A phthalocyanine–subphthalocyanine heterodinuclear dimer has been disclosed for the first time with its unique flat-bowl-shaped structure revealed by X-ray crystallography.

Japan's largest platform for academic e-journals: J-STAGE is a full text database for reviewed academic papers published by Japanese societies

Purpose: The objective of this study was to assess the performance of ChatGPT (GPT-4) on all items, including those with diagrams, in the Japanese National License Examination for Pharmacists (JNLEP) and compare it with the previous GPT-3.5 model’s performance.Methods: The 107th JNLEP, conducted in 2022, with 344 items input into the GPT-4 model, was targeted for this study. Separately, 284 items, excluding those with diagrams, were entered into the GPT-3.5 model. The answers were categorized and analyzed to determine accuracy rates based on categories, subjects, and presence or absence of diagrams. The accuracy rates were compared to the main passing criteria (overall accuracy rate ≥62.9%).Results: The overall accuracy rate for all items in the 107th JNLEP in GPT-4 was 72.5%, successfully meeting all the passing criteria. For the set of items without diagrams, the accuracy rate was 80.0%, which was significantly higher than that of the GPT-3.5 model (43.5%). The GPT-4 model demonstrated an accuracy rate of 36.1% for items that included diagrams.Conclusion: Advancements that allow GPT-4 to process images have made it possible for LLMs to answer all items in medical-related license examinations. This study’s findings confirm that ChatGPT (GPT-4) possesses sufficient knowledge to meet the passing criteria.

An open-cage fullerene bearing an eight-membered ring orifice has been synthesized in one pot by the reaction of C₆₀ with propargylic phosphate in the presence of CuCl. The reaction cascade involves the transformation of the phosphate to the 1,3-dienyl phosphate, which enables the reaction with C₆₀ by [4 + 2] cycloaddition to form the cyclohexene-annulated intermediate, and subsequent intramolecular syn-elimination of the phosphodiester affords the cyclohexadiene-annulated fullerene derivative as the precursor for the open-cage fullerene.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Generative artificial intelligence (AI) has shown rapid advancement and increasing applications in various domains, including healthcare. Previous studies have evaluated AI performance on medical license exams, primarily focusing on ChatGPT. However, the availability of new online chat-based large language models (OC-LLMs) and their potential utility in pharmacy licensing exams remain underexplored. Given that pharmacists require a broad range of expertise in physics, chemistry, biology, and pharmacology, there is a need to verify the knowledge base and problem-solving abilities of these newer models in Japanese pharmacy examinations. Objective: This study aimed to assess the performance of 18 OC-LLM models released in 2024 in the 107th Japanese National License Examination for Pharmacists (JNLEP), comparing their accuracy and identifying areas of improvement relative to earlier models. Methods: The 107th JNLEP, comprising 345 questions in Japanese, was used as the benchmark. Each OC-LLM was prompted with the original text-based questions, and images were uploaded where permitted. No additional prompt engineering or English translation was performed. For questions that included diagrams or chemical structures, models incapable of image input were considered incorrect. Model outputs were compared with publicly available correct answers. Overall accuracy rates were calculated by subject area (pharmacology and chemistry) and question type (text-only, diagram-based, calculation, and chemical structure). Fleiss’ kappa was used to measure answer consistency among the top-performing models. Results: Four flagship models—ChatGPT o1, Gemini 2.0 Flash, Claude 3.5 Sonnet (New), and Perplexity Pro—achieved 80% accuracy, surpassing the official passing threshold and average examinee score. A significant improvement in the overall accuracy was observed between the early and latest 2024 models. Marked improvements were noted in text-only and diagram-based questions compared with those of earlier versions. However, accuracy for chemistry-related and chemical structure questions remained relatively low. Fleiss’ kappa among the four flagship models was 0.334, suggesting moderate consistency, but highlighting variability in more complex questions. Conclusions: OC-LLMs have substantially improved their capacity to handle Japanese pharmacist examination content, with several newer models achieving accuracy rates of over 80%. Despite these advancements, even the best-performing models exhibited an error rate exceeding 10%, underscoring the ongoing need for careful human oversight in clinical settings. The 107th JNLEP serves as a valuable benchmark for current and future generative AI evaluations in pharmacy licensing examinations.

Japan's largest platform for academic e-journals: J-STAGE is a full text database for reviewed academic papers published by Japanese societies