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.

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.

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.

Emergency department (ED) presentations are associated with higher cancer mortality. This study aimed to investigate the prevalence, frequency, and risk factors in Australian patients diagnosed with malignant skin cancers.

This data-linkage cohort study examined adult patients presenting to the ED at the Royal Melbourne and Western Health hospitals within 12 months of a malignant skin cancer diagnosis. Multivariable logistic and Poisson regressions were used to analyze factors influencing the prevalence and frequency of ED presentations.

A total of 3,873 patients were diagnosed with skin malignancies between 2010 and 2018, of which 631 were diagnosed with melanoma. The prevalence of ED presentation was 29%, representing 2,119 episodes of care (median: 0; range: 0–14). Risk factors for a higher prevalence and frequency included: age ≥75 years (odds ratio (OR) = 1.78 [95% confidence interval 1.47–2.15]; incidence risk ratio (IRR) = 1.52 [1.35–1.70]); male (OR = 1.17 [1.01–1.36]; IRR = 1.23 [1.12–1.35]); socioeconomic status levels of 0–30% (OR = 1.59 [1.24–2.03]; IRR = 1.69 [1.45–1.96]) and 71–100% (OR = 1.30 [1.07–1.58]; IRR = 1.27 [1.12–1.45]); preferred language other than English (OR = 1.47 [1.17–1.84]; IRR = 1.49 [1.32–1.69]); and experience with any systemic therapy or radiotherapy (OR = 3.77 [2.12–6.71]; IRR = 2.36 [1.82–3.05]). Age < 65 years was protective (OR = 0.72 [0.59–0.89]; IRR = 0.78 [0.68–0.90]). Other preferred languages and cancer treatment experience were also risk factors in the sub-cohort with melanoma.

This study reports the prevalence and frequency of ED presentations following a skin cancer diagnosis and their association with socioeconomic and linguistic factors in Australia. Increased awareness of these factors could help address health inequities and potentially reduce the need for ED presentations.

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.

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.