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Background and Aims: Metabolic dysfunction–associated steatohepatitis (MASH) is a progressive liver disease that poses significant public health challenges. Although liver biopsy remains the gold standard for diagnostic and prognostic use in patients with MASH, its invasiveness limits its practical application in routine care and large-scale trials. This narrative review explores the growing role of noninvasive tests in the assessment of disease progression and treatment efficacy in MASH. Approach and Results: This review represents expert opinion synthesizing current literature on the most widely used noninvasive tests being assessed as frontline biomarkers for identification of patients with metabolic dysfunction–associated steatotic liver disease at risk of outcomes and/or in need of pharmacological intervention, and that are being used to assess treatment response in the context of clinical trials, and how they may be translated into clinical practice. Among these, elastography and the enhanced liver fibrosis test are highlighted for their potential to replace liver biopsy in clinical settings and trials. These methods are analyzed for their accuracy and ability to monitor therapeutic response, emphasizing their growing importance in providing reliable measures of clinical improvement as drug development efforts for MASH intensify. Conclusions: This synthetic review aims to provide a roadmap for federal regulators updating and potentially revising the requirements for demonstrating histologic improvement in MASH clinical trials, and for practitioners to translate this information into clinical practice. The ultimate goal is to reduce the reliance on liver biopsy while maintaining the rigor necessary to evaluate therapeutic efficacy, thus accelerating the path to approval for new MASH treatments.

PURPOSE Artificial intelligence (AI) has been rapidly evolving in medicine. While there are existing data about the perceptions and concerns of health care workers regarding AI, those of the hematology and oncology (HemOnc) workforce remain unknown. We assessed opinions of physicians (faculty and fellows), advanced practice providers (APPs), and nurses in HemOnc on integrating AI in medical education (MedEd) and clinical practice (CP). METHODS Group-specific electronic surveys were developed. The HemOnc workforce from the three Mayo Clinic sites was invited to participate. Participation was anonymous, with data collection between November 7, 2024, and January 20, 2025. Results were reported as proportions, with 95% binomial CI when appropriate. RESULTS A total of 344 participants responded, 118 physicians (77 faculty, 41 fellows), 49 APPs, and 177 nurses. Most had used AI although less than half felt very or moderately knowledgeable about it. A total of 95% of fellows, 97% of faculty physicians, 96% of APP, and 92% of nurses believe that AI will be integrated in MedEd. A total of 94% of physicians and 88% of nonphysicians believe that this integration would be beneficial. In CP, 98% of fellows, 96% of faculty physicians, 100% of APPs, and 92% of nurses believe in AI's integration. However, 51% of physicians and 68% of nonphysicians had some concerns, including technology time, medical costs, and burnout. Nonetheless, 85% of fellows, 94% of faculty physicians, 98% of APPs, and 86% of nurses would embrace AI. CONCLUSION The majority of this single health system HemOnc's workforce endorsed AI integration into MedEd and CP but highlighted relevant concerns. Proper education, effective partnership among all stakeholders, and well-organized integration are essential for optimizing AI's incorporation into HemOnc's MedEd and CP and ensuring that it fully supports the mission of providing high-quality patient care.

Importance Alzheimer disease (AD) biomarker and genetic testing results are increasingly disclosed to cognitively unimpaired adults in research and could in the future inform clinical treatment decisions in this population. Objectives To assess psychological outcomes after returning 3 categories of amyloid biomarker results as well as apolipoprotein E ( APOE ) genotypes. Design, Setting, and Participants This cohort study was a secondary analysis of data collected as part of screening for the multisite AHEAD preclinical AD trial. Participants were individuals aged 55 to 80 years undergoing screening from July 14, 2020, to October 15, 2024. Exposure Participants were informed whether they had not-detected, intermediate, or elevated amyloid positron emission tomography levels, as well as their APOE genotype, which were categorized as noncarrier, ε4 heterozygote, or ε4 homozygote. Main outcomes and measures Impact of Events Scale (IES; 15 items to assess intrusive thoughts and avoidance; each item is scored as not at all [0], rarely [1], sometimes [3], or often [5]; total range, 0-75), collected 24 to 72 hours after disclosure, and change in a scale measuring concerns about AD dementia (adapted scale using 6 items in which participants indicated their level of agreement with statements related to their perceived probability of developing AD dementia; items scored as strongly disagree [1] through strongly agree [5]; total range, 6-30), calculated by subtracting the score collected before biomarker testing from 1 collected after biomarker and genetic test results disclosure. Results Among 3414 included individuals, the mean (SD) age was 68.8 (6.0) years and 2116 (62%) were female. Group mean IES scores were below clinically significant thresholds. Nevertheless, across genetic groups, learning an elevated amyloid result (1184 participants) was associated with higher IES (mean [SD], 10.5 [10.9]) than intermediate amyloid (482 participants; mean [SD] IES, 8.8 [9.8]), and intermediate amyloid was associated with higher scores than not-detected amyloid (1748 participants; mean [SD] IES, 6.5 [8.4]). Across amyloid groups, learning APOE ε4 homozygosity (337 participants) was associated with higher mean (SD) IES (12.7 [11.6]) than heterozygosity (1609 participants; 9.1 [10.2]), and heterozygosity was associated with higher IES than noncarrier status (1468 participants; mean [SD] IES, 6.2 [8.1]). Both types of information were significant in an analysis of covariance model; no interaction effect was observed. In contrast, only biomarker disclosure was associated with differential change in concerns about AD dementia. Those with elevated amyloid showed a mean (SD) increase in concern (0.8 [3.5]), those with intermediate amyloid showed a smaller increase (0.4 [3.7]), and those with not-detected amyloid showed decreased concerns (−1.1 [4.2]). Conclusions and Relevance In this cohort study of cognitively unimpaired adults, associations with intrusive thoughts were observed to differ among genetic and biomarker subgroups; such associations were limited to biomarker subgroups for measures of perceived dementia risk.

Disclaimer: These guidelines are intended for educational use to build the knowledge of physicians and other health professionals in assessing the conditions and managing the treatment of patients undergoing extracorporeal life support (ECLS)/ECMO and describe what are believed to be useful and safe practice for ECLS and ECMO. The aim of clinical guidelines was to help clinicians to make informed decisions about their patients. However, adherence to a guideline does not guarantee a successful outcome. Healthcare professionals must make their own treatment decisions about care on a case-by-case basis, after consultation with their patients, using their clinical judgment, knowledge, and expertise. These guidelines do not take the place of physicians’ and other health professionals’ judgment in diagnosing and treatment of patients. These guidelines are not intended to and should not be interpreted as setting a standard of care or being deemed inclusive of all proper methods of care nor exclusive of other methods of care directed at obtaining the same results. The ultimate judgment must be made by the physician and other health professionals and the patient considering all the circumstances presented by the individual patient, and the known variability and biologic behavior of the clinical condition. These guidelines reflect the data at the time the guidelines were prepared; the results of subsequent studies or other information may cause revisions to the recommendations in these guidelines to be prudent to reflect new data, but ELSO is under no obligation to provide updates. In no event will ELSO be liable for any decision made or action taken in reliance upon the information provided through these guidelines.

Background: Pancreatitis significantly alters the microbial composition of the oral and intestinal compartments, causing dysbiosis that may contribute to disease mechanisms and potentially serve as a basis for diagnosis or treatment. Objective: To determine whether the oral or gut microbial signature can classify chronic pancreatitis (CP). Methods: Stool samples (n=707) were collected from participants in the Prospective Evaluation of Chronic Pancreatitis for Epidemiologic and Translational Studies (PROCEED). Samples were distributed among 200 healthy (HC), 310 CP, 49 acute pancreatitis (AP), and 148 recurrent acute pancreatitis (RAP). In addition, saliva samples were collected for a subset of participants (n=156). Whole genome sequencing was performed to assess microbiome composition. Machine learning algorithms were utilized to identify a signature with microbial features predictive of CP. Results: Gut alpha diversity was significantly decreased in AP, RAP, and CP compared with HC, with CP exhibiting the lowest diversity. In contrast, oral microbial diversity showed no significant variation across groups. Beta diversity analysis revealed distinct gut microbiome compositions between HC and pancreatitis subtypes, with CP showing the most pronounced differences. Random forest models using gut microbial species demonstrated robust predictive performance for CP using a minimum of 10 species (Area under the curve—AUC: 0.834; accuracy: 0.774). Despite similarities in gut microbiome composition across pancreatitis subtypes, a unique gut microbial signature for CP was identified highlighting the microbiome’s potential in CP diagnosis. Conclusion: Our study reveals a gut microbial signature predictive of CP using machine learning models in a large US multi-institutional cohort.

Background: Vitamin C is a vital nutrient that functions as an antioxidant and is important as a co-factor and regulator of several immune system pathways. The role of vitamin C in the treatment of COVID-19 is largely debatable. We conducted this meta-analysis to evaluate the efficacy and safety of vitamin C in the treatment of COVID-19. Methods: We searched several electronic databases from inception to March 2023 to retrieve randomized controlled trials on the use of vitamin C for COVID-19. RevMan 5.4 was used to calculate risk ratios (RRs) and mean differences (MDs) along with confidence intervals (95% CI) using a random-effects model. Results: We included nine randomized controlled trials in our meta-analysis. Vitamin C did not reduce the all-cause mortality in patients with COVID-19 compared to the standard treatment (RR 0.92, 95% CI: 0.83–1.02; I 2 = 1%). Vitamin C was found to be associated with a similar incidence of ventilation in COVID-19 patients when compared to standard treatment (RR 0.98, 95% CI: 0.87–1.11, I 2 = 0%). There were no significant differences between both groups regarding the incidence of hospitalization (RR 1.00, 95% CI: 0.98–1.02; I 2 = 0%), incidence of recovery (RR 1.57, 95% CI: 0.45–5.50; I 2 = 52%), hospital mortality (RR 0.68, 95% CI: 0.44–1.06; I 2 = 0%) and length of hospital stay (MD −0.63, 95% CI: −3.04 to 1.78; I 2 = 81%). Conclusion: Vitamin C administration did not reduce all-cause mortality in COVID-19 patients. Additional studies are required to evaluate the role of vitamin C in the prevention and treatment of COVID-19 especially in ICU patients.

Conventional paints pose major environmental and health concerns due to their reliance on heavy-metal pigments and volatile organic compound (VOC)-emitting binders, emphasizing the need for sustainable alternatives. Previous formulations of biologic paints that combined bacteria-derived dsRED pigment protein and casein-based binders, while devoid of toxic components, suffered from prolonged drying times (~16 min), limiting their practical applicability. The present study addresses this key limitation by incorporating cellulose nanocrystals (CNC) and chitosan as biologic additives to enhance drying kinetics. Paint formulations containing 2%, 5%, and 10% of each additive were tested under controlled environmental conditions (20 °C, 60% relative humidity) following the GB/T 1728–2020 standard. Both CNC and chitosan significantly reduced drying time in a concentration-dependent manner (p < 0.001). The 10% CNC and 10% chitosan formulations achieved 61% and 44% reductions in drying time, respectively, relative to the unmodified biologic paint (12.96 ± 1.07 min at baseline). Regression analyses indicated that each 1% increase in CNC or chitosan concentration reduced drying time by 0.77 min and 0.58 min, respectively. The optimized paints exhibited acceptable drying times (5–7 min). These findings demonstrate an advancement in the development of biologically derived coatings, providing a feasible pathway toward safe and sustainable alternatives to conventional synthetic paints.