COVID-19 vaccination influences subtypes of γδ-T cells during pregnancy.

Up to now, there has been insufficient clinical data to support the safety and effects of vaccination on pregnancy post COVID-19 vaccination. The γδ-T cells are considered an important component in the immune system to fight against viral infection and exhibit critical roles throughout the pregnancy period. However, the immunological roles of γδ-T cells in pregnant women with the COVID-19 vaccination remain unclear. Therefore, the objective of this study is to investigate the alteration of frequency and expression pattern of activation receptors and inhibitory receptors in γδ-T cell and its subsets in peripheral blood samples collected from non-pregnant vaccinated women, vaccinated pregnant women, and unvaccinated pregnant women. Our findings indicated that the frequency of CD3+γδ-T+ cells is lower in vaccinated pregnant women than in unvaccinated pregnant women. But no significant difference was found in the frequency of CD3+γδ-T+ cells between non-pregnant vaccinated women and vaccinated pregnant women. In addition, there were no significant differences in the frequencies of CD3+γδ-T+Vδ1+T cells, CD3+γδ-T+Vδ2+T cells, CD3+γδ-T+Vδ1-Vδ2-T cells, and Vδ1+T cell/Vδ2+T cell ratio between the pregnant women with or without COVID-19 vaccination. Similar results were found after comparing non-pregnant and pregnant women who received the COVID-19 vaccine. However, there was a significant difference in the fraction of Vδ1-Vδ2-T cells in CD3+γδ-T+ cells between non-pregnant vaccinated women and vaccinated pregnant women. The frequency of NKG2D+ cells in Vδ2+T cells was not significantly different in the vaccinated pregnant women when compared to that in unvaccinated pregnant women or non-pregnant vaccinated women. But the percentage of NKG2D+ cells in Vδ1+T cells was the lowest in pregnant women after COVID-19 vaccination. Furthermore, down-regulation of NKP46 and NKP30 were found in Vδ2+T and Vδ1+T cells in the vaccinated pregnant women, respectively. After the vaccination, up-regulation of PD-1 expression in Vδ1+T cells and Vδ2+T cells indicated γδ-T cells could respond to COVID-19 vaccination and display an exhausted phenotype following activation. In conclusion, COVID-19 vaccination influences subtypes of γδ-T cells during pregnancy, but the side effects might be limited. The phenotypical changes of Vδ1+T cells and Vδ2+T cells will be a promising predictor for evaluating the clinical outcome of the COVID-19 vaccine.

Public Mental Health in Post-COVID-19 Era.

Transmission of the 2019 novel coronavirus (COVID-19) has now rapidly spread around the world, which has alarming implications for individuals and communities, in particular for public mental health. Significant progress has been made in the prevention and control of the COVID-19 pandemic in China, but the psychological crisis caused by the epidemic is still not over and may continue to exist. The public mental health in the post-COVID-19 era should not be ignored. This article provides early warning for the public's mental health in the post-COVID-19 era by listing the characteristics and duration of the public mental health crisis following the SARS outbreak. In addition, based on the current situation, specific methods and measures are proposed in order to provide effective reference for the prevention and control of psychological crisis caused by the COVID-19 epidemic.

COVID-19 vaccination influences subtypes of γδ-T cells during pregnancy

Up to now, there has been insufficient clinical data to support the safety and effects of vaccination on pregnancy post COVID-19 vaccination. The γδ-T cells are considered an important component in the immune system to fight against viral infection and exhibit critical roles throughout the pregnancy period. However, the immunological roles of γδ-T cells in pregnant women with the COVID-19 vaccination remain unclear. Therefore, the objective of this study is to investigate the alteration of frequency and expression pattern of activation receptors and inhibitory receptors in γδ-T cell and its subsets in peripheral blood samples collected from non-pregnant vaccinated women, vaccinated pregnant women, and unvaccinated pregnant women. Our findings indicated that the frequency of CD3+γδ-T+ cells is lower in vaccinated pregnant women than in unvaccinated pregnant women. But no significant difference was found in the frequency of CD3+γδ-T+ cells between non-pregnant vaccinated women and vaccinated pregnant women. In addition, there were no significant differences in the frequencies of CD3+γδ-T+Vδ1+T cells, CD3+γδ-T+Vδ2+T cells, CD3+γδ-T+Vδ1-Vδ2-T cells, and Vδ1+T cell/Vδ2+T cell ratio between the pregnant women with or without COVID-19 vaccination. Similar results were found after comparing non-pregnant and pregnant women who received the COVID-19 vaccine. However, there was a significant difference in the fraction of Vδ1-Vδ2-T cells in CD3+γδ-T+ cells between non-pregnant vaccinated women and vaccinated pregnant women. The frequency of NKG2D+ cells in Vδ2+T cells was not significantly different in the vaccinated pregnant women when compared to that in unvaccinated pregnant women or non-pregnant vaccinated women. But the percentage of NKG2D+ cells in Vδ1+T cells was the lowest in pregnant women after COVID-19 vaccination. Furthermore, down-regulation of NKP46 and NKP30 were found in Vδ2+T and Vδ1+T cells in the vaccinated pregnant women, respectively. After the vaccination, up-regulation of PD-1 expression in Vδ1+T cells and Vδ2+T cells indicated γδ-T cells could respond to COVID-19 vaccination and display an exhausted phenotype following activation. In conclusion, COVID-19 vaccination influences subtypes of γδ-T cells during pregnancy, but the side effects might be limited. The phenotypical changes of Vδ1+T cells and Vδ2+T cells will be a promising predictor for evaluating the clinical outcome of the COVID-19 vaccine.

Artificial intelligence computed tomography helps evaluate the severity of COVID-19 patients: A retrospective study.

BACKGROUND: Computed tomography (CT) is a noninvasive imaging approach to assist the early diagnosis of pneumonia. However, coronavirus disease 2019 (COVID-19) shares similar imaging features with other types of pneumonia, which makes differential diagnosis problematic. Artificial intelligence (AI) has been proven successful in the medical imaging field, which has helped disease identification. However, whether AI can be used to identify the severity of COVID-19 is still underdetermined. METHODS: Data were extracted from 140 patients with confirmed COVID-19. The severity of COVID-19 patients (severe vs. non-severe) was defined at admission, according to American Thoracic Society (ATS) guidelines for community-acquired pneumonia (CAP). The AI-CT rating system constructed by Hangzhou YITU Healthcare Technology Co., Ltd. was used as the analysis tool to analyze chest CT images. RESULTS: A total of 117 diagnosed cases were enrolled, with 40 severe cases and 77 non-severe cases. Severe patients had more dyspnea symptoms on admission (12 vs. 3), higher acute physiology and chronic health evaluation (APACHE) II (9 vs. 4) and sequential organ failure assessment (SOFA) (3 vs. 1) scores, as well as higher CT semiquantitative rating scores (4 vs. 1) and AI-CT rating scores than non-severe patients (P<0.001). The AI-CT score was more predictive of the severity of COVID-19 (AUC=0.929), and ground-glass opacity (GGO) was more predictive of further intubation and mechanical ventilation (AUC=0.836). Furthermore, the CT semiquantitative score was linearly associated with the AI-CT rating system (Adj R 2=75.5%, P<0.001). CONCLUSIONS: AI technology could be used to evaluate disease severity in COVID-19 patients. Although it could not be considered an independent factor, there was no doubt that GGOs displayed more predictive value for further mechanical ventilation.

Veteran Experiences with Telehealth

Research ObjectiveIn FY18‐19, VA Office of Rural Health initiated a pilot program to increase access to services in underserved areas by funding 13 telehealth hubs. Understanding Veterans' experiences with telehealth hubs is essential to its delivery, especially in its scale‐up nationally and in response to the pandemic in FY20. We examine characteristics and ratings of care among Veterans with telehealth hub visits compared to usual primary care (PC) during the pilot program.Our objectives are to:Understand Veteran experiences with access, care coordination, comprehensiveness and primary care provider (PCP) communication during a telehealth pilot program.Examine Veteran characteristics associated with telehealth hub visits during a pilot program.Study DesignRegression analysis of a cross‐sectional data: VA Survey of Healthcare Experiences of Patients – Patient‐Centered Medical Home (SHEP‐PCMH), which uses Consumer Assessment of Healthcare Providers and Systems (CAHPS) questionnaires to survey Veterans on their healthcare experiences. We derived dichotomous outcomes from SHEP‐PCMH composite indices, indicating optimal experiences with access, care coordination, PCP communication, comprehensiveness (based on the percentages of responses that fall in the top category (Always or Yes) and PCP rating (9 or 10 out of 10). Key predictors were telehealth hub v. usual care index visit and proportion of telehealth hub visits in 6 months prior to the index visit. Other covariates included age, gender, race/ethnicity, education, number of PC visits in past year, self‐rated overall and mental health, and year of visit. We conducted multivariate logistic regression analyses to predict optimal care experiences, controlling for demographics, healthcare use, health status and year.Population StudiedVeterans receiving care at VA PC clinics offering telehealth hub visits during FY2018‐2019.Principal FindingsIn FY2018‐2019, 3201 out of 57,793 Veterans had a telehealth hub index visit. Telehealth hub utilization 6 months prior to index visit: 0.25% usual care v. 83% telehealth. Compared to usual care, the telehealth hub group was significantly older (age ≥ 65: 60% telehealth, 50.5% usual care), had fewer PC visits (0–1 visits: 69% telehealth, 56% usual care). In multivariate models, we failed to detect statistically significant difference between telehealth hub and usual care groups for the composite measures of Veteran experiences. Adjusted Odds Ratio (AOR) (95% CI): 0.68 (0.12–3.81), 0.3 (0.07–1.35), 1.55 (0.48–4.95), 0.47 (0.18–1.22), and 0.66 (0.18–2.49) for access, coordination, PCP communication, comprehensiveness, and PCP rating, respectively. Older age and greater number of PC visits were associated with greater likelihood of optimal care ratings;fair/poor overall and mental health were associated with lower likelihood of optimal experiences.ConclusionsCompared to usual care, telehealth hub users did not differ in likelihood ratings of optimal care experience although they are distinct in their demographics and healthcare utilization. Further study is needed, since the population of telehealth hub users is quite different from usual care population during pilot program.Implications for Policy or PracticeTelehealth hub visits were not associated with decrements in care experience. Our findings suggest that Veterans' experiences with tele‐health during the COVID‐19 pandemic might be similar to usual care. This is of importance since the Covid‐19 pandemic necessitates expanded use of virtual care to ensure patient safety.Primary Funding SourceDepartment of Veterans Affairs.

Value of swab types and collection time on SARS-COV-2 detection using RT-PCR assay.

OBJECTIVE: Low viral load from patients infected with SARS-CoV-2 during infection late stage easily lead to false negative nucleic acid testing results, thus having great challenges to the prevention and control of the current pandemic. In present study, we mainly aimed to evaluate specimen types and specimen collection timepoint on the positive detection of 2019 novel coronavirus from patients at infection late stage based on RT-PCR testing. METHODS: Paired nasopharyngeal swabs, nasal swabs, oropharyngeal swabs and anal swabs were collected from patients infected with SARS-CoV-2 during infection late stage before washing in the morning and afternoon on the same day. Then virus RNA was extracted and tested for 2019-nCoV identification by RT-PCR within 24 h. RESULTS: Viral load was low at late infection stage. Specimens collected before washing in the morning would increase the detection ratio of 2019-nCoV. Detection ratio of nasopharyngeal swab [65 (95 % CI: 49.51-77.87) vs 42.5(95 % CI: 28.51-57.8)] or nasal swab [57.5 (95 % CI: 42.2-71.49) vs 35 (95 % CI: 22.13-50.49)] is higher not only than oropharyngeal swab[22.5 (95 % CI: 12.32-37.5) vs 7.5 (95 % CI: 2.58-19.86)], but also anal swab[2.5 (95 % CI: 0.44-12.88) vs 5 (95 % CI: 1.38-16.5)]. CONCLUSIONS: In summary, our research discovers that nasopharyngeal or nasal swab collected before washing in the morning might be more suitable for detecting of large-scale specimens from patients infected with low SARS-CoV-2 load during infection late stage. Those results could facilitate other laboratories in collecting appropriate specimens for improving detection of SARS-CoV-2 from patients during infection late stage as well as initially screening.