Background: ChatGPT is a powerful pretrained large language model. It has both demonstrated potential and raised concerns related to knowledge translation and knowledge transfer. To apply and improve knowledge transfer in the real world, it is essential to assess the perceptions and acceptance of the users of ChatGPT-assisted training. Objective: We aimed to investigate the perceptions of health care trainees and professionals on ChatGPT-assisted training, using biomedical informatics as an example. Methods: We used purposeful sampling to include all health care undergraduate trainees and graduate professionals (n=195) from January to May 2023 in the School of Public Health at the National Defense Medical Center in Taiwan. Subjects were asked to watch a 2-minute video introducing 5 scenarios about ChatGPT-assisted training in biomedical informatics and then answer a self-designed online (web- and mobile-based) questionnaire according to the Kirkpatrick model. The survey responses were used to develop 4 constructs: “perceived knowledge acquisition,” “perceived training motivation,” “perceived training satisfaction,” and “perceived training effectiveness.” The study used structural equation modeling (SEM) to evaluate and test the structural model and hypotheses. Results: The online questionnaire response rate was 152 of 195 (78%); 88 of 152 participants (58%) were undergraduate trainees and 90 of 152 participants (59%) were women. The ages ranged from 18 to 53 years (mean 23.3, SD 6.0 years). There was no statistical difference in perceptions of training evaluation between men and women. Most participants were enthusiastic about the ChatGPT-assisted training, while the graduate professionals were more enthusiastic than undergraduate trainees. Nevertheless, some concerns were raised about potential cheating on training assessment. The average scores for knowledge acquisition, training motivation, training satisfaction, and training effectiveness were 3.84 (SD 0.80), 3.76 (SD 0.93), 3.75 (SD 0.87), and 3.72 (SD 0.91), respectively (Likert scale 1-5: strongly disagree to strongly agree). Knowledge acquisition had the highest score and training effectiveness the lowest. In the SEM results, training effectiveness was influenced predominantly by knowledge acquisition and partially met the hypotheses in the research framework. Knowledge acquisition had a direct effect on training effectiveness, training satisfaction, and training motivation, with β coefficients of .80, .87, and .97, respectively (all P<.001). Conclusions: Most health care trainees and professionals perceived ChatGPT-assisted training as an aid in knowledge transfer. However, to improve training effectiveness, it should be combined with empirical experts for proper guidance and dual interaction. In a future study, we recommend using a larger sample size for evaluation of internet-connected large language models in medical knowledge transfer.

Gene co-expression network analysis (GCNA) can detect alterations in regulatory activities in case/control comparisons. We propose a framework to detect novel genes and networks for predicting breast cancer recurrence. Thirty-four prognosis candidate genes were selected based on a literature review. Four Gene Expression Omnibus Series (GSE) microarray datasets (n = 920) were used to create gene co-expression networks based on these candidates. We applied the framework to four comparison groups according to node (+/−) and recurrence (+/−). We identified a sub-network containing two candidate genes (LST1 and IGHM) and six novel genes (IGHA1, IGHD, IGHG1, IGHG3, IGLC2, and IGLJ3) related to B cell-specific immunoglobulin. These novel genes were correlated with recurrence under the control of node status and were found to function as tumor suppressors; higher mRNA expression indicated a lower risk of recurrence (hazard ratio, HR = 0.87, p = 0.001). We created an immune index score by performing principle component analysis and divided the genes into low and high groups. This discrete index significantly predicted relapse-free survival (RFS) (high: HR = 0.77, p = 0.019; low: control). Public tool KM Plotter and TCGA-BRCA gene expression data were used to validate. We confirmed these genes are correlated with RFS and distal metastasis-free survival (DMFS) in triple-negative breast cancer (TNBC) and general breast cancer.

Background: The COVID-19 outbreak has not only changed the lifestyles of people globally but has also resulted in other challenges, such as the requirement of self-isolation and distance learning. Moreover, people are unable to venture out to exercise, leading to reduced movement, and therefore, the demand for exercise at home has increased. Objective: We intended to investigate the relationships between a Nintendo Ring Fit Adventure (RFA) intervention and improvements in running time, cardiac force index (CFI), sleep quality (Chinese version of the Pittsburgh Sleep Quality Index score), and mood disorders (5-item Brief Symptom Rating Scale score). Methods: This was a randomized prospective study and included 80 students who were required to complete a 1600-meter outdoor run before and after the intervention, the completion times of which were recorded in seconds. They were also required to fill out a lifestyle questionnaire. During the study, 40 participants (16 males and 24 females, with an average age of 23.75 years) were assigned to the RFA group and were required to exercise for 30 minutes 3 times per week (in the adventure mode) over 4 weeks. The exercise intensity was set according to the instructions given by the virtual coach during the first game. The remaining 40 participants (30 males and 10 females, with an average age of 22.65 years) were assigned to the control group and maintained their regular habits during the study period. Results: The study was completed by 80 participants aged 20 to 36 years (mean 23.20, SD 2.96 years). The results showed that the running time in the RFA group was significantly reduced. After 4 weeks of physical training, it took females in the RFA group 19.79 seconds (P=.03) and males 22.56 seconds (P=.03) less than the baseline to complete the 1600-meter run. In contrast, there were no significant differences in the performance of the control group in the run before and after the fourth week of intervention. In terms of mood disorders, the average score of the RFA group increased from 1.81 to 3.31 for males (difference=1.50, P=.04) and from 3.17 to 4.54 for females (difference=1.38, P=.06). In addition, no significant differences between the RFA and control groups were observed for the CFI peak acceleration (CFIPA)_walk, CFIPA_run, or sleep quality. Conclusions: RFA could either maintain or improve an individual’s physical fitness, thereby providing a good solution for people involved in distance learning or those who have not exercised for an extended period. Trial Registration: ClinicalTrials.gov NCT05227040; https://clinicaltrials.gov/ct2/show/NCT05227040

Background: Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann-Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression. Results: The DT models exhibited the lowest predictive power and the poorest extrapolation when applied to the test samples. The ANN models displayed the best predictive power and showed the best extrapolation. The 21 most-associated genes, as determined by integration of each model, were analyzed using Cox regression with a 3.53-fold (95% CI: 2.24-5.58) increased risk of breast cancer five-year recurrence… Conclusions: The 21 selected genes can predict breast cancer recurrence. Among these genes, CCNB1, PLK1 and TOP2A are in the cell cycle G2/M DNA damage checkpoint pathway. Oncologists can offer the genetic information for patients when understanding the gene expression profiles on breast cancer recurrence.

Background: Glioblastoma is currently an incurable cancer. Genome-wide association studies have demonstrated that 41 genetic variants are associated with glioblastoma and may provide an option for drug development. Methods: We investigated FDA-approved antipsychotics for their potential treatment of glioblastoma based on genome-wide association studies data using a ‘pathway/gene-set analysis’ approach. Results: The in-silico screening led to the discovery of 12 candidate drugs. DepMap portal revealed that 42 glioma cell lines show higher sensitivities to 12 candidate drugs than to Temozolomide, the current standard treatment for glioblastoma. Conclusion: In particular, cell lines showed significantly higher sensitivities to Norcyclobenzaprine and Protriptyline which were predicted to bind targets to disrupt a certain molecular function such as DNA repair, response to hormones, or DNA-templated transcription, and may lead to an effect on survival-related pathways including cell cycle arrest, response to ER stress, glucose transport, and regulation of autophagy. However, it is recommended that their mechanism of action and efficacy are further determined.

Background: The successful completion of medical practices often relies on information collection and analysis. Government agencies and medical institutions have encouraged people to use medical information technology (MIT) to manage their conditions and promote personal health. In 2014, Taiwan established the first electronic personal health record (PHR) platform, My Health Bank (MHB), which allows people to access and manage their PHRs at any time. In the face of the COVID-19 pandemic in 2020, Taiwan has used MIT to effectively prevent the spread of COVID-19 and undertaken various prevention measures before the onset of the outbreak. Using MHB to purchase masks in an efficient and orderly way and thoroughly implementing personal protection efforts is highly important to contain disease spread. Objective: This study aims to understand people’s intention to use the electronic PHR platform MHB and to investigate the factors affecting their intention to use this platform. Methods: From March 31 to April 9, 2014, in a promotion via email and Facebook, participants were asked to fill out a structured questionnaire after watching an introductory video about MHB on YouTube. The questionnaire included seven dimensions: perceived usefulness, perceived ease of use, health literacy, privacy and security, computer self-efficacy, attitude toward use, and behavioral intention to use. Each question was measured on a 5-point Likert scale ranging from “strongly disagree” (1 point) to “strongly agree” (5 points). Descriptive statistics and structural equation analysis were performed using SPSS 21 and AMOS 21 software. Results: A total of 350 valid questionnaire responses were collected (female: 219/350, 62.6%; age: 21-30 years: 238/350, 68.0%; university-level education: 228/350, 65.1%; occupation as student: 195/350, 56.6%; average monthly income <NT $30,000 [<US $1054.89]: 230/350, 65.7%; residence in northern Taiwan: 236/350, 67.4%; and health status perceived as “good”: 171/350, 48.9%). Five indicators, including chi-square test (X2310=2.63), goodness-of-fit index (0.85), adjusted goodness-of-fit index (0.81), comparative fit index (0.91), and root mean square error of approximation (0.07), were calculated. The results indicated a good fit. Further analysis indicated that the most important factor affecting respondents’ behavioral intention to use MHB was their attitude toward use (0.78), followed by perceived ease of use (0.65), perceived usefulness (0.41), health literacy (0.10), and privacy and security (0.07). Conclusions: From the perspective of the populace, this study explored the factors affecting the use of MHB and constructed an interpretation model with a strong goodness of fit. The results of our analysis are consistent with the technology acceptance model. Through the diverse value-added services of MHB, Taiwan's experience in pandemic prevention with smart technology can facilitate future responses to unknown, emerging infectious diseases.