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.

Drug-induced liver injury (DILI) represents a prevalent adverse event associated with medication use. However, the exact mechanisms underlying DILI remain incompletely understood, and the lack of specific diagnostic and prognostic biomarkers poses significant challenges to the clinical diagnosis and treatment of this condition. Consequently, our study aimed to endeavor to identify serum and fecal metabolic biomarkers, enabling more accurate DILI diagnosis and improved prediction of chronic progression.

Untargeted metabolomics analysis was performed on serum and fecal samples obtained from a cohort of 32 DILI patients (causality confirmed via the updated Roussel Uclaf Causality Assessment Method) and 36 healthy controls. Utilizing techniques such as partial least squares-discriminant analysis modeling and t-tests, we identified significantly differentially expressed metabolites and metabolite sets. Causality assessment was performed using the updated Roussel Uclaf Causality Assessment Method.

The findings from the analysis of serum and fecal metabolomics association pathways suggested that perturbations in bile acid metabolism might serve as potential mechanisms underlying the progression of DILI. Our study revealed 22 overlapping differential metabolites between serum and feces, displaying significant concentration differences between the DILI and healthy control groups. Notably, we identified chenodeoxycholic acid and deoxycholic acid as promising markers that not only distinguished DILI patients from healthy individuals but also exhibited predictive potential for DILI chronicity.

The integrated analysis of serum and fecal metabolites uncovers the significant disruption of bile acid metabolites as a key contributing factor in the pathogenesis of DILI. Our study offers promising potential biomarkers for the diagnosis and prognosis of DILI, paving the way for a novel perspective in the realm of DILI diagnosis and treatment.

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.

Familial hypobetalipoproteinemia (FHBL), caused by apolipoprotein B (APOB) variants, disrupts APOB-containing lipoprotein synthesis, leading to reduced serum total cholesterol, low-density lipoprotein cholesterol, and APOB. Heterozygous carriers are often asymptomatic, while homozygotes exhibit severe manifestations like malabsorption, vitamin deficiencies, and hepatic steatosis. In recent years, FHBL has attracted increasing attention due to its association with liver disease and its role as a unique monogenic model of steatotic liver disease independent of cardiometabolic risk factors. Mechanistically, lipid overload, endoplasmic reticulum stress, oxidative damage, and impaired autophagy may drive hepatocellular injury and fibrosis. Challenges include insufficient diagnosis, sparse epidemiological data, and unclear disease progression. Enhanced genetic testing, mechanistic research, and longitudinal studies are critical to improving diagnosis, risk assessment, and therapies for FHBL-associated liver disease.

The global integration of traditional medicine (TM) and modern medicine reflects a fundamental shift in healthcare aimed at delivering more holistic, culturally sensitive, and patient-centered care. With over 80% of the global population relying on some form of TM, especially in Asia, Africa, and Latin America, there is growing momentum to institutionalize TM alongside evidence-based biomedicine. Countries like India, China, and Korea have led integration through formal education, government-supported research, and clinical frameworks, while high-income countries are increasingly adopting complementary and integrative medicine models. However, this convergence faces substantial challenges, including differences in epistemology, regulatory standards, evidence hierarchies, and practitioner training. Limited clinical trials, quality assurance concerns, and issues related to intellectual property rights and biopiracy further complicate harmonization. Despite these barriers, the World Health Organization’s Traditional Medicine Strategy (2014–2023) and its newly established Global Centre for Traditional Medicine (India) underscore a growing international commitment to evidence-based integration. Opportunities lie in promoting collaborative research, strengthening regulatory frameworks, enhancing digital health platforms for TM documentation, and fostering intercultural dialogue between health systems. If guided ethically and scientifically, integration can improve access to care, reduce treatment costs, and offer personalized health solutions for chronic and lifestyle-related diseases. This review explored global integration models, evaluated emerging challenges, and identified strategies to support an inclusive, pluralistic, and sustainable healthcare future that respects both traditional wisdom and modern science.

Skin cancer, the most common global malignancy, is linked to ultraviolet (UV)-driven serum 25-hydroxyvitamin D (25(OH)D)synthesis, with its controversial role possibly reflecting cumulative UV exposure. This study aimed to assess the association and causality between 25(OH)D levels and skin cancer risk using the National Health and Nutrition Examination Survey (1999–2018) data and Mendelian randomization (MR) analyses, evaluating 25(OH)D as a screening biomarker.

We integrated data from the National Health and Nutrition Examination Survey (1999–2018; n = 21,357 U.S. adults, including 631 skin cancer cases) with MR analyses using genome-wide association study-derived genetic variants to assess the causal relationship between serum 25(OH)D levels and skin cancer risk.

Higher 25(OH)D levels were associated with increased risks of nonmelanoma skin cancer [odds ratio (OR) (95% confidence interval (CI)) = 2.94 (2.10, 4.20)], melanoma [OR (95% CI) = 2.94 (1.73, 5.28)], and other skin cancers [OR (95% CI) = 2.10 (1.36, 3.36)]. MR analyses supported a causal relationship for nonmelanoma skin cancer [OR (95% CI) = 1.01 (1.00, 1.02)] and melanoma [OR (95% CI) = 1.00 (1.00, 1.01)]. Risks were highest in males, older adults, and individuals with obesity.

Higher serum 25(OH)D levels are associated with increased skin cancer risk, likely reflecting cumulative UV exposure. Routine monitoring of 25(OH)D, combined with UV exposure management, is recommended for risk stratification in skin cancer screening, particularly among high-risk groups. Validation in multiethnic cohorts is needed to confirm these findings.

Breast cancer remains one of the most common cancers affecting women globally, with late detection frequently contributing to its high mortality rate. Multiple factors drive these delays, including a lack of awareness, financial constraints in low-income countries, and limited access to non-invasive and accurate biomarkers. This review aims to introduce biomarkers, particularly hematological and biochemical serum markers, as essential, non-invasive, and accurate tools for improving the diagnosis, prognosis, and therapeutic management of breast cancer. Hematological markers are measurable blood parameters that reflect physiological and pathological processes such as inflammation, infection, cardiovascular stress, autoimmune conditions, and cancer. Routinely measured hematological markers, such as the neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and red blood cell indices, are typically obtained from standard tests like the complete blood count. Regular monitoring through complete blood count is essential during cancer treatment to evaluate changes in blood cell counts and detect potential adverse effects. Because of their affordability, minimal infrastructure requirements, and broad accessibility, hematological parameters have been increasingly studied for their association with high-risk factors in breast cancer, particularly in resource-limited settings. Their utility underscores their critical role in improving patient outcomes across diverse healthcare environments. This review summarizes the clinical value of various hematological and serum-based biochemical markers in the screening and diagnosis of breast cancer. Prediction methods that incorporate hematological and serum-based biochemical parameters can support screening, diagnosis, and staging. Overall, individual or combined blood indicators hold significant potential to enhance diagnostic accuracy and effectiveness.

Nanobiotechnology has driven transformative advancements in healthcare, particularly in the development of innovative solutions for wound treatment, a persistent and costly global health concern. Among these advancements, the combination of biopolymers and metallic nanoparticles has attracted considerable interest due to their excellent biocompatibility and potent antimicrobial activity. This scoping review explores recent technological progress in wound care, with a focus on alginate-based dressings functionalized with metallic nanoparticles. Alginate, a highly versatile biopolymer, was frequently employed in diverse formats, including hydrogels, sponges, beads, films/membranes, and fibers, across the analyzed studies. Silver nanoparticles were the most extensively investigated agents, owing to their well-established efficacy and the development of strategies to mitigate associated risks. Other metallic nanoparticles were also reported, contributing to a growing body of evidence supporting their therapeutic relevance. The synergistic integration of alginate and metallic nanoparticles has shown promising potential to enhance the performance of wound dressings, representing a significant step forward in the design of next-generation materials for effective and targeted wound management.