Category: Statistics

Nat Genet. 2026 Jun 1. doi: 10.1038/s41588-026-02643-6. Online ahead of print.

NO ABSTRACT

PMID:42225866 | DOI:10.1038/s41588-026-02643-6

NPJ Digit Med. 2026 Jun 1;9(1):412. doi: 10.1038/s41746-026-02819-8.

ABSTRACT

Artificial intelligence (AI) has shown strong performance in cardiology, but most approaches rely on sensing modalities whose physical limitations constrain available information. Magnetocardiography (MCG) records the cardiac magnetic field with less tissue distortion than surface potentials and may preserve higher-dimensional spatiotemporal electrophysiological structure. Here, we investigated whether combining MCG with self-supervised learning enables physiologically meaningful cardiac representations. We developed MCG2Vec, a contrastive encoder trained directly on raw 64-channel MCG recordings. Using recordings from 1732 consecutive patients, learned embeddings were evaluated with task-specific probes for multivessel coronary artery disease, reduced left ventricular ejection fraction, and paroxysmal atrial fibrillation risk from sinus-rhythm recordings. The representations enabled discrimination of multivessel coronary artery disease (area under the receiver operating characteristic curve (AUC) 0.89), reduced left ventricular ejection fraction (AUC 0.81), and atrial fibrillation risk (AUC 0.77). Attribution analyses revealed probe-specific temporal and spatial patterns corresponding to ventricular depolarization, repolarization, atrial activation dynamics, and coronary territories, supporting physiological interpretability. These findings suggest that higher-fidelity sensing combined with self-supervised representation learning can yield structured and explainable embeddings from non-invasive cardiac magnetic field recordings. More broadly, the study highlights measurement physics as an important determinant of what medical AI systems can learn.

PMID:42225842 | DOI:10.1038/s41746-026-02819-8

Sci Rep. 2026 Jun 1. doi: 10.1038/s41598-026-54196-7. Online ahead of print.

ABSTRACT

Peritoneal adhesions are common following abdominal surgery and lead to significant morbidity. Both pharmacological agents and barrier methods have been investigated for prevention, but with limited success. In this study, the efficacy of intraperitoneal pitavastatin was evaluated and compared with Seprafilm. Thirty-two female Wistar albino rats were randomly divided into four groups (n = 8): control, saline, pitavastatin (30 mg/kg intraperitoneal), and Seprafilm (30 × 20 mm). Macroscopic adhesions were graded using the Majuzi classification, and microscopic adhesions were assessed using the Zühlke scoring system. Plasma SCUBE1, malondialdehyde, and tissue-type plasminogen activator levels were analyzed. Macroscopic evaluation showed that adhesions were predominantly grade 2 in the control group (62.5%) and the saline group (75%). In contrast, a partial reduction in adhesion severity was observed in both the pitavastatin and Seprafilm groups; 25% of patients in both groups showed no adhesions (grade 0). Lower-grade adhesions (grades 1-2) were observed more frequently in these treatment groups. Microscopically, all rats in the control and Seprafilm groups were classified as stage 2 according to the Zühlke grading system. While grade 1 adhesions were predominantly observed in the saline group (87.5%), a broader distribution was observed in the pitavastatin group, including grade 1 (25%), grade 2 (50%), and grade 3 (25%) adhesions. Biochemical analysis revealed no significant differences among groups in plasma SCUBE1 (p = 0.294) and malondialdehyde levels (p = 0.051). However, plasma tissue-type plasminogen activator levels were significantly higher in the control group compared with both the pitavastatin (p = 0.012) and Seprafilm groups (p = 0.027). Intraperitoneal pitavastatin has demonstrated efficacy comparable to that of Seprafilm; however, neither treatment provided a statistically significant protective effect against postoperative peritoneal adhesions.

PMID:42225809 | DOI:10.1038/s41598-026-54196-7

Sci Rep. 2026 Jun 1. doi: 10.1038/s41598-026-54322-5. Online ahead of print.

ABSTRACT

This study aimed to evaluate the performance of different rootstock-scion combinations on yield and quality improvement in ber (Ziziphus mauritiana Lamk.) under semiarid conditions was conducted at the Regional Research Station, Bawal, Haryana. For statistical analysis, data collected during the stabilized bearing phase (2020-2024) were utilized. The experiment was conducted in a two-factor randomized block design (RBD) comprising three rootstocks (Ziziphus rotundifolia, Ziziphus mauritiana cv. Tikadi and Ziziphus mauritiana cv. Sukhavani) and two scion varieties (Gola and Umran). The pooled data revealed significant variation among rootstocks, scions and their combinations in terms of plant height, trunk girth, leaf area index, chlorophyll content, canopy footprint, fruit physicochemical attributes and yield. Z. mauritiana Tikadi consistently imparted greater vigor, greater trunk cross-sectional area (45.1 cm²), increased canopy footprint (25.8 m²) and greater yield (53.60 kg/plant) in combination with Umran. In contrast, Z. mauritiana Sukhavani presented a comparatively reduced tree size and improved fruit quality traits, such as greater total soluble solids (19.25°B) and ascorbic acid (95.41 mg/100 g), especially with Gola. Fruit size and pulp content are largely governed by the scion genotype, with Umran producing heavier fruits. The scion/stock ratio remained close to unity across the treatments, confirming good graft compatibility. The results highlight the importance of rootstock selection in regulating tree performance, suggesting a practical strategy for improving orchard productivity without the need for the genetic replacement of cultivars.

PMID:42225796 | DOI:10.1038/s41598-026-54322-5

Sci Rep. 2026 Jun 1. doi: 10.1038/s41598-026-54803-7. Online ahead of print.

ABSTRACT

Asthma and chronic obstructive pulmonary disease (COPD) are significant global health burdens with conventional diagnosis relying on resource-intensive spirometry. This paper presents a reproducible multimodal respiratory sound screening model combining complementary acoustic and clinical representations. The proposed method fuses handcrafted spectral-temporal features (MFCCs, chroma, spectral contrast, tonnetz, mel-spectrogram, tempogram) with precomputed cough and vowel embeddings and structured clinical metadata, processed via a class-weighted CatBoost ensemble on the standardized AIRS Kaggle benchmark dataset. The model achieves an overall accuracy of 90.3% with class-wise F1-scores of 0.945 (Healthy), 0.915 (Asthma), and 0.842 (COPD). Systematic ablation experiments confirm the importance of multimodal fusion (-7.8% accuracy without full feature fusion), the attention mechanism (-4.9%), and data augmentation (-6.7%). Additional metrics such as, Matthews Correlation Coefficient (MCC = 0.856) and Cohen’s Kappa (κ = 0.849) – confirm robust classification under class imbalance. Structured multimodal feature fusion with gradient boosting enables scalable, reproducible respiratory disease screening applicable to telemedicine. Future work should address prospective validation on diverse, multi-institutional clinical cohorts.

PMID:42225794 | DOI:10.1038/s41598-026-54803-7

Sci Rep. 2026 Jun 1. doi: 10.1038/s41598-026-54553-6. Online ahead of print.

ABSTRACT

The paper focuses on detecting faults in tapered roller bearings to prevent unplanned shutdowns, accidents, and financial losses. Tapered roller bearings are an indispensable component used in mechanical applications, where handling of combined loads is required in rotating machinery. This study proposes a novel framework ConvECA-Net for advanced fault diagnosis of tapered roller bearings. This innovative architecture combines Efficient Channel Attention (ECA), adaptive kernel size strategy, and Leaky ReLU activation. This fault classification network is designed to classify five fault conditions in the tapered roller bearings. This proposed architecture is compared with deep learning algorithms such as ANN and ResNet50 and traditional machine learning algorithms such as SVM and RF. The proposed ConvECA-Net achieves a better classification accuracy of 95.07% and requires only 563 K trainable parameters and a model size of 2.2 MB. A systematic component-wise ablation study is conducted to validate that each component of this architectural design, namely ECA attention, adaptive kernel sizing, and Leaky-ReLU activation, individually and collectively contribute to the overall performance of this diagnostic model, as it achieves superior performance compared to its baseline variant by 5.45%. Cross-condition robustness is further established by evaluating this diagnostic model on three different rotational speeds and various load levels. Evaluation of noise robustness on this diagnostic model for various levels of Gaussian white noise and pink noise, further establishes its robustness of the proposed model. Further, Statistical validation of this diagnostic model is conducted by running this experiment ten times, using paired t-tests, Shapiro-Wilk normality tests, and stratified 5-fold cross-validation (94.82% ± 0.38%). Finally, analysis of computational efficiency of this diagnostic model reveals that it achieves 192 MFLOPs and an inference latency of 0.82 ms/sample, making it suitable for industrial condition monitoring systems of rotating machinery to prevent the plant shutdown.

PMID:42225790 | DOI:10.1038/s41598-026-54553-6

Sci Rep. 2026 Jun 1. doi: 10.1038/s41598-026-54775-8. Online ahead of print.

ABSTRACT

As demonstrated by numerous environmental disasters worldwide, many sites have suffered from seismically induced liquefaction, resulting in substantial economic losses. Consequently, there is an urgent need for reliable prediction methods to assess vulnerability to liquefaction. In this study, the liquefaction potential (LP) of gravelly soil sites is predicted using available seismological parameters (M_w, R, t, PGA), soil parameters (G, F, D₅₀), and site profile parameters (N’₁₂₀, σ’_v, Dw, H_n, D_n) through AI-based symbolic regression techniques, namely response surface methodology (RSM), genetic programming (GP), evolutionary polynomial regression (EPR), and the group method of data handling neural network (GMDH-NN). A total of 234 data records were compiled from earthquake case histories reported in the literature and divided into 80% for training and 20% for validation. RSM was employed to model the database, whereas GP, EPR, and GMDH-NN were used for liquefaction potential classification in the investigated area. Comparative evaluation of model performance indicates that the RSM yielded a statistically significant parametric LP equation, operating with a degree of variation of 0.61 and a p-value of 0.0001. For the classification models, GP, EPR, and GMDH indicate that liquefaction is expected when the predicted LP ≥ 0.5, while liquefaction is not triggered when LP < 0.5. The total misclassification cases were categorized into positive errors, where liquefaction occurred but was not predicted, and negative errors, where liquefaction was predicted but not observed, with the latter being more conservative. In practice, negative errors are not entirely definitive, as liquefaction may locally occur beneath surface layers without being visibly manifested. Although all four predictive models achieved comparable accuracy levels ranging from 88% to 90%, further analysis revealed that the GP model produced 12% positive errors, whereas both EPR and GMDH models resulted in only 6% positive errors, indicating that they are more conservative and safer than the GP model. In addition, the GMDH model is considerably more complex than the EPR model, providing EPR with a notable practical advantage. Correlation analysis further demonstrated that the vertical effective overburden stress (σ’_v) and the dynamic penetration test blow count (N’₁₂₀) are the most influential parameters, each exhibiting a correlation coefficient greater than 0.3g.

PMID:42225787 | DOI:10.1038/s41598-026-54775-8

Sci Rep. 2026 Jun 1. doi: 10.1038/s41598-026-54314-5. Online ahead of print.

ABSTRACT

The efficient removal of low-concentration nitrogen (N₂) is a critical and challenging task in the industrial production of high-purity oxygen (O₂), particularly in air separation processes and in natural gas purification for generating high-purity methane (CH₄). In this work, three advanced modeling techniques, namely the group method of data handling (GMDH), gene expression programming (GEP), and genetic programming (GP) were applied to develop user-friendly mathematical correlations for predicting nitrogen storage capacity of MOFs. A broad dataset of 3073 laboratory measurements was employed. The developed models were further evaluated for their accuracy and reliability using various statistical and graphical methods. Among the models, the GEP correlation provided the most reliable outcomes with superior statistical metrics, yielding mean absolute error (MAE) values of 0.9924, 1.0101 and 0.9959 for the training, testing, and overall datasets, and R² values of 0.9703, 0.9750, and 0.9714, respectively. All the models closely followed the expected trend of N₂ storage under varying pressure. Additionally, the Pearson, Spearman, and Kendall correlation analyses were employed to examine the influence of individual input factors on the model outputs. The findings revealed that temperature has the most substantial impact on storage capacity across both linear and non-linear dimensions, whereas pressure primarily affects it through nonlinear interactions. At last, the credibility of the collected databank and the applicability of the recommended correlations were confirmed using the leverage method, demonstrating more than 95% of the collected data fell within the acceptable range of the Williams plot.

PMID:42225777 | DOI:10.1038/s41598-026-54314-5

Sci Rep. 2026 May 31. doi: 10.1038/s41598-026-53864-y. Online ahead of print.

ABSTRACT

Quantum annealers natively minimize quadratic unconstrained binary optimization (QUBO) problems, yet faithfully compiling continuous convex objectives into discrete binary forms with formal guarantees remains challenging. We present a complete, algebraically verifiable pipeline for training a linear squared-hinge support vector machine on quantum annealing hardware. The construction comprises four stages with rigorous justification: (i) an exact epigraph reformulation eliminating the hinge nonlinearity, (ii) equality conversion via surplus variables with a quadratic penalty whose exactness on the finite binary domain is formally established, (iii) closed-form QUBO coefficients and a provably energy-preserving Ising mapping, and (iv) a moment-based three-component decoder that reconstructs continuous parameters from noisy annealer samples using empirical first- and second-order statistics. We execute this pipeline end-to-end on D-Wave Advantage systems and evaluate under a rigorous protocol with 30 stratified splits, bootstrap confidence intervals, paired tests, and effect sizes. The feature-wise solver achieves 87-89% test accuracy on the Iris benchmark, competitive with classical baselines at this scale. We contribute a fully auditable reduction from convex SVM training to Ising optimization rather than claiming quantum advantage, and explicitly characterize limitations from discretization, embedding overhead, and feature-wise decomposition.

PMID:42225770 | DOI:10.1038/s41598-026-53864-y

Sci Rep. 2026 Jun 1. doi: 10.1038/s41598-026-51264-w. Online ahead of print.

ABSTRACT

Colorectal cancer frequently spreads to the liver, and current treatments often damage healthy liver tissue, while targeting tumors. Diffusing Alpha-emitters Radiation Therapy (Alpha DaRT) disperses alpha-emitting atoms from an intra-tumoral implanted source to a range of a few millimeters in the tumor. Alpha radiation is highly efficient in killing cancer cells, compared to other radiation types, and its short range in the tissue, potentially enables sparing healthy tissue adjacent to the tumor. Alpha DaRT’s efficacy was previously demonstrated in subcutaneous tumors from various histotypes. Yet its effectiveness in treating tumors located in an internal organ, such as the liver, remains to be tested in animal studies. Here, we used a single liver metastasis orthotopic model by implanting MC38 colorectal metastatic tissue in mice livers. The metastasis was then treated either with a single radioactive or inert Alpha-DaRT source. Alpha-DaRT effectively delayed tumor growth without damaging the surrounding liver parenchymal tissue. F4/80 + immunosuppressive macrophages in the tumor-normal tissue interface were reduced following treatment. The findings suggest that this type of radiation therapy could be a promising option for treating patients with liver tumors.

PMID:42225757 | DOI:10.1038/s41598-026-51264-w