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您的检索式：作者名="Mengling Feng"
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| 1 | Molecular phylogenomic analysis reveals a single origin of monkeypox virus transmission in Guangzhou,China in June 2023显示文摘The monkeypox(mpox)virus has caused worldwide transmission since its initial report in England in early May 2022.Available data from the World Health Organization(WHO)show that Europe and the Americas experienced a huge wave of mpox virus infection.Now the number of infected cases is on the rise in Asia.Several sporadic infections have been reported in China.In this study,we obtained high‐quality whole viral genomic sequences using a mpox virus‐specific amplicon‐based sequencing strategy.Our analysis of the phylogenomic characteristics indicated that all eight mpox virus sequences from Guangzhou belonged to the clade IIb lineage B.1.3 cluster.However,we could not locate the exact origins where the virus was imported,based on all the available mpox virus sequences from the Global Initiative on Sharing Avian Influenza Data(GISAID)database(http://gffzz25fa773688d24fb7sb9vqpcukfx5v6v0q.ffgz.tsg.suse.edu.cn/),except for their closest sequence similarity to that was reported from Japan.Novel amino acid mutations were found among the eight cases,suggesting that a local transmission may have occurred in Guangzhou,China. | Mengling Jiang Xizi Deng Qinghong Fan Fei Gu Huiqin Yang Jian Wang Xiaoping Tang Fengyu Hu Yun Lan Feng Li | 2023 | Biosafety and Health2023，5，5： | 0 |
| 2 | The application of artificial intelligence in the management of sepsis显示文摘Sepsis is a complex and heterogeneous syndrome that remains a serious challenge to healthcare worldwide.Patients afflicted by severe sepsis or septic shock are customarily placed under intensive care unit(ICU)supervision,where a multitude of apparatus is poised to produce high-granularity data.This reservoir of high-quality data forms the cornerstone for the integration of AI into clinical practice.However,existing reviews currently lack the inclusion of the latest advancements.This review examines the evolving integration of artificial intelligence(AI)in sepsis management.Applications of artificial intelligence include early detection,subtyping analysis,precise treatment and prognosis assessment.AI-driven early warning systems provide enhanced recognition and intervention capabilities,while profiling analyzes elucidate distinct sepsis manifestations for targeted therapy.Precision medicine harnesses the potential of artificial intelligence for pathogen identification,antibiotic selection,and fluid optimization.In conclusion,the seamless amalgamation of artificial intelligence into the domain of sepsis management heralds a transformative shift,ushering in novel prospects to elevate diagnostic precision,therapeutic efficacy,and prognostic acumen.As AI technologies develop,their impact on shaping the future of sepsis care warrants ongoing research and thoughtful implementation. | Jie Yang Sicheng Hao Jiajie Huang Tianqi Chen Ruoqi Liu Ping Zhang Mengling Feng Yang He Wei Xiao Yucai Hong Zhongheng Zhang | 2023 | Medical Review2023，3，5： | 0 |
| 3 | Self-Correcting Recurrent Neural Network for Acute Kidney Injury Prediction in Critical Care显示文摘Background.In critical care,intensivists are required to continuously monitor high-dimensional vital signs and lab measurements to detect and diagnose acute patient conditions,which has always been a challenging task.Recently,deep learning models such as recurrent neural networks(RNNs)have demonstrated their strong potential on predicting such events.However,in real deployment,the patient data are continuously coming and there is no effective adaptation mechanism for RNN to incorporate those new data and become more accurate.Methods.In this study,we propose a novel self-correcting mechanism for RNN to fill in this gap.Our mechanism feeds prediction errors from the predictions of previous timestamps into the prediction of the current timestamp,so that the model can“learn”from previous predictions.We also proposed a regularization method that takes into account not only the model’s prediction errors on the labels but also its estimation errors on the input data.Results.We compared the performance of our proposed method with the conventional deep learning models on two real-world clinical datasets for the task of acute kidney injury(AKI)prediction and demonstrated that the proposed model achieved an area under ROC curve at 0.893 on the MIMIC-III dataset and 0.871 on the Philips eICU dataset.Conclusions.The proposed self-correcting RNNs demonstrated effectiveness in AKI prediction and have the potential to be applied to clinical applications. | Hao Du Ziyuan Pan Kee Yuan Ngiam Fei Wang Ping Shum Mengling Feng | 2021 | Health Data Science2021，，1： | 0 |
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