Iterative Innovation Empowers the Training of High-level Interdisciplinary Medical Talents in the New Era: Experiences from School of Medicine, Zhejiang University

COVID-19 highlights the lack of interdisciplinary medical talents. The international history of medical education shows the urgent need of high-level interdisciplinary medical talents. Anchoring the goal to develop a global center of talents and highland of innovation, this article takes medical education of Zhejiang University as an example, focusing on and exploring the training mode of high-level interdisciplinary medical talents in the new era. It includes: Firstly, optimizing the training mode of eight-year program for medical doctors with non-medical bachelor degree followed with complete education for a medical doctorate that innovates the curriculum system of clinical medicine;secondly, creating the training system of postdoctor of clinical medicine and integrating medical resources that include high-quality talents and health care system, in order to build a high-quality teaching staff with a interdisciplinarity background and innovative bases. It not only strengthens the residents' competency and frontier creativity, but also ensures the sustainable development of interdisciplinary medical talents. The reform of training mode, curriculum system, teaching staff and clinical teaching bases all contribute to the goal of building a country with interdisciplinary talents that serve the frontier of science and technology in the world, the major needs of the country and people's health in the new era.Copyright © 2022, Peking Union Medical College Hospital. All rights reserved.

Deep learning coupled model based on TCN-LSTM for particulate matter concentration prediction

In this study, we combined the Temporal Convolutional Network (TCN) model with the Long Short-Term Memory (LSTM) network model and applied it to prediction of atmospheric particulate matter (PM) concentrations. The study area is Xi'an City, Shaanxi Province, and the study period is from January 2015 to July 2022. During this period, Xi'an exceeded China's National Ambient Air Quality Grade Ⅰ standard for PM for up to 70% of the days. The prediction results of the TCN-LSTM model were compared with those of deep learning models (Convolutional Neural Network-LSTM, TCN, and LSTM) and machine learning models (Support Vector Regression and Random Forest). The R2 values of the TCN-LSTM model were all >0.88, indicating better performance than that of the other five models, and the errors of the TCN-LSTM model were all lower than those of the other five models. The results showed that high-accuracy PM predictions using deep learning models can improve air quality monitoring by compensating for problems in the environmental monitoring process such as pollutant monitoring errors caused by instrument failures. Additionally, sensitivity analysis helps to identify the key factors influencing the behavior of PM. A sensitivity analysis of PM for different periods of COVID-19 found that PM2.5 is more sensitive to O3, while PM10 is mainly influenced by PM2.5. The sensitivity analysis for the whole period showed that PM was closely related to CO. Removing variables that do not contribute to the model output based on the sensitivity analysis results improves modeling efficiency while reducing operating costs and improving environmental monitoring activities and management strategies. © 2023 Turkish National Committee for Air Pollution Research and Control

Prediction and evaluation of the SARS-CoV-2 epidemic using an improved SEIR model

The present work propose an improved SEIR model that considers isolation and repeated nucleic acid detection factors. It was applied to the Kashgar Region, Xinjiang, China, to predict the changing trend of the number of confirmed cases and the number of susceptible individuals in Kashgar, and to evaluate and analyze local policy interventions. Model perform four predictive analyses of the epidemic situation in Kashgar. Comprehensive nucleic acid testing, isolation of asymptomatic patients, increasing isolation time and different proportions of the population isolated, control of population flow. Improved kinetic parameters were obtained using the Monte Carlo method. The theoretical estimation of the epidemic using the improved SEIR infectious disease dynamics model was in good agreement with the actuality of the epidemic in Kashgar. The analysis showed that in the areas with large area and dense population, repeat nucleic acid detection, quarantine of asymptomatic individuals and control the contact rate between people can quickly and effectively inhibit development of the local epidemic. © 2022 IEEE.

HOME CARDIAC SURVEILLANCE WITH ARTIFICIAL INTELLIGENCE DIGITAL PATIENT MONITORING DURING TREATMENT WITH PERTUZUMAB, TRASTUZUMAB AND HYALURONIDASE-ZZXF FOR HER2-POSITIVE BREAST CANCER (HARRIET): STUDY DESIGN AND RATIONALE

Background: Cardiotoxicity is frequently monitored when anti-HER2 treatment (Tx) is used and is a potential reason for discontinuation. As patients (pts) live longer, improved surveillance via low-interventional management is critical. Advances in digital tools and the impact of COVID-19 are accelerating opportunities for in-home care. We initiated cardiac monitoring at home in a subset of patients from study NCT04395508, which provides continuity of care during the pandemic and home administration of pertuzumab, trastuzumab and hyaluronidase-zzxf (PH FDC SC) for pts with HER2-positive breast cancer. Methods: Pts must be on/will receive maintenance intravenous pertuzumab + trastuzumab/PH FDC SC/subcutaneous trastuzumab post-chemotherapy completion. Up to 36 pts will be enrolled at Memorial Sloan Kettering Cancer Center and selected Mayo Clinic sites. Pts will undergo remote cardiac surveillance by a Home Health Nursing Provider via Caption Artificial Intelligence (AI)-guided ECHO and ECG (KardiaMobile 6L) after a reference in-clinic ECHO and 12-lead ECG. Images and tracing will be assessed centrally. Key objectives are to evaluate feasibility of LVEF assessment at home based on AI-guided cardiac ultrasound images acquired by novice users without prior ECHO experience and to evaluate feasibility, including recording frequency and signal quality, of an AI-guided ECG algorithm at home by the pt with nurse oversight. Results: Enrollment began Aug 2021. Conclusion: For healthcare systems, HARRIET can improve Tx monitoring to avoid premature discontinuation;is a step toward moving away from specialized sites to flexible healthcare delivery and lower cost of care;and can remove logistical, financial and workload barriers of scheduling in-person ECHO readings. For pts, it can optimize care and increase confidence in anti-HER2 Tx and can provide access to specialty care in the comfort of their own home.

Iterative Innovation Empowers the Training of High-level Interdisciplinary Medical Talents in the New Era: Experiences from School of Medicine, Zhejiang University

COVID-19 highlights the lack of interdisciplinary medical talents. The international history of medical education shows the urgent need of high-level interdisciplinary medical talents. Anchoring the goal to develop a global center of talents and highland of innovation, this article takes medical education of Zhejiang University as an example, focusing on and exploring the training mode of high-level interdisciplinary medical talents in the new era. It includes: Firstly, optimizing the training mode of eight-year program for medical doctors with non-medical bachelor degree followed with complete education for a medical doctorate that innovates the curriculum system of clinical medicine;secondly, creating the training system of postdoctor of clinical medicine and integrating medical resources that include high-quality talents and health care system, in order to build a high-quality teaching staff with a interdisciplinarity background and innovative bases. It not only strengthens the residents' competency and frontier creativity, but also ensures the sustainable development of interdisciplinary medical talents. The reform of training mode, curriculum system, teaching staff and clinical teaching bases all contribute to the goal of building a country with interdisciplinary talents that serve the frontier of science and technology in the world, the major needs of the country and people's health in the new era. © 2022, Peking Union Medical College Hospital. All rights reserved.