Association between clinical symptoms during the COVID-19 infection and SARS-CoV-2 immunoglobulin G titers in COVID-19 convalescent whole-blood donors in China

BACKGROUND

Limited studies have explored the association between clinical symptoms and titers of SARS-CoV-2 antibodies.

STUDY DESIGN AND METHODS

In this cross-sectional study, whole-blood donors who had experienced a confirmed or suspected COVID-19 infection completed questionnaires at the time of blood donation. Plasma SARS-CoV-2 immunoglobulin G (IgG) titers were measured using an enzyme-linked immunosorbent assay. Logistic regression models were used to calculate odds ratios (ORs) for high-titer COVID-19 convalescent plasma (CCP) for each variable.

RESULTS

Among the total 386 donors, 120 (31%) donors with IgG titers ≥1:160 were classified as high-titer donors. The multivariable ORs (95% confidence intervals [CIs]) for high titers were 2.33 (1.45-3.75), 2.11 (1.29-3.43), 1.10 (1.01-1.21), 1.19 (1.00-1.43), and 1.97 (1.05-3.71) for sore throat, cough, symptom count, fever duration, and low fever (compared with non-fever), respectively. No significant association was observed between other symptoms and medical visits and the odds of high-titer CCP. The association between high-titer CCP and fever duration was restricted to confirmed COVID-19-infected donors, while associations with sore throat and cough remained significant in suspected infected donors. In addition, medical visit was positively associated with high-titer CCP in suspected donors, but not in confirmed donors. In bootstrapped logistic regression models, the associations remained significant and reproducible for medical visit in suspected donors and for sore throat and cough in both suspected donors and total donors.

DISCUSSION

Experiencing a sore throat and cough were associated with high-titer CCP in overall donors. We also identified sore throat, cough, and medical visits as potential predictors of high-titer CCP for suspected donors during the pandemic.

Factors associated with the SARS-CoV-2 immunoglobulin-G titer levels in convalescent whole-blood donors: a Chinese cross-sectional study

Blood transfusions from convalescent Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infected patients could be used to treat patients with severe infections or immunocompromised patients. However, it is necessary to select the optimal donors to maximize the utilization of resources. In this study, we investigated the associations among body mass index (BMI), tobacco smoking, exercise frequency and duration, and alcohol consumption with the SARS-CoV-2 immunoglobulin-G (IgG) antibody titer levels with in the Chinese convalescent blood donor population. Here we show that BMI, smoking habits, and exercise frequency appear to be predictive factors for IgG levels in convalescent male blood donors. However, these variables were not observed as predictive of IgG levels in female convalescent blood donors. The findings could be used to optimize the screening for potential blood donors to treat immunocompromised or severely ill COVID-19 patients.

A study on whether deep learning models based on CT images for bone density classification and prediction can be used for opportunistic osteoporosis screening

This study utilized deep learning to classify osteoporosis and predict bone density using opportunistic CT scans and independently tested the models on data from different hospitals and equipment. Results showed high accuracy and strong correlation with QCT results, showing promise for expanding osteoporosis screening and reducing unnecessary radiation and costs.

PURPOSE

To explore the feasibility of using deep learning to establish a model for osteoporosis classification and bone density value prediction based on opportunistic CT scans and to verify its generalization and diagnostic ability using an independent test set.

METHODS

A total of 1219 cases of opportunistic CT scans were included in this study, with QCT results as the reference standard. The training set: test set: independent test set ratio was 703: 176: 340, and the independent test set data of 340 cases were from 3 different hospitals and 4 different CT scanners. The VB-Net structure automatic segmentation model was used to segment the trabecular bone, and DenseNet was used to establish a three-classification model and bone density value prediction regression model. The performance parameters of the models were calculated and evaluated.

RESULTS

The ROC curves showed that the mean AUCs of the three-category classification model for categorizing cases into "normal," "osteopenia," and "osteoporosis" for the training set, test set, and independent test set were 0.999, 0.970, and 0.933, respectively. The F1 score, accuracy, precision, recall, precision, and specificity of the test set were 0.903, 0.909, 0.899, 0.908, and 0.956, respectively, and those of the independent test set were 0.798, 0.815, 0.792, 0.81, and 0.899, respectively. The MAEs of the bone density prediction regression model in the training set, test set, and independent test set were 3.15, 6.303, and 10.257, respectively, and the RMSEs were 4.127, 8.561, and 13.507, respectively. The R-squared values were 0.991, 0.962, and 0.878, respectively. The Pearson correlation coefficients were 0.996, 0.981, and 0.94, respectively, and the p values were all < 0.001. The predicted values and bone density values were highly positively correlated, and there was a significant linear relationship.

CONCLUSION

Using deep learning neural networks to process opportunistic CT scan images of the body can accurately predict bone density values and perform bone density three-classification diagnosis, which can reduce the radiation risk, economic consumption, and time consumption brought by specialized bone density measurement, expand the scope of osteoporosis screening, and have broad application prospects.

PM2.5 Aggravated OVA-Induced Epithelial Tight Junction Disruption Through Fas Associated via Death Domain-Dependent Apoptosis in Asthmatic Mice

BACKGROUND

Exposure to air pollutants cause exacerbation of asthma, but the experimental evidence and the mechanisms still need to be collected and addressed.

METHODS

Asthma model was constructed by ovalbumin (OVA) combined with or without airborne fine particulate matter 2.5 (PM2.5) exposure. Lung sections were stained by hematoxylin-eosin staining (H&E) and Masson's trichrome. RNA-seq and gene set enrichment analysis (GSEA) was performed to identify the key pathway. TdT mediated dUTP Nick End Labeling (TUNEL) assay, real-time qPCR, Western blot, immunofluorescence and lentivirus transfection were applied for mechanism discovery.

RESULTS

In this study, we found PM2.5 aggravated airway inflammation in OVA-induced asthmatic mice. RNA-seq analysis also showed that epithelial mesenchymal transition (EMT) was enhanced in OVA-induced mice exposed to PM2.5 compared with that in OVA-induced mice. In the meantime, we observed that apoptosis was significantly increased in asthmatic mice exposed to PM2.5 by using GSEA analysis, which was validated by TUNEL assay. By using bioinformatic analysis, Fas associated via death domain (FADD), a new actor in innate immunity and inflammation, was identified to be related to apoptosis, EMT and tight junction. Furthermore, we found that the transcript and protein levels of tight junction markers, E-cadherin, zonula occludens (ZO)-1 and Occludin, were decreased after PM2.5 exposure in vivo and in vitro by using RT-qPCR and immunofluorescence, with the increased expression of FADD. Moreover, down-regulation of FADD attenuated PM2.5-induced apoptosis and tight junction disruption in human airway epithelial cells.

CONCLUSION

Taken together, we demonstrated that PM2.5 aggravated epithelial tight junction disruption through apoptosis mediated by up-regulation of FADD in OVA-induced model.