5 results on '"Jiangfeng Bai"'
Search Results
2. Association between Early Treatment and Favorable Clinical Outcomes of COVID-19: Evidence from Nine Provinces in China
- Author
-
Haijun Xie, Hongmei Wo, Yudong Wang, Liufen Mo, Chen Zhao, Nannan Shi, Liying Chen, Shengli Yuan, Ning Liang, Heng Gu, Jingwei Wang, Ruixia Xue, Jinping Liu, Yipin Fan, Li Li, Wei Wu, Shaozhen Huang, Jinbo Zhang, Weiguo Bai, Renbo Chen, Lin Tong, Jia Liu, Sihong Liu, Lanping Wu, Yanhua Xiao, Yang Zhao, Liwen Jiao, Yunhong Hu, Hongde Liu, Guihui Wu, Jiangfeng Bai, Hao Gu, Xiaomei Hu, Yuanyuan Li, Zhifei Wang, Qiuhua Huang, H. S. Chen, Xiao Lei, Shoufang Xu, Mingxuan Wang, Youwen Ge, Kaijun Yang, Yinzhen Wang, Gongqi Zhang, Qiao Feng, Yongyan Wang, Yingchun Zhou, Sheng Sun, Junteng Zhu, Guifen Hu, Tuanmao Guo, Wei Wang, Ya Mao, Yan Ma, Dongting Wang, Honggang Yi, Zhan Shi, Puye Yang, Hongming Xu, Yaxin Tian, Xianyong Li, Huizhen Li, Bin Liu, Liang Ji, Yingjie Zhi, Quntang Li, Wanying Zhao, Linsong Zhang, Guangxi Li, Fangli Song, Kai Zheng, Yong Hou, Shaowen Tang, Shusen Zhao, Ruili Huo, Tianqing Zhu, Zhang Liu, Yibai Xiong, Yuan Kuang, Huamin Zhang, Xiaoyan Wang, Minqing Li, Chun Yang, Haihao Jin, Hui Na, Chunyan Li, Yuting Ma, Yanping Wang, Jike Li, and Jin Huang
- Subjects
Treatment and control groups ,medicine.medical_specialty ,Coronavirus disease 2019 (COVID-19) ,Informed consent ,business.industry ,Internal medicine ,Hazard ratio ,medicine ,Retrospective cohort study ,Traditional Chinese medicine ,business ,Confidence interval ,Western medicine - Abstract
Background: Few studies have examined the association between treatment given time and clinical outcomes, which is indeed of great importance to clinical management of coronavirus disease 2019 (COVID-19). We performed this study to explore whether early treatment brings favorable clinical outcomes. Methods: In this retrospective multicenter study, we included patients aged 18 to 87 years with confirmed COVID-19 on admission from 54 hospitals in nine provinces of China from 21 January to 10 March, 2020. Final date of follow-up was March 17, 2020. All patients were treated by Lung cleansing & detoxifying decoction combined with western medicine. Patients were divided into four groups according to the interval from the first date of onset of symptoms to the date of starting a treatment, i.e., ≤1 week group (≤7 days), 1-2 weeks group (>7 days and ≤14 days), 2-3 weeks group (>14 days and ≤21 days) and >3 weeks group (>21 days). Multivariable Cox proportional hazard ratio (HR) models were used to estimate unadjusted and adjusted HRs and 95% confidence intervals (CIs) for the association between the treatment given time and clinical outcomes (time to recovery, days of viral shedding, duration of hospital stay, course of disease, fever and CT images). Findings: Of the 782 patients (median age was 46 years old, and 405 (52%) were male), there were 321 (41%) patients in ≤1 week group, 221 (28%) in 1-2 weeks group, 123 (16%) in 2-3 weeks group and 117 (15%) in >3 weeks group. Compared to patients in later treatment group (greater than 3 weeks), patients in earlier treatment groups of less than 1 week, 1 to 2 weeks, or 2 to 3 weeks had higher likelihood of recovery, with adjusted HR (95% CI) of 3.81 (2.65-5.48), 2.63 (1.86-3.73) and 1.92 (1.34-2.75), respectively. The median days of viral shedding was 13 days and 12 days in 2-3 weeks group and 3 weeks group (P=0.0137). The median course of disease decreased from 34 days to 24 days, 21 days and 18 days when treatment was given every one week in advance compared to that was given later than 3 weeks from the onset of symptoms (P
- Published
- 2020
- Full Text
- View/download PDF
3. Predictive Value of the Neutrophil-to-Lymphocyte Ratio(NLR) for Diagnosis and Worse Clinical Course of the COVID-19: Findings from Ten Provinces in China
- Author
-
Jin Huang, Yipin Fan, Zhifei Wang, Lanping Wu, Yuanjun Li, Yanhua Xiao, Mingbo Yang, Wei Wang, Jinbo Zhang, Ruixia Xue, Minqing Li, Jinping Liu, Shoufang Xu, Shengli Yuan, Nannan Shi, Weiguo Bai, Sihong Liu, Haihao Jin, Junteng Zhu, Baihua Jiang, Liwen Jiao, Xiaofeng Zhang, Xiao Lei, Xiaomei Hu, Yanping Wang, Xiaoyan Wang, Renbo Chen, Liying Chen, Huamin Zhang, Yunhong Hu, Tianqing Zhu, Tuanmao Guo, Chen Zhao, Puye Yang, Guangxi Li, Yan Ma, Linsong Zhang, Hao Gu, Jingwei Wang, Fangli Song, Yinzhen Wang, Mei Shi, Ning Liang, Zhang Liu, Gongqi Zhang, Yingchun Zhou, Haijun Xie, Guifen Hu, Quntang Li, Dongting Wang, Jingya Wang, Hongming Xu, Shaozhen Huang, Hui Na, Ruili Huo, Yuanyuan Li, H. S. Chen, Zhan Shi, Kai Zheng, Nong Tang, Li Li, Wei Wu, Sheng Sun, Shusen Zhao, Huaben Zhang, Lin Tong, Huizhen Li, Jiangfeng Bai, Furong Xiao, Jia Liu, Qiao Feng, Yongyan Wang, Ya Mao, and Yingjie Zhi
- Subjects
medicine.medical_specialty ,Receiver operating characteristic ,business.industry ,Area under the curve ,Odds ratio ,medicine.disease ,Logistic regression ,Odds ,Internal medicine ,Viral pneumonia ,medicine ,Neutrophil to lymphocyte ratio ,business ,Cohort study - Abstract
Background: Novel coronavirus pneumonia (NCP) is often changing rapidly and fatal. Early detection and early triage of coronavirus disease 2019 (Covid-19) is the key to success management of the disease. An easily obtainable yet accurate variable for both diagnosis and prognosis is urgently needed. We aim to report predictive Value of the Neutrophil-to-Lymphocyte Ratio(NLR) for diagnosis and worse clinical course of the COVID-19, which have not been well demonstrated. Methods: Our study consisted of two stages, at the first stage, a retrospective, single-center, cohort study including was conducted in Heilongjiang, on admission, demographic, clinical, and laboratory data were collected and compared between patients with COVID-19 and patients with non COVID-19; we used multivariable logistic regression methods to explore the risk factors associated with COVID-19;A receiver operating characteristic(ROC) analysis was conducted to calculate the area under the curve(AUC) to assess predictive value of NLR for diagnosis of COVID 19. At the second stage, we conducted retrospective, multi-center and large sample study in 43 hospitals from ten provinces of China, COVID-19 patients with laboratory-confirmed divided into three groups including mild cases, ordinary cases and severe cases. Multivariate logistic regression methods were used to identify the risk factors for the deterioration of COVID-19, along with, a receiver operating characteristic (ROC) curve was also drawn to assess impact on the clinical course of the COVID-19. Findings: We recruited a total of 635 patients with COVID-19 and 27 cases with non COVID-19(Viral pneumonia) from 28 January to 25 February. A total of 88 cases were enrolled with a retrospective, single-center, cohort study from Heilongjiang province, of these, COVID-19 cases were 61(69%) and non COVID-19 cases were 27(31%). On admission, fever (69%) was the most common symptoms, cough (56%) and fatigue(53%). An average(SD) of NLR of COVID-19 patients and non- COVID-19 patients were3.48±2.04 and 2.21±1.14, respectively. multivariable regression showed increasing odds of COVID-19 patients associated with NLR(odds ratio 1.752, 95% CI 1.111-2.763, per 1 unit increase; p=0.016). In addition, the area under the curve (AUC) of NLR was 0.707 and cutoff value was 2.22. At the second stage, 635 patients with COVID were enrolled with a retrospective, multi-center, large sample study in the 43 settings from 10 provinces, of these, mild case were 86(14%), ordinary cases [486(76%)],severe cases[63(10%)], common symptoms was at onset of disease were cough[356(56%)], an average of NLR of 635 patients was 4.04±4.68, and elevated NLR with the deterioration of clinical course[mild case(2.73±2.28), ordinary cases(3.58±3.07), severe cases(9.38±10.52), P
- Published
- 2020
- Full Text
- View/download PDF
4. Association between early treatment with Qingfei Paidu decoction and favorable clinical outcomes in patients with COVID-19: A retrospective multicenter cohort study
- Author
-
Zhan Shi, Jin Huang, Jia Liu, Nannan Shi, Xiaohui Zhang, Liying Chen, Ning Liang, Yong Hou, Kaijun Yang, Kai Zheng, Yang Zhao, Yingchun Zhou, Lin Tong, Heng Gu, Liufen Mo, Gongqi Zhang, Xiaomei Hu, Fangli Song, Jinbo Zhang, Hongming Xu, Hui Na, Qiao Feng, H. S. Chen, Honggang Yi, Yongyan Wang, Youwen Ge, Yan Ma, Chun Yang, Liang Ji, Shusen Zhao, Zhang Liu, Chunyan Li, Yunhong Hu, Wanying Zhao, Huamin Zhang, Ya Mao, Yinzhen Wang, Li Li, Wei Wu, Renbo Chen, Hao Gu, Yipin Fan, Yingjie Zhi, Mingxuan Wang, Xiao Lei, Shengli Yuan, Shoufang Xu, Liwen Jiao, Lanping Wu, Yanhua Xiao, Wei Wang, Junteng Zhu, Haihao Jin, Minqing Li, Guifen Hu, Yibai Xiong, Shaowen Tang, Yuting Ma, Guihui Wu, Qiuhua Huang, Haijun Xie, Xiaoyan Wang, Sheng Sun, Quntang Li, Yaxin Tian, Ruixia Xue, Yanping Wang, Jinping Liu, Jike Li, Bin Liu, Hongde Liu, Jiangfeng Bai, Shaozhen Huang, Yuan Kuang, Puye Yang, Xianyong Li, Linsong Zhang, Guangxi Li, Tuanmao Guo, Chen Zhao, Jingwei Wang, Huizhen Li, Yuanyuan Li, Dongting Wang, Ruili Huo, Zhifei Wang, Tianqing Zhu, Hongmei Wo, Weiguo Bai, Sihong Liu, Jingya Wang, and Yudong Wang
- Subjects
Male ,0301 basic medicine ,RT-PCR, reverse transcription-polymerase-chain-reaction ,Decoction ,Disease ,Cohort Studies ,0302 clinical medicine ,Early treatment ,Prospective cohort study ,COVID-19, coronavirus disease 2019 ,Aged, 80 and over ,Hazard ratio ,RBC, red blood cell ,Middle Aged ,CT, computed tomography ,Treatment Outcome ,030220 oncology & carcinogenesis ,CRP, C-reactive protein ,Female ,Qingfei Paidu decoction ,WBC, white blood cell ,Cohort study ,Unadj., unadjusted ,Adult ,SARS-COV-2, severe acute respiratory syndrome-coronavirus 2 ,China ,medicine.medical_specialty ,Ref., reference ,Article ,Time-to-Treatment ,Young Adult ,03 medical and health sciences ,Internal medicine ,medicine ,Humans ,IFN, interferon ,QFPDD, Qingfei Paidu Decoction ,Viral shedding ,Adj., adjusted ,PO2, partial pressure of oxygen ,ComputingMethodologies_COMPUTERGRAPHICS ,Aged ,ESR, erythrocyte sedimentation rate ,Pharmacology ,business.industry ,COVID-19 ,Length of Stay ,No., number of patients ,HR, hazard ratio ,Confidence interval ,COVID-19 Drug Treatment ,CI, confidence interval ,030104 developmental biology ,COPD, chronic obstructive pulmonary disease ,SpO2, blood oxygen saturation ,Sample size determination ,WISP, work information system platform ,business ,Drugs, Chinese Herbal ,Follow-Up Studies - Abstract
Graphical abstract, The coronavirus disease 2019 (COVID-19) epidemic has been almost controlled in China under a series of policies, including “early diagnosis and early treatment”. This study aimed to explore the association between early treatment with Qingfei Paidu decoction (QFPDD) and favorable clinical outcomes. In this retrospective multicenter study, we included 782 patients (males, 56 %; median age 46) with confirmed COVID-19 from 54 hospitals in nine provinces of China, who were divided into four groups according to the treatment initiation time from the first date of onset of symptoms to the date of starting treatment with QFPDD. The primary outcome was time to recovery; days of viral shedding, duration of hospital stay, and course of the disease were also analyzed. Compared with treatment initiated after 3 weeks, early treatment with QFPDD after less than 1 week, 1-2 weeks, or 2-3 weeks had a higher likelihood of recovery, with adjusted hazard ratio (HR) (95 % confidence interval [CI]) of 3.81 (2.65–5.48), 2.63 (1.86-3.73), and 1.92 (1.34-2.75), respectively. The median course of the disease decreased from 34 days to 24 days, 21 days, and 18 days when treatment was administered early by a week (P < 0.0001). Treatment within a week was related to a decrease by 1-4 days in the median duration of hospital stay compared with late treatment (P
- Published
- 2020
- Full Text
- View/download PDF
5. Non-contact human-computer interaction system based on gesture recognition
- Author
-
Zhuyan Li, Jiangfeng Bai, and Xiaoqiong Wang
- Subjects
Intelligent character recognition ,Computer science ,Sketch recognition ,business.industry ,Operator (linguistics) ,Interaction technique ,Scheduling (computing) ,Large screen ,Human–computer interaction ,Gesture recognition ,Computer vision ,Artificial intelligence ,business ,Gesture - Abstract
A non-contact human-computer interaction system based on gesture recognition is presented in this paper. It tracks and collects the operator's gestures image via two digital high-definition video cameras at a specific location, uses Image Recognition Algorithms to detect gestures change information, and thus controls mouse movements on the computer screen. The experimental results show that this system can support the operator's fingers non-contact accurately to simulate actions of mouse clicks, synchronous positioning and trigger detection operations, etc. This system can be widely used in large screen media show, intelligent scheduling monitoring, interactive teaching entertainment and other many human-computer interaction occasions.
- Published
- 2011
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.