Tag: nevin manimala

Int J Obes (Lond). 2025 Aug 22. doi: 10.1038/s41366-025-01862-x. Online ahead of print.

ABSTRACT

BACKGROUND: The incidence of endometrial cancer has been rising sharply among younger generations, paralleling the growing obesity epidemic in this age group. Abdominal obesity is currently being investigated as an indicator of adiposity and cancer risk, and its prevalence is increasing in young women. This study aimed to examine whether cumulative abdominal obesity exposure in young women was associated with the development of endometrial cancer.

METHODS: We used data from the South Korean National Health Insurance Service for women aged 20-39 years who had completed four consecutive annual health examinations between 2009 and 2012 and had no history of cancer at baseline. Participants were categorized into five groups based on the number of abdominal obesity exposures (waist circumference ≥ 85 cm). Exposure numbers ranged from 0 to 4, indicating the frequency of abdominal obesity across the four health examinations over 4 years. The primary outcome was newly diagnosed endometrial cancer, which was monitored until 2020, with a follow-up period of 7.12 years.

RESULTS: Among the 445,791 young women (mean [SD] age 30.82 [4.55] years), 302 (mean [SD], 32.79 [4.53] years) developed endometrial cancer. The cumulative incidence of endometrial cancer differed significantly according to the number of abdominal obesity exposures (log-rank test, P < .001). The incidence of endometrial cancer has progressively increased with abdominal obesity exposure. The multivariable-adjusted HRs for incident endometrial cancer were 1.480 (95% CI, 0.970-2.258), 2.361 (95% CI, 1.391-4.008), 4.114 (95% CI, 2.546-6.647), and 6.215 (95% CI, 4.250-9.088) for participants with exposure numbers of 1-4, respectively, compared with those with an exposure number of 0.

CONCLUSION: In this population-based nationwide cohort study of young women, we observed a progressive increase in the risk of endometrial cancer with cumulative abdominal obesity exposure.

PMID:40847073 | DOI:10.1038/s41366-025-01862-x

Int J Obes (Lond). 2025 Aug 22. doi: 10.1038/s41366-025-01877-4. Online ahead of print.

ABSTRACT

OBJECTIVES: The association between obesity and cholelithiasis has been identified. However, the causal relationship between age-specific childhood obesity and adult cholelithiasis remains unclear. In addition, the biological basis for the association between childhood obesity and adult cholelithiasis is poorly understood, which poses a challenge for preventing adult cholelithiasis in specific biological pathways.

METHODS: Summary statistics of genome-wide association studies (GWASs) of childhood age-specific body mass index (BMI) at 12 time points and adult cholelithiasis derived from FinnGen were used in this study, with the former covering data from birth to 8 years. Linkage disequilibrium score regression (LDSC) analyses were used to assess the genetic correlations of age-specific childhood BMI to cholelithiasis. Two-sample Mendelian randomization (MR) and multivariable Mendelian randomization (MVMR) analyses were utilized to explore the causal associations. As downstream analyses, summary-based Mendelian randomization (SMR) analyses, transcriptome-wide association studies (TWAS), and Bayesian colocalization were conducted to discover the shared transcriptomic signals. The GWAS summary statistics of cholelithiasis from the UK Biobank were used for sensitivity analyses.

RESULTS: LDSC analyses revealed significant genetic correlations between 11 age-specific childhood BMIs and adult cholelithiasis (except for birth BMI). Two-sample MR and MVMR analyses indicated causal relationships between birth BMI and BMI at 8 months, 1.5 years, 7 years, and 8 years after birth and adult cholelithiasis. SMR, TWAS, and colocalization analyses identified MLXIPL as the strongest overlapping signal between age-specific BMI and adult cholelithiasis.

CONCLUSION: This study provides new evidence on the relationships between childhood obesity and adult cholelithiasis, highlighting the role of early intervention for obesity in childhood at key time points. MLXIPL gene expression was identified as a potential biological pathway, suggesting potential therapeutic targets and precise intervention strategies for childhood obesity and adult cholelithiasis.

PMID:40847070 | DOI:10.1038/s41366-025-01877-4

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