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.

Sedation monitoring is crucial in neurosurgical intensive care units to ensure optimal patient comfort and safety. However, sedation practices vary significantly. This study aimed to evaluate and summarize the evidence related to sedation monitoring in neurocritical care patients, with a focus on identifying best practices for improving monitoring accuracy and patient outcomes.

This study was conducted as an evidence summary, following the evidence summary reporting standards of the Fudan University Evidence-based Nursing Center. The evidence on sedation monitoring management in neurocritical care patients was systematically retrieved using the 6S evidence model, including clinical decisions, best practices, guidelines, expert consensus, evidence summaries, systematic reviews, and more. Searches of domestic and international databases covered all records from the databases’ inception to June 2024. Two researchers independently selected literature that met the inclusion criteria and conducted quality assessment, evidence-level evaluation, and evidence synthesis.

Ten high-quality studies were ultimately included. From these, twenty pieces of best evidence were extracted, covering four categories: monitoring personnel, monitoring targets, monitoring tools, and monitoring timing and content. Among these, fifteen pieces of evidence were classified as strong recommendations, while five were classified as weak recommendations.

This study summarized the best evidence on sedation monitoring for neurocritical care patients, providing guidance for clinical staff to improve sedation monitoring accuracy and patient outcomes in neurosurgical intensive care units.

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.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is now the most common chronic liver disease in the Western world, driven by obesity, insulin resistance, and systemic inflammation. Its progressive form, metabolic dysfunction-associated steatohepatitis (MASH), can culminate in cirrhosis and hepatocellular carcinoma (HCC). While lifestyle modification remains central to MASLD management, there is growing interest in pharmacological interventions, particularly nutrient-stimulated hormone-based therapies (NuSHs), such as GLP-1 receptor agonists. NuSHs exert metabolic and anti-inflammatory effects primarily via weight loss and improved insulin sensitivity. Emerging clinical data support their efficacy in resolving MASH without worsening fibrosis. However, benefits in cirrhotic patients are less evident, suggesting greater utility in early intervention. Observational studies and clinical trials suggest a reduction in liver-related morbidity with GLP-1 receptor agonist use, though fibrosis regression remains inconsistent. Preclinical models indicate that NuSHs may also reduce MASH-related HCC incidence and tumor burden, likely through systemic metabolic improvements rather than direct antineoplastic action. Observational human data following bariatric surgery reinforce this link, suggesting that weight loss itself plays a key preventive role. Herein, we propose that NuSHs are promising candidates for MASH-related HCC prevention. We provide mechanistic suggestions for how this may occur. Furthermore, incorporating NuSHs into the post-locoregional treatment pathway for HCC may delay the need for systemic anti-cancer therapies, improve immunotherapy synergy and transplant eligibility, and even slow disease progression through reversal of carcinogenic drivers. Future studies are needed to target oncological endpoints and clarify immunometabolic mechanisms to guide the integration of NuSHs into MASLD treatment algorithms.

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.

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.

Terminal ileum intubation is considered the completion step of colonoscopy and is usually performed to assess the 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 extracted independently by three reviewers.

Thirty-six studies were included. The subtotal diagnostic yield and the rate of necessary change among the selected patients were much greater than those among the unselected patients (5.1% versus 2.5% and 1.5% versus 0.4%, respectively). In addition, the diagnostic yield was found more frequently for inflammatory bowel disease, anemia, abdominal pain, and chronic diarrhea than for the other indications (26.7%, 16.1%, 14.9%, 12.4%, and 3.2%, respectively). The yield of ileal histopathology with a normal endoscopic appearance was low in both unselected and selected patients (3.5% and 2.4%, respectively).

Terminal ileum intubation is recommended as gold standard for completing colonoscopy. Biopsy should be considered in patients with abnormal endoscopic findings or specific high-risk symptoms.

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.