Laboratory Testing Implications of Risk-Stratification and Management of COVID-19 Patients.

Objective: To distinguish COVID-19 patients and non-COVID-19 viral pneumonia patients and classify COVID-19 patients into low-risk and high-risk at admission by laboratory indicators. Materials and methods: In this retrospective cohort, a total of 3,563 COVID-19 patients and 118 non-COVID-19 pneumonia patients were included. There are two cohorts of COVID-19 patients, including 548 patients in the training dataset, and 3,015 patients in the testing dataset. Laboratory indicators were measured during hospitalization for all patients. Based on laboratory indicators, we used the support vector machine and joint random sampling to risk stratification for COVID-19 patients at admission. Based on laboratory indicators detected within the 1st week after admission, we used logistic regression and joint random sampling to develop the survival mode. The laboratory indicators of COVID-10 and non-COVID-19 were also compared. Results: We first identified the significant laboratory indicators related to the severity of COVID-19 in the training dataset. Neutrophils percentage, lymphocytes percentage, creatinine, and blood urea nitrogen with AUC >0.7 were included in the model. These indicators were further used to build a support vector machine model to classify patients into low-risk and high-risk at admission in the testing dataset. Results showed that this model could stratify the patients in the testing dataset effectively (AUC = 0.89). Our model still has good performance at different times (Mean AUC: 0.71, 0.72, 0.72, respectively for 3, 5, and 7 days after admission). Moreover, laboratory indicators detected within the 1st week after admission were able to estimate the probability of death (AUC = 0.95). We identified six indicators with permutation p < 0.05, including eosinophil percentage (p = 0.007), white blood cell count (p = 0.045), albumin (p = 0.041), aspartate transaminase (p = 0.043), lactate dehydrogenase (p = 0.002), and hemoglobin (p = 0.031). We could diagnose COVID-19 and differentiate it from other kinds of viral pneumonia based on these laboratory indicators. Conclusions: Our risk-stratification model based on laboratory indicators could help to diagnose, monitor, and predict severity at an early stage of COVID-19. In addition, laboratory findings could be used to distinguish COVID-19 and non-COVID-19.

The association of retinoic acid receptor beta2(RARß2) methylation status and prostate cancer risk: a systematic review and meta-analysis.

The retinoic acid receptor beta2(RARß2) is a type of nuclear receptor that is activated by both all-trans retinoic acid and 9-cis retinoic acid, which has been shown to function as a tumor suppressor gene in different types of human tumors. Previous reports demonstrated that the frequency of RARß2 methylation was significantly higher in prostate cancer patients compared with controls, but the relationship between RARß2 promoter methylation and pathological stage or Gleason score of prostate cancer remained controversial. Therefore, a meta-analysis of published studies investigating the effects of RARß2 methylation status in prostate cancer occurrence and association with both pathological stage and Gleason score in prostate cancer was performed in the study. A total of 12 eligible studies involving 777 cases and 404 controls were included in the pooled analyses. Under the random-effects model, the pooled OR of RARß2 methylation in prostate cancer patients, compared to non-cancer controls, was 17.62 with 95%CI = 6.30-49.28. The pooled OR with the fixed-effects model of pathological stage in RASSF1A methylated patients, compared to unmethylated patients, was 0.67 (95%CI = 0.40-1.09) and the pooled OR of low-GS in RARß2 methylated patients by the random-effect model, compared to high-GS RARß2 methylated patients, was 0.54 (95%CI = 0.28-1.04). This study showed that RARß2 might be a potential biomarker in prostate cancer prevention and diagnosis. The detection of RARß2 methylation in urine or serum is a potential non-invasive diagnostic tool in prostate cancer. The present findings also require confirmation through adequately designed prospective studies.

The association of RAS association domain family Protein1A (RASSF1A) methylation states and bladder cancer risk: a systematic review and meta-analysis.

RAS association domain family protein 1a (RASSF1A) is a putative tumor suppressor gene located on 3p21, has been regarded playing important roles in the regulation of different types of human tumors. Previous reports demonstrated that the frequency of RASSF1A methylation was significantly higher in patients group compared with controls, but the relationship between RASSF1A promoter methylation and pathological features or the tumor grade of bladder cancer remains controversial. Therefore, A meta-analysis of published studies investigating the effects of RASSF1A methylation status in bladder cancer occurrence and association with both pTNM (p, pathologic stage; T, tumor size; N, node status; M, metastatic status) and tumor grade in bladder cancer was performed in the study. A total of 10 eligible studies involving 543 cases and 217 controls were included in the pooled analyses. Under the fixed-effects model, the OR of RASSF1A methylation in bladder cancer patients, compared to non-cancer controls, was 8. 40 with 95%CI=4. 96-14. 23. The pooled OR with the random-effects model of pTNM and tumor grade in RASSF1A methylated patients, compared to unmethylated patients, was 0. 75 (95%CI=0. 28-1. 99) and 0. 39 (95%CI=0. 14-1. 09). This study showed that RASSF1A methylation appears to be an independent prognostic factor for bladder cancer. The present findings also require confirmation through adequately designed prospective studies.