Nevin Manimala

JAMA Netw Open. 2025 May 1;8(5):e259119. doi: 10.1001/jamanetworkopen.2025.9119.

ABSTRACT

IMPORTANCE: National guidelines recommend next-generation sequencing (NGS) of tumors in patients diagnosed with metastatic prostate cancer (mPCa) to identify potential actionable alterations. Non-Hispanic Black men are poorly represented in precision oncology cohorts, and therefore differences in alterations frequencies between non-Hispanic Black and White men remain poorly characterized.

OBJECTIVES: To describe the spectrum and frequency of alterations in PCa-related genes and pathways, as well as associations with self-identified race and ethnicity and overall survival in US veterans.

DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study compared alteration frequencies between non-Hispanic Black and White men who underwent NGS testing from January 23, 2019, to November 2, 2023, adjusted by NGS analyte and clinicopathologic covariates. The analytic data file was locked on December 8, 2023. NGS testing was performed through the Department of Veterans Affairs (VA) National Precision Oncology Program, part of the largest near-equal access integrated health care system in the US.

EXPOSURES: Pathogenic alterations identified by NGS testing with a commercially available NGS platform.

MAIN OUTCOMES AND MEASURES: The primary outcome consisted of alteration frequencies in individual genes, actionable targets, and canonical prostate cancer pathways. Associations between alteration frequency and race and ethnicity as well as survival were also examined.

RESULTS: A total of 5015 veterans with mPCa who underwent NGS were included (1784 non-Hispanic Black [35.6%] and 3231 non-Hispanic White [64.4%]; mean [SD] age, 67.4 [9.0] years). Non-Hispanic Black veterans were younger, had higher prostate-specific antigen levels at diagnosis, were less likely to report Agent Orange exposure, and resided in more deprived neighborhoods compared with non-Hispanic White veterans. Nine of the top 10 most commonly altered genes were the same in non-Hispanic Black and non-Hispanic White veterans; however, the frequencies of alterations varied by race and ethnicity. Non-Hispanic Black race and ethnicity was associated with higher odds of genomic alterations in SPOP (odds ratio [OR], 1.7; 95% CI, 1.2-2.6) as well as immunotherapy targets (OR, 1.7; 95% CI, 1.1-2.5) including high microsatellite instability status (OR, 3.1; 95% CI, 1.1-9.4). Furthermore, non-Hispanic Black race and ethnicity was associated with lower odds of genomic alterations in the AKT/PI3K pathway (OR, 0.6; 95% CI, 0.4-0.7), androgen receptor axis (OR, 0.7; 95% CI, 0.5-0.9), and tumor suppressor genes (OR, 0.7; 95% CI, 0.5-0.8). Cox proportional hazards modeling stratified by race and ethnicity found that alterations in tumor suppressor genes, including TP53, were associated with shorter overall survival in both non-Hispanic Black (hazards ratio [HR], 1.54; 95% CI, 1.13-2.11) and non-Hispanic White (HR, 1.52; 95% CI, 1.25-1.85) veterans.

CONCLUSIONS AND RELEVANCE: This retrospective clinical genomic profiling cohort study with a large total and proportional representation of non-Hispanic Black men with mPCa reported significant differences in alteration frequencies from key oncogenic pathways but similar survival rates in the near equal-access VA health care setting. This analysis suggests the utility of genomic testing for identifying candidates irrespective of race and ethnicity for precision oncology treatments, which could contribute to equitable outcomes in patients with mPCa.

PMID:40354055 | DOI:10.1001/jamanetworkopen.2025.9119

JAMA Netw Open. 2025 May 1;8(5):e259792. doi: 10.1001/jamanetworkopen.2025.9792.

ABSTRACT

IMPORTANCE: Intrinsic capacity (IC) is a core component of the World Health Organization’s healthy aging framework. Yet, despite multiple validations of IC across various settings, there is still a lack of longitudinal cross-national analysis.

OBJECTIVE: To validate the IC construct, describe variance between key demographic groups, and create population centile curves across 15 countries using data from the Survey of Health, Aging, and Retirement in Europe (SHARE).

DESIGN, SETTING, AND PARTICIPANTS: In this population-based multicenter cohort study, data from SHARE wave 5 (January to November 30, 2013) were analyzed, and subsequent care dependence in wave 6 (January to November 30, 2015) was determined. Adults 50 years and older from SHARE wave 5 with at least 1 available measure and follow-up data in SHARE wave 6 were included. Data analyses were conducted between December 11, 2022, and June 7, 2024.

EXPOSURE: SHARE waves 5 and 6.

MAIN OUTCOMES AND MEASURES: Changes in activities of daily living (ADL) and instrumental activities of daily living (IADL). Methods included structural equation modeling, bifactor analysis, and path analysis. Construct validity was tested through multiple linear regression and validity of estimates through mediation analysis. Centile curves were established using the generalized additive models for location, scale, and shape.

RESULTS: The sample included 64 872 eligible participants aged 50 to 104 years, with a mean (SD) age of 67.24 (10.01) years, of whom 35 976 (55.46%) were women. The bifactor confirmatory factor analysis model achieved good fit (comparative fit index, 0.986; Tucker-Lewis index, 0.981), suggesting an IC structure consisting of 1 general factor and 5 subdomains. Mediation analysis indicated that IC was associated with subsequent declining performance in ADL (standard coefficient [SD], -0.213 [0.002]; P < .001) and IADL (standard coefficient [SD], -0.209 [0.002]; P < .001) after adjusting for age, gender, educational attainment, socioeconomic status, and country. Socioeconomic status was associated with IC both within and between countries. Centile curves for IC by gender and country (5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles) were constructed.

CONCLUSIONS AND RELEVANCE: Results of this cohort study of older adults suggest that IC was a valid and reliable measure that effectively captured individual-level aspects of functional ability. The centile curves developed during the study suggest that IC has the potential to serve as a benchmark for health status in older populations.

PMID:40354051 | DOI:10.1001/jamanetworkopen.2025.9792

Biomater Sci. 2025 May 12. doi: 10.1039/d5bm00033e. Online ahead of print.

ABSTRACT

Inflammation plays a key role in cartilage damage that occurs in osteoarthritis (OA). However, in vitro assessments of tissue-engineered constructs for cartilage regeneration generally do not consider their performance in the presence of inflammation. In this work, the chondrogenic differentiation potential of mesenchymal stromal cells (MSCs) was evaluated in the presence of both chondrogenic factors and inflammatory cytokines, and cartilage formation, degradative response, and inflammatory response were characterized. The addition of cytokines reduced cartilage production, increased cell proliferation, and resulted in an increase in inflammatory markers. Incorporation of hyaluronic acid (HA) had little impact on both collagen fibril microstructure and mechanical properties, two gel properties known to affect cell response, and thus allows the work to probe the biological impact of HA without the confounding effect of these gel properties. Regardless of in vitro environment, HA did not change cartilage production. The inflammatory response was similar with or without HA in terms of IL-6 and IL-10 secretion whereas IL-8 production exhibited some correlation with HA concentration as observed via a linear regression model. Additionally, in the presence of cytokines, inclusion of HA statistically decreased the gene- and protein-level expression of matrix metalloproteinase-13 (MMP-13). Thus, when exposed to both chondrogenic growth factors and inflammatory cytokines within a chondrogenic-promoting collagen I/II blended hydrogel, chondrogenic differentiation of MSCs was limited by the inflammatory environment. These findings emphasize the importance of understanding how biomaterials affect cell responses within disease-relevant inflammatory environments.

PMID:40354044 | DOI:10.1039/d5bm00033e

Eur Arch Paediatr Dent. 2025 May 12. doi: 10.1007/s40368-025-01046-1. Online ahead of print.

ABSTRACT

PURPOSE: An effective Deep learning (DL) based Early Childhood Caries (ECC) prediction model is crucial for early detection of ECC. This study aims to develop and evaluate a deep learning (DL) based hybrid statistical model for ECC prediction.

METHODS: The study employed a computational cross-sectional design, conducted over a three-year period from March 2021 to March 2024. Data analysis was carried out using a hybrid statistical approach that integrated bootstrap methods, Logistic Regression Modelling (LRM), and Multilayer Feed-Forward Neural Networks (MLFFNN). The sample comprised 157 parent-child pairs, providing a robust dataset for examining the research questions.

RESULTS: In the current study, the predictors named, “mother’s education” (β₁: 0.423; p < 0.25), “parent’s knowledge of bottle-feeding habit during sleep can cause tooth decay” (β₂: -1.264; p < 0.25), “attitude towards the importance of oral health as general health” (β₄: -1.052; p < 0.25) and “parent’s self-reported oral pain among their children” (β₅: -2.107; p < 0.25) showed significant association with ECC. For this model, the Mean Absolute Deviation (MAD) was 0.02211, Predictive Mean Squared Error (PMSE) was 0.07909, and the accuracy level was 99.98%. No significant difference was observed from the t-test between the actual values and the predicted values of the model (p > 0.05).

CONCLUSION: It has been shown that this unique deep learning-based ECC prediction model appears an effective tool with high accuracy and interpretability for ECC prediction. After implementing the oral health intervention program, focusing on the potential predictors of ECC obtained from this innovative model, policymakers could be able to evaluate their prediction models comparing their results with the findings of the current study. This comparison will guide them in understanding, designing, and implementing a more effective intervention program for ECC prevention.

PMID:40354021 | DOI:10.1007/s40368-025-01046-1

Cogn Process. 2025 May 12. doi: 10.1007/s10339-025-01275-8. Online ahead of print.

ABSTRACT

The human brain operates as a complex network, and understanding its functional connectivity is a core challenge in neuroscience. Functional near-infrared spectroscopy (fNIRS) offers a non-invasive, portable method for studying brain activity and connectivity, providing valuable insights into the brain’s network dynamics. In this study, we used fNIRS to examine the functional connectivity of the human brain during the Dual n-back task, a cognitive challenge that varies in memory load (0-back, 1-back, and 2-back). Data were collected from 24 channels in the frontal cortex and pre-processed with discrete wavelet transform. Functional connectivity matrices for each task level were calculated using correlation analysis, and graph theory metrics such as clustering coefficient and local and global efficiency were assessed. Statistical comparisons (t-tests and ANOVA) revealed significant differences in these metrics across memory load levels, with higher memory loads leading to altered brain connectivity patterns (p < 0.05 for clustering coefficient and local efficiency, p < 0.04 for global efficiency). These findings suggest that as cognitive demand increases, the functional connectivity of the brain’s frontal network changes, reflecting the dynamic nature of brain activity during complex tasks. This research highlights the potential of fNIRS for exploring brain network functions and has broader implications for understanding cognitive processes and developing neurocognitive diagnostics and interventions.

PMID:40354005 | DOI:10.1007/s10339-025-01275-8

Discov Oncol. 2025 May 12;16(1):735. doi: 10.1007/s12672-025-02523-1.

ABSTRACT

The growing burden of cancer and recent surge in healthcare data availability call for new ways of analysing this multifactorial disease and improving patient outcomes. The aim of this study is to develop and evaluate prognostic cancer survival models across ten common cancer types based on a large patient sample. We compare the performance of different machine learning algorithms and assess the added value of genetic information in cancer prognosis. We also provide ways to improve model explainabilty which is critical for model adoption in clinical practice. This study included data from 9977 patients with bladder, breast, colorectal, endometrial, glioma, leukaemia, lung, ovarian, prostate, and renal cancers. Genetic data collected through the 100,000 Genomes Project was linked with clinical and demographic data provided by the National Cancer Registration and Analysis Service, Hospital Episode Statistics and Office for National Statistics. More than 500 prognostic features were assessed and four machine learning algorithms including Elastic Net Cox proportional hazards regression, random survival forest, gradient boosting survival and DeepSurv neural network were developed in this study. Most models achieved good performance varying from 60% in bladder cancer to 80% in glioma with the average C-index of 72% across all cancer types. Different machine learning methods achieved similar performance with DeepSurv model slightly underperforming compared to other methods. Addition of genetic data improved performance in endometrial, glioma, ovarian and prostate cancers, showing its potential importance for cancer prognosis. Patient’s age, stage, grade, referral route, waiting times, pre-existing conditions, previous hospital utilisation, tumour mutational burden and mutations in gene TP53 were among the most important features in cancer survival modelling. By offering a comprehensive set of predictive models for cancer survival, this study fills a critical gap in our understanding of cancer prognosis and provides new tools for informing cancer treatment and consequently improving patient outcomes.

PMID:40353995 | DOI:10.1007/s12672-025-02523-1

Biol Futur. 2025 May 12. doi: 10.1007/s42977-025-00260-8. Online ahead of print.

ABSTRACT

Monitoring the root system plays an important role in understanding plant physiological processes; however, its assessment using non-destructive methods remains challenging. Here, we evaluate the utility of root capacitance (C_R) as a practical indicator of root function and its relationship to plant growth parameters in Capsicum annuum L. To improve the accuracy of root function assessment, we applied artificial neural networks (ANN) as a novel data evaluation approach, comparing its predictive performance against multiple linear regression (MLR). Across two soil types (sandy and sandy loam), we applied multiple treatments ranging from microbial inoculants to wool pellet and inorganic nitrogen sources primarily to test whether C_R could detect differences in root activity and biomass production under different conditions. We measured root dry biomass, shoot dry biomass, and leaf N content, treating these variables as independent predictors in a statistical framework. Multiple linear regression (MLR) initially showed strong relationship between C_R and both root and shoot biomass in sandy soil, and between C_R and total plant N content in sandy loam. However, an ANN model consistently outperformed MLR in predicting C_R from plant physiological parameters, as evidenced by lower mean absolute error (MAE) in all treatments. These findings confirm that C_R correlates strongly with plant growth parameters and can reliably distinguish the effects of different soil amendments even those with markedly different nutrient-release profiles.

PMID:40353984 | DOI:10.1007/s42977-025-00260-8

Eur J Nutr. 2025 May 12;64(4):177. doi: 10.1007/s00394-025-03662-5.

ABSTRACT

OBJECTIVE: To investigate the complex relationship between oxidative balance score (OBS), hypertension (HTN) and arterial stiffness in children and adolescents utilizing data gathered from the National Health and Nutrition Examination Survey (NHANES).

STUDY DESIGN: Through utilizing NHANES data (2001-2018), OBS, comprising dietary and lifestyle components, was calculated and categorized into tertiles. The correlation between OBS and HTN was explored employing weighted multivariate logistic regression. Stratified analyses were further performed to evaluate the associations across different subgroups.

RESULTS: A total of 11,754 children and adolescents were ultimately enrolled in analyses. High OBS tertiles demonstrated a consistent negative association with HTN across models. Compared with the lowest OBS tertile, the risk of HTN in the highest OBS tertile was decreased by 37% (95% CI 0.44-0.90, p = 0.011). After dividing OBS into dietary OBS and lifestyle OBS, Lifestyle OBS exhibited a significant inverse association with HTN, while dietary OBS showed no significant correlation. Stratified analyses notably revealed the protective impacts of OBS on the risk of HTN in males. Restricted cubic spline analysis confirmed a nonlinear association between OBS and HTN. Moreover, the elevated OBS was significantly associated with decreased ePWV, indicating a potential link between arterial stiffness and OBS.

CONCLUSION: In summary, the risk of HTN was inversely correlated with high OBS. Adopting a wholesome lifestyle enriched with antioxidants to boost OBS may help shield children from HTN risk.

PMID:40353983 | DOI:10.1007/s00394-025-03662-5

Dokl Biochem Biophys. 2025 May 11. doi: 10.1134/S1607672925700164. Online ahead of print.

ABSTRACT

To study the repertoire of the T-cell receptor in chronically exposed persons in the long-term period. The study involved 48 people who were divided into two groups: a group of exposed persons (31 individuals with the mean accumulated dose to red bone marrow (RBM) of 981 ± 130 mGy) and a comparison group (17 individuals, the mean accumulated dose to RBM of 25.3 ± 5.91 mGy). The study groups did not differ significantly in age, gender, and ethnicity. The repertoire of Vβ-segments of the T-cell receptor of the peripheral blood T-lymphocytes of exposed persons was analyzed by flow cytometry method. 24 Vβ-segments of the T-cell receptor were studied. Statistical processing of the obtained data was carried out using the Wilcoxon signed-rank test, and a direct description of Vβ-segment repertoire of the T-cell receptor was performed using the Lorenz curve and the Gini-TCR index. The study revealed a statistically significant increase in the number of Vβ3 and Vβ5.2 T-cell receptor segments in exposed individuals relative to the comparison group (p = 0.03 and p = 0.003, respectively). It was also shown that the distribution of the Vβ-segments of the T-cell receptor was uneven in both study groups. However, there was no significant difference between the repertoires of the T-cell receptor of the studied groups by the Gini-TCR index (p = 0.14).

PMID:40353972 | DOI:10.1134/S1607672925700164

Dokl Biochem Biophys. 2025 May 11. doi: 10.1134/S1607672925700140. Online ahead of print.

ABSTRACT

The study aims to evaluate the clinical efficacy, safety, pharmacokinetics, pharmacodynamics and immunogenicity of seniprutug (BCD-180) in patients with active radiographic axial spondyloarthritis (r-axSpA, or ankylosing spondylitis).

MATERIALS AND METHODS: . Two hundred sixty patients with active r-axSpA and inadequate response to nonsteroidal anti-inflammatory drugs (NSAIDs) were randomized into three groups to receive either seniprutug (BCD-180) 5 or 7 mg/kg, or placebo. BCD-180 was administered in the respective group dose using a 0-12-36 week regimen. The placebo group patients were switched to BCD-180 5 mg/kg at Week 24, with therapy continued at Week 36. The primary endpoint was the proportion of patients achieving 40% improvement in the Assessment in Spondyloarthritis International Society (ASAS40) score at Week 24. The secondary endpoints included the proportion of patients achieving an ASAS20/40 response, improvement in 5 of 6 ASAS criteria (ASAS5/6), partial remission according to ASAS, ASDAS-CRP clinically important improvement in (Ankylosing Spondylitis Disease Activity Score with C-reactive protein level, ASDAS-CII) and ASDAS-CRP major improvement (ASDAS-MI). An analysis of changes over time in the disease activity status according to ASDAS-CRP, BASDAI (Bath Ankylosing Spondylitis Disease Activity Index) and BASFI (Bath Ankylosing Spondylitis Functional Index) scores, as well as changes over time in laboratory markers (CRP and erythrocyte sedimentation rate (ESR)) was also conducted. Safety was assessed based on the frequency and profile of adverse events (AE) and adverse reactions (AR).

RESULTS: : The proportion of patients who achieved an ASAS40 response at Week 24 on seniprutug (BCD-180) at doses of 7 and 5 mg/kg was 51.4 and 40.8%, respectively, compared with 24% in the Placebo group (p = 0.0012 and p = 0.0417, respectively). Analysis of secondary endpoints showed that the efficacy of BCD-180 at both study doses was statistically significantly superior to placebo in patients with r-axSpA at Week 24 in the following respects: reduction in the proportion of subjects with very high disease activity (ASDAS-CRP > 3.5), achieving ASDAS-CII, ASAS20, ASAS5/6 response. A statistically significant decrease in the ASDAS-CRP, BASDAI, BASFI score, as well as CRP and ESR levels was demonstrated. Tolerability of seniprutug therapy was assessed as acceptable. The most common AEs were infusion-related reactions, most of which were mild to moderate according to CTCAE 5.0 (Common Terminology Criteria for Adverse Events) and developed mainly during the first administration. The proportion of patients with detected binding antibodies was 5.1%. No neutralizing antibodies were detected.

CONCLUSIONS: . Seniprutug (BCD-180) as a therapy for r-axSpA has demonstrated superiority over placebo in the clinical efficacy, a good safety profile and low immunogenicity.

PMID:40353961 | DOI:10.1134/S1607672925700140