Roles of Osteopontin Gene Polymorphism (rs1126616), Osteopontin Levels in Urine and Serum, and the Risk of Urolithiasis: A Meta-Analysis

Objective. Previous studies have investigated the relationships between osteopontin gene polymorphism rs1126616 and OPN levels and urolithiasis, but the results were controversial. Our study aimed to clarify such relationships. Methods. A meta-analysis was performed by searching the databases Pubmed, Embase, and Web of Science for relevant studies. Crude odds ratios (ORs) or standardised mean differences with 95% confidence intervals (CIs) were calculated to evaluate the strength of association. Publication bias was estimated using Begg's funnel plots and Egger's regression test. Results. Overall, a significantly increased risk of urolithiasis was associated with OPN gene polymorphism rs1126616 for all the genetic models except recessive model. When stratified by ethnicity, the results were significant only in Turkish populations. For OPN level association, a low OPN level was detected in the urine of urolithiasis patients in large sample size subgroup. Results also indicated that urolithiasis patients have lower OPN level in serum than normal controls. Conclusion. This meta-analysis revealed that the T allele of OPN gene polymorphism increased susceptibility to urolithiasis. Moreover, significantly lower OPN levels were detected in urine and serum of urolithiasis patients than normal controls, thereby indicating that OPN has important functions in the progression of urolithiasis

Construction and validation of a glioblastoma prognostic model based on immune-related genes

BackgroundGlioblastoma multiforme (GBM) is a common malignant brain tumor with high mortality. It is urgently necessary to develop a new treatment because traditional approaches have plateaued.PurposeHere, we identified an immune-related gene (IRG)-based prognostic signature to comprehensively define the prognosis of GBM.MethodsGlioblastoma samples were selected from the Chinese Glioma Genome Atlas (CGGA). We retrieved IRGs from the ImmPort data resource. Univariate Cox regression and LASSO Cox regression analyses were used to develop our predictive model. In addition, we constructed a predictive nomogram integrating the independent predictive factors to determine the one-, two-, and 3-year overall survival (OS) probabilities of individuals with GBM. Additionally, the molecular and immune characteristics and benefits of ICI therapy were analyzed in subgroups defined based on our prognostic model. Finally, the proteins encoded by the selected genes were identified with liquid chromatography-tandem mass spectrometry and western blotting (WB).ResultsSix IRGs were used to construct the predictive model. The GBM patients were categorized into a high-risk group and a low-risk group. High-risk group patients had worse survival than low-risk group patients, and stronger positive associations with multiple tumor-related pathways, such as angiogenesis and hypoxia pathways, were found in the high-risk group. The high-risk group also had a low IDH1 mutation rate, high PTEN mutation rate, low 1p19q co-deletion rate and low MGMT promoter methylation rate. In addition, patients in the high-risk group showed increased immune cell infiltration, more aggressive immune activity, higher expression of immune checkpoint genes, and less benefit from immunotherapy than those in the low-risk group. Finally, the expression levels of TNC and SSTR2 were confirmed to be significantly associated with patient prognosis by protein mass spectrometry and WB.ConclusionHerein, a robust predictive model based on IRGs was developed to predict the OS of GBM patients and to aid future clinical research

Obstructive sleep apnea in community-dwelling polio survivors: a 5-year longitudinal follow-up study

PurposeObstructive sleep apnea (OSA) is highly prevalent in polio survivors, but longitudinal data on its progression remain limited. This study aimed to characterize OSA progression in community-dwelling polio survivors and compare it with an age-matched control group.MethodsA prospective 5-year longitudinal study recruited 148 polio survivors (48.76 ± 5.97 years, 75% male). At baseline (2014), all participants underwent overnight oximetry. Among them, 42 completed in-lab polysomnography (PSG) testing. Over the 5-year follow-up, 112 polio survivors (76.79% male, mean age 48.48 ± 6.05 years) were successfully tracked, with 33 undergoing follow-up PSG. Additionally, 59 age- and sex-matched OSA patients were enrolled as controls. Primary outcomes included changes in oxygen desaturation index ≥4% (ODI4) and apnea-hypopnea index (AHI). Correlates of OSA progression were analyzed using Pearson’s correlations.ResultsOver 5 years, ODI4 increased significantly in polio survivors from 8.11 ± 9.13 to 10.35 ± 11.63 events/h (p = 0.01), with a shift toward moderate–severe ODI4 (13 to 22%, p = 0.027). AHI also rose in both groups: polio survivors (26.57 ± 21.25 to 33.86 ± 22.43 events/h, p = 0.02) and controls (27.14 ± 21.91 to 37.24 ± 24.55 events/h, p = 0.004), with no significant group difference in AHI progression (p = 0.89). However, polio survivors showed increased mixed apnea index (p = 0.02) and prolonged REM sleep latency (p = 0.009). ODI4 changes correlated with scoliosis (r = 0.27, p = 0.005) and BMI fluctuations (r = 0.25, p = 0.008).ConclusionOSA-related parameters, particularly mixed apnea and REM alterations, progress in polio survivors. Changes in ODI4 were positively correlated with BMI fluctuations and scoliosis

Foundation models and intelligent decision-making: Progress, challenges, and perspectives

Intelligent Decision-Making (IDM) is a cornerstone of artificial intelligence (AI), designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation Models (FMs) have become pivotal in science and industry, trans- forming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM’s evolution, advanced paradigms with FMs, and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems.<br/