Yuewei Ling

All Publications

• Impact of an enhanced recovery after surgery program integrating cardiopulmonary rehabilitation on post-operative prognosis of patients treated with CABG: protocol of the ERAS-CaRe randomized controlled trial. BMC pulmonary medicine Yang, Q., Wang, L., Zhang, X., Lu, P., Pan, D., Li, S., Ling, Y., Zhi, X., Xia, L., Zhu, Y., Chen, Y., Liu, C., Jin, W., Reinhardt, J. D., Wang, X., Zheng, Y. 2024; 24 (1): 512

  Abstract

  Coronary artery bypass grafting is associated with a high occurrence of postoperative cardiopulmonary complications. Preliminary evidence suggested that enhanced recovery after surgery can effectively reduce the occurrence of postoperative cardiopulmonary complications. However, enhanced recovery after surgery with systematic integration of cardiopulmonary rehabilitation (ERAS-CaRe) into for Coronary artery bypass grafting has not been evaluated so far. We thus design the ERAS-CaRe randomized-controlled trial to evaluate possible superiority of embedding cardiopulmonary rehabilitation in ERAS over ERAS alone as well as to investigate effects of differential timing of cardiopulmonary rehabilitation within enhanced recovery after surgery (pre-, post-, perio-operative) on post-operative cardiopulmonary complications following Coronary artery bypass grafting surgery.ERAS-CaRe is a pragmatic, randomized-controlled, parallel four-arm, clinical trial. Three hundred sixty patients scheduled for Coronary artery bypass grafting in two Chinese hospitals will be grouped randomly into (i) Standard enhanced recovery after surgery or (ii) pre-operative ERAS-CaRe or (iii) post-operative ERAS-CaRe or (iv) perio-operative ERAS-CaRe. Primary outcome is the occurrence of cardiopulmonary complications at 10 days after Coronary artery bypass grafting. Secondary outcomes include the occurrence of other individual complications including cardiac, pulmonary, stroke, acute kidney injury, gastrointestinal event, ICU delirium rate, reintubation rate, early drainage tube removal rate, unplanned revascularization rate, all-cause mortality, ICU readmission rate, plasma concentration of myocardial infarction-related key biomarkers etc. DISCUSSION: The trial is designed to evaluate the hypothesis that a cardiopulmonary rehabilitation based enhanced recovery after surgery program reduces the occurrence of cardiopulmonary complications following Coronary artery bypass grafting and to determine optimal timing of cardiopulmonary rehabilitation within enhanced recovery after surgery. The project will contribute to increasing the currently limited knowledge base in the field as well as devising clinical recommendations.The trial was registered at the Chinese Clinical Trials Registry on 25 August 2023 (ChiCTR2300075125; date recorded: 25/8/2023, https://www.chictr.org.cn/ ).

  View details for DOI 10.1186/s12890-024-03286-1

  View details for PubMedID 39402537

  View details for PubMedCentralID PMC11476288

• Optimized air quality management based on air quality index prediction and air pollutants identification in representative cities in China. Scientific reports Guo, Z., Jing, X., Ling, Y., Yang, Y., Jing, N., Yuan, R., Liu, Y. 2024; 14 (1): 17923

  Abstract

  With the ongoing challenge of air pollution posing serious health and environmental threats, particularly in rapidly industrializing regions, accurate forecasting and effective pollutant identification are crucial for enhancing public health and ecological stability. This study aimed to optimize air quality management through the prediction of the Air Quality Index (AQI) and identification of air pollutants. Our study spans nine representative cities (Hohhot, Yinchuan, Lanzhou, Beijing, Taiyuan, Xi'an, Shanghai, Nanjing, Wuhan) in China, with data collected from January 1, 2015, to November 30, 2021. We proposed a new model for daily AQI prediction, termed VMD-CSA-CNN-LSTM, which employed advanced machine learning techniques, including convolutional neural networks (CNN) and long short-term memory (LSTM) networks, and leveraged the chameleon swarm algorithm (CSA) for hyperparameter optimization, integrated through a variational mode decomposition approach. The model was developed using data from Lanzhou, with a split ratio of 8:1:1 into training, validation, and test sets, achieving an RMSE of 2.25, MAPE of 0.02, adjusted R-squared of 98.91%, and training efficiency of 5.31%. The model was further externally validated in the other eight cities, yielding comparable results, with an adjusted R-squared above 96%, MAPE below 0.1, and RMSE below 7.5. Additionally, we employed a random forest algorithm to identify the primary pollutants contributing to AQI levels. Our results indicated that PM2.5 was the most significant pollutant in Beijing, Taiyuan, and Xi'an, while PM10 was dominant in Hohhot, Yinchuan, and Lanzhou. In Shanghai, Nanjing, and Wuhan, both PM2.5 and PM10 were critical, with ozone also identified as a major air pollutant. This study not only advances the predictive accuracy of AQI models but also aids policymakers by providing a reliable tool for air quality management and strategic planning aimed at pollution reduction. The integration of these advanced computational techniques into environmental monitoring practices offers a promising avenue for enhancing air quality and mitigating pollution-related risks.

  View details for DOI 10.1038/s41598-024-68972-w

  View details for PubMedID 39095454

• Trajectories of cognitive function among people aged 45 years and older living with diabetes in China: Results from a nationally representative longitudinal study (2011~2018). PloS one Chen, S., Ling, Y., Zhou, F., Qiao, X., Reinhardt, J. D. 2024; 19 (5): e0299316

  Abstract

  Diabetes is associated with decline of cognitive function. Exploring different trajectories of cognitive function occurring in people with diabetes is important to improved prognosis. This study aimed to investigate differential patterns of trajectories of cognitive function and baseline determinants of trajectory group membership utilizing data from middle-aged and older Chinese adults with diabetes.Participants of the Chinese Health And Retirement Longitudinal Study (CHARLS) aged 45 years and above received biennial assessments between 2011 and 2018. The primary outcome was overall cognitive function score operationalized as sum of mental intactness and episodic memory scores derived from the Telephone Interview of Cognitive Status (TICS). A weighted growth mixture model was used to estimate cognitive function trajectories of CHARLS participants with diabetes, and baseline factors associated with trajectory group membership were investigated with weighted multinomial logistic regression.Data from 1,463 participants with diabetes aged 45 years and above were analyzed, a three-group trajectory model showed the best fit for overall cognitive scores: low baseline, linear declining (22.1%); moderate baseline, linear declining (37.5%) and high-stable (40.3%). Older participants, females, participants with low education, with nighttime sleep <6 h, without daytime napping habits, and with depressive symptoms were at a higher risk of unfavorable cognitive function trajectories.We identified heterogeneous trajectories of cognitive function among middle-aged and older people living with diabetes in China. Socially vulnerable groups including females, rural residents, and those with low education were at a higher risk for unfavorable trajectories. In health programs aimed at preventing and mitigating cognitive decline in individuals with diabetes more attention should be given to vulnerable groups. Reduced nighttime sleep, lack of daytime napping, and depressive symptoms appear to be modifiable risk factors.

  View details for DOI 10.1371/journal.pone.0299316

  View details for PubMedID 38787866

• Portfolio Optimization Model for Gold and Bitcoin Based on Weighted Unidirectional Dual-Layer LSTM Model and SMA-Slope Strategy COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE Xue, Q., Ling, Y., Tian, B. 2022; 2022: 1869897

  Abstract

  Portfolio optimization is one of the most complex problems in the financial field, and technical analysis is a popular tool to find an optimal solution that maximizes the yields. This paper establishes a portfolio optimization model consisting of a weighted unidirectional dual-layer LSTM model and an SMA-slope strategy. The weighted unidirectional dual-layer LSTM model is developed to predict the daily prices of gold/Bitcoin, which addresses the traditional problem of prediction lag. Based on the predicted prices and comparison of two representative investment strategies, simple moving average (SMA) and Bollinger bands (BB), this paper adopts a new investment strategy, SMA-slope strategy, which introduces the concept of k-slope to measure the daily ups and downs of gold/Bitcoin. As two typical financial products, gold and Bitcoin are opposite in terms of their characteristics, which may represent many existing financial products in investors' portfolios. With a principle of $1000, this paper conducts a five-year simulation of gold and Bitcoin trading from 11 September 2016 to 10 September 2021. To compensate for the SMA and BB that may miss buying and selling points, 4 different parameters' values in the k-slope are obtained through particle swarm optimization simulation. Also, the simulation results imply that the proposed portfolio optimization model contributes to helping investors make investment decisions with high profitability.

  View details for DOI 10.1155/2022/1869897

  View details for Web of Science ID 000815095300017

  View details for PubMedID 35720924

  View details for PubMedCentralID PMC9200532