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Neural substrates of treatment-resistant schizophrenia and the response to clozapine: A structural MRI study in a clinical setting
Background Predicting the responsiveness to clozapine among individuals with treatment-resistant schizophrenia (TRS) is difficult. A candidate predictor of clozapine response is the length of time prior to the introduction of clozapine treatment. The relationship between this measure and structural MRI findings has not been established. Patients and methods We compared the cortical-volume ratio between patients with TRS (n = 40 including 20 clozapine-treated patients) and non-TRS patients with schizophrenia (n = 64) and between each of these patient groups and healthy controls (HCs). We then investigated brain regions related to both responsiveness to clozapine and the duration between the TRS designation and the introduction of clozapine. Results The three-group comparison revealed that compared to the HCs, both patient groups had significantly lower cortical-volume ratios in widespread brain regions. However, there was no significant difference in the brain regions between the TRS and non-TRS groups: compared to the non-TRS group, the TRS group showed smaller volumes in a wider range of brain regions only at the uncorrected level. The correlational analysis of regions related to clozapine responsiveness did not identify any region that survived the correction for multiple comparisons. No relationship between any cortical region and the length of time prior to clozapine introduction was observed. Conclusion Overall, these results failed to identify the cortical region responsible for the treatment response to clozapine. The lack of correlations between the length of time prior to clozapine introduction and cortical regions might have been derived by insufficient statistical power, thus necessitating further research.
Expression of Concern: Seroepidemiology and associated risk factors of hepatitis B and C virus infections among pregnant women attending maternity wards at two hospitals in Swabi, Khyber Pakhtunkhwa, Pakistan
Patient safety culture among paramedic university students in Saudi Arabia
Background A strong and well-established patient safety culture is a fundamental component of healthcare systems and is especially vital in Emergency Medical Services (EMS). Understanding the attitudes of paramedic students toward patient safety offers valuable insights into their preparedness and highlights potential gaps in educational curricula requiring targeted enhancement. Objective This study aimed to evaluate the patient safety attitudes among paramedic university students in Saudi Arabia and explore differences based on demographic factors, including gender, academic year, and academic performance. Methods A cross-sectional survey was conducted with 494 paramedic students using the Safety Attitudes Questionnaire (SAQ), covering six domains: Teamwork Climate, Safety Climate, Job Satisfaction, Stress Recognition, Perception of Management, and Working Conditions. Statistical analyses included descriptive statistics, chi-square tests, ANOVA, and Bonferroni post-hoc comparisons. Results A total of 494 paramedic students participated. Overall, 25.1% achieved a positive safety attitude (SAQ ≥ 75). Among domains, Job Satisfaction scored highest (76.2 ± 15.2), while Stress Recognition scored lowest (51.2 ± 28.1). Significant differences were observed across academic years, with interns demonstrating higher domain scores than fourth-year students in teamwork climate, safety climate, stress recognition, job satisfaction, and perception of management (p < 0.01). Conclusion Paramedic students demonstrated low overall safety attitudes, with particularly low scores in stress recognition and reduced perceptions among fourth-year students. Strengthening stress management, teamwork, and supervisory support during training, alongside organisational efforts to enhance the clinical learning environment, may help improve the patient safety culture in paramedic education.
The relationship between the atherogenic index of plasma and hyperuricemia in American adults aged over 20 years: A cross-sectional study
Background The presence of hyperuricemia (HUA) is closely associated with lipid disorders and the development of cardiovascular disease (CVD). However, research on the relationship between the atherogenic index of plasma (AIP) and HUA remains limited among the general adult population in the United States. This study aims to elucidate the association between the AIP and HUA using data from a nationally representative database in the United States. Methods This study included a total of 7,057 participants, with data obtained from the National Health and Nutrition Examination Survey (NHANES) spanning 2011–2018. The AIP was calculated as log10 (triglycerides/high-density lipoprotein cholesterol). HUA served as the outcome variable, defined by serum uric acid (SUA) levels. Multivariate logistic regression, generalized additive models, smoothing fitting curves, subgroup analyses, and interaction tests were employed to reveal the relationship between AIP and HUA. Results After adjusting for all covariates, a statistically significant positive correlation was observed between AIP and the odds of HUA (OR = 3.22, 95%CI [2.54, 4.10], P < 0.001). Participants in the highest AIP quartile (Q4) had a 1.76-fold higher risk of HUA compared to those in the reference AIP quartile (Q1) (OR = 2.76, 95%CI [2.20, 3.45], P < 0.001). Stratified analyses confirmed that the positive correlation between AIP and HUA risk was significant and consistent, regardless of gender and body mass index (BMI) category. Additionally, the study found a nonlinear inverted L-shaped association between AIP and the risk of HUA, with the inflection point at 0.34. Subgroup analysis revealed that gender had a significant interaction with the AIP. Females showed a stronger association than males. Conclusions AIP and the risk of HUA demonstrated an inverted L-shaped positive association in the adult US population. The association was stronger in females than in males.
Optimizing spatial equity of urban park cooling services: Integrating landscape metrics with K-means and PSO algorithms in Nanchang, China
Urban parks and green spaces (UPGS) provide critical cooling services to mitigate urban heat islands, yet their equitable distribution remains poorly addressed. This study integrated landscape metrics with spatial optimization algorithms to quantify and enhance the cooling equity of UPGS in Nanchang, China—a city experiencing severe heat stress. Using Landsat 8TIRS data (2021), we analyzed 85 UPGS to extract cooling indicators (LST, PCI, PCA, PCG) and correlated them with landscape composition (area, perimeter, impervious/green/water coverage) and pattern indices (PD, LPI, etc.). Network analysis based on road networks and 3,024 settlements evaluated accessibility to cooling ranges. Results showed 71 UPGS exhibited significant cooling effects (P < 0.05), with optimal thresholds at 60 hm 2 area and 3 km perimeter. Water coverage was most strongly associated with lower LST (R 2 = 0.4284), while complex green patch morphology extended cooling distance. Crucially, only 71.2% of residents could access cooling services within a 15-min walk, revealing severe suburban disparities (e.g., 59.1% coverage outside Second Ring Road vs. > 73% intra-city). To address gaps, we combined K-means clustering (identifying 18 optimal UPGS additions) and Particle Swarm Optimization (locating placements prioritizing suburban demand). This framework bridges micro-scale UPGS design (e.g., maximizing water bodies) and macro-scale algorithmic spatial planning, offering actionable strategies for thermally equitable cities.
Aeroponic cultivation of lettuce: Unravelling varietal performance and trait interrelationships for enhanced productivity
Aeroponic systems offer a sustainable and efficient platform for cultivating high-quality leafy greens, such as lettuce ( Lactuca sativa L.). This study investigated the performance of five distinct lettuce cultivars (‘Summer Star’, ‘Grand Rapid’, ‘Tango’, ‘Bingo’, and ‘Black Rose’) within a controlled aeroponic environment to identify superior varieties and elucidate the intricate relationships among key agronomic traits. A comprehensive suite of statistical analyses was employed, including Analysis of Variance (ANOVA), Pearson correlation analysis, Principal Component Analysis (PCA), and genetic variability assessment using GCV and PCV. Results revealed significant differences among cultivars for growth parameters, yield components, and quality attributes. Notably, ‘Bingo’ exhibited the highest total leaf weight, while ‘Summer Star’ demonstrated superior yield per hectare. Correlation analysis highlighted strong positive associations between plant height, leaf area index, and chlorophyll content, whereas yield exhibited a negative correlation with total chlorophyll content. PCA identified key underlying factors contributing to the observed variation, with the first two principal components explaining 86.13% of the total variance, underscoring the importance of leaf morphology and chlorophyll concentration in driving aeroponic growth and productivity. The findings underscore the potential of aeroponics to contribute to global food security by enhancing the productivity and quality of leafy greens.
Comparative analysis of the impact of chickenpox and herpes zoster vaccination in Belgium under two different exogenous boosting mechanisms
Background Chickenpox (CP) and herpes zoster (HZ), both caused by the varicella-zoster virus (VZV), present a significant public health burden in unvaccinated populations. Universal CP vaccination has been long debated due to concerns about a potential increase in HZ incidence as a consequence of the exogenous boosting hypothesis. Methods We performed a cost-utility analysis on two deterministic compartmental dynamic transmission models, each of which employed a different underlying mechanism of exogenous boosting: temporal or progressive immunity. We considered four vaccination strategies: the current practice of no widespread vaccination and three strategies involving CP and HZ vaccination, either alone or in combination. The CP vaccines considered were Varivax and ProQuad, while the HZ vaccine considered was the recombinant zoster vaccine (RZV), Shingrix. The vaccine prices per dose were as follows: Varivax - €52.52, ProQuad - €73.69, recombinant zoster vaccine - €170.26. The clinical and economic impact of vaccination on both CP and HZ outcomes were evaluated. The main health outcome of interest was the quality-adjusted life year (QALY) which was used to compare the strategy yielding the highest average net monetary benefits (i.e., the optimal strategy) across a range of willingness-to-pay (WTP) values. Costs and health outcomes were discounted at 3.0% and 1.5% annually, respectively. We used 3 time horizons (i.e., 50, 75 & 100 years) and implemented the healthcare payer perspective throughout the analysis. Results CP vaccination led to a substantial reduction in CP incidence in both models. In the temporary immunity boosting ( Temp ) model, strategies that included HZ vaccination showed a decrease in HZ incidence. For the CP vaccination strategy in the Temp model, and for all CP and HZ vaccination strategies in the progressive immunity boosting ( Prog ) model, we observed both short- and medium-term increases in HZ, followed by a decrease to levels below the no-vaccination scenario. From the healthcare payer’s perspective, using a WTP of €40,000 per QALY gained, the Temp model indicated that the three vaccination strategies were cost-effective when considering time horizons of 50, 75, and 100 years. For the Prog model, only strategies combining both CP and HZ vaccination were cost-effective given a 100-year time horizon. Vaccination strategies under the Temp model became cost-effective at lower values of WTP compared to those under the Prog model. Conclusion Both models predicted that universal CP vaccination would result in significant reductions in the burden of CP disease, however, the HZ disease burden impact varied significantly depending on the assumed boosting mechanism. Hence, the choice of modeled exogenous boosting mechanism leads to different optimal vaccination strategies. Ascertaining the relative accuracy of these structural model choices will require continued research on the mechanism of VZV boosting.
Acceptance of AI-based tools in consumer financial decision-making: An application of the extended technology acceptance model
The development of artificial intelligence has led to an increase in scientific papers on its applications in financial services. Some of them focus on consumers, particularly presenting the aspects of automated servicing through robo-advisory systems. Nonetheless, relatively rarely does the research relate to the aspects of supporting consumers with AI-driven solutions for their own financial decision-making, which we find as a research gap. Our investigation focuses on finding how and to what extent AI-based tools empower consumers, providing support to their personal financial decisions. Our goal is to identify the determinants of acceptance of AI-based tools supporting financial decision-making, including perceptions of ease of use, usefulness, attitudes, and intentions to use. We base our work on the TAM model, for which we developed a dedicated scale to measure the individual items. The investigation is a pilot study that tests the survey on a representative sample of society. We gathered data through the CAWI survey research on a group of 371 respondents from three Polish universities. Our research shows that AI-based tools supporting financial decisions are primarily perceived through their usefulness and the potential to improve financial literacy. Furthermore, the PLS-SEM modelling confirms positive relationships between perceived ease of use (PEOU), perceived usefulness (PU), attitudes (ATT), behavioral intentions (BI), and actual use (AU) of AI-based tools in making financial decisions by consumers, except for the impact of PU on BI. We observed the strongest relation between PU and ATT towards AI-based tools, and between BI and AU. Our findings demonstrate that PU influences BI only indirectly through the construct of attitude (ATT). Such a phenomenon of a fully mediated relationship deviates from the traditional TAM assumptions. Our study confirms the coherence of the survey and the validity of the extended theoretical model of the acceptance and use of AI-based tools in consumers’ financial decision-making.
A hybrid deep learning and residual connection-based architecture for intrusion detection in autonomous vehicles
The emergence of Autonomous and Connected Autonomous Vehicles (CAVs) has transformed the automotive landscape drastically over the past few years by offering enhanced features in the vehicles for drivers’ safety and convenience. These developments have introduced various features in AVs i.e., lane-keeping, cruise control, etc. These features are mainly powered by the Electronic Control Units (ECUs) that communicate using the Controller Area Network (CAN) bus protocol. The components in the AVs communicate with each other by sending and receiving messages via the CAN bus. However, despite increased connectivity, these vehicles have become vulnerable to cyber attacks, as malicious actors can exploit the CAN protocol to manipulate vehicle behavior, which can not only threaten the safety of the passengers but public as well. Hence, several Intrusion Detection Systems (IDS) have been proposed, however, these systems struggle with computational complexity, limited effectiveness against sophisticated attack types, and a lack of interpretability and transparency of detection mechanisms. To address challenges in the existing systems, this paper presents a novel hybrid Deep Learning (DL)-based IDS using DL components such as Convolutional layer and Long Short-Term Memory (LSTM) layers to capture complex patterns in the CAN messages. The proposed IDS uses a residual connection to enhance gradient flow and training stability. The system is evaluated on four common attack types, namely RPM Spoofing, Gear Spoofing, Fuzzy, and Denial of Service (DoS), achieving a detection accuracy of 99.99%. Finally, the outcomes of the proposed IDS are visually interpreted using the Explainable AI (XAI) technique called Local Interpretable Model-agnostic Explanations (LIME) to provide transparency into the model’s decision-making process, thus increasing trust in the system’s deployment in real-world AV environments.
Clinical nurse’s knowledge, attitude, and practice regarding the Intrinsic Capacity of the aged: A cross-sectional study
Background Within the context of Chinese healthcare settings, from the perspective of clinical nursing practice in China, the present study was designed to systematically evaluate registered nurses’ theoretical knowledge, attitude, and practice(KAP)toward assessing the Intrinsic Capacity in the aging population. Methods This cross-sectional study based on online questionnaires included 606 clinical nurses who were employed in a tertiary hospital in Xinjiang from November 21, 2024 to February 18, 2025. Using the Delphi method to create a self-made online questionnaire to collect participants’ sociodemographic information and KAP scores of changes in the Intrinsic Capacity of the elderly. Results Of the collected data, 606 questionnaires were deemed valid for analysis. the research instrument showed high reliability, as evidenced by Cronbach’s α values of 0.979 for knowledge, 0.916 for attitude, and 0.936 for practice sections. Exploratory factor analysis was conducted, and the Bartlett’s test of sphericity result was 0.814.The mean knowledge score was 34.75 ± 11.165(possible range:18–90).The mean attitude score was 20.86 ± 5.488(possible range: 11–35).The mean practice score was 32.69 ± 8.695(possible range:16–72). Knowledge, attitude, and practice (KAP) scores of clinical nurses regarding the intrinsic capacity of older patients were treated as dependent variables. LASSO regression was employed to identify significant predictors. The results indicated that: nursing hierarchy ( λ = 0.509), prior receipt of intrinsic capacity training ( λ = 1.739), hospital support for new technology development in geriatric care ( λ = 2.919), and nurses’ perceived adequacy of existing knowledge to meet clinical needs ( λ = 4.755) were independently associated with knowledge scores. For attitude scores, significant predictors included hospital support for new technology development in geriatric care ( λ = 1.846), perceived adequacy of existing knowledge to meet clinical needs ( λ = 1.580), and expected frequency of training ( λ = 0.699). Practice scores were independently associated with prior receipt of intrinsic capacity training ( λ = 1.914), hospital support for new technology development in geriatric care ( λ = 0.144), the proportion of patients aged over 60 cared for during the past year ( λ = 1.176), and perceived adequacy of existing knowledge to meet clinical needs ( λ = 2.696). The structural equation modeling path coefficients revealed significant direct pathways: from knowledge to attitude( r = 0.732, P < 0.01), knowledge to practice( r = 0.617, P < 0.01), and attitude to practice( r = 0.666, P < 0.01).The bootstrap mediation effect test results indicated an indirect effect ( B = 0.421, 95%CI : 0.296–0.558) and a direct effect ( B = 0.295, 95%CI : 0.146–0.451), with effect proportions of 59% and 41%, respectively. Conclusion This investigation reveals the insufficient understanding, attitude, and practice of clinical nurses in Xinjiang towards changes in the Intrinsic Capacity of elderly patients.
Modeling and benchmarking quantum optical neurons for efficient neural computation
Quantum optical neurons (QONs) are emerging as promising computational units that leverage photonic interference to perform neural operations in an energy-efficient and physically grounded manner. Building on recent theoretical proposals, we introduce a family of QON architectures based on Hong–Ou–Mandel (HOM) and Mach–Zehnder (MZ) interferometers, incorporating different photon modulation strategies—phase, amplitude, and intensity. These physical setups yield distinct pre-activation functions, which we implement as fully differentiable software modules. We evaluate these QONs both in isolation and as building blocks of multilayer networks, training them on binary and multiclass image classification tasks using the MNIST and FashionMNIST datasets. Each experiment is repeated over five independent runs and assessed under both ideal and non-ideal conditions to measure accuracy, convergence, and robustness. Across settings, MZ-based neurons exhibit consistently stable behavior—including under noise—while HOM amplitude modulation performs competitively in deeper architectures, in several cases approaching classical performance. In contrast, phase- and intensity-modulated HOM-based variants show reduced stability and greater sensitivity to perturbations. These results highlight the potential of QONs as efficient and scalable components for future quantum-inspired neural architectures and hybrid photonic–electronic systems. The code is publicly available at https://github.com/gvessio/quantum-optical-neurons .
Using mosquitoes to vaccinate bats could curb the spread of deadly diseases
Memory loss is fuelled by gut microbes in ageing mice
Malaria is hindered by repression of a cell-cycle protein
Reduced cyclin D3 expression in erythroid cells protects against malaria
Abstract The severity of malaria varies substantially between individuals, but the mechanisms that underlie these differences remain unclear. Because erythrocytes have a key role in malaria biology, genetic variants associated with the development of these cells could inform the mechanisms that determine disease severity. Here we investigate the mechanistic basis of the association of the variant rs112233623-T with erythrocyte properties, and examine its role in modulating malaria severity. This variant is associated with increased levels of haemoglobin A2, increased erythrocyte size and reduced erythrocyte number 1,2 . It is found in an erythroid enhancer of CCND3 , which encodes cyclin D3—a cell-division activator that enhances the pentose phosphate pathway and thereby helps to counteract reactive oxygen species (ROS) 3 . We show that rs112233623-T disrupts a binding site for the transcription factor SMAD3, weakens enhancer activity and, in erythrocyte precursors (erythroblasts), is associated with reduced CCND3 expression and inhibition of the G1–S cell-cycle transition, concomitant with a reduction in the number of erythrocytes and an increase in their size. Using population genetic methods, we observe signatures of positive selection for rs112233623-T in the genetic history of Sardinia, a region in which malaria was once prevalent. Furthermore, we show that parasite growth is impaired in cultured Plasmodium falciparum -infected erythrocytes from rs112233623-T carriers, and that this impairment correlates with ROS levels. This mirrors our observations in erythrocytes from individuals who are deficient in the pentose-phosphate-pathway enzyme G6PD—a trait associated with protection against malaria in some settings—and highlights a common ROS-based mechanism of malaria resistance. Our results suggest that a reduction in CCND3 in erythroblasts constitutes a mechanism of resistance to malaria, and could enable therapeutic interventions.
Developmental convergence and divergence in human stem cell models of autism
Abstract Two decades of genetic studies in autism spectrum disorder (ASD) have identified more than 100 genes harbouring rare risk mutations 1–13 . Despite this substantial heterogeneity, transcriptomic and epigenetic analyses have identified convergent patterns of dysregulation across the ASD postmortem brain 14,15–17 . To identify shared and distinct mechanisms of ASD-linked mutations, we assembled a large patient collection of human induced pluripotent stem (hiPS) cells, consisting of 70 hiPS cell lines after stringent quality control representing 8 ASD-associated mutations, idiopathic ASD, and 20 lines from non-affected control individuals. Here we used these hiPS cell lines to generate human cortical organoids, profiling by RNA sequencing at four distinct time points up to 100 days after in vitro differentiation. Early time points harboured the largest mutation-specific changes, but different mutations converged on shared transcriptional changes as development progressed. We identified a shared RNA and protein interaction network, which was enriched in ASD risk genes and predicted to drive the observed downstream changes in gene expression. CRISPR–Cas9 screening of these candidate transcriptional regulators in induced human neural progenitors validated their downstream convergent molecular effects. These data illustrate how risk associated with genetically defined forms of ASD can propagate by means of transcriptional regulation to affect convergently dysregulated pathways, providing new insight into the convergent impact of ASD genetic risk on human neurodevelopment.
Gene editing treats a mouse model of a neurodevelopmental disorder
Multimodal electron microscopy of halide perovskite interfacial dynamics
Abstract Halide perovskite light-emitting diodes promise high-efficiency 1–3 , low-cost optoelectronics, yet their operational instability remains a critical barrier to practical deployment. Here we develop a multimodal in situ electron microscopy approach that integrates four-dimensional scanning transmission electron microscopy, energy-dispersive X-ray spectroscopy and atomic-resolution imaging to directly visualize structural and chemical evolution in a working halide perovskite light-emitting diode with nanometre precision. Our in situ biasing measurements uncover nanoscale structural and chemical transformations initiated at transport layer interfaces, including the formation of metallic lead and lead-rich secondary phases, as well as strain-driven grain fragmentation. On biasing, we observe the partial transformation of the metallic Al contact to insulating AlCl 3 . Crucially, whereas the bulk of the perovskite emitter remains relatively intact, our experiment shows that degradation is localized at interfaces. By comparing in situ and ex situ measurements, these results establish a mechanistic link between interfacial strain, ionic transport and electrochemical reactions in working devices, and provide a broadly applicable framework for nanoscale degradation analysis in complex multilayered optoelectronic systems using multimodal in situ biasing microscopy.