Tag: pubmed

J Orthop Surg Res. 2026 May 3. doi: 10.1186/s13018-026-06910-4. Online ahead of print.

ABSTRACT

OBJECTIVE: To investigate specific radiological risk factors in patients with patellar dislocation accompanied by inferomedial patellar margin avulsion fractures and provide evidence for clinical treatment strategy selection, this study conducted comparative analysis of radiographic measurements and clinical function between patients with patellar dislocation with and without such avulsion fractures.

METHODS: A total of 73 patients with patellar dislocation who underwent medial patellofemoral ligament (MPFL) reconstruction at the Affiliated Hospital of Chengde Medical University were included. Basic patient data, affected side, injury mechanism, and type of patellar dislocation were collected. The subjects were subsequently divided into an experimental group and a control group based on the presence or absence of a concomitant inferomedial patellar marginal avulsion fracture. Imaging evaluations, including MRI, CT, and X-ray examinations, were comprehensively performed for comparative analysis. MRI was used to assess the height of the vastus medialis obliquus (VMO); CT was used to measure patellar thickness, patellar width, lateral patellar facet angle, Wiberg angle, Wiberg index, the length ratio of lateral to medial patellar facets, trochlear depth index, sulcus angle, lateral trochlear inclination, tibial tubercle-trochlear groove (TT-TG) distance, the cross-sectional area ratio of VMO to vastus lateralis muscle (VLM), medial-lateral width of the femoral condyle, and the length of different regions of the femoral condyle, as well as to classify the patella and trochlea. The Caton-Deschamps index was measured on X-ray. Additionally, clinical function was evaluated and compared using Lysholm score, IKDC score, Tegner score, and VAS score preoperatively and at 6 months postoperatively.

RESULTS: Statistically significant differences were observed between the experimental and control groups regarding injury mechanism, type of patellar dislocation, the ratio of lateral to medial patella facet length, and Wiberg scores (P < 0.05).

CONCLUSION: Injury mechanism, types of patellar dislocation, the Wiberg angle, and the length ratio of the lateral to medial patellar facets have significant predictive value for the occurrence of avulsion fractures of the inferomedial patellar margin following patellar dislocation. Furthermore, patients with patellar dislocation, whether accompanied by avulsion fractures of the inferomedial patellar margin or not, undergoing isolated medial patellofemoral ligament reconstruction show no difference in postoperative clinical function.

PMID:42071233 | DOI:10.1186/s13018-026-06910-4

Nutr Metab (Lond). 2026 May 3. doi: 10.1186/s12986-026-01122-3. Online ahead of print.

ABSTRACT

BACKGROUND: Oyster mushrooms (Pleurotus ostreatus, PO) are rich in β-glucans and other ingredients with cholesterol-lowering potential. While human intervention studies suggest that PO intake may reduce total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and triglycerides, current evidence remains limited due to methodological limitations of the studies. Thus, this study investigated whether regular intake of PO powder affects LDL-C concentrations in adults with moderately elevated LDL-C (primary aim). Moreover, the study explored the effect on other lipids (TC, high-density lipoprotein cholesterol, triglycerides), on apolipoproteins A1 and B and possible underlying mechanisms of action (secondary aims).

METHODS: In a double-blind, randomized controlled trial, 46 adults (37 female, 9 male) with moderately elevated LDL-C (116-190 mg/dL) consumed a beverage containing 8.4 g PO powder providing 3 g of β-glucans or a beverage without PO daily over 4 weeks. Plasma concentrations of LDL-C, other lipids and apolipoproteins were measured before and after intervention. The concentrations of noncholesterol sterols in serum, normalized to cholesterol, were determined as validated surrogate markers for cholesterol absorption (sitosterol, campesterol, and 5α-cholestanol), cholesterol synthesis (lathosterol), and bile-acid synthesis (7α-hydroxycholesterol), along with ergosterol, a fungal-specific sterol. Expression of selected target genes involved in cholesterol metabolism was analyzed in blood. Statistical analysis included comparisons of the changes between the groups (treatment effect) and linear modeling.

RESULTS: PO treatment did not modulate LDL-C; no treatment effect was observed for other lipids, apolipoproteins or gene expression (P ≥ 0.05 for all). However, after adjustment for sex, linear model analysis showed a reduction in markers of cholesterol absorption, especially in females (P < 0.05 for all). No effects were observed on markers of cholesterol and bile-acid synthesis (P ≥ 0.05 for all). Ergosterol was detectable in all serum samples after PO intake, confirming high compliance with PO treatment.

CONCLUSIONS: Daily consumption of 8.4 g of PO powder over 4 weeks has no impact on LDL-C concentrations in adults with moderately elevated LDL-C concentrations. However, post-hoc analysis indicates a sex-dependent reduction in cholesterol absorption by PO consumption, especially in females, suggesting that PO may have the potential to beneficially modulate cholesterol metabolism.

TRIAL REGISTRATION: Registration at German Clinical Trials Register; DRKS-ID: DRKS00033943; registration date: 21/03/2024. https://drks.de/search/de/trial/DRKS00033943.

PMID:42071226 | DOI:10.1186/s12986-026-01122-3

Cardiovasc Diabetol. 2026 May 3. doi: 10.1186/s12933-026-03191-3. Online ahead of print.

ABSTRACT

BACKGROUND: Cardiometabolic multimorbidity (CMM) poses a growing global health burden, yet few studies have combined the Triglyceride-Glucose (TyG) index, which reflects metabolic dysfunction, with the Frailty Index (FI), which captures physiological reserve and aging-related vulnerability, to assess CMM risk. Given their complementary biological information, this study examines whether a composite TyG-FI index is associated with incident CMM and whether it improves risk stratification beyond established factors.

METHODS: This prospective cohort study analyzed data from Chinese adults aged ≥ 45 years in the 2011-2020 waves of the China Health and Retirement Longitudinal Study (CHARLS). To assess the association between the TyG-FI index and incident CMM, we used Kaplan-Meier survival curves and multivariable Cox proportional-hazards models adjusted for potential confounders; restricted cubic spline analyses were employed to explore non-linear relationships. Predictive performance was evaluated using eight machine-learning algorithms: CatBoost, Extra Trees, Random Forest (RANGER), XGBoost, Recursive Partitioning (RPART), k-Nearest Neighbors (KKNN), Neural Network (NNET), and Support Vector Machine (SVM). Subgroup and sensitivity analyses were conducted to test the robustness of the results across population subgroups and modeling choices.

RESULTS: The analytic cohort comprised 2961 adults. Kaplan-Meier curves showed a graded, significant increase in cumulative CMM incidence across TyG‑FI quartiles (log‑rank P < 0.001). In multivariable Cox models, each unit increase in TyG‑FI was associated with a 1.80-fold higher CMM risk (HR = 1.80, 95% CI 1.57-2.05; P < 0.001); participants in the highest quartile had markedly elevated risk versus the lowest (Q4 vs. Q1 HR = 7.86, 95% CI 4.16-14.86). Restricted cubic spline analyses revealed significant non-linear relationships (P for non-linearity < 0.001), showing a J-shaped association between TyG-FI and CMM with threshold effects at TyG-FI ≈ 0.7 and cumulative TyG-FI ≈ 2.7. Subgroup analyses indicated stronger associations in participants < 60 years and in normotensive individuals. TyG-FI demonstrated better predictive performance for CMM than TyG index or FI alone, with improved C-statistic, Integrated Discrimination Improvement (IDI), and Net Reclassification Improvement (NRI). Among machine-learning models, RANGER performed best (AUC ≈ 0.81), and SHAP analysis identified cumulative and baseline TyG-FI as the primary predictors. Findings were robust in sensitivity analyses.

CONCLUSIONS: TyG-FI exhibits non‑linear, threshold-defined associations with incident CMM and age‑dependent effect modification. Machine‑learning models incorporating TyG-FI show strong predictive performance. TyG-FI assessment may facilitate cost‑effective risk stratification for CMM and guide targeted prevention.

PMID:42071205 | DOI:10.1186/s12933-026-03191-3

BMC Infect Dis. 2026 May 3. doi: 10.1186/s12879-026-13413-4. Online ahead of print.

ABSTRACT

BACKGROUND: To explore whether preoperative CD4⁺ T-cell count is associated with postoperative infectious outcomes after endoscopic lithotripsy for upper urinary tract calculi in human immunodeficiency virus (HIV)-infected patients.

METHODS: The HIV-infected patients who underwent endoscopic lithotripsy for upper urinary tract stones at Shanghai Public Health Clinical Center from May 2019 to May 2025 were enrolled for this study. The exposure of interest was the preoperative peripheral blood CD4⁺ T-cell count. The primary endpoint was urosepsis. Secondary outcomes included postoperative fever, systemic inflammatory response syndrome (SIRS), and other postoperative complications.

RESULTS: A total of 120 patients were enrolled in this study, including 20 patients with a CD4⁺ T-cell count < 200 cells/µL and 100 patients with a CD4⁺ T-cell count ≥ 200 cells/µL. None of the patients developed urosepsis or SIRS postoperatively. The overall rate of postoperative fever was 17.5% (21/120). Fever was observed in 15.0% of the patients with a CD4⁺ T-cell count < 200 cells/µL, compared with 18.0% of those with a CD4⁺ T-cell count ≥ 200 cells/µL, and there was no statistically significant difference (P > 0.05). Logistic regression analysis further showed that CD4⁺ T-cell count was not significantly associated with fever (OR = 1.001, 95% CI: 0.998-1.003, P = 0.586), whereas urine white blood cell count, stone density, and surgical approach were independently associated with fever. Sensitivity analyses using propensity score matching and inverse probability of treatment weighting showed similar results.

CONCLUSION: In this retrospective single-center cohort, preoperative CD4⁺ T-cell count was not significantly associated with postoperative fever after endoscopic lithotripsy in HIV-infected patients. However, because no urosepsis events occurred and the sample size, particularly in the CD4⁺ T-cell count < 200 cells/µL subgroup, was limited, the study could not adequately evaluate the primary endpoint. These findings should therefore be considered exploratory and require confirmation in larger studies incorporating HIV viral load and procedure-specific analyses.

PMID:42071191 | DOI:10.1186/s12879-026-13413-4

J Nurs Manag. 2026;2026(1):e1853384. doi: 10.1155/jonm/1853384.

ABSTRACT

BACKGROUND: Nurse and patient outcomes in South Africa were poor before COVID-19 and are believed to have worsened during and after the pandemic. Limited evidence exists on modifiable organisational factors contributing to these outcomes hindering targeted interventions.

PURPOSE: This paper aims to develop a better understanding of potentially modifiable organisational factors of hospitals that, if addressed, would likely contribute to improving nurse wellbeing and retention, and quality and safety of patient care.

METHODS: Data were collected from 143 private and public hospitals (n = 4298 nurses) across South Africa using a cross-sectional survey. Independent variables included working time with COVID-19 patients, incidence of death and dying, resources, staffing, and the practice environment; dependent variables focused on nurse outcomes (job satisfaction, intent to leave, burnout, mental and physical health) and patient outcomes (quality of care and patient safety).

RESULTS: Nurse and patient outcomes were worse in public compared to private hospitals. Favourable practice environments had the strongest association with nurse and patient outcomes, followed by staffing and resources. Within the practice environment, nurse management, leadership and support of nurses showed the greatest association with job satisfaction (OR = 4.71^∗∗; 95% CI = 3.97-5.58), lower intent to leave (OR = 2.81^∗∗; 95% CI = 2.33-3.38) and more favourable mental health (OR = 2.58^∗∗; 95% CI = 2.19-3.04). Greater nurse participation in hospital affairs was associated with more favourable nurse assessments of quality of care (OR = 3.74^∗∗; 95% CI = 3.22-4.33 to OR = 6.51^∗∗; 95% CI = 3.81-4.95) and patient safety (OR = 4.35^∗∗; 95% CI = 3.81-4.95).

CONCLUSION: Interventions to improve nurse wellbeing and retention as well as quality and safety of care should focus on improving hospital practice environments, specifically nurse manager expertise, nurse leadership, nurse participation in hospital affairs, and adequate staffing and resources.

PMID:42071176 | DOI:10.1155/jonm/1853384

Adv Exp Med Biol. 2026;1504:303-326. doi: 10.1007/978-3-032-18966-0_15.

ABSTRACT

Human diseases are multi-factorial, affecting multiple aspects of a homeostatic system. Recent advances in high-throughput technology have allowed the generation of various omics datasets from large cohorts at affordable costs and hence made it possible to study the complex dynamical systems perturbed in human diseases. Studying the complex perturbed systems offers a mechanistic understanding to identify druggable targets and offers new avenues for individualised medical intervention. Mechanisms driving complex human diseases cannot be explored merely by single omics-focused studies. In addition, the heterogeneity among the human populations adds additional complexity and limits the possibility for inferring the regulatory mechanisms underlying these diseases. Examining the disease or phenotype of interest through the lens of multiple omics layers may allow the dissection of the perturbed biological processes associated with the disease. Studying a complex disease through multiple omics layers providing vast information is quite a challenging task and, therefore, requires statistical frameworks to achieve integrative multi-omics analysis. In this chapter, we first summarise key characteristics of each of the omics layers and the various considerations important for the implementation of statistical methods. We then shed light on the most common statistical methods used for multi-omics integration studies and highlight various published examples showing the use of these methods for addressing key biological questions. For this, we show integration examples focused on at least two prime omics layers. We next focus on methods and examples showing multi-omics integration to study dynamical systems in large cohort studies. Finally, we discuss some of the multi-omics approaches and examples from single-cell multi-omics datasets.

PMID:42071151 | DOI:10.1007/978-3-032-18966-0_15

Adv Exp Med Biol. 2026;1504:271-286. doi: 10.1007/978-3-032-18966-0_13.

ABSTRACT

Omics data allow us to study complex biological systems. Mathematical models can help us understand these systems and extract more knowledge from the data. In this chapter, we introduce three types of mathematical models that can be used to potentiate the interpretation of omics datasets: statistical, machine learning and mechanistic models. We delve into the characteristics of these modelling approaches, the essential stages involved in their development and their potential applications to omics datasets.

PMID:42071149 | DOI:10.1007/978-3-032-18966-0_13

Adv Exp Med Biol. 2026;1504:247-269. doi: 10.1007/978-3-032-18966-0_12.

ABSTRACT

Mass spectrometry (MS)-based metabolomics is a powerful tool for understanding the complexity of biochemical processes and to identify biomarkers across diverse biological systems. The vast amount of data generated by extreme resolution mass spectrometers poses significant data processing challenges, requiring robust computational approaches and workflows for meaningful data interpretation. This chapter provides a comprehensive overview of current methodologies in MS-based metabolomics data analysis, with a focus on data preprocessing and pretreatment, m/z extraction and annotation, univariate and multivariate statistical approaches, as well as data visualization. We discuss key considerations for ensuring data quality and the growing role of bioinformatics in pathway analysis and metabolite identification. We highlight the transforming role of extreme resolution and mass accuracy enabled by FT-ICR mass spectrometers, and finally, we explore emerging trends, including artificial intelligence-driven insights and real-time data processing, to guide future developments in this rapidly evolving field.

PMID:42071148 | DOI:10.1007/978-3-032-18966-0_12

Adv Exp Med Biol. 2026;1504:205-224. doi: 10.1007/978-3-032-18966-0_10.

ABSTRACT

Omics data, comprising a diverse array of high-throughput molecular datasets, present substantial statistical challenges due to their intrinsic heterogeneity and variability. Effectively distinguishing biologically meaningful variations from random noise requires the application and development of robust statistical approaches. Interdisciplinary collaboration plays a pivotal role in refining these methodologies and enhancing the understanding of intricate biological systems. This chapter reviews the importance of statistical methods in omics data analysis, highlighting the need for ongoing advancements to address key challenges, including experimental design, preprocessing, dimensionality reduction, statistical modeling of complex datasets, and the interpretation of results. The pursuit of improved reliability in biological insights creates opportunities for the development and refinement of advanced statistical methodologies.

PMID:42071146 | DOI:10.1007/978-3-032-18966-0_10

Adv Exp Med Biol. 2026;1504:119-144. doi: 10.1007/978-3-032-18966-0_6.

ABSTRACT

Metabolomics has emerged as a powerful discipline for characterizing the small molecules that define cellular physiology, environmental responses, and disease states. As technologies advance, researchers face an expanding landscape of analytical platforms, data-processing strategies, and integrative approaches that require clear guidance for effective application. This chapter was written to provide a comprehensive and accessible resource for students, clinicians, and researchers entering or advancing in the field. We outline the fundamentals of metabolomics, describe major analytical methodologies-including MS, NMR, chromatography, and imaging-and summarize key considerations for experimental design, data preprocessing, statistical analysis, and functional interpretation. We also address current challenges related to metabolite identification, reproducibility, and multi-omic integration, and highlight emerging innovations such as stable-isotope tracing, spatial metabolomics, and AI-driven analytics. Together, these elements offer a detailed roadmap for conducting robust, reproducible, and insightful metabolomic studies.

PMID:42071142 | DOI:10.1007/978-3-032-18966-0_6