Nevin Manimala

Int J Obes (Lond). 2025 Aug 22. doi: 10.1038/s41366-025-01889-0. Online ahead of print.

ABSTRACT

The primary aim was to develop and assess the performance and applicability of different models utilizing sensor data to determine dietary adherence, specifically within the context of intermittent fasting. Our approach utilized time-series data from two completed human trials, which included continuous glucose monitoring, acceleration data, and food diaries, and a synthetic data set. Machine learning models achieved an average F1-score of 0.88 in distinguishing between fasting and non-fasting times, indicating a high level of reliability in classifying fasting states. The Hutchison Heuristic statistical method, while more moderate in performance, proved to be robust across different cohorts, including individuals with and without type 1 diabetes. A dashboard was developed to visualize results efficiently and in a user-friendly manner. The findings highlight the effectiveness of using sensor data, combined with advanced statistical and machine learning approaches, to passively evaluate dietary adherence in an intermittent fasting context.

PMID:40847068 | DOI:10.1038/s41366-025-01889-0

Sci Rep. 2025 Aug 22;15(1):30964. doi: 10.1038/s41598-025-16531-2.

ABSTRACT

Statistical Process Control is essential for ensuring process stability and detecting variations in a production environment. This study introduces a control chart based on the Exponentially Weighted Moving Average (EWMA) that uses an adaptive sample size. The proposed approach enhances shift detection by dynamically adjusting the sample size in response to changes in process variation. Extensive Monte Carlo simulations were performed to assess the performance of the proposed control chart, focusing on metrics such as the Average Run Length (ARL) and the Standard Deviation of Run Length (SDRL). The findings show that the new chart surpasses both the Fixed Sample Size EWMA (FEWMA) and the Variable Sample Size EWMA charts, particularly in detecting small to moderate shifts in the process. This approach strikes a balance between detection sensitivity and computational efficiency, enabling prompt identification of process changes while maintaining robustness during in-control conditions. To illustrate its practical applicability, a real-world dataset was analyzed, demonstrating the effectiveness of the proposed method in actual process monitoring scenarios.

PMID:40847067 | DOI:10.1038/s41598-025-16531-2

Sci Rep. 2025 Aug 22;15(1):30927. doi: 10.1038/s41598-025-16526-z.

ABSTRACT

High-dimensional longitudinal data present significant analytical challenges due to intricate within-subject correlations and an overwhelming ratio of predictors to observations. To address these challenges, we introduce Mixed-Effect Gradient Boosting (MEGB), a novel R package that synergises gradient boosting with mixed-effects modelling to simultaneously account for population-level fixed effects and subject-specific random variability. MEGB provides a unified framework for analysing repeated measures data that accommodates complex covariance structures while harnessing gradient boosting’s inherent regularisation for robust feature selection and prediction. In comprehensive simulations spanning linear and nonlinear data-generating processes, MEGB achieved 35-76% lower mean squared error (MSE) compared to state-of-the-art alternatives like Mixed-Effect Random Forests (MERF) and REEMForest, while maintaining 55-70% true positive rates for variable selection in ultra-high-dimensional regimes ( p = 2000 ) . Demonstrating practical utility, we applied MEGB to maternal cell-free plasma RNA data ( n = 12 subjects, p = 33 , 297 transcripts), where it identified 9 key placental transcripts driving fetal RNA dynamics across pregnancy trimesters.

PMID:40847064 | DOI:10.1038/s41598-025-16526-z

Sci Rep. 2025 Aug 22;15(1):30858. doi: 10.1038/s41598-025-14612-w.

ABSTRACT

This proposed work is presenting the approximation of higher-order (HO) underwater robotic vehicle (URV) controller with the help of multi-point matching technique by incorporating greywolf optimization algorithm (GWOA). The performance of URV system is affected by external and internal dynamics. The proper momentum of URV system is achieved by designing a controller. The URV can be effectively operated by control action of controller. The URV controller is approximated to comparatively lower-order (LO) to propose an efficient, effective and economical controller for HOURV system. The approximation is accomplished with the help of expansion parameters of HOURV controller and its desired LOURV controller. The errors between these expansion parameters of HOURV controller and its desired LOURV controller are minimized using multi-point matching. The multi-point matching is depicted in the form of objective function (OF). The constructed OF is minimized by exploiting GWOA by fulfilling the steady-state matching condition and Hurwitz stability criterion, as constraints. The effectiveness of proposed approach of multi-point matching is verified by comparing the proposed LOURV model with LOURV models obtained with the help of other approximation approaches. The applicability of proposed LOURV controller is evaluated and validated by analyzing responses and tabulated data obtained in the results. Additionally, the statistical data of performance error values (PEVs) are provided in tabulated form along with its bar plot.

PMID:40847035 | DOI:10.1038/s41598-025-14612-w

Sci Rep. 2025 Aug 22;15(1):30843. doi: 10.1038/s41598-025-09571-1.

ABSTRACT

This work constructs the distinct type of solitons solutions to the nonlinear Perturbed Gerdjikov-Ivanov (PGI) equation with Atangana’s derivative. It interprets its optical soliton solutions in the existence of high-order dispersion. For this purpose, a wave transformation is applied to convert the fractional PGI Equation to a non-linear ODE. Solitons solutions and further solutions of the obtained model are sorted out by using the Sardar sub-equation (SSE) method and the generalized unified method. The different types of soliton solutions such as bright, kink, periodic, and exact dark solitons are achieved. Dynamical and sensitivity analysis is carried out for the obtained results. 3D, 2D, and contour graphs of attained solutions are presented for elaboration. Nonlinear model have played an important role in optic fibber, optical communications and optical sensing.

PMID:40847028 | DOI:10.1038/s41598-025-09571-1

Br J Cancer. 2025 Aug 22. doi: 10.1038/s41416-025-03147-6. Online ahead of print.

ABSTRACT

BACKGROUND: Lung cancer is a health threat, particularly in regions where advanced screening methods like LDCT are limited. In China, chest X-rays (CXRs) are the primary tool for early detection. Integrating AI can enhance CXR diagnostic accuracy, addressing current challenges in early lung cancer detection.

METHODS: We collected 4079 CXRs from 2518 individuals at TMUCIH. These were divided into a training set (1762 patients, 2965 images) and a validation set (756 patients, 1114 images). A deep learning (DL) model, based on the CXR-RANet architecture, was developed and validated using two external cohorts: 24,697 individuals (88,562 images) from the PLCO dataset and 4848 individuals from the ChestDR dataset. The model’s performance was compared with mainstream DL algorithms and traditional machine learning (ML) model in feature extraction and classification.

RESULTS: In the TMUCIH dataset, 47.8% of patients had positive CXR results, compared to 3.9% in PLCO and 13.7% in ChestDR. The CXR-RANet model achieved an AUC of 0.933 in the internal validation set and 0.818 in the ChestDR dataset. In the PLCO dataset, it predicted lung cancer occurrence with AUCs of 0.902, 0.897, and 0.793 for 3, 5, and 10 years, respectively. The model outperformed mainstream DL algorithms in feature extraction and most ML algorithms in classification.

CONCLUSION: The CXR-RANet presents a robust, scalable tool for diagnosing pulmonary nodules and lung cancer, enhancing the capabilities of community physicians in early detection and management, independent of expert experience. Its superior performance in feature extraction and classification underscores its value in lung cancer screening.

PMID:40847012 | DOI:10.1038/s41416-025-03147-6

Stat Med. 2025 Aug;44(18-19):e70109. doi: 10.1002/sim.70109.

ABSTRACT

The benefits of vaccination to protect against the different variants of the SARS-CoV-2 Virus are well-known in the literature. In the United States, public health policy has led to a wide availability of COVID-19 vaccines that are usually freely available to everyone 6 months and older. However, several factors including misinformation create vaccine hesitancy and threaten to undercut the advances of the COVID-19 vaccination program. In this article, we take a network-based approach to investigate community acceptance of vaccines at the county level in the United States, using data from the Centers for Disease Control and Prevention (CDC). We use an exponential random graph model to discover important sociodemographic factors that influence the patterns of vaccination between counties and communities. In addition, we undertake an advanced topological data analysis (TDA) based network clustering method to discover more macrolevel communities that show common trends for COVID-19 vaccine acceptance in the United States. Our study uncovers that sociodemographic features, for example, higher education, household income, and US census regions have significant effects on COVID-19 vaccine acceptance. The cluster analysis demonstrates that different census regions as well as rural and urban areas have distinct preferences in COVID-19 vaccine acceptance.

PMID:40844841 | DOI:10.1002/sim.70109

Nephrol Dial Transplant. 2025 Aug 22:gfaf169. doi: 10.1093/ndt/gfaf169. Online ahead of print.

ABSTRACT

BACKGROUND: Acute kidney injury (AKI) is a serious complication during pediatric cancer treatment. Nephrotoxic medication may increase the risk of developing AKI, which may necessitate modifications to standard treatment and may also increase the risk of chronic kidney disease (CKD). This study investigates the incidence of AKI, the impact of nephrotoxic medications and the association between AKI and the development of CKD.

METHODS: In this retrospective national cohort study, we analyzed 1 525 pediatric cancer patients treated at the Princess Máxima Center between 2015 and 2021. AKI was classified using KDIGO criteria based on serum creatinine. The effect of nephrotoxic medications and other risk factors on AKI incidence and progression was assessed by using a cause specific hazard regression model. The cumulative incidence of AKI was estimated with a competing risk model with death as competing event. The effect of risk factors on CKD, defined as an eGFR < 90 ml/min/1.73m² one year after cancer treatment, was evaluated with a logistic reression.

RESULTS: We included 1525 patients, 37% experienced AKI. A competing risk model identified treatment with ifosfamide, amphotericin B, acyclovir and busulfan as strong, independent risk factors for a first episode of AKI. Older age was also associated with an increased risk of AKI.At one-year follow-up (n = 1 159), 13.6% had CKD (eGFR < 90 mL/min/1.73 m²), and 2.8% had an eGFR < 60. AKI (occurred during treatment) was the strongest predictor of CKD: a single AKI episode increased the risk 2.6-fold, while more episodes increased it nearly 16-fold. Nephrectomy was also identified as independent risk factors for CKD.

CONCLUSION: Acute kidney injury (AKI) is common in children with cancer and is strongly associated with an increased risk of chronic kidney disease (CKD). Awareness is crucial for high-risk patients, particularly those receiving nephrotoxic medications, with a history of multiple AKI episodes or a prior nephrectomy. Comprehensive monitoring strategies should be implemented at diagnosis, during therapy, and during the post-treatment period to enable early detection and timely intervention, ultimately reducing the risk of AKI and its progression to CKD.

PMID:40844823 | DOI:10.1093/ndt/gfaf169

Biostatistics. 2024 Dec 31;26(1):kxaf022. doi: 10.1093/biostatistics/kxaf022.

ABSTRACT

Brain functional connectivity (FC), the temporal synchrony between brain networks, is essential to understand the functional organization of the brain and to identify changes due to neurological disorders, development, treatment, and other phenomena. Independent component analysis (ICA) is a matrix decomposition method used extensively for simultaneous estimation of functional brain topography and connectivity. However, estimation of FC via ICA is often sub-optimal due to the use of ad hoc estimation methods or temporal dimension reduction prior to ICA. Bayesian ICA can avoid dimension reduction, estimate latent variables and model parameters more accurately, and facilitate posterior inference. In this article, we develop a novel, computationally feasible Bayesian ICA method with population-derived priors on both the spatial ICs and their temporal correlation (that is, their FC). For the latter, we consider two priors: the inverse-Wishart, which is conjugate but is not ideally suited for modeling correlation matrices; and a novel informative prior for correlation matrices. For each prior, we derive a variational Bayes algorithm to estimate the model variables and facilitate posterior inference. Through extensive simulation studies, we evaluate the performance of the proposed methods and benchmark against existing approaches. We also analyze fMRI data from over 400 healthy adults in the Human Connectome Project. We find that our Bayesian ICA model and algorithms result in more accurate measures of functional connectivity and spatial brain features. Our novel prior for correlation matrices is more computationally intensive than the inverse-Wishart but provides improved accuracy and inference. The proposed framework is applicable to single-subject analysis, making it potentially clinically viable.

PMID:40844820 | DOI:10.1093/biostatistics/kxaf022

Neurol Ther. 2025 Aug 22. doi: 10.1007/s40120-025-00813-y. Online ahead of print.

ABSTRACT

INTRODUCTION: Perimesencephalic subarachnoid hemorrhage (pmSAH) is a rare, typically benign subtype of non-aneurysmal subarachnoid hemorrhage (SAH). While the majority of patients demonstrate a positive recovery trajectory, a subset of patients experiences complications, including vasospasm, hydrocephalus, or delayed cerebral ischemia (DCI). Reliable imaging markers for risk stratification are lacking. This study evaluates whether volumetric CT-based biomarkers-validated in aneurysmal SAH (aSAH)-are also predictive for pmSAH.

METHODS: In this retrospective single-center study, 72 patients with confirmed pmSAH between 2011 and 2024 were analyzed. The automated volumetric segmentation was performed using 3D Slicer and TotalSegmentator to quantify intracranial volume (ICV), brain volume (BV), cerebrospinal fluid (CSF), and selective sulcal volume (SSV). The associations between volumetric parameters and clinical presentation, complications, and functional outcome (Glasgow Outcome Scale, GOS) were assessed using non-parametric statistics and Spearman correlation.

RESULTS: The median intracranial volume was 1352.7 mL, brain volume 1247.3 mL, cerebrospinal fluid volume 95.9 mL, and selective sulcal volume 19.4 mL. Vomiting at presentation was associated with higher CSF and SSV values (p = 0.04 and p = 0.005, respectively), but no significant volumetric differences were found regarding other symptoms or complications (vasospasm, hydrocephalus, DCI). GOS scores were uniformly high (median = 5), and none of the volumetric markers significantly correlated with outcome or complication rate (all p > 0.05).

CONCLUSION: In contrast to aSAH, volumetric CT biomarkers such as ICV, BV, CSF, and SSV do not offer predictive value in patients with pmSAH. Risk stratification should continue to rely on initial hemorrhage pattern and volume, clinical monitoring, and individualized assessment rather than other volumetric parameters.

PMID:40844796 | DOI:10.1007/s40120-025-00813-y