Fengyi Zhang

China National Pharmaceutical Group Corporation (China)

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Li Luo

Sichuan University

Kezhen Zhao

Ministry of Agriculture and Rural Affairs

Bi-Xian Zhang

Heilongjiang Provincial Academy of Agricultural Sciences

Xueyang Wang

Heilongjiang Provincial Academy of Agricultural Sciences

Sobhi F. Lamlom

Alexandria University

Xiulin Liu

China University of Petroleum, Beijing

Rongqiang Yuan

Heilongjiang Provincial Academy of Agricultural Sciences

Honglei Ren

Heilongjiang Provincial Academy of Agricultural Sciences

Jiajun Wang

Peking University

Chunlei Zhang

Peking University

Cooperative Institutions

Sichuan University

Peking University

West China Hospital of Sichuan University

Chinese Academy of Sciences

Ministry of Agriculture and Rural Affairs

Shandong University

Heilongjiang Provincial Academy of Agricultural Sciences

Sun Yat-sen University

Jilin University

Capital Medical University

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Machine learning for identification of surgeries with high risks of cancellation

Health Informatics Journal (2018)

Li Luo Fengyi Zhang Yao Yao Renrong Gong Martina Fu

Surgery cancellations waste scarce operative resources and hinder patients’ access to operative services. In this study, the Wilcoxon and chi-square tests were used for predictor selection, and three machine learning models – random forest, support vector machine, and XGBoost – were used for the identification of surgeries with high risks of cancellation. The optimal performances of the identification models were as follows: sensitivity − 0.615; specificity − 0.957; positive predictive value − 0.454; negative predictive value − 0.904; accuracy − 0.647; and area under the receiver operating characteristic curve − 0.682. Of the three models, the random forest model achieved the best performance. Thus, the effective identification of surgeries with high risks of cancellation is feasible with stable performance. Models and sampling methods significantly affect the performance of identification. This study is a new application of machine learning for the identification of surgeries with high risks of cancellation and facilitation of surgery resource management.

Identification

10.1177/1460458218813602

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Citations (25)

Estimation of the Effects of Air Pollution on Hospitalization Expenditures for Asthma

International Journal of Health Services (2019)

Fengyi Zhang Li Luo Ziyan Wang Wei Zhang Chunyang Li

This article aims to estimate the effects of air pollutants (PM 2.5 , PM 10 , SO 2 , and NO 2 ) on the hospitalization expenses of adult patients admitted due to asthma. The data is sourced from the medical health database of a city in Southwest China, which involves approximately 14 million residents. A generalized additive model using a Poisson distribution was utilized to identify the effects of air pollution on the cost of hospitalization due to asthma. Hospital admissions were stratified by sex, age, season, and type of medical insurance to specialize the results. Air pollution significantly affects hospitalization expenses for asthma. These effects were more significant in the male, non-senior subgroups than in the female, senior subgroups. The difference also significantly existed in different insurance type subgroups. Air pollution has adverse effects on hospitalization expenses for asthma. Climate change is now deeply affecting the world in the aspects of economy and health. Based on our research, policymakers can establish more effective air pollution reduction policies and measures that can control the levels of ambient air pollutants, which help save health care resources and improve the welfare of the population by easing the climate change.

10.1177/0020731419874996

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Markov Chain-Based Acute Effect Estimation of Air Pollution on Elder Asthma Hospitalization

Journal of Healthcare Engineering (2017)

Li Luo Fengyi Zhang Wei Zhang Lin Sun Chunyang Li

Background . Asthma caused substantial economic and health care burden and is susceptible to air pollution. Particularly, when it comes to elder asthma patient (older than 65), the phenomenon is more significant. The aim of this study is to investigate the Markov-based acute effects of air pollution on elder asthma hospitalizations, in forms of transition probabilities. Methods . A retrospective, population-based study design was used to assess temporal patterns in hospitalizations for asthma in a region of Sichuan province, China. Approximately 12 million residents were covered during this period. Relative risk analysis and Markov chain model were employed on daily hospitalization state estimation. Results . Among PM2.5, PM10, NO 2 , and SO 2 , only SO 2 was significant. When air pollution is severe, the transition probability from a low-admission state (previous day) to high-admission state (next day) is 35.46%, while it is 20.08% when air pollution is mild. In particular, for female-cold subgroup, the counterparts are 30.06% and 0.01%, respectively. Conclusions . SO 2 was a significant risk factor for elder asthma hospitalization. When air pollution worsened, the transition probabilities from each state to high admission states increase dramatically. This phenomenon appeared more evidently, especially in female-cold subgroup (which is in cold season for female admissions). Based on our work, admission amount forecast, asthma intervention, and corresponding healthcare allocation can be done.

10.1155/2017/2463065

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Citations (7)

Transcriptome profiling uncovers differentially expressed genes linked to nutritional quality in vegetable soybean

PLoS ONE (2024)

Xueyang Wang Chunlei Zhang Rongqiang Yuan Xiulin Liu Fengyi Zhang

Vegetative soybean (maodou or edamame) serves as a nutrient-rich food source with significant potential for mitigating global nutritional deficiencies. This study undertook a thorough examination of the nutritional profiles and transcriptomic landscapes of six soybean cultivars, including three common cultivars (Heinong551, Heinong562, and Heinong63) and three fresh maodou cultivars (Heinong527, HeinongXS4, and HeinongXS5). Nutrient analysis of the seeds disclosed notable differences in the levels of protein, fat, soluble sugars, vitamin E, calcium, potassium, magnesium, manganese, iron, and zinc across the cultivars. Through comparative transcriptome profiling and RNA sequencing, distinct variations in differentially expressed genes (DEGs) were identified between fresh and traditional maodou cultivars. Functional enrichment analyses underscored the involvement of DEGs in critical biological processes, such as nutrient biosynthesis, seed development, and stress responses. Additionally, association studies demonstrated robust correlations between specific DEG expression patterns and seed nutrient compositions across the different cultivars. Sankey diagrams illustrated that DEGs are strongly linked with seed quality traits, revealing potential molecular determinants that govern variations in nutritional content. The identified DEGs and their relationships with nutritional profiles offer valuable insights for breeding programs focused on developing cultivars with improved nutritional quality, tailored to specific dietary needs or industrial applications.

10.1371/journal.pone.0313632

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Citations (1)

Author response for "Biotransformation and disposition characteristics of <scp>HSK7653</scp>, a novel long‐acting dipeptidyl peptidase‐4 inhibitor for the treatment of type 2 diabetes"

Yicong Bian Jian Meng Tao Hu Sheng Ma Chenrong Huang

Biotransformation

Disposition

Dipeptidyl peptidase-4 inhibitor

Dipeptidyl peptidase

10.1111/dom.15605/v3/response1

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Development and validation of a nomogram for predicting overall survival of gastric cancer patients after D2R0 resection

European Journal of Cancer Care (2020)

Pengfei Zhang Zedong Du Feng Wen Fengyi Zhang Weihan Zhang

Objective The aim of the study was to find factors associated with overall survival (OS) and establish a nomogram predicting OS of patients with gastric cancer (GC) after D2R0 resection. Methods Demographic and clinicopathologic factors of patients with GC underwent D2R0 surgical resection were retrospectively collected from medical records, telephone interview or outpatient follow-up. To find factors significantly associated with OS, univariate and multivariate analyses were conducted. The concordance index (C-index) was used to measure the accuracy of the nomogram. Discrimination and calibration of the nomogram were tested using the patients in the validation set. Results Overall, patients with 890 GC underwent D2R0 surgical resection were included. Based on the results of univariate analysis and multivariate analysis, T stage, number of metastatic local lymph nodes, lymph node positive rate, adjuvant chemotherapy and diameter of tumour were used to construct a nomogram predicting OS of patients with GC after surgical resection. In the validation cohort, the C-index was 0.73 and the nomogram performed well in predicting OS. Conclusion The nomogram was able to accurately predict OS of patients with GC underwent curative surgery and performed well in internal validation, which would also be useful for the decision-making of doctors.

Nomogram

Univariate

Univariate analysis

Concordance

10.1111/ecc.13260

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Citations (9)

Emergency patient flow forecasting in the radiology department

Health Informatics Journal (2020)

Yumeng Zhang Li Luo Fengyi Zhang Ruixiao Kong Shuicheng Yan

The accurate forecast of radiology emergency patient flow is of great importance to optimize appointment scheduling decisions. This study used a multi-model approach to forecast daily radiology emergency patient flow with consideration of different patient sources. We constructed six linear and nonlinear models by considering the lag effects and corresponding time factors. The autoregressive integrated moving average and least absolute shrinkage and selection operator (Lasso) were selected from the category of linear models, whereas linear-and-radial support vector regression models, random forests and adaptive boosting were chosen from the category of nonlinear models. The models were applied to 4-year daily emergency visits data in the radiology department of West China Hospital in Chengdu, China. The mean absolute percentage error of six models ranged from 8.56 to 9.36 percent for emergency department patients, whereas it varied from 10.90 to 14.39 percent for ward patients. The best-performing model for total radiology visits was Lasso, which yielded a mean absolute percentage error of 7.06 percent. The arrival patterns of emergency department and total radiology emergency patient flows could be modeled by linear processes. By contrast, the nonlinear model performed best for ward patient flow. These findings will benefit hospital managers in managing efficient patient flow, thus improving service quality and increasing patient satisfaction.

Lasso

10.1177/1460458220901889

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Citations (21)

Genetic Adaptations of Soybean to Cold Stress Reveal Key Insights Through Transcriptomic Analysis

Biology (2024)

Xiulin Liu Chunlei Zhang Sobhi F. Lamlom Kezhen Zhao Ahmed M. Abdelghany

Low temperatures greatly restrict the development, growth, and productivity of soybeans, with their effects differing across various cultivars. The present work investigated the transcriptome and physiological reactions of two soybean cultivars, namely "KD52" exhibiting cold tolerance and "DS17" displaying cold sensitivity, to cold stress across a precisely defined period. The soybean plants were subjected to cold treatment at 6 °C for durations of 0, 2, 4, and 8 h. A comparative physiological marker study revealed distinct reactions to cold stress in the two cultivars. The findings showed that increased malondialdehyde levels provided evidence of DS17's heightened vulnerability to lipid peroxidation and membrane degradation. In contrast, the KD52 cultivar exhibited increased activities of antioxidant enzymes, including peroxidase and superoxide dismutase, in response to cold exposure, suggesting a strong antioxidant defense system against oxidative stress. The transcriptomic analysis revealed dynamic responses, mapping 54,532 genes. Within this group, a total of 234 differentially expressed genes (DEGs) were found to be consistently changed at several time intervals, showing unique expression patterns across the two cultivars. Analysis of the association between these important DEGs and the physiological indicators revealed candidate genes that may be involved in controlling oxidative damage and antioxidant defenses. Some key genes showed a progressive rise in expression over time in both cultivars, with a more significant acceleration in KD52, and are probably involved in promoting adaptation processes during extended periods of cold exposure. The identification of improved defense mechanisms in KD52, together with the identification of crucial genes, offers great prospects for enhancing the cold stress resilience of soybean.

Cold stress

10.3390/biology13110856

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Citations (2)

MMHG: Multi-modal Hypergraph Learning for Overall Survival After D2 Gastrectomy for Gastric Cancer

Fei Lu Zhikui Chen Xu Yuan Qiu Li Zedong Du

Overall survival (OS) prediction has been a central topic of oncology. Most existing OS prediction models are based on traditional statistical methods which becomes a limitation when confronted with high dimensional dataset as well complicated internal relation among features in practice. In this paper, we weaken this limitation by exploring the application of multi-modal hypergraph (MMHG) learning framework to improve the accuracy of prediction. More specifically, the proposed hypergraph model unites the demographics, pathologic characteristics and physiological indicators simultaneously to predict the overall survival after D2 gastrectomy for gastric cancer. Experiments are carried out on a real data set of West China Hospital of Sichuan University with 939 patients to evaluate the proposed approach by comparison with random forest (RF) and support vector machine (SVM). Results demonstrate that our scheme outperforms the baseline methods in overall survival classification performance.

Hypergraph

Baseline (sea)

Demographics

10.1109/dasc-picom-datacom-cyberscitec.2017.40

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Genetic analysis and QTL mapping of seed hardness trait in a soybean (Glycine max) recombinant inbred line (RIL) population

Gene (2024)

Honglei Ren Kezhen Zhao Chunlei Zhang Sobhi F. Lamlom Xiulin Liu

Coat

Trait

Inbred strain

Genetic Analysis

10.1016/j.gene.2024.148238

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Citations (4)