Publications by authors named "Zineng Yan"

Artificial intelligence (AI) is an interdisciplinary field that combines computer technology, mathematics, and several other fields. Recently, with the rapid development of machine learning (ML) and deep learning (DL), significant progress has been made in the field of AI. As one of the fastest-growing branches, DL can effectively extract features from big data and optimize the performance of various tasks.

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Objective: To evaluate the accuracy and parsing ability of GPT 4.0 for Japanese medical practitioner qualification examinations in a multidimensional way to investigate its response accuracy and comprehensiveness to medical knowledge.

Methods: We evaluated the performance of the GPT 4.

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Article Synopsis
  • This study compared the effectiveness of optimized vs. unoptimized large language models (LLMs) in answering orthopedic questions using a specialized knowledge base.
  • A knowledge base was created using clinical guidelines and authoritative publications, and 30 orthopedic questions were posed to both types of LLMs, with responses evaluated by experienced orthopedic surgeons.
  • Results indicated that optimization led to significant improvements across all models in quality, accuracy, and comprehensiveness, suggesting that tailored knowledge bases can enhance LLM performance in specialized fields.
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Article Synopsis
  • The brain-computer interface (BCI) system connects external devices to the human brain and reflects mental states through EEG signals, which can often be contaminated by various artifacts.
  • The complexity of unprocessed EEG data makes analysis and cleanup difficult, posing challenges for accurate readings.
  • Recent advancements in artificial intelligence (AI) and machine learning have significantly improved the processing of EEG signals, allowing for better understanding of patients' health and potentially enhancing their quality of life.
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Utilizing transplanted human umbilical cord mesenchymal stem cells (HUMSCs) for cartilage defects yielded advanced tissue regeneration, but the underlying mechanism remain elucidated. Early after HUMSCs delivery to the defects, we observed substantial apoptosis. The released apoptotic vesicles (apoVs) of HUMSCs promoted cartilage regeneration by alleviating the chondro-immune microenvironment.

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Due to its unique structure, articular cartilage has limited abilities to undergo self-repair after injury. Additionally, the repair of articular cartilage after injury has always been a difficult problem in the field of sports medicine. Previous studies have shown that the therapeutic use of mesenchymal stem cells (MSCs) and their extracellular vesicles (EVs) has great potential for promoting cartilage repair.

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Breast cancer has become the most diagnosed cancer type, endangering the health of women. Patients with breast resection are likely to suffer serious physical and mental trauma. Therefore, breast reconstruction becomes an important means of postoperative patient rehabilitation.

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Improving the poor microenvironment in the joint cavity has potential for treating cartilage injury, and mesenchymal stem cell (MSC)-derived exosomes (MSC-Exos), which can modulate cellular behavior, are becoming a new cell-free therapy for cartilage repair. Here, we used acellular cartilage extracellular matrix (ACECM) to prepare 3D scaffolds and 2D substrates by low-temperature deposition modeling (LDM) and tape casting. We aimed to investigate whether MSC-Exos cultured on scaffolds of different dimensions could improve the poor joint cavity microenvironment caused by cartilage injury and to explore the related mechanisms.

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Background: In recent years, there has been significant research progress on in situ articular cartilage (AC) tissue engineering with endogenous stem cells, which uses biological materials or bioactive factors to improve the regeneration microenvironment and recruit more endogenous stem cells from the joint cavity to the defect area to promote cartilage regeneration.

Method: In this study, we used ECM alone as a bioink in low-temperature deposition manufacturing (LDM) 3D printing and then successfully fabricated a hierarchical porous ECM scaffold incorporating GDF-5.

Results: Comparative in vitro experiments showed that the 7% ECM scaffolds had the best biocompatibility.

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Osteoarthritis (OA) is a degenerative joint disease that is common among the middle-aged and older populations, causes patients to experience recurrent pain in their joints and negatively affects their quality of life. Currently, therapeutic options for patients with OA consist of medications to alleviate pain and treat the symptoms; however, due to typically poor outcomes, patients with advanced OA are unlikely to avoid joint replacement. In recent years, several studies have linked disrupted homeostasis of the joint cavity microenvironment to the development of OA.

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Article Synopsis
  • Articular cartilage has limited self-repair capabilities due to a lack of blood vessels, which leads to joint issues like swelling and pain, contributing to osteoarthritis progression.
  • The inflammatory environment created by cartilage injury results in chondrocyte death and poor-quality fibrocartilage formation, complicating repair efforts.
  • A comprehensive approach to managing the inflammatory microenvironment is crucial for improving cartilage healing, highlighting the dual role of immune responses in either hindering or aiding repair processes.
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Regenerating the meniscus remains challenging because of its avascular, aneural, and alymphatic nature. Three-dimensional (3D) printing technology provides a promising strategy to fabricate biomimetic meniscal scaffolds with an anisotropic architecture, a proper biomechanical microenvironment, and bioactive components. Herein, 3D printing technology is adopted by coencapsulating chemokines (platelet-derived growth factor-BB, PDGF-BB) and small chondroinductive molecules (kartogenin, KGN) within biomimetic polycaprolactone/hydrogel composite scaffolds.

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