Objectives: To describe the pharmacy administration and pharmaceutical care in a module hospital during the coronavirus disease 2019 (COVID-19) epidemic and provide reference for domestic and foreign pharmacists participating in the epidemic prevention and control. Setting: The study was performed in a Jianghan module hospital constructed at the Wuhan Convention and Exhibition Center in Wuhan, China. This is 1 of the first 3 module hospitals. Practice description: One thousand eight hundred forty-eight patients were admitted to the Jianghan module hospital, and 1327 cases (71.81% of the total number) were cured and discharged. Pharmacists have successfully completed the tasks of purchase, storage, and free distribution of drugs worth ¥1.03 million (approximately $146,000), reviewed about 20,000 electronic orders, provided one-on-one online medication consultation for 484 patients, and held 5 lectures on rational drug use knowledge, which could help reduce irrational drug use and minimize the risk involved. Practice innovation: The new COVID-19 "module" pharmaceutical care model is equipped with new features such as pharmacy emergency command group, organizational structure for pharmacy administration, electronic control of drug prescription, and "zero contact" pharmaceutical care relying on the new media platform "WeChat." This platform provides relevant pharmaceutical care for patients, such as ensuring drug supply, setting up critical care drug trolleys, designing specific drug packaging bags, creating a module radio station to broadcast rational drug use information to the patients, and other aspects. Evaluation: With the continuous improvement of the module hospital and the progress in indepth knowledge about COVID-19, some aspects such as patient admission criteria and variety of drugs need to be adjusted depending on the actual situation. Results: The pharmacists provided pharmaceutical care for 1848 patients with mild COVID-19 disease. They not only ensured the timely supply of the drugs but also reduced the incidence of drug-induced risks through medication review and guidance, thereby improving patient compliance and helping the patients rebuild their confidence in overcoming the disease. Conclusion:The new COVID-19 module pharmaceutical care model has played an important role in overcoming the epidemic situation of COVID-19 in China and thus can be implemented on a broader scale.

Objective Bone age (BA) is a crucial indicator for revealing the growth and development of children. This study tested the performance of a fully automated artificial intelligence (AI) system for BA assessment of Chinese children with abnormal growth and development. Materials and Methods A fully automated AI system based on the Greulich and Pyle (GP) method was developed for Chinese children by using 8,000 BA radiographs from five medical centers nationwide in China. Then, a total of 745 cases (360 boys and 385 girls) with abnormal growth and development from another tertiary medical center of north China were consecutively collected between January and October 2018 to test the system. The reference standard was defined as the result interpreted by two experienced reviewers (a radiologist with 10 years and an endocrinologist with 15 years of experience in BA reading) through consensus using the GP atlas. BA accuracy within 1 year, root mean square error (RMSE), mean absolute difference (MAD), and 95% limits of agreement according to the Bland-Altman plot were statistically calculated. Results For Chinese pediatric patients with abnormal growth and development, the accuracy of this new automated AI system within 1 year was 84.60% as compared to the reference standard, with the highest percentage of 89.45% in the 12- to 18-year group. The RMSE, MAD, and 95% limits of agreement of the AI system were 0.76 years, 0.58 years, and −1.547 to 1.428, respectively, according to the Bland-Altman plot. The largest difference between the AI and experts’ BA result was noted for patients of short stature with bone deformities, severe osteomalacia, or different rates of maturation of the carpals and phalanges. Conclusions The developed automated AI system could achieve comparable BA results to experienced reviewers for Chinese children with abnormal growth and development.

Effectively identifying COVID-19 patients using non-PCR clinical data is critical for the optimal clinical outcomes. Currently, there is a lack of comprehensive understanding of various biomedical features and appropriate technical approaches to accurately detecting COVID-19 patients. In this study, we recruited 214 confirmed COVID-19 patients in non-severe (NS) and 148 in severe (S) clinical type, 198 non-infected healthy (H) participants and 129 non-COVID viral pneumonia (V) patients. The participants' clinical information (23 features), lab testing results (10 features), and thoracic CT scans upon admission were acquired as three input feature modalities. To enable late fusion of multimodality data, we developed a deep learning model to extract a 10-feature high-level representation of the CT scans. Exploratory analyses showed substantial differences of all features among the four classes. Three machine learning models (k-nearest neighbor kNN, random forest RF, and support vector machine SVM) were developed based on the 43 features combined from all three modalities to differentiate four classes (NS, S, V, and H) at once. All three models had high accuracy to differentiate the overall four classes (95.4%-97.7%) and each individual class (90.6%-99.9%). Multimodal features provided substantial performance gain from using any single feature modality. Compared to existing binary classification benchmarks often focusing on single feature modality, this study provided a novel and effective breakthrough for clinical applications. Findings and the analytical workflow can be used as clinical decision support for current COVID-19 and other clinical applications with high-dimensional multimodal biomedical features.

The causes of seasonal variability in pathogen transmission are not well understood, and have not been comprehensively investigated. In an example for enteric pathogens, incidence of Escherichia coli O157 (STEC) colonization in cattle is consistently higher during warmer months compared to cooler months in various cattle production systems. However, actual mechanisms for this seasonality remain elusive. In addition, the influence of host (cattle) behavior on this pattern has not been thoroughly considered. To that end, we constructed a spatially explicit agent-based model that accounted for the effect of temperature fluctuations on cattle behavior (direct contact among cattle and indirect between cattle and environment), as well as its effect on pathogen survival in the environment. We then simulated the model in a factorial approach to evaluate the hypothesis that temperature fluctuations can lead to seasonal STEC transmission dynamics by influencing cattle aggregation, grazing, and drinking behaviors. Simulation results showed that higher temperatures increased the frequency at which cattle aggregated under shade in pasture, resulting in increased direct contact and transmission of STEC between individual cattle, and hence higher incidence over model simulations in the warm season. In contrast, increased drinking behavior during warm season was not an important transmission pathway. Although sensitivity analyses suggested that the relative importance of direct vs. indirect (environmental) pathways depend to upon model parameterization, model simulations indicated that factors influencing cattle aggregation, such as temperature, were likely strong drivers of transmission dynamics of enteric pathogens.