Tag: nevin manimala

WMJ. 2025;124(4):371-374.

ABSTRACT

INTRODUCTION: HIV remains a significant public health concern. In Wisconsin, new cases increased by 36% during 2020 through 2022, and 22% were 13 to 24 years old. Despite recommendations for routine HIV screening, youth testing remains inadequate. This study aimed to understand HIV screening practices among youth receiving care in pediatric primary care clinics in southeastern Wisconsin.

METHODS: Clinic HIV testing rates were measured in patients aged 12 to 26 undergoing gonorrhea and/or chlamydia testing at pediatric primary care clinics affiliated with a not-for-profit children’s hospital.

RESULTS: Youth HIV testing rates at all clinic sites were low (median 19.7%) ranging from 13.2% to 36.1%. Higher rates were seen in clinics with higher rates of sexually transmitted infections.

CONCLUSIONS: Interventions are needed to enhance HIV testing rates in pediatric primary care clinics.

PMID:41197057

WMJ. 2025;124(4):357-363.

ABSTRACT

BACKGROUND: Housing and health care both play crucial roles in overall health. Though housing and health care barriers negatively impact affect health, little is known about the relative influence of each. This study sought to understand the relationship between housing circumstance, barriers to care, and mental health outcomes among low-income, uninsured patients seen at a free clinic in Milwaukee, Wisconsin. This includes investigating the relative impact of risk factors for homelessness, housing barriers, and health care barriers on mental health.

METHODS: Surveys were administered to clinic patients (n = 94) from June to December 2023. Surveys assessed patient demographics, housing and health care barriers, and mental health outcomes, primarily measured by the Patient Health Questionnaire-2 (PHQ-2), General Anxiety Disorder-2 (GAD-2) questionnaire, modified loneliness scale, and individuals’ subjective mental health rating.

RESULTS: Increased health care barriers and socioenvironmental risk factors for homelessness significantly predicted worse PHQ-2 score, GAD-2 score, loneliness, and mental health rating. Despite significant associations, increased housing barriers did not significantly predict any of the 4 mental health metrics. Furthermore, neither housing barriers nor health care barriers significantly predicted recreational drug use, whereas socioenvironmental risk factors for homelessness were both a significant predictor and response of increased recreational drug use. The most frequently reported mental health care barriers were insurance coverage, financial barriers, and transportation issues. In addition, there was significantly lower patient trust in mental health care providers than in general medical providers, which may reflect increased stigma.

CONCLUSIONS: Compared to housing barriers, increased health care barriers significantly predicted worse mental health outcomes. This study emphasizes the importance of addressing health care barriers to improve mental health.

PMID:41197054

Aging Clin Exp Res. 2025 Nov 6;37(1):314. doi: 10.1007/s40520-025-03215-0.

ABSTRACT

BACKGROUND: Sarcopenic obesity (SO) is increasingly recognized as a significant health concern, particularly among older populations. Existing literature indicates that SO elevates the risk for various adverse health outcomes such as cardiovascular diseases, fractures, higher all-cause mortality. However, evidence regarding its impact on the risk of falls remains limited and inconclusive. Our study aimed to investigate the association between SO and fall incidents.

METHODS: A total of 10,905 participants were enrolled from the baseline survey of the China Health and Retirement Longitudinal Study (CHARLS) 2015 wave. Participants were categorized into four groups according to sarcopenia and obesity status, with the neither sarcopenia nor obesity group serving as the reference. Logistic regression was utilized to evaluate the cross-sectional association between SO and falls. Furthermore, we tracked fall incidents reported in follow-up surveys conducted in CHARLS 2018 and 2020 wave. Cox regression analysis was performed to explore how SO affected the risk of falls. Stratified Cox analyses by age (< 60 vs. ≥60 years) were also performed.

RESULTS: In the cross-sectional analysis (2015), the SO group [OR (95% CI): 1.84 (1.42 ~ 2.37), P < 0.01] showed a higher risk of falls compared to the reference group; however, this association was not statistically significant after adjusting for potential confounding factors. In the longitudinal analysis (2015-2020), the SO group [HR (95%CI): 2.78 (2.03 ~ 3.80), P < 0.01] had a significantly increased risk of falls. The results remained similar after adjusting for age, sex [HR (95%CI): 1.44 (1.02 ~ 2.04), P < 0.05], and additional covariates [HR (95%CI): 1.43 (1.00 ~ 2.03), P < 0.05]. Notably, stratified Cox models showed that SO was significantly associated with fall risk in both age groups, with a stronger effect observed in participants under 60 years [HR (95%CI): 2.85 (1.13 ~ 7.17), P < 0.05] than in those aged 60 and above [HR (95%CI): 1.64 (1.08 ~ 2.50), P < 0.05].

CONCLUSION: Sarcopenic obesity is associated with an increased risk of falls among middle-aged and older adults, especially in longitudinal analyses. Age-stratified results suggest that the impact of SO on falls may be more pronounced in the middle-aged group. Our findings support the need for early identification and targeted interventions for individuals with SO to mitigate fall-related risks in aging populations.

PMID:41196484 | DOI:10.1007/s40520-025-03215-0

Genes Genomics. 2025 Nov 6. doi: 10.1007/s13258-025-01693-0. Online ahead of print.

ABSTRACT

BACKGROUND: The high prevalence of non-biological zero counts, arising from low sequencing depth and sampling variation, presents a significant challenge in microbiome data analysis. These zeros can distort taxon abundance distributions and hinder the identification of true biological signals, complicating downstream analyses.

OBJECTIVE: To address the challenges of non-biological zeros in microbiome datasets, we propose DeepIMB, a deep learning-based imputation method for microbiome data, specifically designed to accurately identify and impute non-biological zero counts while preserving biological integrity.

METHODS: DeepIMB operates in two main phases. First, it identifies non-biological zeros using a gamma-normal mixture model applied to the normalized, log-transformed taxon count matrix. Second, it imputes these zeros with a deep neural network model that integrates diverse sources of information, including taxon abundances, sample covariates, and phylogenetic distances, thereby learning complex, nonlinear relationships within microbiome data.

RESULTS: By leveraging integrated information from multiple data types, DeepIMB accurately imputes non-biological zeros while preserving true biological signals. In our two simulation studies, DeepIMB outperformed existing imputation methods in terms of mean squared error, Pearson correlation coefficient, and Wasserstein distance.

CONCLUSION: DeepIMB effectively addresses the challenges posed by non-biological zeros in microbiome data. By improving the quality of the data and the reliability of downstream analyses, DeepIMB represents a significant advancement in microbiome research methodologies.

PMID:41196474 | DOI:10.1007/s13258-025-01693-0

Clin Transl Oncol. 2025 Nov 6. doi: 10.1007/s12094-025-04093-8. Online ahead of print.

ABSTRACT

BACKGROUND: Early detection of metastasis in cancer patients plays a pivotal role in improving treatment outcomes and increasing patient survival. This study aimed to evaluate the predictive role of inflammatory indices, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune inflammation index (SII), and systemic inflammation response index (SIRI), in identifying metastatic status.

METHODS: In this study, 60 cancer patients were enrolled between December 2023 and June 2024. Clinicopathological data and complete blood counts (CBCs) were collected prior to treatment initiation. The Receiver Operating Characteristic (ROC) curve was used to determine the optimal cutoff values of different baseline inflammatory indices for the metastatic status analysis.

RESULTS: The levels of inflammatory indices were greater in metastatic patients than in nonmetastatic patients; however, only the SIRI was significantly different (1.04 [0.76-1.69] vs. 0.71 [0.45-1.07]; P = 0.044). ROC curve analysis revealed that the area under the curve (AUC) for the SIRI was 0.652 (95% CI 0.507-0.797). Furthermore, broader combinations of the SIRI and MLR, either individually or in conjunction with the NLR, PLR, and/or SII, yielded multi-index models with greater discriminatory power and maintained statistical significance (p < 0.05).

CONCLUSION: The findings indicate that the SIRI, in combination with the MLR, plays a significant role in predicting the metastatic status of cancer patients.

PMID:41196459 | DOI:10.1007/s12094-025-04093-8

Discov Oncol. 2025 Nov 6;16(1):2048. doi: 10.1007/s12672-025-03910-4.

ABSTRACT

BACKGROUND: Lung cancer remains a leading cause of cancer-related mortality, necessitating novel therapeutic targets. The plasma proteome represents a key source for such targets.

METHODS: Proteome-wide Mendelian randomization (MR) and colocalization analyses were conducted to assess the causal effects of plasma proteins on lung cancer subtypes and complications. Genetic instruments (cis-pQTLs) for 2,090 proteins were derived from plasma proteome data (54,306 UK Biobank and 35,559 Icelandic participants). Lung cancer phenotype data were obtained from FinnGen R10.

RESULTS: MR identified seven plasma proteins showing significant causal associations with specific lung cancer phenotypes: high GGT1 increased non-small cell lung cancer (NSCLC) risk (OR 1.27, 95% CI 1.10-1.46; P_FDR = 0.0261), GFRA2 increased the SCLC risk (OR 1.65, 95% CI 1.24-2.21; P_FDR = 0.0462), and higher advanced glycosylation end-product specific receptor reduced the squamous cell carcinoma risk (OR 0.338 per SD increase, 95% CI 0.209-0.548; P_FDR = 0.0138). Fifteen proteins showed associations with lung cancer complications. Colocalization strongly supported causal roles for eight proteins: FKBP1B (OR 1.15, 95% CI 1.09-1.22; P_FDR = 0.00264), F11(OR 1.01, 95% CI 1.01-1.01; P_FDR = 1.47 × 10^{– 23}), ABO (OR 1.11, 95% CI 1.06-1.21; P_FDR = 5.82 × 10^{– 9}), F2 (OR 3.04, 95% CI 1.74-5.31; P_FDR = 0.0102), and VSIG10L (OR 1.006, 95% CI 1.00-1.01; P_FDR = 0.0159).

CONCLUSION: This study reveals causal proteins for various lung cancer phenotypes and complications, emphasizing causal pathways and potential therapeutic targets for lung cancer and providing new insights into its etiology, prevention, treatment, and therapy.

PMID:41196451 | DOI:10.1007/s12672-025-03910-4

Health Econ Rev. 2025 Nov 6;15(1):95. doi: 10.1186/s13561-025-00631-w.

ABSTRACT

BACKGROUND: Health expenditure is influenced by complex interactions between economic, demographic, social factors, with significant variations across countries. This study aims to investigate the determinants of health expenditures employing robust regression methods offering a more flexible and reliable approach to dealing with outliers and high data variation.

METHODS: This study employs robust regression methods, Weighted Least Squares (WLS) and MM-estimator regression, to examine the determinants of health expenditures. The analyses were conducted using data from 179 countries for the year 2021 with the R Studio.

RESULTS: The findings indicate that income and ageing are significant determinants of health expenditures, and sixteen outliers were identified. In contrast, education level, public health expenditure, disease patterns showed no significant effect.

CONCLUSION: This study fills gap in the literature by using robust regression methods to account for outliers and provides new insights into the role of economic and demographic factors in health expenditures.

PMID:41196444 | DOI:10.1186/s13561-025-00631-w

Eur J Nutr. 2025 Nov 6;64(8):314. doi: 10.1007/s00394-025-03842-3.

ABSTRACT

OBJECTIVE: Dietary intake plays a pivotal role in sustaining optimal melatonin levels, while the relationship between dietary patterns and circulating melatonin levels remains unclear. This study aims to investigate the associations between dietary patterns, nutrient intake, and serum melatonin levels in the Chinese population.

METHODS: This cross-sectional study included 6,521 Chinese adults. Three dietary patterns were identified through principal component analysis. Multivariable linear regression was used to assess the associations between dietary patterns and serum melatonin levels. The covariance analysis and partial least squares regression was used to evaluate the association between micronutrient intake and serum melatonin concentrations.

RESULTS: The Dietary pattern 2 (DP2), characterized by high intake of fatty foods and red meat with the lowest Dietary Variety Score (DVS), and DP3 featuring high consumption of red meat, fruits, and vegetables but low intake of white meat and aquatic products with low DVS, were significantly associated with lower serum melatonin levels (DP2: β = – 0.12, P-trend < 0.001; DP3: β = – 0.13, P-trend < 0.001). Insufficient nutrient and quality intake of dietary fiber, potassium, vitamin B2, calcium, and magnesium was found in DP2, whereas DP3 showed inadequate intake of protein, cholesterol, vitamin B2, niacin, calcium, phosphorus, magnesium, selenium, and iron.

CONCLUSION: Specific dietary patterns, low dietary diversity and nutrient deficiencies are associated to reduced melatonin levels. These findings reveal distinct mechanisms linking overall dietary patterns to serum melatonin concentrations, underscoring the importance of appropriate dietary patterns and nutrients intake in sustaining optimal circulating melatonin homeostasis in humans.

PMID:41196434 | DOI:10.1007/s00394-025-03842-3

Environ Monit Assess. 2025 Nov 6;197(12):1303. doi: 10.1007/s10661-025-14700-4.

ABSTRACT

Accurate Air Quality Index (AQI) prediction is essential for timely health risk management in urban environments, yet challenges such as missing data and complex pollutant interactions limit the performance of traditional approaches. This study investigates AQI prediction for three years from six stations in Chennai, a South Indian coastal city, by coupling fourteen imputation techniques with five machine learning (ML) models to identify the most effective framework. Among the tested combinations, the Multi-Layer Perceptron (MLP) model with k-Nearest Neighbor imputation (kNNI-MLP) achieved the best performance, with a coefficient of determination of 0.9999, a root mean squared error of 0.4920, a mean absolute error of 0.2723, a symmetric mean absolute percentage error of 0.4522%, and a mean absolute scaled error of 0.0069%. Residual and calibration analyses confirmed unbiased and well-calibrated predictions, while trend analysis showed strong alignment between actual and predicted AQI values. Seasonal evaluation revealed consistent fluctuations, with AQI peaking in winter and post-monsoon and stabilizing during summer and monsoon. Station-wise patterns further highlighted site-specific pollution drivers such as traffic density, industrial activity, and waste burning. The findings establish kNNI-MLP as a robust AQI prediction framework and provide evidence for targeted interventions, including improved traffic regulation, waste management, and emission controls. Future research will focus on external validation to confirm the model’s generalizability across diverse urban contexts, as well as exploring interpretability techniques such as SHAP or variable importance analysis to enhance understanding of predictor contributions.

PMID:41196422 | DOI:10.1007/s10661-025-14700-4

Eur J Trauma Emerg Surg. 2025 Nov 6;51(1):325. doi: 10.1007/s00068-025-02988-3.

ABSTRACT

PURPOSE: Trauma is a leading cause of mortality worldwide, especially in low- and middle-income countries (LMICs), where diagnostic resources are limited. This study evaluated whether early Focused Assessment with Sonography for Trauma (FAST) reduces mortality and resource use in polytrauma patients in a low-resource setting.

METHODS: We conducted a single-center, randomized controlled trial among adults with severe trauma admitted to the emergency department of a tertiary hospital in Morocco. Patients were randomized to standard care or a protocol incorporating FAST during initial triage. The primary outcome was in-hospital mortality. Secondary endpoints included 30-day mortality, time to surgery, CT use, transfusion needs, and hospital stay. Multivariable logistic regression and survival analyses were performed.

RESULTS: A total of 157 patients were enrolled (77 control, 80 FAST). In-hospital mortality was significantly lower in the FAST group (39.2% vs. 66.2%, p = 0.001). Thirty-day mortality was also reduced (45.6% vs. 72.7%, p = 0.001). FAST use was associated with decreased odds of in-hospital death (adjusted OR 0.48; p = 0.050) and improved survival time (HR 0.56; p = 0.014). Fewer patients underwent CT in the FAST group (82.5% vs. 96.1%, p = 0.006), and time to surgery was shorter (5.16 vs. 9.82 h, p < 0.001).

CONCLUSION: Early use of FAST significantly reduced mortality, CT use, and surgical delays. These findings support guideline recommendations for integrating FAST into trauma triage protocols, particularly in LMICs.

TRIAL REGISTRATION: PACTR202507728817990 (retrospectively registered).

PMID:41196403 | DOI:10.1007/s00068-025-02988-3