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Establishment of a mouse intrauterine adhesion model via transvaginal mechanical injury guided by abdominal ultrasound
Objective To establish a clinically relevant mouse model of intrauterine adhesion (IUA) using a minimally invasive approach that combines real-time ultrasound guidance with controlled transvaginal mechanical injury. Methods Forty female C57 mice in estrus were randomly assigned to four groups (n = 10 each): ultrasound-guided transvaginal mechanical injury, laparotomy mechanical injury, absolute ethanol, and laparotomy control. Fourteen days after surgery, mice were euthanized and uterine tissue was collected for histological evaluation. Hematoxylin–eosin staining was used to assess endometrial morphology and quantify gland density, while Masson trichrome staining was used to fibrosis. Early and late experimental complications were monitored throughout the study. Results The ultrasound-guided transvaginal mechanical injury, laparotomy mechanical injury, and absolute ethanol groups displayed characteristic IUA features, including disrupted endometrial architecture, reduced gland numbers, and significantly increased fibrotic areas compared with controls (P < 0.05). Among the intervention groups, the ultrasound-guided transvaginal group showed the lowest incidence of both early and late complications. Conclusion Ultrasound-guided transvaginal mechanical injury successfully established a mouse model of IUA. This approach avoids peritoneal and full-thickness uterine damage, is safe, produces few complications, and partially mimics key aspects of the mechanical injury associated with clinical IUA, providing a reliable platform for future pathogenesis and therapeutic studies.
Facial soft tissue thicknesses of Azerbaijan adult population: CT study
The aim of this study was to create a facial soft tissue thicknesses database of Azerbaijan adult population and find relation between FSTT and sex, age, body mass index (BMI). The study investigates the FSTT on 300 CT scans of living inviduals of three age groups: I, 18–25 years old; II, 26–45 years old; and III, 46 years and older. The CT images of the patients were divided into two groups according to BMI. The soft tissue thicknesses were measured at 20 landmarks, 10 along the midline and 10 bilateral. The mean, standard deviations, range, median for each anthropometric landmark were determined, and differences related to age, sex, BMI were calculated. This article presents the first database of facial soft tissues thicknesses of Transcaucasia populations.
Feasibility study of prospective gated tomosynthesis using a wide-angle carbon nanotube enabled, stationary digital chest tomosynthesis scanner: In-vivo evaluation in a porcine subject
Background Tomosynthesis offers a valuable alternative to computed tomography (CT) in longitudinal studies for dose reduction and applications where CT is not an option. This study aimed to demonstrate the performance of physiological gated tomosynthesis on the wide-angle-, carbon nanotube (CNT) x-ray based, stationary digital chest tomosynthesis system (s-DCT). Physiologic gating allows for image quality improvements by reducing motion blur and expands the potential applications. Methods Prospective physiological gating was implemented on the system and a porcine subject was imaged under various gating strategies. Respiratory-only, cardiac-only (breath-hold), and dual gated scans were acquired along with breath-hold-only and free-breathing scans to study independent and joint gating performance. Quantitative analysis was performed by measuring full-width-at-half-maximum (FWHM) from the derivative of line profiles across the edge of the heart wall and diaphragm. Results Dual gating had the highest image quality performance both qualitatively and quantitatively. No significant difference was observed between expiratory phase respiratory gating and breath hold, and during breath hold, cardiac gating significantly improved heart wall FWHM. All gated acquisitions performed better overall than free breathing. Conclusions This study successfully demonstrated physiologic gating on the second-generation s-DCT system with high-quality images and imaging times similar to predicted.
Characterizing malignant prognostic signatures in primary glioma based on single-cell and bulk transcriptome sequencing
Glioma, characterized by its highly invasive nature, presents significant challenges in prognosis and treatment resistance. The advent of single-cell RNA sequencing (scRNA-seq) has facilitated a more nuanced understanding of the cellular and molecular landscapes of glioma cells. In this study, conducting a comprehensive analysis of scRNA-seq and bulk RNA-seq data, employing Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) methods, we identified three malignant prognostic signatures: IGFBP2, MDK, and RARRES2. The predictive accuracy of this model was validated across both The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) cohorts. Additionally, we explored the correlation of these signatures with drug responsiveness and immune cell infiltration. Differential expression validation and functional analyses of RARRES2 were performed using external Gene Expression Omnibus (GEO) datasets and in-house samples. In both glioma and pan-cancer contexts, RARRES2 expression is significantly positively correlated with the infiltration of M2-like macrophages, NK cells, and CD8 + T cells. Given that RARRES2 receptors are predominantly found in myeloid and glioma cells, we hypothesize that RARRES2 may regulate tumor progression through autocrine pathways and influence macrophage recruitment and differentiation via paracrine pathways. Collectively, our findings provide valuable insights into potential novel prognostic markers for glioma, potentially enhancing the accuracy of prognostic predictions and serving as promising therapeutic targets.
Urban compactness and Land Use Efficiency in Indochina Capitals: A multi-method spatial assessment (2017–2024)
This study assesses land use efficiency (LUE) and urban spatial structure in three Indochina capitals, Hanoi, Vientiane, and Phnom Penh, during the period 2017–2024. By integrating the SDG 11.3.1 indicators with spatial agglomeration analysis (global Moran’s I) and urban form measurement (Polsby-Popper index), the study provides a multidimensional assessment of urban expansion patterns. The results indicate that land expansion outpaces population growth in all three cities (LUE > 1). Phnom Penh exhibited the highest LUE value (5.03), strong spatial agglomeration, and the most compact urban form. Hanoi recorded the lowest LUE (2.47), characterized by moderate compactness and weak spatial autocorrelation. In contrast, Vientiane displayed fragmented and largely random urban expansion, accompanied by a declining compactness index and no statistically significant agglomeration. These contrasting patterns reflect distinct urban development trajectories shaped by differences in planning regimes, land management practices, and investment orientations. The findings highlight the urgent need for spatially controlled and compact urban development strategies in rapidly growing cities, while also acknowledging data limitations. The proposed analytical framework is transferable and can be applied to other developing cities worldwide.
Pre-analytical variables affecting breast cancer biomarker expression: A controlled single-specimen study of fixation duration, cold ischemia time, and fixative preparation in a low-resource setting
Background Optimal pre-analytical management of breast tissue specimens, particularly formalin fixation, is essential for accurate immunohistochemical (IHC) biomarker assessment in invasive breast cancer. Although international guidelines suggest using 4% neutral buffered formalin with controlled fixation time, many laboratories in low-resource settings deviate from these standards. This study aimed to determine whether three pre-analytical variables — fixation duration, cold ischemia time, and fixative preparation (4% neutral buffered versus 4% non-buffered formaldehyde) — impact the preservation and evaluation of tissue biomarkers in invasive breast cancer. Methods We conducted an exploratory, proof-of-concept, experimental study using fresh mastectomy tissue from a 34-year-old patient with invasive ductal carcinoma (pT4, hormone receptor-positive, HER2-negative, Ki67 = 40%) who had not received neoadjuvant chemotherapy. Fifty microsamples (5–15 mm in length, approximately 1 mm in diameter) were obtained using a 14-gauge core needle biopsy device and divided into four cohorts: (1) 19 samples fixed in 4% neutral buffered formaldehyde for 0.5 to 144 hours; (2) 19 samples fixed in 4% non-buffered formaldehyde for 0.5 to 144 hours; (3) 6 samples with delayed fixation (0.5 to 8 hours) then fixed in neutral buffered formaldehyde for 10 hours; (4) 6 samples with delayed fixation (0.5 to 8 hours) then fixed in non-buffered formaldehyde for 10 hours. Hormone receptors (estrogen receptor-ER, progesterone receptor-PR) and Ki67 expression were evaluated by IHC using the Allred scoring system and current international recommendations. Results Fixative preparation had a statistically significant, yet small, impact on biomarker evaluation. The mean percentage of ER-positive cells was 96.89 ± 0.74% with neutral buffered formaldehyde compared to 94.32 ± 1.51% with non-buffered formaldehyde (p = 0.011). Similar trends were seen for PR (94.89 ± 0.95% vs. 92.63 ± 1.67%, p = 0.027) and staining intensity. However, Allred scores remained unchanged. Fixation duration was significantly correlated with biomarker expression (Spearman ρ between −0.60 and −0.83, p ≤ 0.007), with stable values from 0.5 to 48 h and a significant decline beyond 72 h (one-way ANOVA across fixation windows: all p < 0.01). Cold ischemia time was strongly correlated with decreased biomarker expression regardless of fixative preparation. Hormone receptor expression and Ki67 remained stable with minimal Allred score changes for up to 2 hours of cold ischemia, but significantly decreased after 2 hours, with scores decreasing in proportion to the duration of ischemia (p < 0.05). Conclusions In this single-specimen controlled experiment, non-buffered formaldehyde preserved tissue biomarkers with small but measurable differences relative to neutral buffered formaldehyde for IHC analysis, although these findings require validation in multi-patient studies. Consistent with current guidelines, a cold ischemia time of up to 1 hour maintained adequate biomarker preservation. These preliminary results may be relevant for pathology laboratories in resource-limited settings where neutral buffered formalin may not be easily accessible, and warrant further investigation across diverse tumor types and baseline expression levels, particularly tumors with ER-low-positive (1–10%) or heterogeneous expression.
Association between the triglyceride-glycated hemoglobin index and diabetes risk among patients with Non-alcoholic fatty liver disease: A longitudinal cohort study
Background Emerging evidence suggests that the triglyceride-glycated hemoglobin index (TyH-i) may be a novel predictor of type 2 diabetes (T2D) risk. However, in patients diagnosed with NAFLD, the precise link between TyH-i and T2D is still not well elucidated. Currently, the literature lacks sufficient research on how this index affects the incidence of diabetes in the NAFLD population, highlighting a significant gap in our understanding of the interactions between these metabolic conditions. This research seeks to evaluate the association between the TyH-i and diabetes risk, as well as to investigate its predictive capability in patients with NAFLD concerning the triglyceride-glucose index (TyG-i). Methods Based on the data of 2741 participants who had no diabetes at baseline and suffered from NAFLD, leveraging a nationwide retrospective cohort, we derived the TyH-i as the natural logarithm of [HbA1c (%) × fasting TG (mg/dL) ÷ 2].To assess the relationship between TyH-i and the risk of developing T2D, a Cox proportional hazards regression model was used to estimate the hazard ratio (HR) and 95% confidence interval (CI). Furthermore, the nonlinear correlations between them were also investigated using the restricted cubic spline model. We gauged the relative discriminatory capacity of TyH-i versus TyG-i by constructing time-dependent receiver-operating characteristic (ROC) plots and comparing the corresponding areas under the curve (AUC) with their 95% CI. Results In a median follow-up duration of 5.21 years, incident diabetes was recorded in 223 individuals. The multivariable-adjusted analysis revealed that each unit increase in TyH-i corresponds to a 46% elevation in diabetes risk (HR: 1.75; 95%CI: 1.28-2.41; P = 0.0005). Additionally, a U-shaped relationship was identified between TyH-i levels and the occurrence of type 2 diabetes. Specifically, TyH-i values below 4.95 displayed a notable inverse association with the risk of type 2 diabetes (HR: 0.20, 95%CI: 0.05-0.78, P = 0.0206). Conversely, an elevation in TyH-i levels beyond this threshold was linked to a higher risk of developing type 2 diabetes (HR: 2.01, 95% CI: 1.45-2.39, P < 0.0001). Furthermore, the findings indicate that the Youden Index for both TyH-i and TyG-i is nearly identical, implying that TyH-i may serve as a valid marker for diabetes diagnosis. Conclusion Among the patient population suffering from NAFLD, the initial TyH-i level demonstrated a U-shaped correlation with the emergence of type 2 diabetes. This indicated inflection point can act as a practical clinical threshold, allowing differentiation between individuals at low and high risk. The findings imply that keeping TyH-i close to this inflection may play a role in mitigating the progression to diabetes among NAFLD patients.
Why are some children under 24 months still undernourished in urban and peri-urban Vientiane? A mixed-methods study
Child undernutrition remains a significant public health challenge in many low and middle-income countries (LMICs), including Lao PDR, where high levels persist even in urban areas with generally available and accessible food. This study aimed to explore factors underlying the persistently high rates of undernutrition among young children in urban (Saysetha) and peri-urban (Pakgneum) districts of the Vientiane Capital in Lao PDR. A cross-sectional survey employed a sequential explanatory mixed-methods approach, combining a structured questionnaire of 333 mother–child pairs for quantitative analysis with semi-structured interviews of 47 caregivers for qualitative insights. The prevalence of malnutrition among children under 24 months in Vientiane Capital was 27.3% for stunting, 4.2% for wasting, 14.4% for underweight, and 5.11% for overweight. Multiple logistic regression was applied to identify factors associated with malnutrition, while qualitative data were thematically analyzed. The principal findings revealed that, beyond food access, the quality of caregiving and, critically, caregivers’ capacity to translate nutrition knowledge into effective practices distinguished well-nourished from undernourished children. Caregivers of better-nourished children obtained health and nutrition information from diverse sources, whereas those of undernourished children relied mainly on health services. In conclusion, strengthening practical nutrition communication in various methods and channels, such as through videos and demonstrations, and enhancing caregivers’ ability to apply nutritional knowledge, are central to improving child nutritional outcomes in urban and peri-urban settings in Lao PDR.
Structural consistency in AI governance: A PMC index assessment with evidence from China’s central-level policies
The structural coherence of policy design has become an increasingly important issue in artificial intelligence (AI) governance. This study evaluates the structural consistency of China’s central-level AI policies issued between 2016 and 2025 (n = 54). It combines text mining to identify high-frequency policy terms and semantic co-occurrence patterns with a Policy Modeling Consistency (PMC) index framework comprising nine primary and forty-three secondary indicators. Five representative policies are then selected for detailed quantitative evaluation and visual comparison. The results show that China’s AI policy system is generally well structured, but still exhibits notable weaknesses in temporal planning, intergovernmental coordination, and incentive design. In particular, long-term policy supply remains limited, vertical coordination mechanisms are insufficiently institutionalized, and policy instruments are unevenly configured across key support dimensions. These findings suggest that future policy improvement should focus on strengthening medium- and long-term planning, enhancing coordination across governance levels, and improving the integrated design of policy instruments. Methodologically, the study demonstrates a reproducible analytical framework linking text analysis, indicator construction, quantitative evaluation, and visualization. It contributes to the literature by moving from thematic description toward structural assessment in the study of AI governance.
Comparative impact of insect growth regulators on mortality and development of Amrasca biguttula (Hemiptera: Cicadellidae)
The two-spot cotton leafhopper, Amrasca biguttula (Ishida) (Hemiptera: Cicadellidae), recently detected in the United States, represents an emerging threat to cotton, vegetable, and ornamental crops. Insect growth regulators (IGRs) are considered reduced-risk insecticides. Despite their availability to growers and effectiveness on several piercing and sucking insects, the lethal effects of IGRs on the development of A. biguttula remain poorly understood. Thus, the objective of this study was to determine the effects of common IGRs on various stages of A. biguttula . We evaluated four IGRs: pyriproxyfen, novaluron, azadirachtin, and buprofezin applied at field-recommended rates, alone or combined with nonionic and organosilicone adjuvants, on survival, molting disruption (exuviae production), and longevity of early (1 st -2 nd ), intermediate (3 rd -4 th ), and late (5 th ) nymphal instars, as well as adults using leaf dip and adaxial leaf smear bioassays. All IGRs induced significant, stage-dependent lethal effects. Mortality of 1 st –2 nd instars reached over 90% with buprofezin and novaluron, and molting inhibition reached up to 55%, indicating strong effects of the tested insecticides. The chitin biosynthesis inhibitors buprofezin and novaluron caused rapid mortality, strong molting inhibition, and reduced longevity, particularly in early and intermediate instars. Pyriproxyfen and azadirachtin elicited weaker, delayed responses, with limited effects on late instars and adults. Although adding adjuvants slightly enhanced efficacy, their overall impact was marginal. These findings demonstrate that IGRs can profoundly disrupt A. biguttula population development through interference with insect growth and metamorphosis, supporting their use as selective and sustainable tools in integrated pest management programs targeting this invasive leafhopper.
A system-wide snapshot: A multi-campus survey of open source contributors at the University of California
Academic open source contributors face a wide array of challenges, making it difficult for universities, support staff, and funders to determine which needs they should prioritize. To help address this problem, the University of California (UC) Open Source Program Office (OSPO) Network conducted a multi-campus survey of open source contributors and aspiring contributors. The goals of this survey were two-fold. First, we aimed to understand the needs of university open source contributors, so that we might design programs to address those needs. Second, we sought to characterize open source activity on campus, in order to assess the value of an OSPO. We received 294 valid responses from students, faculty, researchers, and staff. 93% of students and 92% of researchers report that open source software is important for their work. 58% of experienced open source contributors have served as project maintainers, indicating that a large number of university affiliates not only use open source software, they also build and maintain it. The most common challenge is lack of time, particularly time for writing documentation. Regarding opportunities for support, respondents strongly prioritized access to robust computing environments and dedicated grants for sustainability. Finally, comments revealed that institutional norms and priorities can sometimes impede contribution. The survey findings reveal diverse needs across contributor groups, with resources, infrastructure, and culture all playing a role. At the same time, the abundance of maintainers, the prevalence of time and funding-related challenges, and the comments regarding maintenance all underscore a critical need for support for open source sustainability. We conclude by recommending actionable strategies universities can adopt to incorporate sustainability into their open source initiatives. We expect the findings to extend beyond OSPOs to benefit scholars of open source and research software more broadly, providing empirical insights into open source participation and sustainability in academic contexts.
Progress and gaps in childhood immunization among two-year-olds in Ghana (1993–2022): A trend and equity analysis
Background Immunization remains a cornerstone of child survival and population health. While Ghana has made significant strides in vaccine delivery over the past three decades, gaps in equitable and universal coverage persist. This study examined the progress and gaps in full immunization coverage among two-year-olds in Ghana from 1993 to 2022. Methods We used data from the Ghana Demographic Health Survey rounds conducted between 1993 and 2022 to examine full immunization coverage among two-year-olds. Disaggregated data were accessed via the WHO Health Equity Assessment Toolkit (HEAT). Inequality was assessed across six dimensions: maternal age, household wealth status, maternal education, place of residence, child’s sex, and subnational region. Various inequality measures, including difference, ratio, population-attributable risk, and population-attributable fraction were calculated. Results Full immunization coverage improved from 55.5% in 1993 to 71.5% in 2022, peaking at 77.6% in 2008. Inequality analysis showed reduced socioeconomic disparities related to wealth, maternal education, and urban-rural residence by 2022. However, two key inequalities persisted: maternal age-related inequality significantly widened to 26.8 percentage-point gap in 2022 between mothers aged 20–49 and adolescent mothers (15–19 years), and substantial regional disparities remained, with a significant 39.8 percentage-point gap between the best- and worst-performing regions. Conclusion Despite the gains in full immunization coverage in Ghana over the past three decades, significant inequalities persist, particularly among adolescent mothers and populations in disadvantaged regions. Strengthening equity-focused immunization strategies is essential to achieving universal coverage.
Focusing on legal cases: Automatic classification of legal documents with sentence embeddings and deep learning models
The justice system is indispensable to any society as it enforces the rule of law, safeguards fundamental rights, and ensures the equitable resolution of disputes through structured legal frameworks. Artificial Intelligence (AI) has significantly advanced the legal and justice system by automating time-intensive tasks such as document review and contract analysis, thereby enhancing efficiency and reducing human error. Additionally, AI-powered predictive analytics and decision support systems have improved access to justice by providing data-driven insights, enabling faster case resolution, and ensuring more consistent application of the law. Legal document classification using AI techniques is imperative as it enables efficient organization, retrieval, and analysis of vast volumes of legal texts, enhancing accuracy, reducing manual effort, and facilitating faster decision-making in legal processes. In this research study, the main aim is to classify legal text documents using Machine Learning (ML) and state-of-the-art Deep Learning (DL) algorithms. Using a real-world dataset that consists of thousands of legal documents having complex language related to legal cases poses a challenging natural language understanding task by applying various textual features, deep features, and advanced sentence embeddings. The results reveal that the ensemble learning model of Extremely Randomized Trees shows better results with 89% accuracy, as it aggregates the results of multiple decorrelated decision trees to enhance predictive accuracy and control over-fitting. However, the best results of 96% are achieved with sentence embeddings. Sentence embeddings with Long Short-Term Memory (LSTM) networks are highly effective in Natural Language Processing (NLP) due to their ability to capture complex semantic and syntactic information within text.
Construction of a public health emergency information system framework: A case study of Zhuhai city, China
Background A public health emergency information system serves as a critical tool for collecting and analyzing data from sudden public health events, thereby providing a scientific basis for governmental decision-making. However, research on the systematic construction of such information system frameworks within China’s public health infrastructure is lacking. Objective Taking Zhuhai city as a case study, this study aims to construct a comprehensive public health emergency information system framework applicable to public health departments at the municipal, county, and street/township levels. Methods First, through a literature review and expert group discussion, the preliminary framework of system indicators is determined. Second, through two rounds of the Delphi method, 41 experts are invited to qualitatively select the system framework indicators, with the aim of obtaining consensus among experts. Finally, the system is improved through application, feedback, and redesign. Results After two rounds of consultation, the final system at the city and county levels consists of 5 first-level indicator modules and 21 second-level indicator modules, whereas the system at the city, county, and street/township levels consists of 4 first-level indicator modules and 17 second-level indicator modules. Most of the indicators in the “emergency preparedness” and “emergency response” modules are considered important and should be retained as they can play a role in collecting and analysing information on infectious disease outbreaks through practical applications. Conclusion The public health emergency information system constructed in this study can be applied to public health departments such as disease prevention and control centres. Promotion can improve the efficiency of handling infectious disease outbreaks and provide a scientific basis for decision-making analysis.
A U-Net model for epidermal segmentation in optical coherence tomography images of actinic keratosis
Actinic keratosis (AK) is a pre-cancerous skin lesion typically caused by excessive exposure to ultraviolet light. Optical coherence tomography (OCT) can provide sub-surface information relevant for lesion classification, but manual analysis of images is time-consuming and subject to inter-observer variability; automated segmentation based on deep learning models can provide faster and more consistent results. However, structural changes, such as thickening and hyperkeratosis, complicate epidermal segmentation tasks. For this reason, we aimed to develop and optimize a U-Net model for the automated epidermal segmentation of AK lesions in OCT images, combining both accuracy and computational efficiency. Multiple configurations were evaluated by varying hyperparameters, including image sizes (256 × 256, 512 × 512, 1024 × 1024, and 464 × 1356 pixels), batch sizes (2, 4, 8, and 16), and training durations (50, 100, and 150 epochs). Model performance was evaluated quantitatively using several metrics including the Dice coefficient and Jaccard index, comparing automated epidermal segmentations against expert annotations. The optimal configuration, with an image resolution of 256 × 256 pixels and a batch size of 2 over 50 epochs, had a Dice score of 0.86 and a Jaccard index of 0.76. Higher resolutions and longer training increased computation without significantly enhancing the accuracy values and sometimes caused overfitting. These findings show that a simple U-Net architecture could achieve efficient and accurate epidermal segmentation in AK lesions, provided it is fine-tuned to enhance performance.
From cheap entertainment to expensive pleasure: Cinema-going habits and motivations of young people in Türkiye
This study examines the cinema-going habits and motivations of university students in Türkiye from a class-based perspective. This study addresses a gap in the literature by examining cinema-going motivations in Türkiye through a class-based perspective. Participants (N = 1183) were recruited using a convenience sampling method from 12 universities across the NUTS 1 regions of Türkiye. This quantitative study employed three measurement tools to assess participants’ sociodemographic and socioeconomic characteristics, cinema-going habits, and motivations. The findings suggest that socialization was the most prominent factor associated with cinema-going motivation among participants. Cinema was not widely perceived by participants as a means of coping with loneliness, but rather as an activity associated with social interaction. The technical capabilities of cinema also emerged as an important factor associated with cinema attendance. While individuals reported different motivations, class-related differences were associated with variations in cinema-going behaviors within this sample. Participants with higher socioeconomic indicators tended to report higher levels of cinema attendance, whereas those with lower socioeconomic indicators reported lower levels of attendance. Given the convenience sampling design and the student-only composition of the sample, the findings should be interpreted with caution and should not be generalized beyond the study population.
Mapping the landscape of psychological literature on threat from 1961 to 2023 through structural topic modeling
The past decades have generated a substantial volume of psychological literature on threat. However, the absence of systematic cross-field synthesis has resulted in limited understanding of major research domains and relationships between different lines of threat research. We analyzed 51,903 psychological publications on threat retrieved from APA PsycInfo, Scopus, and Web of Science Core Collection that were published between 1961 and 2023. We conducted structural topic modeling on publication titles and abstracts to identify key research topics, and network analysis on the resulting topics to map the thematic structure of the literature. 25 topics emerged, organized into four thematic areas through exploratory graph analysis: 1) threat processing mechanisms, 2) health and clinical threats, 3) social psychological threats, and 4) collective threats. Network analysis revealed differential connectivity patterns within and between thematic areas. Areas showed limited connectivity with each other and no area emerged as a central hub, suggesting gaps in cross-domain integration. Topic prevalence trends revealed diversification in research interest over time, together with responsiveness to broader developments within psychology and evolving societal concerns. Notably, mechanism-focused research declined over the past decade while event-driven research on specific threats increased, indicating reactive rather than theory-driven investigation. These findings provide insights into the landscape of psychological literature on threat and reveal critical gaps in current examinations alongside strategic opportunities to advance cross-field integration.
The effect of yttrium addition on the ratcheting behavior of magnesium
This study provides an in-depth investigation into the influence of yttrium (Y) addition on the ratcheting behavior of magnesium (Mg) alloys under asymmetric cyclic loading. Although pure Mg exhibits higher strength under quasi-static tension, the Mg–Y alloy demonstrates markedly superior resistance to ratcheting during cyclic deformation. The Mg–Y alloy shows a substantially reduced ratcheting strain accumulation rate accompanied by pronounced cyclic hardening, ultimately leading to a significant improvement in fatigue life. Transmission electron microscopy (TEM) reveals that the extensive activation of ⟨c + a⟩ dislocations and the resulting high dislocation density in the Mg–Y alloy play a pivotal role in promoting homogeneous plastic deformation and cyclic hardening, thereby effectively suppressing the accumulation of ratcheting strain. This work provides new insights for the development of high-performance, fatigue resistant Mg alloys.
Genetic association and computational analysis of CYP2R1 gene polymorphisms rs2060793 and rs12794714 with vitamin D deficiency and acute myocardial infarction in the Bangladeshi population: A case control study
Acute myocardial infarction (AMI) remains a leading cause of cardiovascular morbidity and mortality worldwide. Emerging evidence highlights vitamin D as a critical determinant of cardiovascular health. The CYP2R1 gene encodes the key 25-hydroxylase enzyme responsible for converting vitamin D to its principal circulating metabolite, 25-hydroxyvitamin D. However, the influence of CYP2R1 polymorphisms on AMI susceptibility, particularly within South Asian populations, has not been well characterized. This study investigates the association of two CYP2R1 variants, rs2060793 and rs12794714, with AMI risk and their relationship with serum vitamin D levels in a Bangladeshi cohort. A total of 502 participants comprising 251 AMI patients and 251 age- and sex-matched controls were analyzed. Genomic DNA was extracted and genotyped using PCR-RFLP, while serum 25-hydroxyvitamin D 3 levels were quantified by HPLC. AMI patients exhibited markedly lower vitamin D concentrations (23.92 ± 0.94 ng/mL) than controls (30.3 ± 0.86 ng/mL; p < 0.0001). Genotypic analysis revealed a significant association between rs2060793 and AMI risk: the TC (OR = 2.49, 95% CI: 1.34–4.63) and CC (OR = 2.59, 95% CI: 1.37–4.90) genotypes conferred increased susceptibility compared to the TT genotype ( p = 0.0064). The dominant model (TC + CC vs. TT) further confirmed this relationship (OR = 2.53, 95% CI: 1.39–4.61, p = 0.0016). In contrast, rs12794714 showed no significant association with AMI in this population. Stratified analysis indicated that rs2060793 was significantly linked to AMI in males but not females, while both variants were associated with increased risk in individuals aged ≤60 years, but not in those >60 years. Bioinformatic and molecular docking analyses (RegulomeDB, JASPAR, HADDOCK 2.4, DNAproDB) further demonstrated potential regulatory effects of these variants on CYP2R1 function. Collectively, our findings reveal a novel association between CYP2R1 rs2060793 and vitamin D deficiency with AMI risk in the Bangladeshi population, underscoring the interplay of genetic and metabolic determinants in the molecular pathogenesis of AMI.
Topological data analysis for predicting disease outbreaks in humanitarian settings: A machine learning approach
Background Humanitarian settings are highly vulnerable to infectious disease outbreaks because displacement, crowding, disruption of health services, insecurity, and inadequate water and sanitation often interact in ways that are difficult to capture with conventional prediction models. There is a need for forecasting approaches that can integrate heterogeneous data sources and better represent complex system structure. Methods We developed and evaluated a machine-learning framework incorporating topological data analysis to predict cholera and measles surge events (binary indicators per LGA-week) across 97 Local Government Areas in Nigeria between 2018 and 2023. These 97 LGAs represent a selected high-burden subset (12.5%) of Nigeria’s 774 LGAs with sufficient surveillance data. Weekly district-level predictors included climate, conflict, displacement, health-system, and socioeconomic variables. Persistent homology was used to derive topological summaries from multivariate risk profiles, and these were combined with selected raw predictors in gradient-boosting models. Outcomes were defined using surveillance-based outbreak thresholds with a 4-week prediction horizon. Model performance was assessed using temporally ordered hold-out validation, with evaluation of discrimination, calibration, and incremental value over baseline models. Results The topological models achieved ROC-AUC of 0.78 (95% CI: 0.74–0.82) for cholera and 0.81 (95% CI: 0.77–0.85) for measles, representing modest improvements of 0.08–0.12 over models using only conventional predictors. At the optimal decision threshold determined using Youden’s index on validation data, sensitivity was 0.72 (range across folds: 0.68–0.76) and specificity was 0.82 (range: 0.79–0.85) for cholera, with false alert rates varying from 2.8–3.6 per LGA per year across temporal folds. Topological features contributed 35% of predictive importance. Calibration slopes were 0.94 (cholera) and 0.97 (measles). Conclusions Topological feature representations provide a modest but meaningful complementary approach for outbreak prediction in complex humanitarian environments. Their value appears to lie in summarizing higher-order structure across multiple interacting risk domains, rather than replacing established epidemiologic indicators. However, routine deployment requires prospective validation and context-specific threshold tuning. Further external validation, operational threshold analysis, and prospective testing are needed before routine deployment in public-health early warning systems.