NASH is currently the second leading cause for LT waitlist registration/liver transplantation overall, and in females, the leading cause. Given the rate of increase, NASH will likely rise to become the leading indication for LT in males as well.

The contribution of oxidative stress to ischemic brain damage is well established. Nevertheless, for unknown reasons, several clinically tested antioxidant therapies failed to show benefits in human stroke. Based on our previous in vitro work, we hypothesized that the neuroprotective potency of antioxidants is related to their ability to limit release of the excitotoxic amino acids, glutamate and aspartate. We explored the effects of two antioxidants, tempol and edaravone, on amino acid release in the brain cortex, in a rat model of transient occlusion of the middle cerebral artery (MCAo). Amino acid levels were quantified using a microdialysis approach, with the probe positioned in the ischemic penumbra as verified by a laser Doppler technique. Two-hour MCAo triggered a dramatic increase in the levels of glutamate, aspartate, taurine and alanine. Microdialysate delivery of 10 mM tempol reduced the amino acid release by 60–80%, while matching levels of edaravone had no effect. In line with these latter data, an intracerebroventri-cular injection of tempol but not edaravone (500 nmols each, 15 minutes prior to MCAo) reduced infarction volumes by ~50% and improved neurobehavioral outcomes. In vitro assays showed that tempol was superior in removing superoxide anion, whereas edaravone was more potent in scavenging hydrogen peroxide, hydroxyl radical, and peroxynitrite. Overall, our data suggests that the neuroprotective properties of tempol are likely related to its ability to reduce tissue levels of the superoxide anion and pathological glutamate release, and, in such a way, limit progression of brain infarction within ischemic penumbra. These new findings may be instrumental in developing new antioxidant therapies for treatment of stroke.

Background & Aims Liver fibrosis assessed by liver biopsy is predictive of clinical liver events in patients with nonalcoholic fatty liver disease (NAFLD). Magnetic resonance elastography (MRE) correlates with liver biopsy in assessing liver fibrosis. However, data assessing the relationship between MRE and clinical liver events are lacking. We investigated the association between MRE and clinical liver events/death and identified the cut‐off to predict clinical liver events in NAFLD patients. Methods We conducted a multicenter retrospective study of NAFLD patients who underwent MRE between 2016 and 2019. Clinical liver events were defined as decompensation events and death. We categorized patients into noncirrhosis, compensated cirrhosis and decompensated cirrhosis. Fisher's exact test was used to test association strength. Receiver operative curve methods were used to determine the optimal cut‐off of MRE liver stiffness and to maximize the accuracy for classifying noncirrhosis, compensated cirrhosis and decompensated cirrhosis. Logistic regression modelling was used to predict decompensation. Results The study included 320 NAFLD patients who underwent MRE. The best threshold for distinguishing cirrhosis from noncirrhosis was 4.39 kPa (AUROC 0.92) and from decompensated cirrhosis was 6.48 kPa (AUROC 0.71). Odds of decompensation increased as liver stiffness increased (OR 3.28) (P < .001). Increased liver stiffness was associated with ascites, hepatic encephalopathy, oesophageal variceal bleeding and mortality (median 7.10, 10.15 and 10.15 kPa respectively). Conclusion In NAFLD patients, liver stiffness measured by MRE with a cut‐off of ≥6.48 kPa is associated with decompensation and mortality, and specific MRE cut‐offs are predictive of individual clinical liver events.

Background: Patients with cirrhosis and hepatocellular carcinoma (HCC) require extensive care. Personalized education can improve their outcomes. ChatGPT (Generative Pre-trained Transformer), a natural language processing model, has shown potential to provide professional yet patient-freindly responses. Aim: To examine the accuracy and reproducibility of ChatGPT in responding to questions regarding knowledge, management, and emotional support for cirrhosis and HCC. Method: ChatGPT's responses to 164 frequently asked questions were independently graded by two transplant hepatologists, with a third reviewer resolving any discrepancies. We also compared the performance of ChatGPT on two previously validated and published questionnaires to the physicians or trainees who were tested in the included publications. Furthermore, we formulated the 26 quality measures of cirrhosis management into questions and tested ChatGPT's knowledge in cirrhosis care. Finally, the capacity to provide emotional support to patients or caregivers was tested. Results: ChatGPT regurgitated extensive knowledge about both cirrhosis and HCC, but for questions with correct responses, only a small proportion was labelled 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 the cut-off values for making medical decisions and treatment durations. When compared to physicians/trainees, ChatGPT fell short in knowledge of guidelines varying across geographic regions, such as HCC screening criteria. The model also provided practical and multifaceted advice to patients and caregivers regarding the next steps and adjusting to a new diagnosis. Conclusion: In summary, 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.

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.

OBJECTIVES: Despite the importance of adequate bowel cleansing prior to colonoscopy, national societies provide little guidance regarding which bowel preps are best tolerated and most effective; this reflects a lack of comparative effectiveness studies that directly evaluate available preps in a "real-world" setting. To address this gap, we conducted a prospective, naturalistic,

Background and Objectives: Artificial intelligence is increasingly being employed in healthcare, raising concerns about the exacerbation of disparities. This study evaluates ChatGPT and GPT-4's ability to comprehend and respond to cirrhosis-related questions in English, Korean, Mandarin, and Spanish, addressing language barriers that may impact patient care. Methods: A set of 36 cirrhosis-related questions were translated into Korean, Mandarin, and Spanish and prompted to both ChatGPT and GPT-4 models. Non-English responses were graded by native-speaking hepatologists on accuracy and similarity to English responses. Chi-square tests were used to compare the proportions of grading between ChatGPT and GPT-4. Results: GPT-4 showed a marked improvement in the proportion of comprehensive and correct answers compared to ChatGPT across all four languages (p<0.05). GPT-4 demonstrated enhanced accuracy and avoided erroneous responses evident in ChatGPT's output. Significant improvement was observed in Mandarin and Korean subgroups, with a smaller quality gap between English and non-English responses in GPT-4 compared to ChatGPT. Conclusions: GPT-4 exhibited significantly higher accuracy in English and non-English cirrhosis-related questions, highlighting its potential for more accurate and reliable language model applications in diverse linguistic contexts. These advancements have important implications for patients with language discordance, contributing to equalizing health literacy on a global scale.

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