Search Results (3,785)

Background: Acute kidney injury (AKI) frequently occurs following major liver resection, adversely affecting both short- and long-term outcomes. This study aimed to determine the incidence of AKI post-hepatectomy and identify relevant pre- and intraoperative risk factors. Our secondary objectives were to develop a predictive score for postoperative AKI and assess the associations between AKI, chronic kidney disease (CKD), and 1-year mortality. Methods: This was a retrospective study in a cancer referral center in Marseille, France, from 2018 to 2022. Results: Among 169 patients, 55 (32.5%) experienced AKI. Multivariate analysis revealed several independent risk factors for postoperative AKI, including age, body mass index, the use of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, time to liver resection, intraoperative shock, and bile duct reconstruction. Neoadjuvant chemotherapy was protective. The AKIMEBO score was developed, with a threshold of ≥15.6, demonstrating a sensitivity of 89.5%, specificity of 76.4%, positive predictive value of 61.8%, and negative predictive value of 94.4%. AKI was associated with increased postoperative morbidity and one-year mortality following major hepatectomy. Conclusion: AKI is a common complication post-hepatectomy. Factors such as time to liver resection and intraoperative shock management present potential clinical intervention points. The AKIMEBO score can provide a valuable tool for postoperative risk stratification. Full article

Background: Accurate and timely prediction of mortality in intensive care unit (ICU) patients, particularly those with COVID-19, remains clinically challenging due to complex immune responses. Proteomic cytokine profiling holds promise for refining mortality risk assessment. Methods: Serum samples from 89 ICU patients (55 discharged, 34 deceased) were analyzed using a multiplex 21-cytokine panel. Samples were stratified into three groups based on time from collection to outcome: ≤48 h (Group 1: Early), >48 h to ≤7 days (Group 2: Intermediate), and >7 days to ≤14 days (Group 3: Late). Cytokine levels, simple cytokine ratios, and previously unexplored complex ratios between pro- and anti-inflammatory cytokines were evaluated. Machine learning-based feature selection identified the most predictive ratios, with performance evaluated by area under the curve (AUC), sensitivity, and specificity. Results: Complex cytokine ratios demonstrated superior predictive accuracy compared to traditional severity markers (APACHE II, SAPS II, SOFA), individual cytokines, and simple ratios, effectively distinguishing discharged from deceased patients across all groups (AUC: 0.918–1.000; sensitivity: 0.826–1.000; specificity: 0.775–0.900). Conclusions: Multiplex cytokine profiling enhanced by computationally derived complex ratios may offer robust predictive capabilities for ICU mortality risk stratification, serving as a valuable tool for personalized prognosis in critical care. Full article

Background: Pancreatic cystic lesions (PCLs), including intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs), pose a diagnostic challenge due to their variable malignant potential. Current guidelines, such as Fukuoka and American Gastroenterological Association (AGA), have moderate predictive accuracy and may lead to overtreatment or missed malignancies. Artificial intelligence (AI), incorporating machine learning (ML) and deep learning (DL), offers the potential to improve risk stratification, diagnosis, and management of PCLs by integrating clinical, radiological, and molecular data. This is the first systematic review to evaluate the application, performance, and clinical utility of AI models in the diagnosis, classification, prognosis, and management of pancreatic cysts. Methods: A systematic review was conducted in accordance with PRISMA guidelines and registered on PROSPERO (CRD420251008593). Databases searched included PubMed, EMBASE, Scopus, and Cochrane Library up to March 2025. The inclusion criteria encompassed original studies employing AI, ML, or DL in human subjects with pancreatic cysts, evaluating diagnostic, classification, or prognostic outcomes. Data were extracted on the study design, imaging modality, model type, sample size, performance metrics (accuracy, sensitivity, specificity, and area under the curve (AUC)), and validation methods. Study quality and bias were assessed using the PROBAST and adherence to TRIPOD reporting guidelines. Results: From 847 records, 31 studies met the inclusion criteria. Most were retrospective observational (n = 27, 87%) and focused on preoperative diagnostic applications (n = 30, 97%), with only one addressing prognosis. Imaging modalities included Computed Tomography (CT) (48%), endoscopic ultrasound (EUS) (26%), and Magnetic Resonance Imaging (MRI) (9.7%). Neural networks, particularly convolutional neural networks (CNNs), were the most common AI models (n = 16), followed by logistic regression (n = 4) and support vector machines (n = 3). The median reported AUC across studies was 0.912, with 55% of models achieving AUC ≥ 0.80. The models outperformed clinicians or existing guidelines in 11 studies. IPMN stratification and subtype classification were common focuses, with CNN-based EUS models achieving accuracies of up to 99.6%. Only 10 studies (32%) performed external validation. The risk of bias was high in 93.5% of studies, and TRIPOD adherence averaged 48%. Conclusions: AI demonstrates strong potential in improving the diagnosis and risk stratification of pancreatic cysts, with several models outperforming current clinical guidelines and human readers. However, widespread clinical adoption is hindered by high risk of bias, lack of external validation, and limited interpretability of complex models. Future work should prioritise multicentre prospective studies, standardised model reporting, and development of interpretable, externally validated tools to support clinical integration. Full article

Background: Despite therapeutic advances, morbidity and mortality remain high in patients with rheumatoid arthritis (RA) and psoriatic arthritis (PsA), primarily due to increased cardiovascular risk. Objectives: Our study aimed to evaluate the cardiovascular risk profile and biomarker dynamics in patients with RA and PsA treated with Janus kinase inhibitors (JAKis). To our knowledge, this is the first study assessing Lp(a) levels in this context. Methods: This prospective, observational study assessed 48 adult patients. The follow-up period was 12 months. Traditional cardiovascular risk factors and biological markers, including lipid profile, lipoprotein(a) [Lp(a)], and uric acid (UA), were assessed at baseline and follow-up. Correlations between JAKi therapy, lipid profile changes, and cardiovascular risk factors were investigated. Cox regression analysis was used to identify predictors of non-major cardiovascular events. Results: A strong positive correlation was observed between baseline and 12-month Lp(a) levels (r = 0.926), despite minor statistical shifts. No major cardiovascular events occurred during follow-up; however, 47.9% of patients experienced non-major cardiovascular events (e.g., uncontrolled arterial hypertension, exertional angina, and new-onset arrhythmias). Active smoking [hazard ratio (HR) 9.853, p = 0.005], obesity (HR 3.7460, p = 0.050), and arterial hypertension (HR 1.219, p = 0.021) were independent predictors of these events. UA (HR 1.515, p = 0.040) and total cholesterol (TC) (HR 1.019, p = 0.034) were significant biochemical predictors as well. Elevated baseline Lp(a) combined with these factors was associated with an increased event rate, particularly after age 60. Conclusions: Traditional cardiovascular risk factors remain highly prevalent and predictive, underscoring the need for comprehensive cardiovascular risk management. Lp(a) remained stable and may serve as a complementary biomarker for risk stratification in JAKi-treated patients. Full article

Arrhythmogenic cardiomyopathies (ACMs) are a phenotypically and etiologically heterogeneous group of myocardial disorders characterized by fibrotic or fibro-fatty replacement of ventricular myocardium, electrical instability, and an elevated risk of sudden cardiac death. Initially identified as a right ventricular disease, ACMs are now recognized to include biventricular and left-dominant forms. Genetic causes account for a substantial proportion of cases and include desmosomal variants, non-desmosomal variants, and familial gene-elusive forms with no identifiable pathogenic mutation. Nongenetic etiologies, including post-inflammatory, autoimmune, and infiltrative mechanisms, may mimic the phenotype. In many patients, the disease remains idiopathic despite comprehensive evaluation. Cardiac magnetic resonance imaging has emerged as a key tool for identifying non-ischemic scar patterns and for distinguishing arrhythmogenic phenotypes from other cardiomyopathies. Emerging classifications propose the unifying concept of scarring cardiomyopathies based on shared structural substrates, although global consensus is evolving. Risk stratification remains challenging, particularly in patients without overt systolic dysfunction or identifiable genetic markers. Advances in tissue phenotyping, multi-omics, and artificial intelligence hold promise for improved prognostic assessment and individualized therapy. Full article

Heart failure (HF) poses a substantial global burden due to its high morbidity, mortality, and healthcare costs. Accurate prognostication is crucial for optimizing treatment, resource allocation, and patient counseling. Prognostic tools range from simple clinical scores such as ADHERE and MAGGIC to more complex models incorporating biomarkers (e.g., NT-proBNP, sST2), imaging, and artificial intelligence techniques. In acute HF, models like EHMRG and STRATIFY aid early triage, while in chronic HF, tools like SHFM and BCN Bio-HF support long-term management decisions. Despite their utility, most models are limited by poor generalizability, reliance on static inputs, lack of integration into electronic health records, and underuse in clinical practice. Novel approaches involving machine learning, multi-omics profiling, and remote monitoring hold promise for dynamic and individualized risk assessment. However, these innovations face challenges regarding interpretability, validation, and ethical implementation. For prognostic models to transition from theoretical promise to practical impact, they must be continuously updated, externally validated, and seamlessly embedded into clinical workflows. This review emphasizes the potential of prognostic models to transform HF care but cautions against uncritical adoption without robust evidence and practical integration. In the evolving landscape of HF management, prognostic models represent a hopeful avenue, provided their limitations are acknowledged and addressed through interdisciplinary collaboration and patient-centered innovation. Full article

Background: Metabolic syndrome (MetS) is a complex clinical condition characterized by the coexistence of interrelated metabolic abnormalities that significantly increase the risk of cardiovascular diseases and type 2 diabetes mellitus. Endocan—an endothelial cell-specific molecule—is considered a biomarker of endothelial dysfunction and inflammation. This study aimed to evaluate the relationship between serum endocan levels and the severity of MetS, assessed using the MetS-Z score. Methods: This study included 120 patients with MetS and 50 healthy controls. MetS was diagnosed according to the NCEP-ATP III criteria. MetS-Z scores were calculated using the MetS Severity Calculator. Serum levels of endocan, sICAM-1, and sVCAM-1 were measured using the ELISA method. Results: Serum levels of endocan, sICAM-1, and sVCAM-1 were significantly higher in the MetS group compared to the control group (all p < 0.001). When the MetS group was divided into tertiles based on MetS-Z scores, stepwise and statistically significant increases were observed in the levels of endocan and other endothelial markers from the lowest to highest tertile (p < 0.0001). Correlation analysis revealed a strong positive association between the MetS-Z score and serum endocan levels (r = 0.584, p < 0.0001). ROC curve analysis showed that endocan has high diagnostic accuracy for identifying MetS (AUC = 0.967, p = 0.0001), with a cutoff value of >88.0 ng/L. Conclusions: Circulating levels of endocan were significantly increased in MetS and were associated with the severity of MetS, suggesting that endocan may play a role in the cellular response to endothelial dysfunction-related injury in patients with MetS. Full article

Background/Objectives: Androgen deprivation therapy (ADT) is widely used to manage prostate cancer (PC), but the emergence of treatment resistance remains a major clinical challenge. Although the GST family has been implicated in drug resistance, the specific role of GSTM5 remains poorly understood. This study investigates whether GSTM5, alone or in combination with clinical variables, can improve patient stratification based on the risk of early treatment resistance. Methods: In silico analyses were performed to examine GSTM5’s role in protein interactions, molecular pathways, and gene expression. The rs3768490 polymorphism was genotyped in 354 patients with PC, classified by ADT response. Descriptive analysis and logistic regression models were applied to evaluate associations between genotype, clinical variables, and ADT response. GSTM5 expression related to the rs3768490 genotype and ADT response was also analyzed in 129 prostate tissue samples. Results: The T/T genotype of rs3768490 was significantly associated with a lower likelihood of early ADT resistance in both individual (p = 0.0359, Odd Ratios (OR) = 0.18) and recessive models (p = 0.0491, OR = 0.21). High-risk classification according to D’Amico was strongly associated with early progression (p < 0.0004; OR > 5.4). Combining genotype and clinical risk improved predictive performance, highlighting their complementary value in stratifying patients by treatment response. Additionally, GSTM5 expression was slightly higher in T/T carriers, suggesting a potential protective role against ADT resistance. Conclusions: The T/T genotype of rs3768490 may protect against ADT resistance by modulating GSTM5 expression in PC. These preliminary findings highlight the potential of integrating genetic biomarkers into clinical models for personalized treatment strategies, although further studies are needed to validate these observations. Full article

Chromosomal microarray analysis (CMA) was performed for 40 patients with B-ALL undergoing treatment according to the ALL-2016 protocol to investigate the copy number alterations (CNAs) and copy neutral loss of heterozygosity (cnLOH) associated with minimal residual disease (MRD)-positive remission. Aberrations involving over 20,000 genes were identified, and a random forest approach was applied to isolate a subset of genes whose CNAs and cnLOH are significantly associated with poor therapeutic response. We have assembled the triple matched healthy population data and used that data as a reference, but not as a matched control. We identified a recurrent cluster of cnLOH in the 19q13.2–19q13.31 region, significantly enriched in MRD-positive patients (70% vs. 47% in the reference group vs. 16% in MRD-negative patients). This region includes the pregnancy-specific glycoprotein (PSG) gene family and the oncogene ERF, suggesting a potential role in leukemic persistence and treatment resistance. Additionally, we observed significant deletions involving 7p22.3 and 16q13, often as part of large-scale losses affecting almost the entire chromosomes 7 and 16, indicative of global chromosomal instability. These findings highlight specific genomic regions potentially involved in therapy resistance and may contribute to improved risk stratification in B-ALL. Our findings emphasize the value of high-resolution CMA in diagnostics and risk stratification and suggest that PSG genes and other candidate genes could serve as biomarkers for predicting treatment outcomes. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

Background: Breast cancer remains a major public health challenge in Latin America, where access to personalized risk assessment tools is still limited. This study aimed to evaluate the implementation of a polygenic risk score (PRS)-based stratification model combined with remote genomic counseling in Colombian women with sporadic breast cancer and healthy women. Methods: In 2023, an embedded mixed-methods observational study was conducted in Medellín involving 1997 women aged 40–75 years who underwent clinical PRS testing. The intervention integrated PRS-based risk categorization with individualized risk factor assessment and lifestyle recommendations delivered through a remote counseling platform. Results: PRS analysis classified 9.7% of women as high risk and 46% as low risk. Healthier lifestyle patterns were significantly associated with lower PRS categories (p = 0.034). Physical activity showed a protective effect (OR = 0.60, 95% CI: 0.5–0.8), while prior smoking, elevated BMI, and sedentary behavior were associated with higher risk. The counseling model achieved high delivery (93%) and satisfaction (85%) rates. Qualitative insights revealed improved understanding of genomic risk and greater engagement in preventive behaviors. Only one new case of breast cancer was detected among intermediate-risk participants, with a diagnostic lead time of 12 months. Conclusions: These findings support the feasibility, acceptability, and potential impact of integrating PRS and genomic counseling in cancer prevention strategies in middle-income settings. Full article

Respiratory syncytial virus (RSV) is a major cause of acute lower respiratory infections (ALRIs) in young children, especially bronchiolitis, with significant global health and economic impact. Increasing evidence links early-life RSV infection to long-term respiratory complications, notably recurrent wheezing and asthma. This narrative review examines these associations, emphasizing predictive factors and emerging biomarkers for risk stratification. Early RSV infection can trigger persistent airway inflammation and immune dysregulation, increasing the likelihood of chronic respiratory outcomes. Risk factors include severity of the initial infection, age at exposure, genetic susceptibility, prematurity, air pollution, and tobacco smoke. Biomarkers such as cytokines and chemokines are showing promise in identifying children at higher risk, potentially guiding early interventions. RSV-related bronchiolitis may also induce airway remodeling and promote Th2/Th17-skewed immune responses, mechanisms closely linked to asthma development. Advances in molecular profiling are shedding light on these pathways, suggesting novel targets for early therapeutic strategies. Furthermore, passive immunization and maternal vaccination offer promising approaches to reducing both acute and long-term RSV-related morbidity. A deeper understanding of RSV’s prolonged impact is essential to develop targeted prevention, enhance risk prediction, and improve long-term respiratory health in children. Future studies should aim to validate biomarkers and refine immunoprophylactic strategies. Full article

Locally advanced rectal cancer (LARC) remains a major clinical challenge due to its high risk of local recurrence and distant metastasis. Although total mesorectal excision (TME) has been established as the gold standard surgical approach, high recurrence rates associated with surgery alone have driven the development of multimodal preoperative strategies, such as radiotherapy and chemoradiotherapy. More recently, total neoadjuvant therapy (TNT)—which integrates systemic chemotherapy and radiotherapy prior to surgery—and non-operative management (NOM) for patients who achieve a clinical complete response (cCR) have further expanded treatment options. These advances aim not only to improve oncologic outcomes but also to enhance quality of life (QOL) by reducing long-term morbidity and preserving organ function. However, several unresolved issues persist, including the optimal sequencing of therapies, precise risk stratification, accurate evaluation of treatment response, and effective surveillance protocols for NOM. The advent of molecular biomarkers, next-generation sequencing, and artificial intelligence (AI) presents new opportunities for individualized treatment and more accurate prognostication. This narrative review provides a comprehensive overview of the current status of preoperative treatment for LARC, critically examines emerging strategies and their supporting evidence, and discusses future directions to optimize both oncological and patient-centered outcomes. By integrating clinical, molecular, and technological advances, the management of rectal cancer is moving toward truly personalized medicine. Full article

Background: COVID-19 is associated with coagulopathy and increased mortality. The ABO blood group system has been implicated in modulating susceptibility to SARS-CoV-2 infection and disease severity, but its relationship with viral RNAemia, spike gene mutations, and thrombosis remains underexplored. Methods: We analyzed 446 hospitalized COVID-19 patients between 2021 and 2022. SARS-CoV-2 RNAemia was assessed via RT-qPCR on whole blood, and spike gene mutations were identified through whole-genome sequencing in RNAemia-positive samples. ABO blood groups were determined by agglutination testing, and thrombotic events were evaluated using coagulation markers. Statistical analyses included chi-square tests and Kruskal–Wallis tests, with significance set at p < 0.05. Results: RNAemia was detected in 26.9% of patients, with no significant association with ABO blood group (p = 0.175). Omicron was the predominant variant, especially in blood group A (62.5%). The N501Y mutation was the most prevalent in group O (53.2%), and K417N was most prevalent in group B (36.9%), though neither reached statistical significance. Thrombotic events were significantly more common in blood group A (OR = 2.08, 95% CI = 1.3–3.4, p = 0.002), particularly among RNAemia-positive patients. Conclusions: ABO blood group phenotypes, particularly group A, may influence thrombotic risk in the context of SARS-CoV-2 RNAemia. While no direct association was found between blood group and RNAemia or spike mutations, the observed trends suggest potential host–pathogen interactions. Integrating ABO typing and RNAemia screening may enhance risk stratification and guide targeted thromboprophylaxis in COVID-19 patients. Full article

Background: The identification and clinical implementation of robust biomarkers are essential for improving diagnosis, prognosis, and treatment across a wide range of diseases. Pancreatic stone protein (PSP) has recently emerged as a promising candidate biomarker. Objective: This narrative review aims to provide an updated and comprehensive overview of the clinical applications of PSP in infectious, oncological, metabolic, and surgical contexts. Methods: We conducted a structured literature search using PubMed^®, applying the SANRA framework for narrative reviews. Boolean operators were used to retrieve relevant studies on PSP in a wide range of clinical conditions, including sepsis, gastrointestinal cancers, diabetes, and ventilator-associated pneumonia. Results: PSP has shown strong diagnostic and prognostic potential in sepsis, where it may outperform traditional markers such as CRP and PCT. It has also demonstrated relevance in gastrointestinal cancers, type 1 and type 2 diabetes, and perioperative infections. PSP levels appear to rise earlier than other inflammatory markers and may be less affected by sterile inflammation. Conclusion: PSP represents a versatile and clinically valuable biomarker. Its integration into diagnostic protocols could enhance early detection and risk stratification in critical care and oncology settings. However, widespread adoption is currently limited by the availability of point-of-care assay platforms. Full article