaterial-enabled approaches offer a versatile toolkit to address key biological and translational barriers limiting CAR-T cell therapy of solid tumours. Strategies based on clinically familiar materials and simplified designs appear most suitable for near-term clinical translation, emphasising the need to balance engineering innovation with safety, scalability, and integration into existing clinical workflows. Full article

Araucaria angustifolia (Bertol.) Kuntze, a critically endangered tree species, plays an irreplaceable ecological role in its native habitats. Under global climate change, identifying the drivers governing its geographic distribution and assessing climate-related threats can provide scientific guidance for the long-term conservation and habitat restoration of this species. In this study, a total of 287 valid occurrence records from 27 countries were compiled. Combined with 14 screened environmental variables, an optimized Maximum Entropy (MaxEnt) model was used to predict the potential suitable habitats of A. angustifolia under historical climate conditions (1970–2000), as well as under low-emission (SSP126) and high-emission (SSP585) scenarios for the future periods of 2050, 2070, and 2090. Under historical climatic conditions, the average training AUC value from 10 replicate model runs was 0.979, indicating excellent and reliable model performance. Globally, the species has 1.91 × 10⁶ km² of moderately suitable habitat and 0.95 × 10⁶ km² of highly suitable habitat, with a total suitable habitat area of 2.86 × 10⁶ km², accounting for only 1.92% of the global terrestrial area. Mean annual temperature (bio1), mean temperature of the coldest quarter (bio11), and annual temperature range (bio7) are the dominant environmental variables shaping the distribution of A. angustifolia, followed by annual precipitation (bio12). Under future climate scenarios, the overall suitable habitats of A. angustifolia exhibit a slight contracting trend, whereas their spatial distribution patterns remain relatively stable. Full article

Real estate investment decisions are shaped by a complex environment of macroeconomic, demographic, and structural factors, where traditional linear assessment methods often fail to capture nonlinear relationships influencing aggregate housing market performance. The aim of this article is to develop a data-driven artificial intelligence framework for assessing how macroeconomic and structural conditions influence aggregate housing market performance and for providing a conceptual basis for evaluating real estate investment strategies under different economic contexts. The study uses machine learning algorithms that allow for modeling complex relationships between investment return indicators and key macroeconomic factors, such as economic growth rates, price dynamics, population concentration, and long-term structural changes. Unlike traditional econometric methods, the proposed approach identifies nonlinear and regime-dependent relationships between macroeconomic conditions and housing market performance, providing insights that can support the interpretation of different investment strategies. The results show that the factors determining investment returns are not universal, and their significance depends on the broader economic regime and market structure. This allows us to examine how changing macroeconomic conditions influence aggregate housing market performance and to discuss the potential implications for different investment strategies. The study contributes by proposing an artificial intelligence-based methodological framework that combines predictive modelling with explainable AI to support the analysis of macroeconomic influences on housing markets and to inform strategic real estate investment decision-making within complex socioeconomic systems. Full article

Multi-site water quality sequences are jointly affected by seasonal periodicity, meteorological disturbances, and inter-site differences in the Jianghuai Watershed region. Conventional quality prediction models struggle to simultaneously achieve multi-scale feature extraction, spatial heterogeneity characterization, and prediction uncertainty expression. This study used daily-scale monitoring data on dissolved oxygen (DO), pH, and ammonia nitrogen (NH3N) from 32 monitoring stations within the region in 2025 and proposed the FT-TransONet (Fourier-enhanced Temporal Transformer Operator Network) multi-site probabilistic water quality prediction model. Within a Transformer framework, the model employed a FourierTime learnable frequency-domain filtering module, a GeoBias (Geographic Bias) attention bias mechanism, and a multi-residual ensemble strategy composed of a multilayer perceptron (MLP), a gated recurrent unit (GRU), and a temporal convolutional network (TCN) combined with a mass conservation constraint, thereby achieving both point and interval prediction of key water quality indicators. The results showed that FT-TransONet achieved the lowest Macro_RMSE among all compared methods on the multi-site water quality prediction task. At a prediction horizon of three days, its Macro_RMSE reached 0.2255, which was 21.89% lower than that of the long short-term memory network and 5.57% lower than that of the strongest baseline MC-Dropout. For the three individual indicators, the model attained coefficients of determination of 0.9135, 0.9338, and 0.8660 for dissolved oxygen, pH, and ammonia nitrogen, with corresponding root-mean-square errors of 0.5145, 0.1098, and 0.0523, confirming its potential to characterize the temporal variation in the main water quality indicators. Under multi-step prediction, the error grew gently, with the Macro_RMSE rising only from 0.2255 to 0.2384 as the horizon extended from three to seven days, and the ablation experiments, together with the probabilistic prediction results, further supported the effectiveness of the proposed structural design. Validated on 32 water quality monitoring stations in the Jianghuai Watershed, the method improved multi-site prediction accuracy while accounting for stability and uncertainty quantification, providing a preliminary reference for regional water quality early warning and management. Full article

The intersection of artificial intelligence (AI) and gamification in language education has attracted increasing attention, but its link to sustainability is still largely unexamined. This study looks at whether and how much this area of research tackles two aspects of sustainability: the ability of AI-driven gamified tools promoting lasting independent language learning (pedagogical sustainability) and their potential to reduce educational inequalities in line with Sustainable Development Goal 4 (SDG 4) (social sustainability). Using a sequential explanatory mixed-methods design, a bibliometric analysis of 105 documents retrieved from Scopus and Web of Science (WoS) (2018–2026), processed with bibliometrix R package (4.6.0) and VOSviewer (1.6.20), and a thorough qualitative content analysis of 27 studies were combined. The bibliometric mapping uncovered four thematic clusters around gamification design, motivational theory, serious games, and AI-driven vocabulary learning. No sustainability-related terms had enough density to form a clear cluster. The qualitative analysis showed that SDG 4 is missing from all 27 reviewed documents. Only three (11.1%) operationalizing pedagogical sustainability and mediation as a CEFR competence are absent. These findings suggest that sustainability is still on the fringe of this field and support a research agenda focusing on long-term measurement, self-directed learning support, low-resource design and mediation in AI-driven gamified environments. Full article