Nevin Manimala

Med Phys. 2026 May;53(5):e70497. doi: 10.1002/mp.70497.

ABSTRACT

BACKGROUND: In boron neutron capture therapy (BNCT), Monte Carlo (MC) dose calculations are commonly employed because of the complicated neutron reactions. However, MC dose calculations are generally time-consuming. Recently, deep learning (DL)-based dose prediction/calculation has attracted increasing attention; however, the applications of DL models in BNCT are limited and have not been investigated extensively. In addition, there are no practical DL models that can be employed in BNCT clinical practice.

PURPOSE: We propose a practical DL model for head and neck cancers using a commercial treatment planning system (TPS) for BNCT. To increase the speed of the MC dose calculations, the proposed DL model converts the BNCT dose components calculated by the coarse dose calculation grid size and low statistical uncertainty in the MC calculation into the dose components calculated under the fine setting.

METHODS: In this study, we considered 114 head and neck cancer patients who underwent accelerator-based BNCT at our center. Here, we randomly divided 102 patients for training/validation and 12 patients for testing. The BNCT dose components (i.e., boron, nitrogen, hydrogen, and gamma doses) were calculated for all patients using a commercial TPS for BNCT. We employed the hierarchically dense U-net and converted the BNCT dose components calculated by the coarse setting (grid size/uncertainty = 5 mm/10%) into doses calculated by the fine setting (2 mm/5%). In addition, a physical density map was added to the DL input to improve the conversion accuracy. Taking the fine dose as the ground truth, we evaluated the γ-passing rates with various criteria for each dose component of the coarse and DL doses. The calculation time was also measured in the fine, coarse, and DL doses.

RESULTS: In the boron dose, the DL dose exhibited significantly higher γ-passing rates of ≥ 95% with a criterion of 1%/2 mm (dose difference/distance to agreement) than the coarse dose. In the nitrogen and hydrogen doses, the DL dose also demonstrated high γ-passing rates of 95.3% and 94.7% with a criterion of 5%/2 mm. The density map was effective for the hydrogen and nitrogen doses. In addition, the average γ-passing rate with the criterion of 3%/2 mm in the gamma dose achieved 96.2% for the DL dose. The average calculation times for the fine and coarse settings were 984.2 ± 470.2 min and 11.0 ± 2.9 min, respectively, and the average conversion time in the DL model was 0.091 ± 0.020 min.

CONCLUSIONS: In this study, the proposed DL model was developed to convert each dose component calculated in the coarse setting to the fine dose to increase the speed of commercial MC dose calculations in BNCT for head and neck cancers. The conversion speed from the coarse dose to the fine dose was considerably rapid, and its performance was highly accurate. The proposed DL model can provide accurate BNCT dose distributions at high speed, thereby contributing to improving the quality of BNCT treatment planning.

PMID:42192222 | DOI:10.1002/mp.70497

Kaohsiung J Med Sci. 2026 May 26:e70240. doi: 10.1002/kjm2.70240. Online ahead of print.

ABSTRACT

Metabolic dysfunction-associated steatotic liver disease (MASLD) is increasingly prevalent among lean Asian populations, yet effective strategies for identifying high-risk individuals remain limited. We investigated the associations of body fat percentage (BF%) and the triglyceride-glucose (TyG) index with lean MASLD and evaluated their incremental diagnostic value in two independent studies (the NAGALA cohort and a Chinese health check-up study). Lean MASLD was defined as imaging-confirmed hepatic steatosis in individuals with BMI < 23 kg/m². In both studies, participants with MASLD were older, more often male, and exhibited less favorable metabolic profiles. Multivariable analyses showed that the TyG index was consistently associated with increased odds of lean MASLD (adjusted OR per unit increase: 3.41 in NAGALA and 6.37 in the Chinese study), whereas associations of BF% varied by cohort and sex, with significant associations observed in NAGALA men and Chinese women (adjusted OR per unit increase: 1.20 and 1.24, respectively). In ROC analyses, the TyG index showed good discrimination (C-statistics 0.778-0.875), and the addition of BF% further improved performance (0.805-0.901), corresponding to an absolute increase of approximately 0.02-0.05, with consistent improvements in net reclassification and discrimination (all p < 0.05). Mendelian randomization analyses supported a potential causal association between the TyG index and NAFLD, while no significant causal association was observed for BF%. Overall, BF% and the TyG index provide complementary information, and their combined use improves the identification of lean MASLD.

PMID:42192212 | DOI:10.1002/kjm2.70240

Sci Rep. 2026 May 26. doi: 10.1038/s41598-026-54492-2. Online ahead of print.

ABSTRACT

The timing of the initial spring water-level rise represents a key indicator of seasonal hydrological transition in snowmelt-dominated river systems of high-latitude regions. This study evaluates the capability of ensemble machine learning (ML) models to estimate the onset date of the spring water-level rise in Arctic-subarctic rivers of the Anadyr-Kolyma basin district in northeastern Russia using a station-year dataset for the period 2008-2022, combining hydrological observations with meteorological and basin-related predictors. Five regression algorithms were tested using grouped cross-validation by year. CatBoost achieved the highest predictive accuracy with an out-of-fold mean absolute error of 4.54 days, RMSE of 9.79 days, and [Formula: see text], slightly outperforming ExtraTrees (MAE 4.66 days) and RandomForest (MAE 4.70 days). Spatial analysis shows that most gauging stations exhibit prediction errors within 0.5-3 days, whereas errors exceeding 10 days occur mainly in small or topographically complex basins with limited observational coverage. Model interpretation using SHapley Additive exPlanations (SHAP) and partial dependence (PDP) analysis indicates that predictors describing thermal forcing during late winter and early spring dominate the model response, with positive degree days during March-April, the first thaw day, and indicators of rapid water-level rise providing the largest contributions. The onset of spring water-level rise in the studied Arctic-subarctic river systems is primarily associated with the interaction between temperature-driven snowmelt processes and the early hydrological response of the river network, whereas precipitation and spatial descriptors exhibit comparatively smaller contributions. These statistical relationships are conditioned on the 2008-2022 period and may vary under different climatic conditions or longer observational records, which should be considered when applying the model for prediction.

PMID:42192198 | DOI:10.1038/s41598-026-54492-2

Sci Rep. 2026 May 26. doi: 10.1038/s41598-026-50251-5. Online ahead of print.

ABSTRACT

The growing demand for sustainable construction materials has accelerated research into eco-friendly alternatives to traditional Portland cement. This research explores the potential of geopolymer concrete formulated from agricultural-waste ashes as a sustainable replacement for conventional Portland cement. Banana peel ash (BPA) and sugarcane bagasse ash (SCBA) were employed as aluminosilicate precursors, and their combined effects were systematically examined through controlled variations in blend proportion, alkaline activator molarity, sodium silicate-to-sodium hydroxide (SS/SH) ratio, and aggregate-to-binder ratio. The influence of these parameters on fresh and hardened properties-including workability, compressive strength, and flexural strength-was rigorously evaluated. Within the defined experimental domain, an optimal formulation comprising 52.5% SCBA and 47.5% BPA activated with 10 M NaOH achieved compressive and flexural strengths of 33.17 MPa and 9.95 MPa, respectively, demonstrating structural-grade performance suitable for practical applications. Detailed microstructural investigations employing SEM-EDS, XRD, FTIR and TGA techniques confirmed that both ashes exhibit high silica content, significant pozzolanic behaviour, and that increased activator concentration enhanced the dissolution of aluminosilicate phases leading to a denser geopolymeric matrix with improved durability. To further strengthen the analytical framework and enable predictive mix optimization, artificial intelligence-based models-Gene Expression Programming (GEP) and Artificial Neural Networks (ANN)-were developed. Both models achieved excellent predictive performance (R² > 0.98) with respect to slump Flexural and compressive strength; however, the GEP model consistently exhibited superior accuracy, lower error indices and better alignment with measured results than the ANN. Performance was validated through statistical metrics including Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and coefficient of determination (R²), confirming the robustness of the machine-learning framework in capturing the complex, non-linear interactions among mix variables. The novelty of this study lies in demonstrating that low-value agricultural waste ashes can be engineered into reliable, structural-grade geopolymer binders, producing a high-performance BPA-SCBA concrete that repurposes agricultural residues and reduces the carbon footprint of cement production. Additionally, the integration of AI-based optimization provides a robust decision-support tool for mix design, enabling data-driven, sustainable construction practices.

PMID:42192170 | DOI:10.1038/s41598-026-50251-5

Sci Rep. 2026 May 26. doi: 10.1038/s41598-026-55540-7. Online ahead of print.

ABSTRACT

Single-shade composite restorations can adapt to the tooth structure, improve esthetics, and reduce reliance on shade selection. The objective of this study was to assess the effect of four different children’s beverages on the color changes, gloss, and surface microhardness of single-shade resin composites compared to universal composite. Forty-eight specimens were prepared from each Shade A2 universal (Filtek Z250) and single-shade resin composite material (Vittra APS Unique). Then, the specimens were divided into four subgroups: Distilled Water (control group), Pepsi Cola Drink, Orange Juice, and Chocolate Milk, all at 24 °C. These specimens were immersed in their respective beverages for 30 min daily. Color changes, gloss, and microhardness values were evaluated at baseline before immersion and at the 1st, 7th, and 30th days of immersion. Data were collected and statistically analyzed using two-way variance analysis (ANOVA) and Tukey’s post-hoc test (p < 0.05). The results showed color change was significantly greater in Vittra APS than in Filtek Z250 across all time intervals, especially in Pepsi Cola after 30th days. For gloss, Vittra APS and Filtek Z250 at all time periods for four storage media showed statistically significant differences. Microhardness of Vittra APS was significantly affected by all time periods and all media except water. Filtek Z250 showed significant differences across all storage media at baseline and after 1st day. Overall, the single-shade composite showed more color change, gloss loss, and hardness reduction-especially in storage media like Pepsi Cola and Orange Juice-compared to the universal composite.

PMID:42192168 | DOI:10.1038/s41598-026-55540-7

J Perinatol. 2026 May 26. doi: 10.1038/s41372-026-02731-9. Online ahead of print.

ABSTRACT

OBJECTIVE: Evaluate Neonatal Intensive Care Unit (NICU) interpreter access and utilization, unit-based interpreter policies and initiatives, staff awareness and confidence in understanding language-access laws, and perceptions of language-based inequities.

STUDY DESIGN: An exploratory national survey of NICU staff was distributed via the National Association of Neonatal Nurses and the American Academy of Pediatrics (AAP) (10/2024-4/2025). Descriptive statistics and qualitative analysis were used for survey results.

RESULT: The 189 respondents represented all ten AAP districts. Most were aware of NICU-based interpreter policies (76%). 81% were not aware of additional state laws/provisions and many lacked confidence understanding federal (43%) or state (63%) language-access laws. Many respondents disagreed that language-discordance resulted in worse quality of care (40%) and outcomes (59%) in their NICU.

CONCLUSION: Results highlight the need for additional education on federal and state laws and provisions as well as the broad and systemic nature of language-based healthcare inequities across institutions.

PMID:42192163 | DOI:10.1038/s41372-026-02731-9

Sci Rep. 2026 May 26. doi: 10.1038/s41598-026-50687-9. Online ahead of print.

ABSTRACT

Accurate prediction of soil surface wetness (SSW) is vital for effective land management and resource optimization, particularly in sensitive ecosystems like the Western Himalayas. The main objective of the present study is to improve the accuracy of SSW prediction using hybrid and bagging models in the selected sites in Punjab, north-western India. The study utilizes ten machine-learning models comprising five base learners-random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), logistic model tree (LMT), and classification and regression tree (CART) and their corresponding AdaBoost-based hybrid variants: AdaBoost-RF, AdaBoost-XGBoost, AdaBoost-LightGBM, AdaBoost-LMT, and AdaBoost-CART. The SSW data set was collected from NASA POWER platform and Goddard Earth Observing System (GEOS) derived data which covers the period from 1986 to 2021. For model development, we applied a monthly data-lagging technique to generate different model scenarios. For feature selection, we applied greedy stepwise and best-first algorithms to identify the most effective predictors and improve model efficiency. Evaluations of the models were based on a variety of statistical indices. The results show that the RF model achieved the highest correlation coefficients (CC) across the study areas, ranging from 0.64 to 0.76 in Moga, 0.61 to 0.82 (XGBoost) in Hoshiarpur, and 0.41 to 0.64 (LMT) in Firozpur during the testing period. Accordingly, the hybrid AdaBoost-LightGBM model was the best, with CC values of 0.61-0.76, 0.63-0.82, and 0.49-0.60 for Moga, Hoshiarpur, and Firozpur, respectively. Overall model performance was limited to moderate and varied across locations and scenarios; although AdaBoost-LMT showed the best relative performance, the results primarily support its use for reproducing temporal variability in GEOS-derived SSW rather than precise wetness estimation. The findings contribute to improving SSW estimation and support data-driven decision-making for sustainable land and water management in the selected sites in Punjab, north-western India.

PMID:42192144 | DOI:10.1038/s41598-026-50687-9

NPJ Parkinsons Dis. 2026 May 26. doi: 10.1038/s41531-026-01409-5. Online ahead of print.

ABSTRACT

Motor impairment in Parkinson’s disease (PD) is classically attributed to striatal dopaminergic degeneration, yet dopamine loss alone does not fully explain symptom severity, suggesting a key role for circuit-level mechanisms. The cortico-basal ganglia-thalamo-cortical (CBGTC) system is central to dopaminergic motor modulation, but whether dysfunction within individualized CBGTC circuits mediates motor severity across disease stages remains unclear. Here, we studied 76 PD patients (40 mild, 36 moderate-to-severe) who underwent ¹⁸F-FP-CIT PET and multimodal MRI. Individualized long and short CBGTC loops were reconstructed using connectivity profile-based segmentation and probabilistic tractography, with functional connectivity (FC) quantified alongside striatal dopaminergic integrity. Compared with mild PD, moderate-to-severe PD showed a global reduction in striatal dopamine binding, most pronounced in the bilateral caudate. Critically, FC between the caudate and premotor cortex in the more-affected hemisphere was selectively reduced with increasing disease severity in both CBGTC loops (p-FDR = 0.027). The cross-sectional mediation models demonstrated that caudate-premotor FC statistically accounted for the association between caudate dopaminergic loss and motor symptom severity. These findings position individualized cortico-basal ganglia circuit connectivity as a potential mechanistically grounded biomarker linking molecular pathology to motor impairment in PD, with potential relevance for future circuit-guided therapeutic strategies.

PMID:42192116 | DOI:10.1038/s41531-026-01409-5

Nat Commun. 2026 May 26. doi: 10.1038/s41467-026-73397-2. Online ahead of print.

ABSTRACT

Accurate determination of enantiomeric purity is essential for advancing chiral materials in nanotechnology, optoelectronics, and quantum information science. Chiroptical spectroscopic techniques provide rapid, non-destructive measurements of enantiomeric excess (ee), but their use for complex systems like single-walled carbon nanotubes (SWCNTs) is limited by the lack of enantiopure references for calibration. Here we demonstrate an absolute approach combining hyperspectral imaging (HSI) with single-nanotube counting statistics and ensemble chiroptical spectroscopy such as electronic circular dichroism (ECD) and Raman optical activity (ROA) to quantify ee without requiring such standards. Analysis of thousands of individual nanotubes reveals sensitivity of HSI and chiroptical responses to synthesis, purification, and SWCNT concentration, highlighting pronounced source-dependent inhomogeneity. Nevertheless, universal calibration curves for ECD and ROA intensities are established from the purest, most uniform enantiomer-sorted samples. This methodology is extendable to other SWCNT chiralities and chiroptical techniques, enabling quantitative enantiomer sorting and systematic investigations of chirality-dependent properties and applications.

PMID:42192104 | DOI:10.1038/s41467-026-73397-2

Cardiovasc Interv Ther. 2026 May 26. doi: 10.1007/s12928-026-01304-z. Online ahead of print.

ABSTRACT

In patients with ACS undergoing PCI, de-escalation to P2Y12-inhibitor monotherapy reduces bleeding, but the optimal timing of aspirin withdrawal is uncertain. We conducted pairwise and network meta-analyses of randomized trials comparing P2Y12-inhibitor monotherapy after aspirin discontinuation versus dual antiplatelet therapy (DAPT) in ACS post-PCI. Random-effects models were used. The network meta-analysis (NMA) compared four strategies (12-month DAPT [central comparator]; aspirin stop < 1 month, 1-2 months, or 3 months) and ranked treatments using SUCRA. (PROSPERO ID: CRD420251151605). A total of 33,292 participants from 10 RCTs were included. In the pairwise meta-analysis, monotherapy reduced net adverse clinical events (NACE) (5.1% vs. 6.7%; RR: 0.75; 95% CI: 0.65-0.86) and bleeding, including clinically relevant bleeding (RR: 0.45; 95% CI: 0.39-0.53) and major bleeding (RR: 0.47; 95% CI: 0.37-0.60). In the NMA, aspirin discontinuation at 3 months showed a trend toward lower NACE versus 12-month DAPT (RR 0.66; 95% CI 0.42-1.04) and ranked most favorable for net and ischemic outcomes (NACE, MI, MACCE, and all-cause mortality), although estimates for individual ischemic endpoints were imprecise and not statistically different from 12-month DAPT. Aspirin discontinuation at < 1 month showed an unfavorable mortality ranking, with a concordant meta-regression signal suggesting higher mortality with earlier aspirin withdrawal. De-escalation to P2Y₁₂ inhibitor monotherapy after PCI in ACS reduced NACE, mainly by lowering bleeding. In network analyses, aspirin discontinuation at 3 months ranked most favorable for net and ischemic outcomes, whereas discontinuation at < 1 month showed an unfavorable mortality signal.

PMID:42192090 | DOI:10.1007/s12928-026-01304-z