Nevin Manimala

Commun Med (Lond). 2026 Jun 23. doi: 10.1038/s43856-026-01735-y. Online ahead of print.

ABSTRACT

BACKGROUND: Accurate tumor segmentation is essential for early diagnosis, treatment planning, and prognostic evaluation. Although manual annotation can achieve high accuracy, it is time-consuming and requires substantial expert involvement. While deep learning has significantly advanced medical image analysis, fully automated methods often fail to segment atypical lesions within complex abdominal anatomy, leading to missed lesions and misclassification of normal tissues, which may compromise clinical decision-making.

METHODS: To address these challenges, we incorporated guidance masks into a convolutional neural network (CNN)-based deep learning framework. Using our Star-Rain software, users place interactive clicks on lesion locations, and the system adaptively generates task-specific guidance masks. This approach directs the model’s attention to relevant regions, particularly in atypical or anatomically complex cases.

RESULTS: Our method is validated on four independent cohorts comprising 1,217 CT scans from 726 patients, encompassing hepatic, renal, and pancreatic tumors. Across these datasets, our approach outperforms state-of-the-art baseline models on independent test sets, achieving Dice scores consistently above 0.7 and reducing the false negative rate (FNR) by 0.006 to 0.346 compared to the best fully automated approaches. In addition, the model’s segmentation outputs effectively support downstream prognosis tasks, highlighting its clinical value.

CONCLUSIONS: These findings underscore the promise of semi-automatic deep learning frameworks that integrate minimal user input for reliable tumor segmentation. The proposed approach offers a practical and robust solution for clinical applications, enhancing segmentation accuracy and decision support while reducing the annotation burden.

PMID:42337366 | DOI:10.1038/s43856-026-01735-y

Nat Food. 2026 Jun 23. doi: 10.1038/s43016-026-01394-1. Online ahead of print.

NO ABSTRACT

PMID:42337342 | DOI:10.1038/s43016-026-01394-1

Sci Rep. 2026 Jun 23. doi: 10.1038/s41598-026-58833-z. Online ahead of print.

ABSTRACT

Spaceborne optical sensors provide continuous Earth observation, but atmospheric interference still limits their practical reliability. On average, clouds cover 67% of the Earth’s surface. This constant coverage degrades the data continuity needed for precision agriculture, disaster monitoring, and proactive Internet of Things (IoT) systems. Recent deep generative networks produce visually appealing cloud-free images. However, when faced with thick clouds ([Formula: see text] opacity), these models often hallucinate topologies. They synthesize statistical guesses instead of recovering the actual ground reflectance. For high-stakes telemetry, predictable failure is safer than an undetected hallucination. This paper introduces Thermo-Cloud Removal (Thermo-CR), a real-time cloud removal framework. It integrates Radiative Transfer inversion, weather-driven transmission estimates, geographic priors, and multi-scale fusion to restore optical imagery without requiring Synthetic Aperture Radar (SAR). Thermo-CR treats the cloudy atmosphere as a thermodynamic medium. By pulling live meteorological telemetry (Relative Humidity (RH) and Temperature (T)) through the Open-Meteo REST API, the system calculates optical depth and performs a deterministic inversion of the Radiative Transfer Model. Pure inverse models amplify noise under extreme occlusion ([Formula: see text]). To prevent this, we apply a Global Positioning System (GPS)-anchored multi-scale fusion with clear-sky temporal priors. We evaluated Thermo-CR on a synthetically occluded paired dataset covering varied topologies (Amazon, London, Seattle). The system degrades predictably under 90% cloud cover and avoids structural hallucination. It achieves an average Structural Similarity Index Measure (SSIM) of 0.9925 and a Peak Signal-to-Noise Ratio (PSNR) of 55.94 dB in under 13 milliseconds per frame, outperforming standard Dark Channel baselines.

PMID:42337308 | DOI:10.1038/s41598-026-58833-z

Sci Rep. 2026 Jun 23. doi: 10.1038/s41598-026-58392-3. Online ahead of print.

ABSTRACT

Surgical procedures cause significant stress for children and their caregivers. Virtual reality (VR) may reduce perioperative fear and physiological stress by delivering engaging, preparatory information. This study aimed to determine the effect of a VR-delivered animated educational video on perioperative anxiety and fear in children and their mothers. This randomized controlled study was conducted with a total of 60 children aged 7-13 years and their mothers who underwent day surgery at a tertiary hospital and met the inclusion criteria (Intervention: children n = 30, mothers n = 30; Control: children n = 30, mothers n = 30). Measures for children included the Physiological Parameters Form, Child Fear Scale and Multidimensional Perioperative Anxiety Scale for Children (MPAS-C); mothers completed the State Anxiety Inventory-Short Form. The intervention consisted of a pre-recorded, approximately five-minute VR-delivered animated educational video administered on the morning of surgery. The children in the intervention group showed a steady decline in their fear scores before the procedure (M = 1.47), after the procedure (M = 0.80), and after surgery (M = 0.70). Conversely, children in the control group showed an increase in fear scores from before the procedure (M = 0.73) to after the procedure (M = 1.33), followed by a minimal decrease after surgery (M = 0.83) (p = 0.002, η² = 0.12). Children’s anxiety scores showed a sharper decline in the intervention group (from M = 151.33 pre-procedure to M = 41.72 post-surgery) compared to the control group (pre-procedure M = 151.37 to postoperative M = 73.67), indicating a larger reduction over time in the intervention group (p < 0.001, η² = 0.33). Maternal anxiety decreased in both groups, but the VR-delivered animated educational video did not provide a statistically significant additional benefit (p = 0.784). The VR-delivered animated educational video was associated with lower child fear, lower perioperative anxiety, and lower heart rate at specific perioperative time points, while no statistically significant additional benefit was observed for maternal state anxiety.Trial Registration: Clinical Trial Number NCT07131982, registration date 2023-06-20.

PMID:42337300 | DOI:10.1038/s41598-026-58392-3

Sci Rep. 2026 Jun 24. doi: 10.1038/s41598-026-59005-9. Online ahead of print.

ABSTRACT

The objective of this study was to estimate the prevalence of primary dysmenorrhea and associated symptoms among adolescent girls. This school based cross-sectional study was conducted among 5,000 adolescent girls from 55 randomly selected schools. The data were collected by using Dysmenorrhea questionnaire and numerical pain rating scale along with demographic details. The data were analysed using Jamovi -open statistical software version 2.6.44. A bivariate logistic regression model was computed and variables whose p-value was < 0.05 in the bivariate logistic regression analysis were further included in the multiple logistic regression analysis. The prevalence of primary dysmenorrhea was found among 3441 (68.82%), 1686 (49.01%) had mild pain, 1496 (43.4%) had moderate pain, 259 (7.52%) had severe pain. Multiple logistic regression revealed, factors such as mothers with and without dysmenorrhea, having menstruation more than once in a month, menstruation once in two months, lethargy and tiredness day before menstruation, lethargy and tiredness day after, irritability on the day before menstruation, and constipation on the first day of menstruation were associated with primary dysmenorrhea. The prevalence of dysmenorrhea is high among adolescent girls, which highlights the need for early diagnosis and development and implementation of interventions to promote the school health and wellbeing.

PMID:42337292 | DOI:10.1038/s41598-026-59005-9

Sci Rep. 2026 Jun 23. doi: 10.1038/s41598-026-57002-6. Online ahead of print.

ABSTRACT

Visceral leishmaniasis (VL) remains a lethal parasitic disease, disproportionately affecting resource-limited regions where sustained control measures are often economically and logistically impractical. Consequently, cost-effective and sustainable strategies tailored to seasonal transmission patterns are urgently needed. Because temperature and rainfall strongly influence sandfly populations, aligning control efforts with seasonal transmission dynamics may enhance effectiveness while reducing costs. This study proposes an impulsive control strategy within a temperature- and rainfall-dependent VL transmission model to assess how strategically timed, short-term interventions optimize disease control. We examine the effects of intervention timing, frequency, and coverage for measures including sandfly breeding site elimination, insecticide spraying, and culling infected reservoir animals. Theoretical analysis shows that the disease-free periodic solution is locally asymptotically stable when the basic reproduction number ([Formula: see text]) is below one, while endemic persistence occurs when [Formula: see text]. The calibrated model closely reproduced observed seasonal transmission patterns, providing a robust basis for evaluating interventions under climatic forcing. Global sensitivity analysis revealed that vector-related parameters consistently drive infection burden, while reservoir parameters show negligible effects, indicating that vector control should be prioritized over reservoir culling for cost-effective VL management. Simulations indicate that targeted interventions implemented for only a few weeks annually substantially reduce transmission. Although vector control and reservoir culling independently decrease cases, their combined application is more effective. A biannual one-week intervention reduces human cases by 95.11%, increasing to 96.6% when extended to two weeks. Initiating interventions six weeks after peak infection yields the most substantial long-term impact, achieving a 98.89% reduction at 85% coverage.

PMID:42337291 | DOI:10.1038/s41598-026-57002-6

Sci Rep. 2026 Jun 23. doi: 10.1038/s41598-026-51657-x. Online ahead of print.

ABSTRACT

Given the increasing scarcity of potable water and the rising generation of domestic and industrial wastewater, the reuse of greywater to meet part of plant water requirements, along with its environmentally friendly approach, has gained increasing attention. The present study was conducted in 2023 to investigate the effects of microorganisms and biochar on the establishment and growth of Crassula capitella Thunb., as well as on greywater treatment. Cyanobacteria (C0, C0.4, and C0.8 g), mycorrhiza (M0, M5, and M10 g), and biochar (B0, B5, and B10 g) were evaluated under two irrigation regimes including municipal water (MW) and greywater (GW) within a green wall system in Mashhad, Iran. The results indicated that the combined treatments and irrigation water type had statistically significant effects on most of the measured traits. The C0.4-M10-B0 treatment increased chlorophyll content by more than 97% (from 4.93 to 9.74 µg g⁻¹ FW), while the C0-M10-B5 treatment increased RFW and RDW by 189 and 298%, respectively (from 2.530 to 7.309 g and from 0.353 to 1.407 g), compared to their respective minimum values. The initial wastewater COD (376 mg/L) was 118% greater than the COD measured after treatment with C0.4-M10-B0 (173 mg/L). Overall, the C0-M10-B5 and C0.4-M10-B0 treatments showed superior performance in improving plant establishment and growth of C. capitella in the green wall system, while the C0.4-M10-B0 and C0.4-M5-B5 treatments were more effective in greywater treatment. Based on these findings, the integrated use of cyanobacteria, mycorrhizae, and biochar may contribute to the establishment of Crassula in green wall systems and to urban water resource management under the conditions tested in Mashhad, Iran. However, further studies are needed to assess the applicability of these results to other cities or environmental conditions.

PMID:42337260 | DOI:10.1038/s41598-026-51657-x

Ophthalmic Physiol Opt. 2026 Jun 24. doi: 10.1007/s44402-026-00131-2. Online ahead of print.

ABSTRACT

PURPOSE: The purpose of this study was to investigate and quantify the influence of stimulus chromaticity on the perception of visual disturbances, specifically halos, under dim lighting conditions. The study also investigated age-related variations in this perception.

METHOD: Fifty healthy participants were divided into two age groups of 25 each: young adults (<25 years) and older adults (>54 years). The halo perception was quantified using the light disturbance analyser (LDA), a validated device designed to assess visual disturbances such as halos and glare under controlled lighting conditions. To assess the effect of stimulus chromaticity on halo perception, three filters with spectral transmittances centred in the red, green and blue regions of the visible spectrum were used. Measurements were recorded both before and after compensating for longitudinal chromatic aberration (LCA).

RESULTS: In both age groups, white and green colours produced the smallest angular size of the perceived halo, followed by red, whereas blue induced the largest halo size. While LCA compensation under blue light was sufficient for the younger group to perceive a halo size similar to that under white light, this compensation proved insufficient for the older group.

CONCLUSIONS: Perceived halo size was greatest when caused by a blue stimulus, followed by red light, while white and green sources yielded halos with comparable, smaller sizes across both age groups. The influence of age on perceived halo size under blue light was statistically significant. Furthermore, LCA compensation resulted in a greater benefit in perceived halo size for the younger, compared with the older group, under blue light.

PMID:42337218 | DOI:10.1007/s44402-026-00131-2

Discov Oncol. 2026 Jun 24. doi: 10.1007/s12672-026-05488-x. Online ahead of print.

ABSTRACT

PURPOSE: To investigate the feasibility of Simultaneous Amplification and Testing PCA3 (SAT-PCA3, a urine-based prostate cancer-specific biomarker) combined with conventional clinical information in the diagnosis of prostate cancer (PCa).

METHODS: This retrospective study analyzed 137 patients with complete clinical data. Patients with a biopsy Gleason score ≥ 6 were classified as having PCa. Clinical indicators showing significant differences between PCa and non-PCa groups were identified via univariate analysis. A multivariate model was constructed using pathological diagnosis as the outcome and age, digital rectal exam (DRE) result, prostate-specific antigen (PSA), SAT-PCA3 result, and Prostate Imaging Reporting and Data System (PIRADS) score as predictors. The DeLong test was performed to compare differences in the area under the receiver operating characteristic (ROC) area under the curve (AUC) between the univariate model and the multivariate model.

RESULTS: A total of 137 patients were included: 65 were diagnosed with PCa and 72 were non-PCa. Statistical differences existed between the PCa and non-PCa in age, PSA, DRE, PIRADS score, and SAT-PCA3 (p < 0.05). All variables were independently associated with PCa. The coefficient of determination (R²) values is 0.626 in the multivariate model. The AUCs of the age [0.711(95%CI: 0.625-0.796)], DRE [0.626(95%CI: 0.545-0.708)], PSA [0.684(95%CI: 0.593-0.774)], SAT-PCA3 [0.786(95%CI: 0.706-0.866)], PIRADS [0.795(95%CI: 0.722-0.866)] were all less than the multivariate model [0.912(95%CI: 0.866-0.958)], and the difference was statistically significant (p < 0.001).

CONCLUSION: A diagnostic model combining conventional clinical information (age, DRE, PSA, PIRADS score) with the SAT-PCA3 significantly improves the diagnostic accuracy for prostate cancer compared to any single parameter alone.

PMID:42337216 | DOI:10.1007/s12672-026-05488-x

Environ Sci Pollut Res Int. 2026 Jun 24. doi: 10.1007/s11356-026-37943-1. Online ahead of print.

ABSTRACT

Constructed wetlands play a crucial role in urban runoff treatment, enhancing water quality and maintaining ecosystem health, while the water quality index (WQI) serves as a key parameter for evaluating their performance. This study provides a comprehensive assessment of WQI prediction in a constructed wetland at Universiti Sains Malaysia, using 442 samples and 11 physicochemical parameters evaluated across six input scenarios. Feature selection was performed using Pearson correlation and feature-importance rankings from extreme gradient boosting (XGBoost) and categorical boosting (CatBoost) to create reduced-input combinations. SHapley Additive exPlanations (SHAP) analysis further indicated that WQI predictions were mainly driven by organic/solid load and nitrogen-related variables (e.g., chemical oxygen demand (COD), total suspended solids (TSS), and ammoniacal nitrogen (AN)). Fourteen ML models, including adaptive boosting (AdaBoost), adaptive neuro-fuzzy inference system, artificial neural network (ANN), CatBoost, extreme learning machine, gradient boosting regressor, histogram gradient boosting (HGB), Huber regressor, multiple linear regression, ridge regression, stochastic gradient descent regressor (SGD), support vector regression (SVR), XGBoost, and a hybrid Grey Wolf Optimizer-ANN, were developed and evaluated using four statistical metrics such as root mean square error (RMSE), coefficient of determination (R2), percent bias (PBIAS), and mean absolute relative error (MARE), complemented by LP-based multi-metric ranking. Across all scenarios (mean LP; lower is better), CatBoost (0.44) and HGB (0.46) achieved the best overall performance, while SGD (0.91) and SVR (0.75) ranked worst. Notably, several top-performing models maintained competitive performance under reduced inputs (e.g., CatBoost’s LP value of 0.56 in the four-feature scenario), supporting practical WQI estimation when monitoring variables are limited or costly. These findings highlight the critical role of both input selection and model choice in developing robust, scalable frameworks for WQI prediction.

PMID:42337199 | DOI:10.1007/s11356-026-37943-1