Tag: pubmed

J Racial Ethn Health Disparities. 2026 Jul 27. doi: 10.1007/s40615-026-03126-4. Online ahead of print.

ABSTRACT

INTRODUCTION: American Indian/Alaska Native (AI/AN) communities experience persistent oral health disparities shaped by a range of social determinants of health (e.g., social, economic, and healthcare access factors). However, associations between demographic and socioeconomic characteristics and oral health outcomes among AI/AN populations remains understudied. The objective of this study was to conduct an exploratory analysis of demographic and socioeconomic factors and oral health outcomes among the Tribal Behavioral Risk Factor Surveillance System (TBRFSS) population.

METHODS: Demographic and general health variables were assessed through the TBRFSS. Exposure variables included age, sex, income, employment status, and dentist office type. The nine independent, ordinal outcome variables included: difficulty with chewing, difficulty with speech, dry mouth, felt anxious, felt embarrassment, avoided smiling, reduced social activities, problems sleeping, and experienced pain. Multivariable ordinal logistic regressions were conducted to produce proportional ORs and 95% CIs.

RESULTS: A total of 379 responses from participants who identified as AI/AN were included for analysis. Amongst those 379 participants, most identified as female (62% n = 224), had healthcare coverage (92%, n = 343), were Oklahoma residents (78%, n = 295), and were employed full-time (60%, n = 220). Multivariable ordinal logistic regression analyses revealed a statistically significant association between household income and oral health for seven out of the nine outcomes. The age group categories, income, and sex varied in association with the outcomes.

DISCUSSION: Understanding factors associated as it relates with oral health among AI/AN communities is an integral component of addressing health inequities.

PMID:42509519 | DOI:10.1007/s40615-026-03126-4

J Racial Ethn Health Disparities. 2026 Jul 27. doi: 10.1007/s40615-026-03134-4. Online ahead of print.

ABSTRACT

Prostate cancer remains the most prevalent malignancy and a leading cause of cancer-related mortality among men in Puerto Rico. This study evaluates regional disparities in prostate cancer incidence across 76 contiguous municipalities on the main island and characterizes areas exhibiting statistically significant spatial clustering. Using spatial analytical techniques-including Moran’s I and Getis-Ord Gi*-within Geographic Information Systems (GIS), we analyze data from the Puerto Rico Central Cancer Registry (RCCPR) spanning 2016 to 2022. The findings reveal a non-random spatial distribution of prostate cancer incidence, with consistent clustering patterns observed throughout most of the study period, except in 2019. Notably, cold-spot regions were persistently identified in the northwest and western municipalities, while elevated incidence rates were concentrated in clusters located in the southern and eastern regions. These results underscore the presence of enduring geographic disparities in the prostate cancer burden across Puerto Rico.

PMID:42509516 | DOI:10.1007/s40615-026-03134-4

Histochem Cell Biol. 2026 Jul 27;164(1):65. doi: 10.1007/s00418-026-02520-7.

ABSTRACT

Pregnancy induces marked metabolic and physiological adaptations in the maternal liver to maintain systemic homeostasis and support fetal development. Nuclear factor erythroid 2-related factors 1 and 2 (Nrf1 and Nrf2) are Cap’n’Collar (CNC) transcription factors that regulate antioxidant response element (ARE)-dependent gene expression and play essential roles in redox homeostasis and cellular stress responses; however, their gestational stage-dependent intracellular distribution in the maternal liver remains insufficiently characterized. This study investigated the immunohistochemical expression and subcellular localization of Nrf1 and Nrf2 in the maternal rat liver during mid and late gestation. Liver samples were obtained from non-pregnant control rats and pregnant Wistar albino rats on gestational days (GD) 13, 18, and 20 (n = 6 per group). Immunohistochemistry was performed using anti-Nrf1 and anti-Nrf2 antibodies, and staining intensities in nuclear and cytoplasmic compartments were quantified using Fiji (ImageJ). Nuclear-to-cytoplasmic (N/C) ratios were also calculated. Nrf1 immunoreactivity showed gestational stage-dependent changes, with stronger nuclear and cytoplasmic staining at GD13 and reduced staining at GD18 and GD20, accompanied by lower N/C ratios during mid gestation. In contrast, Nrf2 nuclear immunoreactivity remained relatively stable throughout most of gestation, with a significant decrease observed only between GD18 and GD20, whereas cytoplasmic immunoreactivity increased at GD13 and GD18 and declined at GD20. Statistical analysis demonstrated significant gestational stage-related differences in nuclear immunoreactivity, cytoplasmic immunoreactivity, and the N/C ratio for Nrf1. For Nrf2, significant differences were also observed in nuclear immunoreactivity, cytoplasmic immunoreactivity, and the N/C ratio, although pairwise differences in nuclear immunoreactivity were limited to GD18 and GD20. These findings demonstrate distinct gestational stage-dependent patterns of Nrf1 and Nrf2 immunoreactivity in the maternal liver, suggesting differential involvement of these transcription factors in maternal hepatic adaptation during pregnancy.

PMID:42509511 | DOI:10.1007/s00418-026-02520-7

Environ Sci Pollut Res Int. 2026 Jul 27. doi: 10.1007/s11356-026-38077-0. Online ahead of print.

ABSTRACT

Floods are among the most devastating disasters, posing significant risks to communities and infrastructure, particularly in tropical regions where hazard mapping is often constrained by limited data availability. This study applies an integrated geospatial and machine learning (ML) approach to improve flood hazard assessment in a mountainous tropical region of Aceh Jaya, Indonesia, with the aim of evaluating model performance and identifying dominant causative factors linked to spatially targeted mitigation strategies. A set of ten flood causative factors, together with historical flood inventory data, was analyzed using four ML algorithms: Random Forest (RF), Support Vector Machine (SVM), Boosted Regression Tree (BRT), and Generalized Linear Model (GLM). Flood hazard maps were classified into five levels, ranging from very low to very high susceptibility. High to very high hazard zones cover approximately 14-22% of the study area and are primarily concentrated in low-elevation downstream areas. Model evaluation using Area Under the Curve (AUC), True Skill Statistics (TSS), correlation, and deviance indicates that RF achieves the highest predictive performance (AUC = 0.983; TSS = 0.92). The results consistently identify elevation as the dominant controlling factor, underscoring the influence of terrain-driven hydrodynamic processes. The coherence between model outputs, underlying physical mechanisms, and observed spatial patterns strengthens the basis for delineating flood hazard zones and informing mitigation priorities. This integrated approach enhances the applicability of the study by supporting evidence-based decision-making and enabling more targeted flood risk management in data-limited tropical regions, with a transferable framework that can be readily applied to similar mountainous settings.

PMID:42509508 | DOI:10.1007/s11356-026-38077-0

Surg Endosc. 2026 Jul 27. doi: 10.1007/s00464-026-13123-7. Online ahead of print.

ABSTRACT

BACKGROUND: Appendectomy is a leading pediatric surgical procedure, yet the impact of missing data handling on clinical conclusions remains unexplored in pediatric surgery. This study aimed to identify current reporting practices in the pediatric appendectomy literature and quantify how different missing-data strategies influence the identification of postoperative infectious-complication risk factors.

METHODS: A systematic review of pediatric appendectomy literature from 2023 was conducted across PubMed and Web of Science to assess missing data reporting. Subsequently, a retrospective analysis of the National Surgical Quality Improvement Program-Pediatric (NSQIP-P) database (2015-2022) was performed on 142,129 cases. Five strategies were compared: available case analysis, threshold-based exclusion, complete case analysis, simple imputation, and multiple imputation. Multivariable logistic regression models identified risk factors for five infectious outcomes, including combined infectious complications, organ space infections, deep incisional infections, superficial surgical site infections, and wound dehiscence.

RESULTS: The literature review identified 116 eligible articles, of which 54.3% failed to mention missing data. Only 7.3% used advanced statistical techniques, while 86.4% reported that a method relied on complete-case analysis. In the NSQIP-P cohort, 39.3% of records had missing values, primarily in height and race. While core predictors like American Society of Anesthesiologists (ASA) class and operative duration remained stable in multivariate logistic regression analysis, variables such as preoperative white blood cell count and anthropometric measurements fluctuated in significance depending on the handling method. Complete case analysis was the most conservative, identifying the fewest significant predictors.

CONCLUSION: Methodological transparency regarding missing data is critically lacking in pediatric surgical research. Because the choice of data-handling strategy significantly alters the identification of clinical risk factors, researchers should prioritize multiple imputation over simple exclusion methods. Journals must mandate rigorous reporting of missingness to ensure the reliability of evidence-based surgical guidelines.

PMID:42509504 | DOI:10.1007/s00464-026-13123-7

Surg Endosc. 2026 Jul 27. doi: 10.1007/s00464-026-13196-4. Online ahead of print.

ABSTRACT

BACKGROUND: Robotic cholecystectomy is increasingly adopted as an alternative to laparoscopic cholecystectomy and proposed as an entry‑level procedure in robotic training curricula, yet real‑world data on surgeon‑specific learning curves and their impact on perioperative safety remain limited. This study aimed to map the learning curve for robotic cholecystectomy across a multi-surgeon, multicenter cohort and assess associated safety outcomes.

METHODS: This retrospective cohort study analyzed the first 50 consecutive robotic cholecystectomies performed independently by each of five surgeons (n = 250) between January 2023 and June 2025. For each surgeon, the learning-curve endpoint was identified as the breakpoint of a piecewise linear regression of skin-to-skin operative time against case sequence, and the Mann-Whitney U test assessed whether operative time differed significantly between the early and late phases. A linear mixed-effects model, with surgeon as random intercept, identified independent predictors of operative time accounting for within-surgeon clustering.

RESULTS: The learning-curve endpoint ranged from 11 to 34 cases across surgeons. The breakpoint corresponded to a significant reduction in operative time for three of five surgeons (p = 0.017, p = 0.015, p < 0.001), but not for the remaining two (p = 0.853, p = 0.233). Cohort-level median operative time decreased significantly from early to late phase (66 vs 50 min; p < 0.001), while length of stay did not differ (p = 0.354). Case sequence number (- 0.72 min/case) and Nassar difficulty grade (+ 9.8 min/grade) were independent predictors of operative time (p < 0.001). No bile duct injuries occurred and severe complications occurred in 1/250 patients (0.4%).

CONCLUSIONS: Most surgeons showed a statistically confirmed reduction in operative time within their first 11-24 robotic cholecystectomies, while for others no significant improvement was confirmed. Complication rates remained low, although this cohort was not adequately powered to formally demonstrate safety equivalence across the learning process. These findings support robotic cholecystectomy as a feasible early procedure within structured robotic training pathways.

PMID:42509501 | DOI:10.1007/s00464-026-13196-4

Biol Trace Elem Res. 2026 Jul 28. doi: 10.1007/s12011-026-05265-5. Online ahead of print.

ABSTRACT

Selenium (Se) is an essential trace element for human health and longevity. However, the spatial association between soil Se content and longevity remains poorly understood at different spatial scales. Therefore, this study investigated the relationship between soil Se and longevity at both the national scale in China and at six representative smaller regions (Heilongjiang Province, Liaohe River Basin, Enshi Prefecture in Hubei, Ankang City in Shaanxi, Lianzhou City and Yingde City in Guangdong). Both traditional statistical methods (e.g., Pearson correlation, OLS regression) and spatial statistical methods (e.g., Moran’s I, SLM, SEM) were employed. The results revealed that: (1) At the national scale, soil Se and longevity exhibited a highly significant positive correlation and strong spatial clustering. High-Se/high-longevity clusters were mainly located in southern China, while northern provinces were characterized by low-Se/low-longevity clusters. SEM (R² = 0.31) outperformed OLS (R² = 0.22) and SLM (R² = 0.28), and the SEM model parameters indicated that the positive association persists after controlling for spatial error dependence. (2) At the small-scale level, a highly significant positive correlation was only identified in Lianzhou and Yingde. In Lianzhou, clusters of high Se and high longevity were concentrated in the southern region. SLM (R² = 0.87) outperformed OLS (R² = 0.72); the SLM model parameters indicated that the positive effect of soil Se remains after controlling for spatial lag dependence. (3) Spatial error dependence dominated at the broader scale, whereas spatial lag dependence was more prominent in small regions. Linearity and model performance improved substantially at the small scale. Regions with suitable Se levels showed the strongest Se-longevity associations.

PMID:42509495 | DOI:10.1007/s12011-026-05265-5

Pituitary. 2026 Jul 27;29(4):133. doi: 10.1007/s11102-026-01725-2.

ABSTRACT

PURPOSE: Biochemical remission after transsphenoidal resection for acromegaly remains challenging, and early prognostication is limited. This study applies statistical and predictive modeling approaches to identify perioperative predictors of remission.

METHODS: We retrospectively analyzed 86 patients with acromegaly who underwent endoscopic endonasal transsphenoidal resection of GH-secreting primary pituitary adenomas between January 2018 and January 2025. Preoperative demographic, biochemical, radiologic, and histopathologic variables were evaluated. Patients were randomly split into training and test sets. Missing data were addressed using median and KNN imputation. Five machine learning models (GBM, RF, GLMNET, KNN, and Nnet) were trained using exhaustive feature subset selection and evaluated using AUROC and accuracy.

RESULTS: Of 86 patients, 62 (72.1%) achieved biochemical remission. Remission was associated with older age (p = 0.016), round tumor shape (p < 0.0001), gross total resection (p < 0.0001), lower Knosp grade (p < 0.0001), and dense CAM 5.2 staining (p = 0.037). The best-performing model was RF using four features (gender, tumor shape, extent of resection, CAM 5.2), achieving an accuracy of 0.8235 and AUROC of 0.8542 on the test set.

CONCLUSION: Predictive modeling may help estimate biochemical remission after surgery for acromegaly. This exploratory perioperative framework may support postoperative risk stratification and patient counseling while complementing standard endocrine follow-up. Further external validation in larger cohorts is warranted.

PMID:42509491 | DOI:10.1007/s11102-026-01725-2

Neurocrit Care. 2026 Jul 27. doi: 10.1007/s12028-026-02609-w. Online ahead of print.

ABSTRACT

INTRODUCTION: White matter hyperintensities (WMH) are key radiographic biomarkers of cerebral small vessel disease (CSVD) and have been associated with worse outcomes after stroke. Since CSVD may involve blood-brain barrier disruption, we hypothesized that worse outcomes in those with WMH may be mediated through more severe cerebral edema and hemorrhagic transformation (HT).

METHODS: In this retrospective multicenter cohort study, we studied patients from four multinational stroke cohorts who presented within 12 h of anterior circulation large vessel occlusion (LVO) stroke and received at least one follow-up computed tomography (CT) between 12 and 48 h. WMH were rated on baseline CTs using a Fazekas-derived system, with a subset also assessed with Fazekas grading on magnetic resonance imaging (MRI). Automated image analysis measured cerebrospinal fluid displacement (ΔCSF) and lesional-to-contralateral hemispheric CSF volumes (CSF-ratio) as quantitative edema biomarkers. We analyzed the association between WMH presence and edema severity, hemorrhagic transformation, and poor functional outcome (mRS 3-6), adjusting for key covariates, overall and in the subgroup undergoing thrombectomy and those with successful reperfusion.

RESULTS: Of 1290 patients with LVO, 782 were eligible for analysis. Of these, 19% had WMH on CT. Patients with WMH were older, more often female, and had a history of hypertension and higher presenting systolic blood pressure and glucose levels. WMH presence was associated with worse functional outcome, adjusting for age and additional covariates [OR 1.84 (1.06-3.22), p = 0.04] but lower risk of any HT [OR 0.48 (0.25-0.88), p = 0.03]. However, WMH presence by CT or MRI was not associated with edema (ΔCSF, CSF ratio, or midline shift) in the whole population or thrombectomy/reperfusion subgroups. Reperfusion was associated with less edema and improved recovery.

CONCLUSIONS: Presence of CT-graded WMH was associated with worse outcome after LVO stroke, independent of age and other factors. However, this risk appears not to be mediated through greater risk of HT or cerebral edema formation.

PMID:42509480 | DOI:10.1007/s12028-026-02609-w

Arch Osteoporos. 2026 Jul 27;21(1):106. doi: 10.1007/s11657-026-01740-7.

ABSTRACT

We evaluated whether adding a genetic risk score derived from forearm fracture improves fracture prediction beyond standard clinical tools. Incorporation of GPS resulted in a small increase in time-dependent AUC, with improvement in NRI. Decision curve analysis suggested potential clinical benefit across certain threshold ranges.

BACKGROUND: Previous studies have incorporated estimated bone mineral density (eBMD)-derived polygenic scores into FRAX to improve fracture risk prediction, indirectly capturing genetic susceptibility through bone mineral density. However, the genetic architectures of fracture and BMD only partially overlap, suggesting that fracture-specific genetic risk may provide complementary and more direct biological information. With the availability of recently released forearm fracture GWAS summary statistics, we developed Bayesian genome-wide polygenic scores (GPS) derived from fracture risk and evaluated whether integrating fracture-derived Bayesian GPS into FRAX could enhance clinical fracture risk prediction.

METHODS: We constructed two Bayesian GPS using recently released UK Biobank forearm fracture GWAS summary statistics (GWAS Catalog study ID: GCST90281273) with two Bayesian frameworks: polygenic risk score-continuous shrinkage (PRS-CS) and summary-data-based Bayesian regression with continuous shrinkage and functional annotation integration (SBayesRC). The fracture-derived Bayesian GPS were integrated into FRAX to derive fracture-specific GPS-FRAX models. Model performance was evaluated in 10,135 postmenopausal women from the Women’s Health Initiative (WHI) using time-dependent area under the receiver operating characteristic curve (AUC), Brier score, net reclassification improvement (NRI), calibration analysis, and decision curve analysis (DCA).

RESULTS: Compared with the FRAX-CRF model (time-dependent AUC = 0.683), fracture-derived Bayesian GPS-FRAX models demonstrated modest improvements in discrimination, with time-dependent AUCs of 0.693 for PRS-CS and 0.690 for SBayesRC. Overall reclassification proportions were low (1.58% for SBayesRC and 1.82% for PRS-CS), but NRI was significantly improved, with overall NRI estimates of 2.20% (95% CI, 0.83 to 3.58%) for SBayesRC and 2.72% (95% CI, 1.34 to 4.19%) for PRS-CS. These findings indicate incremental predictive gain beyond clinical FRAX factors despite modest increases in discrimination.

CONCLUSIONS: Incorporation of fracture-derived Bayesian GPS into FRAX resulted in a modest but statistically significant improvement in model discrimination, as reflected by a small increase in AUC. In addition, improvements in NRI and higher estimated net benefit in decision curve analysis suggest incremental clinical utility. However, the overall magnitude of improvement remained limited, indicating that the added predictive value beyond established clinical risk factors is modest. Further evaluation in more diverse populations is warranted.

PMID:42509442 | DOI:10.1007/s11657-026-01740-7