Tag: pubmed

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

Clin Imaging. 2026 May 28;136:110854. doi: 10.1016/j.clinimag.2026.110854. Online ahead of print.

ABSTRACT

PURPOSE: To evaluate the association between clinical and MRI characteristics and histological outcomes of MRI-guided vacuum-assisted biopsies (MVAB) performed in the lumpectomy bed after malignant breast-conserving surgery (BCT).

MATERIAL AND METHODS: This retrospective study analyzed all MVABs performed in malignant lumpectomy beds between 2016 and 2022, evaluating the relationship between demographic and imaging characteristics and pathological outcomes (benign vs. malignant) using appropriate statistical methods.

RESULTS: Malignancy was diagnosed in 14 of 72 biopsies (19%), most commonly invasive ductal carcinoma (50%). Fat necrosis was the predominant benign finding (38%). On univariate analysis, benign outcomes were more frequent in younger patients (median 60 vs. 69 years), those with prior radiation therapy (90% vs. 69%, P = 0.048), lesions adjacent to a postoperative cavity (28% vs. 7%, P = 0.1), and when coarse calcifications were present on mammography (43% vs. 0%, P = 0.011). Benign results were also more common for biopsies performed within one year or beyond six years after lumpectomy (97%, P = 0.087). No MRI morphological or kinetic features reliably distinguished benign from malignant lesions.

CONCLUSION: MVAB of lumpectomy bed lesions yielded a 19% malignancy rate, with benign outcomes often associated with specific clinical and imaging features. Larger studies are warranted to validate these findings and refine patient selection.

PMID:42224832 | DOI:10.1016/j.clinimag.2026.110854

Artif Intell Med. 2026 Jun 1;180:103462. doi: 10.1016/j.artmed.2026.103462. Online ahead of print.

ABSTRACT

Machine learning (ML) is transforming healthcare, but safe clinical decisions demand reliable uncertainty estimates that standard ML models fail to provide. Conformal prediction (CP) is a popular tool that allows users to turn heuristic uncertainty estimates into uncertainty estimates with statistical guarantees. CP works by converting predictions of a ML model, together with a calibration sample, into prediction sets that are guaranteed to contain the true label with any desired probability. An often cited advantage is that CP theory holds for calibration samples of arbitrary size, suggesting that uncertainty estimates with practically meaningful statistical guarantees can be achieved even if only small calibration sets are available. We question this promise by showing that, although the statistical guarantees hold for calibration sets of arbitrary size, the practical utility of these guarantees does highly depend on the size of the calibration set. This observation is relevant in medical domains because data is often scarce and obtaining large calibration sets is therefore infeasible. We corroborate our critique in an empirical demonstration on a medical image classification task.

PMID:42224800 | DOI:10.1016/j.artmed.2026.103462

Comput Methods Programs Biomed. 2026 May 28;284:109469. doi: 10.1016/j.cmpb.2026.109469. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Pediatric patients with myocarditis present with heterogeneous symptoms, disease courses and outcomes. Late Gadolinium Enhancement (LGE) in cardiovascular magnetic resonance imaging (CMR) is a routine diagnostic tool, but relationships between LGE patterns and patient characteristics are typically assessed qualitatively. We investigate the use of radiomic features to quantify LGE texture and location to identify signatures that stratify pediatric myocarditis cases.

METHODS: We compared radiomic features in a digital phantom across different resampling strategies to address variability in patient size and imaging parameters. Non-negative matrix factorization (NMF) was applied to spatially resolved radiomic features of the left myocardium to identify distinct radiomic signatures in a pediatric cohort with confirmed myocarditis. Clinical parameters were compared across the resulting groups, and correlations between image meta-features and outcomes explored. A user-friendly software tool offers feature extraction and signature calculation on unseen data and comparison of new patients to the existing cohort.

RESULTS: The phantom experiments showed improved comparability of radiomic features when resampled to uniform voxel density (voxel count per myocardial diameter) rather than uniform voxel size. After appropriate pre-processing, NMF identified four patient groups with distinct LGE signatures within 195 patients (median age 16 years, 19% female). One group separates out patients with signs of heart failure, correlating with left-ventricular ejection fraction (r=-0.38, 95% CI [-0.50,-0.25]) and log(NT-proBNP) (r=0.36,[0.21,0.50]). A second group’s dominant meta-feature correlates with myocardial edema (r=0.27,[0.13,0.40]) and ventricular tachycardia (r=0.19,[0.05,0.32]); a third indicates mild presentation. The clinical relevance of the fourth remains unclear.

CONCLUSIONS: Spatially resolved radiomic features from suitably resampled LGE CMR images yield quantitative LGE signatures associated with clinical characteristics in pediatric myocarditis, supporting improved stratification and personalized management in the long run.

PMID:42224791 | DOI:10.1016/j.cmpb.2026.109469