Lung cancer remains the leading cause of cancer-related mortality worldwide, with marked phenotypic differences observed among its major histological subtypes, adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small cell lung cancer (SCLC), in both clinical presentation and therapeutic response. In recent years, metabolomics has emerged as a powerful tool for studying cancer metabolic reprogramming, providing new insights into the metabolic distinctions among lung cancer subtypes. This review summarizes recent research advances in the metabolomics of ADC, SCC, and SCLC. Studies have revealed that ADC and SCC display distinct metabolic profiles in lipid metabolism, amino acid metabolism, and cell membrane synthesis, while SCLC demonstrates a unique metabolic pattern. Through metabolomic technologies, particularly mass spectrometry and liquid chromatography, it is possible to effectively differentiate lung cancer subtypes and identify potential biomarkers for early diagnosis and personalized treatment. This review also explores the clinical potential of metabolomics in lung cancer, emphasizing its critical role in early diagnosis and subtype stratification. These methodological advances establish a robust foundation for precision oncology paradigms in thoracic malignancies.

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.

Potential celiac disease (PCD) is defined as elevated celiac serology with a preserved small intestinal mucosa. This study aimed to identify baseline characteristics and the outcomes of children with PCD consuming a gluten-containing diet.

This was a retrospective cohort study of pediatric PCD patients diagnosed between 12/2018 and 10/2024. Baseline data included demographics, anthropometrics, clinical symptoms and signs, celiac serology, and biopsy results. Follow-up data included repeat serology and biopsy results when performed.

PCD was diagnosed in 75/517 (14.5%) children undergoing upper endoscopy for suspected celiac disease (CeD). Baseline anti-transglutaminase IgA (TTG) was above 10× the upper limit of normal (ULN) in 18 (24%), between 3–10× ULN in 52 (69.3%), and <3× ULN in five (6.6%). Anti-endomysial antibody was positive in 57 (76%). Among 48 children (64%) with at least one year of follow-up, TTG normalized in 26 (54.1%), decreased to <3× ULN in 13 (27.1%), was between 3–10× ULN in six (12.5%), and was above 10× ULN in three (6.3%). Nine children had a repeat endoscopy, and six (66.7%) were diagnosed with CeD, while three remained PCD. Among the 11 children with TTG >10× ULN and at least one year of follow-up, TTG normalized in three, declined in five, and increased or remained above 10× ULN in three.

PCD is common and may be found in children with TTG above 10× ULN; approximately half will normalize TTG. The omission of biopsies may result in an erroneous diagnosis of CeD.

Given the high burden of hepatocellular carcinoma (HCC), risk stratification in patients with cirrhosis is critical but remains inadequate. In this study, we aimed to develop and validate an HCC prediction model by integrating radiomics and deep learning features from liver and spleen computed tomography (CT) images into the established age-male-ALBI-platelet (aMAP) clinical model.

Patients were enrolled between 2018 and 2023 from a Chinese multicenter, prospective, observational cirrhosis cohort, all of whom underwent 3-phase contrast-enhanced abdominal CT scans at enrollment. The aMAP clinical score was calculated, and radiomic (PyRadiomics) and deep learning (ResNet-18) features were extracted from liver and spleen regions of interest. Feature selection was performed using the least absolute shrinkage and selection operator.

Among 2,411 patients (median follow-up: 42.7 months [IQR: 32.9–54.1]), 118 developed HCC (three-year cumulative incidence: 3.59%). Chronic hepatitis B virus infection was the main etiology, accounting for 91.5% of cases. The aMAP-CT model, which incorporates CT signatures, significantly outperformed existing models (area under the receiver-operating characteristic curve: 0.809–0.869 in three cohorts). It stratified patients into high-risk (three-year HCC incidence: 26.3%) and low-risk (1.7%) groups. Stepwise application (aMAP → aMAP-CT) further refined stratification (three-year incidences: 1.8% [93.0% of the cohort] vs. 27.2% [7.0%]).

The aMAP-CT model improves HCC risk prediction by integrating CT-based liver and spleen signatures, enabling precise identification of high-risk cirrhosis patients. This approach personalizes surveillance strategies, potentially facilitating earlier detection and improved outcomes.

Epidemiological data on bacterial infections in cirrhosis in China remain limited. Therefore, we aimed to conduct a multicenter study to investigate the characteristics and outcomes of patients with cirrhosis and bacterial infections in China.

We retrospectively enrolled 1,438 hospitalized adult patients with cirrhosis and bacterial or fungal infections from 24 hospitals across China between January 2018 and September 2024. Data on demographics, clinical features, microbiology, treatment, and outcomes were collected.

A total of 1,783 infection episodes were recorded, including 1,668 first infections and 115 second infections. Most infections were community-acquired (86.6%). Pneumonia was the most common infection type (26.7%), followed by spontaneous bacterial peritonitis (19.5%) and spontaneous bacteremia (14.1%). Among 754 pathogens isolated from 620 patients, Klebsiella pneumoniae (20.1%) was nearly as common as Escherichia coli (21.7%). Multidrug-resistant (MDR) organisms accounted for 41.0% of all isolates, with extended-spectrum β-lactamase-producing Escherichia coli being the most prevalent MDR strain (8.9% of patients). Adherence to empirical antibiotic treatment guidelines from the European Association for the Study of the Liver was significantly lower in this cohort compared to the global study (21.5% vs. 61.2%, P < 0.001), accompanied by a lower clinical resolution rate (63.5% vs. 79.8%, P < 0.001).

The clinical and microbiological characteristics of bacterial infections in patients with cirrhosis in China differ substantially from those reported in other regions. These findings highlight the need for region-specific management and prevention strategies, particularly in light of the changing microbiological landscape, high MDR prevalence, and suboptimal antibiotic practices.

Metabolic dysfunction-associated steatotic liver disease (MASLD) represents an escalating healthcare burden across the Middle East and North Africa (MENA) region; however, system-level preparedness remains largely undefined. This study aimed to assess existing models of care, clinical infrastructure, policy frameworks, and provider perspectives across 17 MENA countries.

A cross-sectional, mixed-methods survey was distributed to clinicians from MASLD-related specialties across the region. A total of 130 experts (87.2% response rate) from academic, public, and private sectors in 17 countries participated. The questionnaire addressed national policies, diagnostic and therapeutic practices, referral pathways, multidisciplinary team (MDT) integration, and patient/public engagement. Quantitative responses were analyzed descriptively, while qualitative inputs underwent thematic analysis.

Only 35.4% of respondents confirmed the presence of national clinical guidelines for MASLD, and 73.1% reported the absence of a national strategy. Structured referral pathways were reported by 39.2% of participants, and only 31.5% believed the current model adequately addresses MASLD. While 60% supported MDT approaches, implementation remained inconsistent. Limited access to transient elastography was reported by 26.2% of providers. Public education efforts were minimal: 22.3% reported no available tools, and 87.7% indicated the absence of patient-reported outcomes data. Nearly half (47.7%) cited poor patient adherence, attributed to low awareness, financial barriers, and lack of follow-up.

Significant policy, structural, and educational gaps persist in MASLD care across the MENA region. To address this rising burden, countries must adopt integrated national strategies, expand access to non-invasive diagnostic tests, institutionalize MDT care, and invest in both public and provider education as essential pillars of system-wide preparedness.

Systemic light chain (AL) amyloidosis is a rare and potentially fatal disease characterized by the abnormal deposition of homogeneous, amorphous amyloid proteins in tissues and organs. This deposition leads to varying degrees of structural and functional abnormalities, ultimately causing organ dysfunction and failure. The disease often involves multiple systems and organs, including the heart, kidneys, gastrointestinal tract, liver, and nervous system, with cardiac and renal involvement being the most common. Due to its rarity, multisystem involvement, and rapid progression, a comprehensive summary of the diagnosis and treatment of AL amyloidosis is crucial for guiding clinical practice and advancing research in this field. This article reviews the progress in diagnosis and discusses future treatment of AL amyloidosis, aiming to provide expanded options for clinical practice.

Artificial intelligence (AI) is transforming the diagnosis, treatment, monitoring, and research of soft tissue disorders, which include muscles, tendons, ligaments, fascia, nerves, and blood vessels. Traditional diagnostic methods often rely on imaging, histopathology, and clinical evaluation, which can be time-consuming and prone to human error. This review aims to explore the impact of AI on enhancing soft tissue care. The review examines the application of deep learning algorithms in medical imaging, pathology, predictive analytics, and treatment planning. It also evaluates AI’s role in monitoring and rehabilitation, as well as its contributions to research in soft tissue disorders. AI significantly improves the accuracy of medical imaging analysis, facilitating the detection of abnormalities such as tumors and tears. AI-powered pathology tools automate slide analysis, enhancing diagnostic consistency and efficiency. Predictive analytics enable early risk assessment and personalized patient management. In surgical contexts, AI supports preoperative simulations and robotic-assisted procedures, leading to improved outcomes. Additionally, AI enhances patient monitoring through wearable devices and telemedicine. The integration of AI into soft tissue diagnostics and therapeutics presents transformative potential for personalized and efficient healthcare. However, challenges related to data security, algorithm bias, interpretability, and ethical considerations must be addressed. Overall, AI holds promise for improving patient outcomes and advancing medical science in the field of soft tissue disorders.

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.