Effect of female coronavirus disease 2019 vaccination on assisted reproductive outcomes: a systematic review and meta-analysis.

IMPORTANCE: The effect of coronavirus disease 2019 (COVID-19) vaccination on fertility warrants clarification in women undergoing assisted reproductive treatment. OBJECTIVE: To study the association between female COVID-19 vaccination and outcomes of assisted reproductive treatment. DATA SOURCES: PubMed, Embase, the Web of Science, Cochrane Library, and medRxiv and bioRxiv were searched for eligible studies from December 1, 2019, to November 30, 2022, with no language restrictions. STUDY SELECTION AND SYNTHESIS: Observational studies comparing assisted reproductive outcomes between women with and without COVID-19 vaccination were included. The pooled estimates were calculated using the random-effects models as mean differences (MDs), standardized MDs, or odds ratios with 95% confidence intervals (CIs). Heterogeneity was evaluated using the I2 statistic. MAIN OUTCOMES: The number of oocytes retrieved and clinical pregnancy rate. RESULTS: Twenty-one cohort studies involving a total of 19,687 treatment cycles were included. In a comparison of the vaccinated vs. unvaccinated groups, the pooled MD for oocyte number was -0.06 (95% CI, -0.51 to 0.39; I2 = 0), and the pooled odds ratio for clinical pregnancy was 0.95 (95% CI, 0.85-1.05; I2 = 0). Similarly, there were no statistically significant adverse effects identified in other outcomes determined a priori, including 4 cycle characteristics, 6 laboratory parameters, and 3 pregnancy indicators. Most results were consistently unchanged in subgroup and sensitivity analyses, with no evidence of publication bias according to Egger's test. CONCLUSION AND RELEVANCE: Our work did not find significant differences in assisted reproductive outcomes between vaccinated and unvaccinated women. However, more data are warranted to confirm the safety of COVID-19 vaccination for assisted reproductive treatment and in female fertility in general.

Enhanced Interfacial H-Bond Networks Promote Glycan-Glycan Recognition and Interaction.

H-bond networks at heterogeneous interfaces play crucial roles in bioseparation, biocatalysis, biochip array profiling, and functional nanosystem self-assembly, but their precise modulation and enhancement remain challenging. In this study, we have discovered that interfacial hydrophobic hydration significantly enhances H-bond networks at the interface between a glycan-modified adsorbent and a methanol-water-acetonitrile ternary solution. The enhanced H-bond networks greatly promote the adsorbent-solution heterogeneous glycan-glycan recognition and interaction. This novel hydrophobic hydration-enhanced hydrophilic interaction (HEHI) strategy improves the affinity and efficiency of intact glycopeptide enrichment. Compared with the commonly used hydrophilic-interaction enrichment strategy, 23.5 and 48.5% more intact N- and O-glycopeptides are identified, and the enrichment recoveries of half of the glycopeptides are increased >100%. Further, in-depth profiling of both N- and O-glycosylation occurring on SARS-CoV-2 S1 and hACE2 proteins has been achieved with more glycan types and novel O-glycosylation information involved. Interfacial hydrophobic hydration provides a powerful tool for the modulation of hydrophilic interactions in biological systems.

Effect of COVID-19 vaccination on semen parameters: A systematic review and meta-analysis.

The aim of this study was to investigate the effect of coronavirus disease 2019 (COVID-19) vaccination on semen parameters through systematic review and meta-analysis. PubMed, EMBASE, Web of Science, and Cochrane Library were comprehensively searched by June 2022. Studies were considered eligible if they compared semen parameters before and after COVID-19 vaccination or between vaccinated and unvaccinated men, with no restrictions on vaccine types or doses. The effect size was calculated as mean difference (MD) with 95% confidence interval (CI) using a random-effects model. Subgroup and sensitivity analyses were conducted to assess the sources of heterogeneity measured by the I2 statistic, with publication bias evaluated by Egger's test. Twelve cohort studies involving 914 participants fulfilled the inclusion criteria. In a comparison of vaccinated versus unvaccinated group, the pooled data revealed no significant differences in semen volume (MD = 0.18 ml, 95% CI -0.02 to 0.38), sperm concentration (MD = 1.16 million/ml, 95% CI -1.34 to 3.66), total sperm motility (MD = -0.14%, 95% CI -2.84 to 2.56), progressive sperm motility (MD = -1.06%, 95% CI -2.88 to 0.77), total sperm count (MD = 5.92 million, 95% CI -10.22 to 22.05), total motile sperm count (MD = 2.18 million, 95% CI -1.28 to 5.63), total progressively motile sperm count (MD = -3.87 million, 95% CI -13.16 to 5.43), and sperm morphology (MD = 0.07%, 95% CI -0.84 to 0.97). The results also remained similar across messenger ribonucleic acid, viral-vector, and inactivated COVID-19 vaccines. Sensitivity analysis identified two individual studies that contributed to heterogeneity, while the effect size was not materially altered. No obvious publication bias was detected among included studies. Our finding suggested that COVID-19 vaccination had no detrimental impact on semen quality, which could be potentially helpful to reduce male vaccine hesitancy and increase vaccination coverage.

Effect of inactivated COVID-19 vaccination on pregnancy outcomes following frozen-thawed embryo transfer: A retrospective cohort study.

OBJECTIVE: To investigate the effect of inactivated coronavirus disease 2019 (COVID-19) vaccination on frozen-thawed embryo transfer (FET) outcomes. METHODS: This retrospective cohort study enrolled 1,210 patients undergoing FET cycles in a single university-affiliated hospital between July 1, 2021, and May 1, 2022. Of them, 387 women with two full doses of inactivated SARS-CoV-2 vaccines (CoronaVac or BBIBP-CorV) after oocyte retrieval were assigned to the vaccinated group, while 823 were unvaccinated as controls. Propensity score matching and multiple regression analysis were applied to control for baseline and cycle characteristics (19 covariates in total). RESULTS: There were 265 patients in each group after matching. The rates of clinical pregnancy (58.5% vs. 60.8%; P = 0.595) and live birth (44.4% vs. 48.8%; P = 0.693) were similar between vaccinated and unvaccinated patients, with adjusted odds ratios of 0.89 (95% confidence interval [CI] 0.61-1.29) and 1.31 (95% CI 0.37-4.56), respectively. Consistently, no significant differences were found in serum human chorionic gonadotropin levels as well as biochemical pregnancy, biochemical pregnancy loss, and embryo implantation rates. Based on the time interval from vaccination to FET, vaccinated patients were further subdivided into two categories of ≤2 months and >2 months, and the outcomes remained comparable. CONCLUSION: Our study showed that inactivated COVID-19 vaccination in women did not have measurable detrimental impact on implantation performance and live birth outcome during FET treatment cycles. This finding denies the impairment of endometrial receptivity and trophoblast function by vaccine-induced antibodies at the clinical level.

Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study.

OBJECTIVE: The COVID-19 outbreak was first reported in Wuhan, China, and has been acknowledged as a pandemic due to its rapid spread worldwide. Predicting the trend of COVID-19 is of great significance for its prevention. A comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more accurate for anticipating the occurrence of COVID-19 in the USA. DESIGN: Time-series study. SETTING: The USA was the setting for this study. MAIN OUTCOME MEASURES: Three accuracy metrics, mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE), were applied to evaluate the performance of the two models. RESULTS: In our study, for the training set and the validation set, the MAE, RMSE and MAPE of the XGBoost model were less than those of the ARIMA model. CONCLUSIONS: The XGBoost model can help improve prediction of COVID-19 cases in the USA over the ARIMA model.

Investment in energy resources, natural resources and environment: Evidence from China

Energy resource investment has been critical for a sustainable economy. Its significance, however, intensified, notably post COVID-19. The function of energy efficiency in ensuring a sustainable environment has been undervalued. However, unlike previous studies, this study aims to analyze the role of energy efficiency, industrial production and investment in energy on carbon emissions for China employing data from 1990 to 2020. The study uses updated time series econometric methods such as Narayan and Pop unit root test, Bayer-Hanck cointegration method, fully modified, dynamic and canonical cointegration analysis. The results confirmed cointegrating relationship among variables via Bayer-Hanck cointegration approach. The study also revealed that energy efficiency and carbon emissions had an inverse connection. Meanwhile, China's industrial production, energy investment, and gross domestic product have all been found to increase emissions. Consequently, natural resource rents harm the ecosystem. Based on the empirical findings, this study recommends the adoption of energy-efficient technologies and the use of energy from efficient sources in order to assist China to lessen environmental deterioration.

Rare Variants in Inborn Errors of Immunity Genes Associated with Covid-19 Severity (preprint)

Covid-19 is a contagious disease caused by SARS-CoV-2, a novel severe acute respiratory syndrome coronavirus. Common variants and networks underlying host genetic mechanisms have been extensively studied to identify disease-associated genetic factors. However, there are few studies about the rare variants, typically inborn errors of immunity, in understanding the host genetics behind Covid-19 infection, especially in the Chinese population. To fill this gap, we investigate likely-deleterious missense and high-confidence predicted loss-of-function variants by (a) performing gene- and pathway-level association analyses, (b) examining known genes involved in type I interferon signaling and others previously reported in Covid-19 disease, and (c) identifying candidate genes with accumulating mutations and their potential protein-protein interactions with known genes. Based on our analyses, several putative genes and pathways are uncovered and worth further investigation, for example, genes IL12RB1, TBK1, and TLR3, and pathways Tuberculosis (hsa:05152), Primary Immunodeficiency (hsa:05340), and Influenza A (hsa:05164). These regions generally play an essential role in regulating antiviral innate immunity responses to foreign pathogens and in responding to many inflammatory diseases. We believe that to some extent, as an acute inflammatory disease, Covid-19 is also affected by these inborn errors of immunity. We hope that the identification of these rare genetic factors will provide new insights into the genetic architecture of Covid-19.

The correlation between temperature and the incidence of COVID-19 in four first-tier cities of China: a time series study.

The COVID-19 outbreak emerged in Wuhan, China, and was declared a global pandemic in March 2020. This study aimed to explore the association of daily mean temperature with the daily counts of COVID-19 cases in Beijing, Shanghai, Guangzhou, and Shenzhen, China. Data on daily confirmed cases of COVID-19 and daily mean temperatures were retrieved from the 4 first-tier cities in China. Distributed lag nonlinear models (DLNMs) were used to assess the association between daily mean temperature and the daily cases of COVID-19 during the study period. After controlling for the imported risk index and long-term trends, the distributed lag nonlinear model showed that there were nonlinear and lag relationships. The daily cumulative relative risk decreased for every 1.0 °C change in temperature in Shanghai, Guangzhou, and Shenzhen. However, the cumulative relative risk increased with a daily mean temperature below - 3 °C in Beijing and then decreased. Moreover, the delayed effects of lower temperatures mostly occurred within 6-7 days of exposure. There was a negative correlation between the cumulative relative risk of COVID-19 incidence and temperature, especially when the temperature was higher than - 3 °C. The conclusions from this paper will help government and health regulators in these cities take prevention and protection measures to address the COVID-19 crisis and the possible collapse of the health system in the future.

Spatiotemporal cluster analysis of COVID-19 and its relationship with environmental factors at the city level in mainland China.

This study sought to identify the spatial, temporal, and spatiotemporal clusters of COVID-19 cases in 366 cities in mainland China with the highest risks and to explore the possible influencing factors of imported risks and environmental factors on the spatiotemporal aggregation, which would be useful to the design and implementation of critical preventative measures. The retrospective analysis of temporal, spatial, and spatiotemporal clustering of COVID-19 during the period (January 15 to February 25, 2020) was based on Kulldorff's time-space scanning statistics using the discrete Poisson probability model, and then the logistic regression model was used to evaluate the impact of imported risk and environmental factors on spatiotemporal aggregation. We found that the spatial distribution of COVID-19 cases was nonrandom; the Moran's I value ranged from 0.017 to 0.453 (P < 0.001). One most likely cluster and three secondary likely clusters were discovered in spatial cluster analysis. The period from February 2 to February 9, 2020, was identified as the most likely cluster in the temporal cluster analysis. One most likely cluster and seven secondary likely clusters were discovered in spatiotemporal cluster analysis. Imported risk, humidity, and inhalable particulate matter PM2.5 had a significant impact on temporal and spatial accumulation, and temperature and PM10 had a low correlation with the spatiotemporal aggregation of COVID-19. The information is useful for health departments to develop a better prevention strategy and potentially increase the effectiveness of public health interventions.

Plasma cell-free DNA promise disease monitoring and tissue injury assessment of COVID-19 (preprint)

COVID-19 is a huge threat to global health. Due to the lack of definitive etiological therapeutics currently, effective disease monitoring is of high clinical value for better healthcare and management of the large number of COVID-19 patients. In this study, we recruited 37 COVID-19 patients, collected 176 blood samples upon diagnosis and during treatment, and analyzed cell-free DNA (cfDNA) in these samples. We report gross abnormalities in cfDNA of COVID-19 patients, including elevated GC content, altered molecule size and end motif patterns. More importantly, such cfDNA characteristics reflect patient-specific physiological conditions during treatment. Further analysis on tissue origin tracing of cfDNA reveals frequent tissue injuries in COVID-19 patients, which is supported by clinical diagnoses. Hence, we demonstrate the translational merit of cfDNA as valuable analyte for effective disease monitoring, as well as tissue injury assessment in COVID-19 patients.

The Association of Suicidal Ideation With Family Characteristics and Social Support of the First Batch of Students Returning to a College During the COVID-19 Epidemic Period: A Cross Sectional Study in China.

Objective: To investigate the prevalence of suicidal ideation among the first batch of students returning to a college during the COVID-19 epidemic, and to explore the correlation of suicidal ideation with family characteristics and social support. Methods: A cluster sampling survey with a self-designed questionnaire was conducted among the first batch of students returning to a college in Wuhu, China. The Positive and Negative Suicidal ideation (PANSI) and Social Support Scale (SSRS) were used to define students' suicidal ideation and social support, respectively. The influence of family characteristics and social support on the students' suicidal ideation was investigated using multivariate unconditional logistic regression analysis. Results: Two thousand seven hundred valid questionnaires were collected, including 673 males (24.9%) and 2,027 females (75.1%), in this study. A total of 146 students (5.4%) showed suicidal ideation. Male respondents reported higher rates (7.9%) than females (4.6%). Multivariate logistic regression analysis showed that a higher risk level of residence before returning to school and lower objective support were the risk factors for suicidal ideation in males. In contrast, a higher level of maternal education, a poorer relationship with the mother, and lower scores for subjective support and support availability had significant effects on females' suicidal ideation. Limitations: This is a cross-sectional study, and lacks comparison to the time point unaffected by COVID-19. Moreover, it was limited by COVID-19 epidemic prevention and control restrictions, and the differences in returning to school in different regions. Only one college was investigated in this study, and all of the respondents were sophomores, so there may be some limitations in the representativeness of the sample and extrapolation of the results. Conclusion: Family characteristics and social support have had an important influence on suicidal ideation among students returning to school during the COVID-19 epidemic. Some gender differences were identified. Targeted interventions are needed for early prevention and control.

A Chinese host genetic study discovered type I interferons and causality of cholesterol levels and WBC counts on COVID-19 severity (preprint)

As of early May 2021, the ongoing pandemic COVID-19 has caused over 160 million of infections and over 3 million deaths worldwide. Many risk factors, such as age, gender, and comorbidities, have been studied to explain the variable symptoms of infected patients. However, these effects may not fully account for the diversity in disease severity. Here, we present a comprehensive analysis of a broad range of patients laboratory and clinical assessments to investigate the genetic contributions to COVID-19 severity. By performing GWAS analysis, we discovered several concrete associations for laboratory features. Based on these findings, we performed Mendelian randomization (MR) analysis to investigate the causality of laboratory traits on disease severity. From the MR study, we identified two causal traits, cholesterol levels and WBC counts. The functional gene related to cholesterol levels is ApoE and people with particular ApoE genotype are more likely to have higher cholesterol levels, facilitating the process that SARS-CoV-2 binds on its receptor ACE2 and aggravating COVID-19 disease. The functional gene related to WBC counts is MHC system that plays a central role in the immune system. The host immune response to the SARS-CoV-2 infection greatly affects the patients severity status and clinical outcome. Additionally, our gene-based and GSEA analysis revealed interferon pathways, including type I interferon receptor binding, regulation of IFNA signaling, and SARS coronavirus and innate immunity. We hope that our work will make a contribution in studying the genetic mechanisms of disease illness and serve as useful reference for the clinical diagnosis and treatment of COVID-19.

Efficacy and safety of tocilizumab in COVID-19 patients.

In this retrospective study we assessed the efficacy and safety of tocilizumab in patients with critical or severe coronavirus disease 2019 (COVID-19). We enrolled 181 patients admitted to Huoshenshan Hospital (Wuhan, China) with confirmed COVID-19 between January 2020 and February 2020. Ninety-two patients were treated with tocilizumab, and 89 patients were treated conventionally. We analyzed the clinical manifestations, changes in CT scan images, and laboratory tests before and after tocilizumab treatment, and compared these results with the conventionally treated group. A significant reduction in the level of C-reactive protein was observed 1 week after tocilizumab administration. In some cases this meant the end of the IL-6-related cytokine storm. In addition, tocilizumab relieved fever, cough, and shortness of breath with no reported adverse drug reactions. These findings suggest tocilizumab improves clinical outcomes and is effective for treatment of patients with critical or severe COVID-19. However, future clinical trials are needed to better understand the impact of tocilizumab interference with IL-6 and provide a therapeutic strategy for treatment of COVID-19.

Chest CT Findings in Patients With Coronavirus Disease 2019 and Its Relationship With Clinical Features.

OBJECTIVES: The aim of this study was to investigate the chest computed tomography (CT) findings in patients with confirmed coronavirus disease 2019 (COVID-19) and to evaluate its relationship with clinical features. MATERIALS AND METHODS: Study sample consisted of 80 patients diagnosed as COVID-19 from January to February 2020. The chest CT images and clinical data were reviewed, and the relationship between them was analyzed. RESULTS: Totally, 80 patients diagnosed with COVID-19 were included. With regards to the clinical manifestations, 58 (73%) of the 80 patients had cough, and 61 (76%) of the 80 patients had high temperature levels. The most frequent CT abnormalities observed were ground glass opacity (73/80 cases, 91%), consolidation (50/80 cases, 63%), and interlobular septal thickening (47/80, 59%). Most of the lesions were multiple, with an average of 12 ± 6 lung segments involved. The most common involved lung segments were the dorsal segment of the right lower lobe (69/80, 86%), the posterior basal segment of the right lower lobe (68/80, 85%), the lateral basal segment of the right lower lobe (64/80, 80%), the dorsal segment of the left lower lobe (61/80, 76%), and the posterior basal segment of the left lower lobe (65/80, 81%). The average pulmonary inflammation index value was (34% ± 20%) for all the patients. Correlation analysis showed that the pulmonary inflammation index value was significantly correlated with the values of lymphocyte count, monocyte count, C-reactive protein, procalcitonin, days from illness onset, and body temperature (P < 0.05). CONCLUSIONS: The common chest CT findings of COVID-19 are multiple ground glass opacity, consolidation, and interlobular septal thickening in both lungs, which are mostly distributed under the pleura. There are significant correlations between the degree of pulmonary inflammation and the main clinical symptoms and laboratory results. Computed tomography plays an important role in the diagnosis and evaluation of this emerging global health emergency.

Dynamic changes in the immune response correlate with disease severity and outcomes during infection with SARS-CoV-2 (preprint)

Background:The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly spread throughout China and all over the world. Little is known about the dynamic changes in the patient immune responses to SARS-CoV-2, and how different responses are correlated with disease severity and outcomes.Method:74 patients with confirmed COVID-19 were enrolled in this prospective research. The demographic information, medical history, symptoms, signs and laboratory results were analyzed and compared between severe and non-severe patients. The leukocytes, lymphocyte subsets and inflammatory cytokines were longitudinally collected.Results:Of the 74 patients included, 17 suffered from severe disease. The severe patients tended be older (65.29 ± 12.33 years vs. 45.37 ± 18.66 years), and had a greater degree of underlying disease (41.18% vs. 24.56%) , lower baseline lymphocytes counts (0.69 ± 0.36 × 10⁹ vs. 1.46 ± 0.75 × 10⁹) , higher neutrophil-lymphocyte-ratios (NLRs; 3.76 (3.15–5.51) vs. 2.07 (1.48–2.93)) and lower baseline eosinophil counts (0.01 ± 0.01 × 10⁹ vs. 0.05 ± 0.07 × 10⁹), than that in non-severe patients. The baseline helper T (Th) cells (335.47 vs. 666.46/mL), suppressor T(Ts) cells (158 vs. 334/mL), B cells (95 vs. 210/mL), and natural killer (NK) cells (52 vs. 122/mL) were significantly decreased in severe cases compared to that in non-severe cases. In addition, the baseline neutrophils and B cells were positively correlated with the severity of COVID-19 and the baseline lymphocytes and Th cells were negatively correlated with the severity of COVID-19. The dynamic change of T cells, Th cells and IFN-γ in the severe cases were parallel to the amelioration of the disease.Conclusions:Collectively, our study provides novel information on the kinetics of the immune responses in a cohort of COVID-19 patients with different disease severities. Furthermore, our study indicates that both innate and adaptive immune responses correlate with better clinical outcomes.

Nomogram Prediction of Severe Risk in Patients With COVID-19 Pneumonia: A Retrospective Study (preprint)

Background COVID-19 elicits a range of different responses in patients and can manifest into mild to very severe cases in different individuals, depending on many factors. We aimed to establish a prediction model of severe risk in COVID-19 patients, to help clinicians achieve early prevention, intervention, and aid them in choosing effective therapeutic strategy.Methods We selected confirmed COVID-19 patients who admitted to First Hospital of Changsha city between January 29 and February 15, 2020 and collected their clinical data. Multivariate logical regression was used to identify the risk factors associated with severe risk. These factors were incorporated into the nomogram to establish the model. The ROC curve, calibration plot and decision curve were used to assess the performance of model.Results 239 patients were enrolled and 45 (18.83%) patients developed severe pneumonia. Univariate and multivariate analysis showed that age, COPD, shortness of breath, fatigue, creatine kinase, D-dimer, lymphocytes and h CRP were independent risk factors for severe risk in COVID-19 patients. Incorporating these factors, the nomogram achieved good concordance indexes of 0.873 (95% CI: 0.819–0.927), and well-fitted calibration plot curves. The model provided superior net benefit when clinical decision thresholds were between 10–70% predicted risk.Conclusions Using the model, clinicians can intervene early, improve therapeutic effects and reduce the severity of COVID-19, thus ensuring more targeted and efficient use of medical resources.

CT Quantitative Analysis and Its Relationship with Clinical Features for Assessing the Severity of Patients with COVID-19.

OBJECTIVE: To investigate the value of initial CT quantitative analysis of ground-glass opacity (GGO), consolidation, and total lesion volume and its relationship with clinical features for assessing the severity of coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: A total of 84 patients with COVID-19 were retrospectively reviewed from January 23, 2020 to February 19, 2020. Patients were divided into two groups: severe group (n = 23) and non-severe group (n = 61). Clinical symptoms, laboratory data, and CT findings on admission were analyzed. CT quantitative parameters, including GGO, consolidation, total lesion score, percentage GGO, and percentage consolidation (both relative to total lesion volume) were calculated. Relationships between the CT findings and laboratory data were estimated. Finally, a discrimination model was established to assess the severity of COVID-19. RESULTS: Patients in the severe group had higher baseline neutrophil percentage, increased high-sensitivity C-reactive protein (hs-CRP) and procalcitonin levels, and lower baseline lymphocyte count and lymphocyte percentage (p < 0.001). The severe group also had higher GGO score (p < 0.001), consolidation score (p < 0.001), total lesion score (p < 0.001), and percentage consolidation (p = 0.002), but had a lower percentage GGO (p = 0.008). These CT quantitative parameters were significantly correlated with laboratory inflammatory marker levels, including neutrophil percentage, lymphocyte count, lymphocyte percentage, hs-CRP level, and procalcitonin level (p < 0.05). The total lesion score demonstrated the best performance when the data cut-off was 8.2%. Furthermore, the area under the curve, sensitivity, and specificity were 93.8% (confidence interval [CI]: 86.8-100%), 91.3% (CI: 69.6-100%), and 91.8% (CI: 23.0-98.4%), respectively. CONCLUSION: CT quantitative parameters showed strong correlations with laboratory inflammatory markers, suggesting that CT quantitative analysis might be an effective and important method for assessing the severity of COVID-19, and may provide additional guidance for planning clinical treatment strategies.

Short-term Moderate-dose Corticosteroid Plus Immunoglobulin Effectively Reverses Covid-19 Patients Who Have Failed Low-dose Therapy (preprint)

Background: The coronavirus disease-19 (COVID-19) has spread globally with more than 6,750,000 people infected, and nearly 400,000 patients died. Currently, we are in an urgent need for effective treatment strategy to control the clinical deterioration of COVID-19 patients.Methods: The clinical data of 10 COVID-19 patients receiving short-term moderate-dose corticosteroid (160mg/d) plus immunoglobulin (20g/d) were studied in the North Yard of The First Hospital of Changsha, Hunan from January 17th to February 27th, 2020. Epidemiological, clinical, laboratory, radiological findings were analyzed. Results: After treatment with combination of low-dose corticosteroid (40-80mg/d) and immunoglobulin (10g/d), patients’ lymphocyte count (0.88±0.34 vs 0.59±0.18, P<0.05), oxygenation index including SPO2 (94.90±2.51 vs 90.50±5.91, P<0.05) and PaO2/FiO2 (321.36±136.91 vs 129.30±64.97, P<0.05) were significantly lower than pre-treatment, and CT showed that the pulmonary lesion deteriorated in all patients. While after treatment of short-term moderate-dose corticosteroid plus immunoglobulin, patients’ APACHE Ⅱ score (9.10±6.15 vs 5.50±9.01, P<0.05), body temperature (37.59±1.16 vs 36.46±0.25, P<0.05), lymphocyte count (0.59±0.18 vs 1.36±0.51, P<0.05), Lactate dehydrogenase (419.24±251.31 vs 257.40±177.88, P<0.05), and C-reactive protein (49.94±26.21 vs 14.58±15.25, P<0.05) significantly improved compared with post-treatment with low-dose therapy. In addition, oxygenation index including SPO2 (90.50±5.91 vs 97.50±1.18, P<0.05), PaO2 (60.47±14.53 vs 99.07±34.31, P<0.05), and PaO2/FiO2 (129.30±64.97 vs 340.86±146.72, P<0.05) significant improved. Furthermore, CT showed that pulmonary lesions obviously improved in 7 patients. After systematic therapy, 4 out of 10 COVID-19 patients recovered and discharged.Conclusions: Short-term moderate-dose corticosteroid plus immunoglobulin is effective for reversing the continued deterioration of COVID-19 patients who failed to respond to the low-dose therapy.