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Experimental and numerical investigation of the mechanical performance of natural–synthetic hybrid composite laminates
Fiber-reinforced polymer composites have attracted considerable attention in engineering applications owing to their high strength-to-weight ratio, corrosion resistance, and tailorable mechanical properties. Hybridization provides an effective approach for combining the advantageous characteristics of different reinforcement materials to achieve improved overall laminate performance. In the present study, three six-ply hybrid composite laminates, namely J2/C2/J2, J2/G2/J2, and C2/G2/C2, with a [0°/90°] stacking sequence and a relatively high fiber content obtained through controlled hand lay-up fabrication, were fabricated using the hand lay-up technique. Test specimens were prepared according to the relevant ASTM standards and subjected to tensile, compressive, and flexural testing. In addition, finite element simulations were performed using ANSYS Mechanical APDL 2020 R1 to validate the experimental observations. The experimental results demonstrated that natural–synthetic fiber hybridization significantly enhanced the mechanical performance of the investigated laminates. The incorporation of carbon fiber into jute-based laminates resulted in an average strength improvement of approximately 88%, whereas glass-fiber incorporation produced an improvement of approximately 55%. Among the investigated configurations, the C2/G2/C2 laminate exhibited the highest tensile strength (approximately 140 MPa), compressive strength (approximately 39 MPa), and flexural strength (approximately 109 MPa). The numerical predictions showed good agreement with the experimental measurements, with maximum deviations below 1.2%. The results confirm the effectiveness of hybridization in enhancing the mechanical response of composite laminates and identify the C2/G2/C2 configuration as the most promising among the investigated laminate systems. These findings demonstrate the influence of natural–synthetic fiber hybridization on laminate mechanical performance and provide useful information for the design of lightweight composite structural components. Further studies involving pressure-vessel fabrication and internal-pressure testing are required before pressure-vessel applications can be established.
A novel dynamic Optuna hybrid Harris Hawks Optimization approach for classification of CAD
Coronary Artery Disease (CAD) is a leading cause of mortality worldwide and is primarily associated with atherosclerotic plaque formation, resulting in coronary artery stenosis. The accurate prediction of CAD using deep learning models is often constrained by the limitations of conventional optimization techniques, including premature convergence and limited adaptability of the models. To address these challenges, this study proposes a Dynamic Optuna Hybrid Harris Hawks Optimization (Dynamic Optuna H-HHO) framework to enhance the performance of deep learning–based CAD prediction models. The proposed approach integrates dynamic parameter adjustment, adaptive escape energy mechanisms, and Optuna-based hyperparameter tuning. The framework was applied to optimize several deep-learning classifiers, including ResNet-50, VGG-16, InceptionV3, and MobileNet, using a coronary artery stenosis dataset. The performance was evaluated through a comparative analysis with models optimized using the conventional Hybrid Harris Hawks Optimization (H-HHO) algorithm. The experimental results indicate that the proposed Dynamic Optuna H-HHO framework consistently improves the predictive accuracy across all evaluated models. InceptionV3 achieved the highest accuracy of 97.9%, followed by MobileNet with 97.6%, compared with the maximum accuracy of 82.46% obtained using traditional HHO-based optimization. By combining adaptive optimization strategies with automated hyperparameter tuning, the proposed framework provides a robust and scalable solution for improving the accuracy of coronary artery disease prediction.
Pacing strategy patterns and performance outcomes in marathon running: A large-scale analysis of split time data
Although pacing strategy is widely recognized as critical for marathon performance, the actual distribution of pacing patterns among recreational runners and their associations with finish time across demographic groups have not been systematically characterized in large samples. This study analyzed split time data (i.e., cumulative elapsed times recorded at intermediate checkpoints including 5K, 10K, 15K, 20K, half-marathon, 25K, 30K, 35K, and 40K) from 78,912 finishers of the Boston Marathon (2015–2017), characterizing pacing profiles using the ratio of first-half to second-half completion times and segment-by-segment pace variations across these nine timing points. Cluster analysis was employed to identify distinct pacing patterns, and the relationships between pacing strategy and finish time were examined across age groups, sex, and performance levels using analysis of variance and multiple regression analysis. Four distinct pacing patterns were identified: even-pacing (maintaining consistent pace throughout; 47.9%), mild positive-splitting (controlled late-race slowing; 31.9%), strong positive-splitting (pronounced late-race slowing; 12.2%), and variable-pacing (highly inconsistent pace fluctuations; 8.0%). Even-pacing was consistently associated with the fastest finish times across age and performance strata ( p < 0.001), with mean finish times markedly lower than those observed for strong positive-splitting (217 vs. 276 minutes). Variable-pacing was associated with the highest incidence of “hitting the wall” (pace decline >20% in the final segment; 85.4%). High-performance runners (<3:00 finish time) demonstrated significantly higher rates of even-pacing (82.0%) compared to casual participants (19.6%). Age and sex significantly influenced pacing strategy selection ( p < 0.001). These findings may help inform marathon pacing guidance by indicating that age, sex, and performance level should be considered when interpreting pacing patterns and developing individualized race plans.
N-acetyl-L-leucine normalizes Transcription Factor EB activity by stereospecific bidirectional modulation in a HeLa cell model of Niemann-Pick disease type C
Levacetylleucine (Aqneursa™), an acetylated derivative and pro-drug of L-leucine, is the only FDA-approved monotherapy for Niemann–Pick disease type C (NPC). Its acetyl group enables transport via monocarboxylate transporters, supporting blood–brain barrier penetration and efficient cellular uptake. Inside cells, levacetylleucine is metabolised by acylases, generating elevated levels of L-leucine that enhance mitochondrial bioenergetics and is thought to ameliorate lysosomal dysfunction indirectly. Here, we describe a direct effect of levacetylleucine on lysosomal regulation through modulation of TFEB, the master transcription factor for lysosomal and autophagy genes. Levacetylleucine rapidly alters TFEB translocation between the cytoplasm and the nucleus in a biphasic, homeostasis-restoring manner. In wild-type HeLa cells, levacetylleucine promotes TFEB activation and nuclear localisation. However, in NPC1 disease models, where we show that TFEB is over-activated and enriched in the nucleus due to lysosomal stress, levacetylleucine reduces nuclear TFEB and restores a more normal cytoplasmic-to-nuclear balance. These effects occur at clinically relevant concentrations associated with lysosomal storage reduction. The effects of the drug are stereospecific: while the L-enantiomer is active, the D-enantiomer and racemate show no effect, revealing the antagonistic properties of the D-enantiomer. This bidirectional normalisation of TFEB activity highlights a direct mechanism through which levacetylleucine modulates lysosomal and autophagic pathways in the HeLa cell model, giving mechanistic insight into its therapeutic potential in NPC, and also across diverse neurological and neurodevelopmental disorders.
Positive mental attitude and depressive symptoms among medical students and residents: a cross-sectional study
Background Depression is highly prevalent among medical trainees and negatively affects academic performance, empathy, and patient care. While healthy lifestyle behaviors are known to support mental health, the contribution of cognitive-emotional orientations, such as Positive Mental Attitude (PMA), remains underexplored. This study examined the associations between health behaviors, PMA, and depressive symptoms among medical students and residents in Ecuador. Methods A cross-sectional survey was conducted in 2025 among 700 medical students and postgraduate residents at a private university. Participants completed the Patient Health Questionnaire-9 (PHQ-9) and the Health Behavior Inventory (HBI). Logistic regression models estimated associations between total HBI scores, its four subscales, and screening positive for clinically significant depressive symptoms (PHQ-9 ≥ 10), adjusting for sociodemographic and academic variables. Results Nearly half of participants screened positive on the PHQ-9 (score ≥10) (47.7%). Higher total HBI scores were associated with lower odds of depressive symptoms (AOR = 0.94, 95% CI: 0.93–0.95). When HBI subscales were analyzed simultaneously, only PMA remained independently associated with lower odds of depressive symptoms (AOR = 0.79, 95% CI: 0.74–0.84). Female gender, younger age, and the presence of comorbidities were also associated with higher odds of depressive symptoms. Conclusions PMA emerged as the behavioral domain most strongly associated with lower odds of clinically significant depressive symptoms in medical trainees, after adjustment for other lifestyle domains. These findings underscore the relevance of cognitive-emotional orientations in behavioral medicine and support the inclusion of positive psychology approaches in medical education to promote well-being.
Atlas of Brain Aging Derived from Cortical Surface Area Changes
During brain aging, atrophy of cortical surface area exhibits region-specific patterns that extend beyond classical anatomical divisions. However, a specialized brain atlas of healthy brain aging has not yet been developed. We propose a two-stage spatially regularized non-negative matrix factorization (NMF) strategy to construct a brain aging atlas. In the first stage, we extract initial morphological covariance features from 1,066 cortical surface area scans of 436 (196 males, 240 females) cognitively normal adults (aged 51–96) from the ADNI cohort using standard NMF to ensure sparsity and biological interpretability. In the second stage, we perform refined smoothing on the manifold structure to rectify boundary noise. This strategy enhances the spatial continuity of the atlas while preserving data-driven patterns. Using this atlas, we identified three distinct cortical atrophy patterns: frontal–temporal–occipital dominant (FTOD), parietal slow-changing (PSC), and Diffuse patterns. Our findings revealed that: (1) Cortical aging exhibits pronounced spatiotemporal heterogeneity, with the frontal, temporal, and occipital cortices showing sensitivity to age-related changes. Significant interhemispheric differences were observed in atrophy rates, with no sex-related effects. (2) The right hemisphere exhibited faster atrophy than the left, and all atrophy patterns showed limited interhemispheric spatial overlap (Dice = 0.653), indicating pronounced hemispheric asymmetry in cortical surface area aging. (3) Different atrophy patterns demonstrate pattern-specific associations with cognitive performance: higher relative preservation of left-hemispheric FTOD pattern is associated with less subjective cognitive decline, whereas greater relative preservation of Diffuse and PSC patterns correlates with higher subjective cognitive decline. Significance statement Using a two-stage spatially regularized NMF (ST-NMF) strategy, we constructed the first systematic cortical surface area (SA) aging atlas for healthy middle-aged and older adults. Our atlas reveals three distinct cortical atrophy patterns: frontal-temporal-occipital dominant, parietal slow-changing, and Diffuse patterns. These patterns exhibit spatiotemporal heterogeneity, hemispheric asymmetry, and differential associations with cognitive domains, providing a novel framework for understanding cortical aging.
Malnutrition in maintenance hemodialysis: Prevalence, patterns, and clinical implications in Bangladesh
Malnutrition is a major clinical challenge among individuals receiving maintenance hemodialysis (HD), contributing to increased complications and reduced survival. The burden is particularly pronounced in resource-limited settings, where gaps in nutritional assessment and management persist. This cross-sectional study evaluated the prevalence and determinants of malnutrition among 181 HD patients from four districts in Bangladesh. Based on the International Society of Renal Nutrition and Metabolism (ISRNM) criteria, 42.5% of patients were malnourished, including 10.5% with severe malnutrition, while the Malnutrition Inflammation Score (MIS) identified malnutrition in 72.9% of participants. Patients who reused dialyzers more than twice had significantly higher odds of malnutrition [AOR = 5.8; 95% CI: 1.2–28.7]. Age, body mass index (BMI), and diabetes mellitus were also independently associated with poor nutritional status. These findings underscore the need for strengthened nutritional assessment and early identification of at-risk patients in HD care. Implementing validated diagnostic tools, such as those used in this study, may help reduce disease burden and improve survival outcomes in resource-constrained dialysis settings.
Between neuroaffirmation and access: Parents’ experiences of autism caregiving in England
Despite growing recognition of the challenges faced by families of autistic children in the United Kingdom, existing research has largely focused on parental stress and individual burden, with comparatively limited qualitative attention to how caregiving demands, service systems, and sociocultural contexts interact to shape family quality of life. Addressing this gap, the present study explored caregivers lived experiences of raising autistic children in England, examining how caregiving demands, access to support, and adaptive responses operate within broader family and systemic contexts to influence family quality of life. Semi-structured interviews were conducted with 12 caregivers of autistic children from diverse ethnic backgrounds, including both White British and ethnic minority backgrounds families, and analysed using reflexive thematic analysis. Six interrelated themes were identified that characterised families’ experiences: (1) The 24/7 Care Ecology; (2) Culture, Religion, and Gender as Contextual Amplifiers (3) Building (and lacking) the Social Support Architecture; (4) Fighting the System(s); (5) Adaptive Resilience and Neuroaffirmation; and (6) Family Quality of Life Consequences. Findings illustrate how intensive and ongoing caregiving demands intersect with fragmented service systems and sociocultural expectations, particularly where cultural, religious, and gendered norms shaped caregiving roles and help-seeking. Notably, neuroaffirmation emerged as a protective and identity-affirming response for some families, reframing caregiving beyond deficit-oriented narratives and contributing to resilience within structurally constrained environments. These findings highlight the need for more accessible, coordinated, and family-centred autism support services that recognise the diverse contexts in which caregiving takes place.
Gene expression profiling of recipient immune cells induced by 7 × 19 CAR-T cell dosing in a syngeneic mouse model
Chimeric antigen receptor (CAR)-T cell therapy is effective for hematologic malignancies; however, the response of solid tumors is limited because of the immunosuppressive tumor microenvironment, antigen heterogeneity, and lack of persistence of transferred T cells. To overcome these challenges, CAR-T cells expressing interleukin-7 and CC chemokine ligand 19 (7 × 19 CAR-T) were generated to achieve potent antitumor efficacy through the recruitment and proliferation of CAR-T cells and endogenous immune cells. To elucidate the underlying mechanism of 7 × 19 CAR-T cells against solid tumors, we analyzed the cellular composition and gene expression profiles of host immune cells following CAR-T cell infusion in a murine solid tumor model. Antihuman CD20 7 × 19 CAR-T cells were prepared using Thy 1.1 congenic mice and administered to C57BL/6N mice bearing subcutaneous MC38 tumors expressing human CD20. The tumors were harvested 4 days postinfusion to capture early immune responses before overt tumor regression. CD90.1- recipient immune cells were subjected to flow cytometry analysis, and transcriptomic changes were determined using AmpliSeq and single-cell RNA seq. An increase in recipient CD8 + T cells and macrophages was observed in the tumor of mice treated with 7 × 19 CAR-T cells, but not with conventional CAR-T. The expression of chemokines and genes associated with the inflammatory pathway was upregulated only in recipient immune cells of the 7 × 19 CAR-T-treated mice. Single-cell RNA-seq analysis revealed upregulation of pro-inflammatory genes and chemokines in the dendritic cell and monocyte/macrophage populations. These results indicate that 7 × 19 CAR-T cells initiate the early recruitment and activation of host immune cells, which contributes to their superior antitumor activity compared with conventional CAR-T cells.
Governing digital misinformation: A computational analysis of platform intervention using a three-party evolutionary game model
The proliferation of misinformation on short video platforms poses a significant challenge to digital governance and public trust. While platforms are central actors, their strategic role in rumor governance remains underexplored. This study introduces a tripartite evolutionary game model that incorporates the platform as a bounded rational strategic actor alongside rumor spreaders and information clarifiers, and combines this model with the extended SICR (Susceptible-Rumor spreader-Clarifier-Recovered) information diffusion framework, where game strategies and payoffs are defined based on user roles. This combined framework allows us to analyze the strategic interactions among these three agents. Numerical simulations identify key evolutionary stable states and reveal that increasing the probability and perceived benefits of platform intervention can effectively steer the system towards an ideal equilibrium where platforms strictly supervise, and clarifiers actively debunk rumors. A case study of the “Hengshan Bus” incident supports the model’s applicability, demonstrating that proactive platform intervention accelerates the dissemination of corrective information. Our findings illustrate the important and strategically complex role of platforms in self-regulation, and provide insights for co-governance mechanism design on Chinese short-video platforms.
Epidemiology and determinants of dyslipidemia in Iranian population: Results from the PERSIAN cohort
Background Dyslipidemia is one of the main risk factors for cardiovascular diseases worldwide. The current study aimed to determine the prevalence and correlates of dyslipidemia in Iran, in addition to factors associated with receiving treatment and attaining treatment goals. Methods The current study used the data of a very large cross-sectional survey, Prospective Epidemiological Research Studies in IrAN (PERSIAN), including 163,770 Iranians aged 35–70 years from 18 distinct areas across Iran, between 2014 and 2020. Participants’ characteristics were recorded using validated questionnaires including biological samples, physical measurements, and self-reported information. Dyslipidemia was defined according to the Third Report of the Adult Treatment Panel. Triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL) and low-density lipoprotein (LDL) were measured as components of dyslipidemia. We calculated participants’ 10-years cardiovascular risk using the World Health Organization risk charts to assess the eligibility for treatment and achievement of control. Multivariable logistic regression analyses were conducted to investigate the correlates of dyslipidemia, receiving treatment, and control. Results The mean age of participants was 49.4 (±9.1) years, among whom 55% were women and 70% were urban residents. The weighted prevalence of dyslipidemia was 73.2% (95% CI: 71.5–75.0%). High TG (40.4%, 36.9%−44.4%) and high TC (36.2%, 32.9%−39.4%) comprised the most prevalent components of dyslipidemia, followed by low HDL-C (32.1%, 25.8%−38.4%), high non-HDL-C (26.6%, 24.0%−29.1%), and high LDL-C (23.4%, 21.1%−25.7%). In multivariable logistic regression analysis, younger age, tobacco consumption, low physical activity, high BMI, diabetes, and hypertension were associated with low HDL-C and high TG. Older age, alcohol consumption, low physical activity, and high BMI were associated with high TC and high LDL-C. Women had higher likelihood of having abnormal levels for all components of dyslipidemia, except for high TG. Older age, high wealth score, high BMI, and low physical activity were associated with higher utilization of statin treatment. Good lipid control in those who were treated with statin was higher in males and diabetics. Conclusion The prevalence of dyslipidemia in Iranian population is extremely high, and certain groups are at higher risk of poor lipid control. Findings of this study highlight the necessity of developing a national guideline for management of dyslipidemia.
An evaluation of strategies commonly used by health advocate programs
Many non-urgent and routine medical procedures, such as MRI and CT Scans, are performed under standardized industry protocols with minimal process variations. However, large price variations exist among providers offering these services within the same geographic region. Many insurers and independent vendors have implemented health advocate/concierge programs to steer beneficiaries to high-quality and low-cost providers. These programs also aim to reduce costs. The Blue Cross Blue Shield (BCBS) of Texas’ Benefits-Value-Advisor (BVA) program is one of the most cited examples. It uses three key strategies: recommendation, unconditional monetary rewards, and persuasion. However, their effectiveness in influencing beneficiaries’ choices, particularly for routine medical procedures, has not been tested. This study fills the gap through a behavioral experiment, which provides insights into how beneficiaries may respond to these strategies and providing guidance for health plan managers. A full-factorial-between-subjects experiment was conducted with 500 subjects recruited through Amazon Mechanical Turk. The experiment design included three treatments: recommendation, copay waiver, and persuasion, each with two levels (Yes or No), resulting in eight distinct scenarios. Subjects were randomly assigned to one of eight scenarios. The survey data were analyzed using a logit regression model. We found that subjects who received recommendations were 32.7% more likely to select the lowest-cost provider and had a 27.3% greater chance of choosing lower-cost providers. However, the effectiveness of recommendations diminished when subjects mistrusted their insurance companies. Neither copay waiver nor persuasion had a significant impact on provider choice. We acknowledge that these findings are derived from a hypothetical experimental setting using an online sample, and that actual beneficiary behavior in clinical settings may differ due to additional factors such as physician referrals, appointment availability, urgency of medical needs and convenience. Our results should be interpreted as providing suggestive evidence rather than directly generalizable predictions of real-world behavior.
MMFNet: A multi-branch multi-scale framework with adaptive sparse self-attention and cross-modal fusion for sleep stage assessment
Accurate sleep stage classification serves as a crucial foundation for sleep health assessment and disease diagnosis. However, existing approaches still encounter several challenges, including limited feature representation, redundancy in extracted information, and difficulties in effectively integrating cross-modal physiological signals. To address these issues, we propose a novel framework entitled Multi-Branch Multi-Scale Fusion with Adaptive Sparse Self-Attention and Cross-Modal Integration (MFFNet). Specifically, considering the prominent time-frequency characteristics of sleep signals, multiple branches are designed to capture multi-scale information, including a time-granular feature extraction branch and a time-frequency extraction branch. Furthermore, a multi-head adaptive sparse self-attention mechanism is introduced to suppress redundant information while emphasizing discriminative features. In addition, we employ an adaptive cross-modal fusion strategy that dynamically integrates information from EEG and EOG, and further visualize the contribution of each modality to sleep stage classification. Experiments conducted on the Sleep-EDF-39 and Sleep-EDF-153 datasets demonstrate the effectiveness of the proposed approach. Using the Fpz-Cz EEG channel and EOG signals, MFFNet achieves accuracy rates of 84.26% and 81.86%, with F1 scores of 75.91% and 73.38%, respectively, highlighting its competitive performance in sleep stage assessment.
Development and initial validation of a computerized adaptive test prototype for ECG interpretation using item response theory
Aims Accurate electrocardiogram (ECG) interpretation is essential for appropriate clinical decision-making, yet competency remains suboptimal among many healthcare professionals. Although computerized adaptive testing (CAT) has the potential to provide rapid, individualized, and standardized assessment of ECG interpretation skills, its implementation requires a well-calibrated item bank based on item response theory. This study aimed to establish item parameters, evaluate the latent structure underlying ECG interpretation competency using multidimensional item response theory (MIRT), and develop an initial CAT prototype for ECG competency assessment. Methods and results We analyzed responses from 535 healthcare professionals who completed a 50-item online ECG test previously developed and administered via an online platform. Participants represented diverse clinical roles and experience levels. Dimensionality was examined using exploratory and confirmatory MIRT analyses. A unidimensional model and a two-factor model both emerged as plausible latent structures. Item parameters, including discrimination and difficulty, were estimated from item characteristic curves. Of 50 items, 47 demonstrated strong discrimination, and difficulty parameters were appropriately distributed. Model fit analyses demonstrated that the selected model adequately represented the response data and provided a suitable basis for CAT development. Conclusions Although both unidimensional and two-factor models demonstrated acceptable statistical performance, the unidimensional model was selected as the initial operational framework because of its simplicity and suitability for computerized adaptive testing. The calibrated ECG item bank enabled development of a preliminary CAT prototype. These findings support the feasibility of CAT as an efficient and standardized approach for assessing ECG interpretation competency across healthcare professionals. Further studies are warranted to validate its performance and educational utility in broader clinical settings.
Surveillance of hepatocellular carcinoma (HCC) patients using Protein Induced by Vitamin K (PIVKA-II): A cost-utility analysis for Hong Kong
Background & Aims Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death in Hong Kong. Common risk factors include chronic hepatitis B (CHB) infection and/ or liver cirrhosis (LC). Routine HCC surveillance using ultrasound plus alpha-fetoprotein (AFP) for patients with these conditions is recommended, but many patients still face limited access to ultrasound. For these, biomarker-based surveillance using a combination of protein induced by vitamin K absence or antagonist-II (PIVKA-II) plus AFP could provide a valuable alternative. Methods This study compared the cost-utility of routine HCC surveillance using ‘PIVKA-II + AFP’ to the recommended standard-of-care in Hong Kong: surveillance using ‘US + AFP’. A health economic model using a Markov-microsimulation framework was adapted using published local data and settings. The model was used to compare diagnostic outcomes, overall costs, quality-adjusted life years (QALYs), and cost-utility of the alternative HCC surveillance methods. Results Compared to the current recommended standard-of-care, the results suggested that HCC surveillance using ‘PIVKA-II + AFP’ could improve detection of HCC during early-stage disease by 4.6 percentage points, thus enabling access to more effective treatment options for some patients. It could also reduce false positive diagnoses by 7.2%, which could alleviate the need for costly confirmatory testing. Overall, PIVKA-II + AFP’ was associated with a gain of 0.019 QALYs and cost savings of HK $ 4,144 per individual. The results were particularly favorable for non-cirrhotic CHB patients. Conclusions HCC surveillance using ‘PIVKA-II + AFP’ could be a feasible option for patients with CHB and/ or LC who face limited access to ultrasound-based surveillance. The combination has the potential to improve early-stage HCC detection, facilitating timely intervention and improving both survival outcomes and patient quality of life.
Sampling bias and uneven data coverage shape observed biodiversity patterns in a megadiverse island archipelago hotspot
Where and how species are sampled can shape biodiversity knowledge, spatial patterns, and data-driven conservation. In megadiverse regions of the Global South, sampling remains uneven, and available data often lack synthesis needed to assess spatial heterogeneity in recorded biodiversity for conservation planning. This limitation is also evident in areas designated for biodiversity conservation. We examine this in the Philippines, where long-term research effort and policy development have supported expansion of conservation-relevant areas (hereafter “Philippine conservation-relevant areas” or PCAs). Using over a century of digitally accessible species occurrence records compiled and manually curated, we analyzed the country-wide spatial distribution of observed amphibian and squamate reptile species richness. We further tested whether observed richness across PCAs is associated with area, topographic relief, and sampling effort, and whether these relationships differ between protected and conserved areas (with management or governance schemes) and key biodiversity areas (unmanaged but priority areas for conservation). Results show strong spatial heterogeneity in occurrence records, with preserved specimens comprising 92% of all records and strongly influencing observed richness patterns. Citizen-science data tend to reinforce already well-sampled regions, whereas literature-derived records provide additional coverage in comparatively undersampled areas. PCAs show variable representation in the dataset, with observed richness and sampling effort generally increasing with area. However, this richness–area relationship weakens with increasing topographic relief. Differences in predictor–richness relationships between PCA types are also evident, with stronger area and sampling effects in protected and conserved areas and a relatively stronger elevational effect in key biodiversity areas. Notably, several high-richness areas occur outside formally designated protected areas. These results highlight uneven biodiversity data coverage and suggest that observed richness patterns may reflect both sampling structure and spatial variation. We emphasize integrating multiple biodiversity data sources and identifying undersampled regions to improve future biodiversity assessments.
Agricultural land degradation consequences as a migration driver in Egypt
Environmental migration is a global phenomenon in which increasing soil salinity reduces productivity and income, thereby increasing migration intention (MI) in affected regions. This study investigates how agricultural land degradation (ALD), particularly soil salinity, reduces productivity and influences MIs in Egypt. Employing Protection Motivation Theory (PMT) as a framework, we analyzed primary data from 1,782 respondents using a multinomial logistic model. The empirical findings revealed that socioeconomic characteristics, agricultural engagement, and ALD significantly shape and determine MI. Furthermore, over 35.8% of respondents reported a decline in land productivity in the past decade due to environmental change, with 22.4% actively seeking relocation. Among those willing to migrate, the capital Cairo (40.2%) and international destinations (29.4%) were the most desired. Additionally, gender, age, and income adequacy were identified as crucial predictors in migration dynamics. The findings challenge conventional demographic assumptions by revealing a significant propensity to migrate among older populations and women, groups often considered less mobile. The primary motivations (employment and income) are directly exacerbated by ecological pressures, such as declining land productivity. Therefore, effective policy must address this nexus to reduce threat appraisal by implementing sustainable land and water management and investing in both environmental adaptation (e.g., soil restoration) and human capital (e.g., education, local economic opportunities) to mitigate displacement pressures and foster sustainable rural resilience.
PsiAudit: An open-source toolkit for auditing symmetry-organised complexity in equivariant quantum neural networks
Parameterised quantum circuits are commonly assessed using measures such as expressibility, gradient behaviour, and entanglement. While useful, these measures do not indicate whether a circuit respects the symmetry it was designed to respect. This is especially important in equivariant quantum machine learning, where symmetry is central to the model. We introduce PsiAudit, an open-source Python toolkit for auditing symmetry-aware quantum neural network ansätze before training. Given an ansatz, a target symmetry, and a state trajectory, PsiAudit reports how the circuit occupies symmetry sectors, maintains coherence between them, fluctuates across the trajectory, and complies with the target symmetry. These outputs are combined into a configurable dashboard-style summary. PsiAudit supports phase, spin, and permutation symmetries, with the permutation audit implemented using Hamming-weight orbits. Tests on five ansatz families, across twenty random seeds and four to eight qubits, show, in the tested setting, that PsiAudit can identify inactive equivariant circuits, recover structure when multiple symmetry sectors are activated, and distinguish ansätze that appear similar under standard diagnostics. The toolkit also includes a unitary-level compliance check, reproducible examples, and a notebook for regenerating the reported results.
Content validity of the quality of dying and death questionnaire- revised global-version in North American inpatient hospices
Context Enhancing end-of-life care requires valid measurement of the quality of dying and death, but existing measures of this outcome have limited applicability across varied cultural and resource settings. To address this gap, we revised the Quality of Dying and Death Questionnaire (QODD) to create the QODD-Revised Global Version (QODD-RGV). Objectives Our objective was to conduct a preliminary evaluation of the content validity of the QODD-RGV questionnaire in North American inpatient hospice settings based on item relevance, comprehensibility, and comprehensiveness. Methods Bereaved caregivers of patients who died in inpatient hospices in Canada and the United States were recruited 4–6 months after death of the patient. Participants completed the QODD-RGV, a 26-item measure assessing the experience of patients in the last week of life regarding symptoms, care preferences, and social support. Cognitive interviews were conducted with verbal probing and think-aloud techniques to evaluate the participants’ understanding of the items and the information they relied upon to make their ratings. Interview transcripts were coded independently by two team members followed by consensus coding. Qualitative content analysis was performed. Results Eighteen cognitive interview protocols were completed. Participants indicated that their ratings were most commonly based on information from the perspective of the patient or themselves. The most common judgment strategy was comparison to “a hoped for or ideal dying experience,” followed by “a state of distress/no distress.” All respondents used multiple standards of comparison. Response difficulty was most frequently due to lack of communication with the patient. No single item was consistently misunderstood, but there was uncertainty about the ratings of some items due to limited caregiver knowledge or time spent communicating with the patient. Conclusion The present study with bereaved caregivers in North American inpatient hospice settings provides preliminary evidence supporting the content validity of the QODD-RGV in North American hospice settings based on relevance, comprehensibility, and comprehensiveness of the items.
Long-term medical and surgical treatment patterns in Korean patients newly diagnosed with primary open-angle glaucoma: A 10-year retrospective cohort study
Purpose To investigate the long-term medical and surgical treatment of patients with primary open-angle glaucoma (POAG). Methods This retrospective cohort study analyzed data from the Health Insurance Review and Assessment Service (HIRA) database in Korea, focusing on patients newly diagnosed with POAG in 2010. The study examined glaucoma eye drop prescriptions and the occurrence of glaucoma surgery over a 10-year period, from 2010 to 2020. The main outcome measures included changes in glaucoma eye drop prescriptions and the cumulative incidence of glaucoma surgery following the initial diagnosis of POAG. Results A total of 1,599 patients with POAG were enrolled. Prostaglandin analogue eye drops monotherapy was the most frequently prescribed glaucoma eye drop prescriptions in the first year (39.9%). The average number of prescribed glaucoma eye drops per patient was 1.19 in the first year and significantly increased to 1.40 by the tenth year. Similarly, the average number of prescribed ingredients per patient increased from 1.54 to 1.99 over the same period. The cumulative incidence of glaucoma surgery over 10 years was 3.1% (2.9% in males and 3.4% in females). Conclusions This study quantitatively confirmed an increase in glaucoma medication use in patients newly diagnosed with POAG over a 10-year treatment period. Additionally, it reveals the incidence of glaucoma surgery based on sex and age. These findings provide valuable evidence for consulting patients with glaucoma.