[Technical Difficulties in the Implementation of the Continuous Glucose Monitoring System].

The continuous glucose monitoring system (CGMS) has been clinically applied to monitor the dynamic change of the subcutaneous interstitial glucose concentration which is a function of the blood glucose level by glucose sensors. It can track blood glucose levels all day along, and thus provide comprehensive and reliable information about blood glucose dynamics. The clinical application of CGMS enables monitoring of blood glucose fluctuations and the discovery of hidden hyperglycemia and hypoglycemia that are difficult to be detected by traditional methods. As a CGMS needs to work subcutaneously for a long time, a series of factors such as biocompatibility, enzyme inactivation, oxygen deficiency, foreign body reaction, implant size, electrode flexibility, error correction, comfort, device toxicity, electrical safety, et al. should be considered beforehand. The study focused on the difficulties in the technology, and compared the products of Abbott, Medtronic and DexCom, then summarized their cutting-edge. Finally, this study expounded some key technologies in dynamic blood glucose monitoring and therefore can be utilized as a reference for the development of CGMS.

Early Prediction Model for Critical Illness of Hospitalized COVID-19 Patients Based on Machine Learning Techniques.

Motivation: Patients with novel coronavirus disease 2019 (COVID-19) worsen into critical illness suddenly is a matter of great concern. Early identification and effective triaging of patients with a high risk of developing critical illness COVID-19 upon admission can aid in improving patient care, increasing the cure rate, and mitigating the burden on the medical care system. This study proposed and extended classical least absolute shrinkage and selection operator (LASSO) logistic regression to objectively identify clinical determination and risk factors for the early identification of patients at high risk of progression to critical illness at the time of hospital admission. Methods: In this retrospective multicenter study, data of 1,929 patients with COVID-19 were assessed. The association between laboratory characteristics measured at admission and critical illness was screened with logistic regression. LASSO logistic regression was utilized to construct predictive models for estimating the risk that a patient with COVID-19 will develop a critical illness. Results: The development cohort consisted of 1,363 patients with COVID-19 with 133 (9.7%) patients developing the critical illness. Univariate logistic regression analysis revealed 28 variables were prognosis factors for critical illness COVID-19 (p < 0.05). Elevated CK-MB, neutrophils, PCT, α-HBDH, D-dimer, LDH, glucose, PT, APTT, RDW (SD and CV), fibrinogen, and AST were predictors for the early identification of patients at high risk of progression to critical illness. Lymphopenia, a low rate of basophils, eosinophils, thrombopenia, red blood cell, hematocrit, hemoglobin concentration, blood platelet count, and decreased levels of K, Na, albumin, albumin to globulin ratio, and uric acid were clinical determinations associated with the development of critical illness at the time of hospital admission. The risk score accurately predicted critical illness in the development cohort [area under the curve (AUC) = 0.83, 95% CI: 0.78-0.86], also in the external validation cohort (n = 566, AUC = 0.84). Conclusion: A risk prediction model based on laboratory findings of patients with COVID-19 was developed for the early identification of patients at high risk of progression to critical illness. This cohort study identified 28 indicators associated with critical illness of patients with COVID-19. The risk model might contribute to the treatment of critical illness disease as early as possible and allow for optimized use of medical resources.

Development and Validation of a Predictive Nomogram with Age and Laboratory Findings for Severe COVID-19 in Hunan Province, China.

Purpose: To identify more objectively predictive factors of severe outcome among patients hospitalized for coronavirus disease 2019 (COVID-19). Patients and Methods: A retrospective cohort of 479 hospitalized patients diagnosed with COVID-19 in Hunan Province was selected. The prognostic effects of factors such as age and laboratory indicators were analyzed using the Kaplan-Meier method and Cox proportional hazards model. A prognostic nomogram model was established to predict the progression of patients with COVID-19. Results: A total of 524 patients in Hunan province with COVID-19 from December 2019 to October 2020 were retrospectively recruited. Among them, 479 eligible patients were randomly assigned into the training cohort (n = 383) and validation cohort (n = 96), at a ratio of 8:2. Sixty-eight (17.8%) and 15 (15.6%) patients developed severe COVID-19 after admission in the training cohort and validation cohort, respectively. The differences in baseline characteristics were not statistically significant between the two cohorts with regard to age, sex, and comorbidities (P > 0.05). Multivariable analyses included age, C-reactive protein, fibrinogen, lactic dehydrogenase, neutrophil-to-lymphocyte ratio, urea, albumin-to-globulin ratio, and eosinophil count as predictive factors for patients with progression to severe COVID-19. A nomogram was constructed with sufficient discriminatory power (C index = 0.81), and proper consistency between the prediction and observation, with an area under the ROC curve of 0.81 and 0.86 in the training and validation cohort, respectively. Conclusion: We proposed a simple nomogram for early detection of patients with non-severe COVID-19 but at high risk of progression to severe COVID-19, which could help optimize clinical care and personalized decision-making therapies.

Immunological evaluation of Vibrio alginolyticus, Vibrio harveyi, Vibrio vulnificus and infectious spleen and kidney necrosis virus (ISKNV) combined-vaccine efficacy in Epinephelus coioides.

Combined vaccines are immunological products intended for immunization against multifactorial infectious diseases caused by different types or variants of pathogens. In this study, the effectiveness of Vibrio alginolyticus (Va), Vibrio harveyi (Vh), Vibrio vulnificus (Vv) and infectious spleen and kidney necrosis virus (ISKNV), an iridovirus, combined-vaccine (Vibrio and ISKNV combined vaccines, VICV), Va+Vh+Vv inactive vaccine (VIV) and ISKNV whole cell inactive vaccine (IWCIV) in Epinephelus coioides were evaluated using various immunological parameters including antibody titer, serum lysozyme activity (LA), respiratory burst (RB) activity, bactericidal activity (BA) and relative percentage survival (RPS). E. coioides immunized with VICV and challenged with Va+Vh+Vv+ISKNV had an RPS of 80%. The RPS was 73.3% in E. coioides immunized with VIV and challenged with Va+Vh+Vv. E. coioides immunized with IWCIV and challenged with ISKNV had an RPS of 69.6%. Serum LA in the vaccinated group was significantly higher than the control group on days 21 and 28 post-vaccination (P<0.01). The RB activity of head kidney cells in the vaccinated group was significantly higher (P<0.01) compared to that in the control group. However, RB activity of spleen cells in the vaccinated group and the control group were not significantly different (P>0.05). After immunization with VICV, BA values of blood leucocytes and head kidney cells increased significantly more than spleen cells. BA value of blood leucocytes was higher than that in head kidney cells. There were distinct difference between BA values in head kidney cells and in spleen cells (P<0.05) as well as between BA value of blood leucocytes and head kidney cells (P<0.01). E. coioides vaccinated with VICV have significantly higher antibody levels than control groupers (P<0.01). Our study suggests that the VICV candidate can effectively protect groupers against multiple bacterial and viral pathogens.