Empowering inclusivity: improving readability of living kidney donation information with ChatGPT.

Background: Addressing disparities in living kidney donation requires making information accessible across literacy levels, especially important given that the average American adult reads at an 8th-grade level. This study evaluated the effectiveness of ChatGPT, an advanced AI language model, in simplifying living kidney donation information to an 8th-grade reading level or below. Methods: We used ChatGPT versions 3.5 and 4.0 to modify 27 questions and answers from Donate Life America, a key resource on living kidney donation. We measured the readability of both original and modified texts using the Flesch-Kincaid formula. A paired t-test was conducted to assess changes in readability levels, and a statistical comparison between the two ChatGPT versions was performed. Results: Originally, the FAQs had an average reading level of 9.6 ± 1.9. Post-modification, ChatGPT 3.5 achieved an average readability level of 7.72 ± 1.85, while ChatGPT 4.0 reached 4.30 ± 1.71, both with a p-value <0.001 indicating significant reduction. ChatGPT 3.5 made 59.26% of answers readable below 8th-grade level, whereas ChatGPT 4.0 did so for 96.30% of the texts. The grade level range for modified answers was 3.4-11.3 for ChatGPT 3.5 and 1-8.1 for ChatGPT 4.0. Conclusion: Both ChatGPT 3.5 and 4.0 effectively lowered the readability grade levels of complex medical information, with ChatGPT 4.0 being more effective. This suggests ChatGPT's potential role in promoting diversity and equity in living kidney donation, indicating scope for further refinement in making medical information more accessible.

Meta-analysis comparing different ultrasound detection methods to accurately assess wound healing and scar formation after caesarean section.

The accurate assessment of wound healing post-caesarean section, especially in twin pregnancies, remains a pivotal concern in obstetrics, given its implications for maternal health and recovery. Traditional methods, including conventional abdominal ultrasonography (CU), have been challenged by the advent of transvaginal ultrasonography (TU), offering potentially enhanced sensitivity and specificity. This meta-analysis directly compares the efficacy of TU and CU in evaluating wound healing and scar formation, crucial for optimizing postoperative care. Results indicate that TU is associated with significantly better outcomes in wound healing, demonstrated by lower REEDA scores (SMD = -20.56, 95% CI: [-27.34.20, -13.77], p < 0.01), and in scar formation reduction, evidenced by lower Manchester Scar Scale scores (SMD = -25.18, 95% CI: [-29.98, -20.39], p < 0.01). These findings underscore the potential of integrating TU into routine post-caesarean evaluation protocols to enhance care quality and patient recovery.

Performance of GPT-4 Vision on kidney pathology exam questions.

OBJECTIVES: ChatGPT (OpenAI, San Francisco, CA) has shown impressive results across various medical examinations, but its performance in kidney pathology is not yet established. This study evaluated proficiencies of GPT-4 Vision (GPT-4V), an updated version of the platform with the ability to analyze images, on kidney pathology questions and compared its responses with those of nephrology trainees. METHODS: Thirty-nine questions (19 text-based questions and 20 with various kidney biopsy images) designed specifically for the training of nephrology fellows were employed. RESULTS: GPT-4V displayed comparable accuracy rates in the first and second runs (67% and 72%, respectively, P = .50). The aggregated accuracy, however-particularly, the consistent accuracy-of GPT-4V was lower than that of trainees (72% and 67% vs 79%). Both GPT-4V and trainees displayed comparable accuracy in responding to image-based and text-only questions (55% vs 79% and 81% vs 78%, P = .11 and P = .67, respectively). The consistent accuracy in image-based, directly asked questions for GPT-4V was 29%, much lower than its 88% consistency on text-only, directly asked questions (P = .02). In contrast, trainees maintained similar accuracy in directly asked image-based and text-based questions (80% vs 77%, P = .65). Although the aggregated accuracy for correctly interpreting images was 69%, the consistent accuracy across both runs was only 39%. The accuracy of GPT-4V in answering questions with correct image interpretation was significantly higher than for questions with incorrect image interpretation (100% vs 0% and 100% vs 33% for the first and second runs, P = .001 and P = .02, respectively). CONCLUSIONS: The performance of GPT-4V in handling kidney pathology questions, especially those including images, is limited. There is a notable need for enhancement in GPT-4V proficiency in interpreting images.

Perspectives on AI-based recommendations for mask-wearing and COVID-19 vaccination for transplant recipients in the post-COVID-19 era.

In the aftermath of the COVID-19 pandemic, the ongoing necessity for preventive measures such as mask-wearing and vaccination remains particularly critical for organ transplant recipients, a group highly susceptible to infections due to immunosuppressive therapy. Given that many individuals nowadays increasingly utilize Artificial Intelligence (AI), understanding AI perspectives is important. Thus, this study utilizes AI, specifically ChatGPT 4.0, to assess its perspectives in offering precise health recommendations for mask-wearing and COVID-19 vaccination tailored to this vulnerable population. Through a series of scenarios reflecting diverse environmental settings and health statuses in December 2023, we evaluated the AI's responses to gauge its precision, adaptability, and potential biases in advising high-risk patient groups. Our findings reveal that ChatGPT 4.0 consistently recommends mask-wearing in crowded and indoor environments for transplant recipients, underscoring their elevated risk. In contrast, for settings with fewer transmission risks, such as outdoor areas where social distancing is possible, the AI suggests that mask-wearing might be less imperative. Regarding vaccination guidance, the AI strongly advocates for the COVID-19 vaccine across most scenarios for kidney transplant recipients. However, it recommends a personalized consultation with healthcare providers in cases where patients express concerns about vaccine-related side effects, demonstrating an ability to adapt recommendations based on individual health considerations. While this study provides valuable insights into the current AI perspective on these important topics, it is crucial to note that the findings do not directly reflect or influence health policy. Nevertheless, given the increasing utilization of AI in various domains, understanding AI's viewpoints on such critical matters is essential for informed decision-making and future research.

Sex- and Age-Specific Prevalence of Osteopenia and Osteoporosis: Sampling Survey.

BACKGROUND: Osteopenia and osteoporosis are posing a long-term influence on the aging population's health contributing to a higher risk of mortality, loss of autonomy, hospitalization, and huge health system costs and social burden. Therefore, more pertinent data are needed to demonstrate the current state of osteoporosis. OBJECTIVE: This sampling survey seeks to assess the trends in the prevalence of osteopenia and osteoporosis in a Chinese Han population. METHODS: A community-based cross-sectional study involving 16,377 participants used a multistage sampling method. Bone mineral density was measured using the quantitative ultrasonic densitometry. Student t test and Mann-Whitney U test were used to test the difference between normally and nonnormally distributed quantitative variables between male and female participants. A chi-square (χ2) test was used to compare categorized variables. Stratified analysis was conducted to describe the prevalence rates of osteoporosis (T score ≤-2.5) and osteopenia (T score -2.5 to -1.0) across age, sex, calcium intake, and menopause. A direct standardization method was used to calculate the age-standardized prevalence rates of osteoporosis and osteopenia. T-score was further categorized into quartiles (T1-T4) by age- and sex-specified groups. RESULTS: The prevalence rates of osteopenia and osteoporosis were 40.5% (6633/16,377) and 7.93% (1299/16,377), respectively, and the age-standardized prevalence rates were 27.32% (287,877,129.4/1,053,861,940) and 3.51% (36,974,582.3/1,053,861,940), respectively. There was an increase in osteopenia and osteoporosis prevalence from 21.47% (120/559) to 56.23% (754/1341) and 0.89% (5/559) to 17.23% (231/1341), respectively, as age increased from 18 years to 75 years old. The prevalence rates of osteopenia and osteoporosis were significantly higher in female participants (4238/9645, 43.94% and 1130/9645, 11.72%) than in male participants (2395/6732, 35.58% and 169/6732, 2.51%; P<.001), and in postmenopausal female participants (3638/7493, 48.55% and 1053/7493, 14.05%) than in premenopausal female participants (538/2026, 26.55% and 53/2026, 2.62%; P<.001). In addition, female participants with a history of calcium intake had a lower osteoporosis prevalence rate than female participants without any history of calcium intake in all age groups (P=.004). From low quartile to high quartile of T-score, the prevalence of diabetes mellitus (752/4037, 18.63%; 779/4029, 19.33%; 769/3894, 19.75%; and 869/3879, 22.4%) and dyslipidemia (2228/4036, 55.2%; 2304/4027, 57.21%; 2306/3891, 59.26%; and 2379/3878, 61.35%) were linearly increased (P<.001), while the prevalence of cancer (112/4037, 2.77%; 110/4029, 2.73%; 103/3894, 2.65%; and 77/3879, 1.99%) was decreased (P=.03). CONCLUSIONS: Our data imply that as people age, osteopenia and osteoporosis are more common in females than in males, particularly in postmenopausal females than in premenopausal females, and bone mineral density significantly affects the prevalence of chronic diseases. These findings offer information that can be applied to intervention programs meant to prevent or lessen the burden of osteoporosis in China.

Evaluating ChatGPT's efficacy in assessing the safety of non-prescription medications and supplements in patients with kidney disease.

Background: This study investigated the efficacy of ChatGPT-3.5 and ChatGPT-4 in assessing drug safety for patients with kidney diseases, comparing their performance to Micromedex, a well-established drug information source. Despite the perception of non-prescription medications and supplements as safe, risks exist, especially for those with kidney issues. The study's goal was to evaluate ChatGPT's versions for their potential in clinical decision-making regarding kidney disease patients. Method: The research involved analyzing 124 common non-prescription medications and supplements using ChatGPT-3.5 and ChatGPT-4 with queries about their safety for people with kidney disease. The AI responses were categorized as "generally safe," "potentially harmful," or "unknown toxicity." Simultaneously, these medications and supplements were assessed in Micromedex using similar categories, allowing for a comparison of the concordance between the two resources. Results: Micromedex identified 85 (68.5%) medications as generally safe, 35 (28.2%) as potentially harmful, and 4 (3.2%) of unknown toxicity. ChatGPT-3.5 identified 89 (71.8%) as generally safe, 11 (8.9%) as potentially harmful, and 24 (19.3%) of unknown toxicity. GPT-4 identified 82 (66.1%) as generally safe, 29 (23.4%) as potentially harmful, and 13 (10.5%) of unknown toxicity. The overall agreement between Micromedex and ChatGPT-3.5 was 64.5% and ChatGPT-4 demonstrated a higher agreement at 81.4%. Notably, ChatGPT-3.5's suboptimal performance was primarily influenced by a lower concordance rate among supplements, standing at 60.3%. This discrepancy could be attributed to the limited data on supplements within ChatGPT-3.5, with supplements constituting 80% of medications identified as unknown. Conclusion: ChatGPT's capabilities in evaluating the safety of non-prescription drugs and supplements for kidney disease patients are modest compared to established drug information resources. Neither ChatGPT-3.5 nor ChatGPT-4 can be currently recommended as reliable drug information sources for this demographic. The results highlight the need for further improvements in the model's accuracy and reliability in the medical domain.

Evaluating ChatGPT's Accuracy in Responding to Patient Education Questions on Acute Kidney Injury and Continuous Renal Replacement Therapy.

INTRODUCTION: Acute Kidney Injury (AKI) and Continuous Renal Replacement Therapy (CRRT) are critical areas in nephrology. The effectiveness of ChatGPT in simpler, patient education-oriented questions has not been thoroughly assessed. This study evaluates the proficiency of ChatGPT 4.0 in responding to such questions, subjected to various linguistic alterations. METHODS: Eighty-nine questions were sourced from the Mayo Clinic Handbook for educating patients on AKI and CRRT. These questions were categorized as original, paraphrased with different interrogative adverbs, paraphrased resulting in incomplete sentences, and paraphrased containing misspelled words. Two nephrologists verified the questions for medical accuracy. A chi-squared test was conducted to ascertain notable discrepancies in ChatGPT 4.0's performance across these formats. RESULTS: ChatGPT provided notable accuracy in handling a variety of question formats for patient education in AKI and CRRT. Across all question types, ChatGPT demonstrated an accuracy of 97% for both original and adverb-altered questions and 98% for questions with incomplete sentences or misspellings. Specifically for AKI-related questions, the accuracy was consistently maintained at 97% for all versions. In the subset of CRRT-related questions, the tool achieved a 96% accuracy for original and adverb-altered questions, and this increased to 98% for questions with incomplete sentences or misspellings. The statistical analysis revealed no significant difference in performance across these varied question types (p-value: 1.00 for AKI and 1.00 for CRRT), and there was no notable disparity between the AI's responses to AKI and CRRT questions (p-value: 0.71). CONCLUSION: ChatGPT 4.0 demonstrates consistent and high accuracy in interpreting and responding to queries related to AKI and CRRT, irrespective of linguistic modifications. These findings suggest that ChatGPT 4.0 has the potential to be a reliable support tool in the delivery of patient education, by accurately providing information across a range of question formats. Further research is needed to explore the direct impact of AI-generated responses on patient understanding and education outcomes.

Co-impacts of cation type and humic acid on migration of polystyrene microplastics in saturated porous media.

The aging process of microplastics (MPs) could significantly change their physical and chemical characteristics and impact their migration behavior in soil. However, the complex effects of different cations and humic acids (HA) on the migration of aged MPs through saturated media are not clear. In this research, the migration and retention of pristine/aged PSMPs (polystyrene microplastics) under combined effects of cations (Na+, Ca2+) (ionic strength = 10 mM) and HA (0, 5, 15 mg/L) were investigated and analyzed in conjunction with the two-site kinetic retention model and DLVO theory. The findings showed that the aging process accelerated PSMPs migration under all tested conditions. Aged PSMPs were less susceptible to Ca2+ than pristine PSMPs. Under Ca2+ conditions, pristine/aged PSMPs showed higher retention than under Na+ conditions in the absence of HA. Furthermore, under Na+ conditions, the migration of aged PSMPs significantly increased at higher concentrations of HA. However, under Ca2+ conditions, the migration of aged PSMPs decreased significantly at higher concentrations of HA. In higher HA conditions, HA, Ca2+, and PSMPs interact to cause larger aggregations, resulting in the sedimentation of aged PSMPs. The DLVO calculations and two-site kinetic retention models' results showed the detention of PSMPs was irreversible under higher HA conditions (15 mg/L) with Ca2+, and aged PSMPs were more susceptible to clogging. These findings may help to understand the potential risk of migration behavior of PSMPs in the soil-groundwater environment.

Hedan tablet ameliorated non-alcoholic steatohepatitis by moderating NF-κB and lipid metabolism-related pathways via regulating hepatic metabolites.

Non-alcoholic steatohepatitis (NASH) is a severe form of fatty liver disease. If not treated, it can lead to liver damage, cirrhosis and even liver cancer. However, advances in treatment have remained relatively slow, and there is thus an urgent need to develop appropriate treatments. Hedan tablet (HDP) is used to treat metabolic syndrome. However, scientific understanding of the therapeutic effect of HDP on NASH remains limited. We used HDP to treat a methionine/choline-deficient diet-induced model of NASH in rats to elucidate the therapeutic effects of HDP on liver injury. In addition, we used untargeted metabolomics to investigate the effects of HDP on metabolites in liver of NASH rats, and further validated its effects on inflammation and lipid metabolism following screening for potential target pathways. HDP had considerable therapeutic, anti-oxidant, and anti-inflammatory effects on NASH. HDP could also alter the hepatic metabolites changed by NASH. Moreover, HDP considerable moderated NF-κB and lipid metabolism-related pathways. The present study found that HDP had remarkable therapeutic effects in NASH rats. The therapeutic efficacy of HDP in NASH mainly associated with regulation of NF-κB and lipid metabolism-related pathways via arachidonic acid metabolism, glycine-serine-threonine metabolism, as well as steroid hormone biosynthesis.

Remedial Dosing Recommendations for Sirolimus Delayed or Missed Dosages Caused by Poor Medication Compliance in Pediatric Tuberous Sclerosis Complex Patients.

BACKGROUND: Delayed or missed dosages caused by poor medication compliance significantly affected the treatment of diseases in children. AIMS: The present study aimed to investigate the influence of delayed or missed dosages on sirolimus pharmacokinetics (PK) in pediatric tuberous sclerosis complex (TSC) patients and to recommend remedial dosages for nonadherent patients. METHODS: A published sirolimus population PK model in pediatric TSC patients was used to assess the influence of different nonadherence scenarios and recommend optimally remedial dosages based on Monte Carlo simulation. Thirteen nonadherent scenarios were simulated in this study, including delayed 2h, 4 h, 6 h, 8 h, 10 h, 12 h, 14 h, 16 h, 18 h, 20 h, 22 h, 23.5 h, and missed one dosage. Remedial dosing strategies contained 10-200% of scheduled dosages. The optimal remedial dosage was that with the maximum probability of returning the individual therapeutic range. RESULTS: For delayed or missed sirolimus dosages in pediatric TSC patients, when the delayed time was 0-8 h, 8-10 h, 10-18 h, 18-22.7 h, 22.7-24 h, 70%, 60%, 40%, 30%, 20% scheduled dosages were recommended to take immediately. When one dosage was missed, 120% of scheduled dosages were recommended at the next dose. CONCLUSION: It was the first time to recommend remedial dosages for delayed or missed sirolimus therapy caused by poor medication compliance in pediatric TSC patients based on Monte Carlo simulation. Meanwhile, the present study provided a potential solution for delayed or missed dosages in clinical practice.

Personalized Medicine Transformed: ChatGPT's Contribution to Continuous Renal Replacement Therapy Alarm Management in Intensive Care Units.

The accurate interpretation of CRRT machine alarms is crucial in the intensive care setting. ChatGPT, with its advanced natural language processing capabilities, has emerged as a tool that is evolving and advancing in its ability to assist with healthcare information. This study is designed to evaluate the accuracy of the ChatGPT-3.5 and ChatGPT-4 models in addressing queries related to CRRT alarm troubleshooting. This study consisted of two rounds of ChatGPT-3.5 and ChatGPT-4 responses to address 50 CRRT machine alarm questions that were carefully selected by two nephrologists in intensive care. Accuracy was determined by comparing the model responses to predetermined answer keys provided by critical care nephrologists, and consistency was determined by comparing outcomes across the two rounds. The accuracy rate of ChatGPT-3.5 was 86% and 84%, while the accuracy rate of ChatGPT-4 was 90% and 94% in the first and second rounds, respectively. The agreement between the first and second rounds of ChatGPT-3.5 was 84% with a Kappa statistic of 0.78, while the agreement of ChatGPT-4 was 92% with a Kappa statistic of 0.88. Although ChatGPT-4 tended to provide more accurate and consistent responses than ChatGPT-3.5, there was no statistically significant difference between the accuracy and agreement rate between ChatGPT-3.5 and -4. ChatGPT-4 had higher accuracy and consistency but did not achieve statistical significance. While these findings are encouraging, there is still potential for further development to achieve even greater reliability. This advancement is essential for ensuring the highest-quality patient care and safety standards in managing CRRT machine-related issues.

Integrating Retrieval-Augmented Generation with Large Language Models in Nephrology: Advancing Practical Applications.

The integration of large language models (LLMs) into healthcare, particularly in nephrology, represents a significant advancement in applying advanced technology to patient care, medical research, and education. These advanced models have progressed from simple text processors to tools capable of deep language understanding, offering innovative ways to handle health-related data, thus improving medical practice efficiency and effectiveness. A significant challenge in medical applications of LLMs is their imperfect accuracy and/or tendency to produce hallucinations-outputs that are factually incorrect or irrelevant. This issue is particularly critical in healthcare, where precision is essential, as inaccuracies can undermine the reliability of these models in crucial decision-making processes. To overcome these challenges, various strategies have been developed. One such strategy is prompt engineering, like the chain-of-thought approach, which directs LLMs towards more accurate responses by breaking down the problem into intermediate steps or reasoning sequences. Another one is the retrieval-augmented generation (RAG) strategy, which helps address hallucinations by integrating external data, enhancing output accuracy and relevance. Hence, RAG is favored for tasks requiring up-to-date, comprehensive information, such as in clinical decision making or educational applications. In this article, we showcase the creation of a specialized ChatGPT model integrated with a RAG system, tailored to align with the KDIGO 2023 guidelines for chronic kidney disease. This example demonstrates its potential in providing specialized, accurate medical advice, marking a step towards more reliable and efficient nephrology practices.

Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review

BACKGROUND: ChatGPT is a large language model (LLM) based on artificial intelligence (AI) capable of responding in multiple languages and generating nuanced and highly complex responses. While ChatGPT holds promising applications in medical education, its limitations and potential risks cannot be ignored. METHODS: A scoping review was conducted for English articles discussing ChatGPT in the context of medical education published after 2022. A literature search was performed using PubMed/MEDLINE, Embase, and Web of Science databases, and information was extracted from the relevant studies that were ultimately included. RESULTS: ChatGPT exhibits various potential applications in medical education, such as providing personalized learning plans and materials, creating clinical practice simulation scenarios, and assisting in writing articles. However, challenges associated with academic integrity, data accuracy, and potential harm to learning were also highlighted in the literature. The paper emphasizes certain recommendations for using ChatGPT, including the establishment of guidelines. Based on the review, 3 key research areas were proposed: cultivating the ability of medical students to use ChatGPT correctly, integrating ChatGPT into teaching activities and processes, and proposing standards for the use of AI by medical students. CONCLUSION: ChatGPT has the potential to transform medical education, but careful consideration is required for its full integration. To harness the full potential of ChatGPT in medical education, attention should not only be given to the capabilities of AI but also to its impact on students and teachers.

Effect of Age, Comedications, and CYP3A4/5 Polymorphisms on Perampanel Exposure in Chinese Pediatric Patients With Epilepsy.

Perampanel (PER) is a new type of antiseizure medication used for partial or generalized seizures. However, the plasma concentration shows obvious individual variability in children. The present study aims to ascertain the effect of age, comedications, and cytochrome P450 (CYP) 3A4/5 polymorphisms on PER exposure in Chinese pediatric patients with epilepsy. Clinical data were retrospectively collected in a tertiary children's hospital medical records system from January 2021 to December 2022. The influence factors on the daily dose, plasma concentration, and concentration-to-dose ratio (CDR) of PER were investigated. A total of 135 pediatric patients with 178 blood samples were involved. With a median daily dose of 4.0 mg (interquartile range, 3.0-5.0 mg), the median plasma concentration was 409.4 ng/mL (interquartile range, 251.7-639.4 ng/mL). The CDR in patients aged less than 4 years was significantly decreased by 48.0% and 39.1% compared with those aged 4-11 years and 12 years or older, respectively. Enzyme inducers significantly decreased the CDR of PER by 34.5%, while valproic acid showed an increase of 71.7%. In addition, genotype CYP3A5*3/*3 carriers presented a significant increase of 21.5% compared to the CYP3A5*1/*3 expresser. No correlations were observed between the CDR and CYP3A4∗1G polymorphism. PER showed high variations in individual plasma concentrations. Age younger than 4 years, comedication with enzyme inducers or valproic acid, and possession of the CYP3A5*3 genotype potentially predicted PER exposure in pediatric patients with epilepsy.

Safety and efficacy of glucagon-like peptide-1 receptor agonists among kidney transplant recipients: a systematic review and meta-analysis.

Background: Evidence supporting glucagon-like peptide-1 receptor agonists (GLP-1RAs) in kidney transplant recipients (KTRs) remains scarce. This systematic review and meta-analysis aims to evaluate the safety and efficacy of GLP-1RAs in this population. Methods: A comprehensive literature search was conducted in the MEDLINE, Embase and Cochrane databases from inception through May 2023. Clinical trials and observational studies that reported on the safety or efficacy outcomes of GLP-1RAs in adult KTRs were included. Kidney graft function, glycaemic and metabolic parameters, weight, cardiovascular outcomes and adverse events were evaluated. Outcome measures used for analysis included pooled odds ratios (ORs) with 95% confidence intervals (CIs) for dichotomous outcomes and standardized mean difference (SMD) or mean difference (MD) with 95% CI for continuous outcomes. The protocol was registered in the International Prospective Register of Systematic Reviews (CRD 42023426190). Results: Nine cohort studies with a total of 338 KTRs were included. The median follow-up was 12 months (interquartile range 6-23). While treatment with GLP-1RAs did not yield a significant change in estimated glomerular filtration rate [SMD -0.07 ml/min/1.73 m2 (95% CI -0.64-0.50)] or creatinine [SMD -0.08 mg/dl (95% CI -0.44-0.28)], they were associated with a significant decrease in urine protein:creatinine ratio [SMD -0.47 (95% CI -0.77 to -0.18)] and haemoglobin A1c levels [MD -0.85% (95% CI -1.41 to -0.28)]. Total daily insulin dose, weight and body mass index also decreased significantly. Tacrolimus levels remained stable [MD -0.43 ng/ml (95% CI -0.99 to 0.13)]. Side effects were primarily nausea and vomiting (17.6%), diarrhoea (7.6%) and injection site pain (5.4%). Conclusions: GLP-1RAs are effective in reducing proteinuria, improving glycaemic control and supporting weight loss in KTRs, without altering tacrolimus levels. Gastrointestinal symptoms are the main side effects.

Formononetin promotes fatty acid ß-oxidation to treat non-alcoholic steatohepatitis through SIRT1/PGC-1α/PPARα pathway.

BACKGROUND: Non-alcoholic steatohepatitis (NASH), the progressive form of non-alcoholic fatty liver disease (NAFLD), carries a high risk of cirrhosis and hepatocellular carcinoma. With the increasing incidence of NASH, the accompanying medical burden is also increasing rapidly, so the development of safe and reliable drugs is urgent. Formononetin (FMNT) has a variety of pharmacological effects such as antioxidant and anti-inflammation, and plays a major role in regulating lipid metabolism, reducing hepatic steatosis and so on, but the mechanism for alleviating NASH is unclear. MATERIALS AND METHODS: We firstly established a mouse model on NASH through methionine-choline deficient (MCD) diet to investigate the improvement of FMNT as well as the effects of fatty acid ß oxidation and SIRT1/PGC-1α/PPARα pathway. Then, we explored the mechanisms of FMNT regulation in SIRT1/PGC-1α/PPARα pathway and fatty acid ß oxidation based on genes silencing of SIRT1 and PGC1A. In addition, SIRT1 agonist (SRT1720) and inhibitor (EX527) were used to verify the mechanism of FMNT on improvement of NASH. RESULTS: Our study found that after FMNT intervention, activities of ALT and AST and TG level were improved, and liver function and hepatocellular steatosis on NASH mice were significantly improved. The detection of ß oxidation related indicators showed that FMNT intervention up-regulated FAO capacity, level of carnitine, and the levels of ACADM and CPT1A. The detection of factors related to the SIRT1/PGC-1α/PPARα pathway showed that FMNT activated and promoted the expression of SIRT1/PGC-1α/PPARα pathway, including up-regulating the expression level of SIRT1, improving the activity of SIRT1, promoting the deacetylation of PGC-1α, and promoting the transcriptional activity of PPARα. Furthermore, after genes silencing of SIRT1 and PGC1A, we found that FMNT intervention could not alleviate NASH, including improvement of hepatocellular steatosis, enhancement of ß oxidation, and regulation of SIRT1/PGC-1α/PPARα pathway. Afterwards, we used SRT1720 as a positive control, and the results indicated that FMNT and SRT1720 intervention had no significant difference on improving hepatocellular steatosis and promoting fatty acid ß oxidation. Besides, we found that when EX527 intervention inhibited expression of SIRT1, the improvement of FMNT on NASH was weakened or even disappeared. CONCLUSION: In summary, our results demonstrated that FMNT intervention activated SIRT1/PGC-1α/PPARα pathway to promote fatty acid ß oxidation and regulate lipid metabolism in liver, ultimately improved hepatocellular steatosis on NASH mice.

Chain of Thought Utilization in Large Language Models and Application in Nephrology.

Chain-of-thought prompting enhances the abilities of large language models (LLMs) significantly. It not only makes these models more specific and context-aware but also impacts the wider field of artificial intelligence (AI). This approach broadens the usability of AI, increases its efficiency, and aligns it more closely with human thinking and decision-making processes. As we improve this method, it is set to become a key element in the future of AI, adding more purpose, precision, and ethical consideration to these technologies. In medicine, the chain-of-thought prompting is especially beneficial. Its capacity to handle complex information, its logical and sequential reasoning, and its suitability for ethically and context-sensitive situations make it an invaluable tool for healthcare professionals. Its role in enhancing medical care and research is expected to grow as we further develop and use this technique. Chain-of-thought prompting bridges the gap between AI's traditionally obscure decision-making process and the clear, accountable standards required in healthcare. It does this by emulating a reasoning style familiar to medical professionals, fitting well into their existing practices and ethical codes. While solving AI transparency is a complex challenge, the chain-of-thought approach is a significant step toward making AI more comprehensible and trustworthy in medicine. This review focuses on understanding the workings of LLMs, particularly how chain-of-thought prompting can be adapted for nephrology's unique requirements. It also aims to thoroughly examine the ethical aspects, clarity, and future possibilities, offering an in-depth view of the exciting convergence of these areas.