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Yu-Chuan Li

Publications and source records attributed to Yu-Chuan Li.

23 records · Page 2Linked to original sources
PubMed2004

Characterization of the 3a protein of SARS-associated coronavirus in infected vero E6 cells and SARS patients.

Proteomics was used to identify a protein encoded by ORF 3a in a SARS-associated coronavirus (SARS-CoV). Immuno-blotting revealed that interchain disulfide bonds might be formed between this protein and the spike protein. ELISA indicated that sera from SARS patients have significant positive reactions with synthesized peptides derived from the 3a protein. These results are concordant with that of a spike protein-derived peptide. A tendency exists for co-mutation between the 3a protein and the spike protein of SARS-CoV isolates, suggesting that the function of the 3a protein correlates with the spike protein. Taken together, the 3a protein might be tightly correlated to the spike protein in the SARS-CoV functions. The 3a protein may serve as a new clinical marker or drug target for SARS treatment.

Animals↗
PubMed2004

Predicting blood pressure change caused by rapid injection of propofol during anesthesia induction with a logistic regression model.

BACKGROUND: Propofol is a common intravenous agent for induction and maintenance of anesthesia. The advantage of propofol is rapid recovery of consciousness when the continuous infusion is stopped. Additionally, it has antiemetic effect of reducing postoperative nausea and vomiting. On the other hand, rapid infusion of propofol is painful and may cause hypotension. In this study, we aimed to develop a logistic regression model to accurately predict blood pressure change caused by rapid infusion of propofol. METHODS: Seventeen variables (including demographic data, past medical history, laboratory data, and blood pressure before induction) were assessed in 200 patients who received propofol for induction of anesthesia for routine surgery. A logistic regression model was derived using these values as independent variables to predict whether a patient would suffer a significant blood pressure change (> 30% decrease from baseline). Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of our prediction model. RESULTS: A cut-off value of 0.17 in the logistic regression model predicted decreased blood pressure with 90.0% sensitivity and, 67.3% specificity. The area under the receiver operating characteristic curve was 0.855. CONCLUSIONS: Our prediction model predicts propofol-induced hypotension with acceptable accuracy. Because of the straightforward mathematic formula used, our model can be integrated effortlessly into a hospital information system, providing a reliable and useful decision support for clinical anesthesia staff.

Adolescent↗
PubMed2003

Stratification of adverse outcomes by preoperative risk factors in coronary artery bypass graft patients: an artificial neural network prediction model.

We constructed and internally validated an artificial neural network (ANN) model for prediction of in-hospital major adverse outcomes (defined as death, cardiac arrest, coma, renal failure, cerebrovascular accident, reinfarction, or prolonged mechanical ventilation) in patients who received "on-pump" coronary artery bypass grafting (CABG) surgery. We retrospectively analyzed a 5-year CABG surgery database with a final study population of 563 patients. Predictive variables were limited to information available before the procedure, and outcome variables were represented only by events that occurred postoperatively. The ANN's ability to discriminate outcomes was assessed using receiver-operating characteristic (ROC) analysis and the results were compared with a multivariate logistic regression (LR) model and the QMMI risk score (RS) model. A major adverse outcome occurred in 12.3% of all patients and 18 predictive variables were identified by the ANN model. Pairwise comparison showed that the ANN model significantly outperformed the RS model (AUC = 0.886 vs.0.752, p = 0.043). However, the other two pairs, ANN vs. LR models (AUC = 0.886 vs. 0.807, p = 0.076) and LR vs. RS models (AUC = 0.807 vs. 0.752, p = 0.453) performed similarly well. ANNs tend to outperform regression models and might be a useful screening tool to stratify CABG candidates preoperatively into high-risk and low-risk groups.

Cardiopulmonary Bypass↗
PubMed2002

Neural network modeling to predict the hypnotic effect of propofol bolus induction.

Dose requirements of propofol to achieve loss of consciousness depend on the interindividual variability. Until now when propofol was administered by a single bolus, how to define the optimal individual dose and to assess its hypnotic effect have not been clearly studied. The goal of this study is to develop an artificial neural network model to predict the hypnotic effect of propofol on the basis of common clinical parameters. Ten parameters were chosen as the input factors based on the related literatures and clinical experiences. The bispectral index of EEG was used to record the consciousness level of patients and served as the output factor. The predictive results of neural net models were superior to that of clinician. This model could potentially help determine the optimal dose of propofol and thus reduce the anesthetic cost.

Anesthesia, Intravenous↗