Nevin Manimala

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

Parasitol Res. 2025 Aug 22;124(8):96. doi: 10.1007/s00436-025-08508-x.

ABSTRACT

Ehrlichia ruminantium, the causative agent of heartwater, is a tick-borne pathogen affecting livestock in Africa and the Caribbean. This disease is transmitted primarily by Amblyomma variegatum ticks and poses a significant threat to animal health. In Madagascar, the prevalence of E. ruminantium remains poorly documented. During a Rift Valley fever (RVF) outbreak in Mananjary, Madagascar (April-May 2021), we conducted a field study to assess the circulation of vector-borne pathogens in ticks collected from ruminants. Ticks were morphologically identified, and DNA was extracted for quantitative PCR targeting the pCS20 gene of E. ruminantium. Statistical analyses were performed to explore associations between tick infection status, ruminant health, and infestation levels. A total of 332 ticks were collected from 25 ruminants. The tick species identified included Rhipicephalus microplus (51.5%) and Amblyomma variegatum (48.2%). E. ruminantium DNA was detected in 5.1% (17/332) of ticks, consisting of 16 A. variegatum and one R. microplus, with the majority being male. No association was observed between ruminant clinical signs and the presence of infected ticks. This study provides the first molecular evidence of E. ruminantium circulation in ticks from Madagascar during an RVF outbreak. Our findings emphasize the need for improved disease surveillance and integrated tick control strategies to mitigate the impact of heartwater on livestock.

PMID:40844790 | DOI:10.1007/s00436-025-08508-x

Eur J Trauma Emerg Surg. 2025 Aug 22;51(1):279. doi: 10.1007/s00068-025-02950-3.

ABSTRACT

BACKGROUND: Severe trauma continues to pose a substantial burden on survivors, particularly in terms of long-term physical, psychological, and social functioning. While survival rates have improved, data on long-term outcomes remain limited. This study evaluates ten-year post-injury outcomes in patients with major trauma, focusing on return to work and social participation.

METHODS: In this single-center, retrospective cohort study, adult patients (≥ 18 years) with an Injury Severity Score (ISS) ≥ 9 treated between 2010 and 2013 were surveyed and distributed minimally 10 years later. Patients completed standardized questionnaires assessing sociodemographic and occupational data, functional status, and psychological well-being using the Trauma Outcome Profile (TOP).

RESULTS: Ninety-one patients completed the follow-up. The mean age at injury was 43.0 years, with a mean ISS of 20.8. Ten years post-trauma, 82.4% of patients had returned to work; 10.6% required vocational retraining, and 25.3% changed occupations. Failure to return to work was significantly associated with higher ISS (p = 0.027), increased anxiety (p = 0.005), post-traumatic stress disorder (PTSD, p = 0.039), and reduced mental functioning (p = 0.009), but not with physical functioning ten years after the trauma. Patients with mental health impairments were more likely to experience reduced independence, impaired social participation, and difficulties in activities of daily living.

CONCLUSION: A majority of patients successfully reintegrated into the workforce ten years after trauma. Mental health, rather than physical disability, emerged as the primary determinant of long-term occupational reintegration. These findings underscore the necessity for comprehensive, long-term rehabilitation programs that prioritize psychosocial support.

PMID:40844786 | DOI:10.1007/s00068-025-02950-3

JAMA Netw Open. 2025 Aug 1;8(8):e2527284. doi: 10.1001/jamanetworkopen.2025.27284.

ABSTRACT

IMPORTANCE: Long-term back problems impact an individual’s ability to participate in the workforce productively, potentially resulting in financial stress and furthering inequities. Estimates of future productivity losses could inform advocacy and policy making.

OBJECTIVE: To estimate the productivity losses of long-term back problems in working-age Australians (aged 15-64 years) over the next 10 years (2024-2033).

DESIGN, SETTING, AND PARTICIPANTS: This modeling study used a dynamic population-level model to simulate the population of working Australians with long-term back problems. Age- and sex-specific prevalence and workforce participation data were obtained from the 2022 National Health Survey. Excess all-cause mortality, absenteeism, and presenteeism data due to long-term back problems were derived from published sources.

MAIN OUTCOMES AND MEASURES: Primary outcomes were years of life lost, full-time equivalent workers lost, and productivity losses due to long-term back problems. Productivity losses were estimated as productivity-adjusted life-years and associated costs to Australia’s gross domestic product (GDP).

RESULTS: In 2024, 2 950 538 Australians had long-term back problems, which was projected to increase to 3 258 612 million by 2033. Long-term back problems resulted in an estimated loss of 3 394 255 productivity-adjusted life-years over the 10-year period, equating to a loss of more than 638 billion Australian dollars in Australia’s GDP. Reducing the relative prevalence and incidence of long-term back problems by 10% was estimated to result in a gain of 41.4 billion Australian dollars in GDP over the 10-year period.

CONCLUSIONS AND RELEVANCE: In this modeling study estimating future productivity losses from long-term back problems, substantial economic gains could be achieved from reducing the prevalence and impact of the condition. This model highlights the need to assess the effectiveness of interventions on work-related outcomes.

PMID:40844779 | DOI:10.1001/jamanetworkopen.2025.27284