Tag: pubmed

Alzheimers Dement. 2026 May;22(5):e71458. doi: 10.1002/alz.71458.

ABSTRACT

INTRODUCTION: Subtle symptoms, like subjective cognitive decline (SCD) and minor neuropsychological deficits (MNPD), can improve the risk stratification in preclinical Alzheimer´s disease (AD) but their importance is insufficiently elaborated.

METHODS: We pooled data from cognitively normal individuals participating in three longitudinal cohort studies (N = 13,192, 8,359[63.3%] female, mean [SD] age 71.0[8.4]).

RESULTS: Compared to participants without SCD and MNPD (SCD-/MNPD-), SCD-/MNPD+, SCD+/MNPD-, and SCD+/MNPD+ participants had an increased risk for mild cognitive impairment (MCI) and dementia, including in amyloid-positive individuals. Focusing on SCD+/MNPD+ participants triples the positive predictive value of amyloid biomarker testing for the 5-year prediction of MCI and reduces the required samples size for trials in preclinical AD to one fourth, compared to considering all cognitively normal participants regardless of subtle symptoms.

DISCUSSION: SCD and MNPD offer a powerful approach for risk stratification in preclinical AD, which can improve clinical trial designs, risk counseling, and future case identifications for early treatment.

PMID:42129577 | DOI:10.1002/alz.71458

Nat Cancer. 2026 May 13. doi: 10.1038/s43018-026-01161-y. Online ahead of print.

ABSTRACT

Over 50% of persons with unresectable stage III non-small cell lung cancer (NSCLC) treated with standard-of-care concurrent chemoradiotherapy (CCRT) and durvalumab consolidation progress or die within 18 months. Here adults with untreated, unresectable stage III NSCLC were randomized to nivolumab plus CCRT followed by consolidation with nivolumab plus ipilimumab (arm A) or nivolumab alone (arm B) or CCRT followed by consolidation with durvalumab (arm C). The primary endpoint was progression-free survival (PFS) in arm A versus arm C and secondary endpoints included overall survival (OS), PFS in arm B versus arm C, response rates and safety. At a median follow-up of 30.5 months, there was no statistically significant difference in the primary endpoint of PFS in the nivolumab plus ipilimumab arm versus durvalumab arm (hazard ratio (HR): 0.95, 96% confidence interval (CI): 0.77-1.19; P = 0.65). Descriptive OS analysis showed no improvement (HR: 1.12, 95% CI: 0.87-1.43). Nivolumab alone did not improve PFS or OS versus durvalumab (PFS, HR: 0.84, 95% CI: 0.69-1.04; OS, HR: 0.97, 95% CI: 0.76-1.24). Nivolumab plus ipilimumab and nivolumab alone plus CCRT resulted in increased pneumonitis. These results emphasize the need for novel efficacious treatments for these individuals. (ClinicalTrials.gov: NCT04026412 ).

PMID:42129521 | DOI:10.1038/s43018-026-01161-y

Commun Med (Lond). 2026 May 13. doi: 10.1038/s43856-026-01646-y. Online ahead of print.

ABSTRACT

BACKGROUND: We aimed to investigate associations between newborn metabolite concentrations and the development of early-life wheezing and asthma. Our goal was to advance understanding of pathways involved in childhood asthma pathogenesis and identify potential targets for disease prevention.

METHODS: Our study populations included children enrolled in two Environmental influences on Child Health Outcomes (ECHO) cohorts (INSPIRE, discovery; Healthy Start, replication) with linked newborn screening metabolic and outcome data (4-year recurrent wheeze and 5-year current asthma). We used elastic net penalized regression, followed by multivariable logistic regression, to determine metabolite-wheeze and metabolite-asthma associations. We secondarily assessed whether metabolite-asthma associations differed by asthma phenotype in the discovery cohort.

RESULTS: Among 1554 INSPIRE children, the prevalence of recurrent wheeze and current asthma is 11% and 18%, respectively. Newborn concentrations of butyrylcarnitine + isobutyrylcarnitine (C4) and decenoylcarnitine (C10:1) are associated with recurrent wheeze (C4: aOR 0.75 [95% CI 0.59, 0.95]; C10:1: aOR 1.42 [95% CI 1.13, 1.78]), while linoleoylcarnitine (C18:2) and citrulline (CIT) are associated with current asthma (C18:2: aOR 1.20 [95% CI 1.02, 1.41]; CIT: aOR 0.74 [95% CI 0.58, 0.93]). The effect size and directionality of the association between C18:2 and childhood asthma is similar in Healthy Start (n = 518), although the relationship is not statistically significant. C18:2 is additionally associated with increased odds of non-allergic asthma compared to no asthma in INSPIRE.

CONCLUSIONS: These findings suggest biologic pathways that may be involved in childhood asthma pathogenesis and support investigation of the mechanisms underlying these relationships given the potential for targeted prevention strategies.

PMID:42129510 | DOI:10.1038/s43856-026-01646-y

Sci Rep. 2026 May 13;16(1):15058. doi: 10.1038/s41598-026-52851-7.

ABSTRACT

A novel stereological framework to generate synthetic three-dimensional cellular material structures using Voronoi tessellations is presented. While conventional investigations of microstructural features rely on costly and often destructive three-dimensional imaging techniques, our method enables the reconstruction of 3D cellular structures from two-dimensional planar-sectional image data. By representing 3D cell architectures through Voronoi tessellations, we obtain an analytical representation requiring only three parameters per cell, ensuring efficient storage and computational processing. Our framework employs a differentiable approximation of Voronoi tessellations combined with a discriminator neural network in an adversarial learning context, enabling gradient-based optimization of tessellation parameters to generate random 3D cellular structures with statistically similar 2D planar sections as observed in measured 2D image data. We demonstrate the framework on image data of various cellular materials including metallic alloys, biological cells, and foam structures. The presented framework shows state-of-the-art capability of stereologically reconstructing 3D cellular microstructures, while introducing a low-parameter representation, preserving physical interpretability, and ensuring computational efficiency.

PMID:42129505 | DOI:10.1038/s41598-026-52851-7

Sci Rep. 2026 May 13. doi: 10.1038/s41598-026-52827-7. Online ahead of print.

ABSTRACT

Mycoplasmas are known for being fastidious, with scientists still struggling to propagate them in vitro. Improving their culture is vital for future research, despite limited metabolomics studies available. This study explored the chemical composition changes in four liquid media (A, B, C, D) inoculated with Mycoplasma gallisepticum (MG), aiming to uncover overlooked metabolic features. We used Direct Analysis in Real Time High-Resolution Mass Spectrometry (DART-HRMS) across five time points over 71 h. Integration of DART-HRMS data with statistical analysis showed that media A and D initially contained higher glucose levels, which declined over time. Lactic acid levels rose in all media, with signals reaching saturation at the latest time points. Subsequent pathway enrichment analysis revealed an unexpected overexpression of arginine metabolism. Spermidine accumulation in certain media suggests a potential link to inhibited biofilm formation, opening questions about polyamine function in MG. Histidinal accumulation indicated an unpredicted amino acid synthesis capability in a mycoplasma and MG’s inability to convert arginine into glutamic acid. Additionally, MG was observed to utilize creatine when present. These findings highlight the importance of metabolomics in understanding enigmatic microorganisms like mycoplasmas, reaffirming that environmental factors drive alternative metabolic routes in MG and opening new research avenues.

PMID:42129501 | DOI:10.1038/s41598-026-52827-7

Sci Rep. 2026 May 13. doi: 10.1038/s41598-026-52289-x. Online ahead of print.

ABSTRACT

This study compared two widely used biomechanical models-Plug-in Gait (PiG) and Conventional Gait Model 2.3 (CGM2.3)-during overground walking (WALK) and single-leg squats (SLS) in 24 healthy adults. Data was collected using a 20-camera Vicon system and force plates. Static trials were analyzed with medial knee and ankle markers to align joint axes across models. Kinematic and kinetic outputs were compared using root mean square differences (RMSD) and statistical parametric mapping (SPM) paired t-tests. During WALK, PiG produced greater internal rotation at the knee (RMSD 17.8°, p < 0.001) and hip (RMSD 5.0°, p < 0.001), and smaller sagittal-plane flexion angles (RMSD 2.6° knee, 2.3° hip) compared with CGM2.3. In single-leg squats, these discrepancies increased to 29.1° and 9.0°, respectively, with sagittal-plane differences of 4.4° at the knee and 5.1° at the hip. CGM2.3 yielded higher knee flexion moments (31% in WALK, 104% in SLS), while PiG produced higher frontal-plane knee moments (28% and 89%). The differences were most pronounced at deeper flexion angles. These results demonstrate that biomechanical outcomes differ systematically between models, emphasizing the impact of model selection on joint kinematics and kinetics in human movement analysis.

PMID:42129499 | DOI:10.1038/s41598-026-52289-x

Sci Rep. 2026 May 13. doi: 10.1038/s41598-026-52543-2. Online ahead of print.

ABSTRACT

In this study, we explore the (2+1)-dimensional Heisenberg ferromagnetic spin chain (HFSC) equation because of its significant role in modeling the nonlinear spin-wave propagation and magnetic excitations in ferromagnetic materials. The aim is to develop exact analytical solutions of the model through two different methods: a modified (addendum-type) Kudryashov method and a unified Riccati equation method. These methods provide a range of exact wave solutions, such as periodic, hyperbolic, trigonometric and rational structures, which exhibit a rich nonlinear behavior of the model. The solutions are discussed and depicted graphically in 2D and 3D forms, exhibiting stable, bounded, and finite propagation of waves without singularities. A key novelty of this study lies in the combined application of the two analytical methods to the HFSC model, which has not been extensively explored in previous literature. The outcome indicates the success and compatibility of these methods in describing the nonlinear behavior of spin-wave structures. The results could be applied for the study of nonlinear magnetic structures and may find applications in spintronics and modeling of ferromagnetic materials.

PMID:42129492 | DOI:10.1038/s41598-026-52543-2

Sci Rep. 2026 May 13. doi: 10.1038/s41598-026-52883-z. Online ahead of print.

ABSTRACT

High predictive accuracy is frequently misinterpreted as evidence of causal understanding or population-level signal. Models can exploit spurious correlations, confounding, or protocol-induced artefacts, while post-hoc explanations may faithfully describe model behaviour yet remain misleading about the underlying phenomenon. We propose a framework that separates three layers of evidence: (i) causal relations in the phenomenon, (ii) population-level statistical dependence, and (iii) finite-sample, protocol-dependent predictive effects. This separation clarifies why predictive success and feature attributions do not license mechanistic interpretations without additional assumptions. Under log-loss and Bayes-risk-consistent protocols, the population predictive value of adding a feature equals the conditional mutual information, providing a principled reference for “true signal”. Using controlled simulations, we illustrate that bootstrap resampling can induce persistent false positives by amplifying chance correlations, and that SHAP can assign high importance to confounded variables while remaining faithful to the fitted model. These results suggest that “feature importance” is best treated as protocol-bounded evidence, and that interpretation benefits from reporting the protocol, robustness checks, and the intended inferential scope.

PMID:42129446 | DOI:10.1038/s41598-026-52883-z

Commun Biol. 2026 May 13. doi: 10.1038/s42003-026-10231-x. Online ahead of print.

ABSTRACT

Body size is a fundamental driver of metabolism, yet it remains unclear whether colonial organisms such as corals conform to the universal ¾-power scaling law. As climate change accelerates metabolic demands, characterizing these scaling relationships is essential to identifying which species are most physiologically vulnerable to environmental shifts. Here, we test whether coral polyp morphological traits can predict aerobic metabolism across a diverse range of reef-building species. We examine relationships between respiration and polyp biovolume, surface area, and corallite width, finding isometric scaling with biovolume and slight positive allometry with surface area, with both exponents close to one. Using median corallite width, we further extrapolate our model to theoretically predict per-polyp respiration for 727 coral species from a publicly available trait database.

PMID:42129431 | DOI:10.1038/s42003-026-10231-x

Sci Rep. 2026 May 13. doi: 10.1038/s41598-026-53171-6. Online ahead of print.

ABSTRACT

Cerebral small vessel disease (CSVD) poses major public health challenges, yet current MRI based diagnosis detects only established damage, and existing auxiliary methods, mostly based on conventional statistics, lack sufficient feature extraction capability and generalizability, thereby limiting early warning and precision management. Accordingly, we developed an intelligent auxiliary diagnostic system grounded in an interpretable ensemble learning framework, aiming to enable early detection and warning of CSVD. To support this development, a total of 597 sets of electronic medical record data from Quzhou Affiliated Hospital of Wenzhou Medical University were used as the study cohort. Firstly, a multidimensional feature evaluation and selection method was proposed, identifying 12 key predictive factors out of 23 relevant variables. Subsequently, the optimal algorithm was selected from Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting Machine, XGBoost, and Multilayer Perceptron Classifier, based on Area Under the Curve (AUC) and Accuracy metrics, and a stacking ensemble learning strategy was then employed for model construction. The developed model demonstrated excellent discriminative performance, achieving an AUC of 0.881 while maintaining a low Brier score of 0.1271. By integrating the SHAP interpretability algorithm, the model provided intuitive visualizations of feature importance, thereby enhancing transparency and facilitating clinical adoption. Ultimately, this study achieved effective integration of early warning and auxiliary diagnostic functions for CSVD. These results indicate that the proposed system possesses high accuracy, interpretability, and deployability, underscoring its broad potential for early warning and personalized management of CSVD.

PMID:42129429 | DOI:10.1038/s41598-026-53171-6