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Chen B, Cui J, Li C, Xu P, Xu G, Jiang J, Xue P, Sun Y, Cui Z. Application of radiomics model based on lumbar computed tomography in diagnosis of elderly osteoporosis. J Orthop Res 2024; 42:1356-1368. [PMID: 38245854 DOI: 10.1002/jor.25789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 12/31/2023] [Accepted: 01/04/2024] [Indexed: 01/22/2024]
Abstract
A metabolic bone disease characterized by decreased bone formation and increased bone resorption is osteoporosis. It can cause pain and fracture of patients. The elderly are prone to osteoporosis and are more vulnerable to osteoporosis. In this study, radiomics are extracted from computed tomography (CT) images to screen osteoporosis in the elderly. Collect the plain scan CT images of lumbar spine, cut the region of interest of the image and extract radiomics features, use Lasso regression to screen variables and adjust complexity, use python language to model random forests, support vector machines, K nearest neighbor, and finally use receiver operating characteristic curve to evaluate the performance of the model, including precision, recall, accuracy and area under the curve (AUC). For the model, 14 radiolomics features were selected. The diagnosis performance of random forest model and support vector machine is good, all around 0.9. The AUC of K nearest neighbor model in training set and test set is 0.828 and 0.796, respectively. We selected the plain scan CT images of the elderly lumbar spine to build radiomics features model, which has good diagnostic performance and can be used as a tool to assist the diagnosis of osteoporosis in the elderly.
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Affiliation(s)
- Baisen Chen
- Department of Orthopedics, Nantong City No. 1 People's Hospital and Second Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, China
- Nantong University, Nantong, Jiangsu Province, China
| | - Jiaming Cui
- Department of Orthopedics, Nantong City No. 1 People's Hospital and Second Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, China
| | - Chaochen Li
- Department of Orthopedics, Nantong City No. 1 People's Hospital and Second Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, China
- Nantong University, Nantong, Jiangsu Province, China
- Key Laboratory for Restoration Mechanism and Clinical Translation of Spinal Cord Injury, Nantong, China
- Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, China
| | - Pengjun Xu
- Department of Orthopedics, Nantong University Affiliated Hospital, Nantong, Jiangsu, China
| | - Guanhua Xu
- Department of Orthopedics, Nantong City No. 1 People's Hospital and Second Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, China
| | - Jiawei Jiang
- Department of Orthopedics, Nantong City No. 1 People's Hospital and Second Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, China
| | - Pengfei Xue
- Department of Orthopedics, Nantong City No. 1 People's Hospital and Second Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, China
| | - Yuyu Sun
- Department of Orthopedic, Nantong Third People's Hospital, Nantong, Jiangsu Province, China
| | - Zhiming Cui
- Department of Orthopedics, Nantong City No. 1 People's Hospital and Second Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, China
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Yu T, Song J, Yu L, Deng W. A systematic evaluation and meta-analysis of early prediction of post-thrombotic syndrome. Front Cardiovasc Med 2023; 10:1250480. [PMID: 37692043 PMCID: PMC10484413 DOI: 10.3389/fcvm.2023.1250480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 08/11/2023] [Indexed: 09/12/2023] Open
Abstract
Objective Post-thrombotic syndrome (PTS) is the most common long-term complication in patients with deep venous thrombosis, and the prevention of PTS remains a major challenge in clinical practice. Some studies have explored early predictors and constructed corresponding prediction models, whereas their specific application and predictive value are controversial. Therefore, we conducted this systematic evaluation and meta-analysis to investigate the incidence of PTS and the feasibility of early prediction. Methods We systematically searched databases of PubMed, Embase, Cochrane and Web of Science up to April 7, 2023. Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the included articles, and the OR values of the predictors in multi-factor logistic regression were pooled to assess whether they could be used as effective independent predictors. Results We systematically included 20 articles involving 8,512 subjects, with a predominant onset of PTS between 6 and 72 months, with a 2-year incidence of 37.5% (95% CI: 27.8-47.7%). The results for the early predictors were as follows: old age OR = 1.840 (95% CI: 1.410-2.402), obesity or overweight OR = 1.721 (95% CI: 1.245-2.378), proximal deep vein thrombosis OR = 2.335 (95% CI: 1.855-2.938), history of venous thromboembolism OR = 3.593 (95% CI: 1.738-7.240), history of smoking OR = 2.051 (95% CI: 1.305-3.224), varicose veins OR = 2.405 (95% CI: 1.344-4.304), and baseline Villalta score OR = 1.095(95% CI: 1.056-1.135). Meanwhile, gender, unprovoked DVT and insufficient anticoagulation were not independent predictors. Seven studies constructed risk prediction models. In the training set, the c-index of the prediction models was 0.77 (95% CI: 0.74-0.80) with a sensitivity of 0.75 (95% CI: 0.68-0.81) and specificity of 0.69 (95% CI: 0.60-0.77). In the validation set, the c-index, sensitivity and specificity of the prediction models were 0.74(95% CI: 0.69-0.79), 0.71(95% CI: 0.64-0.78) and 0.72(95% CI: 0.67-0.76), respectively. Conclusions With a high incidence after venous thrombosis, PTS is a complication that cannot be ignored in patients with venous thrombosis. Risk prediction scoring based on early model construction is a feasible option, which helps to identify the patient's condition and develop an individualized prevention program to reduce the risk of PTS.
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Affiliation(s)
- Tong Yu
- Pharmacy Laboratory, College of Pharmacy, Shenyang Pharmaceutical University, Benxi, China
| | - Jialin Song
- Microbiology laboratory, College of Life Sciences and Pharmacy, Shenyang Pharmaceutical University, Benxi, China
| | - LingKe Yu
- Department of Encephalopathy, Internal Medicine Department, Liaoning University of Traditional Chinese Medicine Affiliated Second Hospital, Shenyang, China
| | - Wanlin Deng
- Electrical Engineering, Information Engineering College, Shenyang University of Chemical Technology, Shenyang, China
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Jyani G, Yang Z, Sharma A, Goyal A, Stolk E, Purba FD, Grover S, Kaur M, Prinja S. Evaluation of EuroQol Valuation Technology (EQ-VT) Designs to Generate National Value Sets: Learnings from the Development of an EQ-5D Value Set for India Using an Extended Design (DEVINE) Study. Med Decis Making 2023; 43:692-703. [PMID: 37480281 PMCID: PMC10422850 DOI: 10.1177/0272989x231180134] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 04/27/2023] [Indexed: 07/23/2023]
Abstract
INTRODUCTION Countries develop their EQ-5D-5L value sets using the EuroQol Valuation Technology (EQ-VT) protocol. This study aims to assess if extension in the conventional EQ-VT design can lead to development of value sets with improved precision. METHODS A cross-sectional survey was undertaken in a representative sample of 3,548 adult respondents, selected from 5 different states of India using a multistage stratified random sampling technique. A novel extended EQ-VT design was created that included 18 blocks of 10 health states, comprising 150 unique health states and 135 observations per health state. In addition to the standard EQ-VT design, which is based on 86 health states and 100 observations per health state, 3 extended designs were assessed for their predictive performance. The extended designs were created by 1) increasing the number of observations per health state in the design, 2) increasing the number of health states in the design, and 3) implementing both 1) and 2) at the same time. Subsamples of the data set were created for separate designs. The root mean squared error (RMSE) and mean absolute error (MAE) were used to measure the predictive accuracy of the conventional and extended designs. RESULTS The average RMSE and MAE for the standard EQ-VT design were 0.055 and 0.041, respectively, for the 150 health states. All 3 types of design extensions showed lower RMSE and MAE values as compared with the standard design and hence yielded better predictive performance. RMSE and MAE were lowest (0.051 and 0.039, respectively) for the designs that use a greater number of health states. Extending the design with inclusion of more health states was shown to improve the predictive performance even when the sample size was fixed at 1,000. CONCLUSION Although the standard EQ-VT design performs well, its prediction accuracy can be further improved by extending its design. The addition of more health states in EQ-VT is more beneficial than increasing the number of observations per health state. HIGHLIGHTS The EQ-5D-5L value sets are developed using the standardized EuroQol Valuation Technology (EQ-VT) protocol. This is the first study to empirically assess how much can be gained from extending the standard EQ-VT design in terms of sample size and/or health states. It not only presents useful insights into the performance of the standard design of the EQ-VT but also tests the potential extensions in the standard EQ-VT design in terms of increasing the health states to be directly valued as well as the number of observations recorded to predict the utility value of each of these health states.The study demonstrates that the standard EQ-VT design performs good, and an extension in the design of the standard EQ-VT can lead to further improvement in its performance. The addition of more health states in EQ-VT is more beneficial than increasing the number of observations per health state. Extending the design with inclusion of more health states marginally improves the predictive performance even when the sample size was fixed at 1,000.The findings of the study will streamline the systematic process for generating precise EQ-5D-5L value sets, thus facilitating the conduct of credible, transparent, and robust outcome valuation in health technology assessments.
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Affiliation(s)
- Gaurav Jyani
- Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Zhihao Yang
- Guizhou Medical University, Guiyang, People’s Republic of China
| | - Atul Sharma
- Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Aarti Goyal
- Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Elly Stolk
- EuroQol Research Foundation, Rotterdam, South Holland, the Netherlands
| | - Fredrick Dermawan Purba
- Department of Developmental Psychology, Faculty of Psychology, Universitas Padjadjaran, Bandung, Jawa Barat, Indonesia
| | - Sandeep Grover
- Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Manmeet Kaur
- Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Shankar Prinja
- Postgraduate Institute of Medical Education and Research, Chandigarh, India
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Feng C, Huang W, Qiao Y, Liu D, Li H. Research Progress and New Ideas on the Theory and Methodology of Water Quality Criteria for the Protection of Aquatic Organisms. Toxics 2023; 11:557. [PMID: 37505523 PMCID: PMC10386067 DOI: 10.3390/toxics11070557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 07/29/2023]
Abstract
Water quality criteria (WQC) for the protection of aquatic organisms mainly focus on the maximum threshold values of the pollutants that do not have harmful effects on aquatic organisms. The WQC value is the result obtained based on scientific experiments in the laboratory and data fitting extrapolation and is the limit of the threshold value of pollutants or other harmful factors in the water environment. Until now, many studies have been carried out on WQC for the protection of aquatic organisms internationally, and several countries have also issued their own relevant technical guidelines. Thus, the WQC method for the protection of aquatic organisms has been basically formed, with species sensitivity distribution (SSD) as the main method and the assessment factor (AF) as the auxiliary method. In addition, in terms of the case studies on WQC, many scholars have conducted relevant case studies on various pollutants. At the national level, several countries have also released WQC values for typical pollutants. This study systematically discusses the general situation, theoretical methodology and research progress of WQC for the protection of aquatic organisms, and deeply analyzes the key scientific issues that need to be considered in the research of WQC. Furthermore, combined with the specific characteristics of the emerging pollutants, some new ideas and directions for future WQC research for the protection of aquatic organisms are also proposed.
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Affiliation(s)
- Chenglian Feng
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Wenjie Huang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yu Qiao
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Daqing Liu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
- College of Water Science, Beijing Normal University, Beijing 100875, China
| | - Huixian Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
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Li Q, Chi L, Zhao W, Wu L, Jiao C, Zheng X, Zhang K, Li X. Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis. Front Neurol 2023; 14:1039794. [PMID: 37388543 PMCID: PMC10299899 DOI: 10.3389/fneur.2023.1039794] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 05/25/2023] [Indexed: 07/01/2023] Open
Abstract
Background Recent studies have reported that machine learning (ML), with a relatively strong capacity for processing non-linear data and adaptive ability, could improve the accuracy and efficiency of prediction. The article summarizes the published studies on ML models that predict motor function 3-6 months post-stroke. Methods A systematic literature search was conducted in PubMed, Embase, Cochorane and Web of Science as of April 3, 2023 for studies on ML prediction of motor function in stroke patients. The quality of the literature was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). A random-effects model was preferred for meta-analysis using R4.2.0 because of the different variables and parameters. Results A total of 44 studies were included in this meta-analysis, involving 72,368 patients and 136 models. Models were categorized into subgroups according to the predicted outcome Modified Rankin Scale cut-off value and whether they were constructed based on radiomics. C-statistics, sensitivity, and specificity were calculated. The random-effects model showed that the C-statistics of all models were 0.81 (95% CI: 0.79; 0.83) in the training set and 0.82 (95% CI: 0.80; 0.85) in the validation set. According to different Modified Rankin Scale cut-off values, C-statistics of ML models predicting Modified Rankin Scale>2(used most widely) in stroke patients were 0.81 (95% CI: 0.78; 0.84) in the training set, and 0.84 (95% CI: 0.81; 0.87) in the validation set. C-statistics of radiomics-based ML models in the training set and validation set were 0.81 (95% CI: 0.78; 0.84) and 0.87 (95% CI: 0.83; 0.90), respectively. Conclusion ML can be used as an assessment tool for predicting the motor function in patients with 3-6 months of post-stroke. Additionally, the study found that ML models with radiomics as a predictive variable were also demonstrated to have good predictive capabilities. This systematic review provides valuable guidance for the future optimization of ML prediction systems that predict poor motor outcomes in stroke patients. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022335260, identifier: CRD42022335260.
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Affiliation(s)
- Qinglin Li
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Lei Chi
- Department of Acupuncture, The Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Weiying Zhao
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Lei Wu
- Department of Acupuncture, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Chuanxu Jiao
- Department of Neurorehabilitation, Taizhou Enze Medical Center Luqiao Hospital, Taizhou, Zhejiang, China
| | - Xue Zheng
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Kaiyue Zhang
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Xiaoning Li
- Department of Acupuncture, The Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
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Tang Y, Dong J, Gruda N, Jiang H. China Requires a Sustainable Transition of Vegetable Supply from Area-Dependent to Yield-Dependent and Decreased Vegetable Loss and Waste. Int J Environ Res Public Health 2023; 20:1223. [PMID: 36673990 PMCID: PMC9859069 DOI: 10.3390/ijerph20021223] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/05/2023] [Accepted: 01/06/2023] [Indexed: 06/17/2023]
Abstract
China, the largest country in vegetable supply, faces a transition to sustainable vegetable production to counteract resource waste and environmental pollution. However, there are knowledge gaps on the main constraints and how to achieve sustainable vegetable supply. Herein, we integrated the vegetable production and supply data in China, compared its current status with five horticulture-developed countries US, the Netherlands, Greece, Japan and South Korea, using data from the Food and Agriculture Organization (FAO) and National Bureau of Statistics of China, and predicted the vegetable supply in 2030 and 2050 by a model prediction. The vegetable supply in China increased from 592 g capita-1 d-1 in 1995 to 1262 g capita-1 d-1 in 2018 and will keep constant in 2030 and 2050. Compared to the five countries, the greater vegetable supply is primarily achieved by higher harvested areas rather than higher yield. However, it is predicted that the harvested areas will decrease by 13.6% and 24.7% in 2030 and 2050. Instead, steady increases in vegetable yield by 11.8% and 28.3% are predicted for this period. The high vegetable supply and greater vegetable preference indicated by the high vegetable-to-meat production ratio cannot guarantee recommended vegetable intake, potentially due to the high rate of vegetable loss and waste. Under the scenarios of decreased vegetable loss and waste, the harvested area will decrease by 37.3-67.2% in 2030 and 2050. This study points out that the sustainable transition of Chinese vegetable supply can be realized by enhancing yield and limiting vegetable loss and waste instead of expanding the harvested area.
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Affiliation(s)
- Ying Tang
- Department of Agronomy and Horticulture, Jiangsu Vocational College of Agriculture and Forestry, Jurong 212400, China
| | - Jinlong Dong
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
| | - Nazim Gruda
- Institute of Crop Science and Resource Conservation, Division of Horticultural Sciences, University of Bonn, 53121 Bonn, Germany
| | - Haibo Jiang
- Jiangsu Station for Protection of Arable Land Quality and Agricultural Environment, Nanjing 210029, China
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Kong X, Peng R, Dai H, Li Y, Lu Y, Sun X, Zheng B, Wang Y, Zhao Z, Liang S, Xu M. Disease-specific data processing: An intelligent digital platform for diabetes based on model prediction and data analysis utilizing big data technology. Front Public Health 2022; 10:1053269. [PMID: 36579056 PMCID: PMC9791221 DOI: 10.3389/fpubh.2022.1053269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 11/24/2022] [Indexed: 12/14/2022] Open
Abstract
Background Artificial intelligence technology has become a mainstream trend in the development of medical informatization. Because of the complex structure and a large amount of medical data generated in the current medical informatization process, big data technology to assist doctors in scientific research and analysis and obtain high-value information has become indispensable for medical and scientific research. Methods This study aims to discuss the architecture of diabetes intelligent digital platform by analyzing existing data mining methods and platform building experience in the medical field, using a large data platform building technology utilizing the Hadoop system, model prediction, and data processing analysis methods based on the principles of statistics and machine learning. We propose three major building mechanisms, namely the medical data integration and governance mechanism (DCM), data sharing and privacy protection mechanism (DPM), and medical application and medical research mechanism (MCM), to break down the barriers between traditional medical research and digital medical research. Additionally, we built an efficient and convenient intelligent diabetes model prediction and data analysis platform for clinical research. Results Research results from this platform are currently applied to medical research at Shanghai T Hospital. In terms of performance, the platform runs smoothly and is capable of handling massive amounts of medical data in real-time. In terms of functions, data acquisition, cleaning, and mining are all integrated into the system. Through a simple and intuitive interface operation, medical and scientific research data can be processed and analyzed conveniently and quickly. Conclusions The platform can serve as an auxiliary tool for medical personnel and promote the development of medical informatization and scientific research. Also, the platform may provide the opportunity to deliver evidence-based digital therapeutics and support digital healthcare services for future medicine.
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Affiliation(s)
- Xiangyong Kong
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China,*Correspondence: Xiangyong Kong
| | - Ruiyang Peng
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Huajie Dai
- Department of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Institute of Endocrine and Metabolic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yichi Li
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Yanzhuan Lu
- School of Food Science, Shihezi University, Shihezi, China
| | - Xiaohan Sun
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Bozhong Zheng
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yuze Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Zhiyun Zhao
- Department of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Institute of Endocrine and Metabolic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shaolin Liang
- STI-Zhilian Research Institute for Innovation and Digital Health, Beijing, China,Institute for Six-sector Economy, Fudan University, Shanghai, China
| | - Min Xu
- Department of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Institute of Endocrine and Metabolic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China,Min Xu
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Zhang Y, Wan D, Chen M, Li Y, Ying H, Yao G, Liu Z, Zhang G. Automated machine learning-based model for the prediction of delirium in patients after surgery for degenerative spinal disease. CNS Neurosci Ther 2022; 29:282-295. [PMID: 36258311 PMCID: PMC9804056 DOI: 10.1111/cns.14002] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 09/25/2022] [Accepted: 10/01/2022] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVE This study used machine learning algorithms to identify critical variables and predict postoperative delirium (POD) in patients with degenerative spinal disease. METHODS We included 663 patients who underwent surgery for degenerative spinal disease and received general anesthesia. The LASSO method was used to screen essential features associated with POD. Clinical characteristics, preoperative laboratory parameters, and intraoperative variables were reviewed and were used to construct nine machine learning models including a training set and validation set (80% of participants), and were then evaluated in the rest of the study sample (20% of participants). The area under the receiver-operating characteristic curve (AUROC) and Brier scores were used to compare the prediction performances of different models. The eXtreme Gradient Boosting algorithms (XGBOOST) model was used to predict POD. The SHapley Additive exPlanations (SHAP) package was used to interpret the XGBOOST model. Data of 49 patients were prospectively collected for model validation. RESULTS The XGBOOST model outperformed the other classifier models in the training set (area under the curve [AUC]: 92.8%, 95% confidence interval [CI]: 90.7%-95.0%), validation set (AUC: 87.0%, 95% CI: 80.7%-93.3%). This model also achieved the lowest Brier Score. Twelve vital variables, including age, serum albumin, the admission-to-surgery time interval, C-reactive protein level, hypertension, intraoperative blood loss, intraoperative minimum blood pressure, cardiovascular-cerebrovascular disease, smoking, alcohol consumption, pulmonary disease, and admission-intraoperative maximum blood pressure difference, were selected. The XGBOOST model performed well in the prospective cohort (accuracy: 85.71%). CONCLUSION A machine learning model and a web predictor for delirium after surgery for the degenerative spinal disease were successfully developed to demonstrate the extent of POD risk during the perioperative period, which could guide appropriate preventive measures for high-risk patients.
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Affiliation(s)
- Yu Zhang
- Outpatient DepartmentThe Second Affiliated Hospital of Nanchang UniversityNanchangChina,Medical Innovation Centerthe First Affiliated Hospital of Nanchang UniversityNanchangChina,Institute of Spine and Spinal CordNanchang UniversityNanchangChina
| | - Dong‐Hua Wan
- Department of OrthopedicsThe Second Affiliated Hospital of Nanchang UniversityNanchangChina
| | - Min Chen
- Department of OrthopedicsThe Second Affiliated Hospital of Nanchang UniversityNanchangChina
| | - Yun‐Li Li
- Department of OrthopedicsThe Second Affiliated Hospital of Nanchang UniversityNanchangChina
| | - Hui Ying
- Medical Innovation Centerthe First Affiliated Hospital of Nanchang UniversityNanchangChina,Institute of Spine and Spinal CordNanchang UniversityNanchangChina
| | - Ge‐Liang Yao
- Medical Innovation Centerthe First Affiliated Hospital of Nanchang UniversityNanchangChina,Institute of Spine and Spinal CordNanchang UniversityNanchangChina
| | - Zhi‐Li Liu
- Medical Innovation Centerthe First Affiliated Hospital of Nanchang UniversityNanchangChina,Institute of Spine and Spinal CordNanchang UniversityNanchangChina
| | - Guo‐Mei Zhang
- Outpatient DepartmentThe Second Affiliated Hospital of Nanchang UniversityNanchangChina
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Zhang CY, Li X, Keil Stietz KP, Sethi S, Yang W, Marek RF, Ding X, Lein PJ, Hornbuckle KC, Lehmler HJ. Machine Learning-Assisted Identification and Quantification of Hydroxylated Metabolites of Polychlorinated Biphenyls in Animal Samples. Environ Sci Technol 2022; 56:13169-13178. [PMID: 36047920 PMCID: PMC9573770 DOI: 10.1021/acs.est.2c02027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 08/15/2022] [Accepted: 08/22/2022] [Indexed: 06/02/2023]
Abstract
Laboratory studies of the disposition and toxicity of hydroxylated polychlorinated biphenyl (OH-PCB) metabolites are challenging because authentic analytical standards for most unknown OH-PCBs are not available. To assist with the characterization of these OH-PCBs (as methylated derivatives), we developed machine learning-based models with multiple linear regression (MLR) or random forest regression (RFR) to predict the relative retention times (RRT) and MS/MS responses of methoxylated (MeO-)PCBs on a gas chromatograph-tandem mass spectrometry system. The final MLR model estimated the retention times of MeO-PCBs with a mean absolute error of 0.55 min (n = 121). The similarity coefficients cos θ between the predicted (by RFR model) and experimental MS/MS data of MeO-PCBs were >0.95 for 92% of observations (n = 96). The levels of MeO-PCBs quantified with the predicted MS/MS response factors approximated the experimental values within a 2-fold difference for 85% of observations and 3-fold differences for all observations (n = 89). Subsequently, these model predictions were used to assist with the identification of OH-PCB 95 or OH-PCB 28 metabolites in mouse feces or liver by suggesting candidate ranking information for identifying the metabolite isomers. Thus, predicted retention and MS/MS response data can assist in identifying unknown OH-PCBs.
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Affiliation(s)
- Chun-Yun Zhang
- Department
of Occupational and Environmental Health, The University of Iowa, Iowa City, Iowa 52242, United States
| | - Xueshu Li
- Department
of Occupational and Environmental Health, The University of Iowa, Iowa City, Iowa 52242, United States
| | - Kimberly P. Keil Stietz
- Department
of Molecular Biosciences, School of Veterinary Medicine, University of California Davis, Davis, California 95616, United States
| | - Sunjay Sethi
- Department
of Molecular Biosciences, School of Veterinary Medicine, University of California Davis, Davis, California 95616, United States
| | - Weizhu Yang
- Department
of Pharmacology and Toxicology, College of Pharmacy, University of Arizona, Tucson, Arizona 85721, United States
| | - Rachel F. Marek
- Department
of Civil and Environmental Engineering and IIHR Hydroscience and Engineering, The University of Iowa, Iowa City, Iowa 52242, United States
| | - Xinxin Ding
- Department
of Pharmacology and Toxicology, College of Pharmacy, University of Arizona, Tucson, Arizona 85721, United States
| | - Pamela J. Lein
- Department
of Molecular Biosciences, School of Veterinary Medicine, University of California Davis, Davis, California 95616, United States
| | - Keri C. Hornbuckle
- Department
of Civil and Environmental Engineering and IIHR Hydroscience and Engineering, The University of Iowa, Iowa City, Iowa 52242, United States
| | - Hans-Joachim Lehmler
- Department
of Occupational and Environmental Health, The University of Iowa, Iowa City, Iowa 52242, United States
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10
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Wu W, Xu J, Dou Y, Yu J, Kong D, Zhou L. Bioaccumulation of Pyraoxystrobin and Its Predictive Evaluation in Zebrafish. Toxics 2021; 10:5. [PMID: 35051047 DOI: 10.3390/toxics10010005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 12/13/2021] [Accepted: 12/21/2021] [Indexed: 11/17/2022]
Abstract
This paper aims to understand the bioaccumulation of pyraoxystrobin in fish. Using a flow-through bioconcentration method, the bioconcentration factor (BCF) and clearance rate of pyraoxystrobin in zebrafish were measured. The measured BCF values were then compared to those estimated from three commonly used predication models. At the exposure concentrations of 0.1 μg/L and 1.0 μg/L, the maximum BCF values for pyraoxystrobin in fish were 820.8 and 265.9, and the absorption rate constants (K1) were 391.0 d−1 and 153.2 d−1, respectively. The maximum enrichment occurred at 12 d of exposure. At the two test concentrations, the clearance rate constant (K2) in zebrafish was 0.5795 and 0.4721, and the half-life (t1/2) was 3.84 d and 3.33 d, respectively. The measured BCF values were close to those estimated from bioconcentration predication models.
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11
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Hu XY, Liu H, Zhao X, Sun X, Zhou J, Gao X, Guan HL, Zhou Y, Zhao Q, Han Y, Cao JL. Automated machine learning-based model predicts postoperative delirium using readily extractable perioperative collected electronic data. CNS Neurosci Ther 2021; 28:608-618. [PMID: 34792857 PMCID: PMC8928919 DOI: 10.1111/cns.13758] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 10/13/2021] [Accepted: 10/16/2021] [Indexed: 12/19/2022] Open
Abstract
Objective Postoperative delirium (POD) is a common postoperative complication that is relevant to poor outcomes. Therefore, it is critical to find effective methods to identify patients with high risk of POD rapidly. Creating a fully automated score based on an automated machine‐learning algorithm may be a method to predict the incidence of POD quickly. Materials and methods This is the secondary analysis of an observational study, including 531 surgical patients who underwent general anesthesia. The least absolute shrinkage and selection operator (LASSO) was used to screen essential features associated with POD. Finally, eight features (age, intraoperative blood loss, anesthesia duration, extubation time, intensive care unit [ICU] admission, mini‐mental state examination score [MMSE], Charlson comorbidity index [CCI], postoperative neutrophil‐to‐lymphocyte ratio [NLR]) were used to established models. Four models, logistic regression, random forest, extreme gradient boosted trees, and support vector machines, were built in a training set (70% of participants) and evaluated in the remaining testing sample (30% of participants). Multivariate logistic regression analysis was used to explore independent risk factors for POD further. Results Model 1 (logistic regression model) was found to outperform other classifier models in testing data (area under the curve [AUC] of 80.44%, 95% confidence interval [CI] 72.24%–88.64%) and achieve the lowest Brier Score as well. These variables including age (OR = 1.054, 95%CI: 1.017~1.093), extubation time (OR = 1.027, 95%CI: 1.012~1.044), ICU admission (OR = 2.238, 95%CI: 1.313~3.793), MMSE (OR = 0.929, 95%CI: 0.876~0.984), CCI (OR = 1.197, 95%CI: 1.038~1.384), and postoperative NLR (OR = 1.029, 95%CI: 1.002~1.057) were independent risk factors for POD in this study. Conclusions We have built and validated a high‐performing algorithm to demonstrate the extent to which patient risk changes of POD during the perioperative period, thus leading to a rational therapeutic choice.
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Affiliation(s)
- Xiao-Yi Hu
- Department of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, Xuzhou City, China.,Jiangsu Province Key Laboratory of Anesthesiology & NMPA Key Laboratory for Research and Evaluation of Narcotic and Psychotropic Drugs, Xuzhou Medical University, Jiangsu Province, Xuzhou City, China
| | - He Liu
- Department of Anesthesiology, The Affiliated Huzhou Hospital, Zhejiang University School of Medicine, Huzhou Central Hospital, Zhejiang Province, Huzhou City, China
| | - Xue Zhao
- Department of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, Xuzhou City, China
| | - Xun Sun
- Department of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, Xuzhou City, China.,Jiangsu Province Key Laboratory of Anesthesiology & NMPA Key Laboratory for Research and Evaluation of Narcotic and Psychotropic Drugs, Xuzhou Medical University, Jiangsu Province, Xuzhou City, China
| | - Jian Zhou
- Department of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, Xuzhou City, China.,Jiangsu Province Key Laboratory of Anesthesiology & NMPA Key Laboratory for Research and Evaluation of Narcotic and Psychotropic Drugs, Xuzhou Medical University, Jiangsu Province, Xuzhou City, China
| | - Xing Gao
- Jiangsu Province Key Laboratory of Anesthesiology & NMPA Key Laboratory for Research and Evaluation of Narcotic and Psychotropic Drugs, Xuzhou Medical University, Jiangsu Province, Xuzhou City, China.,Department of Anesthesiology, Changzhou First People's Hospital, Changzhou, Jiangsu, China
| | - Hui-Lian Guan
- Jiangsu Province Key Laboratory of Anesthesiology & NMPA Key Laboratory for Research and Evaluation of Narcotic and Psychotropic Drugs, Xuzhou Medical University, Jiangsu Province, Xuzhou City, China.,Department of Anesthesiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, China
| | - Yang Zhou
- Jiangsu Province Key Laboratory of Anesthesiology & NMPA Key Laboratory for Research and Evaluation of Narcotic and Psychotropic Drugs, Xuzhou Medical University, Jiangsu Province, Xuzhou City, China
| | - Qiu Zhao
- Department of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, Xuzhou City, China.,Jiangsu Province Key Laboratory of Anesthesiology & NMPA Key Laboratory for Research and Evaluation of Narcotic and Psychotropic Drugs, Xuzhou Medical University, Jiangsu Province, Xuzhou City, China
| | - Yuan Han
- Department of Anesthesiology, Eye & ENT Hospital of Fudan University, Shanghai, China
| | - Jun-Li Cao
- Department of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, Xuzhou City, China.,Jiangsu Province Key Laboratory of Anesthesiology & NMPA Key Laboratory for Research and Evaluation of Narcotic and Psychotropic Drugs, Xuzhou Medical University, Jiangsu Province, Xuzhou City, China
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12
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Jones RN, Tommet D, Steingrimsson J, Racine AM, Fong TG, Gou Y, Hshieh TT, Metzger ED, Schmitt EM, Tabloski PA, Travison TG, Vasunilashorn SM, Abdeen A, Earp B, Kunze L, Lange J, Vlassakov K, Dickerson BC, Marcantonio ER, Inouye SK. Development and internal validation of a predictive model of cognitive decline 36 months following elective surgery. Alzheimers Dement (Amst) 2021; 13:e12201. [PMID: 34046520 PMCID: PMC8140204 DOI: 10.1002/dad2.12201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 04/09/2021] [Accepted: 04/13/2021] [Indexed: 12/03/2022]
Abstract
INTRODUCTION Our goal was to determine if features of surgical patients, easily obtained from the medical chart or brief interview, could be used to predict those likely to experience more rapid cognitive decline following surgery. METHODS We analyzed data from an observational study of 560 older adults (≥70 years) without dementia undergoing major elective non-cardiac surgery. Cognitive decline was measured using change in a global composite over 2 to 36 months following surgery. Predictive features were identified as variables readily obtained from chart review or a brief patient assessment. We developed predictive models for cognitive decline (slope) and predicting dichotomized cognitive decline at a clinically determined cut. RESULTS In a hold-out testing set, the regularized regression predictive model achieved a root mean squared error (RMSE) of 0.146 and a model r-square (R2 ) of .31. Prediction of "rapid" decliners as a group achieved an area under the curve (AUC) of .75. CONCLUSION Some of our models could predict persons with increased risk for accelerated cognitive decline with greater accuracy than relying upon chance, and this result might be useful for stratification of surgical patients for inclusion in future clinical trials.
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Affiliation(s)
- Richard N. Jones
- Department of Psychiatry and Human BehaviorBrown University Warren Alpert Medical SchoolProvidenceRhode IslandUSA
- Department of NeurologyBrown University Warren Alpert Medical SchoolProvidenceRhode IslandUSA
| | - Douglas Tommet
- Department of Psychiatry and Human BehaviorBrown University Warren Alpert Medical SchoolProvidenceRhode IslandUSA
| | - Jon Steingrimsson
- Department of BiostatisticsBrown University School of Public HealthProvidenceRhode IslandUSA
| | | | - Tamara G. Fong
- Biogen IncCambridgeMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
- Department of NeurologyBeth Israel Deaconess Medical CenterBostonMassachusettsUSA
| | - Yun Gou
- Biogen IncCambridgeMassachusettsUSA
| | - Tammy T. Hshieh
- Harvard Medical SchoolBostonMassachusettsUSA
- Department of MedicineBeth Israel Deaconess Medical CenterBostonMassachusettsUSA
| | - Eran D. Metzger
- Harvard Medical SchoolBostonMassachusettsUSA
- Department of PsychiatryBeth Israel Deaconess Medical CenterBostonMassachusettsUSA
| | | | | | - Thomas G. Travison
- Biogen IncCambridgeMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
| | - Sarinnapha M. Vasunilashorn
- Harvard Medical SchoolBostonMassachusettsUSA
- Department of MedicineBeth Israel Deaconess Medical CenterBostonMassachusettsUSA
| | - Ayesha Abdeen
- Harvard Medical SchoolBostonMassachusettsUSA
- Department of Orthopedic SurgeryBeth Israel Deaconess Medical CenterBostonMassachusettsUSA
| | - Brandon Earp
- Harvard Medical SchoolBostonMassachusettsUSA
- Department of Orthopedic SurgeryBrigham and Women's HospitalBostonMassachusettsUSA
| | - Lisa Kunze
- Harvard Medical SchoolBostonMassachusettsUSA
- Department of AnesthesiaBeth Israel Deaconess Medical CenterBostonMassachusettsUSA
| | - Jeffrey Lange
- Harvard Medical SchoolBostonMassachusettsUSA
- Department of Orthopedic SurgeryBrigham and Women's HospitalBostonMassachusettsUSA
| | - Kamen Vlassakov
- Harvard Medical SchoolBostonMassachusettsUSA
- Department of AnesthesiaBrigham and Women's HospitalBostonMassachusettsUSA
| | - Bradford C. Dickerson
- Department of Neurology and Massachusetts Alzheimer's Disease Research CenterMassachusetts General HospitalBostonMassachusettsUSA
| | - Edward R. Marcantonio
- Biogen IncCambridgeMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
- Department of MedicineBeth Israel Deaconess Medical CenterBostonMassachusettsUSA
| | - Sharon K. Inouye
- Biogen IncCambridgeMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
- Department of MedicineBeth Israel Deaconess Medical CenterBostonMassachusettsUSA
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13
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Kakui H, Tsurisaki E, Shibata R, Moriguchi Y. Factors Affecting the Number of Pollen Grains per Male Strobilus in Japanese Cedar ( Cryptomeria japonica). Plants (Basel) 2021; 10:856. [PMID: 33922663 PMCID: PMC8146487 DOI: 10.3390/plants10050856] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/14/2021] [Accepted: 04/19/2021] [Indexed: 11/16/2022]
Abstract
Japanese cedar (Cryptomeria japonica) is the most important timber species in Japan; however, its pollen is the primary cause of pollinosis in Japan. The total number of pollen grains produced by a single tree is determined by the number of male strobili (male flowers) and the number of pollen grains per male strobilus. While the number of male strobili is a visible and well-investigated trait, little is known about the number of pollen grains per male strobilus. We hypothesized that genetic and environmental factors affect the pollen number per male strobilus and explored the factors that affect pollen production and genetic variation among clones. We counted pollen numbers of 523 male strobili from 26 clones using a cell counter method that we recently developed. Piecewise Structural Equation Modeling (pSEM) revealed that the pollen number is mostly affected by genetic variation, male strobilus weight, and pollen size. Although we collected samples from locations with different environmental conditions, statistical modeling succeeded in predicting pollen numbers for different clones sampled from branches facing different directions. Comparison of predicted pollen numbers revealed that they varied >3-fold among the 26 clones. The determination of the factors affecting pollen number and a precise evaluation of genetic variation will contribute to breeding strategies to counter pollinosis. Furthermore, the combination of our efficient counting method and statistical modeling will provide a powerful tool not only for Japanese cedar but also for other plant species.
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Affiliation(s)
- Hiroyuki Kakui
- Graduate School of Science and Technology, Niigata University, Niigata City, Niigata 950-2181, Japan;
| | - Eriko Tsurisaki
- Faculty of Agriculture, Niigata University, Niigata City, Niigata 950-2181, Japan; (E.T.); (R.S.)
| | - Rei Shibata
- Faculty of Agriculture, Niigata University, Niigata City, Niigata 950-2181, Japan; (E.T.); (R.S.)
| | - Yoshinari Moriguchi
- Graduate School of Science and Technology, Niigata University, Niigata City, Niigata 950-2181, Japan;
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14
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Dong W, Xie S. Construction of a prediction model and a prevention control system for cesarean section rate based on the Robson classification system. Am J Transl Res 2021; 13:3238-3245. [PMID: 34017494 PMCID: PMC8129404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 12/09/2020] [Indexed: 06/12/2023]
Abstract
OBJECTIVE To systematically explore the risk factors that influence cesarean section rate, and establish a prediction model to investigate a system effectively reducing cesarean section rates. METHODS This retrospective study was carried out in the medical institutions in Xingtai city, where cesarean section could be conducted. The data of parturients who gave birth to children in the past five years were collected using the hospital information system. Based on the Robson's ten group classification system, parturients were grouped. The difference of cesarean section rate in each group and its main influencing factors were then analyzed. The above factors and factors such as age, education background, and knowledge on childbirth were independent variables, while cesarean section was the dependent variable. A logistic regression model was constructed to determine the correlation between relevant influencing factors and cesarean section. RESULTS In the past 5 years, cesarean section rate in Xingtai city had been maintained at a relatively high level. Cesarean section rates in the R2 and R5 groups were the highest. Parity, fetal position, number of fetuses, and gestational weeks were all factors affecting cesarean section rate (all P < 0.01). After screening the above factors using logistic regression analysis, a regression equation was established: logistic (p) = -1.061 + 1.107 * parity + 0.196 * fetal position + 2.245 * number of fetuses - 0.070 * gestational week + 0.234 * age - 0.278 * education background + 0.623 * knowledge on childbirth. CONCLUSION The Robson classification system plays an important role in evaluating and supervising parturients' conditions. Based on the Robson classification system, we find that parity, fetal position, number of fetuses, and gestational weeks are the main factors influencing cesarean section rate. Using logistic regression analysis, a prediction model, with guiding significance on the control of cesarean section rate, is established.
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Affiliation(s)
- Wei Dong
- Department of Obstetrics and Gynecology, Xingtai People's Hospital Xingtai, Hebei Province, China
| | - Shasha Xie
- Department of Obstetrics and Gynecology, Xingtai People's Hospital Xingtai, Hebei Province, China
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15
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Mohammed R, Zhang ZF, Jiang C, Hu YH, Liu LY, Ma WL, Song WW, Nikolaev A, Kallenborn R, Li YF. Occurrence, Removal, and Mass Balance of Polycyclic Aromatic Hydrocarbons and Their Derivatives in Wastewater Treatment Plants in Northeast China. Toxics 2021; 9:toxics9040076. [PMID: 33918398 PMCID: PMC8066243 DOI: 10.3390/toxics9040076] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 03/23/2021] [Accepted: 03/24/2021] [Indexed: 11/16/2022]
Abstract
Polycyclic aromatic hydrocarbons (PAHs), 33 methylated PAHs (Me-PAHs), and 14 nitrated PAHs (NPAHs) were measured in wastewater treatment plants (WWTPs) to study the removal efficiency of these compounds through the WWTPs, as well as their source appointment and potential risk in the effluent. The concentrations of ∑PAHs, ∑Me-PAHs, and ∑NPAHs were 2.01–8.91, 23.0–102, and 6.21–171 µg/L in the influent, and 0.17–1.37, 0.06–0.41 and 0.01–2.41 µg/L in the effluent, respectively. Simple Treat 4.0 and meta-regression methods were applied to calculate the removal efficiencies (REs) for the 63 PAHs and their derivatives in 10 WWTPs and the results were compared with the monitoring data. Overall, the ranges of REs were 55.3–95.4% predicated by the Simple Treat and 47.5–97.7% by the meta-regression. The results by diagnostic ratios and principal component analysis PCA showed that “mixed source” biomass, coal composition, and petroleum could be recognized to either petrogenic or pyrogenic sources. The risk assessment of the effluent was also evaluated, indicating that seven carcinogenic PAHs, Benzo[a]pyrene, Dibenz[a,h]anthracene, and Benzo(a)anthracene were major contributors to the toxics equivalency concentrations (TEQs) in the effluent of WWTPs, to which attention should be paid.
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Affiliation(s)
- Rashid Mohammed
- International Joint Research Center for Persistent Toxic Substances (IJRC-PTS), State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology (HIT), Harbin 150090, China; (R.M.); (L.-Y.L.); (W.-L.M.); (W.-W.S.); (R.K.)
- International Joint Research Center for Arctic Environment and Ecosystem (IJRC-AEE), Polar Academy, School of Environment, Harbin Institute of Technology (HIT), Harbin 150090, China
- Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem (HPKL-PEE), Harbin Institute of Technology (HIT), Harbin 150090, China
| | - Zi-Feng Zhang
- International Joint Research Center for Persistent Toxic Substances (IJRC-PTS), State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology (HIT), Harbin 150090, China; (R.M.); (L.-Y.L.); (W.-L.M.); (W.-W.S.); (R.K.)
- International Joint Research Center for Arctic Environment and Ecosystem (IJRC-AEE), Polar Academy, School of Environment, Harbin Institute of Technology (HIT), Harbin 150090, China
- Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem (HPKL-PEE), Harbin Institute of Technology (HIT), Harbin 150090, China
- Correspondence: or (Z.-F.Z.); or (Y.-F.L.); Tel.: +86-451-8628-9130 (Z.-F.Z.)
| | - Chao Jiang
- Heilongjiang Institute of Labor Hygiene and Occupational Diseases, Harbin 150028, China; (C.J.); (Y.-H.H.)
| | - Ying-Hua Hu
- Heilongjiang Institute of Labor Hygiene and Occupational Diseases, Harbin 150028, China; (C.J.); (Y.-H.H.)
| | - Li-Yan Liu
- International Joint Research Center for Persistent Toxic Substances (IJRC-PTS), State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology (HIT), Harbin 150090, China; (R.M.); (L.-Y.L.); (W.-L.M.); (W.-W.S.); (R.K.)
- International Joint Research Center for Arctic Environment and Ecosystem (IJRC-AEE), Polar Academy, School of Environment, Harbin Institute of Technology (HIT), Harbin 150090, China
- Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem (HPKL-PEE), Harbin Institute of Technology (HIT), Harbin 150090, China
| | - Wan-Li Ma
- International Joint Research Center for Persistent Toxic Substances (IJRC-PTS), State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology (HIT), Harbin 150090, China; (R.M.); (L.-Y.L.); (W.-L.M.); (W.-W.S.); (R.K.)
- International Joint Research Center for Arctic Environment and Ecosystem (IJRC-AEE), Polar Academy, School of Environment, Harbin Institute of Technology (HIT), Harbin 150090, China
- Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem (HPKL-PEE), Harbin Institute of Technology (HIT), Harbin 150090, China
| | - Wei-Wei Song
- International Joint Research Center for Persistent Toxic Substances (IJRC-PTS), State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology (HIT), Harbin 150090, China; (R.M.); (L.-Y.L.); (W.-L.M.); (W.-W.S.); (R.K.)
- International Joint Research Center for Arctic Environment and Ecosystem (IJRC-AEE), Polar Academy, School of Environment, Harbin Institute of Technology (HIT), Harbin 150090, China
- Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem (HPKL-PEE), Harbin Institute of Technology (HIT), Harbin 150090, China
| | - Anatoly Nikolaev
- Institute of Natural Sciences, North-Eastern Federal University, 677000 Yakutsk, Russia;
| | - Roland Kallenborn
- International Joint Research Center for Persistent Toxic Substances (IJRC-PTS), State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology (HIT), Harbin 150090, China; (R.M.); (L.-Y.L.); (W.-L.M.); (W.-W.S.); (R.K.)
- International Joint Research Center for Arctic Environment and Ecosystem (IJRC-AEE), Polar Academy, School of Environment, Harbin Institute of Technology (HIT), Harbin 150090, China
- Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem (HPKL-PEE), Harbin Institute of Technology (HIT), Harbin 150090, China
- Faculty of Chemistry, Biotechnology & Food Sciences (KBM), Norwegian University of Life Sciences (NMBU), 1432 Ås, Norway
| | - Yi-Fan Li
- International Joint Research Center for Persistent Toxic Substances (IJRC-PTS), State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology (HIT), Harbin 150090, China; (R.M.); (L.-Y.L.); (W.-L.M.); (W.-W.S.); (R.K.)
- International Joint Research Center for Arctic Environment and Ecosystem (IJRC-AEE), Polar Academy, School of Environment, Harbin Institute of Technology (HIT), Harbin 150090, China
- Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem (HPKL-PEE), Harbin Institute of Technology (HIT), Harbin 150090, China
- IJRC-PTS-NA, Toronto, ON M2N 6X9, Canada
- Correspondence: or (Z.-F.Z.); or (Y.-F.L.); Tel.: +86-451-8628-9130 (Z.-F.Z.)
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16
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Kharrazi H, Ma X, Chang HY, Richards TM, Jung C. Comparing the Predictive Effects of Patient Medication Adherence Indices in Electronic Health Record and Claims-Based Risk Stratification Models. Popul Health Manag 2021; 24:601-609. [PMID: 33544044 DOI: 10.1089/pop.2020.0306] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Multiple indices are available to measure medication adherence behaviors. Medication adherence measures, however, have rarely been extracted from electronic health records (EHRs) for population-level risk predictions. This study assessed the value of medication adherence indices in improving predictive models of cost and hospitalization. This study included a 2-year retrospective cohort of patients younger than age 65 years with linked EHR and insurance claims data. Three medication adherence measures were calculated: medication regimen complexity index (MRCI), medication possession ratio (MPR), and prescription fill rate (PFR). The authors examined the effects of adding these measures to 3 predictive models of utilization: a demographics model, a conventional model (Charlson index), and an advanced diagnosis-based model. Models were trained using EHR and claims data. The study population had an overall MRCI, MPR, and PFR of 14.6 ± 17.8, .624 ± .310, and .810 ± .270, respectively. Adding MRCI and MPR to the demographic and the morbidity models using claims data improved forecasting of next-year hospitalization substantially (eg, AUC of the demographic model increased from .605 to .656 using MRCI). Nonetheless, such boosting effects were attenuated for the advanced diagnosis-based models. Although EHR models performed inferior to claims models, adding adherence indices improved EHR model performances at a larger scale (eg, adding MRCI increased AUC by 4.4% for the Charlson model using EHR data compared to 3.8% using claims). This study shows that medication adherence measures can modestly improve EHR- and claims-derived predictive models of cost and hospitalization in non-elderly patients; however, the improvements are minimal for advanced diagnosis-based models.
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Affiliation(s)
- Hadi Kharrazi
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.,Division of Health Sciences Informatics, Johns Hopkins School of Medicine, Baltimore Maryland, USA
| | - Xiaomeng Ma
- Dalla Lana School of Public Health, Institute of Health Policy Management and Evaluations, University of Toronto, Toronto, Canada
| | - Hsien-Yen Chang
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Thomas M Richards
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Changmi Jung
- Carey Business School, Johns Hopkins University, Baltimore, Maryland, USA
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17
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Racine AM, Tommet D, D'Aquila ML, Fong TG, Gou Y, Tabloski PA, Metzger ED, Hshieh TT, Schmitt EM, Vasunilashorn SM, Kunze L, Vlassakov K, Abdeen A, Lange J, Earp B, Dickerson BC, Marcantonio ER, Steingrimsson J, Travison TG, Inouye SK, Jones RN. Machine Learning to Develop and Internally Validate a Predictive Model for Post-operative Delirium in a Prospective, Observational Clinical Cohort Study of Older Surgical Patients. J Gen Intern Med 2021; 36:265-273. [PMID: 33078300 PMCID: PMC7878663 DOI: 10.1007/s11606-020-06238-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 09/11/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND Our objective was to assess the performance of machine learning methods to predict post-operative delirium using a prospective clinical cohort. METHODS We analyzed data from an observational cohort study of 560 older adults (≥ 70 years) without dementia undergoing major elective non-cardiac surgery. Post-operative delirium was determined by the Confusion Assessment Method supplemented by a medical chart review (N = 134, 24%). Five machine learning algorithms and a standard stepwise logistic regression model were developed in a training sample (80% of participants) and evaluated in the remaining hold-out testing sample. We evaluated three overlapping feature sets, restricted to variables that are readily available or minimally burdensome to collect in clinical settings, including interview and medical record data. A large feature set included 71 potential predictors. A smaller set of 18 features was selected by an expert panel using a consensus process, and this smaller feature set was considered with and without a measure of pre-operative mental status. RESULTS The area under the receiver operating characteristic curve (AUC) was higher in the large feature set conditions (range of AUC, 0.62-0.71 across algorithms) versus the selected feature set conditions (AUC range, 0.53-0.57). The restricted feature set with mental status had intermediate AUC values (range, 0.53-0.68). In the full feature set condition, algorithms such as gradient boosting, cross-validated logistic regression, and neural network (AUC = 0.71, 95% CI 0.58-0.83) were comparable with a model developed using traditional stepwise logistic regression (AUC = 0.69, 95% CI 0.57-0.82). Calibration for all models and feature sets was poor. CONCLUSIONS We developed machine learning prediction models for post-operative delirium that performed better than chance and are comparable with traditional stepwise logistic regression. Delirium proved to be a phenotype that was difficult to predict with appreciable accuracy.
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Affiliation(s)
- Annie M Racine
- Aging Brain Center, Institute for Aging Research, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Douglas Tommet
- Department of Psychiatry & Human Behavior, and Neurology, Brown University Warren Alpert Medical School, Providence, RI, USA
| | | | - Tamara G Fong
- Aging Brain Center, Institute for Aging Research, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Yun Gou
- Aging Brain Center, Institute for Aging Research, Boston, MA, USA
| | | | - Eran D Metzger
- Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Tammy T Hshieh
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Eva M Schmitt
- Aging Brain Center, Institute for Aging Research, Boston, MA, USA
| | - Sarinnapha M Vasunilashorn
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Lisa Kunze
- Harvard Medical School, Boston, MA, USA
- Department of Anesthesia, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Kamen Vlassakov
- Harvard Medical School, Boston, MA, USA
- William F Connell School of Nursing at Boston College, Boston, MA, USA
| | - Ayesha Abdeen
- Harvard Medical School, Boston, MA, USA
- Department of Orthopedic Surgery, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Jeffrey Lange
- Harvard Medical School, Boston, MA, USA
- Department of Orthopedic Surgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Brandon Earp
- Harvard Medical School, Boston, MA, USA
- Department of Orthopedics, Brigham and Women's Faulkner Hospital, Boston, MA, USA
| | - Bradford C Dickerson
- Department of Neurology and Massachusetts Alzheimer's Disease Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Edward R Marcantonio
- Aging Brain Center, Institute for Aging Research, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Thomas G Travison
- Aging Brain Center, Institute for Aging Research, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Sharon K Inouye
- Aging Brain Center, Institute for Aging Research, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Richard N Jones
- Department of Psychiatry & Human Behavior, and Neurology, Brown University Warren Alpert Medical School, Providence, RI, USA.
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Shull PB, Xia H. Modeling and Prediction of Wearable Energy Harvesting Sliding Shoes for Metabolic Cost and Energy Rate Outside of the Lab. Sensors (Basel) 2020; 20:s20236915. [PMID: 33287288 PMCID: PMC7730444 DOI: 10.3390/s20236915] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 11/27/2020] [Accepted: 11/30/2020] [Indexed: 11/16/2022]
Abstract
The recent explosion of wearable electronics has led to widespread interest in harvesting human movement energy, particularly during walking, for clinical and health applications. However, the amount of energy available to harvest and the required metabolic rate for wearable energy harvesting varies across subjects. In this paper, we utilize custom energy harvesting sliding shoes to develop and evaluate multivariate linear regression models to predict metabolic rate and energy harvesting rate during overground walking outside of the lab. Subjects performed 200 m self-selected normal and fast walking trials on flat ground with custom sliding shoes. Metabolic rate was measured with a portable breathing analysis system and energy harvesting rate was measured directly from the generator on the custom sliding shoes. Model performance was determined by comparing the difference between actual and predicted metabolic and energy harvesting rates. Overall, predictive modeling closely matched the actual values, and there was no statistical difference between actual and predicted average metabolic rate or between actual and predicted average energy harvesting rate. Energy harvesting sliding shoes could potentially be used for a variety of wearable devices to reduce onboard energy storage, and these findings could serve to inform expected energy harvesting rates and associated required metabolic cost for a diverse array of medical and health applications.
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Affiliation(s)
- Peter B. Shull
- The State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
| | - Haisheng Xia
- Department of Automation, University of Science and Technology of China, Hefei 230026, China
- Correspondence:
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19
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Liu J, Zhou Y, Tzvetanov T. Globally Normal Bistable Motion Perception of Anisometropic Amblyopes May Profit From an Unusual Coding Mechanism. Front Neurosci 2018; 12:391. [PMID: 29930497 PMCID: PMC5999761 DOI: 10.3389/fnins.2018.00391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2017] [Accepted: 05/22/2018] [Indexed: 11/19/2022] Open
Abstract
Anisometropic amblyopia is a neurodevelopmental disorder of the visual system. There is evidence that the neural deficits spread across visual areas, from the primary cortex up to higher brain areas, including motion coding structures such as MT. Here, we used bistable plaid motion to investigate changes in the underlying mechanisms of motion integration and segmentation and, thus, help us to unravel in more detail deficits in the amblyopic visual motion system. Our results showed that (1) amblyopes globally exhibited normal bistable perception in all viewing conditions compared to the control group and (2) decreased contrast led to a stronger increase in percept switches and decreased percept durations in the control group, while the amblyopic group exhibited no such changes. There were few differences in outcomes dependent upon the use of the weak eye, the strong eye, or both eyes for viewing the stimuli, but this was a general effect present across all subjects, not specific to the amblyopic group. To understand the role of noise and adaptation in such cases of bistable perception, we analyzed predictions from a model and found that contrast does indeed affect percept switches and durations as observed in the control group, in line with the hypothesis that lower stimulus contrast enhances internal noise effects. The combination of experimental and computational results presented here suggests a different motion coding mechanism in the amblyopic visual system, with relatively little effect of stimulus contrast on amblyopes' bistable motion perception.
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Affiliation(s)
- Jiachen Liu
- Hefei National Laboratory for Physical Sciences at Microscale, School of Life Science, University of Science and Technology of China, Hefei, China
| | - Yifeng Zhou
- Hefei National Laboratory for Physical Sciences at Microscale, School of Life Science, University of Science and Technology of China, Hefei, China.,State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Science, Beijing, China
| | - Tzvetomir Tzvetanov
- Hefei National Laboratory for Physical Sciences at Microscale, School of Life Science, University of Science and Technology of China, Hefei, China.,Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine and School of Computer and Information, Hefei University of Technology, Hefei, China
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20
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Chambers U, Jones VP. Effect of Over-Tree Evaporative Cooling in Orchards on Microclimate and Accuracy of Insect Model Predictions. Environ Entomol 2015; 44:1627-1633. [PMID: 26331306 DOI: 10.1093/ee/nvv137] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2015] [Accepted: 08/07/2015] [Indexed: 06/05/2023]
Abstract
Orchard design and management practices can alter microclimate and, thus, potentially affect insect development. If sufficiently large, these deviations in microclimate can compromise the accuracy of phenology model predictions used in integrated pest management (IPM) programs. Sunburn causes considerable damage in the Pacific Northwest, United States, apple-producing region. Common prevention strategies include the use of fruit surface protectants, evaporative cooling (EC), or both. This study focused on the effect of EC on ambient temperatures and model predictions for four insects (codling moth, Cydia pomonella L.; Lacanobia fruitworm, Lacanobia subjuncta Grote and Robinson; oblique-banded leafroller, Choristoneura rosaceana Harris; and Pandemis leafroller, Pandemis pyrusana Kearfott). Over-tree EC was applied in July and August when daily maximum temperatures were predicted to be ≥30°C between 1200-1700 hours (15/15 min on/off interval) in 2011 and between 1200-1800 hours (15/10 min on/off interval, or continuous on) in 2012. Control plots were sprayed once with kaolin clay in early July. During interval and continuous cooling, over-tree cooling reduced average afternoon temperatures compared with the kaolin treatment by 2.1-3.2°C. Compared with kaolin-treated controls, codling moth and Lacanobia fruitworm egg hatch in EC plots was predicted to occur up to 2 d and 1 d late, respectively. The presence of fourth-instar oblique-banded leafroller and Pandemis leafroller was predicted to occur up to 2 d and 1 d earlier in EC plots, respectively. These differences in model predictions were negligible, suggesting that no adjustments in pest management timing are needed when using EC in high-density apple orchards.
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Affiliation(s)
- Ute Chambers
- Washington State University, Tree Fruit Research and Extension Center, 1100 N Western Avenue, Wenatchee, WA 98801.
| | - Vincent P Jones
- Washington State University, Tree Fruit Research and Extension Center, 1100 N Western Avenue, Wenatchee, WA 98801
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21
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Abstract
A system of ordinary differential equations is formulated to describe the pathogenesis of HIV infection, wherein certain features that have been shown to be important by recent experimental research are incorporated in the model. These include the role of CD4+ memory cells that serve as a major reservoir of latently infected cells, a critical role for T-helper cells in the generation of CD8 memory cells capable of efficient recall response, and stimulation by antigens other than HIV. A stability analysis illustrates the capability of this model in admitting multiple locally asymptotically stable (locally a.s.) off-treatment equilibria.We show that this more biologically detailed model can exhibit the phenomenon of transient viremia experienced by some patients on therapy with viral load levels suppressed below the detection limit. We also show that the loss of CD4+ T-cell help in the generation of CD8+ memory cells leads to larger peak values for the viral load during transient viremia. Censored clinical data is used to obtain parameter estimates. We demonstrate that using a reduced set of 16 free parameters, obtained by fixing some parameters at their population averages, the model provides reasonable fits to the patient data and, moreover, that it exhibits good predictive capability. We further show that parameter values obtained for most clinical patients do not admit multiple locally a.s off-treatment equilibria. This suggests that treatment to move from a high viral load equilibrium state to an equilibrium state with a lower (or zero) viral load is not possible for these patients.
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Affiliation(s)
- H T Banks
- Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC, USA.
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