The development of highly active and low-cost catalysts for electrochemical reactions is one of the most attractive topics in the renewable energy technology. Herein, the site-specific nitrogen doping of graphdiyne (GDY) including grap-N, sp-N(I) and sp-N(II) GDY is systematically investigated as metal-free oxygen reduction electrocatalysts via density functional theory (DFT). Our results indicate that the doped nitrogen atom can significantly improve the oxygen (O2) adsorption activity of GDY through activating its neighboring carbon atoms. The free-energy landscape is employed to describe the electrochemical oxygen reduction reaction (ORR) in both O2 dissociation and association mechanisms. It is revealed that the association mechanism can provide higher ORR onset potential than dissociation mechanism on most of the substrates. Especially, sp-N(II) GDY exhibits the highest ORR electrocatalytic activity through increasing the theoretical onset potential to 0.76 V. This work provides an atomic-level insight for the electrochemical ORR mechanism on metal-free N-doped GDY.
Myocardial ischemia-reperfusion injury (MIRI) is a major hindrance to the success of cardiac reperfusion therapy. Although increased neutrophil infiltration is a hallmark of MIRI, the subtypes and alterations of neutrophils in this process remain unclear. Here, we performed single-cell sequencing of cardiac CD45+ cells isolated from the murine myocardium subjected to MIRI at six-time points. We identified diverse types of infiltrating immune cells and their dynamic changes during MIRI. Cardiac neutrophils showed the most immediate response and largest changes and featured with functionally heterogeneous subpopulations, including Ccl3hi Neu and Ym-1hi Neu, which were increased at 6 h and 1 d after reperfusion, respectively. Ym-1hi Neu selectively expressed genes with protective effects and was, therefore, identified as a novel specific type of cardiac cell in the injured heart. Further analysis indicated that neutrophils and their subtypes orchestrated subsequent immune responses in the cardiac tissues, especially instructing the response of macrophages. The abundance of Ym-1hi Neu was closely correlated with the therapeutic efficacy of MIRI when neutrophils were specifically targeted by anti-Lymphocyte antigen 6 complex locus G6D (Ly6G) or anti-Intercellular cell adhesion molecule-1 (ICAM-1) neutralizing antibodies. In addition, a neutrophil subtype with the same phenotype as Ym-1hi Neu was detected in clinical samples and correlated with prognosis. Ym-1 inhibition exacerbated myocardial injury, whereas Ym-1 supplementation significantly ameliorated injury in MIRI mice, which was attributed to the tilt of Ym-1 on the polarization of macrophages toward the repair phenotype in myocardial tissue. Overall, our findings reveal the anti-inflammatory phenotype of Ym-1hi Neu and highlight its critical role in myocardial protection during the early stages of MIRI.
Diabetes poses a considerable global health challenge, with varying levels of diabetes knowledge among healthcare professionals, highlighting the importance of diabetes training. Large Language Models (LLMs) provide new insights into diabetes training, but their performance in diabetes-related queries remains uncertain, especially outside the English language like Chinese. We first evaluated the performance of ten LLMs: ChatGPT-3.5, ChatGPT-4.0, Google Bard, LlaMA-7B, LlaMA2-7B, Baidu ERNIE Bot, Ali Tongyi Qianwen, MedGPT, HuatuoGPT, and Chinese LlaMA2-7B on diabetes-related queries, based on the Chinese National Certificate Examination for Primary Diabetes Care in China (NCE-CPDC) and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United Kingdom. Second, we assessed the training of primary care physicians (PCPs) without and with the assistance of ChatGPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical assistants. We found that ChatGPT-4.0 outperformed other LLMs in the English examination, achieving a passing accuracy of 62.50%, which was significantly higher than that of Google Bard, LlaMA-7B, and LlaMA2-7B. For the NCE-CPFC examination, ChatGPT-4.0, Ali Tongyi Qianwen, Baidu ERNIE Bot, Google Bard, MedGPT, and ChatGPT-3.5 successfully passed, whereas LlaMA2-7B, HuatuoGPT, Chinese LLaMA2-7B, and LlaMA-7B failed. ChatGPT-4.0 (84.82%) surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination (improving by 1 %-6.13%). In summary, LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language, and hold great potential to assist future diabetes training for physicians globally.