Emerging role of long non-coding RNA MALAT1 related signaling pathways in the pathogenesis of lung disease.

Long non-coding RNAs (lncRNAs) are endogenously expressed RNAs longer than 200 nt that are not translated into proteins. In general, lncRNAs bind to mRNA, miRNA, DNA, and proteins and regulate gene expression at various cellular and molecular levels, including epigenetics, transcription, post-transcription, translation, and post-translation. LncRNAs play important roles in many biological processes, such as cell proliferation, apoptosis, cell metabolism, angiogenesis, migration, endothelial dysfunction, endothelial-mesenchymal transition, regulation of cell cycle, and cellular differentiation, and have become an important topic of study in genetic research in health and disease due to their close link with the development of various diseases. The exceptional stability, conservation, and abundance of lncRNAs in body fluids, have made them potential biomarkers for a wide range of diseases. LncRNA MALAT1 is one of the best-studied lncRNAs in the pathogenesis of various diseases, including cancers and cardiovascular diseases. A growing body of evidence suggests that aberrant expression of MALAT1 plays a key role in the pathogenesis of lung diseases, including asthma, chronic obstructive pulmonary diseases (COPD), Coronavirus Disease 2019 (COVID-19), acute respiratory distress syndrome (ARDS), lung cancers, and pulmonary hypertension through different mechanisms. Here we discuss the roles and molecular mechanisms of MALAT1 in the pathogenesis of these lung diseases.

The effectiveness of a computer-aided system in improving the detection rate of gastric neoplasm and early gastric cancer: study protocol for a multi-centre, randomized controlled trial.

BACKGROUND: This protocol is for a multi-centre randomised controlled trial to determine whether the computer-aided system ENDOANGEL-GC improves the detection rates of gastric neoplasms and early gastric cancer (EGC) in routine oesophagogastroduodenoscopy (EGD). METHODS: Study design: Prospective, single-blind, parallel-group, multi-centre randomised controlled trial. SETTINGS: The computer-aided system ENDOANGEL-GC was used to monitor blind spots, detect gastric abnormalities, and identify gastric neoplasms during EGD. PARTICIPANTS: Adults who underwent screening, diagnosis, or surveillance EGD. Randomisation groups: 1. Experiment group, EGD examinations with the assistance of the ENDOANGEL-GC; 2. Control group, EGD examinations without the assistance of the ENDOANGEL-GC. RANDOMISATION: Block randomisation, stratified by centre. PRIMARY OUTCOMES: Detection rates of gastric neoplasms and EGC. SECONDARY OUTCOMES: Detection rate of premalignant gastric lesions, biopsy rate, observation time, and number of blind spots on EGD. BLINDING: Outcomes are undertaken by blinded assessors. SAMPLE SIZE: Based on the previously published findings and our pilot study, the detection rate of gastric neoplasms in the control group is estimated to be 2.5%, and that of the experimental group is expected to be 4.0%. With a two-sided α level of 0.05 and power of 80%, allowing for a 10% drop-out rate, the sample size is calculated as 4858. The detection rate of EGC in the control group is estimated to be 20%, and that of the experiment group is expected to be 35%. With a two-sided α level of 0.05 and power of 80%, a total of 270 cases of gastric cancer are needed. Assuming the proportion of gastric cancer to be 1% in patients undergoing EGD and allowing for a 10% dropout rate, the sample size is calculated as 30,000. Considering the larger sample size calculated from the two primary endpoints, the required sample size is determined to be 30,000. DISCUSSION: The results of this trial will help determine the effectiveness of the ENDOANGEL-GC in clinical settings. TRIAL REGISTRATION: ChiCTR (Chinese Clinical Trial Registry), ChiCTR2100054449, registered 17 December 2021.

Prevalence and correlators of burnout among health professionals during different stages of the COVID-19 pandemic in China.

Background: Persistently increased workload and stress occurred in health professionals (HPs) during the past 3 years as the COVID-19 pandemic continued. The current study seeks to explore the prevalence of and correlators of HPs' burnout during different stages of the pandemic. Methods: Three repeated online studies were conducted in different stages of the COVID-19 pandemic: wave 1: after the first peak of the pandemic, wave 2: the early period of the zero-COVID policy, and wave 3: the second peak of the pandemic in China. Two dimensions of burnout, emotional exhaustion (EE) and declined personal accomplishment (DPA), were assessed using Human Services Survey for Medical Personnel (MBI-HSMP), a 9-item Patient Health Questionnaire (PHQ-9), and a 7-item Generalized Anxiety Disorder (GAD-7) to assess mental health conditions. An unconditional logistic regression model was employed to discern the correlators. Results: There was an overall prevalence of depression (34.9%), anxiety (22.5%), EE (44.6%), and DPA (36.5%) in the participants; the highest prevalence of EE and DPA was discovered in the first wave (47.4% and 36.5%, respectively), then the second wave (44.9% and 34.0%), and the third wave had the lowest prevalence of 42.3% and 32.2%. Depressive symptoms and anxiety were persistently correlated with a higher prevalence risk of both EE and DPA. Workplace violence led to a higher prevalence risk of EE (wave 1: OR = 1.37, 95% CI: 1.16-1.63), and women (wave 1: OR = 1.19, 95% CI: 1.00-1.42; wave 3: OR =1.20, 95% CI:1.01-1.44) and those living in a central area (wave 2: OR = 1.66, 95% CI: 1.20-2.31) or west area (wave 2: OR = 1.54, 95% CI: 1.26-1.87) also had a higher prevalence risk of EE. In contrast, those over 50 years of age (wave 1: OR = 0.61, 95% CI: 0.39-0.96; wave 3: OR = 0.60, 95% CI: 0.38-0.95) and who provided care to patients with COVID-19 (wave 2: OR = 0.73, 95% CI: 0.57-0.92) had a lower risk of EE. Working in the psychiatry section (wave 1: OR = 1.38, 95% CI: 1.01-1.89) and being minorities (wave 2: OR = 1.28, 95% CI: 1.04-1.58) had a higher risk of DPA, while those over 50 years of age had a lower risk of DPA (wave 3: OR = 0.56, 95% CI: 0.36-0.88). Conclusion: This three-wave cross-sectional study revealed that the prevalence of burnout among health professionals was at a high level persistently during the different stages of the pandemic. The results suggest that functional impairment prevention resources and programs may be inadequate and, as such, continuous monitoring of these variables could provide evidence for developing optimal strategies for saving human resources in the coming post-pandemic era.

N-Butanol Extract of Glycyrrhizae Radix et Rhizoma Inhibits Dengue Virus through Targeting Envelope Protein.

BACKGROUND: At present, about half of the world's population is at risk of being infected with dengue virus (DENV). However, there are no specific drugs to prevent or treat DENV infection. Glycyrrhizae Radix et Rhizome, a well-known traditional Chinese medicine, performs multiple pharmacological activities, including exerting antiviral effects. The aim of this study was to investigate the anti-DENV effects of n-butanol extract from Glycyrrhizae Radix et Rhizome (GRE). METHODS: Compounds analysis of GRE was conducted via ultra-performance liquid chromatography/tandem mass spectrometry (UHPLC-MS/MS). The antiviral activities of GRE were determined by the CCK-8 assay, plaque assay, qRT-PCR, Western blotting, and the immunofluorescence assay. The DENV-infected suckling mice model was constructed to explore the antiviral effects of GRE in vivo. RESULTS: Four components in GRE were analyzed by UHPLC-MS/MS, including glycyrrhizic acid, glycyrrhetnic acid, liquiritigenin, and isoliquiritigenin. GRE inhibited the attachment process of the virus replication cycle and reduced the expression of the E protein in cell models. In the in vivo study, GRE significantly relieved clinical symptoms and prolong survival duration. GRE also significantly decreased viremia, reduced the viral load in multiple organs, and inhibited the release of pro-inflammatory cytokines in DENV-infected suckling mice. CONCLUSIONS: GRE exhibited significant inhibitory activities in the adsorption stage of the DENV-2 replication cycle by targeting the envelope protein. Thus, GRE might be a promising candidate for the treatment of DENV infection.

Moral injury and suicidal ideation among health professionals: The role of mental health.

BACKGROUND: The prolonged COVID-19 pandemic has burdened health professionals mentally and physically. This study aims to explore the relationship between moral injury (MI) and suicidal ideation (SI), and the role of mental health conditions in this relationship. METHODS: Three-wave repeated online cross-sectional study with a total of 10,388 health professionals were conducted in different stages (2020-2022) of the COVID-19 pandemic in mainland China. Participants completed the Chinese version of the Moral Injury Symptoms Scale-Health Professional, Post-Traumatic Stress Disorder (PTSD) Checklist for DSM-5 coupled with a blanket of scales. RESULTS: The prevalence of SI and MI among health professionals was 9.8 % and 40.2 %, respectively. The prevalence risk of SI was lower in wave 2 (OR = 0.64, 95 % CI: 0.54-0.77) and wave 3 (OR = 0.71, 95 % CI: 0.60-0.84) when compared with wave 1. MI (OR = 4.66, 95 % CI: 3.99-5.43), medical error (OR = 1.15, 95 % CI: 1.00-1.32), workplace violence (OR = 1.13, 95 % CI: 0.97-1.32), depression (OR = 94.08, 95 % CI: 63.37-139.69), anxiety (OR = 25.54, 95 % CI: 21.22-30.74), PTSD (OR = 24.51, 95 % CI: 19.01-31.60) were associated with a higher risk of SI. The mediation model revealed that depressive, anxiety, and PTSD symptoms explained 90.6 % of the total variance in the relationship between MI and SI. CONCLUSIONS: The risk of SI has reduced among health professionals since the first peak of the COVID-19 pandemic in China. MI may contribute to prevalent SI, and mental health conditions, especially depressive symptoms, play a significant role as mediators. LIMITATIONS: Cross-sectional design precludes the investigation of casual relationships. The nonrandom sampling method limits the generalization.

Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data.

The COVID-19 pandemic has had unprecedented impacts on the way we get around, which has increased the need for physical and social distancing while traveling. Shared mobility, as an emerging travel mode that allows travelers to share vehicles or rides has been confronted with social distancing measures during the pandemic. On the contrary, the interest in active travel (e.g., walking and cycling) has been renewed in the context of pandemic-driven social distancing. Although extensive efforts have been made to show the changes in travel behavior during the pandemic, people's post-pandemic attitudes toward shared mobility and active travel are under-explored. This study examined Alabamians' post-pandemic travel preferences regarding shared mobility and active travel. An online survey was conducted among residents in the State of Alabama to collect Alabamians' perspectives on post-pandemic travel behavior changes, e.g., whether they will avoid ride-hailing services and walk or cycle more after the pandemic. Machine learning algorithms were used to model the survey data (N = 481) to identify the contributing factors of post-pandemic travel preferences. To reduce the bias of any single model, this study explored multiple machine learning methods, including Random Forest, Adaptive Boosting, Support Vector Machine, K-Nearest Neighbors, and Artificial Neural Network. Marginal effects of variables from multiple models were combined to show the quantified relationships between contributing factors and future travel intentions due to the pandemic. Modeling results showed that the interest in shared mobility would decrease among people whose one-way commuting time by driving is 30-45 min. The interest in shared mobility would increase for households with an annual income of $100,000 or more and people who reduced their commuting trips by over 50% during the pandemic. In terms of active travel, people who want to work from home more seemed to be interested in increasing active travel. This study provides an understanding of future travel preferences among Alabamians due to COVID-19. The information can be incorporated into local transportation plans that consider the impacts of the pandemic on future travel intentions.

Correlation between clinical course and radiographic development on CT scan in patients with COVID-19.

Background: To analyze the correlation between clinical course and radiographic development on computed tomography (CT) in patients with confirmed coronavirus disease 2019 (COVID-19) and to provide more evidence for treatment. Methods: This retrospective, observational, cohort study enrolled 49 patients with Reverse transcription-polymerase chain reaction (RT-PCR)-confirmed COVID-19, which included 30 patients admitted to the intensive care unit (ICU) of Wuhan Third Hospital and 19 patients either admitted to or receiving telemedicine consultation from Shanghai General Hospital, Shanghai Xuhui Dahua Hospital, and hospitals in other provinces. CT scans were performed in all enrolled patients and the radiographic features including simple ground-glass opacities (GGOs), GGO with interlobular septal thickening, consolidations with GGO, and consolidations only were monitored by repeating the CT. The progression of these radiographic features was analyzed in combination with their clinical staging and the time interval between onset of symptoms to CT. Results: Based on illness severity, the 49 patients were classified into four stages: mild (n = 6), moderate (n = 12), severe (n = 16), and critically ill (n = 15). The CT findings were classified into three phases: early (n = 5), progression (n = 39), and recovery (n = 5). Among the 49 patients, 9 had bilateral diffuse GGO or diffuse consolidations (white lungs) and were counted as 18 lesions. Three patients had no abnormal findings on initial CT, but their repeat CT showed new lesions. In all, we identified 892 lesions including simple GGO, GGO with interlobular septal thickening, consolidations with GGO, and consolidations only. Conclusions: Most patients had pulmonary lesions on the posterior, inferior, and peripheral lung fields on CT. The development of GGO with interlobular septal thickening, GGO with consolidations, and consolidations only happened mainly between day 8 and 14. The emergence of consolidations may suggest the progression to the severe phase of the illness, whereas simple consolidations or "white lung" may suggest a critically ill phase.

A SARS-CoV-2-specific CAR-T-cell model identifies felodipine, fasudil, imatinib, and caspofungin as potential treatments for lethal COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-induced cytokine storm is closely associated with coronavirus disease 2019 (COVID-19) severity and lethality. However, drugs that are effective against inflammation to treat lethal COVID-19 are still urgently needed. Here, we constructed a SARS-CoV-2 spike protein-specific CAR, and human T cells infected with this CAR (SARS-CoV-2-S CAR-T) and stimulated with spike protein mimicked the T-cell responses seen in COVID-19 patients, causing cytokine storm and displaying a distinct memory, exhausted, and regulatory T-cell phenotype. THP1 remarkably augmented cytokine release in SARS-CoV-2-S CAR-T cells when they were in coculture. Based on this "two-cell" (CAR-T and THP1 cells) model, we screened an FDA-approved drug library and found that felodipine, fasudil, imatinib, and caspofungin were effective in suppressing the release of cytokines, which was likely due to their ability to suppress the NF-κB pathway in vitro. Felodipine, fasudil, imatinib, and caspofungin were further demonstrated, although to different extents, to attenuate lethal inflammation, ameliorate severe pneumonia, and prevent mortality in a SARS-CoV-2-infected Syrian hamster model, which were also linked to their suppressive role in inflammation. In summary, we established a SARS-CoV-2-specific CAR-T-cell model that can be utilized as a tool for anti-inflammatory drug screening in a fast and high-throughput manner. The drugs identified herein have great potential for early treatment to prevent COVID-19 patients from cytokine storm-induced lethality in the clinic because they are safe, inexpensive, and easily accessible for immediate use in most countries.

Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace.

In recent years, there has been a growing amount of discussion on the use of big data to prevent and treat pandemics. The current research aimed to use CiteSpace (CS) visual analysis to uncover research and development trends, to help academics decide on future research and to create a framework for enterprises and organizations in order to plan for the growth of big data-based epidemic control. First, a total of 202 original papers were retrieved from Web of Science (WOS) using a complete list and analyzed using CS scientometric software. The CS parameters included the date range (from 2011 to 2022, a 1-year slice for co-authorship as well as for the co-accordance assessment), visualization (to show the fully integrated networks), specific selection criteria (the top 20 percent), node form (author, institution, region, reference cited, referred author, journal, and keywords), and pruning (pathfinder, slicing network). Lastly, the correlation of data was explored and the findings of the visualization analysis of big data pandemic control research were presented. According to the findings, "COVID-19 infection" was the hottest cluster with 31 references in 2020, while "Internet of things (IoT) platform and unified health algorithm" was the emerging research topic with 15 citations. "Influenza, internet, China, human mobility, and province" were the emerging keywords in the year 2021-2022 with strength of 1.61 to 1.2. The Chinese Academy of Sciences was the top institution, which collaborated with 15 other organizations. Qadri and Wilson were the top authors in this field. The Lancet journal accepted the most papers in this field, while the United States, China, and Europe accounted for the bulk of articles in this research. The research showed how big data may help us to better understand and control pandemics.

Therapeutic equine hyperimmune antibodies with high and broad-spectrum neutralizing activity protect rodents against SARS-CoV-2 infection.

The emergence of SARS-CoV-2 variants stresses the continued need for broad-spectrum therapeutic antibodies. Several therapeutic monoclonal antibodies or cocktails have been introduced for clinical use. However, unremitting emerging SARS-CoV-2 variants showed reduced neutralizing efficacy by vaccine induced polyclonal antibodies or therapeutic monoclonal antibodies. In our study, polyclonal antibodies and F(ab')2 fragments with strong affinity produced after equine immunization with RBD proteins produced strong affinity. Notably, specific equine IgG and F(ab')2 have broad and high neutralizing activity against parental virus, all SARS-CoV-2 variants of concern (VOCs), including B.1.1,7, B.1.351, B.1.617.2, P.1, B.1.1.529 and BA.2, and all variants of interest (VOIs) including B.1.429, P.2, B.1.525, P.3, B.1.526, B.1.617.1, C.37 and B.1.621. Although some variants weaken the neutralizing ability of equine IgG and F(ab')2 fragments, they still exhibited superior neutralization ability against mutants compared to some reported monoclonal antibodies. Furthermore, we tested the pre-exposure and post-exposure protective efficacy of the equine immunoglobulin IgG and F(ab')2 fragments in lethal mouse and susceptible golden hamster models. Equine immunoglobulin IgG and F(ab')2 fragments effectively neutralized SARS-CoV-2 in vitro, fully protected BALB/c mice from the lethal challenge, and reduced golden hamster's lung pathological change. Therefore, equine pAbs are an adequate, broad coverage, affordable and scalable potential clinical immunotherapy for COVID-19, particularly for SARS-CoV-2 VOCs or VOIs.

Drug-induced liver injury in COVID-19 patients during hospitalization.

Since coronavirus disease 2019 (COVID-19) outbreaks in December 2019 in Wuhan, almost no studies have systematically described drug-induced liver injury (DILI) in COVID-19 patients. This study aimed to assess the characteristics of liver test abnormality or liver injury in patients with COVID-19, and further to explore DILI in COVID-19 patients during hospitalization. It was a single-center retrospective analysis of confirmed severe acute respiratory syndrome coronavirus 2 infected patients in the hospital from January 2020 to March 2020. Univariate and multivariate logistic regression analysis were used to assess the risk factors associated with liver test abnormality or liver injury. At admission, 148 (48.8%, 148/303) patients had abnormal liver test results and 7 (2.4%, 7/303) had liver injury, while 195 (64.4%, 195/303) had abnormal liver test results and 17 (5.6%, 17/303) had liver injury during hospitalization. After excluding these patients with liver disease and liver function abnormalities or liver injury at admission, 15 (11.1%, 15/135) patients developed DILI during hospitalization. Further regression analysis indicated that methylprednisolone (odds ratio = 4.177, 95% confidence interval [1.106-15.771], P = .035), but not Chinese herbal medicine or other used drug, was associated with DILI in patients during hospitalization. Abnormal liver function results were in more than half of patients with COVID-19, and the incidence of DILI in COVID-19 patients was 11.1% during hospitalization. Liver test abnormality or liver injury in patients might be directly caused by the viral infection at admission, but the detrimental effects on liver injury mainly related to certain medications used during hospitalization, particularly methylprednisolone. Severe COVID-19 could increase the occurrence of liver injury (P = .007) during hospitalization, but not a risk factor of liver injury. However, Chinese herbal medicine was a protective factor for liver injury.

Prospective validation of a dynamic prognostic model for identifying COVID-19 patients at high risk of rapid deterioration.

BACKGROUND: We sought to develop and prospectively validate a dynamic model that incorporates changes in biomarkers to predict rapid clinical deterioration in patients hospitalized for COVID-19. METHODS: We established a retrospective cohort of hospitalized patients aged ≥18 years with laboratory-confirmed COVID-19 using electronic health records (EHR) from a large integrated care delivery network in Massachusetts including > 40 facilities from March to November 2020. A total of 71 factors, including time-varying vital signs and laboratory findings during hospitalization were screened. We used elastic net regression and tree-based scan statistics for variable selection to predict rapid deterioration, defined as progression by two levels of a published severity scale in the next 24 hours. The development cohort included the first 70% of patients identified chronologically in calendar time; the latter 30% served as the validation cohort. A cut-off point was estimated to alert clinicians of high risk of imminent clinical deterioration. RESULTS: Overall, 3,706 patients (2,587 in the development and 1,119 in the validation cohort) met the eligibility criteria with a median of 6 days of follow-up. Twenty-four variables were selected in the final model, including 16 dynamic changes of laboratory results or vital signs. Area under the ROC curve was 0.81 (95% CI, 0.79 - 0.82) in the development set and 0.74 (95% CI, 0.71-0.78) in the validation set. The model was well calibrated (slope = 0.84 and intercept = -0.07 on the calibration plot in the validation set). The estimated cut-off point, with a positive predictive value of 83%, was 0.78. CONCLUSIONS: Our prospectively validated dynamic prognostic model demonstrated temporal generalizability in a rapidly evolving pandemic and can be used to inform day-to-day treatment and resource allocation decisions based on dynamic changes in biophysiological factors. This article is protected by copyright. All rights reserved.

Development of Bivalent mRNA Vaccines against SARS-CoV-2 Variants.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected billions of individuals and is the cause of the current global coronavirus disease 2019 (COVID-19) pandemic. We previously developed an mRNA vaccine (LVRNA009) based on the S protein of the Wuhan-Hu-1 strain; the phases I and II clinical trials showed that LVRNA009 has a promising safety and immunogenicity profile. In order to counteract the immune escape by SARS-CoV-2 variants of concern, a panel of mRNA vaccines was developed based on the S proteins of the Wuhan-Hu-1, Delta, Omicron BA.1, BA.2, and BA.5 strains, and each vaccine's protective potency against the virus variants was evaluated. Furthermore, to achieve excellent neutralization against SARS-CoV-2 variants, bivalent vaccines were developed and tested against the variants. We found that the monovalent Wuhan-Hu-1 or the Delta vaccines could induce high level of neutralization antibody and protect animals from the infection of the SARS-CoV-2 Wuhan-Hu-1 or Delta strains, respectively. However, serum samples from mice immunized with monovalent Delta vaccine showed relatively low virus neutralization titers (VNTs) against the pseudotyped virus of the Omicron strains. Serum samples from mice immunized with bivalent Delta/BA.1 vaccine had high VNTs against the pseudotyped Wuhan-Hu-1, Delta, and BA.1 strains but low VNTs against BA.2 and BA.5 (p < 0.05). Serum samples from mice immunized with Delta/BA.2 vaccine had high VNTs against the pseudotyped Wuhan-Hu-1, Delta, BA.1 and BA.2 strains but low VNTs against BA.5. Finally, serum samples from mice immunized with Delta/BA.5 vaccine had high VNTs against all the tested pseudotyped SARS-CoV-2 strains including the Wuhan-Hu-1, Delta, and Omicron variants (p > 0.05). Therefore, a bivalent mRNA vaccine with Delta/BA.5 combination is promising to provide broad spectrum immunity against all VOCs.

Two-year follow-up of brain structural changes in patients who recovered from COVID-19: A prospective study.

The long-term effects of COVID-19 on brain structure remain unclear. A prospective study was conducted to explore the changes in brain structure in COVID-19 survivors at one and two years after discharge (COVID-19one, COVID-19two). The difference in gray matter volume (GMV) was analyzed using the voxel-based morphometry method, and correlation analyses were conducted. The dynamic changes in clinical sequelae varied. The GMVs in the cerebellum and vermis were reduced in COVID-19one and COVID-19two, positively correlated with lymphocyte count, and negatively correlated with neutrophil count, neutrophil/lymphocyte ratio (COVID-19one), and systemic immune-inflammation index (COVID-19two). The decreased GMVs in the left middle frontal gyrus, inferior frontal gyrus of the operculum, right middle temporal gyrus, and inferior temporal gyrus returned to normal in COVID-19two. The decreased GMV in the left frontal lobe was negatively correlated with the Athens Insomnia Scale (AIS). The GMV in the left temporal lobe was aggravated in COVID-19two and positively correlated with C-reactive protein. In conclusion, GMV recovery coexisted with injury, which was associated with AIS and inflammatory factors. This may shed some light on the dynamic changes in brain structure and the possible predictors that may be related to GMV changes in COVID-19two.

Brief Video Intervention to Increase Treatment-Seeking Intention Among U.S. Health Care Workers: A Randomized Controlled Trial.

OBJECTIVE: Many health care workers avoid seeking mental health care, despite COVID-19-related increases in risk of psychopathology. This study assessed the effects of two versions (distinguished by the race of the protagonist) of a brief social contact-based video on treatment-seeking intention and stigma toward mental health services among U.S. health care workers. METHODS: Participants (N=1,402) were randomly assigned to view a 3-minute video in which a Black or White female nurse described struggles with COVID-19-related anxiety and depression, barriers to care, and how therapy helped, or to view a control video unrelated to mental health. Half of the participants receiving the intervention watched the same video (i.e., booster) again 14 days later. Treatment-seeking intention and treatment-related stigma were assessed at baseline, postintervention, and 14- and 30-day follow-ups. RESULTS: Both intervention videos elicited an immediate increase in treatment-seeking intention in the intervention groups (p<0.001, effect size [ES]=21%), with similar effects among those who watched the booster video (p=0.016, ES=13%) and larger effects among those who had never sought treatment (p<0.001, ES=34%). The increased effects were not sustained 14 days after the initial video or at 30-day follow-up. The results showed an immediate reduction in stigma, but with no booster effect. The race of the protagonist did not influence outcomes. CONCLUSIONS: This easily administered intervention could increase the likelihood of care seeking by proactively encouraging health care workers with mental health challenges to pursue treatment. Future studies should examine whether the inclusion of linkable referrals to mental health services helps to increase treatment-seeking behavior.

Long-term passaging of pseudo-typed SARS-CoV-2 reveals the breadth of monoclonal and bispecific antibody cocktails.

The continuous emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants poses challenges to the effectiveness of neutralizing antibodies. Rational design of antibody cocktails is a realizable approach addressing viral immune evasion. However, evaluating the breadth of antibody cocktails is essential for understanding the development potential. Here, based on a replication competent vesicular stomatitis virus model that incorporates the spike of SARS-CoV-2 (VSV-SARS-CoV-2), we evaluated the breadth of a number of antibody cocktails consisting of monoclonal antibodies and bispecific antibodies by long-term passaging the virus in the presence of the cocktails. Results from over two-month passaging of the virus showed that 9E12 + 10D4 + 2G1 and 7B9-9D11 + 2G1 from these cocktails were highly resistant to random mutation, and there was no breakthrough after 30 rounds of passaging. As a control, antibody REGN10933 was broken through in the third passage. Next generation sequencing was performed and several critical mutations related to viral evasion were identified. These mutations caused a decrease in neutralization efficiency, but the reduced replication rate and ACE2 susceptibility of the mutant virus suggested that they might not have the potential to become epidemic strains. The 9E12 + 10D4 + 2G1 and 7B9-9D11 + 2G1 cocktails that picked from the VSV-SARS-CoV-2 system efficiently neutralized all current variants of concern and variants of interest including the most recent variants Delta and Omicron, as well as SARS-CoV-1. Our results highlight the feasibility of using the VSV-SARS-CoV-2 system to develop SARS-CoV-2 antibody cocktails and provide a reference for the clinical selection of therapeutic strategies to address the mutational escape of SARS-CoV-2.

Research progress on adenovirus vector vaccines

Adenovirus（Ad） vectors have been widely used as gene delivery vehicles in gene therapy studies since Ad does not integrate into the host genome, thus the risk of insertion mutation is very low.Ad vectors induce immune responses and have relatively high thermal stability, which make them potential vaccine vectors.The outbreak of coronavirus disease 2019（COVID-19） has drawn more attention to the application of Ad vector vaccines.Vaccination is still the most economical and effective means to prevent and control infectious diseases, including COVID-19, and a variety of Ad vector vaccines have been developed.In this review, we describe the basic characteristics, immune mechanism, clinical application and research progress of Ad vectors.

Dynamic white matter changes in recovered COVID-19 patients: a two-year follow-up study.

Background and purpose: Long COVID with regard to the neurological system deserves more attention, as a surge of treated patients are being discharged from the hospital. As the dynamic changes in white matter after two years remain unknown, this characteristic was the focus of this study. Methods: We investigated 17 recovered COVID-19 patients at two years after discharge. Diffusion tensor imaging, neurite orientation dispersion and density imaging were performed to identify white matter integrity and changes from one to two years after discharge. Data for 13 revisited healthy controls were collected as a reference. Subscales of the Wechsler Intelligence scale were used to assess cognitive function. Repeated-measures ANOVA was used to detect longitudinal changes in 17 recovered COVID-19 patients and 13 healthy controls after one-year follow-up. Correlations between diffusion metrics, cognitive function, and other clinical characteristics (i.e., inflammatory factors) were also analyzed. Results: Longitudinal analysis showed the recovery trends of large-scale brain regions, with small-scale brain region deterioration from one year to two years after SARS-CoV-2 infection. However, persistent white matter abnormalities were noted at two years after discharge. Longitudinal changes of cognitive function showed no group difference. But cross-sectional cognitive difference between recovered COVID-19 patients and revisited HCs was detected. Inflammation levels in the acute stage correlated positively with white matter abnormalities and negatively with cognitive function. Moreover, the more abnormal the white matter was at two years, the greater was the cognitive deficit present. Conclusion: Recovered COVID-19 patients showed longitudinal recovery trends of white matter. But also had persistent white matter abnormalities at two years after discharge. Inflammation levels in the acute stage may be considered predictors of cognition and white matter integrity, and the white matter microstructure acts as a biomarker of cognitive function in recovered COVID-19 patients. These findings provide an objective basis for early clinical intervention.