Search Results (6,446)

Background: Despite clinical and pathological risk tools, predicting outcomes in non-muscle-invasive bladder cancer (NMIBC), particularly high-grade (HG) cases, remains challenging due to its unpredictable recurrence and progression. There is an urgent need for molecular biomarkers to enhance risk stratification and guide treatment. Methods: We assessed the prognostic potential of eight miRNAs (miR-9, miR-143, miR-182, miR-205, miR-27a, miR-369, let-7c, and let-7g) in a cohort of ninety patients with primary bladder cancer. Expression data were retrieved from our previously published studies. Kaplan–Meier’s and Cox’s regression analyses were used to evaluate the associations with overall survival (OS), metastasis-free survival (MFS), and clinical outcomes. Principal component analysis (PCA) was performed to identify informative miRNA combinations. Target gene prediction, pathway enrichment (DAVID), and drug–gene interaction mapping (DGIdb) were conducted in silico. Results: A high expression of let-7g and miR-9 was significantly associated with better OS in HG NMIBC and MIBC, respectively (p = 0.013 and p = 0.000). MiR-9 downregulation correlated with metastasis in MIBC (p = 0.018). Among all combinations, miR-205 and miR-27a best predicted intermediate-risk NMIBC progression and recurrence (r² = 0.982, p = 0.000). A functional analysis revealed that these miRNAs regulate key cancer-related pathways (MAPK, mTOR, and p53) through genes such as TP53, PTEN, and CDKN1A. Drug interaction mapping identified nine target genes (e.g., DAPK1, ATR, and MTR) associated with eight FDA-approved bladder cancer therapies, including cisplatin and gemcitabine. Conclusion: Let-7g, miR-9, miR-143, miR-182, and miR-205 emerged as promising biomarkers for outcome prediction in NMIBC. Their integration into liquid biopsy platforms could support non-invasive monitoring and personalized treatment strategies. These findings warrant validation in larger, prospective studies and through functional assays. Full article

Background/Objectives: Cardiac sarcoidosis (CS) is an inflammatory cardiomyopathy with a strong clinical impact on patients affected by the disease and a challenging diagnosis. Methods: This comprehensive narrative review evaluates the role of [18F]fluorodesoxyglucose ([18F]FDG) positron emission tomography (PET)-based radiomics and machine learning (ML) analyses in the assessment of CS. Results: The value of [18F]FDG PET-based radiomics and ML has been investigated for the clinical settings of diagnosis and prognosis of patients affected by CS. Even though different radiomics features and ML models have proved their clinical role in these settings in different cohorts, the clear superiority and added value of one of them across different studies has not been demonstrated. In particular, textural analysis and ML showed high diagnostic value for the diagnosis of CS in some papers, but had controversial results in other works, and may potentially provide prognostic information and predict adverse clinical events. When comparing these analyses with the classic semiquantitative evaluation, a conclusion about which method best suits the final objective cannot be drawn with the available references. Different methodological issues are present when comparing different papers, such as image segmentation and feature extraction differences that are more evident. Furthermore, the intrinsic limitations of radiomics analysis and ML need to be overcome with future research developed in multicentric settings with protocol harmonization. Conclusions: [18F]FDG PET-based radiomics and ML show preliminary promising results for CS evaluation, but remain investigational tools since the current evidence is insufficient for clinical adoption due to methodological heterogeneity, small sample sizes, and lack of standardization. Full article

Background/Objectives: Immune checkpoint inhibitors targeting the PD-1/PD-L1 axis have become a standard in the treatment of all stages of non-small lung cancer. Beyond tumor-intrinsic biomarkers like PD-L1 expression, evidence points to the role of patient-related factors, such as body mass index, sarcopenia, and cachexia. These body composition parameters may reflect metabolic reserve or even immune competence and could help stratify outcomes in patients treated with PD-1 and PD-L1. This systematic review aims to evaluate the impact of body composition—specifically BMI, pretreatment weight loss, sarcopenia, and cachexia—on clinical outcomes such as progression-free and overall survival in NSCLC patients treated with immune checkpoint inhibitors. Methods: A systematic literature search was conducted across multiple databases including PubMed, Google Scholar, and Science Direct. We included full-text original research articles (1 January 2020–1 May 2025) reporting clinical outcomes of NSCLC patients treated with PD-1 or PD-L1 inhibitors, in relation to body composition factors (BMI, pretreatment weight loss, sarcopenia, cachexia). Eligible studies involved adults (>18 years) and included observational cohorts or controlled trials; animal or in vitro studies were excluded. Data extraction and risk of bias assessments were performed independently by two reviewers, with discrepancies being resolved through a third one. Results: From 12,358 records identified, 21 studies met the inclusion criteria. Most were retrospective cohorts assessing the impact of pre-treatment weight loss, cachexia, and sarcopenia on ICI outcomes in NSCLC. These factors consistently predicted poorer survival and response, while BMI alone showed limited prognostic value. Considerable heterogeneity in body composition definitions and outcome reporting was observed. Conclusions: Body composition—particularly weight loss, cachexia, and sarcopenia—significantly impacts survival and response in NSCLC patients treated with ICIs. These factors reflect immune–metabolic dysfunction that may impair treatment efficacy. BMI alone is insufficient; routine assessment of muscle mass and cachexia could improve risk stratification. Full article

Background and Objectives: Postoperative physical recovery, particularly the acquisition of independent ambulation, is a critical milestone in rehabilitation following living donor liver transplantation (LDLT). Although liver function markers are conventionally used to assess hepatic physiology, emerging evidence has suggested their potential role as prognostic indicators of physical performance. Materials and Methods: This study investigated the association between liver function parameters at the initiation of postoperative physical therapy (total bilirubin [T-Bil], aspartate aminotransferase [AST], and alanine aminotransferase [ALT]) and the independent walking ability of 63 patients who underwent LDLT. A logistic regression model was constructed using these variables, and a receiver-operating characteristic (ROC) curve analysis was performed to evaluate its discriminative performance. Predicted probabilities of each patient were calculated, and the optimal cutoff value was determined based on the Youden Index. Results: The multivariate logistic regression model demonstrated a statistically significant association between liver function markers and the ambulation status of a cohort of 63 patients. The ROC curve analysis yielded an area under the ROC curve (AUC) of 0.8416 (95% confidence interval [CI]: 0.715–0.968), indicating strong predictive performance. The optimal cutoff value was 0.865, with sensitivity and specificity of 74.1% and 88.9%, respectively. The bootstrap CI for sensitivity at this threshold ranged from 0.6111 to 0.8519. The Hosmer–Lemeshow test indicated good model fit (p = 0.363), and the correct classification rate was 87.3%. Conclusions: Liver function test results may be indicators of hepatic dysfunction as well as functional biomarkers that could predict ambulatory outcomes following LDLT. This predictive model may enhance early clinical decision-making regarding rehabilitation and discharge planning. Future prospective studies should be performed to validate the generalizability of these results to broader clinical contexts. Full article

Hepatocellular carcinoma (HCC) remains a global health challenge due to molecular heterogeneity and frequent delayed diagnosis. This comprehensive review synthesizes recent immunohistochemistry (IHC) advancements for HCC diagnosis, prognostication, and therapeutic prediction. We systematically evaluate conventional markers, such as hepatocyte paraffin 1 (HepPar1), arginase-1 (Arg-1), and glypican-3 (GPC3), as well as emerging biomarkers, detailing their diagnostic sensitivities and specificities in HCC with varied tumor differentiation. Prognostic immunostaining markers, such as Ki-67 proliferation index and vascular endothelial growth factor (VEGF) overexpression, correlate with reduced 5-year survival, while novel immune checkpoint IHC markers (PD-L1 and CTLA-4) predict response to immunotherapy, particularly in advanced HCC. This work provides evidence-based recommendations for optimizing IHC utilization in clinical practice while identifying knowledge gaps in biomarker validation and standardization. Full article

Background and Objectives: Otitis media with effusion (OME), frequently associated with obstructive adenoid hypertrophy (OAH), is a leading cause of paediatric hearing loss. Clinically distinguishing effusion types (serous vs. mucoid) and predicting postoperative hearing recovery are unresolved challenges. This study evaluated the utility of preoperative blood inflammatory markers in predicting effusion characteristics and short-term hearing outcomes following adenoidectomy with tympanostomy tube (TT) insertion. Materials and Methods: In this retrospective cohort study, 232 children under 12 years old in 2024 and undergoing adenoidectomy (with or without TT insertion) were categorised into serous OME (n = 42), mucoid OME (n = 78), and non-effusion (n = 112) groups. Preoperative blood sample analyses assessed neutrophil, lymphocyte, eosinophil, basophil, and platelet counts, along with derived indices, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), eosinophil-to-basophil ratio (EBR), mean platelet volume (MPV), and systemic immune–inflammation index (SII). Hearing was evaluated at 2 weeks and 1 month postoperatively. Statistical analyses used SPSS v.28, with significance set at p < 0.05. Result: Mucoid OME patients exhibited significantly elevated neutrophil counts, platelet counts, eosinophils, NLR, and SII compared to those in serous OME and non-effusion groups (p < 0.05). All serous OME children achieved normal hearing by the first follow-up, whereas 15.4% of mucoid OME cases had transient mild hearing loss persisting after 2 weeks (p = 0.008; OR=15.97) but resolving by 1 month. Preoperative neutrophil count independently predicted delayed hearing recovery (p = 0.021). Conclusions: Systemic inflammatory markers, particularly neutrophil count, NLR, and SII, effectively differentiate mucoid OME from other effusion types and correlate with short-term hearing recovery. Neutrophil count may serve as a prognostic tool for surgical planning and patient counselling. Prospective studies are warranted to validate these findings in broader paediatric populations. Full article

Cardiovascular disease remains the world’s leading cause of mortality, yet everyday care still relies on episodic, symptom-driven interventions that detect ischemia, arrhythmias, and remodeling only after tissue damage has begun, limiting the effectiveness of therapy. A narrative review synthesized 183 studies published between 2016 and 2025 that were located through PubMed, MDPI, Scopus, IEEE Xplore, and Web of Science. This review examines CVD diagnostics using innovative technologies such as digital cardiovascular twins, which involve the collection of data from wearable IoT devices (electrocardiography (ECG), photoplethysmography (PPG), and mechanocardiography), clinical records, laboratory biomarkers, and genetic markers, as well as their integration with artificial intelligence (AI), including machine learning and deep learning, graph and transformer networks for interpreting multi-dimensional data streams and creating prognostic models, as well as generative AI, medical large language models (LLMs), and autonomous agents for decision support, personalized alerts, and treatment scenario modeling, and with cloud and edge computing for data processing. This multi-layered architecture enables the detection of silent pathologies long before clinical manifestations, transforming continuous observations into actionable recommendations and shifting cardiology from reactive treatment to predictive and preventive care. Evidence converges on four layers: sensors streaming multimodal clinical and environmental data; hybrid analytics that integrate hemodynamic models with deep-, graph- and transformer learning while Bayesian and Kalman filters manage uncertainty; decision support delivered by domain-tuned medical LLMs and autonomous agents; and prospective simulations that trial pacing or pharmacotherapy before bedside use, closing the prediction-intervention loop. This stack flags silent pathology weeks in advance and steers proactive personalized prevention. It also lays the groundwork for software-as-a-medical-device ecosystems and new regulatory guidance for trustworthy AI-enabled cardiovascular care. Full article

Background/Objectives: There is a need for prediction models which enable weighing benefits against risks of different treatment strategies for individual Hodgkin lymphoma (HL) patients. Therefore, we aimed to predict absolute risk of progression, first relapse or death (PRD) with and without incorporating HL treatment. Methods: The prognostic and treatment information of 2343 patients treated for classical HL at ages 15–60 years between 2008 and 2018 in the Netherlands was used to predict absolute risk of PRD up to 5 years after diagnosis using Cox proportional hazard models allowing for time-varying coefficients. Models were externally validated in 1675 patients treated for classical HL in Denmark between 2000 and 2018. Results: In early stages, gender, leukocyte, and lymphocyte counts were associated with risk of PRD. Additionally, receiving >4 cycles of ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine) or ABVD plus radiotherapy predicted lower risk of relapse compared with receiving ≤4 cycles of ABVD. In advanced stages, age, albumin and leukocyte counts predicted PRD risk. Receiving (escalated) BEACOPP (bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, prednisone) predicted lower PRD risk compared to ABVD. In Danish patients treated between 2008 and 2018, adding treatment information improved 5-year Inverse Probability of Censoring Weighted (IPCW) Area Under the Curve (AUC) values from 0.63 (95% Confidence Interval (CI): 0.55–0.72) to 0.71 (95% CI: 0.63–0.79) in early stages (p-value = 0.04) and from 0.59 (95% CI: 0.52–0.65) to 0.62 (95% CI: 0.55–0.68) in advanced stages (p-value = 0.33). Conclusions: We developed well calibrated models with reasonable discrimination, not only incorporating pre-treatment prognostic factors but also treatment effect enabling the prediction of absolute risk of first relapse/progression. Full article

The evolving paradigm of precision medicine is redefining the landscape of orthobiologic therapies by moving beyond traditional diagnosis-driven approaches toward biologically tailored interventions. This review synthesizes current evidence supporting precision orthobiologics, emphasizing the significance of individualized treatment strategies in musculoskeletal regenerative medicine. This narrative review synthesized literature from PubMed, Embase, and Web of Science databases (January 2015–December 2024) using search terms, including ‘precision medicine,’ ‘orthobiologics,’ ‘regenerative medicine,’ ‘biomarkers,’ and ‘artificial intelligence’. Biological heterogeneity among patients with ostensibly similar clinical diagnoses—reflected in diverse inflammatory states, genetic backgrounds, and tissue degeneration patterns—necessitates patient stratification informed by molecular, genetic, and multi-omics biomarkers. These biomarkers not only enhance diagnostic accuracy but also improve prognostication and monitoring of therapeutic responses. Advanced imaging modalities such as T2 mapping, DTI, DCE-MRI, and molecular PET offer non-invasive quantification of tissue health and regenerative dynamics, further refining patient selection and treatment evaluation. Simultaneously, bioengineered delivery systems, including hydrogels, nanoparticles, and scaffolds, enable precise and sustained release of orthobiologic agents, optimizing therapeutic efficacy. Artificial intelligence and machine learning approaches are increasingly employed to integrate high-dimensional clinical, imaging, and omics datasets, facilitating predictive modeling and personalized treatment planning. Despite these advances, significant challenges persist—ranging from assay variability and lack of standardization to regulatory and economic barriers. Future progress requires large-scale multicenter validation studies, harmonization of protocols, and cross-disciplinary collaboration. By addressing these limitations, precision orthobiologics has the potential to deliver safer, more effective, and individualized care. This shift from generalized to patient-specific interventions holds promise for improving outcomes in degenerative and traumatic musculoskeletal disorders through a truly integrative, data-informed therapeutic framework. Full article

As the leading cause of global mortality, cardiovascular diseases demand improved and innovative strategies for early detection and risk assessment to enhance prevention and timely treatment. This comprehensive review examines the potential of combining high-sensitivity cardiac troponins (hs-cTns) and homocysteine (Hcy) as complementary biomarkers for enhanced cardiovascular risk prediction. hs-cTn assays have revolutionized cardiovascular diagnostics by enabling the detection of minimal myocardial injury, improving early diagnosis of acute coronary syndrome, and providing robust prognostic information in both symptomatic and asymptomatic populations. Hcy, while established as a marker of vascular dysfunction, presents an interpretative challenge due to multiple confounding factors and inconsistent therapeutic responses. Emerging evidence demonstrates significant correlations between elevated Hcy and troponins across various clinical conditions, suggesting that their combined assessment—reflecting both myocardial injury and vascular dysfunction—may improve cardiovascular risk stratification. While initial findings are promising, additional studies are required to validate the clinical value of the combined marker approach. Future development of personalized interpretation algorithms, and multi-marker panels incorporating these biomarkers, may significantly advance cardiovascular medicine and enable more effective population-specific risk management strategies. Full article

Background/Objectives: Prognostic biomarkers are essential for guiding the clinical management of pulmonary hypertension (PH). This study aimed to assess both established and novel biomarkers—specifically, the red cell distribution width-to-estimated glomerular filtration rate ratio (RGR) and the NT-proBNP-to-albumin ratio (NTAR)—for their ability to predict length of hospital stay (LOS), prolonged LOS (ELOS), in-hospital mortality, and 3-month all-cause mortality in patients with pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH). Methods: A retrospective analysis was conducted on 275 PH-related hospital regular admissions (148 PAH; 127 CTEPH). Established biomarkers—including serum albumin, neutrophil-to-lymphocyte ratio (NLR), Log NT-proBNP, red cell distribution width (RDW), and estimated glomerular filtration rate (eGFR)—as well as novel indices (RGR, and NTAR) were examined for their relationships with LOS, ELOS, in-hospital mortality, and 3-month all-cause mortality. Spearman correlation, univariate logistic regression, and ROC analyses evaluated biomarker relationships and predictive performance. Results: Serum albumin independently predicted in-hospital and 3-month mortality in PAH, while in CTEPH, it inversely correlated with LOS and strongly predicted prolonged hospitalization and mortality (AUC = 0.833). NLR had limited correlation with LOS but predicted mortality across both groups. RDW correlated weakly with LOS, significantly predicting prolonged hospitalization (threshold > 52.1 fL) in PAH but not in CTEPH. Preserved renal function (eGFR > 60 mL/min/1.73 m²) was inversely associated with LOS in CTEPH patients, suggesting a protective effect. Additionally, reduced eGFR significantly predicted mortality in both PAH (AUC = 0.701; optimal cut-off ≤ 97.4 mL/min/1.73 m²) and CTEPH (AUC = 0.793; optimal cut-off ≤ 59.2 mL/min/1.73 m²) groups. NTAR (AUC = 0.817) outperformed Log NT-proBNP alone in predicting extended hospitalization and mortality, whereas RGR correlated with LOS and predicted in-hospital mortality. Phenotype-specific analysis demonstrated that inflammatory and renal biomarkers had a stronger prognostic impact in CTEPH. Conclusions: Stratification by PH phenotype highlighted the greater prognostic significance of inflammatory and renal indices, particularly in patients with CTEPH. Incorporating NTAR and RGR into clinical workflows may enhance risk stratification and enable more precisely targeted interventions to improve outcomes in pulmonary hypertension. Full article

Background: Most patients with hypertension exhibit elevated and detectable levels of natriuretic peptides, particularly BNP and NT-proBNP, as well as troponin concentrations. However, the prognostic relevance of this finding has not been clearly established in patients who have hypertension without heart failure (HF). In this review, we aimed to evaluate the prognostic utility of BNP/NT-proBNP alongside troponin T/I for risk stratification in hypertensive patients, excluding those with HF. Methods: This systematic review was registered in PROSPERO (CRD42024552031). A systematic literature search was conducted using two online databases, Ovid Medline and Web of Science, to identify studies. Data retrieved from articles were used in line with the PRISMA statement guidelines. Participants were aged ≥ 18 years with hypertension. The primary end point was a major adverse cardiac event (MACE) and its individual components. Descriptive synthesis was performed, and data are presented in tabular form. Results: Seventeen studies (70,021 participants) were retrieved for analysis comprising eight prospective cohort studies, six randomized controlled trials, and three retrospective studies. The review evaluated cardiac biomarkers: BNP (n = 6), NT proBNP (n=9), troponin T (n = 4), and troponin I (n = 7). Studies predicted composite MACE (n = 8), all-cause mortality (n = 7), HF (n = 6), and atrial fibrillation (n = 3) outcomes. Cardiac biomarkers showed a strong association with reported outcomes. However, heterogeneity in biomarker thresholds and methodologies limited comparability. Conclusions: The obtained results suggest that elevated cardiac biomarkers BNP/NT-proBNP and troponin I are associated with significantly higher risk of MACE and are powerful predictors in clinical setting. However, large-scale studies are required to validate the robustness and prognostic utility of these biomarkers Full article

Gastrointestinal (GI) cancers, particularly colorectal and gastric cancers, majorly contribute to global cancer mortality due to frequent late-stage diagnosis and poor therapeutic response in advanced disease. Earlier detection of GI cancers is needed for a better prognosis. This review examines both traditional and emerging biomarkers that contribute significantly to early detection, prognostication, and prediction of therapeutic resistance or sensitivity. Specifically, we highlight the diagnostic utility of non-invasive liquid biopsy biomarkers such as circulating tumor DNA (ctDNA), microRNAs (miRNAs), and exosomes. Moreover, we discuss the prognostic and predictive value of conventional genetic alterations, including KRAS, BRAF, and HER2. Although new findings have shown the advantages of liquid biopsy over colonoscopy, there are still limitations to the technique, such as cost-effectiveness, technological gaps in low-resource settings, and uncertain detection rates. Further studies are required to test the validity and accessibility of liquid biopsy and its biomarkers in order to advance personalized diagnosis and treatments for GI cancers. Such a study will be helpful for clinicians to better manage patients with GI cancers. Full article

Coronary artery disease (CAD) is a leading cause of death worldwide, encompassing a broad spectrum of pathological conditions ranging from chronic to acute coronary syndromes. It underlies complex biological mechanisms, among which an emerging role is played by extracellular vesicles (EVs). EVs are non-replicable cell-derived particles enclosed by lipid bilayers acting as mediators of cellular interactions. In the past two decades, there has been a growing interest in EVs as potential diagnostic, prognostic and therapeutic tools in cardiovascular disease. We reviewed the most recent studies on circulating EVs in CAD with a particular focus on their role in biomarker discovery. Our aim was to evaluate the feasibility of translating these findings into routine clinical practice. To this end, we underlie the development and application of integrated indicators, referred to as “Bioscores”, which combine clinical, laboratory, and molecular data to enhance diagnostic and prognostic accuracy. We briefly discuss the opportunity and pitfalls related to the emerging use of Machine Learning (ML) algorithms. Moreover, we highlight that further investigation of mechanistic pathways is required beyond the initially predicted associations generated by in silico studies. Finally, we analyzed the key limitations, challenges, and unmet needs in the field, including small and unrepresentative sample sizes, a lack of external validation, overlapping and often contradictory effects on targeted pathways, difficulties in standardizing EV isolation and characterization methods, as well as concerns regarding affordability and clinical reliability. Full article

Objective: To evaluate the diagnostic performance of fluorine 18 fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (¹⁸F-FDG PET/MR) in the preoperative staging of hypopharyngeal cancer (HPC), compare it with conventional enhanced computed tomography (CT) and MR, and further explore the prognostic value of its metabolic and diffusion metrics for HPC. Methods: This retrospective study included 33 patients with pathologically confirmed HPC. All patients underwent preoperative ¹⁸F-FDG PET/MR, CT, and MR examination. The staging performance of the three modalities was evaluated using pathological staging as a reference. Additionally, metabolic indicators and diffusion-related parameters from PET/MR were collected to investigate their impact on larynx preservation and survival. Results: PET/MR demonstrated accuracies of 90.9% and 71.4% in the preoperative T and N staging, respectively, significantly higher than those of CT (54.5%, p = 0.001; 42.9%, p = 0.021) and MR (66.7%, p = 0.016; 42.9%, p = 0.021). On the whole, significant differences emerged in the maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), minimum apparent diffusion coefficient (ADCmin), and mean ADC (ADCmean) and combined ratios across different T stages, while SUVmax, mean SUV (SUVmean), total lesion glycolysis (TLG), and MTV varied significantly across different N stages. The ADCmin and ADCmean showed good predictive capability for larynx preservation, with AUCs of 0.857 and 0.920 (p < 0.05), respectively. In Cox multivariate analysis of overall survival, high-level ADCmean (p = 0.004) and low-level TLG/ADCmean (p = 0.022) were significantly associated with better survival. Conclusion: In HPC, ¹⁸F-FDG PET/MR imaging significantly surpasses CT and MR in preoperative diagnostic staging. Its diffusion-related parameters have substantial prognostic value, with high ADC values associated with larynx preservation. ADCmean and TLG/ADCmean are potential prognostic indicators for HPC. Full article