Background: We examined trends in NAFLD-related mortality in the United States from 1999 to 2022, focusing on sex, racial differences, and specific age groups. Methods: We analyzed age-adjusted mortality rates (AAMRs) for NAFLD-related deaths using the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database and assessed differences between sex and racial groups. Results: Between 1999 and 2022, NAFLD-related mortality rose from an age-adjusted mortality rate (AAMR) of 0.2 to 1.7 per 100,000, with an average annual percent change (AAPC) of 10.0% (p < 0.001). In all, 85.4% of the cases were reported after 2008. Females (0.2–2 per 100,000, AAPC: 11.7%, p < 0.001) saw a steeper increase than males (0.2–1.3 per 100,000, AAPC: 9.3%, p < 0.001). White individuals’ AAMR rose from 0.2 to 1.9 per 100,000 (AAPC: 10.8%, p < 0.001). Asian or Pacific Islanders (AAPI) increased from 0.2 in 2013 to 0.5 in 2022 (AAPC: 12.13%, p = 0.002), and American Indians or Alaska Natives (AI/AN) from 1 in 2013 to 2.2 in 2022 (AAPC: 7.9%, p = 0.001). African Americans (AA) showed an insignificant change (0.3–0.5 per 100,000, AAPC: 0.7%, p = 0.498). Regarding age, individuals 45–64 saw AAMR rise from 0.3 to 1.2 per 100,000 (AAPC: 6.5%, p < 0.001), and those 65+ from 0.2 to 6 per 100,000 (AAPC: 16.5%, p < 0.001). No change was observed in the 25–44 age group (AAMR: 0.2 per 100,000, AAPC: 0.0%, p = 0.008). Conclusion: We report increased NAFLD-related mortality among both sexes and certain racial groups. The mortality rate increased for older populations, emphasizing the need for targeted public health measures and evidence-based interventions.

We examined trends in alcohol-associated liver disease (ALD)–related mortality in the United States from 1999 to 2022, focusing on sex, racial differences, and specific age groups. We analyzed age-adjusted mortality rates for ALD-related deaths using the CDC WONDER database and assessed differences between sex and racial groups. ALD-related mortality rates increased significantly between 1999 and 2022, with a more pronounced increase in females. White, Asian, Pacific Islander (AAPI), and American Indian or Alaska Native (AI/AN) groups showed significant uptrends in ALD-related mortality, while African Americans (AA) experienced a nonsignificant decline. Age-specific trends revealed substantial increases in crude mortality rates across various age groups, with the largest increase observed in the younger age groups of 25–34 years, with an average percent change of 11.12% from 2006 to 2022 (average annual percent change of 7.1% for the study period), and 35–44 years, which showed an average percent change of 17.2% from 2018 to 2022 (average annual percent change of 3.8% for the study period). This study reveals increased ALD-related mortality rates in the United States from 1999 to 2022, with disparities among sex, racial groups, and younger age groups. Continued monitoring and evidence-based interventions are needed to address the growing burden of ALD-related mortality, particularly in the younger population. Export

Beyond Human Limits: Harnessing Artificial Intelligence to Optimize Immunosuppression in Kidney Transplantation

Diabetic kidney disease (DKD) is a significant complication of type 2 diabetes, posing a global health risk. Detecting and predicting diabetic kidney disease at an early stage is crucial for timely interventions and improved patient outcomes. Artificial intelligence (AI) has demonstrated promise in healthcare, and several tools have recently been developed that utilize Machine Learning with clinical data to detect and predict DKD. This review aims to explore the current landscape of AI and machine learning applications in DKD, specifically examining existing literature on risk scores and machine learning approaches for predicting DKD development. A literature search was conducted using Medline (PubMed), Google Scholar, and Scopus databases until July 2023. Relevant keywords were used to extract studies that described the role of AI in DKD. The review revealed that AI and machine learning have been successfully used to predict DKD progression, outperforming traditional risk score models. Artificial intelligence-driven research for DKD extends beyond prediction models, offering opportunities for integrating genetic and epigenetic data, advancing understanding of the disease’s molecular basis, personalizing treatment strategies, and fostering the development of novel drugs. However, challenges remain, including the requirement for large datasets and the lack of standardization in AI-driven tools for DKD. Artificial intelligence and machine learning have the potential to revolutionize the management and care of DKD patients, surpassing the limitations of traditional methods reliant on existing knowledge. Future research should address the challenges associated with AI and machine learning in DKD and focus on developing AI-driven tools for clinical practice. Graphical abstract

During the COVID-19 pandemic, numerous co-infections have been reported, with some studies indicating that patients with HIV/AIDS have worse outcomes when co-infected with COVID-19. Here, we present the case of a young adult male who presented with disseminated Varicella and was simultaneously diagnosed with AIDS and COVID-19 virus with several infection-related complications. A 25-year-old African-American male presented to the Emergency Department with vesicular, blistering rashes in multiple dermatomes including his eyelids. The screening test in the ED was positive for COVID-19. Given his high-risk sexual history, he was tested for HIV which returned positive with a CD4 count of zero. He was started on IV antivirals for disseminated varicella with zoster ophthalmicus. The patient was intubated for worsening respiratory failure and required intensive care. During the hospital course, he developed worsening encephalopathy and CSF analysis was positive for CMV and VZV. The patient had a prolonged hospital stay and exhibited evidence of infectious CNS vasculitis and HIV myelopathy. Anti-retroviral therapy was started after the acute period and the patient showed slow but definite clinical improvement. To the best of our knowledge, this is the first case report of a patient with AIDS with COVID-19 and disseminated VZV and with multiple complex infection-related complications (www.actabiomedica.it)

Acetone poisoning, although not very common, can present with varied signs and symptoms. High acetone levels in serum can be due to exogenous exposure or endogenous production of acetone. Unlike certain alcohol toxicities, acetone does not cause high anion gap metabolic acidosis. A 69-year-old male presented to our service with shock and acute encephalopathy and required intensive care support. Initial laboratory investigation showed high anion gap metabolic acidosis with high osmolar gap. Serum acetone level was elevated. Clinicians need to be aware of how to elucidate such metabolic disturbances and associated toxicities.