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Oroxylum indicum ameliorates D-galactose-induced aging related memory impairments via enhancing rat hippocampal neurogenesis

Abstract Brain aging significantly affects human lives, particularly among the elderly, leading to various diseases and constituting memory and cognitive decline. Hippocampal neurogenesis, the process of generating new neurons throughout life, decline with aging, which contribute to cognitive decline and dementia. D-galactose (D-gal) induces brain aging by increasing oxidative stress and inflammation, resulting in neurodegeneration and memory dysfunction. Oroxylum indicum fruit extract (OIFE), a traditional Thai herb, possesses strong antioxidant and neuroprotective properties. We investigated whether OIFE extract mitigates D-gal-induced memory decline, assessing its phytochemical profile, antioxidant capacity and neuroprotective effects. The results revealed that OIFE possessed strong phytochemical properties using measures such as total phenolic content (TPC), total flavonoid content (TFC), 2,2-diphenyl-1-picrylhydrazyl (DPPH), ferric reducing antioxidant power (FRAP), and high-performance liquid chromatography–ultraviolet (HPLC–UV). Liver and kidney function tests on blood samples showed no significant differences among the groups. For memory function testing, sixty male Sprague Dawley rats were divided into six groups and treated with OIFE (125 or 250 mg/kg, p.o.) and D-gal (50 mg/kg, i.p.) for eight weeks. D-gal administration resulted in significant memory deficits (accessed via the novel object location [NOL] and novel object recognition [NOR] test). These deficits were correlated with a decrease in the number of immature neurons and reduced neuronal cell survival in the subgranular zone (SGZ), as observed using doublecortin (DCX) and 5-Bromo-2′-deoxyuridine/Neuronal nuclear protein (BrdU/NeuN) immunofluorescence staining, respectively. However, co-treatment with OIFE ameliorates D-gal-induced these impairments. Therefore, OIFE could mitigate memory and neurogenesis impairments in D-gal-induced brain aging in rats.

Dose- and tissue-dependent effects of cannabidiol on antioxidant status in developing chicken embryos

A migrasome-related LncRNA signatures for predicting prognosis and immunotherapeutic response in colorectal cancer

Study protocol for a pilot study for Remote ADHD Monitoring Program (RAMP) for children in rural areas

Background Attention-deficit/hyperactivity Disorder (ADHD) is the most common neurobehavioral condition of childhood and can be controlled with stimulant medication. Evidence-based guidelines endorse use of standardized ADHD symptom reports to facilitate medication titration to therapeutic dosage. Children living in under-resourced areas experience barriers to receiving this recommended evidence-based care. The Remote ADHD Monitoring Program (RAMP) uses a text-based platform to relay symptom reports from caregivers and teachers to healthcare providers. This pilot study is a feasibility study examining intervention uptake. It compares the submission of structured symptom reports in those children enrolled in RAMP compared to usual care as well as utilization of the RAMP platform by providers. Methods This paper describes the protocol to evaluate the feasibility of deploying RAMP in practices serving rural or underserved children. We will recruit 36 dyads from 4 practices in 2 separate states. Each dyad will include a caregiver and their child aged 5–11 years with a diagnosis of ADHD who is starting or reinitiating stimulants. Dyads will be randomized 1:1 to receive the RAMP intervention or usual care with attention controls. Our primary outcome is number of symptom reports (paper assessments in control arm and RAMP reports in intervention arm) per participant that are completed by caregivers and teachers and returned to providers. Our secondary outcome is proportion of submitted RAMP reports that are reviewed by providers. Discussion As telehealth use increases, it is critical that we improve access to high quality care for children with chronic conditions. Leveraging technology may be a meaningful approach to improve efficiency in optimizing medication management. This pilot study tests a text-based platform designed to improve communication between the caregivers and teachers of children with ADHD and health care providers. If successful, a future trial will examine the effectiveness of the RAMP intervention on improvement in symptoms. Trial registration ClinicalTrials.gov NCT06743425 .

Development and validation of a machine learning model for critical progression risk in pediatric severe community-acquired pneumonia

Correction: Evaluating advance peace in Fresno, California: An interrupted times series analysis of a community-based gun violence intervention

AI assistance improves reader agreement in digital mammography: A multireader crossover study of general and breast subspecialty radiologists

The spatiotemporal evolution and driving factors of urban–rural integration over the past eight years in China: Evidence from 31 provinces

Urban-rural integration development is essential for overcoming the inherent barriers between urban and rural areas and achieving coordinated regional development. This study uses panel data, along with methods such as the entropy method, coupling coordination degree model, and geographic detector model, to analyze the spatiotemporal evolution and main influencing factors of urban-rural integration development across 31 provinces, autonomous regions, and municipalities in China from 2015 to 2022. The findings indicate that: (1) The overall level of urban-rural integration in China shows an upward trend, but the growth rate is uneven, exhibiting a phased pattern of “rapid growth—slow development—fluctuating rise.” (2) There are significant regional differences, with eastern regions leading in development, central regions rising rapidly, and western regions showing huge potential. (3) Key driving factors promoting urban-rural integration include internet broadband access, per capita disposable income, education expenditure, unemployment insurance coverage, and greening coverage. Based on these findings, it is recommended to develop phased strategies, implement a regional gradient development approach, and prioritize strengthening key areas to systematically promote urban-rural integration.

Chemical exchange saturation transfer imaging and diffusion weighted imaging for colon 26 tumor bearing mice

Evaluation of the delivery of the family nurse partnership programme in Scotland during the COVID-19 pandemic

Background The Family Nurse Partnership (FNP) is an intensive and structured person-centred home-visiting programme delivered by specially trained nurses, who offer support services to first-time young mothers. The COVID-19 pandemic prompted the quick adoption of telehealth within the FNP, as healthcare services moved rapidly to implement remote delivery systems in line with infection control measures. The aim of this study was 1) to understand the features of telehealth employed to deliver the FNP programme during COVID-19 in Scotland; 2) to examine how FNP nurses and clients responded to the delivery of FNP through telehealth; 3) to evaluate the challenges of delivering the FNP through telehealth during COVID-19 and its implications for future delivery of the programme. Methods The study employed a mixed-methods parallel design, where qualitative (one-to-one interviews and focus groups) and quantitative (survey) data were collected and analysed concurrently. Thirty-one family nurses took part in the focus groups and one-to-one interviews and a further 90 responded to the online survey. Fifteen FNP clients participated in one-to-one interviews. Interview data were analysed using thematic analysis and survey data were analysed by descriptive analysis. Results Family nurses combined both home visiting and remote delivery such as phone calls, SMS text messaging, emails, video calls to deliver the programme. Family nurses felt well equipped and supported to conduct their work remotely. Clients, particularly those who became isolated during COVID-19, overwhelmingly acknowledged this support and felt their family nurses provided stability, advice and care. However, both family nurses and clients found the rapid move to remote delivery challenging, because it affected both recruitment of clients with complex vulnerabilities to the programme and therapeutic relationship building. Nevertheless, 42% of family nurse respondents in the survey indicated that they would prefer mixed-mode delivery of face-to-face and telehealth as part of future FNP programme delivery. Conclusion Despite the challenges of delivering the programme remotely during COVID-19, telehealth has the potential to play a valuable role in post COVID-19 FNP programme delivery. A hybrid delivery approach could be appropriate in certain instances, for example clients not deemed to have complex vulnerabilities or those living in remote locations. Future studies could robustly examine the impact of the quality of modes of FNP delivery, for instance home visiting, telehealth and hybrid delivery and how these influence outcomes across different client groups. An economic evaluation of the value for money of different modes of delivery could also be insightful for decision makers.

Editorial Expression of Concern: Intranasal vaccination of hamsters with a Newcastle disease virus vector expressing the S1 subunit protects animals against SARS-CoV-2 disease

Integrating graph neural networks and LSTM for path optimization in smart port multi-modal systems

This paper addresses the challenges of dynamic environments and multimodal data fusion in multimodal transport path optimization for smart ports by proposing a GL-SSL Model that integrates Graph Neural Networks (GCN), Long Short-Term Memory (LSTM), and Self-Supervised Learning (SSL). The model fully exploits the graph-structured information of port transport networks and their temporal variations, while SSL enhances feature representation, enabling efficient optimization of path planning. Experiments were conducted on multiple public datasets, including AIS data from the Port of Rotterdam, global shipping data, and port net revenue data. Results show that the GL-SSL Model achieved significant improvements in key performance metrics. Specifically, the optimized path length reached 80 km , the transport cost was reduced to 200 cost-units (a composite metric reflecting fuel consumption, equipment wear, and labor cost), and the delay rate was maintained at 0.05 (5%) , all of which are substantially better than traditional algorithms and other deep learning models. Furthermore, the model demonstrated stable performance under complex scenarios such as peak traffic, adverse weather, and equipment failures, with rapid convergence of training loss and strong robustness. These findings highlight the model’s adaptability and practical application potential. Overall, this work provides effective technical support for multimodal transport path optimization in smart ports and carries important theoretical significance and broad application prospects.

High-temperature wood silicification: constraints from fluid and carbonaceous inclusions in quartz from Qitai, NW China

Socio-ecological factors influencing dietary behaviours among adolescents and young adults in rural Eastern Uganda: A qualitative study

Introduction Adolescents and young adults (AYAs) worldwide display poor dietary behaviors, including high consumption of sugar-sweetened beverages and insufficient intake of fruits and vegetables. These issues are more pronounced in Sub-Saharan Africa, such as rural Eastern Uganda, where 45.3% of adolescents eat low-diversity diets high in refined grains and fats. Such diets raise the risk of malnutrition and diet-related non-communicable diseases (NCDs). However, there is limited contextual evidence on the multi-level factors influencing AYAs’ dietary behaviors in rural Uganda. This study examined socio-ecological factors shaping dietary behaviors among AYAs in this setting. Methods A qualitative study guided by the socio-ecological model (SEM) was conducted in Mayuge District, Eastern Uganda. Focus group discussions (FGDs) were held with AYAs, including male and female, aged 10–14, 15–19, and 20–24 years. To have a nuanced understanding of how AYAs’ dietary behaviours are shaped, additional FGDs were conducted with parents or guardians and teachers, and key informant interviews with the district education officer, nutrition focal person, civil society staff, and food vendors. Data were analyzed in ATLAS.ti using both inductive and deductive thematic approaches: data-driven sub-themes were first identified inductively, then deductively mapped onto pre-determined themes of the SEM. Results Dietary behaviors were shaped by satiety, energy needs, sensory appeal, and nutrition knowledge at the individual level. Peer influence, parental control, and food’s perceived link to attractiveness acted interpersonally, while community factors included gendered cultural taboos, norms, and health worker advice. At the societal level, cultural identity, ancestral restrictions, and media exposure strongly influenced choices. Conclusions This study contributes novel rural-specific evidence from rural Uganda, where AYAs’ diets are uniquely constrained by satiety demands, parental dominance, cultural taboos, and seasonal scarcity; contrasting with urban contexts where convenience, autonomy, and wider food environments prevail. Multi-level interventions integrating nutrition education, family and peer engagement, cultural dialogue, and household food security support are essential for promoting healthier diets in resource-limited rural settings.

Feature selective inhibitory mechanisms enable expectation suppression in cortical microcircuits

From data to diagnosis: An innovative approach to epilepsy prediction with CGTNet incorporating spatio-temporal features

Epilepsy affects around 50 million people globally, causing significant burdens. While many methods predict seizures, current models struggle with handling spatiotemporal features and balancing accuracy with computational efficiency.This paper introduces a novel deep learning architecture called CGTNet, which is composed of a multi-scale convolutional network, gated recurrent units (GRUs), and Sparse Transformers. It is specifically designed for analyzing elec-troencephalogram (EEG) data to predict epileptic seizures. CGTNet enhances the ability to extract spatiotemporal features from EEG signals, demonstrating its exceptional performance in seizure prediction through rigorous evaluation on the renowned CHB-MIT and SWEC-ETHZ EEG datasets. The model achieved an accuracy of 98.89%, sensitivity of 98.52%, specificity of 98.53%, an AUROC value of 0.97, and an MCC value of 0.975 on these datasets. These results not only highlight the technical innovations of CGTNet but also validate the immense potential of deep learning in processing medical signals. Our research provides an effective new tool for early detection and continuous monitoring of epilepsy, laying the foundation for advancing healthcare with artificial intelligence technology.

Multiscale finite element analysis of TRC-strengthened concrete columns under lateral cyclic loading

Understanding smartphone use patterns in higher education: A latent class approach to behavioral and health risk typologies

Introduction The widespread use of smartphones among university students has raised concern because of their potential effects and the need to detect profiles of problematic use. This study aimed to identify, characterize and differentiate different profiles of smartphone users in a sample of university students on the basis of variables such as use, nomophobia, risk and sociodemographic characteristics. Methods A total of 681 university students participated. A total of 681 university students participated in this study. The sample was recruited using a non-probabilistic, convenience sampling method. Latent class analysis -LCA- was performed to identify profiles from variables that included smartphone use patterns such as daily hours, messaging, social networks, browsing, history of technology adoption, situational use, NMPQ nomophobia questionnaire -a scale designed to assess the fear of being without a smartphone-, and reported consequences such as accidents, visual or musculoskeletal problems. The resulting classes were compared in subsequent analyses using chi-square tests for categorical variables and Mann‒Whitney U tests for ordinal variables. Results LCA revealed two clearly differentiated user profiles. Class 1 (n = 348) grouped users with moderate use and less exposure to risks and was characterized by shorter daily use of smartphones (mean = 5.46 hours), significantly lower scores on the total scale of nomophobia (mean NMPQ = 65.4 out of 140 possible points, moderate level), a lower frequency of accidents reported due to mobile use and lower reports of visual and musculoskeletal health problems. Class 2 (n = 333) grouped users with high digital involvement and multiple vulnerabilities and showed a significantly more intensive use pattern (mean = 11.01 hours per day), higher levels of nomophobia (mean NMPQ = 74.3 out of 140 possible points, moderate level), and a higher frequency of accidents and major visual and musculoskeletal health problems. Conclusion While both groups of undergraduate students could benefit from awareness and training programs, interventions could be differentiated and designed to mitigate the risks associated with problematic smartphone use. These findings provide evidence for higher education institutions and health professionals in the development of programs aimed at promoting digital well-being among university students.

The protective role of nuclear Heme oxygenase-1 in blood-spinal cord barrier after hypoxia in vitro

HAttFFNN: Hybridized attention mechanism-based feedforward neural network deep learning model for the plastic material classification of three stage materials on spectroscopic data

Classification of plastic materials based on spectroscopic data is a very crucial task in a variety of applications, including automated recycling, environmental monitoring, quality control in manufacturing, quality control of products, and analysis of complex material properties. These applications demand high precision in identifying and separating plastic types to enhance sustainability and ensure regulatory compliance. In this work, we presented a novel technique Hybridized Attention mechanism-based Feedforward Neural Network (HAttFFNN) to detect three stage Polyethylene Terephthalate (PET) materials. Dataset used in this methodology is basically comprised of 295,327 samples, and contains the parameters like absorbance, wavelengths, references, samples. We collected the spectral data (900–1700 nm) using the Digital Light Processing (DLP) Near-Infrared (NIR) scan Nano Evaluation Module (EVM). We utilized various preprocessing techniques for better and improved detection result, such as Savitzky-Golay filter, interference, Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC). The preprocessed and organized spectral data is provided to the proposed HAttFFNN model for the detection of three stage PET material. To validate the performance of the proposed model, we experimented various State-Of-The-Art (SOTA) models, Multi-Head Neural Network (MHNN), Virtual Geometry Group (VGG16), One-Dimensional Convolutional Neural Network (1D-CNN), Residual Network (ResNet), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The proposed model outperforms state of the art techniques across all metrics including accuracy, precision, recall, F1 score, and specificity with Stage 1 (PET Clear vs PET Hazard) achieving 99.33% accuracy, Stage 2 (PET vs Others) 99.32%, and Stage 3 (PET Coloured vs PET Transparent) 99.28%, along with consistently high precision, recall, and specificity values for each class. These results confirm that our proposed model, HAttFFNN, is able to achieve higher accuracy in spectroscopic classification domain, especially in complex cases such as differentiating between visually and spectrally similar materials (PET Clear vs PET Hazard, PET vs Others and PET Colored vs PET Transparent) where traditional models often fail. Furthermore, the Root Mean Square Error (RMSE) values 0.1408 for Stage 1, 0.1249 for Stage 2, and 0.1403 for Stage 3, further validate the model’s low-error performance, reinforcing its effectiveness as a less error-prone approach for spectrometry-based plastic material classification.