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Large language models (LLMs) are gaining increasing interests to improve clinical efficiency for medical diagnosis, owing to their unprecedented performance in modelling natural language. Ensuring the safe and reliable clinical applications, the evaluation of LLMs indeed becomes critical for better mitigating the potential risks, e.g., hallucinations. However, current evaluation methods heavily rely on labor-intensive human participation to achieve human-preferred judgements. To overcome this challenge, we propose an automatic evaluation paradigm tailored to assess the LLMs' capabilities in delivering clinical services, e.g., disease diagnosis and treatment. The evaluation paradigm contains three basic elements: metric, data, and algorithm. Specifically, inspired by professional clinical practice pathways, we formulate a LLM-specific clinical pathway (LCP) to define the clinical capabilities that a doctor agent should possess. Then, Standardized Patients (SPs) from the medical education are introduced as the guideline for collecting medical data for evaluation, which can well ensure the completeness of the evaluation procedure. Leveraging these steps, we develop a multi-agent framework to simulate the interactive environment between SPs and a doctor agent, which is equipped with a Retrieval-Augmented Evaluation (RAE) to determine whether the behaviors of a doctor agent are in accordance with LCP. The above paradigm can be extended to any similar clinical scenarios to automatically evaluate the LLMs' medical capabilities. Applying such paradigm, we construct an evaluation benchmark in the field of urology, including a LCP, a SPs dataset, and an automated RAE. Extensive experiments are conducted to demonstrate the effectiveness of the proposed approach, providing more insights for LLMs' safe and reliable deployments in clinical practice.

Recent advancements in Large Language Models (LLMs) and Large Multi-modal Models (LMMs) have shown potential in various medical applications, such as Intelligent Medical Diagnosis. Although impressive results have been achieved, we find that existing benchmarks do not reflect the complexity of real medical reports and specialized in-depth reasoning capabilities. In this work, we introduced RJUA-MedDQA, a comprehensive benchmark in the field of medical specialization, which poses several challenges: comprehensively interpreting imgage content across diverse challenging layouts, possessing numerical reasoning ability to identify abnormal indicators and demonstrating clinical reasoning ability to provide statements of disease diagnosis, status and advice based on medical contexts. We carefully design the data generation pipeline and proposed the Efficient Structural Restoration Annotation (ESRA) Method, aimed at restoring textual and tabular content in medical report images. This method substantially enhances annotation efficiency, doubling the productivity of each annotator, and yields a 26.8% improvement in accuracy. We conduct extensive evaluations, including few-shot assessments of 5 LMMs which are capable of solving Chinese medical QA tasks. To further investigate the limitations and potential of current LMMs, we conduct comparative experiments on a set of strong LLMs by using image-text generated by ESRA method. We report the performance of baselines and offer several observations: (1) The overall performance of existing LMMs is still limited; however LMMs more robust to low-quality and diverse-structured images compared to LLMs. (3) Reasoning across context and image content present significant challenges. We hope this benchmark helps the community make progress on these challenging tasks in multi-modal medical document understanding and facilitate its application in healthcare., Comment: 15 pages, 13 figures

We introduce RJUA-QA, a novel medical dataset for question answering (QA) and reasoning with clinical evidence, contributing to bridge the gap between general large language models (LLMs) and medical-specific LLM applications. RJUA-QA is derived from realistic clinical scenarios and aims to facilitate LLMs in generating reliable diagnostic and advice. The dataset contains 2,132 curated Question-Context-Answer pairs, corresponding about 25,000 diagnostic records and clinical cases. The dataset covers 67 common urological disease categories, where the disease coverage exceeds 97.6\% of the population seeking medical services in urology. Each data instance in RJUA-QA comprises: (1) a question mirroring real patient to inquiry about clinical symptoms and medical conditions, (2) a context including comprehensive expert knowledge, serving as a reference for medical examination and diagnosis, (3) a doctor response offering the diagnostic conclusion and suggested examination guidance, (4) a diagnosed clinical disease as the recommended diagnostic outcome, and (5) clinical advice providing recommendations for medical examination. RJUA-QA is the first medical QA dataset for clinical reasoning over the patient inquiries, where expert-level knowledge and experience are required for yielding diagnostic conclusions and medical examination advice. A comprehensive evaluation is conducted to evaluate the performance of both medical-specific and general LLMs on the RJUA-QA dataset. Our data is are publicly available at \url{https://github.com/alipay/RJU_Ant_QA}., Comment: An initial version

Yingfeng Tu,1,2 Xiaowei Ma,3 Hao Chen,2,4 Yuhua Fan,5 Lei Jiang,2 Ruiping Zhang,2,6 Zhen Cheng2,4 1Department of Cardiology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, People’s Republic of China; 2Molecular Imaging Program at Stanford, Department of Radiology and Bio-X Program, Stanford University, Stanford, CA, USA; 3Department of Nuclear Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China; 4Molecular Imaging Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, People’s Republic of China; 5College of Pharmacy, Harbin Medical University, Daqing, Heilongjiang, People’s Republic of China; 6The Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan, People’s Republic of ChinaCorrespondence: Zhen Cheng, Molecular Imaging Program at Stanford, Department of Radiology and Bio-X Program, Canary Center at Stanford for Cancer Early Detection, 1201 Welch Road, Lucas Expansion, P095, Stanford University, Stanford, CA, 94305, USA, Tel +01-650-723-7866, Email zcheng@simm.ac.cn Ruiping Zhang, Department of Radiology, the Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan, 030032, People’s Republic of China, Email 1900547207@qq.comBackground: Matrix metalloproteinases from macrophages are important intraplaque components that play pivotal roles in plaque progression and regression. This study sought to develop a novel multifunctional positron emission tomography (PET) and magnetic resonance imaging (MRI) contrast agents based on MMP-2 cleavable nanoparticles to noninvasive assessment of MMP-2 activity in mouse carotid atherosclerotic plaques.Results: Macrophage-rich vascular lesions were induced by carotid ligation plus high-fat diet and streptozotocin-induced diabetes in CL57

Na Yu,1 Nan Gu,1 Yuxin Wang,1 Bin Zhou,2 Difei Lu,1 Jianping Li,3 Xiaowei Ma,1 Junqing Zhang,1 Xiaohui Guo1 1Department of Endocrinology, Peking University First Hospital, Beijing, 100034, People’s Republic of China; 2Department of Cardiology, Fuwai Hospital, National Centre for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, People’s Republic of China; 3Department of Cardiology, Peking University First Hospital, Beijing, 100034, People’s Republic of ChinaCorrespondence: Xiaowei MaDepartment of Endocrinology, Peking University First Hospital, Beijing, People’s Republic of ChinaTel/Fax +86-010-83572574Email xiaowei.ma@pkufh.comPurpose: We aimed to examine the association between plasma trimethylamine N-oxide (TMAO), a gut microbial metabolite from dietary phosphatidylcholine, and coronary atherosclerotic burden in patients with type 2 diabetes (T2D).Methods: In total, 349 patients with T2D were studied, including 70 controls and 279 patients with coronary artery disease (CAD) by coronary angiography. Coronary atherosclerotic burden is quantified by the number of diseased coronary branches and SYNTAX (Synergy between PCI with Taxus and Cardiac Surgery) score. Plasma TMAO levels were determined by UHPLC–MS/MS technique.Results: The TMAO concentration was significantly higher in the patients with triple vessel disease (TVD) (3.33 [IQR: 1.81– 6.65] μM) than those without TVD (2.62 [IQR: 1.50– 4.73] μM) (P = 0.015). A similar difference was found between patients with SYNTAX score > 22 (3.93 [IQR: 1.81– 6.82] μM) and those with SYNTAX score ≤ 22 (2.54 [IQR: 1.44– 4.54] μM) (P = 0.014). TMAO was not significantly correlated with the presence of CAD. Among patients with eGFR < 60 mL/min/1.73 m2, the highest tertile of TMAO was significantly associated with TVD (OR = 25.28

X-ray-based analytics are routinely applied in many fields, including physics, chemistry, materials science, and engineering. The full potential of such techniques in the life sciences and medicine, however, has not yet been fully exploited. We highlight current and upcoming advances in this direction. We describe different X-ray-based methodologies (including those performed at synchrotron light sources and X-ray free-electron lasers) and their potentials for application to investigate the nano-bio interface. The discussion is predominantly guided by asking how such methods could better help to understand and to improve nanoparticle-based drug delivery, though the concepts also apply to nano-bio interactions in general. We discuss current limitations and how they might be overcome, particularly for future use in vivo.

X-ray-based analytics are routinely applied in many fields, including physics, chemistry, materials science, and engineering. The full potential of such techniques in the life sciences and medicine, however, has not yet been fully exploited. We highlight current and upcoming advances in this direction. We describe different X-ray-based methodologies (including those performed at synchrotron light sources and X-ray free-electron lasers) and their potentials for application to investigate the nano-bio interface. The discussion is predominantly guided by asking how such methods could better help to understand and to improve nanoparticle-based drug delivery, though the concepts also apply to nano-bio interactions in general. We discuss current limitations and how they might be overcome, particularly for future use in vivo.

X-ray-based analytics are routinely applied in many fields, including physics, chemistry, materials science, and engineering. The full potential of such techniques in the life sciences and medicine, however, has not yet been fully exploited. We highlight current and upcoming advances in this direction. We describe different X-ray-based methodologies (including those performed at synchrotron light sources and X-ray free-electron lasers) and their potentials for application to investigate the nano–bio interface. The discussion is predominantly guided by asking how such methods could better help to understand and to improve nanoparticle-based drug delivery, though the concepts also apply to nano–bio interactions in general. We discuss current limitations and how they might be overcome, particularly for future use in vivo.

The design and use of materials in the nanoscale size range for addressing medical and health-related issues continues to receive increasing interest. Research in nanomedicine spans a multitude of areas, including drug delivery, vaccine development, antibacterial, diagnosis and imaging tools, wearable devices, implants, high-throughput screening platforms, etc. using biological, nonbiological, biomimetic, or hybrid materials. Many of these developments are starting to be translated into viable clinical products. Here, we provide an overview of recent developments in nanomedicine and highlight the current challenges and upcoming opportunities for the field and translation to the clinic.

The design and use of materials in the nanoscale size range for addressing medical and health-related issues continues to receive increasing interest. Research in nanomedicine spans a multitude of areas, including drug delivery, vaccine development, antibacterial, diagnosis and imaging tools, wearable devices, implants, high-throughput screening platforms, etc. using biological, nonbiological, biomimetic, or hybrid materials. Many of these developments are starting to be translated into viable clinical products. Here, we provide an overview of recent developments in nanomedicine and highlight the current challenges and upcoming opportunities for the field and translation to the clinic.

The design and use of materials in the nanoscale size range for addressing medical and health-related issues continues to receive increasing interest. Research in nanomedicine spans a multitude of areas, including drug delivery, vaccine development, antibacterial, diagnosis and imaging tools, wearable devices, implants, high-throughput screening platforms, etc. using biological, nonbiological, biomimetic, or hybrid materials. Many of these developments are starting to be translated into viable clinical products. Here, we provide an overview of recent developments in nanomedicine and highlight the current challenges and upcoming opportunities for the field and translation to the clinic.

Shu-wen Wang,1 Juan Ren,1 Yan-li Yan,2 Chao-fan Xue,2 Li Tan,2 Xiao-wei Ma2 1Department of Radiotherapy, First Affiliated Hospital of Xian Jiaotong University, 2Medical School of Xian Jiaotong University, Xi’an, Shaanxi, People’s Republic of China Abstract: The objective of this study was to compare the effects of image-guided hypofractionated radiotherapy and conventional fractionated radiotherapy on non-small-cell lung cancer (NSCLC). Fifty stage- and age-matched cases with NSCLC were randomly divided into two groups (A and B). There were 23 cases in group A and 27 cases in group B. Image-guided radiotherapy (IGRT) and stereotactic radiotherapy were conjugately applied to the patients in group A. Group A patients underwent hypofractionated radiotherapy (6–8 Gy/time) three times per week, with a total dose of 64–66 Gy; group B received conventional fractionated radiotherapy, with a total dose of 68–70 Gy five times per week. In group A, 1-year and 2-year local failure survival rate and 1-year local failure-free survival rate were significantly higher than in group B (P<0.05). The local failure rate (P<0.05) and distant metastasis rate (P>0.05) were lower in group A than in group B. The overall survival rate of group A was significantly higher than that of group B (P=0.03), and the survival rate at 1 year was 87% vs 63%, (P<0.05). The median survival time of group A was longer than that of group B. There was no significant difference in the incidence of complications between the two groups (P>0.05). Compared with conventional fractionated radiation therapy, image-guided hypofractionated stereotactic radiotherapy in NSCLC received better treatment efficacy and showed good tolerability. Keywords: non-small-cell lung cancer, hypofractionated radiotherapy, stereotactic radiotherapy, segmentation, intensity-modulated radiotherapy, image-guided radiation therapy technology

Three phage-displayed peptides designated H, S and F that recognize porcine aminopeptidase N (pAPN), the cellular receptor of porcine transmissible gastroenteritis virus (TGEV) were able to inhibit cell infection by TGEV. These same peptides had no inhibitory effects on infection of Vero cells by porcine epidemic diarrhea virus (PEDV). However, when PEDV, TGEV and porcine pseudorabies virus were incubated with peptide H (HVTTTFAPPPPR), only infection of Vero cells by PEDV was inhibited. Immunofluorescence assays indicated that inhibition of PEDV infection by peptide H was independent of pAPN. Western blots demonstrated that peptide H interacted with PEDV spike protein and that pre-treatment of PEDV with peptide H led to a higher inhibition than synchronous incubation with cells. These results indicate direct interaction with the virus is necessary to inhibit infectivity. Temperature shift assays demonstrated that peptide H inhibited pre-attachment of the virus to the cells.

Xiaowei Ma*, Huan Wang*, Shubin Jin, Yan Wu, Xing-Jie LiangLaboratory of Nanomedicine and Nanosafety, Division of Nanomedicine and Nanobiology, National Center for Nanoscience and Technology, People’s Republic of China; and CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Chinese Academy of Sciences, Beijing, People’s Republic of China *These authors contributed equally to this workBackground: There is an urgent need to develop drug-loaded biocompatible nanoscale packages with improved therapeutic efficacy for effective clinical treatment. To address this need, a novel poly (2-hydroxyethyl methacrylate)-poly (lactide)-1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine [PHEMA-g-(PLA-DPPE)] copolymer was designed and synthesized to enable these nanoparticles to be pH responsive under pathological conditions.Methods: The structural properties and thermal stability of the copolymer was measured and confirmed by Fourier transform infrare d spectroscopy, nuclear magnetic resonance, and thermogravimetric analysis. In order to evaluate its feasibility as a drug carrier, paclitaxel-loaded PHEMA-g-(PLA-DPPE) nanoparticles were prepared using the emulsion-solvent evaporation method.Results: The PHEMA-g-(PLA-DPPE) nanoparticles could be efficiently loaded with paclitaxel and controlled to release the drug gradually and effectively. In vitro release experiments demonstrated that drug release was faster at pH 5.0 than at pH 7.4. The anticancer activity of the PHEMA-g-(PLA-DPPE) nanoparticles was measured in breast cancer MCF-7 cells in vivo and in vitro. In comparison with the free drug, the paclitaxel-loaded PHEMA-g-(PLA-DPPE) nanoparticles could induce more significant tumor regression.Conclusion: This study indicates that PHEMA-g-(PLA-DPPE) nanoparticles are promising carriers for hydrophobic drugs. This system can passively target cancer tissue and release drugs in a controllable manner, as determined by the pH value of the area in

Xiaowei Ma*, Huan Wang*, Shubin Jin, Yan Wu, Xing-Jie LiangLaboratory of Nanomedicine and Nanosafety, Division of Nanomedicine and Nanobiology, National Center for Nanoscience and Technology, People’s Republic of China; and CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Chinese Academy of Sciences, Beijing, People’s Republic of China *These authors contributed equally to this workBackground: There is an urgent need to develop drug-loaded biocompatible nanoscale packages with improved therapeutic efficacy for effective clinical treatment. To address this need, a novel poly (2-hydroxyethyl methacrylate)-poly (lactide)-1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine [PHEMA-g-(PLA-DPPE)] copolymer was designed and synthesized to enable these nanoparticles to be pH responsive under pathological conditions.Methods: The structural properties and thermal stability of the copolymer was measured and confirmed by Fourier transform infrare d spectroscopy, nuclear magnetic resonance, and thermogravimetric analysis. In order to evaluate its feasibility as a drug carrier, paclitaxel-loaded PHEMA-g-(PLA-DPPE) nanoparticles were prepared using the emulsion-solvent evaporation method.Results: The PHEMA-g-(PLA-DPPE) nanoparticles could be efficiently loaded with paclitaxel and controlled to release the drug gradually and effectively. In vitro release experiments demonstrated that drug release was faster at pH 5.0 than at pH 7.4. The anticancer activity of the PHEMA-g-(PLA-DPPE) nanoparticles was measured in breast cancer MCF-7 cells in vivo and in vitro. In comparison with the free drug, the paclitaxel-loaded PHEMA-g-(PLA-DPPE) nanoparticles could induce more significant tumor regression.Conclusion: This study indicates that PHEMA-g-(PLA-DPPE) nanoparticles are promising carriers for hydrophobic drugs. This system can passively target cancer tissue and release drugs in a controllable manner, as determined by the pH value of the area in