Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.

Oxidative stress due to hyperglycemia damages the functions of retinal pigment epithelial (RPE) cells and is a major risk factor for diabetic retinopathy (DR). Paeoniflorin is a monoterpenoid glycoside found in the roots of Paeonia lactiflora Pall and has been reported to have a variety of health benefits. However, the mechanisms underlying its therapeutic effects on high glucose (HG)-induced oxidative damage in RPE cells are not fully understood. In this study, we investigated the protective effect of paeoniflorin against HG-induced oxidative damage in cultured human RPE ARPE-19 cells, an in vitro model of hyperglycemia. Pretreatment with paeoniflorin markedly reduced HG-induced cytotoxicity and DNA damage. Paeoniflorin inhibited HG-induced apoptosis by suppressing activation of the caspase cascade, and this suppression was associated with the blockade of cytochrome c release to cytoplasm by maintaining mitochondrial membrane stability. In addition, paeoniflorin suppressed the HG-induced production of reactive oxygen species (ROS), increased the phosphorylation of nuclear factor erythroid 2-related factor 2 (Nrf2), a key redox regulator, and the expression of its downstream factor heme oxygenase-1 (HO-1). On the other hand, zinc protoporphyrin (ZnPP), an inhibitor of HO-1, abolished the protective effect of paeoniflorin against ROS production in HG-treated cells. Furthermore, ZnPP reversed the protective effects of paeoniflorin against HG-induced cellular damage and induced mitochondrial damage, DNA injury, and apoptosis in paeoniflorin-treated cells. These results suggest that paeoniflorin protects RPE cells from HG-mediated oxidative stress-induced cytotoxicity by activating Nrf2/HO-1 signaling and highlight the potential therapeutic use of paeoniflorin to improve the symptoms of DR.

Cynaropicrin, a sesquiterpene lactone found in artichoke leaves exerts diverse pharmacological effects. This study investigated whether cynaropicrin has a paraptosis-like cell death effect in human hepatocellular carcinoma Hep3B cells in addition to the apoptotic effects reported in several cancer cell lines. Cynaropicrin-induced cytotoxicity and cytoplasmic vacuolation, a key characteristic of paraptosis, were not ameliorated by inhibitors of necroptosis, autophagy, or pan caspase inhibitors in Hep3B cells. Our study showed that cynaropicrin-induced cytotoxicity was accompanied by mitochondrial dysfunction and endoplasmic reticulum stress along with increased cellular calcium ion levels. These effects were significantly mitigated by endoplasmic reticulum stress inhibitor or protein synthesis inhibitor. Moreover, cynaropicrin treatment in Hep3B cells increased reactive oxygen species generation and downregulated apoptosis-linked gene 2-interacting protein X (Alix), a protein that inhibits paraptosis. The addition of the reactive oxygen species scavenger N-acetyl-L-cysteine (NAC) neutralized cynaropicrin-induced changes in Alix expression and endoplasmic reticulum stress marker proteins counteracting endoplasmic reticulum stress and mitochondrial impairment. This demonstrates a close relationship between endoplasmic reticulum stress and reactive oxygen species generation. Additionally, cynaropicrin activated p38 mitogen activated protein kinase and a selective p38 mitogen activated protein kinase blocker alleviated the biological phenomena induced by cynaropicrin. NAC pretreatment showed the best reversal of cynaropicrin induced vacuolation and cellular inactivity. Our findings suggest that cynaropicrin induced oxidative stress in Hep3B cells contributes to paraptotic events including endoplasmic reticulum stress and mitochondrial damage.

Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.

Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.

Heart failure (HF) is a major cause of mortality and morbidity in South Korea, imposing substantial physical, emotional, and financial burdens on patients and society. Despite the high burden of symptom and complex care needs of HF patients, palliative care and hospice services remain underutilized in South Korea due to cultural, institutional, and knowledge-related barriers. This position statement from the Korean Society of Heart Failure emphasizes the need for integrating palliative and hospice care into HF management to improve quality of life and support holistic care for patients and their families. By clarifying the role of palliative care in HF and proposing practical referral criteria, this position statement aims to bridge the gap between HF and palliative care services in South Korea, ultimately improving patient-centered outcomes and aligning treatment with the goals and values of HF patients.

Oxidative stress due to hyperglycemia damages the functions of retinal pigment epithelial (RPE) cells and is a major risk factor for diabetic retinopathy (DR). Paeoniflorin is a monoterpenoid glycoside found in the roots of Paeonia lactiflora Pall and has been reported to have a variety of health benefits. However, the mechanisms underlying its therapeutic effects on high glucose (HG)-induced oxidative damage in RPE cells are not fully understood. In this study, we investigated the protective effect of paeoniflorin against HG-induced oxidative damage in cultured human RPE ARPE-19 cells, an in vitro model of hyperglycemia. Pretreatment with paeoniflorin markedly reduced HG-induced cytotoxicity and DNA damage. Paeoniflorin inhibited HG-induced apoptosis by suppressing activation of the caspase cascade, and this suppression was associated with the blockade of cytochrome c release to cytoplasm by maintaining mitochondrial membrane stability. In addition, paeoniflorin suppressed the HG-induced production of reactive oxygen species (ROS), increased the phosphorylation of nuclear factor erythroid 2-related factor 2 (Nrf2), a key redox regulator, and the expression of its downstream factor heme oxygenase-1 (HO-1). On the other hand, zinc protoporphyrin (ZnPP), an inhibitor of HO-1, abolished the protective effect of paeoniflorin against ROS production in HG-treated cells. Furthermore, ZnPP reversed the protective effects of paeoniflorin against HG-induced cellular damage and induced mitochondrial damage, DNA injury, and apoptosis in paeoniflorin-treated cells. These results suggest that paeoniflorin protects RPE cells from HG-mediated oxidative stress-induced cytotoxicity by activating Nrf2/HO-1 signaling and highlight the potential therapeutic use of paeoniflorin to improve the symptoms of DR.

Cynaropicrin, a sesquiterpene lactone found in artichoke leaves exerts diverse pharmacological effects. This study investigated whether cynaropicrin has a paraptosis-like cell death effect in human hepatocellular carcinoma Hep3B cells in addition to the apoptotic effects reported in several cancer cell lines. Cynaropicrin-induced cytotoxicity and cytoplasmic vacuolation, a key characteristic of paraptosis, were not ameliorated by inhibitors of necroptosis, autophagy, or pan caspase inhibitors in Hep3B cells. Our study showed that cynaropicrin-induced cytotoxicity was accompanied by mitochondrial dysfunction and endoplasmic reticulum stress along with increased cellular calcium ion levels. These effects were significantly mitigated by endoplasmic reticulum stress inhibitor or protein synthesis inhibitor. Moreover, cynaropicrin treatment in Hep3B cells increased reactive oxygen species generation and downregulated apoptosis-linked gene 2-interacting protein X (Alix), a protein that inhibits paraptosis. The addition of the reactive oxygen species scavenger N-acetyl-L-cysteine (NAC) neutralized cynaropicrin-induced changes in Alix expression and endoplasmic reticulum stress marker proteins counteracting endoplasmic reticulum stress and mitochondrial impairment. This demonstrates a close relationship between endoplasmic reticulum stress and reactive oxygen species generation. Additionally, cynaropicrin activated p38 mitogen activated protein kinase and a selective p38 mitogen activated protein kinase blocker alleviated the biological phenomena induced by cynaropicrin. NAC pretreatment showed the best reversal of cynaropicrin induced vacuolation and cellular inactivity. Our findings suggest that cynaropicrin induced oxidative stress in Hep3B cells contributes to paraptotic events including endoplasmic reticulum stress and mitochondrial damage.

Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.

Oxidative stress due to hyperglycemia damages the functions of retinal pigment epithelial (RPE) cells and is a major risk factor for diabetic retinopathy (DR). Paeoniflorin is a monoterpenoid glycoside found in the roots of Paeonia lactiflora Pall and has been reported to have a variety of health benefits. However, the mechanisms underlying its therapeutic effects on high glucose (HG)-induced oxidative damage in RPE cells are not fully understood. In this study, we investigated the protective effect of paeoniflorin against HG-induced oxidative damage in cultured human RPE ARPE-19 cells, an in vitro model of hyperglycemia. Pretreatment with paeoniflorin markedly reduced HG-induced cytotoxicity and DNA damage. Paeoniflorin inhibited HG-induced apoptosis by suppressing activation of the caspase cascade, and this suppression was associated with the blockade of cytochrome c release to cytoplasm by maintaining mitochondrial membrane stability. In addition, paeoniflorin suppressed the HG-induced production of reactive oxygen species (ROS), increased the phosphorylation of nuclear factor erythroid 2-related factor 2 (Nrf2), a key redox regulator, and the expression of its downstream factor heme oxygenase-1 (HO-1). On the other hand, zinc protoporphyrin (ZnPP), an inhibitor of HO-1, abolished the protective effect of paeoniflorin against ROS production in HG-treated cells. Furthermore, ZnPP reversed the protective effects of paeoniflorin against HG-induced cellular damage and induced mitochondrial damage, DNA injury, and apoptosis in paeoniflorin-treated cells. These results suggest that paeoniflorin protects RPE cells from HG-mediated oxidative stress-induced cytotoxicity by activating Nrf2/HO-1 signaling and highlight the potential therapeutic use of paeoniflorin to improve the symptoms of DR.