ABSTRACT
With long-term impact of COVID-19 on education, online interactive live courses have been an effective method to keep learning and teaching from being interrupted, attracting more and more attention due to their synchronous and real-time interaction. However, there is no suitable method for predicting academic performance for students participating in online class. Five machine learning models are employed to predict academic performance of an engineering mechanics course, taking online learning behaviors, comprehensive performance as input and final exam scores (FESs) as output. The analysis shows the gradient boosting regression model achieves the best performance with the highest correlation coefficient (0.7558), and the lowest RMSE (9.3595). Intellectual education score (IES) is the most important factor of comprehensive performance while the number of completed assignment (NOCA), the live viewing rate (LVR) and the replay viewing rate (RVR) of online learning behaviors are the most important factors influencing FESs. Students with higher IES are more likely to achieve better academic performance, and students with lower IES but higher NOCA tend to perform better. Our study can provide effective evidences for teachers to adjust teaching strategies and provide precise assistance for students at risk of academic failure in advance.
ABSTRACT
The coronavirus disease 2019 (COVID-19) has extensively promoted the application of nucleic acid testing technology in the field of clinical testing. The most widely used polymerase chain reaction (PCR)-based nucleic acid testing technology has problems such as complex operation, high requirements of personnel and laboratories, and contamination. The highly miniaturized microfluidic chip provides an essential tool for integrating the complex nucleic acid detection process. Various microfluidic chips have been developed for the rapid detection of nucleic acid, such as amplification-free microfluidics in combination with clustered regularly interspaced short palindromic repeats (CRISPR). In this review, we first summarized the routine process of nucleic acid testing, including sample processing and nucleic acid detection. Then the typical microfluidic chip technologies and new research advances are summarized. We also discuss the main problems of nucleic acid detection and the future developing trend of the microfluidic chip. © 2022 Elsevier B.V.
ABSTRACT
Objective To rapidly screen patients with novel coronavirus pneumonia (COVID-19) infection including asymptomatic ones. Method Established a rapid detection test kit, and evaluated analytical and clinical performance of it. Result The minimum limit of detection of the reagent was 9.75×102 TCID50/mL;there was no cross-reaction and interference in the high-concentration samples of 29 common respiratory pathogens tested. The diagnostic sensitivity of clinical samples was 98.56%, specificity was 99.00%, and the total coincidence rate was 98.85%;the consistency test Kappa value is 0.974 5. The stratified analysis of positive samples with different Ct values showed that the coincidence rate within each stratum was greater than 95%. Conclusion This COVID-19 antigen test kit with excellent detection performance, fast detection speed, and portable operation. It can be used as a supplementary method for existing nucleic acid detection methods for early screening of new coronavirus.
ABSTRACT
OBJECTIVE: Transplant recipients have a higher risk of SARS-CoV-2 infection owing to the use of immunosuppressive drugs like tacrolimus (FK506). FK506 and nirmatrelvir (NMV) (an anti-SARS-CoV-2 drug) are metabolized by cytochrome P450 3A4 and may have potential drug-drug interactions. It is important to determine the effect of NMV on FK506 concentrations. PATIENTS AND METHODS: Following protein precipitation from blood, FK506 and its internal standard (FK506-13C,2d4) were detected by ultra-high performance liquid chromatography/tandem mass spectrometry (UHPLC-MS/MS). Total 22 blood samples (valley concentrations) from two coronavirus disease 2019 (COVID-19) patients were collected and analyzed for FK506 concentrations. RESULTS: Blood levels of FK506 (0.5-100 ng/mL) showed good linearity. The UHPLC-MS/MS method was validated with intra- and inter-batch accuracies of 104.55-107.85%, and 99.52-108.01%, respectively, and precisions of < 15%. Mean blood FK506 concentration was 12.01 ng/mL (range, 3.15-33.1 ng/mL). Five-day co-administration with NMV increased the FK506 concentrations from 3.15 ng/mL to 33.1 ng/mL, returning to 3.36 ng/mL after a 9-day-washout. CONCLUSIONS: We developed a simple quantification method for therapeutic drug monitoring of FK506 in patients with COVID-19 using UHPLC-MS/MS with protein precipitation. We found that NMV increased FK506 blood concentration 10-fold. Therefore, it is necessary to re-consider co-administration of FK506 with NMV.