Search Results (18,439)

High levels of low-density lipoprotein cholesterol (LDL-c) have been recognized as the main causal factor of atherosclerotic cardiovascular disease (ASCVD) and are influenced by both genetic and environmental factors. Among genetic determinants, Familial Hypercholesterolemia (FH) is the most common monogenic disorder, caused by rare high-impact variants in genes involved in LDL uptake. Other monogenic causes of hypercholesterolemia include sitosterolemia, cerebrotendinous xanthomatosis and lysosomal acid lipase deficiency (LALD). However, monogenic disorders only account for a small proportion of inherited hypercholesterolemia. In many individuals, increased LDL-c levels are caused by the contemporary presence of different single-nucleotide polymorphisms (SNPs) with a moderate/low impact. These SNPs could be summarized through polygenic risk scores (PRS) that attribute relative weight to each of these. Another genetic determinant of hypercholesterolemic phenotypes is high levels of lipoprotein(a)—Lp(a). Lp(a) is an LDL particle modified by the binding of apolipoprotein(a)—apo(a)—which represents an independent risk factor for ASCVD. Lp(a) levels are mainly genetically determined by variation in the number of kringle IV type 2 (K-IV₂) repeats, as well as by several SNPs, and remain stable throughout life. The aim of this narrative review is to report an updated overview of the genetic mechanisms underlying hypercholesterolemia, including monogenic disorders, PRS and Lp(a), focusing on their potential repercussion in clinical practice by the integration into cardiovascular risk stratification beyond traditional clinical assessment. This integration could lead to a more comprehensive and individualized approach to cardiovascular prevention, with emerging perspectives including the possible use of artificial intelligence (AI). Full article

Background: Dry Eye Disease (DED) is a multifactorial ocular surface disorder characterized by tear film instability and inflammation. Cyclosporine A, an immunomodulator, and Diquafosol sodium, a mucin secretagogue, represent two distinct therapeutic pathways. However, current evidence directly comparing their clinical efficacy is inconsistent. This meta-analysis aimed to compare treatment outcomes and efficacy between 0.05% Cyclosporine A and 3% Diquafosol sodium in patients with moderate-to-severe DED. Methods: In January 2026, we conducted a systematic search of PubMed, Scopus, Web of Science, and the Cochrane Library for randomized controlled trials directly comparing 0.05% Cyclosporine A to 3% Diquafosol sodium in adult patients with moderate-to-severe DED. For the meta-analysis, we used R 4.5.0 with R Studio 2024.12.1+563. Results: We included six RCTs with a total of 859 patients. No significant differences were found between Cyclosporine A and Diquafosol sodium in Tear Break-Up Time (TBUT) at 4, 8, or 12 weeks. Cyclosporine A showed a suggestive greater improvement in Schirmer test scores at 4 weeks (SMD = 0.35, 95% CI 0.07 to 0.63). A modest benefit in symptom scores favoring Diquafosol sodium was observed at 12 weeks (SMD = 0.23, 95% CI 0.06 to 0.41). Subgroup analysis suggested this symptomatic benefit may be more pronounced in patients with severe disease, although subgroup interaction tests were not statistically significant. There were no significant differences in corneal or conjunctival staining at any time point. The risk of adverse events did not differ significantly between treatments. Conclusions: Early improvement in tear production showed a potential benefit for Cyclosporine A, while longer-term symptomatic relief showed a potential benefit for Diquafosol sodium, with suggestive evidence in severe disease. However, these findings should be interpreted cautiously, given the methodological limitations and inconclusive TSA evidence for several outcomes. Future large-scale, standardized trials with extended follow-up are warranted to confirm these findings. Full article

The growing worldwide prevalence of Diabetes Mellitus highlights the urgent need for effective early detection methods to enable prompt intervention. This study develops a machine learning-based decision-support prototype for predicting diabetes risk using health metrics from the DiaHealth dataset, a recently published Bangladeshi open-source dataset for Type 2 diabetes prediction. Five supervised learning algorithms were evaluated: Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Tree (DT), and Random Forest (RF). Models were assessed across three stages: before feature scaling, after standardisation, and following hyperparameter optimisation via GridSearchCV, using accuracy, precision, recall, and F1-score as evaluation metrics. LR and SVM showed marked improvements after standardisation, consistent with their sensitivity to feature magnitude, whilst tree-based approaches such as DT and RF remained largely unchanged. KNN displayed minimal sensitivity to scaling, which is discussed in relation to the feature distributions of the dataset. Following hyperparameter tuning, RF achieved the highest accuracy of 95%, outperforming all other models. RF predictions were interpreted using Local Interpretable Model-agnostic Explanations (LIME) to promote transparency in model decision-making. The best-performing model was subsequently deployed as an interactive web-based prototype application using Streamlit, providing real-time prediction outputs. These findings demonstrate how preprocessing choices and hyperparameter tuning can differentially affect algorithm performance and illustrate the potential of combining explainable AI with practical deployment for diabetes risk assessment in a research context. Full article

Background/Objectives: Enzalutamide and abiraterone acetate are commonly used androgen receptor pathway inhibitors in metastatic castration-resistant prostate cancer (mCRPC), including after docetaxel. However, real-world outcomes remain heterogeneous, and simple prognostic markers may help describe this variability. This study aimed to describe survival outcomes with enzalutamide and abiraterone acetate after docetaxel and to explore the prognostic value of a routine clinical-inflammatory risk classification. Methods: This retrospective single-center study included 136 patients with mCRPC treated with enzalutamide or abiraterone acetate after docetaxel. A composite risk classification was defined using four routinely available variables: pan-immune-inflammation value (PIV) > 457.99, time to castration resistance < 12 months, baseline hemoglobin ≤ 12 g/dL, and Gleason score ≥ 8. One point was assigned for each adverse factor, and patients were classified as low, moderate, or high risk. Overall survival (OS) was assessed using Kaplan–Meier estimates and Cox regression. The prognostic score and Cox regression-based nomogram were evaluated as exploratory tools. Results: Of the 136 patients, 8 (5.9%) were classified as low risk, 67 (49.3%) as moderate risk, and 61 (44.9%) as high risk. Median OS was not reached in the low-risk group, compared with 33.84 months in the moderate-risk group and 9.66 months in the high-risk group. In multivariable analysis, high-risk status was independently associated with worse OS (HR = 9.87; 95% CI: 2.38–40.92; p = 0.002). No statistically significant OS difference was observed between enzalutamide and abiraterone acetate in this non-randomized cohort (HR = 1.36; 95% CI: 0.90–2.06; p = 0.142). Conclusions: In this real-world post-docetaxel mCRPC cohort, no statistically significant OS difference was observed between enzalutamide and abiraterone acetate; however, the study was not designed to establish comparative effectiveness or therapeutic equivalence. The exploratory risk classification based on routine clinical and inflammatory variables was associated with distinct survival outcomes. External validation is required before clinical application. Full article

Background/Objectives: Limited research has evaluated the association between the triglyceride-to-high-density lipoprotein (TG/HDL) ratio and premature coronary artery disease (PCAD), particularly in Saudi Arabia. Therefore, this study aimed to investigate the association of the TG/HDL ratio with PCAD and to assess its sensitivity and specificity in a young Saudi population. Methods: This comparative retrospective case–control study utilized data collected from patients’ electronic medical records at King Saud University Medical City (KSUMC) between 2015 and 2023. The vessel score and Gensini score were used to evaluate the severity of coronary occlusion. The study population was divided into two groups: (1) a healthy control group consisting of blood bank donors, selected to exclude individuals with chronic diseases such as metabolic disorders and hypertension, with no evidence of coronary artery disease and aged ≤50 years (as confirmed by a cardiologist to rule out cardiovascular disease); and (2) patients with PCAD, aged ≤51 years, who underwent selective coronary angiography using the standard hospital procedure (right femoral artery approach). Coronary angiographic images were evaluated using right and left oblique views with cranial and caudal angulations. Results: A total of 898 subjects were included in the study, comprising 440 healthy controls and 458 patients with PCAD. Higher HbA1c levels were significantly associated with PCAD (adjusted OR = 13.03, 95% CI [7.32, 23.18], p < 0.001). Importantly, the TG/HDL ratio, the primary biomarker of interest, remained significantly associated with PCAD after full adjustment. Each unit increase in the TG/HDL ratio was associated with more than a threefold increase in the odds of PCAD (adjusted OR = 3.39, 95% CI [2.22, 5.16], p < 0.001), independent of age, sex, BMI, HbA1c, smoking, and total cholesterol levels. Among females, the TG/HDL ratio demonstrated an area under the curve (AUC) of 0.796, with an optimal cut-off value of 0.91, yielding 77.8% sensitivity and 71.4% specificity. Among males, the TG/HDL ratio yielded an AUC of 0.786, with a higher optimal cut-off value of 1.09 providing 73.4% sensitivity and 65.4% specificity. Conclusions: Our study indicates that the TG/HDL ratio and HbA1c are significantly associated with PCAD in young Saudi male and female populations, demonstrating good sensitivity and specificity. Females exhibited a lower cut-off value than males. Smoking and elevated cholesterol levels were also identified as prominent risk factors. However, the TG/HDL ratio did not distinguish between moderate and severe coronary stenosis, as assessed by the Gensini score. Full article

Objective: This study was conducted to examine the relationship between ruminative thinking styles and suicide probability in individuals presenting to the emergency department with suspected alcohol and substance intoxication/use, and to investigate whether these variables differ according to various demographic characteristics. Methods: The sample of this descriptive study consisted of 45 cases presenting to the emergency departments of two hospitals in eastern Turkey. Data were collected using a Sociodemographic Data Form, the Rumination Scale, and the Suicide Probability Scale. Descriptive statistics, Pearson correlation, independent samples t-test, and Linear Regression analysis were used for data analysis. Results: Of the participants, 66.7% were male and 44.4% were in the 18–23 age group. A positive and moderately significant relationship was found between rumination and suicide probability (r = 0.441; p = 0.001). Regression analysis revealed that rumination explained 34% of the variance in the suicide probability. Furthermore, suicide probability scores of those using non-alcohol or multiple substances were significantly higher than those using only alcohol (p = 0.025). Conclusions: Ruminative thinking is a significant associated factor of suicide risk in patients with alcohol and substance use disorders presenting to the emergency department. It is recommended that cognitive assessments of these patients be conducted during clinical processes and that multiple-substance users, in particular, should be closely monitored for suicide risk. Full article

Wheat is a major staple crop, and storage mold growth poses a severe threat to grain safety and quality stability. Natural mold development in stored wheat exhibits subtle, localized, and highly heterogeneous characteristics. Existing unimodal methods and global fusion approaches generally suffer from insufficient local feature sensitivity, hindering fine-grained mold severity grading. To address this limitation, we propose a Mask-Guided Fine-Grained Fusion Network, a weakly supervised framework based on local RGB–HSI fusion. This framework employs a dynamic parallel A/B experimental design to construct time-matched proxy labels via weakly supervised learning. A standardized preprocessing pipeline including single-kernel extraction, foreground segmentation, and cross-modal registration is established to resolve RGB–HSI spatial misalignment, ensuring physical-level spatial consistency of multimodal features. The model incorporates a Foreground-Aware Spectral Recalibration (FASR) module to suppress background noise, a Mask-Guided Dilated Cross-modal Local Attention (MDCLA) mechanism to establish fine-grained local mappings between RGB visual phenotypes and hyperspectral responses, and a sample-level adaptive fusion strategy to dynamically weight features by modal reliability, enhancing representation of complex samples across all mold stages. Experiments show that the Mask-Guided Fine-Grained Fusion Network achieves 0.9689 classification accuracy, 0.9698 Macro-F1 score, and 0.0593 Mean Absolute Error (MAE), significantly outperforming state-of-the-art unimodal deep models and global attention fusion baselines. This work provides a proof-of-principle framework for fine-grained non-destructive mold risk assessment in stored wheat. Full article

Background/Objectives: This investigation was performed because corticosteroid injections are commonly used for symptomatic relief in patients with meniscal deficiency, yet their effect on graft survivorship and postoperative outcomes following meniscal allograft transplantation (MAT) remains poorly understood, with limited literature specifically addressing this topic. The aim of this study is to evaluate whether preoperative intra-articular corticosteroid injections (ICS) are associated with reoperation after MAT. Secondary aims included comparing reoperation-free survival, patient-reported outcome measures (PROMs), and patient acceptable symptom state (PASS) achievement. Methods: A retrospective review of 130 adults undergoing meniscal allograft transplantation (MAT) between 2011 and 2023 was performed. Patients with documented corticosteroid injection (CSI) status and ≥2 years of follow-up were included. Exclusion criteria included prior meniscal allograft transplantation, receipt of non-corticosteroid injections (e.g., hyaluronic acid or platelet-rich plasma), concomitant osteotomy procedures, multi-ligament knee reconstruction or inadequate follow-up. Propensity score matching (2:1 no steroid: steroid) based on age, sex, body mass index, fixation technique, operative compartment, and concomitant procedures yielded 54 matched patients (35 no steroid, 19 steroid). The primary outcome was ipsilateral knee reoperation, categorized as major reoperation (revision MAT, anterior cruciate ligament reconstruction, osteochondral allograft transplantation, conversion to total knee arthroplasty, meniscectomy and meniscus repair). Minor reoperations included irrigation and debridement, lysis of adhesions or manipulation under anesthesia, hardware removal, chondroplasty, and synovectomy. Reoperation-free survival was assessed using Kaplan–Meier analysis. PROMs and PASS were compared using adjusted regression models. Statistical significance was set at p < 0.05. Results: Baseline characteristics and follow-up were comparable between groups (7.6 ± 3.5 vs. 6.6 ± 3.2 years; p = 0.30). Overall reoperation occurred in 37.1% of patients in the no-steroid group and 31.6% in the steroid group (p = 0.771). Major reoperation rates were similar (17.1% vs. 15.8%; p = 1.000. There was no significant difference in minor reoperations between groups (20.0% vs. 10.5%; p = 0.468). Kaplan–Meier analysis demonstrated no difference in reoperation-free survival (p = 0.903), with comparable survival at the 1-, 2-, and 5-year time points. No individual subtypes differed significantly between groups. PROMs and PASS achievement were also similar, with no statistically significant differences observed. Conclusions: Preoperative corticosteroid injection was not associated with increased reoperation risk, inferior reoperation-free survival, or worse patient-reported outcomes following meniscal allograft transplantation. However, given the study’s limited power, lack of detailed injection characteristics, and the use of a heterogeneous complication outcome, these findings should be interpreted cautiously, as further investigation is warranted. Full article

Background: Cardiovascular (CV) risk is increased in psoriatic arthritis (PsA), yet vascular assessment has largely focused on carotid arteries, potentially underestimating systemic atherosclerosis. Objective: The objective of this study was to characterize the distribution and concordance of atherosclerotic plaques across carotid, femoral, and aortic territories in PsA and evaluate their incremental value over SCORE2. Methods: In this cross-sectional study, 250 unselected patients with PsA underwent carotid and femoral ultrasound and abdominal X-ray. Plaque prevalence and multiterritorial involvement (≥2 vascular beds) were assessed. Agreement between territories was evaluated using Cohen’s κ. In patients aged 50–69 years, the incremental value of vascular territories over SCORE2 was evaluated using ROC curves, bootstrap-corrected decision curve analysis (DCA), and reclassification metrics (IDI and continuous NRI). Results: Plaques were detected in carotid (36.0%), femoral (62.8%), and aortic (31.6%) territories, with multiterritorial involvement in 43.2%. Agreement between vascular beds was moderate (κ ≈ 0.35). Notably, 48.1% of patients without carotid plaques had femoral involvement. SCORE2 categories showed a strong gradient with plaque prevalence (p < 0.0001). In patients aged 50–69 years, adding vascular imaging improved discrimination for multiterritorial disease (AUC 0.73 vs. 0.86–0.90). Reclassification analyses demonstrated that carotid plaque substantially improved the identification of multiterritorial atherosclerosis (IDI 0.32, 95% CI 0.18–0.50; continuous NRI 1.33, 95% CI 1.08–1.60), with similar results observed for aortic plaque (IDI 0.33, 95% CI 0.20–0.50; continuous NRI 1.24, 95% CI 0.99–1.48). Femoral plaque provided a more modest improvement (IDI 0.26, 95% CI 0.16–0.37; continuous NRI 1.11, 95% CI 0.80–1.33). Conversely, when the outcome was defined as the presence of any plaque, femoral plaque provided the greatest incremental value over SCORE2 (AUC 0.96, 95% CI 0.93–0.99). Bootstrap-corrected DCA confirmed improved net benefit. Conclusions: The incremental value of vascular imaging over SCORE2 appears to be phenotype-dependent. Femoral plaque provided the greatest improvement for detecting the presence of subclinical atherosclerosis, whereas carotid and aortic plaques offered greater incremental value for identifying multiterritorial vascular involvement. These findings support a tailored, multiterritorial approach to cardiovascular risk assessment in patients with PsA. Full article

Background: Lepidium meyenii Walp (L. meyenii), traditionally known as maca, is widely recognized for its health-promoting properties, including potential protection against exercise-induced muscle damage (EIMD). However, its precise effect on post-exercise blood biomarkers remains unclear. Objective: This study aimed to qualitatively review research published until April 2026 examining L. meyenii supplementation to reduce blood markers of muscle damage and protein degradation post-exertion in animal studies. Specifically, the effect size (ES) of L. meyenii supplementation on post-exercise levels of creatine kinase (CK), lactate dehydrogenase (LDH), and blood urea nitrogen (BUN) was estimated. Methods: This systematic review and meta-analysis were conducted in accordance with the PRISMA guidelines. The certainty of the evidence was assessed using the GRADE framework. Relevant studies were identified through Web of Science, Scopus, SPORTDiscus, PubMed, and MEDLINE. Eligible studies included in vivo experiments in animals with controlled designs and pre-/post-intervention assessments. Methodological quality and risk of bias were evaluated using the CAMARADES tool. Statistical analysis involved standardized mean differences (SMD) using Hedges’ g with 95% confidence intervals. Results: 15 studies were included in the systematic review, and 14 studies in animals in the meta-analysis. The CAMARADES scores ranged from 5 to 7 points, indicating moderate methodological quality. Supplementation with L. meyenii was not associated with statistically significant changes in LDH (SMD = −1.37; 95% CI −3.34 to 0.59), BUN (SMD = −0.37; 95% CI −2.16 to 1.42) nor CK (SMD = 0.29; 95% CI −5.45 to 6.03), with very high heterogeneity (I² > 97%). Exploratory subgroup analyses and meta-regression analyses by formulation type and dose did not identify any moderators that could robustly explain this heterogeneity. Conclusions: The available evidence does not support a robust overall effect of L. meyenii supplementation on blood biomarkers of muscle damage or protein catabolism in animals subjected to physical stress. The high degree of heterogeneity could not be robustly explained by either the type of formulation or the dose. These findings, which are exploratory and hypothesis-generating in nature, highlight the need for standardized, well-characterized formulations and trials with adequate statistical power. Full article

Background: Antimicrobial resistance (AMR) poses an escalating threat to global health, agriculture, and the environment, demanding urgent multisectoral action under the One Health framework. Despite global awareness efforts, understanding of AMR among the general population remains insufficient, particularly in low- and middle-income countries such as Brazil. This study aimed to evaluate the knowledge, attitudes, and perceptions (KAP) of the Brazilian population regarding AMR. Methods: An online questionnaire was distributed through social media platforms between April and August 2025, resulting in 945 valid responses after data cleaning. Quasi-Poisson models were applied to identify demographic predictors of KAP scores while logistic regression models were used to assess the association between KAP scores and antibiotic use-related practices. Results: Education level was the strongest predictor of higher KAP scores, whereas age and gender showed inconsistent influence. Only 40.3% of respondents correctly identified antibiotics among commonly used medicines, and 25.9% reported proper disposal of antibiotic packaging. More than half (54.2%) were willing to pay more for antibiotic-free products, although only 26.7% had ever noticed such labeling. Network analysis of open-ended responses indicated that concerns about potential health risks and AMR awareness were the primary motivators for purchasing antibiotic-free products. Conclusions: These findings reveal significant gaps in public understanding of antibiotic use and resistance in Brazil, emphasizing the urgent need for targeted educational initiatives, improved public communication, and behavioral interventions to support antimicrobial stewardship and sustainable antibiotic use. Full article

Background/Objectives: Discharging patients after hospitalization for an acute exacerbation of chronic obstructive pulmonary disease (COPD) is a critical transition in care associated with a high risk of early readmission. This survey aimed to describe physician-reported discharge practices following COPD exacerbations, identify perceived gaps and organizational barriers, explore attitudes toward structured COPD discharge summaries, and use a SWOT analysis as an interpretative framework. Methods: In this cross-sectional observational survey, 100 physicians involved in COPD care were recruited from the official mailing list of the Respiratory Society of Serbia, which represents approximately 71% of the Society’s members. The survey assessed discharge procedures, multidisciplinary practices, patient education, comorbidity management, perceived causes of readmission, and barriers to structured discharge summaries. Data were analyzed descriptively and complemented with a structured SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis. Results: Most respondents worked in tertiary care settings and were involved in managing patients hospitalized for COPD exacerbations. Although 24% of physicians routinely used structured discharge summaries, 45% reported never using them. The most frequently perceived contributors to 30-day readmissions were active smoking (90%), poor treatment adherence (81%), comorbidities (77%), and incorrect inhaler technique (72%). Major barriers to implementing structured discharge summaries included the lack of standardized templates, time constraints, poor coordination across healthcare levels, and technical limitations. Willingness to implement structured discharge tools was high (mean score 8.86/10). SWOT analysis identified strong professional support for discharge standardization alongside organizational and system-level barriers to implementation. Conclusions: This exploratory survey identified important gaps between recommended and routine COPD discharge practices and highlighted organizational barriers to implementation. The findings may inform future evaluation and development of structured discharge tools in this healthcare setting. Full article

Corporate sustainability and the reliability of ESG reporting have gained relevance in the evaluation of listed companies, particularly in emerging capital markets, where reporting practices are still in their early stages of development. The purpose of this study is to analyze the relationship between the quality of ESG reporting, the risk of greenwashing estimated using a proxy derived from reported information, and the market value of companies listed on the Bucharest Stock Exchange. The research employs a mixed-methods design, involving content analysis of annual reports, sustainability reports, and sustainability statements for 25 companies over the 2020–2024 period. The scores corresponding to the Environmental, Social, and Governance dimensions, as well as the proxy for greenwashing risk, were developed using an ordinal scoring grid, which was validated through inter-rater assessment. During the course of the study, the empirical relationships were tested using pooled OLS specifications on short panel data, incorporating the natural logarithm of market capitalization, financial controls, year effects, and sector dummy variables. The results highlight the presence of an association between the quality of ESG reporting and market value, particularly for environmental and social dimensions, while the greenwashing risk proxy exhibits a limited statistical influence. The study contributes to the literature on ESG reporting in emerging markets and highlights the need for a cautious interpretation of indicators constructed based on corporate disclosures. Full article

Pharmacogenomics AI offers significant potential for individualized drug therapy; however, its clinical benefits remain unevenly distributed. Models trained predominantly on European-ancestry data consistently underperform in non-European populations, with polygenic risk scores (PRS) showing an estimated 39–73% reduction in predictive accuracy in African-ancestry cohorts across complex traits. These disparities have driven increased interest in moving beyond single-layer genomic approaches. Multi-omics frameworks integrating genomic, transcriptomic, proteomic, and metabolomic data have emerged as a promising strategy to improve prediction across heterogeneous clinical populations, as each molecular layer provides distinct and complementary biological information. Among these layers, metabolomics may represent a particularly transferable component across populations. Metabolite profiles capture the downstream functional output of biological systems influenced by genetic, environmental, dietary, and microbiome-related factors, and may therefore be less reliant on ancestry-stratified allele frequency structures that underlie performance disparities in genomic models. This review synthesizes evidence regarding the mechanistic basis of genomic bias in pharmacogenomics AI, the emerging role of multi-omics integration, especially metabolomics, in improving predictive performance, and the current landscape of computational strategies for bias mitigation, including federated learning, transfer learning, domain adaptation, and synthetic data generation. Collectively, current evidence supports metabolomics-inclusive multi-omics frameworks as a biologically plausible, hypothesis-generating strategy to reduce reliance on ancestry-linked genomic features. However, direct evidence that such frameworks reduce ancestry-related bias in clinical AI outputs remains limited, underscoring the need for globally diverse datasets and prospective multi-population validation. Full article

Early detection of hepatocellular carcinoma (HCC) is crucial for curative treatment, yet current screening strategies for high-risk liver cirrhosis (LC) patients lack sufficient sensitivity. This study evaluates plasma cell-free DNA(cfDNA) concentration and fragmentomics as biomarkers to improve HCC diagnosis and prognosis. Plasma samples from 39 HCC and 46 LC patients were analyzed for cfDNA concentration and fragment patterns. A multivariate logistic regression model (CMAC), integrating cfDNA concentration, mononucleosome proportion (%MN), alpha-fetoprotein (AFP), and c-reactive protein (CRP), was developed and validated using Leave-One-Out Cross-Validation and bootstrapping. HCC patients exhibited significantly higher cfDNA concentrations (p < 0.0001) and longer fragment lengths (p < 0.05) compared to LC patients. The CMAC model demonstrated superior diagnostic performance (AUROC = 0.946) compared to AFP alone (AUROC = 0.777, p < 0.001). Notably, in early-stage HCC, the CMAC model remained highly accurate (AUROC = 0.941), whereas AFP failed to reach statistical significance. Higher CMAC scores were significantly associated with advanced BCLC stages (p = 0.009), lymphovascular invasion (p = 0.0063) and reduced overall survival (p = 0.0037). Integration of cfDNA analysis with established clinical markers in the CMAC model shows promise as a complementary tool for the early detection of HCC in LC patients. Validation in larger, multicenter cohorts will be necessary to confirm these findings and their clinical applicability. Full article