搜索结果：validated

Internalization of appearance ideals and sociocultural pressures contribute to body dissatisfaction and eating disorders. Japanese adolescents report high levels of body dissatisfaction despite relatively low body mass index compared to peers in other countries, highlighting potential cultural influences. However, the Sociocultural Attitudes Towards Appearance Questionnaire-4-Revised (SATAQ-4R) has not been validated in Japan. This study aimed to develop a Japanese version of the SATAQ-4R and examine its psychometric properties among Japanese adolescents. A nationwide population-based study was conducted in 2023 among 1,368 adolescents aged 11-17 years (girls: n = 694; boys: n = 674). Participants completed self-reported questionnaires. The factor structure of the SATAQ-4R was evaluated using exploratory and confirmatory factor analyses (EFA and CFA), internal consistency was assessed using Cronbach's alpha, and convergent validity was examined using eating-related measures. EFA suggested a five-factor 23-item structure for girls and a seven-factor 23-item structure for boys. CFA showed suboptimal fit for the female model and comparatively better fit for the male model. Most subscales showed acceptable internal consistency, although one female pressure-related subscale showed inadequate reliability. Correlations with external measures provided partial evidence of convergent validity, with associations varying by sex and subscale. These findings provide preliminary evidence for psychometric properties of the Japanese SATAQ-4R. However, suboptimal female model fit, inadequate reliability of one female subscale, and mixed convergent validity evidence indicate that the resulting Japanese models should be used with caution. Level III, cross-sectional observational study.

Accelerating molecular probe discovery and lead optimization requires accurate and efficient binding affinity prediction. Here we present PBCNet2.0, a Cartesian tensor-based Siamese neural network for protein-ligand relative binding affinity prediction. Trained on 8.6 million protein-ligand complex pairs, PBCNet2.0 achieves zero-shot accuracy similar to computationally intensive physics-based simulations while remaining highly efficient. Retrospective prioritization experiments show that PBCNet2.0 improves optimization efficiency by 7.18-fold and reduces resource use by 41%. Mechanistic analyses indicate that the model captures intermolecular interactions and encodes spatial geometric constraints, enabling sensitivity to subtle effects such as fluorine orthogonal multipolar interactions. Notably, although not trained on mutation data, PBCNet2.0 exhibits an emergent capability to predict affinity changes induced by binding pocket residue variations, supporting resistance analysis. We prospectively validated these capabilities on ENPP1 and ALDH1B1, accurately resolving affinity shifts from minor interaction and conformational differences and identifying critical binding residues with a hit rate of five out of six selected residues.

The accurate ability to predict the distribution of contact stress under reinforced concrete (RC) footings is important for the safety and serviceability of shallow foundations. Conventional analytical models idealizing the footing as rigid and soil as homogenous fail to capture the stress concentration and redistribution effects, especially under non-uniform loading. Earlier studies are mostly concentrated on sand; however, basalt soil has different mechanical characteristics as it possesses high stiffness, angularity, and interlocking effects. It also ignores stiffness loss due to concrete cracking. This study aims to fill these gaps through an experimental and numerical investigation of RC square footings resting on basaltic soil and the influence of the reinforcement ratio, yield strength of steel and strength of concrete. Within the laboratory conditions, four footings having different reinforcement ratios of 0.19%, 0.36%, 0.54% and 3.43% were tested under monotonic loading. Central and edge displacements were measured. Using a validated finite element model, a parametric study expanded the investigation to include reinforcement ratios of 0.54% to 4.80%, steel yield stresses of 240 MPa to 450 MPa and concrete compressive strengths of 20 MPa to 60 MPa, allowing systematic consideration of these parameters on central contact stress, ultimate load, deformation and energy absorption. The findings revealed that enhancing the reinforcement ratio from 0.54% to 4.80% resulted in an increase of 73.4% in central contact stress, 34.1% in ultimate load, and 55% in energy absorption, respectively. Increase in steel yield stress from 240 MPa to 450 MPa caused a 25.2% increase in central contact stress, 13% in ultimate load and 3.74% in energy absorption in laminated composite panel. The increase of concrete compressive strength from 20 MPa to 60 MPa increased central contact stress by 117.4% ultimate load by 70.4% and energy absorption by 270% showing this factor as dominant. These results show that the performance of footing on stiff basaltic soil mainly depends on the concrete strength and amount of reinforcement whereas careful use of steel yield stress. The insights provided by the study are critical for practical design. Furthermore, non-uniform contact stresses, stiffness degradation, and soil-structure interaction need to be accounted for optimizing strength and ductility.

Inefficient hardware configuration in Health Information Systems (HIS) is a global informatics challenge that undermines healthcare delivery. This study designed and content-validated a novel quantitative hardware assessment framework to enable systematic hardware evaluation. We developed an informatics-driven framework through a methodological study in 2024. Initial items were derived from a scoping literature review and expert input from computer engineering. Content validity was assessed using Content Validity Ratio (CVR) and Index (CVI). Inter-rater reliability was evaluated via Intraclass Correlation Coefficient (ICC) across 122 identical computers. The final framework comprises 23 items across three domains. A quantitative scoring model (0-150 points) was established, demonstrating excellent inter-rater reliability (ICC&#x2009;=&#x2009;0.87). The tool effectively differentiates between obsolete (score&#x2009;<&#x2009;50), marginal (50-80), and optimal (>&#x2009;80) configurations, providing IT managers with a clear upgrade priority map. This study presents a development and content validation of a reliable and content-valid informatics framework that bridges hardware assessment with system performance evaluation. Predictive validity against real-world HIS performance metrics (e.g., login time, response latency, freeze frequency) has not yet been empirically tested and remains a critical next step.

This study aimed to investigate whether the progression of lumbar facet joint angle (FJA) in adjacent segment degeneration (ASD) occurs more rapidly in the sagittal plane compared to a matched control group. This retrospective longitudinal study included 35 individuals from three hospitals who underwent L4-L5 posterior lumbar interbody fusion (PLIF) surgery and had preoperative lumbar magnetic resonance imaging (MRI) scans. These patients also underwent follow-up MRI scans approximately four to five years postoperatively. A control group, matched for age and gender, was included for comparative analysis. MRI data were assessed using grading scales for facet joint degeneration (FJD) and lumbar intervertebral disc degeneration (IDD). FJA measurements were derived from T2-weighted axial images. No significant progression of IDD was found in either group. Within the PLIF group, a more rapid progression of FJD was observed at the bilateral L5-S1 and right L3-L4 segments in comparison to the control group (P&#x2009;<&#x2009;0.05). In contrast, the control group exhibited consistent lumbar FJA across all segments. Postoperative MRI scans of the PLIF group, however, demonstrated a significant reduction in FJA on both sides of the L3-L4 segment and on the left side of the L5-S1 segment (P&#x2009;<&#x2009;0.05), with no significant changes observed at the L2-L3 segment and the right side of the L5-S1 segment. A significant negative correlation was found between the change in FJA (&#x394;FJA) and the change in FJD (&#x394;FJD) at the left L5-S1 segment in the PLIF group (&#x3c1; = -0.335, P&#x2009;=&#x2009;0.049). Accelerated sagittal facet remodeling occurs at segments adjacent to L4-L5 PLIF and correlates with facet degeneration. These hypothesis-generating preliminary findings suggest that FJA measurement has the potential to serve as an imaging biomarker for ASD risk, though this interpretation remains to be validated in larger prospective cohorts. The findings underscore the need for long-term postoperative surveillance.

Acute myeloid leukemia (AML) is a genetically and phenotypically heterogeneous hematological malignancy. Here, to better define this clinically taxing and translationally challenging malignancy, we applied a multiomics approach, consisting of 13 modalities to analyze 173 treatment-naive individuals with AML. By integrating these 'omes', we identified distinct AML subtypes, genotype-phenotype associations, biomarkers and pathobiological mechanisms. Across the spectrum of primitive and committed AML, we found extensive metabolomic and lipidomic reprogramming driven by divergent MYC and mTOR activity. We linked metabolic changes to striking hyperacetylation of mitochondrial proteins in CEBPA-mutant AML. Protein-centric subtyping revealed a distinct NPM1-mutant subset characterized by outlier expression of FOXC1 and HOXB8/9. To nominate therapeutic targets across subtypes, we developed a multiomic machine-learning approach and validated MTA1 as a contributor to panobinostat resistance. Altogether our findings underscore the complex nature of AML and provide a clinically and translationally informed unified view that reveals coalescent phenotypes across multiomic layers.

Multiple myeloma (MM) is a common hematological malignancy, while the prognostic value of tumor-infiltrating immune cells in MM remains elusive. This study aimed to construct an immune-related prognostic model and identify potential therapeutic targets for MM. RNA-seq and clinical data of 751 newly diagnosed MM patients were analyzed. LASSO regression was applied to establish an immune pathway-based prognostic model for patient risk stratification. WGCNA was used to screen hub genes, and in vitro and in vivo experiments validated gene functions and therapeutic effects. Five immune cell pathways were significantly correlated with MM prognosis. MCM2 was identified as the key hub gene associated with risk scores. MCM2 knockdown induced G2-phase cell cycle arrest and suppressed MM proliferation both in vitro and in vivo. Moreover, MCM2 inhibition enhanced the antitumor efficacy of PD1/PDL1 inhibitor BMS1, and CDK inhibitor PHA767491 sensitized MM to immunotherapy. The immune-based model reliably predicts MM prognosis. MCM2 serves as a vital prognostic biomarker. Targeting MCM2 combined with PD1/PDL1 and CDK inhibitors represents a promising therapeutic strategy for MM.

Postpartum depression (PPD) is a debilitating condition with adverse consequences for mothers and infants. Kangaroo mother care (KMC), involving prolonged skin-to-skin contact often combined with breastfeeding, has been proposed as an alternative intervention for improving maternal mental health. To examine the effect of KMC on PPD outcomes, scholarly databases, PubMed, and Google Scholar were searched for controlled trials published between 1998 and 2025 and reporting validated depression measures. Outcome measures in standardized mean differences (SMDs) were pooled using random-effects models. Publication quality was assessed with the Cochrane Risk of Bias tool or Newcastle-Ottawa scale. Thirteen studies comprising 439 mothers in KMC groups and 453 controls were included. KMC was associated with a significant reduction in depression symptoms (SMD&#x2009;=&#x2009;-0.58 [95% CI: -0.88, -0.28], I&#xb2;&#x2009;=&#x2009;78%). Analyses of publication bias and sensitivity showed an overall negative effect, although a small number of studies exerted greater influence. Subgroup analyses indicated a stronger effect of KMC in developing countries, compared to developed countries. Similarly, mothers with preterm as opposed to term delivery appeared to have greater reductions in depression scores upon KMC implementation. The evidence suggests that KMC reduces depressive symptoms in birthing women, possibly with larger effects in resource-limited settings. KMC may have potential benefits in repressing depressive phenotype in birthing women, particularly those delivering preterm infants. This meta-analysis provides updated, quantitative evidence synthesizing diverse study designs and highlights differential effects across socioeconomic settings. Inclusion of controlled trials with both pre- and post-test depression measures enhances the robustness of our findings compared with previous meta-analyses. Our results indicate varied but stronger antidepressant effects of KMC, particularly in developing economies under resource-limited settings, compared to developed countries. The evidence supports KMC as a low-cost, scalable, non-pharmacological intervention to improve maternal mental health and strengthen mother-infant outcomes across diverse settings.

Mindfulness has been linked to lower psychological distress. Yet, it remains unclear how, or the mechanism(s) by which, mindfulness reduces psychological distress, particularly during times of heightened, prolonged stress. The present research utilised secondary analysis of data from two independent studies to 1) assess whether greater trait mindfulness protected U.S. adults against psychological distress during the COVID-19 pandemic and 2) test whether negatively biased cognition (pessimism) mediated the association between trait mindfulness and psychological distress. Both studies utilised a two-wave longitudinal design with an initial survey conducted prior to the COVID-19 pandemic and a follow-up survey conducted during the pandemic. Participants completed validated measures of trait mindfulness, negatively biased cognition (i.e. pessimism), and psychological distress (i.e. anxiety and depression symptoms). Across two studies, pre-pandemic levels of trait mindfulness prospectively predicted less psychological distress during the pandemic, controlling for pre-pandemic psychological distress. Further, lower pessimism during the pandemic mediated the association between greater pre-pandemic trait mindfulness and lower psychological distress during the pandemic. These findings suggest that mindfulness may protect against, or reduce, psychological distress by decreasing negatively biased cognition. As such, mindfulness and negatively biased cognition may be important targets for interventions aimed at promoting psychological health.

Enlarged facial pores are a common aesthetic concern associated with aging and reduced dermal support. Hybrid Cooperative Complex (HCC), a stabilized hyaluronic acid formulation, has been proposed to improve skin quality through bioremodeling. This prospective observational pilot study evaluated changes in facial pore size after treatment with Hybrid Cooperative Complex (HCC) using standardized photography, clinical assessment, and validated rating scales. Ten healthy adult participants received two treatment sessions according to the Bio Aesthetic Points protocol, and outcomes were assessed at baseline and on Days 30, 60, 120, and 180. Visible improvement in facial pore size and skin texture was observed after treatment. Peak pore refinement was noted at Day 120 in most participants, while partial regression was observed in many cases by Day 180, although skin quality remained improved compared with baseline. HCC may be associated with visible improvement in facial pore size and skin quality in this pilot cohort. Changes in pore morphology may represent a practical clinical marker for estimating maintenance treatment timing, although larger controlled studies are needed.

Maintaining a favorable balance between antioxidants and pro-oxidants, rather than merely increasing antioxidant intake, may be critical for mental health. This study examined the association between Oxidative Balance Score (OBS), an integrated measure of both antioxidant and pro-oxidant exposures, and odds of psychological disorders. This cross-sectional analysis used baseline data from the Isfahan Functional Disorders (ISFUN) cohort. Dietary intakes were assessed using a self-administered, Willett-format, Dish-based, 106-item Semi-quantitative Food Frequency Questionnaire (DS-FFQ). OBS was calculated based on 16 dietary and 4 lifestyle factors. Depression and anxiety symptoms were evaluated using the Iranian-validated Hospital Anxiety and Depression Scale (HADS). After adjusting for confounding variables, higher OBS was significantly associated with reduced odds of depression (OR: 0.65; 95% CI: 0.49-0.86) and anxiety (OR: 0.70; 95% CI: 0.53-0.92). In sex-stratified analyses, a significant inverse association was found between OBS with depression (OR: 0.65; 95% CI: 0.45-0.93) and anxiety (OR: 0.64; 95% CI: 0.45-0.91) among women. However, there was no significant association between OBS with depression and anxiety among men. In conclusion, a higher OBS was associated with reduced odds of depression and anxiety in this population, with this inverse association being statistically significant only in women, highlighting the potential importance of a balanced antioxidant-pro-oxidant profile for mental health. Prospective studies are needed to confirm these findings.

Dengue transmission remains a major public health challenge in rapidly urbanising regions, yet spatial risk patterns may vary across residential environments. This study investigated how housing typology, surrounding land use, and built-vegetation interfaces may represent environmental zones associated with elevated dengue occurrence in Kuala Selangor, Malaysia. Using validated georeferenced dengue surveillance records collected between 2020 and 2024, we applied GIS-based spatial analyses across three dominant residential housing typologies: terrace/landed housing, high-rise housing, and traditional/rural housing. Terrace housing demonstrated relatively stronger and more spatially continuous clustering patterns compared with other housing environments. Dengue occurrence in these areas was more closely associated with dense built-up surroundings and nearby vegetation. High-rise housing showed more localized clustering within highly built environments, whereas traditional and rural housing exhibited broader but weaker clustering patterns across more environmentally mixed landscapes. Overall, the findings suggest that dengue spatial distribution patterns differ according to housing structure and surrounding environmental context. The built-vegetation interface may represent an important environmental zone associated with elevated residential dengue occurrence, particularly in dense terrace housing settings. These findings highlight the importance of adapting vector control strategies to local residential environments, including targeted management of peri-domestic vegetation in terrace housing, infrastructure-focused interventions in high-rise settings, and broader environmental management approaches in rural areas.

Subjective well-being is a central component of physical and mental health and is increasingly recognized as a key indicator of quality of life. Despite the widespread use of the PERMA framework, no validated French version of the PERMA-Profiler is currently available. The aim of this study was to translate and culturally adapt the PERMA-Profiler into French and to examine its psychometric properties in a large sample of adults. A total of 612 French-speaking adults completed the French version of the PERMA-Profiler as well as measures of flourishing, anxiety and depression. The sample was randomly divided into two independent subsamples to conduct exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The EFA suggested a parsimonious three-factor solution, while comparative model tests confirmed the theoretical five-factor structure of the PERMA model. The CFA conducted on an independent subsample strongly confirmed a correlated five-factor model, as well as a second-order hierarchical model reflecting a general well-being factor. The PERMA-Profiler demonstrated good to excellent internal consistency for most dimensions and for the total score, although the "engagement" dimension showed lower reliability, which is consistent with previous validation studies. Test-retest analyses indicated good temporal stability for the total score and most subscales. Convergent validity was confirmed by strong positive associations with flourishing and perceived happiness. Discriminant validity was evidenced by weaker associations with anxiety, depression, loneliness and negative emotions than with flourishing, although some correlations with HADS anxiety and depression scores reached moderate to strong magnitude. Overall, the French version of the PERMA-Profiler has robust psychometric properties and supports the multidimensional structure of psychological well-being proposed by the PERMA framework. This instrument is a reliable and theoretically grounded tool for assessing the well-being of French-speaking adult population, while highlighting the distinct psychometric behavior of the Engagement dimension.

Lung cancer remains one of the leading causes of cancer-associated mortality globally, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of all cases. The PI3K p110&#x3b1; catalytic subunit (PIK3CA) signaling pathway is often dysregulated in non-small cell lung cancer (NSCLC) and plays a vital role in promoting tumor cell proliferation, survival, and resistance to apoptosis, making it a clinically validated therapeutic target. Despite the approval of several PI3K p110&#x3b1; inhibitors, challenges including off-target toxicity, drug resistance, and suboptimal pharmacokinetic profiles require the continued discovery of novel, drug-like candidates. In this study, virtual screening of 650,000 compounds targeting the PI3K p110&#x3b1; pathway identified five lead candidates based on the lowest docking binding energy. MD simulations over 100&#xa0;ns, MM-GBSA/MM-PBSA binding free energy calculations were performed using 1000 frames extracted at 100&#xa0;ps intervals from the 100&#xa0;ns molecular dynamics simulation trajectory. The results showed negative binding free energy values for all complexes, computationally indicating favorable binding interactions and potential stability of the compounds with the target protein. DFT analysis and SwissADME ADMET profiling were performed. All five complexes showed stable RMSD profiles and consistently negative binding free energies. DFT confirmed acceptable HOMO-LUMO energy gaps. ADMET profiling demonstrated satisfactory drug-likeness for all candidates. Five promising PI3K p110&#x3b1; inhibitor candidates with potential anti-lung cancer activity were identified; experimental validation is required to confirm these computational results.

To examine the surgical outcomes of surgical aortic valve replacement in the transcatheter aortic valve replacement era and propose a novel patient-specific prognostic model. We randomly divided 772 patients with aortic stenosis who underwent surgical aortic valve replacement in 2016-2021 into two cohorts (derivation, 515; validation, 257). In the derivation cohort, no data were missing for any patients for the candidate predictors including age, sex, body mass index, left ventricular ejection fraction, levels of albumin, hemoglobin, and serum creatinine, presence of chronic atrial fibrillation, and end-stage renal disease requiring hemodialysis. We developed possible scoring models using Cox proportional hazards regression with overall survival as the endpoint and calculated the cross-validated 5-year C-statistics to assess accuracy. The mean patient age was 74.2&#xa0;years, and 46.9% were female. Kaplan-Meier analysis revealed overall 1- and 5-year survival rates of 96.6 and 88.7%, respectively. The 5-year C-statistic of the derivation cohort was 0.785 (95% confidence interval: 0.716-0.853), while the estimated 1-, 3-, and 5-year C-statistics of the validation cohort were 0.885 (0.806-0.965), 0.888 (0.824-0.953), and 0.801 (0.702-0.901), respectively. Calibration plots revealed good agreement between predicted and actual 5-year survival (intraclass correlation coefficient&#x2009;=&#x2009;0.955; 95% confidence interval: 0.827-0.989). This novel survival prediction model after isolated surgical aortic valve replacement in the transcatheter aortic valve replacement era showed good survival prediction, and may guide the decision-making process for surgical aortic valve replacement versus transcatheter aortic valve replacement with lifetime management.

We demonstrate how Large Language Models (LLMs) accelerate biomedical data harmonization through automated Common Data Element (CDE) generation. We processed 31 datasets including clinical taxonomies and research data dictionaries through OpenAI's Generative Pre-trained Transformer - 4 (API Model gpt-4-0613), generating comprehensive metadata for each element using a template-based system. Subject-matter experts validated outputs, finding 94% of generated metadata fields required no revision overall, with an unweighted accuracy of 83.8%, unweighted, for semi-structured sources. Dramatically faster than manual approaches. Our system uses ElasticSearch with weighted field matching to identify semantic equivalences between variables, avoiding duplicate CDEs while building a standardized repository. Testing with Alzheimer's Disease Neuroimaging Initiative (ADNI) and Global Parkinson's Genetic Program (GP2) datasets showed 32.4% of previously unseen headers successfully mapped to our CDEs, with interoperability scores averaging 53.8/100 based on matching, completeness, and compliance metrics. This approach automates the most tedious aspects of data integration, reducing barriers to cross-study collaboration in biomedical research.

Beauveria caledonica offers great potential as a biocontrol agent for certain pests and diseases. However, its ability to colonize plants as an endophyte and its interactions with pathogenic microorganisms within the plant are still not fully understood. In this study, we investigated the potential of B. caledonica isolated from banana weevils to colonize banana cultivar "Baxijiao" (Musa spp. AAA) and control the Tropical Race 4 (TR4) strain of Fusarium Wilt of Banana (Fusarium oxysporum f. sp. cubense (Foc). A four-point dual-culture confrontation test revealed that B. caledonica effectively inhibited the in vitro growth of Foc TR4, with an inhibition rate of 70.14%. Colonization experiments showed that B. caledonica could colonize the roots, corms, and pseudostems of banana plantlets. Finally, greenhouse experiments, arranged in a randomized complete block design, confirmed that B. caledonica could act as an endophyte, surviving inside the banana plant, and demonstrated that it significantly reduced Foc TR4 in the host plant, with an efficacy of 34.73%, without adversely affecting plant growth. This groundbreaking study confirms that an insect-pathogenic fungus, B. caledonica, isolated from banana weevils, can colonize banana plants and establish itself as an endophyte within host plants. Its demonstrated potential to antagonize Foc TR4 highlights its effectiveness as a biocontrol agent in banana production, which opens a new possibility for B. caledonica's dual roles in disease and pest management to be validated in large-scale field trials.

Selective eating often leads to nutritional imbalance and mealtime stress, making early identification essential. This study aimed to compare the predictive performance of machine learning (ML) models using two types of input data-namely, vegetable intake and food frequency questionnaire (FFQ) responses-to identify children with selective eating behaviour (cutoff score &#x2265;1.59). We analysed a cross-sectional dataset of 283 children aged 3-6 years using up to 8 predictors selected from 15 food-frequency items or 15 designated vegetables, along with age and sex. Selective eating behaviour was defined as a selective eating score of &#x2265; 1.59. Eleven ML algorithms were trained and evaluated, resulting in 11,253 feature-model combinations. Model performance was assessed using 5-fold cross-validation based on accuracy, precision, recall, F1 score, and ROC-AUC. Permutation importance analysis was conducted to identify key predictive features. Among the 11 ML algorithms tested, vegetable intake-based L1 logistic regression showed the best performance (ROC-AUC = 0.717, accuracy = 0.647, precision = 0.722, recall = 0.659, F1 = 0.808). Permutation importance further identified tomato, green onion, and taro as key contributors in the vegetable intake-based L1 logistic regression model. In contrast, the FFQ-based Na&#xef;ve Bayes model showed relatively good discrimination (ROC-AUC = 0.619) despite moderate recall. Vegetable intake-based ML models were more effective than those using FFQ data in identifying children with selective eating behaviour. Detailed dietary assessment aids early detection.

Isolated rapid eye movement sleep behavior disorder (iRBD) is a major prodromal marker of &#x3b1;-synucleinopathies, often preceding the clinical onset of Parkinson's disease, dementia with Lewy bodies, or multiple system atrophy. While wrist-worn actimeters hold significant potential for detecting RBD in large-scale screening efforts by capturing abnormal nocturnal movements, they require a reliable and efficient analysis pipeline. This study presents ActiTect, a fully automated, open-source machine learning tool to identify RBD from actigraphy recordings. To ensure generalizability across heterogeneous acquisition settings, our pipeline includes robust preprocessing and automated sleep-wake detection to harmonize multi-device data and extract physiologically interpretable motion features. Model development was conducted on a cohort of 78 individuals, yielding strong discrimination under nested cross-validation (AUROC&#x2009;=&#x2009;0.95). Generalization was confirmed on a blinded local test set (n&#x2009;=&#x2009;31, AUROC&#x2009;=&#x2009;0.86) and two independent external cohorts (n&#x2009;=&#x2009;113, AUROC&#x2009;=&#x2009;0.84; n&#x2009;=&#x2009;57, AUROC&#x2009;=&#x2009;0.94). To assess robustness, leave-one-dataset-out cross-validation across cohorts demonstrated consistent performance (AUROC range&#x2009;=&#x2009;0.84-0.89). Complementary stability analysis showed that predictive features remained reproducible across datasets, supporting the pooled multi-center pre-trained model for broader deployment. As an open-source, easy-to-use tool, ActiTect promotes adoption, independent validation, and collaborative improvements, thereby advancing generalizable wearable-based RBD detection.