RJUA-MedDQA: A Multimodal Benchmark for Medical Document Question Answering and Clinical Reasoning

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 figure

Single-step synthesis of hierarchical flower-like silver structures with assistance of gallic acid

We synthesize hierarchical flower-like silver microspheres with tunable size and shape, using ascorbic acid as reducing agent and gallic acid as structure-directing agent. The chemical nature of the gallic acid plays a vital role in the process of assembling silver particles into Chinese rose hierarchical structures. By changing the amount of gallic acid or silver nitrate solution (AgNO _3 ), it is easy to adjust the anisotropic morphologies of as-synthesized silver structures and promote the preferential growth, resulting in a complete, clear, and stable multi-layered floral silver structure. This single-step wet-chemical synthesis method provides a new synthetic strategy for the anisotropic growth and morphology control of flower-like silver particles

Gold-Coated Flower-Like TiO₂ Microparticles Wrapped with Reduced Graphene Oxide for SERS Monitoring and Photocatalytic Degradation of Organic Pollutants

The development of powerful technologies to simultaneously monitor and eliminate toxic organic pollutants is a vital focus of environmental research. For this purpose, a recyclable surface-enhanced Raman scattering (SERS) substrate was successfully prepared by successively depositing gold nanoparticles (AuNPs) and a reduced graphene oxide (rGO) film on TiO2 microflower particles by two-step in situ reduction processes. The prepared TiO2@Au-rGO ternary composites exhibit not only good SERS performance via molecule enrichment of rGO and surface plasmon resonance (SPR) capability, but also enhanced photocatalytic degradation activity because of the increased broadband UV–visible-light-harvesting efficiency and SPR-enhanced charge transfer. The detection limit of rhodamine 6G (R6G) as low as 10–11 M and excellent reproducibility were achieved. Additionally, the TiO2@Au-rGO ternary composites yield complete removal of R6G within 15 min under broadband UV–visible light illumination, wherein their apparent reaction-rate constant is 2.8 and 5.5 times larger than those of binary TiO2@Au and pure TiO2, respectively. Meanwhile, no distinct SERS and photocatalytic activity loss were observed even after five reusability tests, achieving the recycling SERS application by photocatalytic degradation. Most importantly, the as-fabricated ternary samples can be explored for the efficient capture, sensitive detection, and self-cleaning degradation of 2,2′,4,4′-tetrabromodiphenyl ether (BDE-47), revealing great potential applications in the SERS-based simultaneous monitoring and removal of trace aromatic persistent organic pollutants