Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Purpose: Postoperative adhesions following thyroid surgery can lead to multiple complications that significantly impact quality of life. The use of an acellular dermal matrix (ADM) adhesion barrier device has been proposed as a potential solution to reduce the risk of such adhesions. This study aimed to evaluate the safety and anti-adhesive effect of an ADM in patients undergoing thyroid surgery. Methods: In this multicenter randomized controlled trial, patients undergoing thyroid surgery were randomly assigned to receive either ADM (n = 42) or no ADM (n = 39) during surgery. The primary outcome was the Swallowing Impairment Score (SIS-6), measured 6 weeks after surgery and compared between groups. Secondary outcomes included intergroup comparisons of the SIS-6, the Voice Handicap Index (VHI)-10, and the Glasgow-Edinburgh Throat Scale (GETS) at baseline, and 2, 6, and 18 weeks after surgery. Results: At week 6, the mean SIS-6 scores were 4.0 ± 4.1 and 3.3 ± 4.2 in the ADM and control groups, respectively, which was not significantly different. Both groups showed similar postoperative improvements in SIS-6, VHI-10, and GETS scores over time, without significant differences between groups at any time point, indicating that the ADM did not reduce the incidence of postoperative adhesions or alter the course of recovery compared to the control group. Conclusion Although application of the ADM is safe for use in patients undergoing thyroid surgery, it did not produce a clinically significant advantage in preventing postoperative adhesions. Future research should focus on identifying specific patient populations or surgical scenarios where the use of the ADM may be beneficial.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Objective To address the challenges of constructing large language models for traditional Chinese medicine(TCM)classics,which are complex and expensive to fine-tune,this study explores a lightweight fine-tuning method for such models,aiming to develop a question-answering model centered on TCM classics,particularly various editions of Shang Han Lun through the ages.Methods Dataset construction involved designing prompts to guide GPT-4 in generating Q&A pairs based on Shang Han Lun and integrating them with the ShenNong_TCM_Dataset and cMedQA2 datasets.Five general-purpose large models were selected for Lora fine-tuning.The best model was chosen through evaluation,and the performance of multiple quantized versions was validated.Results After fine-tuning,the BLEU,ROUGE-1,ROUGE-2,and ROUGE-L metrics for the Qwen-7B-Chat model improved by 17.61,19.63,14.3,and 21.4,respectively,compared to the base model.Conclusion The selected model in this study is capable of effectively understanding and utilizing professional terms and concepts from TCM classics,such as Shang Han Lun,to provide accurate answers to user queries.Compared to similar models,it requires lower fine-tuning costs and computational power,contributing to the dissemination of TCM knowledge and the development of intelligent systems.

This study was aimed at investigating the effects of demethylase fat mass and obesity-associated protein(FTO)and methyltransferase methyltransferase like protein 3(METTL3),key molecules in N6-methyladenosine(m6A)modification,on the replication and proliferation of Japanese encephalitis virus(JEV).Recombinant lentiviruses were generated by packaging the FTO and green fluorescent protein into lentiviral vectors.Neuro2a cells,a mouse neuroblastoma cell line,were infected with the lentivirus,and stable FTO-expressing cell lines were obtained through puromycin selection.Successful overexpression of FTO was confirmed through fluorescence microscopy,real-time quantitative PCR,and western blot analysis.When Neuro2a cells overexpressing FTO were infected with JEV,the overexpression of FTO decreased JEV replication in the cells,and increased the expression of interferon(IFN)and related molecules.Additionally,treatment of JEV-infected Neuro2a cells with the METTL3-specific inhibitor STM2457 resulted in a dose-dependent decrease in JEV replication and viral protein expression.These findings suggested that lowering m6A methylation levels inhibits JEV replication,thus shedding light on the regulatory role of methylation modification in JEV replication.

Oral squamous cell carcinoma(OSCC)is a malignant lesion originating from the oral mucosal squamous epithelium,account-ing for over 80％of oral and maxillofacial malignancies.Key etiological factors include tobacco,alcohol abuse,and betel quid chewing.In China,its incidence has shown an overall upward trend,posing a significant threat to public health.OSCC exhibits high local invasive-ness,making early diagnosis critical for improving prognosis.Its clinical management requires close multidisciplinary collaboration among oral and maxillofacial surgery,head and neck surgery,radiation oncology,medical oncology,reconstructive surgery,radiology,patholo-gy,and nutritional support teams.Given the increasing disease burden of OSCC and rapid development of multidisciplinary collaborative models,an expert panel has formulated this integrated management consensus based on evidence-based medicine and extensive deliber-ation.Centered on the'Prevention-Screening-Diagnosis-Treatment-Rehabilitation'framework,the consensus provides comprehensive guidance for the entire disease course of OSCC patients,aiming to standardize clinical practice.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Metachromatic leukodystrophy (MLD) is an inherited disease caused by a deficiency of the enzyme arylsulfatase A (ARSA). Lentivirus-modified autologous hematopoietic stem cell gene therapy (HSCGT) has recently been approved for clinical use in pre and early symptomatic children with MLD to increase ARSA activity. Unfortunately, this advanced therapy is not available for most patients with MLD who have progressed to more advanced symptomatic stages at diagnosis. Patients with late-onset juvenile MLD typically present with a slower neurological progression of symptoms and represent a significant burden to the economy and healthcare system, whereas those with early onset infantile MLD die within a few years of symptom onset. We conducted a pilot study to determine the safety and benefit of HSCGT in patients with postsymptomatic juvenile MLD and report preliminary results. The safety profile of HSCGT was favorable in this long-term follow-up over 9 years. The most common adverse events (AEs) within 2 months of HSCGT were related to busulfan conditioning, and all AEs resolved. No HSCGT-related AEs and no evidence of distorted hematopoietic differentiation during long-term follow-up for up to 9.6 years. Importantly, to date, patients have maintained remarkably improved ARSA activity with a stable disease state, including increased Functional Independence Measure (FIM) score and decreased magnetic resonance imaging (MRI) lesion score. This long-term follow-up pilot study suggests that HSCGT is safe and provides clinical benefit to patients with postsymptomatic juvenile MLD.
Humans ; Leukodystrophy, Metachromatic/genetics* ; Pilot Projects ; Genetic Therapy/methods* ; Hematopoietic Stem Cell Transplantation ; Male ; Follow-Up Studies ; Female ; Lentivirus/genetics* ; Child ; Child, Preschool ; Hematopoietic Stem Cells/metabolism* ; Cerebroside-Sulfatase/metabolism* ; Adolescent