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The treatment of painful KOA in adult patients with ITP has not been well studied yet. We conducted a prospective, double-blind, randomized, placebo-controlled trial to evaluate the efficacy of intra-articular allogeneic PRP injections on symptoms and joint structure in patients with KOA and ITP. 80 participants were randomly allocated in a 1:1 ratio to allogeneic PRP group or saline group. The primary outcome was the WOMAC total score at 12 months post-injection. The number of patients in each group who achieved MCID of primary outcome showed a statistically significant difference only at 3-month (27/39 vs. 5/39, p = 0.001) and 6-month (15/39 vs. 3/38, p = 0.032). The difference in WOMAC total score exceeded the MCID only at 3 month (mean difference of -15.1 [95% CI -20.7 to -9.5], p < 0.001). Results suggest that allogeneic PRP was superior to placebo only with respect to symptoms at 3-month of follow-up.

BACKGROUND: ChatGPT is a large language model designed to generate responses based on a contextual understanding of user queries and requests. This study utilised the entrance examination for the Master of Clinical Medicine in Traditional Chinese Medicine to assesses the reliability and practicality of ChatGPT within the domain of medical education.
METHODS: We selected 330 single and multiple-choice questions from the 2021 and 2022 Chinese Master of Clinical Medicine comprehensive examinations, which did not include any images or tables. To ensure the test\'s accuracy and authenticity, we preserved the original format of the query and alternative test texts, without any modifications or explanations.
RESULTS: Both ChatGPT3.5 and GPT-4 attained average scores surpassing the admission threshold. Noteworthy is that ChatGPT achieved the highest score in the Medical Humanities section, boasting a correct rate of 93.75%. However, it is worth noting that ChatGPT3.5 exhibited the lowest accuracy percentage of 37.5% in the Pathology division, while GPT-4 also displayed a relatively lower correctness percentage of 60.23% in the Biochemistry section. An analysis of sub-questions revealed that ChatGPT demonstrates superior performance in handling single-choice questions but performs poorly in multiple-choice questions.
CONCLUSIONS: ChatGPT exhibits a degree of medical knowledge and the capacity to aid in diagnosing and treating diseases. Nevertheless, enhancements are warranted to address its accuracy and reliability limitations. Imperatively, rigorous evaluation and oversight must accompany its utilization, accompanied by proactive measures to surmount prevailing constraints.

BACKGROUND: Narrative Medicine (NM), a contemporary medical concept proposed in the 21st century, emphasizes the use of narrative as a literary form in medicine. This study aims to explore the understanding about NM and willingness to learn NM among medical students in our hospital.
METHODS: A questionnaire survey was conducted among 130 students at Xiangya Medical College of Central South University.
RESULTS: The findings revealed that a small percentage of students (3.1%) were familiar with narrative medicine and its training methods. Knowledge about the treatment skills (77.7%) and core content (55.4%) of narrative medicine was limited among the students. Despite this, a majority (63.1%) expressed a lack of interest in further understanding and learning about narrative medicine. Surprisingly, the survey indicated that students possessed a high level of narrative literacy, even without formal training in narrative medicine. Additionally, over half of the surveyed students (61.5%) believed that narrative medicine could benefit their clinical practice.
CONCLUSIONS: This study serves as a preliminary basis for the future development of narrative medicine education in China. It highlights the need to prioritize medical humanities education and provide medical students with more opportunities to access information on narrative medicine. By doing so, we can strive to enhance the visibility and promote the integration of narrative medicine into medical humanities education in China.

BACKGROUND: ChatGPT, an artificial intelligence (AI) based on large-scale language models, has sparked interest in the field of health care. Nonetheless, the capabilities of AI in text comprehension and generation are constrained by the quality and volume of available training data for a specific language, and the performance of AI across different languages requires further investigation. While AI harbors substantial potential in medicine, it is imperative to tackle challenges such as the formulation of clinical care standards; facilitating cultural transitions in medical education and practice; and managing ethical issues including data privacy, consent, and bias.
OBJECTIVE: The study aimed to evaluate ChatGPT\'s performance in processing Chinese Postgraduate Examination for Clinical Medicine questions, assess its clinical reasoning ability, investigate potential limitations with the Chinese language, and explore its potential as a valuable tool for medical professionals in the Chinese context.
METHODS: A data set of Chinese Postgraduate Examination for Clinical Medicine questions was used to assess the effectiveness of ChatGPT\'s (version 3.5) medical knowledge in the Chinese language, which has a data set of 165 medical questions that were divided into three categories: (1) common questions (n=90) assessing basic medical knowledge, (2) case analysis questions (n=45) focusing on clinical decision-making through patient case evaluations, and (3) multichoice questions (n=30) requiring the selection of multiple correct answers. First of all, we assessed whether ChatGPT could meet the stringent cutoff score defined by the government agency, which requires a performance within the top 20% of candidates. Additionally, in our evaluation of ChatGPT\'s performance on both original and encoded medical questions, 3 primary indicators were used: accuracy, concordance (which validates the answer), and the frequency of insights.
RESULTS: Our evaluation revealed that ChatGPT scored 153.5 out of 300 for original questions in Chinese, which signifies the minimum score set to ensure that at least 20% more candidates pass than the enrollment quota. However, ChatGPT had low accuracy in answering open-ended medical questions, with only 31.5% total accuracy. The accuracy for common questions, multichoice questions, and case analysis questions was 42%, 37%, and 17%, respectively. ChatGPT achieved a 90% concordance across all questions. Among correct responses, the concordance was 100%, significantly exceeding that of incorrect responses (n=57, 50%; P<.001). ChatGPT provided innovative insights for 80% (n=132) of all questions, with an average of 2.95 insights per accurate response.
CONCLUSIONS: Although ChatGPT surpassed the passing threshold for the Chinese Postgraduate Examination for Clinical Medicine, its performance in answering open-ended medical questions was suboptimal. Nonetheless, ChatGPT exhibited high internal concordance and the ability to generate multiple insights in the Chinese language. Future research should investigate the language-based discrepancies in ChatGPT\'s performance within the health care context.

A brain tumor magnetic resonance image processing algorithm can help doctors to diagnose and treat the patient\'s condition, which has important application significance in clinical medicine. This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. The standard convolution blocks of the coding path and decoding path on the original network are improved to dense blocks, which enhances the transmission of features. The mixed loss function composed of the Binary Cross Entropy Loss function and the Tversky coefficient is used to replace the original single cross-entropy loss, which restrains the influence of irrelevant features on segmentation accuracy. Compared with U-Net, U-Net++, and PA-Net the algorithm in this paper has significantly improved the segmentation accuracy, reaching 0.846, 0.861, and 0.782 respectively in the Dice coefficient index of WT, TC, and ET. The PPV coefficient index has reached 0.849, 0.883, and 0.786 respectively. Compared with the traditional U-net network, the Dice coefficient index of the proposed algorithm exceeds 0.8%, 4.0%, and 1.4%, respectively, and the PPV coefficient index in the tumor core area and tumor enhancement area increases by 3% and 1.2% respectively. The proposed algorithm has the best performance in tumor core area segmentation, and its Sensitivity index has reached 0.924, which has good research significance and application value.

Breast cancer remains a significant public health issue, being a leading cause of cancer-related mortality among women globally. Timely diagnosis and efficient treatment are crucial for enhancing patient outcomes, reducing healthcare burdens and advancing community health. This systematic review, following the PRISMA guidelines, aims to comprehensively synthesize the recent advancements in computer-aided diagnosis and treatment for breast cancer. The study covers the latest developments in image analysis and processing, machine learning and deep learning algorithms, multimodal fusion techniques and radiation therapy planning and simulation. The results of the review suggest that machine learning, augmented and virtual reality and data mining are the three major research hotspots in breast cancer management. Moreover, this paper discusses the challenges and opportunities for future research in this field. The conclusion highlights the importance of computer-aided techniques in the management of breast cancer and summarizes the key findings of the review.

The qualitative, quantitative, and localization analysis of hearing loss is one of the important contents of forensic clinical research and identification. Pure-tone audiometry is the \"gold standard\" for hearing loss assessment, but it is affected by the subjective cooperation of the assessed person. Due to the complexity of the auditory pathway and the diversity of hearing loss, the assessment of hearing loss requires the combination of various subjective and objective audiometric techniques, along with comprehensive evaluation based on the case situation, clinical symptoms, and other examinations to ensure the scientificity, accuracy and reliability of forensic hearing impairment assessment. Objective audiometry includes acoustic impedance, otoacoustic emission, and various auditory evoked potentials. The frequency-specific auditory brainstem response (ABR), 40 Hz auditory event related potential, and auditory steady-state response are commonly used for objective hearing threshold assessment. The combined application of acoustic impedance, otoacoustic emission and ABR can be used to locate hearing loss and determine whether it is located in the middle ear, cochlea, or posterior cochlea. This article reviews the application value of objective audiometry techniques in hearing threshold assessment and hearing loss localization, aiming to provide reference for forensic identification of hearing loss.
听力损失的定性、定量和定位分析是法医临床学科研和鉴定的重要内容之一。纯音测听是评估听力损失的“金标准”，但受被鉴定人主观配合的影响。由于听觉通路的复杂性和听力损失的多样性，听力损失评估需联合应用多种主客观测听技术，并结合案情、临床症状及其他检查进行综合判断，以保证法医学听力障碍评定结果的科学性、准确性和可靠性。客观测听技术包括声导抗、耳声发射及各类听觉诱发电位等。具有频率特异性的听性脑干反应（auditory brainstem response，ABR）、40 Hz听觉相关电位和听性稳态反应常用于客观听阈评估。声导抗、耳声发射和ABR联合可用于听力损失定位，判断损伤位于中耳、耳蜗还是蜗后。本文综述了不同客观测听技术在听阈评估和听力损失定位中的应用价值，以期为听力损失的法医学鉴定提供参考。.

OBJECTIVE: This study aimed to assess the effectiveness of a low trough serum concentration of vancomycin on acute kidney injury in infants and toddlers in the paediatric intensive care unit (PICU).
METHODS: A retrospective cohort study was performed of 126 infants and toddlers (aged between 29 days and 3 years) from the PICU of a tertiary care hospital who were administered intravenous vancomycin between January 2019 and December 2022. Information about their demographic factors, duration of PICU stay, time of administration and trough levels of vancomycin were retrieved. Descriptive statistics were used for demographic factors and multivariable logistic regression analyses were conducted to assess the determining factors.
RESULTS: Based on the trough concentration of vancomycin, the participants were divided into three groups as follows: 4-5 mg/L, 5-15 mg/L and >15 mg/L. The serum vancomycin concentration was significantly related to body weight, albumin, cystatin C, urea nitrogen in serum, serum creatinine and creatinine clearance (p<0.05) in these patients. Multivariate analysis showed that body weight, albumin, cystatin C, urea nitrogen in serum and creatinine clearance were independent contributors to the trough vancomycin concentration. There was no difference in the effectiveness of different trough concentrations on patients (p=0.241). The cumulative incidence of acute kidney injury was highest in the group with a trough concentration of vancomycin >15 mg/L (p<0.01).
CONCLUSIONS: Patients with a vancomycin trough concentration of 4-5 mg/L in the PICU had a high cure rate (79.4%) and a low incidence of acute kidney injury (HR 18.3, 95% CI 5.135 to 87.621; p<0.001). Therefore, the serum trough concentration should be considered but it should also be combined with the treatment effect to achieve individualised administration for the clinical application of vancomycin.