Application of problem-based learning combined with a virtual simulation training platform in clinical biochemistry teaching during the COVID-19 pandemic.

Background: The coronavirus disease 2019 (COVID-19) pandemic has had a great impact on the traditional teaching mode (Lecture-based Learning, LBL) and laboratory teaching. To address this challenge, the researchers conducted online Problem-based learning (PBL) teaching and virtual simulation laboratory teaching through DingTalk, and evaluated the effectiveness of this method in teaching clinical biochemistry. Methods: With the method of cluster sampling, the researchers randomly selected 60 students from two classes of the Class 2019 as the experimental group for this prospective experimental study. The theory class was taught online PBL through DingTalk, and experimental lectures were given by virtual simulation. After the experimental teaching, students were assessed for theory and operation. Self-administered questionnaires were administered through DingTalk. 65 students from our 2018 medical laboratory class were randomly selected as the control group, and offline LBL and traditional experimental teaching methods were used. Examination results were obtained through teaching portfolios. Results: The experimental group had significantly better examination scores in theoretical knowledge and experimental operational skills than the control group (87.45 ± 5.91 vs. 83.52 ± 9.94, P = 0.0095; 87.08 ± 12.42 vs. 80.18 ± 14.04, P = 0.0044). The results of the questionnaire survey revealed that the experimental group was more receptive to the DingTalk-PBL teaching method and virtual simulation laboratory teaching. Moreover, this hybrid teaching method was more effective in promoting basic knowledge understanding (95.0%, 57/60), facilitating the mastery of operational skills (93.3, 56/60), cultivating interest in learning (96.7%, 58/60), training clinical thinking (95.0%, 57/60), improving communication skills (95.0%, 57/60), and enhancing self-learning ability (91.7%, 55/60) and was more satisfying than traditional teaching method (all P < 0.05). Conclusion: The DingTalk-based PBL method combined with virtual simulation experiments was an effective and acceptable teaching strategy during the pandemic compared with the traditional teaching method.

Composition identification and UV-C irradiation growth inhibition effect of green shading on the greenhouse cover.

Greenhouse cover pollution with green shading composed of dust, microalgae and bacteria is a severe problem in tropical areas. The shading results in lower greenhouse indoor light intensity reducing the yield and quality of protected horticulture crops. However, few studies have focused on environmentally efficient ways to remove green shading to increase greenhouse production. In this study, five purified microalgae were isolated from the green shading of three greenhouse roofs and were identified using morphological and molecular assessments. The effects of Ultraviolet-C irradiation (UV-C, 254 nm) at doses of 100, 200 and 300 mJ cm-2 on the growth of GLY-1 microalgae were investigated. The results indicated that five purified microalgae all appeared to belong to the genus of Jaagichlorella. The purified microalgae cell density and chlorophyll content decreased respectively by 26.89-74.44 % and 42.02-77.31 % at 1-3 d after UV-C treatment with doses ranging from 100 to 300 mJ cm-2. The inhibition of the growth rate of microalgae was significantly positively correlated with the UV-C irradiation dose and significantly negatively correlated with treatment time. In summary, UV-C irradiation treatment at 300 mJ cm-2 and 3 d could substantially inhibit microalgae growth in green shading on greenhouse covers. UV-C irradiation could be an effective method for solving the problem of greenhouse cover pollution with microalgae.

Agreement of Potassium, Sodium, Glucose, and Hemoglobin Measured by Blood Gas Analyzer With Dry Chemistry Analyzer and Complete Blood Count Analyzer: A Two-Center Retrospective Analysis.

Background: Blood gas analyzers (BGAs) and dry biochemistry analyzers for potassium and sodium are based on direct electrode methods, and both involve glucose oxidase for glucose detection. However, data are lacking regarding whether the results of the two assay systems can be used interchangeably. In addition, there remains controversy over the consistency between BGA-measured hemoglobin and complete blood count analyzer data. Here, we compared the consistency of sodium, potassium, glucose, and hemoglobin levels measured by BGA and dry chemistry and complete blood count analyzers. Methods: Data from two teaching hospitals, the Zhejiang Provincial People's Hospital (ZRY) and the Qianfoshan Hospital (QY), were retrospectively analyzed based on dry biochemistry and complete blood count analyzer results as the reference system (X) and BGA as the experimental system (Y). Plasma was used for biochemical analysis at the ZRY Hospital, and serum at the QY Hospital. Paired data from the respective hospitals were evaluated for consistency, and biases between methods were assessed by simple correlation, Passing-Bablok regression, and Bland-Altman analyses. Results: The correlations of potassium, sodium, glucose, and hemoglobin measured by BGA and dry biochemistry and complete blood count analyzers were high, at 0.9573, 0.8898, 0.9849, and 0.9883 for the ZRY Hospital and 0.9198, 0.8591, 0.9764, and 0.8666, respectively, for the QY Hospital. The results of Passing to Bablok regression analysis showed that the predicted biases at each medical decision level were within clinically acceptable levels for potassium, sodium, glucose, and hemoglobin at the ZRY Hospital. Only the predicted bias of glucose was below the clinically acceptable medical decision levels at the QY Hospital, while potassium, sodium, and hemoglobin were not. Compared with the reference system, the mean bias for BGA measurements at the ZRY Hospital was -0.08 mmol/L (95% confidence interval [CI] -0.091 to -0.069) for potassium, 1.2 mmol/L (95% CI 1.06 to 1.42) for sodium, 0.20 mmol/L (95% CI 0.167 to 0.228) for glucose, and -2.8 g/L for hemoglobin (95% CI -3.14 to -2.49). The mean bias for potassium, sodium, glucose, and hemoglobin at the QY Hospital were -0.46 mmol/L (95% CI -0.475 to -0.452), 3.7 mmol/L (95% CI 3.57 to 3.85), -0.36 mmol/L (95% CI -0.433 to -0.291), and -8.7 g/L (95% CI -9.40 to -8.05), respectively. Conclusion: BGA can be used interchangeably with plasma electrolyte results from dry biochemistry analyzers but does not show sufficient consistency with serum electrolyte results from dry biochemistry analyzers to allow data interchangeability. Good consistency was observed between BGA and plasma or serum glucose results from dry biochemistry analyzers. However, BGA-measured hemoglobin and hematocrit assay results should be treated with caution.

Promoter prediction in nannochloropsis based on densely connected convolutional neural networks.

Promoter is a key DNA element located near the transcription start site, which regulates gene transcription by binding RNA polymerase. Thus, the identification of promoters is an important research field in synthetic biology. Nannochloropsis is an important unicellular industrial oleaginous microalgae, and at present, some studies have identified some promoters with specific functions by biological methods in Nannochloropsis, whereas few studies used computational methods. Here, we propose a method called DNPPro (DenseNet-Predict-Promoter) based on densely connected convolutional neural networks to predict the promoter of Nannochloropsis. First, we collected promoter sequences from six Nannochloropsis strains and removed 80% similarity using CD-HIT for each strain to yield a reliable set of positive datasets. Then, in order to construct a robust classifier, within-group scrambling method was used to generate negative dataset which overcomes the limitation of randomly selecting a non-promoter region from the same genome as a negative sample. Finally, we constructed a densely connected convolutional neural network, with the sequence one-hot encoding as the input. Compared with commonly used sequence processing methods, DNPPro can extract long sequence features to a greater extent. The cross-strain experiment on independent dataset verifies the generalization of our method. At the same time, T-SNE visualization analysis shows that our method can effectively distinguish promoters from non-promoters.

[Feasibility of Preheating at 41 ℃ to Correct Red Blood Cell Parameters in the Presence of High-titer Cold Agglutinins].

Objective To explore the feasibility of preheating in 41 ℃ water bath for 30 minutes to correct the red blood cell parameters in the specimens containing high-titer cold agglutinins(CAs). Methods Two specimens containing high-titer CAs were selected during work,and the parameters of complete blood count at room temperature or after preheating in 37 ℃ or 41 ℃ water bath were compared.The smears were stained,and the distribution of red blood cells was observed with a microscope.Further,74 specimens without CAs were collected for complete blood count,and then the test results at room temperature and after preheating at 41 ℃ were compared. Results At room temperature,the specimens containing high-titer CAs showed significantly reduced red blood cell count(RBC)and hematocrit(HCT),abnormally increased mean corpuscular hemoglobin(MCH)and mean cell hemoglobin concentration(MCHC),abnormal percents of hemoglobin(HGB)and RBC,and aggregation of a large number of red blood cells.After being preheated at 37 ℃ for a certain time,the specimens demonstrated obviously improved parameters while still aggregation of a small number of red blood cells.After being preheated at 41 ℃ for 30 minutes,the specimens showed significantly increased RBC,normal HCT,MCH,and MCHC,and evenly distributed red blood cells.The 74 specimens without CAs showed the comparability was ≥80% between room temperature and preheating at 41 ℃ for 30 minutes or 60 minutes. Conclusion We can preheat the specimens containing high-titer CAs in a water bath at 41 ℃ to obtain accurate red blood cell parameters.

Establishment of a regression model of bone metabolism markers for the diagnosis of bone metastases in lung cancer.

BACKGROUND: The aim of this study was to establish a regression equation model of serum bone metabolism markers. We analyzed the diagnostic value of bone metastases in lung cancer and provided laboratory evidence for the early clinical treatment of bone metastases in lung cancer. METHODS: A total of 339 patients with non-metastatic lung cancer, patients with lung cancer with bone metastasis, and patients with benign lung disease who were treated in our hospital from July 2012 to October 2015 were included. A total of 103 patients with lung cancer in the non-metastatic group, 128 patients with lung cancer combined with bone metastasis group, and 108 patients with benign lung diseases who had nontumor and nonbone metabolism-related diseases were selected as the control group. Detection and analysis of type I collagen carboxyl terminal peptide ß-special sequence (ß-CTX), total type I procollagen amino terminal propeptide (TPINP), N-terminal-mid fragment of osteocalcin (N-MID), parathyroid hormone (PTH), vitamin D (VitD3), alkaline phosphatase (ALP), calcium (CA), phosphorus (P), cytokeratin 19 fragment (F211), and other indicators were performed. Four multiple regression models were established to determine the best diagnostic model for lung cancer with bone metastasis. RESULTS: Analysis of single indicators of bone metabolism markers in lung cancer was performed, among which F211, ß-CTX, TPINP, and ALP were significantly different (P < 0.05). The ROC curve of each indicator was less than 0.712. Based on the multiple regression models, the fourth model was the best and was much better than a single indicator with an AUC of 0.856, a sensitivity of 70.0%, a specificity of 91.0%, a positive predictive value of 82.5%, and a negative predictive value of 72.0%. CONCLUSION: Multiple regression models of bone metabolism markers were established. These models can be used to evaluate the progression of lung cancer and provide a basis for the early treatment of bone metastases.

The expression of death decoy receptor 3 was increased in the patients with primary Sjögren's syndrome.

Previous studies suggested a pathological role for the death decoy receptor 3 (DcR3) in systemic lupus erythematosus (SLE) and rheumatic arthritis (RA). Herein, the expression of DcR3 in primary Sjögren's syndrome (pSS) and the relationship with clinical characteristics were investigated. The serum DcR3 levels of pSS patients and healthy controls were measured by ELISA. Pearson's correlation analysis was used to evaluate the relationship between the DcR3 levels with the clinical characterstics of pSS patients. Additionally, the DcR3 expression in salivary glands of pSS patients was investigated by the immunohistochemistry method. The serum DcR3 expression in pSS patients was significantly higher than healthy controls (p < 0.001), especially in new onset pSS patients (p = 0.036). Moreover, Pearson's correlation analysis show that DcR3 levels were positively correlated with age (p = 0.013), platelet (PLT) (p = 0.002), hemoglobin (Hb) (p = 0.004), Sjögren's syndrome disease damage activity index (SSDAI) score (p = 0.005), Sjögren's syndrome disease damage index (SSDDI) score (p < 0.001) and EULAR Sjögren's syndrome disease activity index (ESSDAI) score (p = 0.010). Furthermore, the DcR3 levels were significantly lower when the pSS patients were treated with the disease-modifying anti-rheumatic drugs. At last, DcR3 expression in salivary glands of pSS patients was significantly higher than healthy controls. The DcR3 expression was significantly elevated in the pSS patients and positively correlated with the clinical characteristics, and it might be an important factor involved in the progression of pSS patients and could be a potential therapeutic target.