Chronic diabetes mellitus is marked by hyperglycemia and metabolic dysfunction, increasing the risk of complications such as nephropathy. This study aimed to evaluate key biochemical parameters among participants with diabetic nephropathy (DNp), diabetes control (DC), nephropathy control (NC), and healthy control groups.

A prospective case-control study was conducted with 200 participants categorized into four groups: DNp, NC, DC, and healthy controls. Biochemical parameters, including glucose, glycated hemoglobin, waste metabolites, proteins, enzymes, electrolytes, and lipids, were analyzed using an Advia 1800 chemical system analyzer (Siemens, Germany) with standard kits.

Among the four investigated groups, the DNp group exhibited augmented fasting glucose (178.75 ± 61 mg/dL), glycated hemoglobin (8.13 ± 1.7%), creatinine (5.67 ± 1.8 mg/dL), and blood urea nitrogen (72.02 ± 22.8 mg/dL), indicating poor glycemic control and impaired kidney function. In contrast, the DC group showed elevated random glucose levels (280 ± 3.1 mg/dL). Elevated inflammatory markers (C-reactive protein, 6.35 ± 6.3 mg/L; lactate dehydrogenase, 1,216.43 ± 634 U/L) were observed in the NC group. Compared to the other groups, the DC group demonstrated augmented lipid profiles, including elevated triglycerides (230.67 ± 59 mg/dL), very low-density lipoprotein (48.5 ± 16.5 mg/dL), low-density lipoprotein (107.41 ± 16 mg/dL), and cholesterol (169 ± 19 mg/dL). Statistical analysis was performed using one-way analysis of variance followed by a t-test to investigate differences among groups at P < 0.05.

Altered biochemical variations were noted among groups. The DNp group showed renal dysfunction and poor glycemic control, the DC group had dyslipidemia and hyperglycemia, and the NC group showed elevated inflammatory markers. Early testing is indispensable for the timely diagnosis and management of diabetic complications.

Myosteatosis is associated with poor outcomes in various liver diseases. However, standardized methods for assessing, defining, and diagnosing myosteatosis in the context of liver diseases remain unclear. Furthermore, the underlying mechanisms by which myosteatosis leads to pathophysiological progression and adverse health outcomes remain elusive. Therefore, in this review, we elaborate on the currently available measures, definitions, and diagnostic criteria of myosteatosis in the existing literature. We thoroughly clarify the recent evidence and data regarding the possible involvement of myosteatosis in the progression and deterioration of various liver diseases and resulting complications, including liver cirrhosis, chronic viral hepatitis, non-alcoholic/metabolic-associated fatty liver disease, primary sclerosing cholangitis, liver transplantation, and hepatocellular carcinoma. Additionally, it synthesizes insights from basic research on the pathogenesis of myosteatosis, which involves multifactorial mechanisms, including insulin resistance, mitochondrial dysfunction, and chronic inflammation. Finally, from an operational and pragmatic perspective, several regimens, including physical, nutritional, and pharmacological therapies, have been discussed as potential treatments for myosteatosis.

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.

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.

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.