Search Results (1,031)

Background: Perioperative factors significantly impact oncologic outcomes after radical cystectomy (RC) for bladder cancer. This study aimed to identify key perioperative predictors for overall (OS) and progression-free survival (PFS) and to develop a prognostic nomogram for the identification of high-risk patients adapted to the clinical routines and standard of care of our country. Methods: We retrospectively analyzed 121 patients undergoing RC (2014–2024). Data on patient demographics, comorbidities, tumor pathology, neoadjuvant treatments, extensive intraoperative factors, and postoperative events were assessed using COX models. A prognostic nomogram for 3-year OS was constructed. Results: Median follow-up was 44.33 months. Significant predictors for worse OS included lymphovascular invasion (LVI) (HR 2.22), higher T stage (HR 8.75), N+ status (HR 1.10), and intraoperative complications (HR 3.04). Similar predictors were noted for PFS. The developed nomogram incorporated T-, N-stages, sex, grade, intraoperative complications and early (12 months) recurrence, and was able to significantly identify patients with a higher mortality risk (p < 0.001) with a C-index of 0.74. Conclusions: Our nomogram for mortality prediction of BC patients offers a promising tool for individualized risk stratification. Further studies are required for its external validation. Full article

Objectives: Hepatocellular carcinoma (HCC) remains a major indication for liver transplantation (LT), but accurate pretransplant risk stratification is critical to improve long-term outcomes. Traditional morphometric criteria such as tumor size and number are limited in predicting recurrence and survival. The HALP (hemoglobin, albumin, lymphocyte, platelet), gamma-glutamyl transpeptidase to platelet ratio (GPR), and FIB-4 indices are emerging systemic inflammatory and nutritional biomarkers that may provide additional prognostic value in HCC patients undergoing LT. Materials and Methods: This retrospective, two-center cohort study included 200 patients who underwent LT for HCC between 2012 and 2023. Preoperative HALP, GPR, and FIB-4 scores were calculated, and their associations with overall survival (OS) and recurrence-free survival (RFS) were assessed using ROC analyses and Cox proportional hazard models. Cut-off values were determined for each biomarker, and survival outcomes were analyzed using Kaplan–Meier methods. Results: A low HALP score (≤0.39) was independently associated with reduced OS but not with RFS. Conversely, low GPR (≤0.45) and FIB-4 (≤3.1) values were significantly associated with both poor OS and higher recurrence risk. Tumor size, number of lesions, and microvascular invasion also independently predicted poor outcomes. Multivariate analysis confirmed HALP, GPR, and FIB-4 as significant preoperative predictors of prognosis in this population. Conclusions: HALP, GPR, and FIB-4 are readily available, cost-effective indices that provide significant prognostic information in HCC patients undergoing LT. Their integration with morphometric criteria may improve pretransplant risk stratification and support individualized clinical decision-making. Full article

Background/Objectives: Chronic low-grade inflammation drives cardiometabolic risk; functional SNPs may influence individual cytokine and hematologic phenotypes. We investigated genotype-specific relationships between circulating immuno-inflammatory biomarkers and routine blood indices in apparently healthy adults. Methods: In this cross-sectional study, 155 fasting volunteers (26–72 years) were genotyped for IL1RN rs1149222 and TNF-proximal rs2071645. Serum IL-1β, TNF-α, oxidized LDL (oxLDL) and C-reactive protein (CRP) were quantified by ELISA, and complete blood counts were recorded simultaneously. Genotype effects were tested with ANOVA/Kruskal–Wallis; Spearman correlations and age-, sex-, BMI-adjusted linear models explored genotype-stratified associations. Results: Among 155 adults, IL1RN rs1149222 significantly affected IL-1β (TT > TG ≈ GG; ANOVA p = 0.042) and oxLDL (overall p = 0.036), with the clearest difference between heterozygotes and major-allele homozygotes. The same variant produced a modest fall in erythrocyte count and hemoglobin restricted to heterozygotes (RBC p = 0.036; Hb p = 0.041). TNF-proximal rs2071645 strongly raised TNF-α (GG > GA > AA; p < 0.0001) and led to a moderate oxLDL increase, driven by GA versus AA carriers (pairwise p = 0.013), while leaving red-cell indices and CRP unchanged. Baseline leukocyte counts, differentials and derived ratios showed no genotype dependence, and multivariable models revealed no epistatic interaction between the two loci. Conclusions: IL1RN rs1149222 and TNF-related rs2071645 generate two independent inflammatory signatures—an IL-1β-oxidative axis linked to mild erythropoietic suppression and a TNF-lipid axis without hematologic shift. Integrating targeted genotyping with inexpensive hematologic ratios may refine early risk stratification and guide tailored preventive strategies in ostensibly healthy populations. Full article

Background: Myasthenia gravis (MG), a chronic autoimmune disorder with variable disease trajectories, presents considerable challenges for clinical stratification and acute care management. This systematic review evaluated machine learning models developed for prognostic assessment in patients with MG. Methods: Following PRISMA guidelines, we systematically searched PubMed, Embase, and Scopus for relevant articles published from January 2010 to May 2025. Studies using machine learning techniques to predict MG-related outcomes based on structured or semi-structured clinical variables were included. We extracted data on model targets, algorithmic strategies, input features, validation design, performance metrics, and interpretability methods. The risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool. Results: Eleven studies were included, targeting ICU admission (n = 2), myasthenic crisis (n = 1), treatment response (n = 2), prolonged mechanical ventilation (n = 1), hospitalization duration (n = 1), symptom subtype clustering (n = 1), and artificial intelligence (AI)-assisted examination scoring (n = 3). Commonly used algorithms included extreme gradient boosting, random forests, decision trees, multivariate adaptive regression splines, and logistic regression. Reported AUC values ranged from 0.765 to 0.944. Only two studies employed external validation using independent cohorts; others relied on internal cross-validation or repeated holdout. Of the seven prognostic modeling studies, four were rated as having high risk of bias, primarily due to participant selection, predictor handling, and analytical design issues. The remaining four studies focused on unsupervised symptom clustering or AI-assisted examination scoring without predictive modeling components. Conclusions: Despite promising performance metrics, constraints in generalizability, validation rigor, and measurement consistency limited their clinical application. Future research should prioritize prospective multicenter studies, dynamic data sharing strategies, standardized outcome definitions, and real-time clinical workflow integration to advance machine learning-based prognostic tools for MG and support improved patient care in acute settings. Full article

Introduction: Mitral valve transcatheter edge-to-edge repair (M-TEER) has emerged as a minimally invasive option for high-risk surgical candidates with severe and symptomatic mitral regurgitation (MR), but post-procedure residual mitral valve (MV) dysfunction remains a significant concern. This study evaluates the clinical utility of combining artificial intelligence applied to electrocardiograms (ECG-AI) for diastolic dysfunction (DD) grading and the echocardiography-derived velocity time integral of the MV and left ventricular outflow tract ratio (VTI_MV/_LVOT) in predicting prognosis in patients post-M-TEER. Methods: A retrospective analysis of patients who underwent M-TEER between 2014 and 2021 was conducted. Patients were categorized based on VTI_MV/LVOT and ECG-AI scores into three groups: both normal parameters, either abnormal parameter, or both abnormal parameters to compare outcomes (mortality, major adverse cardiovascular events [MACE], and the need for subsequent MV reintervention) using Kaplan–Meier analysis, multivariable Cox regression models, and net reclassification improvement. Results: Overall, 250 patients were included; the median age was 79.5 (IQR: 73.1, 84.6) and 66.4% were male. The combined abnormal VTI_MV/LVOT (≥2.5) and ECG-AI score for DD (>1) was associated with higher risk of one-year mortality (adjusted HR: 4.56 [1.04–19.89], p = 0.044) and MACE (adjusted HR: 3.72 [1.09–12.72], p = 0.037) compared to patients with both normal parameters. Conclusions: This study highlights the potential additive value of integrating VTI_MV/LVOT and ECG-AI scores as a prognostic tool for a personalized approach to the post-operative evaluation and risk stratification in M-TEER patients. Full article

Background: Inherent to the challenge of acute pulmonary embolism (APE), the breadth of presentation ranges from asymptomatic pulmonary emboli to sudden death. Risk stratification of patients with APE is mandatory for determining the appropriate therapeutic management approach. However, the optimal clinically most relevant combination of predictors of death remains to be determined. Radiomics is an emerging discipline in medicine that extracts and analyzes quantitative data from medical images using mathematical algorithms. In APE, these data can reveal thrombus characteristics that are not visible to the naked eye, which may help to more accurately identify patients at higher risk of early clinical deterioration or mortality. We conducted a scoping review to explore the current evidence on the prognostic performance of radiomic models in patients with APE. Methods: PubMed, Web of Science, EMBASE, and Scopus were searched for studies published between January 2010 and April 2025. Eligible studies evaluated the use of radiomics to predict adverse outcomes in patients with APE. The PROSPERO registration number is CRD420251083318. Results: Nine studies were included in this review. There was significant heterogeneity in the methodology for feature selection and model development. Radiomic models demonstrated variable performance across studies. Models that combined radiomic features with clinical data tended to show better predictive accuracy. Conclusions: This scoping review underscores the potential of radiomic models, particularly when combined with clinical data, to improve risk stratification in patients with APE. Full article

Obstructive sleep apnea (OSA) is a common condition characterized by repeated airway collapses during sleep, contributing to oxygen desaturation, arousals, and significant cardiovascular complications. This meta-analysis aims to evaluate the association between blood ICAM-1 levels and OSA, exploring its potential as a biomarker for cardiovascular disease (CVD) and for identifying factors contributing to result heterogeneity. Following PRISMA guidelines, this meta-analysis addressed a PECO framework to assess circulating ICAM-1 levels in adults with OSA compared to controls. A systematic search was conducted across PubMed, Web of Science, Scopus, Cochrane Library, and CNKI until 23 April 2025, complemented by citation reviews and Google Scholar. Statistical analyses, including subgroup and meta-regression, were performed using RevMan, CMA 3.0, and TSA software to calculate mean differences, assess heterogeneity, and evaluate publication bias. Results were analyzed under random-effect models, with significance set at p < 0.05 for all metrics except publication bias (p < 0.10). This systematic review and meta-analysis included 34 articles. The pooled mean difference (MD) of ICAM-1 levels was 184.06 ng/mL (95% CI: 143.83 to 224.28; p < 0.00001), significantly higher in OSA patients with high heterogeneity (I² = 100%). Subgroup analysis highlighted larger MDs in Asians and plasma samples, as well as greater ICAM-1 elevations in severe OSA cases. Despite publication bias indicated by Begg’s (p = 0.036) and Egger’s (p = 0.016) tests, the findings remained robust, supported by sensitivity and meta-regression analyses. This meta-analysis underscores a significant association between elevated ICAM-1 levels and OSA, highlighting its potential as a biomarker for CVD risk stratification in OSA patients. Full article

Background: Lung cancer remains the leading cause of global cancer mortality. The HALP (hemoglobin, albumin, lymphocyte, platelet) score integrates nutritional, immune, and inflammatory status and may offer prognostic value. This meta-analysis evaluates the association between the HALP score and survival outcomes in lung cancer patients. Methods: Following PRISMA guidelines, PubMed, Embase, Web of Science, CNKI, Wanfang, and Google Scholar were searched. Inclusion criteria covered observational studies in lung cancer reporting hazard ratios (HRs) for overall survival (OS), progression-free survival (PFS), or disease-free survival (DFS). Study quality was assessed via the Newcastle–Ottawa Scale (NOS). Random-effects models were used to pool HRs (95% confidence intervals [CIs]), with subgroup and sensitivity analyses used to address heterogeneity. Results: Fourteen studies (N = 10,182 patients) were included. A high HALP score predicted significantly improved OS in multivariate analysis (HR = 0.56, 95% CI: 0.46–0.69, p < 0.001), representing a 44% mortality risk reduction. The results were consistent for surgical (HR = 0.60, CI: 0.43–0.84), advanced (HR = 0.47, CI: 0.32–0.69), and all-stage subgroups. High HALP also correlated with superior PFS (multivariate HR = 0.56, CI: 0.39–0.78, p = 0.001) but not DFS (HR = 0.50, CI: 0.22–1.16, p = 0.107). Significant heterogeneity persisted (I² > 75% for OS), likely due to stage variability and non-standard HALP cutoffs. Publication bias was detected for OS studies (Egger′s p = 0.003). Conclusions: The HALP score is a low-cost, accessible prognostic biomarker for lung cancer. A high HALP score independently predicts better OS and PFS but not DFS, suggesting utility for long-term risk stratification. Standardized HALP thresholds and validation in diverse populations are needed for clinical implementation. Full article

Background/Objectives: Preeclampsia (PE) is a major cause of maternal and perinatal morbidity and mortality, particularly in low- and middle-income countries. Early-onset PE (EOP) and late-onset PE (LOP) are distinct clinical entities with differing pathophysiological mechanisms and prognoses. However, few studies have explored differential risk factors for EOP and LOP in Latin American populations. This study aimed to identify and assess clinical risk factors for predicting EOP and LOP in a cohort of pregnant women from Bogotá, Colombia, using traditional statistics and machine learning (ML). Methods: A cross-sectional observational study was conducted on 190 pregnant women diagnosed with PE (EOP = 80, LOP = 110) at a tertiary hospital in Bogotá between 2017 and 2018. Risk factors and perinatal outcomes were collected via structured interviews and clinical records. Traditional statistical analyses were performed to compare the study groups and identify associations between risk factors and outcomes. Eleven ML techniques were used to train and externally validate predictive models for PE subtype and secondary outcomes, incorporating permutation-based feature importance to enhance interpretability. Results: EOP was significantly associated with higher maternal education and history of hypertension, while LOP was linked to a higher prevalence of allergic history. The best-performing ML model for predicting PE subtype was linear discriminant analysis (recall = 0.71), with top predictors including education level, family history of perinatal death, number of sexual partners, primipaternity, and family history of hypertension. Conclusions: EOP and LOP exhibit distinct clinical profiles in this cohort. The combination of traditional statistics with ML may improve early risk stratification and support context-specific prenatal care strategies in similar settings. Full article

Background and Objectives: Electrocardiography (ECG), though non-specific, is widely applied as a valuable tool in the diagnostic work-up of acute myocarditis. Cardiac magnetic resonance (CMR) has become a key non-invasive tool. This study assessed the association of ECG findings (at baseline), echocardiographic parameters, circulating biomarkers, and CMR imaging features (myocardial edema and late gadolinium enhancement—LGE) in patients with acute myocarditis. Materials and Methods: This single-center, retrospective observational study included 86 patients admitted with acute myocarditis from January 2021 to December 2024. Data collected included demographics, clinical presentation, ECG, echocardiography, biomarkers (CRP, troponin I), and CMR imaging performed during hospitalization and at the six-month follow-up. Based on ECG findings, patients were stratified into three groups: no ST elevation or T-wave abnormalities (NSTG, n = 27), T-wave abnormalities (TWAG, n = 24), and ST elevation (STEG, n = 35). Results: We enrolled 86 patients (median age: 26 years; 87.2% male), and the most frequent CMR findings were either LGE (80.2%) and/or myocardial edema (75.6%). The prevalence of edema and LGE was higher in the STEG (both 91.2%) compared to TWAG (65.2%, 77.3%, respectively) and NSTG (57.7, 65.4%, respectively) (p < 0.05). Peak troponin levels were also higher in the STEG than other groups (p = 0.005). In logistic regression analysis, TWAs were independently associated with both edema (OR = 3.15, 95% CI: 1.078–9.189, p = 0.036) and LGE (OR = 3.93, 95% CI: 1.256–12.276, p = 0.019). Biomarkers were associated with lower LVEF in univariate analysis, but not in multivariate models. Conclusions: ECG abnormalities, particularly STE and TWA, are common in acute myocarditis and significantly associated with CMR findings. Although CMR remains essential for definitive diagnosis and risk stratification in acute myocarditis, ECG may serve as a valuable initial screening tool in the context of a multimodal diagnostic approach. Full article

Subarachnoid hemorrhage (SAH) and its major complication, cerebral vasospasm (CVS), present significant challenges for early diagnosis and risk stratification. In this study, we developed interpretable decision tree models to differentiate between healthy controls, SAH patients, and SAH patients with vasospasm using serum N-glycomic data. Building on previously published glycomic profiles, we introduced a refined modeling approach combining systematic preprocessing, feature selection, and interpretable machine learning. Our methodology included outlier removal, standard scaling, and a novel correlation-based feature reduction guided by feature importance scores derived from preliminary decision trees. Binary classification tasks (Control vs. SAH and Control vs. CVS, and SAH vs. CVS) were evaluated through stratified repeated cross-validation and hyperparameter optimization. Models achieved high accuracy (up to 0.91) and stable F1-scores across configurations. Key glycans such as FA2(6)G1 (bi-antennary, fucosylated, monogalactosylated), A4G4S3(2) (tetra-antennary, tetra-galactosylated, tri-sialylated), and A3G3S3(5) (tri-antennary, tri-galactosylated, tri-sialylated) emerged as the most discriminative. Visualizations that combine joint feature distributions and decision boundaries provided intuitive insight into the classifier’s logic. These findings support the integration of interpretable glycomics-based models into clinical workflows. Full article

Background/Objectives: Endometrial cancer (EC) is the most common gynecological tumor in developed countries, and presents a wide variety of histological and molecular characteristics that make its treatment increasingly complex. In recent years, advances in molecular imaging, particularly with [¹⁸F]FDG-PET/CT and PET/MRI, have changed clinicians’ management of diagnosis, treatment planning, and prognosis of EC. Methods: In this narrative review, a search was conducted for current evidence on the role of [¹⁸F]FDG-PET/CT and PET/MRI throughout the treatment of EC, focusing on their diagnostic performance, clinical relevance, and prognostic implications. Their use in areas such as initial staging, treatment monitoring, recurrence detection, and individualized risk assessment was also discussed. Results: [¹⁸F]FDG-PET/CT is effective in detecting lymph node and distant metastases, while [¹⁸F]FDG-PET/MRI offers greater accuracy for T and N staging. In addition, [¹⁸F]FDG-PET/CT provides early metabolic indicators of therapeutic response and facilitates differentiation between viable tumors and post-treatment changes. Radiomics-derived parameters have shown promising associations with tumor aggressiveness and lymphovascular invasion and survival, supporting their role as prognostic imaging biomarkers. In addition, the use of non-FDG radiotracers, as well as predictive models based on machine learning, can further refine preoperative stratification and treatment planning in certain subtypes of EC. Conclusions: Molecular imaging enhances the accuracy of TNM staging in EC. The incorporation of molecular imaging biomarkers into daily clinical practice could significantly improve personalized decision-making in EC. However, large prospective studies are needed to confirm their efficacy. Full article

Systematic Background/Objectives: Type 1 diabetes mellitus (T1DM) is an autoimmune condition in which pancreatic β-cells are selectively destroyed, predominantly by autoreactive T lymphocytes. Despite decades of research, the achievement of durable immune tolerance remains elusive. This review presents a historically grounded and forward-looking perspective on the evolution of immunotherapy in T1DM, from early immunosuppressive interventions to advanced precision-based cellular approaches. Specifically, we focus on systemic immunosuppressants (e.g., corticosteroids, cyclosporine), monoclonal antibodies (e.g., anti-CD3, anti-IL-1, anti-TNF), regulatory cell-based approaches (e.g., Tregs, CAR-Tregs, MDSCs), and β-cell replacement strategies using stem cell-derived islets. Methods: We analyzed major clinical and translational milestones in immunotherapy for T1DM, with particular attention to the transition from broad immunosuppression to targeted modulation of immune pathways. Emerging data on cell-based therapies, artificial intelligence (AI)-driven stratification, and personalized intervention timing have been incorporated to provide a comprehensive overview of current and future directions. Results: Initial therapies such as corticosteroids and cyclosporine offered proof-of-concept for immune modulation, yet suffered from relapse and toxicity. The introduction of monoclonal antibodies (e.g., teplizumab) marked a shift toward immune-specific intervention, particularly in stage 2 preclinical T1DM. More recent approaches include low-dose IL-2, checkpoint modulation, and antigen-specific tolerance strategies. Cellular therapies such as Treg adoptive transfer, chimeric antigen receptor Tregs (CAR-Tregs), and stem cell-derived islet replacements (e.g., VX-880) have shown promise in preserving β-cell function and modulating autoimmunity. Myeloid-derived suppressor cells (MDSCs), although still preclinical, represent a complementary avenue for immune tolerance induction. Concurrently, AI-based models are emerging as tools to stratify risk and personalize immunotherapeutic timing, enhancing trial design and outcome prediction. Conclusions: In conclusion, the historical progression from broad immunosuppression to precision-driven strategies underscores the importance of stage-specific, mechanism-based interventions in T1DM. The convergence of targeted biologics, regenerative cell therapies, and β-cell replacement approaches, supported by AI-enabled patient stratification, offers a realistic path toward durable immune tolerance and functional β-cell preservation. Continued integration of these modalities, coupled with rigorous long-term evaluation, will be essential to transform these scientific advances into sustained clinical benefit. Full article

Metastatic dissemination defines a complex phenomenon driven by genetic forces and, importantly, determined by interaction between cancer cells and the surrounding stroma. Although the biologic and immune reactions which characterize the process have been widely and extensively evaluated, fewer data are available regarding the mechanical and physical forces to which circulating neoplastic clones are exposed. It should be hypothesized that this interaction can be modified in case of concomitant pathologic conditions, such as chronic vasculopathy, which frequently occurs in lung cancer patients. We here aim at analyzing and discussing the complex interplay between lung malignant transformation and arteriopathy, mainly focusing on the immune–inflammatory systemic reaction. Notably—in most instances—smoking-related fixed airflow obstruction, including but not limited to COPD, frequently coexists and contributes to both tumor progression and vascular complications. Attention is paid mainly to the analysis of the role of immune checkpoint inhibitors and their interaction with triple bronchodilation and antiaggregants. Understanding the biomechanical and molecular dynamics of lung cancer progression in altered vascular territories has several translational implications in defining risk stratification and in surgical planning and therapeutic targeting. Moreover, computational modeling of the physical forces which regulate the transit and extravasation of metastatic clones in altered contexts could be of help in deciphering the whole process and in determining more effective blockade strategies. Full article

As people with cystic fibrosis (PwCF) live longer, kidney disease is emerging as a significant comorbidity that is increasingly linked to cardiovascular complications and progression to end-stage kidney disease. In our recent review, we proposed the unifying term CF-related kidney disease (CFKD) to encompass the spectrum of kidney dysfunction observed in this population. Early detection of kidney injury is critical for improving long-term outcomes, yet remains challenging due to the limited sensitivity of conventional laboratory tests, particularly in individuals with altered muscle mass and unique CF pathophysiology. Emerging approaches, including novel blood and urinary biomarkers, urinary extracellular vesicles, and genetic risk profiling, offer promising avenues for identifying subclinical kidney damage. When integrated with machine learning algorithms, these tools may enable the development of personalized risk stratification models and targeted therapeutic strategies. This precision medicine approach has the potential to transform kidney disease management in PwCF, shifting care from reactive treatment of late-stage disease to proactive monitoring and early intervention. Full article