Patient-facing large language models (LLMs) hold potential to streamline inefficient transitions from primary to specialist care. We developed the preassessment (PreA), an LLM chatbot co-designed with local stakeholders, to perform the general medical consultations for history-taking, preliminary diagnoses, and test ordering that would normally be performed by primary care providers and to generate referral reports for specialists. PreA was tested in a randomized controlled trial involving 111 specialists from 24 medical disciplines across two health centers, where 2,069 patients (1,141 women; 928 men) were randomly assigned to use PreA independently (PreA-only), use it with staff support (PreA-human), or not use it (No-PreA) before specialist consultation. The trial met its primary end points with the PreA-only group showing significantly reduced physician consultation duration (28.7% reduction; 3.14 ± 2.25 min) compared to the No-PreA group (4.41 ± 2.77 min; P < 0.001), alongside significant improvements in physician-perceived care coordination (mean scores 113.1% increase; 3.69 ± 0.90 versus 1.73 ± 0.95; P < 0.001) and patient-reported communication ease (mean scores 16.0% increase; 3.99 ± 0.62 versus 3.44 ± 0.97; P < 0.001). Equivalent outcomes between the PreA-only and PreA-human groups confirmed the autonomous operation capability. Co-designed PreA outperformed the same model with additional fine-tuning on local dialogues across clinical decision-making domains. Co-design with local stakeholders, compared to passive local data collecting, represents a more effective strategy for deploying LLMs to strengthen health systems and enhance patient-centered care in resource-limited settings. Chinese Clinical Trial Registry identifier: ChiCTR2400094159 .

Background: In megacities, there is an urgent need to establish more sensitive forecasting and early warning methods for acute respiratory infectious diseases. Existing prediction and early warning models for influenza and other acute respiratory infectious diseases have limitations and therefore there is room for improvement. Objective: The aim of this study was to explore a new and better-performing deep-learning model to predict influenza trends from multisource heterogeneous data in a megacity. Methods: We collected multisource heterogeneous data from the 26th week of 2012 to the 25th week of 2019, including influenza-like illness (ILI) cases and virological surveillance, data of climate and demography, and search engines data. To avoid collinearity, we selected the best predictor according to the weight and correlation of each factor. We established a new multiattention-long short-term memory (LSTM) deep-learning model (MAL model), which was used to predict the percentage of ILI (ILI%) cases and the product of ILI% and the influenza-positive rate (ILI%×positive%), respectively. We also combined the data in different forms and added several machine-learning and deep-learning models commonly used in the past to predict influenza trends for comparison. The R2 value, explained variance scores, mean absolute error, and mean square error were used to evaluate the quality of the models. Results: The highest correlation coefficients were found for the Baidu search data for ILI% and for air quality for ILI%×positive%. We first used the MAL model to calculate the ILI%, and then combined ILI% with climate, demographic, and Baidu data in different forms. The ILI%+climate+demography+Baidu model had the best prediction effect, with the explained variance score reaching 0.78, R2 reaching 0.76, mean absolute error of 0.08, and mean squared error of 0.01. Similarly, we used the MAL model to calculate the ILI%×positive% and combined this prediction with different data forms. The ILI%×positive%+climate+demography+Baidu model had the best prediction effect, with an explained variance score reaching 0.74, R2 reaching 0.70, mean absolute error of 0.02, and mean squared error of 0.02. Comparisons with random forest, extreme gradient boosting, LSTM, and gated current unit models showed that the MAL model had the best prediction effect. Conclusions: The newly established MAL model outperformed existing models. Natural factors and search engine query data were more helpful in forecasting ILI patterns in megacities. With more timely and effective prediction of influenza and other respiratory infectious diseases and the epidemic intensity, early and better preparedness can be achieved to reduce the health damage to the population.

Background: Influenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve. Objective: This study aimed to develop a predictive model for influenza outbreaks by combining Baidu search query data with traditional virological surveillance data. The goal was to improve early detection and preparedness for influenza outbreaks in both northern and southern China, providing evidence for supplementing modern intelligence epidemic surveillance methods. Methods: We collected virological data from the National Influenza Surveillance Network and Baidu search query data from January 2011 to July 2018, totaling 3,691,865 and 1,563,361 respective samples. Relevant search terms related to influenza were identified and analyzed for their correlation with influenza-positive rates using Pearson correlation analysis. A distributed lag nonlinear model was used to assess the lag correlation of the search terms with influenza activity. Subsequently, a predictive model based on the gated recurrent unit and multiple attention mechanisms was developed to forecast the influenza-positive trend. Results: This study revealed a high correlation between specific Baidu search terms and influenza-positive rates in both northern and southern China, except for 1 term. The search terms were categorized into 4 groups: essential facts on influenza, influenza symptoms, influenza treatment and medicine, and influenza prevention, all of which showed correlation with the influenza-positive rate. The influenza prevention and influenza symptom groups had a lag correlation of 1.4-3.2 and 5.0-8.0 days, respectively. The Baidu search terms could help predict the influenza-positive rate 14-22 days in advance in southern China but interfered with influenza surveillance in northern China. Conclusions: Complementing traditional disease surveillance systems with information from web-based data sources can aid in detecting warning signs of influenza outbreaks earlier. However, supplementation of modern surveillance with search engine information should be approached cautiously. This approach provides valuable insights for digital epidemiology and has the potential for broader application in respiratory infectious disease surveillance. Further research should explore the optimization and customization of search terms for different regions and languages to improve the accuracy of influenza prediction models.

It is important to understand the cognition, willingness, and psychological anxiety state of Chinese guardians toward COVID-19 vaccination for their children to predict the future vaccination rate and to help the design of policies that aim to expand the population with immunity against COVID-19. This study collected data with a professional vaccination registration platform for children named “Xiao Dou Miao” in February 2021. The psychological anxiety state of the guardians was self-evaluated using the psychological anxiety scale. Factors that might influence the willingness of guardians to vaccinate their children were identified using logistic regression analysis. This study included 12,872 questionnaires with 70.9% of guardians showing willingness to vaccinate their children. Guardians who were male, aged 40–49 and from rural area were more willing to vaccinate their children. Fathers, guardians with higher education and income, whose children have a history of adverse vaccine reactions and allergies were less willing to vaccinate their children (p < .001). More than 80% of the guardians expressed a high level of trust for vaccine information released by official and health-related agencies. Guardians who were not vaccinated were more anxious than those who were vaccinated (χ2 = 27.99, p < .001). To protect children from COVID-19, vaccine coverage in children should be expanded rapidly and public awareness on vaccine safety and effectiveness should be improved.

This study aimed to investigate the changes in the willingness of guardians to administer the COVID-19 vaccine to their children, allow the coadministration of other vaccines, and administer the COVID-19 vaccine booster dose. This was a follow-up study conducted 6 months after a similar previous study. The self-administered questionnaire was distributed through the “Xiao Dou Miao” app and 9424 guardians with access to this app participated in the survey that was conducted from September 15 to October 8, 2021. Of all the participating guardians, 86.68% were willing to vaccinate their children with the COVID-19 vaccine, which was approximately 16% more than those in our previous study. Guardians aged ≥40 years, healthcare workers, and those with children aged ≥3 years were more willing to vaccinate their children. Approximately 77% of the guardians were willing toward the coadministration of COVID-19 and influenza vaccines. Approximately 64% of the guardians were willing toward the coadministration of other nonimmunization program vaccines with the COVID-19 vaccine for their children. The primary reasons for reluctance toward the coadministration of vaccines were concerns about vaccine safety and effectiveness. If necessary, 92% of the guardians were willing to receive a COVID-19 vaccine booster and 82% were willing to vaccinate their children with a COVID-19 vaccine booster. We hope that this research will facilitate the formulation of successful strategies for the implementation of COVID-19 vaccinations, covaccinations, and COVID-19 booster doses, particularly for children aged <6 years.

Objective: Dipeptidyl peptidase 4 (DPP4), also known as CD26, is a transmembrane glycoprotein with peptidase activity expressed on epithelial cells and some immune cells. It also occurs as a soluble form. Studies have revealed that the expression level of lymphocyte sCD26/sDPP4 was elevated in the asthmatic patients. Airway remodeling increases in asthma severity and these structural changes include, amongst others, the loss of epithelial integrity because of cell shedding, goblet cell hyperplasia, destruction of ciliated cells, and EMT. So we try to find whether sCD26/sDPP4 has a role in pathological/dysregulated transition from bronchial epithelial cells into fibroblasts cells in response to TGFβ1 exposure in vitro. Therefore, our purpose in the present work was to identify the role of sCD26/sDPP4 in airway EMT regulation. Methods: The EMT cell model was established based on human 16HBE cells. The effects of sCD26/sDPP4 and its inhibitors on airway EMT and that of sCD26/sDPP4 on Th17/IL-17 and its role in airway EMT were investigated in vitro. Results: The mRNA and protein level of E-Cadherin decreased after the treatment of TGF-β1 in 16HBE cells, while α-SMA was up-regulated. The level of E-Cadherin was significantly down-regulated after the sCD26/sDPP4 stimulation, and that of α-SMA was dramatically elevated. DPP4 inhibitors promoted the level of E-cadherin and inhibited that of α-SMA. Additionally, in the DPP4-treated IL-17 cells group, E-Cadherin was markedly down-regulated at the mRNA and protein level, while α-SMA was reversely up-regulated. Conclusion: The TGF-β1-induced EMT of human bronchial epithelial cells could be promoted by sCD26/sDPP4. The suppression of EMT in human bronchial epithelial cells was achieved by DPP4 inhibitor, and the TGF-β1-mediated EMT of human airway cells was promoted by the synergy of IL-17 and sCD26/sDPP4 in vitro.