Category: Statistics

Int J Obes (Lond). 2026 Feb 28. doi: 10.1038/s41366-026-02038-x. Online ahead of print.

ABSTRACT

BACKGROUND: Obesity is a known cardiovascular risk factor, but the “obesity paradox” has been observed in patients with acute myocardial infarction (AMI), where obesity may be linked to better survival outcomes. The relationship between body mass index (BMI) and recurrent myocardial infarction, particularly with age-specific effects, remains unclear.

METHODS: This retrospective cohort study included 4023 AMI patients from a tertiary medical center (2015-2023). Patients were stratified by age: ≤60 years (n = 1277) and >60 years (n = 2746). Multivariable-adjusted Cox proportional hazards models were used to assess the association between BMI and recurrent myocardial infarction, adjusting for demographics, biomarkers [N-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-cTnT)], imaging parameters [left ventricular ejection fraction (LVEF)], comorbidities, and treatment regimens. Curve-fitting models were also applied. The median follow-up time was 35 months (Q1-Q3 25-58).

RESULTS: In the ≤60 years group, higher BMI was associated with a significantly lower risk of recurrent myocardial infarction [adjusted hazard ratio (HR) = 0.965, 95% confidence interval (CI) 0.936-0.994, P = 0.018]. In contrast, the >60 years group showed a trend toward higher risk (unadjusted HR = 1.032, 95% CI 1.012-1.053, P = 0.001), which lost statistical significance after adjustment (adjusted HR = 1.015, 95% CI 0.994-1.037, P = 0.151). Curve fitting revealed a negative linear correlation in the ≤60 years group and a positive relationship in the >60 years group.

CONCLUSIONS: This study presents the first evidence of an age-dependent obesity paradox in AMI. In patients aged ≤60 years, higher BMI reduced recurrent myocardial infarction risk, whereas in those aged >60 years, the protective effect disappeared and reversed, indicating potential harm. These findings highlight the need for age-stratified secondary prevention strategies for AMI. Summary of Principal Study Outcomes.

PMID:41764327 | DOI:10.1038/s41366-026-02038-x

Sci Rep. 2026 Feb 28. doi: 10.1038/s41598-026-42346-w. Online ahead of print.

ABSTRACT

Socioeconomic status (SES) has been linked to brain-based markers, but most studies rely on conventional statistical methods that overlook the complexity and inherent multicollinearity of the brain. We trained elastic net models to predict SES from multimodal neuroimaging data: diffusion tensor imaging (DTI), structural MRI (sMRI), and resting-state functional connectivity (RSFC) data. Neural features independently predicted SES, and demographic information only minimally enhanced performance. The income and multimodal models performed best; accordingly, the best-performing primary model predicted income using multimodal data, achieving AUCs of 0.75 (test) and 0.811 (train) without demographic information and 0.779 (test) and 0.836 (train) with demographic information. The performance of the secondary multimodal models for predicting income had a positive relationship with income disparity; expectedly, the best performing model distinguished between children from the top and bottom ~ 10-20% of income brackets, reaching AUCs of 0.81 (test) and 0.969 (train) without demographic information and 0.863 (test) and 0.986 (train) with demographic information. Among the modalities, DTI was the most discriminative, followed by sMRI. Globally distributed along with executive functioning (EF) and language features were the most discriminative. Multimodal neuroimaging can predict SES, especially income, even without demographic data, and the most discriminative features tended to be measurements of white matter integrity and organization; more globally distributed than isolated to specific regions; and linked to cognitive control and language.

PMID:41764315 | DOI:10.1038/s41598-026-42346-w

Sci Rep. 2026 Feb 28. doi: 10.1038/s41598-026-40388-8. Online ahead of print.

NO ABSTRACT

PMID:41764310 | DOI:10.1038/s41598-026-40388-8

NPJ Digit Med. 2026 Feb 28. doi: 10.1038/s41746-026-02486-9. Online ahead of print.

ABSTRACT

Objective, scalable biomarkers are needed for continuous monitoring of major depressive disorder. Smartphone-collected speech is promising, yet clinically useful signals remain elusive. We analyzed 3151 weekly voice diaries from 284 German-speaking adults (128 MDD, 156 controls) to predict Beck Depression Inventory (BDI) scores. Sentence-embedding models outperformed lexical and acoustic baselines: Qwen3-8B achieved MAE 4.65 and R² 0.34, and stacked generalization of multilingual-E5 with Qwen3-8B further improved performance (MAE 4.37, R² 0.41). Audio embeddings added little incremental value. In an MDD-only analysis, multilingual-E5 was the top single modality (MAE 6.74, R² 0.20). To aid interpretation, BERTopic uncovered six coherent themes; BDI scores were highest for “Distress & care”, supporting clinical face validity. Together, LLM embeddings paired with lightweight topic analysis capture the dominant signal of depression severity in everyday speech and offer a scalable route to ecologically valid digital phenotyping.

PMID:41764298 | DOI:10.1038/s41746-026-02486-9

Sci Rep. 2026 Feb 28. doi: 10.1038/s41598-026-40367-z. Online ahead of print.

ABSTRACT

Heterozygous familial hypercholesterolemia (FH), a monogenic cause for premature coronary artery disease (CAD) is often underdiagnosed. In individuals who meet the FH diagnostic criteria and lack pathogenic variants, polygenic factors are recognized as potential contributors. This study aimed to characterize the spectrum of genetic variants and determine the low-density lipoprotein polygenic risk score (LDL-PRS) among clinically diagnosed FH participants from South India. We recruited 116 unrelated participants with a pretreatment LDL- C concentration ≥ 190 mg/dl and a DLCN (Dutch Lipid Clinic Network) score ≥ 3. Targeted next-generation sequencing (NGS) of 23 lipid related genes and 12-SNP (Single nucleotide polymorphism) genotyping were performed. NGS identified 39 variants including 13 pathogenic and 26 variants of unknown significance (VUS) some of which were in non-classical genes: ABCG5, ABCG8, APOE, PPP1R17, SREBF2. Pathogenic variants were detected in 66.7% of those with definite FH,19.7% in probable FH and 2.7% in possible FH. Overall,66% were variant negative. Among variant negative (FH/V-) participants, 64% demonstrated high LDL-PRS, whereas 70% of variant positive participants also exhibited elevated scores; suggesting a contributory role of polygenic factors across both groups. Additionally, the observation that variant positive individuals with high LDL-PRS have an increased risk of coronary artery disease (CAD) adds important nuance to risk stratification within genetically confirmed FH patients. Confirmation of diagnosis by genetic testing is essential for the diagnosis of FH. Although LDL-PRS may offer little benefit in variant negative cases and improve CAD risk prediction in variant positive individuals, large scale studies are essential to validate its clinical utility and assess whether inclusion of additional LDL- raising SNPs could enhance the detection of polygenic FH in the Indian population.

PMID:41764281 | DOI:10.1038/s41598-026-40367-z

Sci Rep. 2026 Feb 28. doi: 10.1038/s41598-026-39514-3. Online ahead of print.

ABSTRACT

Ore particle size distribution is an important metric for evaluating blasting outcomes and affects the energy consumption of ore crushing equipment. Faced with dense accumulation of ore, nonuniform size distributions, dust occlusion, and target loss due to motion, using computer vision methods, we propose a blasting ore size detection method based on efficient dehazing network and multi-dimensional feature fusion, which is an improvement to YOLOv8. Firstly, we constructs an efficient defogging backbone network that combines feature attention and composite scalable backbone so that the model can efficiently extract the features of ore images and enhance the robustness of the model to dust interference in the ore crushing process. Secondly, we introduces a new feature fusion network that combines the convolution model and the Vmamba sequence model as well as cross-layer fusion of multi-scale features so that the model can effectively adapt to the dramatic scale change of blasting ore, capture fine ore and large-size ore, avoid ore omission, and improve the accuracy of particle size statistics. Finally, the multi-dimensional feature fusion ability of Dynamic Head was introduced to optimize the target detection head, and the feature fusion was further optimized so that the feature tensor obtained from the ore image was adapted to the detection and positioning task of ore, and the discrimination ability of the model for ore was improved. Experiments were conducted on a manually labeled jaw fracture ore dataset. Compared to the YOLOv8n algorithm, the average precision ([Formula: see text]) for detecting eight size categories of ore increased by 7%. On datasets containing interference such as smoke, dust, and wet conditions, the mean average precision at the IoU threshold of 0.5 (mAP50) improved by 7.6%. For fine ores below D5 (72 mm), the detection precision ([Formula: see text]) increased by 18.8%, while the recall rate ([Formula: see text]) rose by 13.8%. On the total one-class dataset, the recall rate ([Formula: see text]) and mAP50 reached 84% and 88.1%, respectively.

PMID:41764266 | DOI:10.1038/s41598-026-39514-3

Sci Rep. 2026 Feb 28. doi: 10.1038/s41598-025-32082-y. Online ahead of print.

ABSTRACT

Rapidly increasing municipal solid waste (MSW) generation, reaching 160,039 tonnes per day in India, and the environmental burdens of conventional disposal highlight the need for efficient waste-to-biofuel solutions. This study conducts a comparative Life Cycle Assessment (LCA) of seven MSW-to-biofuel pathways: open landfilling, landfill gas recovery, incineration, torrefaction, gasification, hydrothermal carbonization, and integrated gasification. Using a functional unit of 1 tonne of MSW, the assessment quantifies environmental impacts across five midpoint categories (GWP, SOD, FEP, LU, WC) following ISO 14040/44 guidelines. The methodology integrates experimental MSW characterization, national waste statistics, and ± 10% sensitivity analysis to address uncertainties in methane capture, energy recovery, and grid displacement. Results show substantial differences across pathways, with integrated gasification (MIG) emerging as the most sustainable option, achieving an avoided GWP of – 1095 kg CO₂ eq, water savings of – 1125.61 m³, and the lowest land-use requirement (- 32.39 m²·a). Material Flow Analysis further validates MIG’s superior mass-energy conversion when combined with recycling. The study’s novelty lies in its first holistic comparison of seven thermochemical and conventional MSW pathways tailored to India, integrating LCA and MFA evidence. the findings support prioritizing advanced thermochemical routes, particularly MIG, for climate-resilient, resource-efficient, and circular MSW management.

PMID:41764252 | DOI:10.1038/s41598-025-32082-y

Sci Rep. 2026 Feb 28. doi: 10.1038/s41598-026-40752-8. Online ahead of print.

ABSTRACT

The role of Nature ^**** in supporting human life, health, and well-being has been recognized and appreciated since ancient times, and has become a topic of scientific investigation with early studies dating back several decades. In recent years, this field has gained renewed attention and methodological refinement, driven by interdisciplinary frameworks and advances in environmental psychology, ecology, and health sciences, including new ecosystem-based approaches that highlight the deep human dependence on Nature for both mental and physical health. Among Nature-based Interventions that aim at exposing people to the natural environment, Green Prescriptions (GRx) represent a promising strategy to address human health challenges in ways that can also support environmental sustainability, in line with the Planetary Health framework. However, significant gaps remain in our understanding of the specific ecological factors that influence health outcomes during therapeutic activities in natural settings; in particular, it remains unclear how ecosystem structure and functions modulate health responses in individuals. This nine-month pilot study examined the therapeutic efficacy of GRx within a Mediterranean woodland ecosystem, to assess if and how variations in ecosystem structure influence health outcomes in individuals with complex chronic conditions. Using a novel aggregated index to characterize four distinct woodland patches, we identified a gradient in structural complexity where greater ecosystem functionality was consistently associated with greater alleviation of psychological and physical symptoms. Notably, health outcomes were independent of weather conditions and participants’ baseline connectedness to Nature, whereas temporal dynamics and the presence of peaks in the productivity of some species influenced both perceptions and physical responses. This underscores the intrinsic role of ecosystem properties and dynamic functions in modulating human health responses, while also suggesting the potential presence of a complex set of signals pervading complex ecosystems that is worth further exploration. The results demonstrated cumulative health benefits, including significant reductions in medication use over time, particularly among individuals with respiratory challenges and chronic pain. Furthermore, participants showed improved environmental awareness and behavior, embracing the interconnectedness principle, which is integral to effective environmental conservation. This study highlights the potential of well-functioning ecosystems to serve as co-effectors in healthcare interventions, advancing the goals of Planetary Health while reinforcing the importance of preserving ecological integrity. (**In this paper, “Nature” is written with a capital “N” to indicate the living biosphere and the abiotic matrices (soil, air, and water) in which life is embedded, including the ecological processes they sustain. This capitalization reflects the scientific perspective of Nature not merely as a passive backdrop, but as an active ecological system that interacts and influences human health. It also avoids confusion with “nature” as the intrinsic quality of a phenomenon**).

PMID:41764240 | DOI:10.1038/s41598-026-40752-8

Sci Data. 2026 Feb 28. doi: 10.1038/s41597-026-06926-9. Online ahead of print.

ABSTRACT

Observer bias and inconsistencies in traditional plant phenotyping methods limit the accuracy and reproducibility of fine-grained plant analysis. To address these limitations, TomatoMAP is introduced as a comprehensive dataset for Solanum lycopersicum. The dataset contains 68,080 RGB images: 3,616 high-resolution macrophotographs (3648 × 5472) with semantic annotations, and 64,464 moderate-resolution images (1080 × 1440) captured from 12 plant poses at four camera elevations. Each image is accompanied by manually annotated bounding boxes for seven regions of interest (leaves, panicle, flower clusters, fruit clusters, axillary shoot, shoot, and whole-plant area) and by labels spanning 50 BBCH classes representing phenologically growth stages. A general cascading structure is proposed. For real-time applicability, models emphasizing the accuracy-efficiency trade-off (MobileNetv3, YOLOv11, and Mask R-CNN) are prioritized and benchmarked against multiple state-of-the-art models. Performance is assessed using accuracy, mAP, inference FPS, and normalized confusion matrices. In a study involving five domain experts, AI models trained on TomatoMAP achieves comparable accuracy levels. Reliability of automated fine-grained phenotyping is supported by Cohen’s Kappa statistics and inter-rater agreement heatmaps.

PMID:41764239 | DOI:10.1038/s41597-026-06926-9

NPJ Prim Care Respir Med. 2026 Feb 28. doi: 10.1038/s41533-026-00496-4. Online ahead of print.

ABSTRACT

We aimed to assess an obstructive sleep apnoea (OSA) diagnostic approach performed solely in primary care centres (PCC) with the support of an autoscoring home sleep apnoea testing (aHSAT, ApneaLink^TM Air) device and compare the diagnoses with those undertaken by the manual analysis of home sleep apnoea testing (mHSAT), and polysomnography (PSG) if necessary, of a certified sleep specialist. This multicentre, cross-sectional study was undertaken between April 2016 and November 2020. We randomly selected patients aged 30-70 years with a high probability of OSA (≥ 3 points on the STOP-Bang questionnaire) who were visiting any of the four PCCs assigned for referral to the University Hospital Doctor Josep Trueta, Girona, Spain. 2599 patients were assessed for eligibility; 403 provided a high probability of OSA and 329 could be compared between PCC and hospital. 210 (63.8%) patients were male and the mean age was 56.5 (SD: 9.2) years. The global agreement between PCC and hospital diagnoses was 41.6% and severe OSA showed the highest level of agreement (96.2%). The Kappa index for severe OSA was 0.46 (95% CI: 0.37, 0.55) and the specificity was 0.99 (95% CI: 0.97, 1.00). The ApneaLink^TM Air device showed high specificity for severe OSA in a high-risk primary care population with a high pre-test probability of OSA. When aHSAT indicates severe OSA, hospital confirmation may be unnecessary, whereas negative or moderate findings may still require specialist assessment.

PMID:41764227 | DOI:10.1038/s41533-026-00496-4