Parameters and Morphological Changes of Erythrocytes and Platelets of COVID-19 Subjects: A Longitudinal Cohort Study.

Purpose: Information about dynamic changes occurring in the parameters and morphology of erythrocytes and platelets during the coronavirus disease 2019 (COVID-19) infection and convalescence is scarce. To explore potential associations between dynamic erythrocyte and platelet parameters, morphological changes, and the course or severity of the disease is essential. Patients and Methods: From January 17th, 2020, to February 20th, 2022, we followed up on 35 patients with non-severe and 11 patients with severe COVID-19 following their discharge. We collected clinical features, dynamic complete blood count (CBC), and peripheral blood smears (PBS) and analyzed parameter and morphological changes of erythrocytes and platelets depending on the course or severity of the disease. The course of the disease included four periods, namely onset (T1), discharge (T2), 1-year follow-up (T3), and 2-year follow-up (T4). Results: Red blood cell (RBC) counts and hemoglobin were the lowest in T2, followed by T1, and lower in T1 and T2 than in T3 and T4. Inversely, the red blood cell distribution width (RDW) was the highest in T2, followed by T1, and higher than in T3 and T4. Compared to non-severe patients, the platelet of severe patients was lower in T1 and T2. In contrast, the mean platelet volume (MPV) and platelet distribution width (PDW) tended to be higher in severe patients. Similarly, anisocytosis was more common in peripheral blood smears at early stages and in severe patients. Finally, large platelets were more common in severe patients. Conclusion: Anisocytosis of erythrocytes and large platelets are found in patients with severe COVID-19, these changes may help primary hospitals to identify patients with a high risk of severe COVID-19 at an early stage.

Xuanfei Baidu Formula attenuates LPS-induced acute lung injury by inhibiting the NF-κB signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Acute lung injury (ALI) is a common manifestation of COVID-19. Xuanfei Baidu Formula(XFBD) is used in China to treat mild or common damp-toxin obstructive pulmonary syndrome in COVID-19 patients. However, the active ingredients of XFBD have not been extensively studied, and its mechanism of action in the treatment of ALI is not well understood. AIM OF THE STUDY: The purpose of this study was to investigate the mechanism of action of XFBD in treating ALI in rats, by evaluating its active components. MATERIALS AND METHODS: Firstly, the chemical composition of XFBD was identified using ultra-high performance liquid chromatography with quadrupole time-of-flight mass spectrometry. The potential targets of XFBD for ALI treatment were predicted using network pharmacological analysis. Finally, the molecular mechanism of XFBD was validated using a RAW264.7 cell inflammation model and a mouse ALI model. RESULTS: A total of 113 compounds were identified in XFBD. Network pharmacology revealed 34 hub targets between the 113 compounds and ALI. The results of Kyoto Encyclopedia of Genes and Genomes and gene ontology analyses indicated that the NF-κB signaling pathway was the main pathway for XFBD in the treatment of ALI. We found that XFBD reduced proinflammatory factor levels in LPS-induced cellular models. By examining the lung wet/dry weight ratio and pathological sections in vivo, XFBD was found that XFBD could alleviate ALI. Immunohistochemistry results showed that XFBD inhibited ALI-induced increases in p-IKK, p-NF-κB p65, and iNOS proteins. In vitro experiments demonstrated that XFBD inhibited LPS-induced activation of the NF-κB pathway. CONCLUSION: This study identified the potential practical components of XFBD, combined with network pharmacology and experimental validation to demonstrate that XFBD can alleviate lung injury caused by ALI by inhibiting the NF-κB signaling pathway.

Comparative pharmacokinetics of 24 major bioactive components in normal and ARDS rats after oral administration of Xuanfei Baidu granules.

ETHNOPHARMACOLOGICAL RELEVANCE: Xuanfei Baidu prescription, consisting of 13 Chinese medicines, was formulated by academicians Boli Zhang and Professor Qingquan Liu based on their experience in first-line clinical treatment of COVID-19. Xuanfei Baidu granules (XFBD granules) are a proprietary Chinese medicine preparation developed based on Xuanfei Baidu prescription. It is recommended for the treatment of patients with the common wet toxin and lung stagnation syndrome of COVID-19. However, the pharmacokinetic characteristics of its major bioactive components in rats under different physiological and pathological conditions are unclear. MATERIALS AND METHODS: A rapid and sensitive analytical method, ultra-performance liquid chromatography coupled with mass spectrometry (UPLC-MS/MS), was developed and applied to 24 major bioactive components in normal and ARDS rats after oral administration of XFBD granules. We studied the metabolic process of XFBD granules in vivo to compare the differences in pharmacokinetic parameters between normal and model metabolic processes. RESULTS: This method was successfully applied to the pharmacokinetic investigation of 24 major components of XFBD granules following oral administration in normal and ARDS rats. Eight components, including ephedrine and amygdalin, were more highly absorbed and had shorter Tmax values than the model group; the absorption of six components, such as rhein, decreased in ARDS rats, and there was no significant difference in the absorption of ten components, such as verbenalin and naringin, between the normal and ARDS rats. The results showed that the peak times of other analytes were very short, and 80% of these target constituents were eliminated in both normal and ARDS rats within 6 h except for liquiritigenin and 18ß-glycyrrhetinic acid. CONCLUSIONS: In this study, a rapid and sensitive UPLC-MS/MS analytical method was developed and applied to 24 major bioactive components in normal and ARDS rats after the oral administration of XFBD granules. This will serve to form the basis for further studies on the pharmacokinetic-pharmacodynamic correlation of XFBD granules.

The hemocyte counts as a potential biomarker for predicting disease progression in COVID-19: a retrospective study.

Objectives In December 2019, there was an outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, China, and since then, the disease has been increasingly spread throughout the world. Unfortunately, the information about early prediction factors for disease progression is relatively limited. Therefore, there is an urgent need to investigate the risk factors of developing severe disease. The objective of the study was to reveal the risk factors of developing severe disease by comparing the differences in the hemocyte count and dynamic profiles in patients with severe and non-severe COVID-19. Methods In this retrospectively analyzed cohort, 141 confirmed COVID-19 patients were enrolled in Taizhou Public Health Medical Center, Taizhou Hospital, Zhejiang Province, China, from January 17, 2020 to February 26, 2020. Clinical characteristics and hemocyte counts of severe and non-severe COVID patients were collected. The differences in the hemocyte counts and dynamic profiles in patients with severe and non-severe COVID-19 were compared. Multivariate Cox regression analysis was performed to identify potential biomarkers for predicting disease progression. A concordance index (C-index), calibration curve, decision curve and the clinical impact curve were calculated to assess the predictive accuracy. Results The data showed that the white blood cell count, neutrophil count and platelet count were normal on the day of hospital admission in most COVID-19 patients (87.9%, 85.1% and 88.7%, respectively). A total of 82.8% of severe patients had lymphopenia after the onset of symptoms, and as the disease progressed, there was marked lymphopenia. Multivariate Cox analysis showed that the neutrophil count (hazard ratio [HR] = 4.441, 95% CI = 1.954-10.090, p = 0.000), lymphocyte count (HR = 0.255, 95% CI = 0.097-0.669, p = 0.006) and platelet count (HR = 0.244, 95% CI = 0.111-0.537, p = 0.000) were independent risk factors for disease progression. The C-index (0.821 [95% CI, 0.746-0.896]), calibration curve, decision curve and the clinical impact curve showed that the nomogram can be used to predict the disease progression in COVID-19 patients accurately. In addition, the data involving the neutrophil count, lymphocyte count and platelet count (NLP score) have something to do with improving risk stratification and management of COVID-19 patients. Conclusions We designed a clinically predictive tool which is easy to use for assessing the progression risk of COVID-19, and the NLP score could be used to facilitate patient stratification management.