Yalan Chen

Background: Lifestyle interventions for patients with prostate cancer have been shown to improve treatment adherence and quality of life. However, there remains a lack of large language models (LLMs) capable of delivering individualized and professional lifestyle recommendations under clearly defined medical safety boundaries and controlled evidence sources. Objective: This study aimed to develop and evaluate a supervised fine-tuned LLM—PCaPLMM_SFT (Prostate Cancer Patient Lifestyle Management Model via Supervised Fine-Tuning)—to support health literacy improvement and lifestyle self-management among patients with prostate cancer. Methods: We searched English-language literature primarily from PubMed (February 2015 to February 2025) to build a structured lifestyle management knowledge base covering diet, physical activity, weight management, medication adherence, and psychological support. We used a retrieval-augmented generation pipeline to generate patient-style question-answer (QA) pairs from retrieved knowledge slices. Bilingual English-Chinese QA data were generated from English-language source evidence through patient-oriented reformulation and retrieval-augmented generation–based answer generation, and independent English and Chinese test sets were constructed to assess bilingual QA performance. We trained Baichuan2-7B-Chat using a 2-stage strategy, consisting of continued pretraining, followed by supervised fine-tuning with low-rank adaptation. Model outputs were evaluated in 2 double-blind rounds by referee LLMs (Qwen3-Max and DeepSeek-R1) and compared with GPT-3.5-Turbo and the base Baichuan2-7B-Chat using 2500 queries across 5 lifestyle scenarios. Additionally, 3 domain experts conducted a blinded review of 50 QA samples (10 per scenario). We used the Mann-Whitney U test with effect size r , and Benjamini-Hochberg false discovery rate correction, and examined consistency using intraclass correlation coefficients. Results: Based on 2211 included publications, we constructed the PCaPLMM_SFT-Train dataset. The knowledge base yielded >150,000 structured knowledge slices. After 2 rounds of review, we obtained 42,330 single-turn QA pairs and 3008 multiturn dialogues, and the supervised fine-tuning phase used 45,338 structured QA samples. In the dual-round referee LLM assessment, PCaPLMM_SFT consistently outperformed Baichuan2-7B-Chat across dimensions and showed comparable or superior performance to GPT-3.5-Turbo across 5 lifestyle scenarios. Consistency analyses indicated moderate to good agreement between referee models across rounds, supporting the robustness of the comparative evaluation. Conclusions: PCaPLMM_SFT demonstrates the feasibility of constructing a medical lifestyle–focused LLM by integrating structured medical knowledge, QA-style training data, and a multilayer evaluation system. This framework provides a reproducible methodological foundation for evidence-based health education and lifestyle management and establishes groundwork for future evaluation in real-world health management settings.

Objectives: To achieve a rapid assessment of health literacy (HL) levels among diabetic patients. Methods: A questionnaire survey was conducted among diabetic patients from nine communities in Nantong City, Jiangsu Province, China, using convenient sampling. Based on the survey results, data from three communities were randomly selected as the test set, with the remaining data used as the training set. Feature selection was performed using recursive feature elimination. Predictive models were established and compared using logistic regression (LR), random forest (RF), and support vector machine (SVM). Calibration curves, decomposition plots, and partial dependence plots were drawn to evaluate and interpret the models. Results: In November 2023, a total of 802 valid questionnaires were received. Eight variables were selected for modeling: educational level, exercise habits, average monthly household income, dietary control, age, medication for blood sugar control, duration of diabetes, and number of cohabitants. The recall for LR in the three communities was 0.778, 0.800, and 0.862 [area under the curve (AUC): 0.810, 0.792, and 0.775]. For RF, the recall values were 0.879, 0.877, and 0.923 (AUC: 0.781, 0.710, and 0.710). For SVM, the recall values were 0.859, 0.862, and 0.877 (AUC: 0.813, 0.759, and 0.770). Model evaluation showed that as the data volume increased, the calibration curves became more ideal. Conclusions: As one of the few HL prediction models for diabetic patients in mainland China that is built based on multi-center survey data and evaluated through multi-center assessment, this model can quickly identify patients with insufficient HL using a small amount of objective personal information.

Prostate cancer (PCa) is commonly occurred among males worldwide and its prognosis could be influenced by biochemical recurrence (BCR). MicroRNAs (miRNAs) are functional regulators in carcinogenesis, and miR-221-3p was reported as one of the significant candidates deregulated in PCa. However, its regulatory pattern in PCa BCR across literature reports was not consistent, and the targets and mechanisms in PCa malignant transition and BCR are less explored. In this study, an evidence-based and knowledge-guided approach was proposed to decipher the role and mechanism of miR-221-3p in PCa development. First, the literature-reported inconsistency between miR-221-3p and PCa BCR was quantitatively measured by meta-analysis. Then a knowledge-guided network strategy was applied to prioritize key targets of miR-221-3p in PCa progression based both on topological and functional characterization of genes in multi-omics miRNA-mRNA and protein-protein interaction networks. Finally, a key gene was computationally identified and experimentally validated using cell line and clinical samples through EdU assay, scratch assay, transwell assay, dual-luciferase reporter assay and the epithelial-to-mesenchymal transition (EMT)-related analysis. Down-regulation of miR-221-3p was correlated with a lower biochemical recurrence-free survival (BRFS) in PCa (HR: 0.72, 95%, CI: 0.64–0.81, P < 0.00001). A significant down-regulation of miR-221-3p was observed in most of the PCa cells compared with the normal control. KPNA2 was identified as a key target of miR-221-3p and it was over-expressed in all the PCa cells and human PCa tissues. Moreover, elevated miR-221-3p inhibited the proliferation, migration, invasion, and EMT of PCa cells in vitro via directly and negatively mediating KPNA2 expression. miR-221-3p down-regulation was a risk factor for PCa BRFS, and its over-expression could inhibit the malignant phenotype and EMT of PCa cells by directly targeting KPNA2. Translational and personalized applications of the findings will be conducted in the future.

Background: Shared decision‐making (SDM) is a patient‐centred approach to improve the quality of care. An essential requirement for the SDM process is to be fully aware of patient information needs.Objectives: Our study aimed to assess patient information needs for new antidiabetic medications using the best–worst scaling (BWS) experiment.Methods: BWS tasks were developed according to a literature review and the focus group discussion. We used a balanced incomplete block design and blocking techniques to generate choice sets. The final BWS contains 11 attributes, with 6‐choice scenarios in each block. The one‐to‐one, face‐to‐face BWS survey was conducted among type 2 diabetic patients in Jiangsu Province. Results were analyzed using count‐based analysis and modelling approaches. We also conducted a subgroup analysis to observe preference heterogeneity.Results: Data from 539 patients were available for analysis. The most desired information domain was the comparative effectiveness of new antidiabetic medications. It consists of the incidence of macrovascular complications, the length of extended life years, changes in health‐related quality of life, the incidence of microvascular complications, and the control of glycated haemoglobin. Of all the attributes, the incidence of macrovascular complications was the primary concern. Patients' glycemic control and whether they had diabetes complications exerted a significant influence on their information needs.Conclusions: Information on health benefits is of critical significance for diabetic patients. Patients have different information needs as their disease progresses. Personalized patient decision aids that integrate patient information needs and provide evidence of new antidiabetic medications are worthy of being established.Patient or Public Contribution: Before data collection, a pilot survey was carried out among diabetic patients to provide feedback on the acceptability and intelligibility of the attributes.

Disease ontologies facilitate the semantic organization and representation of domain-specific knowledge. In the case of prostate cancer (PCa), large volumes of research results and clinical data have been accumulated and needed to be standardized for sharing and translational researches. A formal representation of PCa-associated knowledge will be essential to the diverse data standardization, data sharing and the future knowledge graph extraction, deep phenotyping and explainable artificial intelligence developing. In this study, we constructed an updated PCa ontology (PCAO2) based on the ontology development life cycle. An online information retrieval system was designed to ensure the usability of the ontology. The PCAO2 with a subclass-based taxonomic hierarchy covers the major biomedical concepts for PCa-associated genotypic, phenotypic and lifestyle data. The current version of the PCAO2 contains 633 concepts organized under three biomedical viewpoints, namely, epidemiology, diagnosis and treatment. These concepts are enriched by the addition of definition, synonym, relationship and reference. For the precision diagnosis and treatment, the PCa-associated genes and lifestyles are integrated in the viewpoint of epidemiological aspects of PCa. PCAO2 provides a standardized and systematized semantic framework for studying large amounts of heterogeneous PCa data and knowledge, which can be further, edited and enriched by the scientific community. The PCAO2 is freely available at https://bioportal.bioontology.org/ontologies/PCAO, http://pcaontology.net/ and http://pcaontology.net/mobile/.

Purpose: This study is aimed at investigating health literacy (HL) among diabetes mellitus (DM) patients using a comprehensive, scientific, feasible, and suitable HL assessment indicator system tailored for the diabetic population in mainland China and systematically analyzing the factors influencing HL in this population.Methods: The Delphi expert consultation method was employed to initially draft and refine the Diabetes Health Literacy Evaluation Indicator System (DHLEIS). The reliability and representativeness of the indicator system were tested through metrics including the active coefficient, authority degree, and coordination degree. A HL survey questionnaire for diabetic patients was developed based on DHLEIS and administered to diabetic patients across five hospitals in Nantong and Yancheng cities, Jiangsu Province. The random forest method was utilized to deeply analyze the impact of various factors on HL and its four dimensions and to identify the core influencing factors.Results: Analysis of 707 questionnaires based on the DHLEIS revealed that nine factors—age, sex, body shape, income, exercise, education level, duration of DM, whether insulin is injected, and the number of cohabitants—significantly impact the HL levels. Among these, age, duration of DM, education level, and number of cohabitants were particularly influential across the four dimensions of health knowledge, awareness, behavior, and skills. Factors related to health knowledge and skills were the most significant contributors to overall HL.Conclusions: The multidimensional analysis of factors influencing HL offers valuable insights into characterizing varying levels of HL among diabetic patients. This approach supports targeted cognitive improvements and the effective enhancement of health skills, ultimately leading to better health outcomes.

There are many problems in traditional hospital staff sign-in systems, such as low efficiency, time required to obtain result statistics, and fraud. A system based on face recognition is a very promising strategy. In 2022, the highest accuracy of face recognition algorithms around the world has exceeded 99%, with fewer than one-thousandth of one percent false positives. Face recognition technology (FRT) has been widely used in finance, intelligent security, access control, and many other fields, and the market has seen sustained rapid growth. Baidu's face recognition algorithm has won a leading place in the most authoritative public evaluation competition in the world. In this study, a hospital staff sign-in system (H3S) has been designed and developed using the Baidu AI face recognition software development kit (SDK) along with PyQt, Python, MySQL, JSON, and other tools. Compared to traditional sign-in systems, the proposed H3S was more convenient, non-contact, highly efficient and accurate, easy for data analysis, and resistant to fraud. After its deployment, sign-in efficiency has been significantly improved, checking and analysis of sign-in data were more convenient and accurate, and fraud has been essentially eliminated.

Relevant studies published between January 2010 and June 2021 were identified through relevant databases, including the Science Citation Index Expanded (SCIE) database of Web of Science, PubMed, and Embase, in order to assess the effect of health literacy (HL) intervention on patients with diabetes. A total of 21 articles were eligible. The results showed that: (1) this review involved different HL assessment tools, most of which were self-designed scales and assessment tools focused on measuring functional HL. (2) The differences in glycosylated hemoglobin (HbA1c) (weighted mean difference [WMD] = −0.78, 95% confidence interval [CI]: −0.94, −0.62) and medication adherence (standardized mean difference [SMD] = 1.85, 95% CI: 0.19, 3.52) between the HL intervention group and the usual care group were statistically significant. There was no significant improvement in systolic blood pressure (SMD = −0.05, 95% CI: −0.34, 0.25). Furthermore, this review reported that self-efficacy (SMD = 0.85, 95% CI: 0.65, 1.04) was increased, and the level of HL was improved. In the assessments of risk of bias, 90% of the studies were classified as medium. The quality of the evidence of medication adherence was very low, and the reliability of the conclusions was not enough to confirm the effect of HL.

Neuroimaging has been widely used as a diagnostic technique for brain diseases. With the development of artificial intelligence, neuroimaging analysis using intelligent algorithms can capture more image feature patterns than artificial experience-based diagnosis. However, using only single neuroimaging techniques, e.g., magnetic resonance imaging, may omit some significant patterns that may have high relevance to the clinical target. Therefore, so far, combining different types of neuroimaging techniques that provide multimodal data for joint diagnosis has received extensive attention and research in the area of personalized medicine. In this study, based on the regularized label relaxation linear regression model, we propose a multikernel version for multimodal data fusion. The proposed method inherits the merits of the regularized label relaxation linear regression model and also has its own superiority. It can explore complementary patterns across different modal data and pay more attention to the modal data that have more significant patterns. In the experimental study, the proposed method is evaluated in the scenario of Alzheimer’s disease diagnosis. The promising performance indicates that the performance of multimodality fusion via multikernel learning is better than that of single modality. Moreover, the decreased square difference between training and testing performance indicates that overfitting is reduced and hence the generalization ability is improved.

Alzheimer’s disease (AD) is a progressive neurodegenerative disease, characterized by Amyloid-β accumulation-induced neuronal injury. Emerging evidence shows that circular RNA (circRNA) is involved in AD development. The aim of this study was to illustrate the role of circ-HUWE1 in Amyloid-β accumulation-induced neuronal injury. Quantitative real-time PCR (qPCR) or western blot was conducted for the expression analysis of circ-HUWE1, miR-433-3p, and fibroblast growth factor 7 (FGF7). In functional assays, cell viability was determined by CCK-8 assay, and cell apoptosis was examined by flow cytometry assay, the protein levels of apoptosis-related markers, and caspase1 or caspase3 activity. The release of pro-inflammatory factors was monitored by ELISA. The predicted binding relationship between miR-433-3p and circ-HUWE1 or FGF7 was validated by dual-luciferase reporter assay. We discovered that circ-HUWE1 absence alleviated Amyloid-β-induced cell viability degradation, cell apoptosis, and inflammatory responses in SK-N-SH cells. MiR-433-3p was a target of circ-HUWE1, and miR-433-3p inhibition reversed the effects of circ-HUWE1 knockdown. In addition, FGF7 was a downstream target of miR-433-3p whose function could be abolished by FGF7 reintroduction. Circ-HUWE1 positively regulated FGF7 expression via competitively targeting miR-433-3p. Moreover, circ-HUWE1 knockdown activated the WNT signaling pathway in Amyloid-β-treated SK-N-SH cells by targeting the miR-433-3p/FGF7 axis. In conclusion, circ-HUWE1 knockdown alleviates Amyloid-β-induced neuronal injury in SK-N-SH cells via miR-433-3p release-mediated FGF7 depletion.