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Abstract Background During the pandemic, a notable increase in diabetic ketoacidosis (DKA) and hyperosmolar hyperglycemic state (HHS), conditions that warrant emergent management, was reported. We aimed to investigate the trend of DKA‐ and HHS‐related mortality and excess deaths during the pandemic. Methods Annual age‐standardized mortality rates related to DKA and HHS between 2006 and 2021 were estimated using a nationwide database. Forecast analyses based on prepandemic data were conducted to predict the mortality rates during the pandemic. Excess mortality rates were calculated by comparing the observed versus predicted mortality rates. Subgroup analyses of demographic factors were performed. Results There were 71 575 DKA‐related deaths and 8618 HHS‐related deaths documented during 2006–2021. DKA, which showed a steady increase before the pandemic, demonstrated a pronounced excess mortality during the pandemic (36.91% in 2020 and 46.58% in 2021) with an annual percentage change (APC) of 29.4% (95% CI: 16.0%–44.0%). Although HHS incurred a downward trend during 2006–2019, the excess deaths in 2020 (40.60%) and 2021 (56.64%) were profound. Pediatric decedents exhibited the highest excess mortality. More than half of the excess deaths due to DKA were coronavirus disease 2019 (COVID‐19) related (51.3% in 2020 and 63.4% in 2021), whereas only less than a quarter of excess deaths due to HHS were COVID‐19 related. A widened racial/ethnic disparity was observed, and females exhibited higher excess mortality than males. Conclusions The DKA‐ and HHS‐related excess mortality during the pandemic and relevant disparities emphasize the urgent need for targeted strategies to mitigate the escalated risk in these populations during public health crises.

Summary: Background: The prevalence of metabolic-associated steatotic liver disease (MASLD) is rising precipitously among children, particularly in regions or countries burdened with high prevalence of obesity. However, identifying those at high risk remains a significant challenge, as the majority do not exhibit distinct symptoms of MASLD. There is an urgent need for a widely accepted non-invasive predictor to facilitate early disease diagnosis and management of the disease. Our study aims to 1) evaluate and compare existing predictors of MASLD, and 2) develop a practical screening strategy for children, tailored to local prevalence of obesity. Methods: We utilized a school-based cross-sectional survey in Beijing as the training dataset to establish predictive models for screening MASLD in children. An independent school-based study in Ningbo was used to validate the models. We selected the optimal non-invasive MASLD predictor by comparing logistic regression model, random forest model, decision tree model, and support vector machine model using both the Beijing and Ningbo datasets. This was followed by serial testing using the best performance index we identified and indices from previous studies. Finally, we calculated the potential MASLD screening recommendation categories and corresponding profits based on national and subnational obesity prevalence, and applied those three categories to 200 countries according to their obesity prevalence from 1990 to 2022. Findings: A total of 1018 children were included (NBeijing = 596, NNingbo = 422). The logistic regression model demonstrated the best performance, identifying the waist-to-height ratio (WHtR, cutoff value ≥0.48) as the optimal noninvasive index for predicting MASLD, with strong performance in both training and validation set. Additionally, the combination of WHtR and lipid accumulation product (LAP) was selected as an optimal serial test to improve the positive predictive value, with a LAP cutoff value of ≥668.22 cm × mg/dL. Based on the obesity prevalence among 30 provinces, three MASLD screening recommendations were proposed: 1) “Population-screening-recommended”: For regions with an obesity prevalence ≥12.0%, where MASLD prevalence ranged from 5.0% to 21.5%; 2) “Resources-permitted”: For regions with an obesity prevalence between 8.4% and 12.0%, where MASLD prevalence ranged from 2.3% to 4.4%; 3) “Population-screening-not-recommended”: For regions with an obesity prevalence

Summary: Background: Drug overdose is an escalating public health crisis in the United States (U.S.). This study evaluated the temporal trend of drug overdose-related deaths in the U.S., with an emphasis on identifying disparities across subpopulations and the contributing drugs. Methods: Using the nationwide death dataset from the National Vital Statistics System (NVSS), we estimated the drug overdose-related age-standardized mortality rate (ASMR) and temporal trends for individuals aged 12 and older from 2012 to 2022. Excess mortality during the Coronavirus disease 2019 (COVID-19) pandemic was evaluated based on the pre-pandemic trends using predictive modeling analysis. Findings: Among 809,967 overdose-related deaths during 2012–2022, ASMR increased by 8.9% [95% confidence interval (CI): 6.0%–11.9%] per year from 2012 to 2019 and increased to 12.9% (95% CI: 2.1%–24.8%) from 2019 to 2022, with the excess ASMR of 16.9% in 2020, increased to 26.4% in 2021 and then decreased to 19.3% in 2022. Significant age, sex, racial/ethnic and geographic disparities were demonstrated, with adolescents (annual percentage change [APC]:21.6%) and males (APC:13.6%) having the most pronounced increase during the pandemic. Ethnic minorities especially the non-Hispanic American Indian/Alaska Native experienced the highest excess ASMR (33.1% in 2020). Illicit fentanyl and synthetics starting with the lowest ASMR in 2012 (1.0 per 100,000), marked the most dramatic increase and became the leading cause of overdose-related death since 2016 (7.5 per 100,000), leading to the highest ASMR by 2022 (27.4 per 100,000). All drug types except heroin experienced varying degrees of excess ASMRs, with prescription opioid pain relievers (23.5%–55.1%), benzodiazepines (27.4%–40.9%) and antidepressants (10.4%–17.8%) exhibiting consistent increases from 2020 to 2022, while the excess ASMRs for illicit fentanyl and synthetics (25.3%–10.0%), psychostimulants (32.8%–14.6%) and methadone (35.0%–33.3%) decreased between 2021 and 2022. Interpretation: Overdose-related mortality is increasing at an alarming rate, and the stark differences point to the need for targeted interventions to reduce the burden of drug overdose deaths. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

An increase in acute myocardial infarction (AMI)-related deaths has been reported during the COVID-19 pandemic. Despite evidence suggesting the association between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and AMI, the underlying mechanisms remain unclear. Here, we integrated mRNA and microRNA expression profiles related to SARS-CoV-2 infection and AMI from public databases. We then performed transcriptomic analysis using bioinformatics and systems biology approaches to explore the potential molecular mechanisms of SARS-CoV-2 infection affects AMI. First, twenty-one common differentially expressed genes (DEGs) were identified from SARS-CoV-2 infection and AMI patients in endothelial cells datasets and then we performed functional analysis to predict the roles of these DEGs. The functional analysis emphasized that the endothelial cell response to cytokine stimulus due to excessive inflammation was essential in these two diseases. Importantly, the tumor necrosis factor and interleukin-17 signaling pathways appeared to be integral factors in this mechanism. Interestingly, most of these common genes were also upregulated in transcriptomic datasets of SARS-CoV-2-infected cardiomyocytes, suggesting that these genes may be shared in cardiac- and vascular-related injuries. We subsequently built a protein-protein interaction network and extracted hub genes and essential modules from this network. At the transcriptional and post-transcriptional levels, regulatory networks with common DEGs were also constructed, and some key regulator signatures were further identified and validated. In summary, our research revealed that a highly activated inflammatory response in patients with COVID-19 might be a crucial factor for susceptibility to AMI and we identified some candidate genes and regulators that could be used as biomarkers or potential therapeutic targets.

Background/Aims Nonalcoholic fatty liver disease (NAFLD) is associated with a multitude of adverse outcomes. We aimed to estimate the pooled incidence of NAFLD-related adverse events. Methods We performed a systematic review and meta-analysis of cohort studies of adults with NAFLD to evaluate the pooled incidence of adverse events. Results 19,406 articles were screened, 409 full-text articles reviewed, and 79 eligible studies (1,377,466 persons) were included. Mean age was 51.47 years and body mass index 28.90 kg/m2. Baseline comorbidities included metabolic syndrome (41.73%), cardiovascular disease (CVD) (16.83%), cirrhosis (21.97%), and nonalcoholic steatohepatitis (NASH) (58.85%). Incidence rate per 1,000 person-years for mortality included: all-cause (14.6), CVD-related (4.53), non-liver cancer-related (4.53), and liver-related (3.10). Incidence for liver-related events included overall (24.3), fibrosis progression (49.0), cirrhosis (10.9), liver transplant (12.0), and hepatocellular carcinoma (HCC) (3.39). Incidence for non-liver events included metabolic syndrome (25.4), hypertension (25.8), dyslipidemia (26.4), diabetes (19.0), CVD (24.77), renal impairment (30.3), depression/anxiety (29.1), and non-liver cancer (10.5). Biopsy-proven NASH had higher incidence of HCC (P=0.043) compared to non-NASH. Higher rates of CVD and mortality were observed in North America and Europe, hypertension and non-liver cancer in North America, and HCC in Western Pacific/Southeast Asia (P

Background and Aims: The change in hepatocellular carcinoma (HCC) care continuum during the coronavirus disease 2019 (COVID-19) pandemic remains unknown at a national level in the United States. We sought to determine the impact of the pandemic on incident HCC cases, clinical characteristics, and treatment in the United States. Methods: Using the National Cancer Database, we analyzed incident HCC cases from 2010 to 2020. The incidence rate was calculated using the population data for each year from the census bureau. Joinpoint regression analysis was applied for trend analysis, and a polynomial regression model estimated the number of projected HCC cases in 2020 according to the trend of rates from 2010 to 2019. The distribution of cancer stage and treatment modality were assessed. Results: The pandemic led to a significant reduction in reported HCC cases, from 19,597 in 2019 to 16,188 in 2020. The projected number of HCC for 2020 was 19,011, corresponding to a 14.8% reduction in 2020. Extent of reduction in the number of incident HCC cases relative to estimated cases remains consistent in racial and ethnic subgroups. Despite underdiagnosis of HCC in 2020, proportion of patients with early tumor stage (30.5% for Tumour, Node, Metastasis stage 1) and curative treatment receipt (9.1% for surgical resection, 13% for ablation, 4.2% for liver transplant) for HCC remained stable in the first year of the COVID-19 pandemic. Conclusion: There was a significant reduction in HCC cases in 2020 compared to pre-COVID years. While tumor stage and proportion of patients receiving curative treatment remained stable, continued follow-up is needed to assess potential changes during subsequent years.

BackgroundAmyloidosis, a rare multisystem condition, often requires complex, multidisciplinary care. Its low prevalence underscores the importance of efforts to ensure the availability of high-quality patient education materials for better outcomes. ChatGPT (OpenAI) is a large language model powered by artificial intelligence that offers a potential avenue for disseminating accurate, reliable, and accessible educational resources for both patients and providers. Its user-friendly interface, engaging conversational responses, and the capability for users to ask follow-up questions make it a promising future tool in delivering accurate and tailored information to patients. ObjectiveWe performed a multidisciplinary assessment of the accuracy, reproducibility, and readability of ChatGPT in answering questions related to amyloidosis. MethodsIn total, 98 amyloidosis questions related to cardiology, gastroenterology, and neurology were curated from medical societies, institutions, and amyloidosis Facebook support groups and inputted into ChatGPT-3.5 and ChatGPT-4. Cardiology- and gastroenterology-related responses were independently graded by a board-certified cardiologist and gastroenterologist, respectively, who specialize in amyloidosis. These 2 reviewers (RG and DCK) also graded general questions for which disagreements were resolved with discussion. Neurology-related responses were graded by a board-certified neurologist (AAH) who specializes in amyloidosis. Reviewers used the following grading scale: (1) comprehensive, (2) correct but inadequate, (3) some correct and some incorrect, and (4) completely incorrect. Questions were stratified by categories for further analysis. Reproducibility was assessed by inputting each question twice into each model. The readability of ChatGPT-4 responses was also evaluated using the Textstat library in Python (Python Software Foundation) and the Textstat readability package in R software (R Foundation for Statistical Computing). ResultsChatGPT-4 (n=98) provided 93 (95%) responses with accurate information, and 82 (84%) were comprehensive. ChatGPT-3.5 (n=83) provided 74 (89%) responses with accurate information, and 66 (79%) were comprehensive. When examined by question category, ChatGTP-4 and ChatGPT-3.5 provided 53 (95%) and 48 (86%) comprehensive responses, respectively, to “general questions” (n=56). When examined by subject, ChatGPT-4 and ChatGPT-3.5 performed best in response to cardiology questions (n=12) with both models producing 10 (83%) comprehensive responses. For gastroenterology (n=15), ChatGPT-4 received comprehensive grades for 9 (60%) responses, and ChatGPT-3.5 provided 8 (53%) responses. Overall, 96 of 98 (98%) responses for ChatGPT-4 and 73 of 83 (88%) for ChatGPT-3.5 were reproducible. The readability of ChatGPT-4’s responses ranged from 10th to beyond graduate US grade levels with an average of 15.5 (SD 1.9). ConclusionsLarge language models are a promising tool for accurate and reliable health information for patients living with amyloidosis. However, ChatGPT’s responses exceeded the American Medical Association’s recommended fifth- to sixth-grade reading level. Future studies focusing on improving response accuracy and readability are warranted. Prior to widespread implementation, the technology’s limitations and ethical implications must be further explored to ensure patient safety and equitable implementation.

Background and aimsWith the rapid growth of artificial intelligence (AI) applications in various fields, understanding its impact on liver cancer research is paramount. This scientometrics project aims to investigate publication trends and topics in AI-related publications in liver cancer.Materials and MethodsWe employed a search strategy to identify AI-related publications in liver cancer using Scopus database. We analyzed the number of publications, author affiliations, and journals that publish AI-related publications in liver cancer. Finally, the publications were grouped based on intended application.ResultsWe identified 3950 eligible publications (2695 articles, 366 reviews, and 889 other document types) from 1968 to August 3, 2023. There was a 12.7-fold increase in AI-related publications from 2013 to 2022. By comparison, the number of total publications on liver cancer increased by 1.7-fold. Our analysis revealed a significant shift in trends of AI-related publications on liver cancer in 2019. We also found a statistically significant consistent increase in numbers of AI-related publications over time (tau = 0.756, p < 0.0001). Eight (53%) of the top 15 journals with the most publications were radiology journals. The largest number of publications were from China (n=1156), the US (n=719), and Germany (n=236). The three most common publication categories were “medical image analysis for diagnosis” (37%), “diagnostic or prognostic biomarkers modeling & bioinformatics” (19%), and “genomic or molecular analysis” (18%).ConclusionOur study reveals increasing interest in AI for liver cancer research, evidenced by a 12.7-fold growth in related publications over the past decade. A common application of AI is in medical imaging analysis for various purposes. China, the US, and Germany are leading contributors.

Summary: Background: With increasingly prevalent coexistence of chronic hepatitis B (CHB) and hepatic steatosis (HS), simple, non-invasive diagnostic methods to accurately assess the severity of hepatic inflammation are needed. We aimed to build a machine learning (ML) based model to detect hepatic inflammation in patients with CHB and concurrent HS. Methods: We conducted a multicenter, retrospective cohort study in China. Treatment-naive CHB patients with biopsy-proven HS between April 2004 and September 2022 were included. The optimal features for model development were selected by SHapley Additive explanations, and an ML algorithm with the best accuracy to diagnose moderate to severe hepatic inflammation (Scheuer's system ≥ G3) was determined and assessed by decision curve analysis (DCA) and calibration curve. This study is registered with ClinicalTrials.gov (NCT05766449). Findings: From a pool of 1,787 treatment-naive patients with CHB and HS across eleven hospitals, 689 patients from nine of these hospitals were chosen for the development of the diagnostic model. The remaining two hospitals contributed to two independent external validation cohorts, comprising 509 patients in validation cohort 1 and 589 in validation cohort 2. Eleven features regarding inflammation, hepatic and metabolic functions were identified. The gradient boosting classifier (GBC) model showed the best performance in predicting moderate to severe hepatic inflammation, with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI 0.83–0.88) in the training cohort, and 0.89 (95% CI 0.86–0.92), 0.76 (95% CI 0.73–0.80) in the first and second external validation cohorts, respectively. A publicly accessible web tool was generated for the model. Interpretation: Using simple parameters, the GBC model predicted hepatic inflammation in CHB patients with concurrent HS. It holds promise for guiding clinical management and improving patient outcomes. Funding: This research was supported by the National Natural Science Foundation of China (No. 82170609, 81970545), Natural Science Foundation of Shandong Province (Major Project) (No. ZR2020KH006), Natural Science Foundation of Jiangsu Province (No.BK20231118), Tianjin Key Medical Discipline (Specialty), Construction Project, TJYXZDXK-059B, Tianjin Health Science and Technology Project key discipline special, TJWJ2022XK034, and Research project of Chinese traditional medicine and Chinese traditional medicine combined with Western medicine of Tianjin municipal health and Family Planning Commission (2021022).

Background/Aims Patients with cirrhosis and hepatocellular carcinoma (HCC) require extensive and personalized care to improve outcomes. ChatGPT (Generative Pre-trained Transformer), a large language model, holds the potential to provide professional yet patient-friendly support. We aimed to examine the accuracy and reproducibility of ChatGPT in answering questions regarding knowledge, management, and emotional support for cirrhosis and HCC. Methods ChatGPT’s responses to 164 questions were independently graded by two transplant hepatologists and resolved by a third reviewer. The performance of ChatGPT was also assessed using two published questionnaires and 26 questions formulated from the quality measures of cirrhosis management. Finally, its emotional support capacity was tested. Results We showed that ChatGPT regurgitated extensive knowledge of cirrhosis (79.1% correct) and HCC (74.0% correct), but only small proportions (47.3% in cirrhosis, 41.1% in HCC) were labeled as comprehensive. The performance was better in basic knowledge, lifestyle, and treatment than in the domains of diagnosis and preventive medicine. For the quality measures, the model answered 76.9% of questions correctly but failed to specify decision-making cut-offs and treatment durations. ChatGPT lacked knowledge of regional guidelines variations, such as HCC screening criteria. However, it provided practical and multifaceted advice to patients and caregivers regarding the next steps and adjusting to a new diagnosis. Conclusions We analyzed the areas of robustness and limitations of ChatGPT’s responses on the management of cirrhosis and HCC and relevant emotional support. ChatGPT may have a role as an adjunct informational tool for patients and physicians to improve outcomes.

Abstract Studies have examined nonalcoholic fatty liver disease (NAFLD) prevalence and severity in Asians; however, this is not well understood in Asian Americans (both East and South Asian Americans) as few studies have analyzed this population. We aimed to describe characteristics, prevalence of NAFLD, and its severity in Asian Americans in the National Health and Nutrition Examination Surveys (NHANES) from 2017 to 2018. Respondents 18 years and older with interview, laboratory testing, and transient elastography data were included. Other causes of liver disease were excluded. Controlled attenuation parameter (CAP) cutoff ≥ 274 dB/m, as published in the literature, defined NAFLD. Sensitivity analysis for CAP cutoffs ≥ 248 and ≥302 dB/m were performed. We found that 450 out of 3639 respondents were Asian Americans, and prevalence using CAP ≥ 274 dB/m was 43.23%. Using sensitivity analysis cutoffs of CAP ≥ 248 dB/m and CAP ≥ 302 dB/m, the prevalence was 57.38% and 28.03%, respectively. Compared with non‐Asian Americans with NAFLD, Asian Americans with NAFLD had significantly lower body mass index (BMI) and less prevalent smoking history. Comorbidities, such as prediabetes, diabetes, and hypertension, were not significantly different between Asian and non‐Asian Americans with NAFLD. Compared to non‐Asian Americans with NAFLD, Asian Americans with NAFLD exhibited higher aminotransferases and triglycerides. Fibrosis assessed by transient elastography was not significantly different between Asian and non‐Asian Americans with NAFLD. Despite decreased prevalence of BMI ≥ 30 kg/m2, Asian Americans experienced similar NAFLD prevalence with increased hepatocellular injury and triglyceridemia compared to non‐Asian Americans. Fibrosis stages were similar to non‐Asian Americans.

BackgroundLower psychological wellbeing is associated with poor outcomes in a variety of diseases and healthy populations. However, no study has investigated whether psychological wellbeing is associated with the outcomes of COVID-19. This study aimed to determine whether individuals with lower psychological wellbeing are more at risk for poor outcomes of COVID-19.MethodsData were from the Survey of Health, Aging, and Retirement in Europe (SHARE) in 2017 and SHARE's two COVID-19 surveys in June–September 2020 and June–August 2021. Psychological wellbeing was measured using the CASP-12 scale in 2017. The associations of the CASP-12 score with COVID-19 hospitalization and mortality were assessed using logistic models adjusted for age, sex, body mass index, smoking, physical activity, household income, education level, and chronic conditions. Sensitivity analyses were performed by imputing missing data or excluding cases whose diagnosis of COVID-19 was solely based on symptoms. A confirmatory analysis was conducted using data from the English Longitudinal Study of Aging (ELSA). Data analysis took place in October 2022.ResultsIn total, 3,886 individuals of 50 years of age or older with COVID-19 were included from 25 European countries and Israel, with 580 hospitalized (14.9%) and 100 deaths (2.6%). Compared with individuals in tertile 3 (highest) of the CASP-12 score, the adjusted odds ratios (ORs) of COVID-19 hospitalization were 1.81 (95% CI, 1.41–2.31) for those in tertile 1 (lowest) and 1.37 (95% CI, 1.07–1.75) for those in tertile 2. As for COVID-19 mortality, the adjusted ORs were 2.05 (95% CI, 1.12–3.77) for tertile 1 and 1.78 (95% CI, 0.98–3.23) for tertile 2, compared with tertile 3. The results were relatively robust to missing data or the exclusion of cases solely based on symptoms. This inverse association of the CASP-12 score with COVID-19 hospitalization risk was also observed in ELSA.ConclusionThis study shows that lower psychological wellbeing is independently associated with increased risks of COVID-19 hospitalization and mortality in European adults aged 50 years or older. Further study is needed to validate these associations in recent and future waves of the COVID-19 pandemic and other populations.

Summary: Background: Diabetes mellitus (DM) is a critical risk factor for severe SARS-CoV-2 infection, and SARS-CoV-2 infection contributes to worsening glycemic control. The COVID-19 pandemic profoundly disrupted the delivery of care for patients with diabetes. We aimed to determine the trend of DM-related deaths during the pandemic. Methods: In this serial population-based study between January 1, 2006 and December 31, 2021, mortality data of decedents aged ≥25 years from the National Vital Statistics System dataset was analyzed. Decedents with DM as the underlying or contributing cause of death on the death certificate were defined as DM-related deaths. Excess deaths were estimated by comparing observed versus expected age-standardized mortality rates derived from mortality during 2006-2019 with linear and polynomial regression models. The trends of mortality were quantified with joinpoint regression analysis. Subgroup analyses were performed by age, sex, race/ethnicity, and state. Findings: Among 4·25 million DM-related deaths during 2006-2021, there was a significant surge of more than 30% in mortality during the pandemic, from 106·8 (per 100,000 persons) in 2019 to 144·1 in 2020 and 148·3 in 2021. Adults aged 25-44 years had the most pronounced rise in mortality. Widened racial/ethnic disparity was observed, with Hispanics demonstrating the highest excess deaths (67·5%; 95% CI 60·9-74·7%), almost three times that of non-Hispanic whites (23·9%; 95% CI 21·2-26·7%). Interpretation: The United States saw an increase in DM-related mortality during the pandemic. The disproportionate rise in young adults and the widened racial/ethnic disparity warrant urgent preventative interventions from diverse stakeholders. Funding: National Natural Science Foundation of China.

Background and objectives: The COVID-19 pandemic imperiled the global health system. We aimed to determine the impact of COVID-19 on the care continuum of HCV-infected patients.Material and Methods: Two hundred and fifty-six patients who were prescribed a course of DAA therapy at three tertiary medical centers in the US and China between January 1, 2019 to June 30, 2020 were included. We assessed the proportions of patients who completed DAA therapy and had HCV RNA testing during and after the end of therapy. We also assessed the impact of utilization of telemedicine.Results: The proportion of patients undergoing HCV RNA testing during DAA treatment decreased from >81.7% before pandemic to 67.8% during the pandemic (P=0.006), with a more prominent decrease in the US. There were significant decreases in HCV RNA testing >12 (P20 weeks (P

The current alanine aminotransferase (ALT) upper limit of normal was defined using selected healthy Caucasian blood donors. Given the global rise in obesity and different body habitus in Asians, we aimed to perform a systematic review and meta‐analysis combined with bootstrap modeling and individual patient data validation to estimate the ALT upper threshold for Asians, including the overweight and diabetics. We included studies from PubMed, Embase, and Cochrane database searches that identified individuals without known liver diseases (i.e., viral hepatitis, alcohol, and ultrasound‐detected nonalcoholic fatty liver disease). The mean ALT (U/L) was estimated using a random‐effects mixed model and upper threshold (95th‐percentile value, U/L) via a bootstrap model with 10,000 resamples. We screened 4,995 studies and identified 86 studies that reported ALT values for 526,641 individuals without excessive alcohol intake or known liver diseases, yielding a mean ALT of 19 and ALT upper threshold of 32. The ALT upper threshold was 37 in males versus 31 in females, 39 in overweight versus 28 in normal‐weight individuals, and 36 for diabetics versus 33 for nondiabetics. We validated our study level data with individual patient level data in 6,058 individuals from five study centers in Japan. Consistent with our study‐level data, we found that the ALT upper threshold in our individual patient data analysis was indeed higher in overweight versus normal‐weight individuals (39 vs. 32) and in diabetics versus nondiabetics (42 vs. 33). Conclusion: We provide validated reference ranges for ALT upper threshold derived from Asians without known liver disease, including individuals with ultrasound‐detected nonalcoholic fatty liver disease who are normal weight, overweight, nondiabetic, and diabetic, to inform practice.

Objectives. We assessed the potential of glial cell line-derived neurotrophic factor (GDNF) as a useful biomarker to predict cirrhosis in chronic hepatitis B (CHB) patients. Methods. A total of 735 patients from two medical centers (385 CHB patients and 350 healthy controls) were included to determine the association of serum and tissue GDNF levels with biopsy-proven cirrhosis. The diagnostic accuracy of serum GDNF (sGDNF) was estimated and compared with other indices of cirrhosis. Results. We showed significantly higher levels of sGDNF in CHB patients with fibrosis (28.4 pg/ml vs. 11.6 pg/ml in patients without) and patients with cirrhosis (33.8 pg/ml vs. 23.5 pg/ml in patients without). The areas under receiver operating curve (AUROCs) of sGDNF were 0.83 (95% confidence interval (CI): 0.80–0.87) for predicting liver fibrosis and 0.84 (95% CI: 0.79–0.89) for cirrhosis. Findings from the serum protein level and hepatic mRNA expression were consistent. Using the best cutoff to predict cirrhosis, we categorized the patients into sGDNF-high and sGDNF-low groups. The sGDNF-high group had significantly larger Masson’s trichrome and reticulin staining-positive area, higher Scheuer score, and METAVIR fibrosis stage (all p

Background: In-hospital cardiac arrest (IHCA) carries a high mortality and significant morbidity in survivors. Gastrointestinal bleeding (GIB) can complicate cardiac arrests. We aim to study the association between GIB and the in-hospital outcomes of patients with IHCA. Methods and results: The National Inpatient Sample 2016–2018 databases were used. IHCA were identified using ICD-10-PCS code for cardiopulmonary resuscitation. Other diagnoses including GIB were identified using ICD-10-CM codes. Multivariate logistic regression was used to study the effect of GIB on in-hospital mortality. Gamma regression with log link was used to determine the effect of GIB on length of stay and cost of admission. In patients with IHCA, GIB as a secondary diagnosis is associated with an increased in hospital mortality (unadjusted 74.2% vs 68.3%, adjusted OR 1.17, 95% confidence interval [CI] 1.09–1.25, p

There is growing evidence that links nonalcoholic fatty liver disease (NAFLD) with impairment of renal function. As such, we aimed to demonstrate the trend of NAFLD, NAFLD with renal insufficiency (RI), disease awareness, and mortality over time. Patient data were extracted from the National Health and Nutrition Examination Survey (NHANES) 1999‐2016. A total of 14,255 adult study participants without competing liver disease or heavy drinking and with complete laboratory data were included. NAFLD was defined using the U.S. Fatty Liver Index (USFLI) and RI was defined using the Chronic Kidney Disease Epidemiology Collaboration equation and urine albumin:creatinine ratio. Death data were obtained from the National Death Index (up to December 31, 2015). Prevalence of NAFLD in participants was 31.2% (95% confidence interval [CI], 30.01‐32.46); of these participants, 22.05% (95% CI, 20.34‐23.85) had RI. From 1999 to 2016, prevalence of both NAFLD without RI (P = 0.048) and NAFLD‐RI (P = 0.006) increased significantly. Among those with NAFLD‐RI, awareness of kidney disease was 8.56% (95% CI, 6.69‐10.89), while awareness of liver disease among all NAFLD was 4.49% (95% CI, 3.17‐6.33). Among those with NAFLD, mortality incidence per 1,000 person years was highest among those with severe RI in all‐cause mortality (104.4; 95% CI, 83.65‐130.39) and other residual causes of mortality (mean, 50.88; 95% CI, 37.02‐69.93). Conclusion: Prevalence of NAFLD and NAFLD‐RI has increased over the past 2 decades in the United States. Low kidney disease and liver disease awareness are major public health issues as those with NAFLD‐RI have significantly higher mortality than those with only NAFLD.

Matriptase is ectopically expressed in neoplastic B-cells, in which matriptase activity is enhanced by negligible expression of its endogenous inhibitor, hepatocyte growth factor activator inhibitor (HAI)-1. HAI-1, however, is also involved in matriptase synthesis and intracellular trafficking. The lack of HAI-1 indicates that other related inhibitor, such as HAI-2, might be expressed. Here, we show that HAI-2 is commonly co-expressed in matriptase-expressing neoplastic B-cells. The level of active matriptase shed after induction of matriptase zymogen activation in 7 different neoplastic B-cells was next determined and characterised. Our data reveal that active matriptase can only be generated and shed by those cells able to activate matriptase and in a rough correlation with the levels of matriptase protein. While HAI-2 can potently inhibit matriptase, the levels of active matriptase are not proportionally suppressed in those cells with high HAI-2. Our survey suggests that matriptase proteolysis might aberrantly remain high in neoplastic B-cells regardless of the levels of HAI-2.

The ability of cells to rapidly detect and react to alterations in their chemical environment, such as pH, ionic strength and redox potential, is essential for cell function and survival. We present here evidence that cells can respond to such environmental alterations by rapid induction of matriptase autoactivation. Specifically, we show that matriptase autoactivation can occur spontaneously at physiological pH, and is significantly enhanced by acidic pH, both in a cell-free system and in living cells. The acid-accelerated autoactivation can be attenuated by chloride, a property that may be part of a safety mechanism to prevent unregulated matriptase autoactivation. Additionally, the thio-redox balance of the environment also modulates matriptase autoactivation. Using the cell-free system, we show that matriptase autoactivation is suppressed by cytosolic reductive factors, with this cytosolic suppression being reverted by the addition of oxidizing agents. In living cells, we observed rapid induction of matriptase autoactivation upon exposure to toxic metal ions known to induce oxidative stress, including CoCl2 and CdCl2. The metal-induced matriptase autoactivation is suppressed by N-acetylcysteine, supporting the putative role of altered cellular redox state in metal induced matriptase autoactivation. Furthermore, matriptase knockdown rendered cells more susceptible to CdCl2-induced cell death compared to control cells. This observation implies that the metal-induced matriptase autoactivation confers cells with the ability to survive exposure to toxic metals and/or oxidative stress. Our results suggest that matriptase can act as a cellular sensor of the chemical environment of the cell that allows the cell to respond to and protect itself from changes in the chemical milieu.

BACKGROUND: Blood culture is viewed as the golden standard for the diagnosis of sepsis but suffers from low sensitivity and long turnaround time. LightCycler SeptiFast (LC-SF) is a real-time multiplex polymerase chain reaction test able to detect 25 common pathogens responsible for bloodstream infections within hours. We aim to assess the accuracy of LC-SF by systematically reviewing the published studies. METHOD: Related literature on Medline, Embase, and Cochrane databases was searched up to October 2012 for studies utilizing LC-SF to diagnose suspected sepsis and that provided sufficient data to construct two-by-two tables. RESULTS: A total of 34 studies enrolling 6012 patients of suspected sepsis were included. The overall sensitivity and specificity for LC-SF to detect bacteremia or fungemia was 0·75 (95% CI: 0·65-0·83) and 0·92 (95%CI:0·90-0·95), respectively. LC-SF had a high positive likelihood ratio (10·10) and a moderate negative likelihood ratio (0·27). Specifically, LC-SF had a sensitivity of 0·80 (95%CI: 0·70-0·88) and a specificity of 0·95(95%CI: 0·93-0·97) for the bacteremia outcome, and a sensitivity of 0·61 (95%CI: 0·48-0·72) and a specificity of 0·99 (95%CI: 0·99-0·99) for the fungemia outcome. High heterogeneity was found in the bacteremia outcome subgroup but not in the fungemia outcome subgroup. CONCLUSION: LC-SF is of high rule-in value for early detection of septic patients. In a population with low pretest probability, LC-SF test can still provide valuable information for ruling out bacteremia or fungemia.

Background and aims: With the rapid growth of artificial intelligence (AI) applications in various fields, understanding its impact on liver cancer research is paramount. This scientometrics project aims to investigate publication trends and topics in AI-related publications in liver cancer. Materials and Methods: We employed a search strategy to identify AI-related publications in liver cancer using Scopus database. We analyzed the number of publications, author affiliations, and journals that publish AI-related publications in liver cancer. Finally, the publications were grouped based on intended application. Results: We identified 3950 eligible publications (2695 articles, 366 reviews, and 889 other document types) from 1968 to August 3, 2023. There was a 12.7-fold increase in AI-related publications from 2013 to 2022. By comparison, the number of total publications on liver cancer increased by 1.7-fold. Our analysis revealed a significant shift in trends of AI-related publications on liver cancer in 2019. We also found a statistically significant consistent increase in numbers of AI-related publications over time (tau = 0.756, p < 0.0001). Eight (53%) of the top 15 journals with the most publications were radiology journals. The largest number of publications were from China (n=1156), the US (n=719), and Germany (n=236). The three most common publication categories were "medical image analysis for diagnosis" (37%), "diagnostic or prognostic biomarkers modeling & bioinformatics" (19%), and "genomic or molecular analysis" (18%). Conclusion: Our study reveals increasing interest in AI for liver cancer research, evidenced by a 12.7-fold growth in related publications over the past decade. A common application of AI is in medical imaging analysis for various purposes. China, the US, and Germany are leading contributors. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The efficacy and safety of combined immunotherapy and transarterial radioembolization (TARE) were suggested in preclinical and early-phase trials, but these were limited by small sample sizes. We sought to compare the efficacy of combined therapy and immunotherapy alone in patients with advanced hepatocellular carcinoma (HCC). METHODS: The National Cancer Database was used to identify patients with advanced HCC diagnosed between January 1, 2017, and December 31, 2019. We included patients who received combined therapy or immunotherapy alone as first-line treatment. Multivariable logistic regression was conducted to determine predictors of combined therapy. Kaplan-Meier and Cox regression approaches were used to identify predictors of overall survival and to compare hazards of mortality between the patients who received combined therapy and immunotherapy alone. RESULTS: Of 1,664 eligible patients with advanced-stage HCC, 142 received combined TARE/immunotherapy and 1,522 received immunotherapy alone. Receipt of combination therapy was associated with care at an academic center and inversely associated with racial/ethnic minority status (Hispanic and Black individuals). The median overall survival was significantly higher in the combination group than in the immunotherapy alone group (19.8 vs 9.5 months). In multivariable analysis, combined therapy was independently associated with reduced mortality (adjusted hazard ratio 0.50, 95% confidence interval: 0.36-0.68, P < 0.001). Results were consistent across subgroups and in sensitivity analyses using propensity score matching and inverse probability of treatment weighting. DISCUSSION: The combination of TARE and immunotherapy was associated with improved survival compared with immunotherapy alone in patients with advanced-stage HCC. Our findings underly the importance of large clinical trials evaluating combination therapy in these patients. [ABSTRACT FROM AUTHOR]