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.

Objective: We aimed to investigate the effectiveness of buspirone as an adjunctive therapy for alleviating anxiety symptoms in patients with depressive disorders who are already taking antidepressants. Methods: This was an open-label prospective multicenter non-interventional observational study conducted over 12 weeks. We enrolled 180 patients diagnosed with depressive disorders according to DSM-5 criteria and Hamilton Anxiety Rating Scale (HAMA) scores ≥ 18. Participants were already taking selective serotonin reuptake inhibitors or serotoninnorepinephrine reuptake inhibitors and were prescribed adjunctive buspirone. Efficacy was assessed using HAMA, Hamilton Depression Rating Scale (HAMD), Clinical Global Impression Scale-Improvement, Clinical Global Impression Scale-Severity, Sheehan Disability Scale (SDS), and WHO-5 Well-Being Index. Results: The efficacy analysis included 161 patients. HAMA scores decreased significantly from 25.2 ± 6.7 at baseline to 15.4 ± 8.6 at 12 weeks (p ＜ 0.001), whereas HAMD scores decreased from 19.4 ± 4.6 to 12.7 ± 5.7 (p ＜ 0.001).WHO-5 and SDS scores showed significant improvements. The HAMA response rate was 39.1% and the remission rate was 13.7% at 12 weeks. Adverse drug reactions were reported in 3.7% of participants. Subgroup analyses showed no significant differences in treatment response based on buspirone dosage, baseline anxiety/depression severity, or benzodiazepine use. Conclusion Adjunctive buspirone therapy effectively improved anxiety symptoms in depressed patients taking antidepressants, regardless of baseline symptom severity or buspirone dosage. The treatment was well-tolerated with few adverse events. Future studies using a control group are needed.

Background: and Purpose: This clinical practice guideline provides evidence-based recommendations for treatment of dementia, focusing on cholinesterase inhibitors and N-methyl-D-aspartate (NMDA) receptor antagonists for Alzheimer’s disease (AD) and other types of dementia. Methods: Using the Population, Intervention, Comparison, Outcomes (PICO) framework, we developed key clinical questions and conducted systematic literature reviews. A multidisciplinary panel of experts, organized by the Korean Dementia Association, evaluated randomized controlled trials and observational studies. Recommendations were graded for evidence quality and strength using Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology. Results: Three main recommendations are presented: (1) For AD, cholinesterase inhibitors (donepezil, rivastigmine, galantamine) are strongly recommended for improving cognition and daily function based on moderate evidence; (2) Cholinesterase inhibitors are conditionally recommended for vascular dementia and Parkinson’s disease dementia, with a strong recommendation for Lewy body dementia; (3) For moderate to severe AD, NMDA receptor antagonist (memantine) is strongly recommended, demonstrating significant cognitive and functional improvements. Both drug classes showed favorable safety profiles with manageable side effects. Conclusions This guideline offers standardized, evidence-based pharmacologic recommendations for dementia management, with specific guidance on cholinesterase inhibitors and NMDA receptor antagonists. It aims to support clinical decision-making and improve patient outcomes in dementia care. Further updates will address emerging treatments, including amyloid-targeting therapies, to reflect advances in dementia management.

Anticancer activities of Licochalcone D (LCD) in human colorectal cancer (CRC) cells HCT116 and oxaliplatin-resistant HCT116 (HCT116-OxR) were determined. Cell viability assay and soft agar assay were used to analyze antiproliferative activity of LCD.Flow cytometry was performed to determine effects of LCD on apoptosis, cell cycle distribution, reactive oxygen species (ROS), mitochondrial membrane potential (MMP) dysfunction, and multi-caspase activity in CRC cells. Western blot analysis was used to monitor levels of proteins involved in cell cycle and apoptosis signaling pathways. LCD suppressed the growth and anchorageindependent colony formation of both HCT116 and HCT116-OxR cells. Cell cycle analysis by flow cytometry indicated that LCD induced cell cycle arrest and increased cells in sub-G1 phase. In parallel with the antiproliferative effect of LCD, LCD up-regulated levels of p21 and p27 while downregulating cyclin B1 and cdc2. In addition, phosphorylation levels of JNK and p38 mitogen-activated protein kinase (MAPK) were increased by LCD. Inhibition of these kinases somehow prevented the antiproliferative effect of LCD. Moreover, LCD increased ROS and deregulated mitochondrial membrane potential, leading to the activation of multiple caspases. An ROS scavenger N-acetyl-cysteine (NAC) or pan-caspase inhibitor Z-VAD-FMK prevented the antiproliferative effect of LCD, supporting that ROS generation and caspase activation mediated LCD-induced apoptosis in CRC cells. In conclusion, LCD exerted antitumor activity in CRC cells by inducing ROS generation and phosphorylation of JNK and p38 MAPK. These results support that LCD could be further developed as a chemotherapeutic agent for treating CRC.

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: We aimed to investigate the effectiveness of buspirone as an adjunctive therapy for alleviating anxiety symptoms in patients with depressive disorders who are already taking antidepressants. Methods: This was an open-label prospective multicenter non-interventional observational study conducted over 12 weeks. We enrolled 180 patients diagnosed with depressive disorders according to DSM-5 criteria and Hamilton Anxiety Rating Scale (HAMA) scores ≥ 18. Participants were already taking selective serotonin reuptake inhibitors or serotoninnorepinephrine reuptake inhibitors and were prescribed adjunctive buspirone. Efficacy was assessed using HAMA, Hamilton Depression Rating Scale (HAMD), Clinical Global Impression Scale-Improvement, Clinical Global Impression Scale-Severity, Sheehan Disability Scale (SDS), and WHO-5 Well-Being Index. Results: The efficacy analysis included 161 patients. HAMA scores decreased significantly from 25.2 ± 6.7 at baseline to 15.4 ± 8.6 at 12 weeks (p ＜ 0.001), whereas HAMD scores decreased from 19.4 ± 4.6 to 12.7 ± 5.7 (p ＜ 0.001).WHO-5 and SDS scores showed significant improvements. The HAMA response rate was 39.1% and the remission rate was 13.7% at 12 weeks. Adverse drug reactions were reported in 3.7% of participants. Subgroup analyses showed no significant differences in treatment response based on buspirone dosage, baseline anxiety/depression severity, or benzodiazepine use. Conclusion Adjunctive buspirone therapy effectively improved anxiety symptoms in depressed patients taking antidepressants, regardless of baseline symptom severity or buspirone dosage. The treatment was well-tolerated with few adverse events. Future studies using a control group are needed.

Background: and Purpose: This clinical practice guideline provides evidence-based recommendations for treatment of dementia, focusing on cholinesterase inhibitors and N-methyl-D-aspartate (NMDA) receptor antagonists for Alzheimer’s disease (AD) and other types of dementia. Methods: Using the Population, Intervention, Comparison, Outcomes (PICO) framework, we developed key clinical questions and conducted systematic literature reviews. A multidisciplinary panel of experts, organized by the Korean Dementia Association, evaluated randomized controlled trials and observational studies. Recommendations were graded for evidence quality and strength using Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology. Results: Three main recommendations are presented: (1) For AD, cholinesterase inhibitors (donepezil, rivastigmine, galantamine) are strongly recommended for improving cognition and daily function based on moderate evidence; (2) Cholinesterase inhibitors are conditionally recommended for vascular dementia and Parkinson’s disease dementia, with a strong recommendation for Lewy body dementia; (3) For moderate to severe AD, NMDA receptor antagonist (memantine) is strongly recommended, demonstrating significant cognitive and functional improvements. Both drug classes showed favorable safety profiles with manageable side effects. Conclusions This guideline offers standardized, evidence-based pharmacologic recommendations for dementia management, with specific guidance on cholinesterase inhibitors and NMDA receptor antagonists. It aims to support clinical decision-making and improve patient outcomes in dementia care. Further updates will address emerging treatments, including amyloid-targeting therapies, to reflect advances in dementia management.

Anticancer activities of Licochalcone D (LCD) in human colorectal cancer (CRC) cells HCT116 and oxaliplatin-resistant HCT116 (HCT116-OxR) were determined. Cell viability assay and soft agar assay were used to analyze antiproliferative activity of LCD.Flow cytometry was performed to determine effects of LCD on apoptosis, cell cycle distribution, reactive oxygen species (ROS), mitochondrial membrane potential (MMP) dysfunction, and multi-caspase activity in CRC cells. Western blot analysis was used to monitor levels of proteins involved in cell cycle and apoptosis signaling pathways. LCD suppressed the growth and anchorageindependent colony formation of both HCT116 and HCT116-OxR cells. Cell cycle analysis by flow cytometry indicated that LCD induced cell cycle arrest and increased cells in sub-G1 phase. In parallel with the antiproliferative effect of LCD, LCD up-regulated levels of p21 and p27 while downregulating cyclin B1 and cdc2. In addition, phosphorylation levels of JNK and p38 mitogen-activated protein kinase (MAPK) were increased by LCD. Inhibition of these kinases somehow prevented the antiproliferative effect of LCD. Moreover, LCD increased ROS and deregulated mitochondrial membrane potential, leading to the activation of multiple caspases. An ROS scavenger N-acetyl-cysteine (NAC) or pan-caspase inhibitor Z-VAD-FMK prevented the antiproliferative effect of LCD, supporting that ROS generation and caspase activation mediated LCD-induced apoptosis in CRC cells. In conclusion, LCD exerted antitumor activity in CRC cells by inducing ROS generation and phosphorylation of JNK and p38 MAPK. These results support that LCD could be further developed as a chemotherapeutic agent for treating CRC.

Objective: We aimed to investigate the effectiveness of buspirone as an adjunctive therapy for alleviating anxiety symptoms in patients with depressive disorders who are already taking antidepressants. Methods: This was an open-label prospective multicenter non-interventional observational study conducted over 12 weeks. We enrolled 180 patients diagnosed with depressive disorders according to DSM-5 criteria and Hamilton Anxiety Rating Scale (HAMA) scores ≥ 18. Participants were already taking selective serotonin reuptake inhibitors or serotoninnorepinephrine reuptake inhibitors and were prescribed adjunctive buspirone. Efficacy was assessed using HAMA, Hamilton Depression Rating Scale (HAMD), Clinical Global Impression Scale-Improvement, Clinical Global Impression Scale-Severity, Sheehan Disability Scale (SDS), and WHO-5 Well-Being Index. Results: The efficacy analysis included 161 patients. HAMA scores decreased significantly from 25.2 ± 6.7 at baseline to 15.4 ± 8.6 at 12 weeks (p ＜ 0.001), whereas HAMD scores decreased from 19.4 ± 4.6 to 12.7 ± 5.7 (p ＜ 0.001).WHO-5 and SDS scores showed significant improvements. The HAMA response rate was 39.1% and the remission rate was 13.7% at 12 weeks. Adverse drug reactions were reported in 3.7% of participants. Subgroup analyses showed no significant differences in treatment response based on buspirone dosage, baseline anxiety/depression severity, or benzodiazepine use. Conclusion Adjunctive buspirone therapy effectively improved anxiety symptoms in depressed patients taking antidepressants, regardless of baseline symptom severity or buspirone dosage. The treatment was well-tolerated with few adverse events. Future studies using a control group are needed.