Cardiovascular diseases account for approximately 80% of all deaths caused by known medical conditions, making them the leading cause of mortality worldwide. The present study investigates the use of electrocardiogram (ECG) non-linear features and different topological medical features (heart rate, anthropometry, blood, glucose, and lipid profile, and heart rate variability) to discriminate between different Framingham Cardiovascular Risk Scale status groups in adult obesity using machine learning.

We conducted a cross-sectional study between November 2023 and May 2024 in Fortaleza, Ceará, Brazil. Based on the Framingham Cardiovascular Risk Scale, patients were categorized into three cardiovascular risk groups: Low (22 participants), Moderate (14 participants), and High (17 participants). From ECG signals at two different positions (ECG_Down and ECG_UP), 27 non-linear features were extracted using multi-band analysis. Additionally, 42 medical features provided by physicians were included. From a pool of 19 machine learning classifiers, models were trained and tested within a nested leave-one-out cross-validation procedure using information solely from ECG, solely from medical features, and combining both (multimodal), respectively, to distinguish between Low vs. Moderate, Low vs. High, Moderate vs. High, and All vs. All.

The multimodal model presented the best results for every comparison group, reaching (1) 88.89% Accuracy and 0.8831 area under the curve (AUC) for Low vs. Moderate; (2) 97.44% Accuracy and 0.9706 AUC for Low vs. High; (3) 93.55% Accuracy and an AUC of 0.9412 for Moderate vs. High; (4) 86.79% Accuracy and 0.9346 AUC for All vs. All.

The multimodal model outperformed single-source models in cardiovascular risk classification. ECG-derived non-linear features, especially from ECG_Down, were key drivers, with medical features adding complementary value. The results support its potential use in clinical triage and diagnosis.

Microsatellite-stable colorectal cancer, which accounts for roughly 80–85% of cases, remains largely refractory to immune checkpoint inhibitors compared with microsatellite instability-high tumors. This review synthesizes current evidence on tumor-intrinsic and microenvironmental mechanisms underlying immune checkpoint inhibitor resistance in microsatellite-stable colorectal cancer—including low neoantigen burden and impaired antigen presentation, activation of Wnt/β-catenin and MAPK signaling that exclude T cells, an immunosuppressive cellular milieu (regulatory T cells, myeloid-derived suppressor cells, M2-like tumor-associated macrophages, cancer-associated fibroblasts), metabolic reprogramming, and gut microbiome dysbiosis—and evaluates translational strategies aimed at overcoming these barriers. Preclinical and early-phase clinical data indicate that rational, mechanism-guided combinations (vascular normalization, myeloid reprogramming, metabolic inhibitors, antigen-priming approaches, and microbiome modulation) can enhance immune infiltration and produce benefits in biomarker-defined subgroups. Moving the field forward will require biomarker-driven, adaptive clinical trials with embedded translational endpoints to optimize patient selection and manage toxicity.

Terminal ileum intubation is an adjunct to colonoscopy that permits direct assessment of the terminal ileum. The histological examination of the ileal mucosa, which is acquired during terminal ileum intubation, may allow an accurate diagnosis. However, there is no absolute consensus on when ileoscopy and biopsy should be attempted. As a result, we aimed to evaluate whether terminal ileum intubation and biopsy should be performed routinely.

Systematic searches were performed in the PubMed, EMBASE, and Cochrane Library databases, as well as the Science Citation Index via the Web of Science platform. Reference lists from the identified papers were manually searched. Systematic searches were performed from January 1, 1971, to October 1, 2025. Studies reporting on terminal ileum intubation and biopsy during colonoscopy were included. Case reports, letters, reviews, and animal studies were excluded. The primary outcomes were the diagnostic yield of terminal ileum intubation and the rate of necessitating a change in management. Data were independently extracted by two reviewers and cross-verified by a third.

Thirty-six observational studies were included. Crude aggregate diagnostic-yield proportions were 5.1% (122/2,383) in selected samples and 2.5% (871/35,339) in unselected samples. Among studies reporting management changes, the corresponding crude proportions were 2.0% (36/1,807) and 0.6% (139/24,285), respectively. Because of clinical heterogeneity, these proportions were summarized descriptively without between-group hypothesis testing. The crude diagnostic-yield proportions for inflammatory bowel disease, anemia, abdominal pain, chronic diarrhea, and other indications were 26.7%, 16.1%, 14.9%, 12.4%, and 3.2%, respectively; indication categories could overlap. The yield of ileal histopathology with a normal endoscopic appearance was low in both unselected and selected samples (3.5% and 2.4%, respectively).

Cecal intubation remains the standard marker of complete colonoscopy. Terminal ileum intubation may be considered selectively when clinically indicated or when additional documentation of examination extent is needed. Biopsy should be considered for abnormal terminal ileal mucosa or when clinical findings raise a high pretest probability of ileal disease.

Hepatocellular carcinoma (HCC) remains one of the most fatal cancers, primarily due to late diagnosis and the lack of effective early biomarkers. Recent advances in multi-omics and liquid biopsy technologies hold promise for improving early detection, prognostication, and monitoring of HCC. Understanding the immune landscape of HCC through genetic and epigenetic signatures is essential for identifying therapeutic targets and improving immunotherapy outcomes. This review aims to present current findings on immune-related biomarkers, multi-omics strategies, and biomarker validation in HCC. It also aims to evaluate the role of liquid biopsy and gene signatures in predicting treatment responses, with a specific focus on their applications in immunotherapy. The goal is to provide a comprehensive framework for integrating these emerging tools into clinical practice. The integration of multi-omics approaches has led to the identification of robust gene signatures that predict HCC prognosis and response to immune checkpoint inhibitors. Liquid biopsy technologies, including circulating tumor DNA, provide non-invasive alternatives for monitoring tumor evolution and therapeutic responses. Despite promising results, challenges remain in clinical validation, particularly in cross-platform reproducibility and the interpretation of complex multi-omics data. While genetic biomarkers are rapidly advancing, their clinical application in personalized medicine remains hindered by technical and ethical challenges, such as data privacy, informed consent, and method standardization. The integration of multi-omics data and liquid biopsies offers a promising path toward real-time, personalized treatment and the development of universal prognostic signatures for HCC. However, successful clinical adoption depends on cross-disciplinary collaboration to standardize data protocols and overcome challenges regarding accuracy, reproducibility, and patient privacy.

Pancreatic cancer remains a highly lethal malignancy owing to the difficulty of early detection. The 2021 Nanjing consensus on early screening and surveillance for pancreatic cancer in high-risk individuals was published in Chinese in 2022. This article presents 26 consensus recommendations. The development methods reported in the published article included a systematic literature review, GRADE-based evidence assessment, and a modified Delphi process involving 53 specialists from 17 provincial-level regions, with consensus predefined as at least 75% agreement. The recommendations address screening objectives, high-risk populations, starting ages, surveillance intervals, imaging modalities, surgical indications, and lifestyle measures. They are intended to support more standardized, multidisciplinary screening and surveillance in China. Their effects on early detection, survival, quality of life, feasibility, and cost-effectiveness require prospective evaluation in Chinese populations.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is now considered to be among the most prevalent chronic liver diseases worldwide. Its comprehensive management encompasses multiple stages, including risk assessment, early detection, stratified intervention, and long-term follow-up. Among these, improving diagnostic accuracy and optimizing individualized therapeutic strategies remain key challenges in both research and clinical practice. In recent years, artificial intelligence and smart devices have developed rapidly and have gradually been applied in the medical field, offering novel tools and pathways for MASLD risk stratification, non-invasive diagnosis, therapeutic evaluation, and patient self-management. This review summarizes the current applications of artificial intelligence and smart devices in MASLD care, highlights their benefits and limitations, and discusses future directions to support precision diagnosis and treatment strategies.

The history of screening for cervical cancer is rich with implementing cutting-edge ideas and technologies. From the very first “Pap smear” to the semi-automated and computerized systems of today, the way we screen for cervical cancer has changed dramatically in the past 75 years. With the advent of new techniques and more advanced machine learning algorithms, we sought to understand the current and future applications of artificial intelligence in clinical pathology around cervical cancer screening, diagnosis, and treatment.

A structured narrative review was conducted to examine the historical evolution and contemporary advances in cervical cancer screening, diagnosis, excisional management, and artificial intelligence applications. Peer-reviewed articles, consensus guidelines, and global policy documents published between January 1990 and March 2025 were identified through targeted searches of PubMed and review of reference lists from relevant publications. Search terms included combinations of “cervical cancer screening,” “Papanicolaou test,” “liquid-based cytology,” “HPV testing,” “colposcopy,” “loop electrosurgical excision procedure,” “digital pathology,” “deep learning,” and “artificial intelligence.” Emphasis was placed on multi-center validation studies, systematic reviews, regulatory and implementation guidance, and global health frameworks. Publications lacking methodological transparency or direct relevance to clinical or translational practice were excluded.

Through a review of the literature, we describe how innovations in conventional and liquid-based cytology, human papillomavirus testing, and organized screening programs established the current prevention framework. Building on this foundation, recent studies demonstrate promising performance of deep learning algorithms applied to conventionally prepared cervical cytology slides, with systems capable of binary normal versus abnormal classification as well as more granular diagnostic categorization. Artificial intelligence-assisted colposcopy and computer-vision approaches have also shown improved diagnostic concordance, workflow efficiency, and potential to expand screening capacity in resource-limited environments.

There has been much work done in the past several years surrounding the implementation of deep learning algorithms in regard to cervical cancer screening. The work in this field shows promise in enhancing diagnostic accuracy, streamlining diagnostic workflow, and decreasing turnaround times from specimen collection to rendering a diagnosis. However, there are still many technical, legal, and ethical questions that must be answered prior to widespread adoption of these algorithms for patient care.

Parenteral nutrition (PN)-associated cholestasis (PNAC) is frequently diagnosed in premature infants; however, not all PN-exposed infants develop PNAC. We propose that, in premature infants receiving PN and varying amounts of enteral feeds, differences in the gut microbiome and fecal bile acid content are associated with PNAC development. This study aimed to examine the fecal microbiome and bile acid content of premature infants on PN to determine if there is a relationship with the development of PNAC.

Twenty-two preterm infants had serial bilirubin measurements and fecal samples collected during their neonatal intensive care unit admission. Fecal samples underwent 16S rRNA gene sequencing and bile acid analysis. Binomial regression, adjusting for postmenstrual age with feed amount as a moderator, was used to assess the impact of the fecal microbiome and bile acids on PNAC development.

Cholestatic patients (n = 11) had greater PN and antibiotic exposure (p = 0.020; p = 0.010) and longer neonatal intensive care unit stays (p = 0.0038) than non-cholestatic patients. Microbiome richness was higher in non-cholestatic infants (p < 2E-16), with no difference in β diversity (p = 1.0). Cholestatic infants had a significantly higher abundance of Proteobacteria and Fusobacteriota and a lower abundance of Bacteroidota (p < 2E-16). Akkermansia was abundant in all infants on low feeds; as feed volume increased, Akkermansia abundance significantly increased in non-cholestatic infants (p < 2E-16). Bile acid analysis demonstrated significantly lower deoxycholic acid concentrations in cholestatic infants (p < 2E-16). Metagenomic analysis revealed an increase in Proteobacteria requiring augmented stress responses in non-cholestatic infants.

This is the first study to directly explore the relationship between PNAC susceptibility, the microbiome, and fecal bile acids in preterm infants. The microbiome and bile acid patterns identified here may inform the development of targeted therapeutics for this vulnerable population.

Acute-on-chronic liver failure (ACLF) is a life-threatening syndrome characterized by systemic inflammation and immune dysregulation, in which macrophages play a key role in organ injury. This study aimed to investigate the role and mechanism of pyruvate dehydrogenase kinase 4 (PDK4) in ACLF to identify therapeutic targets that modulate macrophage function and mitigate ACLF progression.

Single-cell RNA sequencing data from healthy and ACLF liver tissues were analyzed from the Sequence Read Archive database. Transcriptomic data of peripheral blood mononuclear cells from ACLF patients (GSE168048) were also examined. In vitro experiments assessed PDK4 expression and macrophage polarization, and conditioned-medium studies evaluated effects on LO2 hepatocytes. In vivo validation was performed in ACLF mouse models treated with a PDK4 inhibitor.

Single-cell analysis revealed a predominance of M1-polarized hepatic macrophages in ACLF with marked upregulation of PDK4. Peripheral blood mononuclear cell transcriptomics showed that higher PDK4 expression correlated with 28-day mortality. In vitro, PDK4 expression increased in M1 macrophages; PDK4 inhibition attenuated M1 polarization and reduced cytotoxic effects on LO2 cells. In vivo, pharmacologic inhibition of PDK4 suppressed M1 polarization in macrophages, alleviated liver inflammation, and reduced tissue injury. Mechanistically, PDK4 promoted M1 polarization via activation of signal transducer and activator of transcription 1 signaling.

PDK4 is a key pro-inflammatory regulator in ACLF by promoting M1 macrophage polarization. Targeting PDK4 may be a promising strategy to attenuate inflammation and improve clinical outcomes in ACLF.

In the past decade, immune checkpoint inhibitors (ICIs) have dramatically changed cancer treatment, significantly improving outcomes for patients with various malignancies. Nonetheless, their widespread application has resulted in a rise in immune-related adverse events due to excessive immune activation, including immune-mediated hepatotoxicity (IMH). IMH can cause serious complications and even death, underscoring the need for early prediction and intervention. This review outlines the current understanding of risk factors and predictive biomarkers for IMH in cancer patients undergoing ICI therapy, with risk factors divided into patient-associated, tumor-associated, and agent-associated categories. Higher IMH risk is related to female sex, younger age, extreme BMI, Asian ethnicity, and chronic liver disease. Cancer type, prior ICI treatment, dual ICI combination therapy, and the concurrent use of chemotherapy, targeted agents, or other hepatotoxic drugs (e.g., acetaminophen, statins) also increase the risk of IMH. Potential predictive biomarkers encompass circulating blood cells, serum proteins, autoantibodies, cytokines, gene profiles, and the gut microbiome. Despite promising findings, the predictive value of these biomarkers remains inconsistent, and no definitive biomarker has been established for routine clinical use. Large-scale prospective studies are essential to verify the predictive value of these biomarkers and facilitate their integration into clinical practice, thereby providing deeper insights into the early identification and individualized management of IMH during ICI therapy.