Association of small, dense LDL-cholesterol concentration and lipoprotein particle characteristics with coronary heart disease: A systematic review and meta-analysis.

OBJECTIVES: The aim of this study was to systematically collate and appraise the available evidence regarding the associations between small, dense low-density lipoprotein (sdLDL) and incident coronary heart disease (CHD), focusing on cholesterol concentration (sdLDL-C) and sdLDL particle characteristics (presence, density, and size). BACKGROUND: Coronary heart disease (CHD) is the leading cause of death worldwide. Small, dense low-density lipoprotein (sdLDL) has been hypothesized to induce atherosclerosis and subsequent coronary heart disease (CHD). However, the etiological relevance of lipoprotein particle size (sdLDL) versus cholesterol content (sdLDL-C) remains unclear. METHODS: PubMed, MEDLINE, Web of Science, and EMBASE were systematically searched for studies published before February 2020. CHD associations were based on quartile comparisons in eight studies of sdLDL-C and were based on binary categorization in fourteen studies of sdLDL particle size. Reported hazards ratios (HR) and odds ratios (OR) with 95% confidence interval (CI) were standardized and pooled using a random-effects meta-analysis model. RESULTS: Data were collated from 21 studies with a total of 30,628 subjects and 5,693 incident CHD events. The average age was 67 years, and 53% were men. Higher sdLDL and sdLDL-C levels were both significantly associated with higher risk of CHD. The pooled estimate for the high vs. low categorization of sdLDL was 1.36 (95% CI: 1.21, 1.52) and 1.07 (95% CI: 1.01, 1.12) for comparing the top quartiles versus the bottom of sdLDL-C. Several studies suggested a dose response relationship. CONCLUSIONS: The findings show a positive association between sdLDL or sdLDL-C levels and CHD, which is supported by an increasing body of genetic evidence in favor of its causality as an etiological risk factor. Thus, the results support sdLDL and sdLDL-C as a risk marker, but further research is required to establish sdLDL or sdLDL-C as a potential therapeutic marker for incident CHD risk reduction.

Blood routine test in mild and common 2019 coronavirus (COVID-19) patients.

The new 2019 coronavirus disease (COVID-19), according to the World Health Organization (WHO), has been characterized as a pandemic. As more is being discovered about this virus, we aim to report findings of the complete blood count (CBC) of COVID-19 patients. This would serve in providing physicians with important knowledge on the changes that can be expected from the CBC of mild and normal COVID-19 patients. A total of 208 mild and common patients were admitted at the Dongnan Hospital located in the city of Xiaogan, Hubei, China. The CBCs of these patients, following a confirmed diagnosis of COVID-19, were retrospectively analyzed and a significant P<0.05 was found after a full statistical analysis was conducted using the Statistical Package for the Social Sciences (IBM SPSS). CBC analysis revealed changes in the levels of red blood cells (RBCs), hemoglobin (HGB), hematocrit (HCT), mean corpuscular volume (MCV), and C-reactive protein (CRP). Clinicians should expect similar findings when dealing with the new COVID-19.

Clinical features in 52 patients with COVID-19 who have increased leukocyte count: a retrospective analysis.

Recent reports have showed that a proportion of patients with Coronavirus Disease 2019 (COVID-19) presented elevated leukocyte count. Clinical data about these patients is scarce. We aimed to evaluate the clinical findings of patients with COVID-19 who have increased leukocyte at admission. We retrospectively collected the clinical data on the 52 patients who have increased leukocyte count at admission from the 619 patients with confirmed COVID-19 who had pneumonia with abnormal features on chest CT scan in Renmin Hospital of Wuhan University in Wuhan, China, from February 3 to March 3, 2020. The mean age of the 52 patients with increased leukocyte count was 64.7 (SD 11.4) years, 32 (61.5%) were men and 47 (90.4%) had fever. Compared with the patients with non-increased leukocyte count, the patients with increased leukocyte count were significantly older (P < 0.01), were more likely to have underlying chronic diseases (P < 0.01), more likely to develop critically illness (P < 0.01), more likely to admit to an ICU (P < 0.01), more likely to receive mechanical ventilation (P < 0.01), had higher rate of death (P < 0.01) and the blood levels of neutrophil count and the serum concentrations of CRP and IL-6 were significantly increased, (P < 0.01). The older patients with COVID-19 who had underlying chronic disorders are more likely to develop leukocytosis. These patients are more likely to develop critical illness, with a high admission to an ICU and a high mortality rate.

Combination of four clinical indicators predicts the severe/critical symptom of patients infected COVID-19.

BACKGROUND: Despite the death rate of COVID-19 is less than 3%, the fatality rate of severe/critical cases is high, according to World Health Organization (WHO). Thus, screening the severe/critical cases before symptom occurs effectively saves medical resources. METHODS AND MATERIALS: In this study, all 336 cases of patients infected COVID-19 in Shanghai to March 12th, were retrospectively enrolled, and divided in to training and test datasets. In addition, 220 clinical and laboratory observations/records were also collected. Clinical indicators were associated with severe/critical symptoms were identified and a model for severe/critical symptom prediction was developed. RESULTS: Totally, 36 clinical indicators significantly associated with severe/critical symptom were identified. The clinical indicators are mainly thyroxine, immune related cells and products. Support Vector Machine (SVM) and optimized combination of age, GSH, CD3 ratio and total protein has a good performance in discriminating the mild and severe/critical cases. The area under receiving operating curve (AUROC) reached 0.9996 and 0.9757 in the training and testing dataset, respectively. When the using cut-off value as 0.0667, the recall rate was 93.33 % and 100 % in the training and testing datasets, separately. Cox multivariate regression and survival analyses revealed that the model significantly discriminated the severe/critical cases and used the information of the selected clinical indicators. CONCLUSION: The model was robust and effective in predicting the severe/critical COVID cases.