A novel capsule endoscopy for upper and mid-GI tract: the UMGI capsule.

BACKGROUNDS AND AIMS: Complete and consecutive observation of the gastrointestinal (GI) tract continues to present challenges for current endoscopy systems. We developed a novel upper and mid gastrointestinal (UMGI) capsule endoscopy using the modified detachable string magnetically controlled capsule endoscopy (DS-MCE) and inspection method and aimed to assess the clinical application. METHODS: Patients were recruited to undergo UMGI capsule endoscopy followed by esophagogastroduodenoscopy. All capsule procedures in the upper gastrointestinal (UGI) tract were conducted under the control of magnet and string. The main outcome was technical success, and the secondary outcomes included visualization of the UMGI tract, examination time, diagnostic yield, compliance, and safety evaluation. RESULTS: Thirty patients were enrolled and all UMGI capsule procedures realized repeated observation of the esophagus and duodenum with detection rates of 100.0%, 80.0%, and 86.7% of Z-line, duodenal papilla, and reverse side of pylorus, respectively. String detachment was succeeded in 29 patients (96.7%) and the complete examination rate of UMGI tract was 95.45% (21/22). All UMGI capsule procedures were well tolerated with low discomfort score, and had a good diagnostic yield with per-lesion sensitivity of 96.2% in UGI diseases. No adverse events occurred. CONCLUSIONS: This new capsule endoscopy system provides an alternative screening modality for the UMGI tract, and might be indicated in cases of suspected upper and small bowel GI bleeding. Trial registration DS-MCE-UGI and SB, NCT04329468. Registered 27 March 2020, https://clinicaltrials.gov/ct2/results?cond=&term=NCT04329468 .

Serious adverse reaction associated with the COVID-19 vaccines of BNT162b2, Ad26.COV2.S, and mRNA-1273: Gaining insight through the VAERS.

Background and purpose: Serious adverse events following immunization (AEFI) associated with the COVID-19 vaccines, including BNT162b2 (Pfizer-BioNTech), Ad26.COV2.S (Janssen), and mRNA-1273 (Moderna), have not yet been fully investigated. This study was designed to evaluate the serious AEFI associated with these three vaccines. Methods: A disproportionality study was performed to analyze data acquired from the Vaccine Adverse Event-Reporting System (VAERS) between 1 January 2010 and 30 April 2021. The reporting odds ratio (ROR) method was used to identify the association between the COVID-19 vaccines BNT162b2, Ad26.COV2.S, and mRNA-1273 and each adverse event reported. Moreover, the ratio of the ROR value to the 95% CI span was applied to improve the credibility of the ROR. The median values of time from vaccination to onset (TTO) for the three vaccines were analyzed. Results: Compared with BNT162b2 and mRNA-1273, Ad26.COV2.S vaccination was associated with a lower death frequency (p < 0.05). Ad26.COV2.S vaccination was associated with a lower birth defect and emergency room visit frequency than BNT162b2 (p < 0.05). There were 6,605, 830, and 2,292 vaccine recipients who suffered from COVID-19-related symptoms after vaccination with BNT162b2, Ad26.COV2.S, and mRNA-1273, respectively, including people who were infected by COVID-19, demonstrated a positive SARS-CoV-2 test, and were asymptomatic. Serious AEFI, including thromboembolism, hemorrhage, thrombocytopenia, cardiac arrhythmia, hypertension, and hepatotoxicity, were associated with all three vaccines. Cardiac failure and acute renal impairment events were associated with BNT162b2 and mRNA-1273, while seizure events were associated with BNT162b2 and Ad26.COV2.S. The median values of TTO associated with the three vaccinations were similar. Conclusion: These findings may be useful for health workers and the general public prior to inoculation, especially for patients with underlying diseases; however, the risk/benefit profile of these vaccines remains unchanged. The exact mechanism of SARS-CoV-2 vaccine-induced AEFI remains unknown, and further studies are required to explore these phenomena.

Determinants of COVID-19 testing among late middle-aged and older adults: Applying the health belief model.

Objectives: The purpose of this study was to examine correlates of taking a COVID-19 test among late middle-aged and older adults using nationally representative data. Methods: Data were obtained from the 2020 Health and Retirement Study midway release COVID-19 module. Our sample was representative of community residing adults aged 51 and over in the United States (n = 2,870). Measurements: We regressed taking a COVID-19 test on demographic characteristics, medical comorbidities, and measures related to the health belief model (i.e., perceived severity, perceived susceptibility, cues to action, and perceived barriers) using logistic regression, stratifying the model by 10-year age categories. Results: Concern about the pandemic was associated with an increase in the likelihood of taking a test among late middle-aged adults. Knowing someone who was diagnosed with COVID-19 was associated with taking a test in most age categories. Financial barriers and knowing someone who died of COVID-19 were not associated with taking a test. Conclusions: How late middle-aged and older adults perceive the COVID-19 pandemic may significantly influence their likelihood of taking a COVID-19 test.

Clinical Applicable AI System Based on Deep Learning Algorithm for Differentiation of Pulmonary Infectious Disease.

Objective: To assess the performance of a novel deep learning (DL)-based artificial intelligence (AI) system in classifying computed tomography (CT) scans of pneumonia patients into different groups, as well as to present an effective clinically relevant machine learning (ML) system based on medical image identification and clinical feature interpretation to assist radiologists in triage and diagnosis. Methods: The 3,463 CT images of pneumonia used in this multi-center retrospective study were divided into four categories: bacterial pneumonia (n = 507), fungal pneumonia (n = 126), common viral pneumonia (n = 777), and COVID-19 (n = 2,053). We used DL methods based on images to distinguish pulmonary infections. A machine learning (ML) model for risk interpretation was developed using key imaging (learned from the DL methods) and clinical features. The algorithms were evaluated using the areas under the receiver operating characteristic curves (AUCs). Results: The median AUC of DL models for differentiating pulmonary infection was 99.5% (COVID-19), 98.6% (viral pneumonia), 98.4% (bacterial pneumonia), 99.1% (fungal pneumonia), respectively. By combining chest CT results and clinical symptoms, the ML model performed well, with an AUC of 99.7% for SARS-CoV-2, 99.4% for common virus, 98.9% for bacteria, and 99.6% for fungus. Regarding clinical features interpreting, the model revealed distinctive CT characteristics associated with specific pneumonia: in COVID-19, ground-glass opacity (GGO) [92.5%; odds ratio (OR), 1.76; 95% confidence interval (CI): 1.71-1.86]; larger lesions in the right upper lung (75.0%; OR, 1.12; 95% CI: 1.03-1.25) with viral pneumonia; older age (57.0 years ± 14.2, OR, 1.84; 95% CI: 1.73-1.99) with bacterial pneumonia; and consolidation (95.8%, OR, 1.29; 95% CI: 1.05-1.40) with fungal pneumonia. Conclusion: For classifying common types of pneumonia and assessing the influential factors for triage, our AI system has shown promising results. Our ultimate goal is to assist clinicians in making quick and accurate diagnoses, resulting in the potential for early therapeutic intervention.

Higher Star, Better Performance in US Nursing Homes during the Covid-19 Pandemic ?

The Covid-19 pandemic has brought terrible difficulties to nursing homes, as they were locations with the highest number of confirmed Covid-19 cases and deaths in the US. The Centers for Medicare & Medicaid Services (CMS) applies the Five-Star Quality Ratings to indicate the quality of care in nursing homes based on health inspection survey, staffing, and resident outcome. Studies to date have contradictory findings regarding the relationship between nursing home reported Quality Ratings and Covid-19 cases and deaths based on US regional data. The purpose of this study is to examine whether nursing homes’ Quality Ratings were related to the total number of resident Covid-19 cases and deaths at the US National level. The study examined US nursing homes (N=13,494) linked with CMS Nursing Home Compare data and Covid-19 Nursing Home data. Using multiple linear regression analyses, results showed nursing home Quality Ratings were significantly associated with Covid-19 residents’ cases and deaths controlling for ownership type, size, occupation rate, and years of operation (p<.001;p<.001). Five-star nursing homes were less likely to have Covid-19 cases and deaths. Further, comparing lower Star Ratings nursing homes, 1-Star nursing homes showed no significant difference to 2-Star and 3-Star nursing homes when examining Covid-19 cases and deaths. Overall, the Five-Star Quality Ratings is a useful measure when investigating nursing homes’ performance during the Covid-19 pandemic. Future policymakers and administrators should also focus on nursing homes with lower star ratings when improving the quality of nursing homes, particularly with regard to resident health.

Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT

Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses.

Coronary artery calcification and risk of mortality and adverse outcomes in patients with COVID-19: a Chinese multicenter retrospective cohort study.

Background: Coronary artery calcification (CAC) is an independent risk factor of major adverse cardiovascular events; however, the impact of CAC on in-hospital death and adverse clinical outcomes in patients with coronavirus disease 2019 (COVID-19) remains unclear. Objective: To explore the association between CAC and in-hospital mortality and adverse events in patients with COVID-19. Methods: This multicenter retrospective cohort study enrolled 2067 laboratory-confirmed COVID-19 patients with definitive clinical outcomes (death or discharge) admitted from 22 tertiary hospitals in China between January 3, 2020 and April 2, 2020. Demographic, clinical, laboratory results, chest CT findings, and CAC on admission were collected. The primary outcome was in-hospital death and the secondary outcome was composed of in-hospital death, admission to intensive care unit (ICU), and requiring mechanical ventilation. Multivariable Cox regression analysis and Kaplan-Meier plots were used to explore the association between CAC and in-hospital death and adverse clinical outcomes. Results: The mean age was 50 years (SD,16) and 1097 (53.1%) were male. A total of 177 patients showed high CAC level, and compared with patients with low CAC, these patients were older (mean age: 49 vs. 69 years, P < 0.001) and more likely to be male (52.0% vs. 65.0%, P = 0.001). Comorbidities, including cardiovascular disease (CVD) ([33.3%, 59/177] vs. [4.7%, 89/1890], P < 0.001), presented more often among patients with high CAC, compared with patients with low CAC. As for laboratory results, patients with high CAC had higher rates of increased D-dimer, LDH, as well as CK-MB (all P < 0.05). The mean CT severity score in high CAC group was also higher than low CAC group (12.6 vs. 11.1, P = 0.005). In multivariable Cox regression model, patients with high CAC were at a higher risk of in-hospital death (hazard ratio [HR], 1.731; 95% CI 1.010-2.971, P = 0.046) and adverse clinical outcomes (HR, 1.611; 95% CL 1.087-2.387, P = 0.018). Conclusion: High CAC is a risk factor associated with in-hospital death and adverse clinical outcomes in patients with confirmed COVID-19, which highlights the importance of calcium load testing for hospitalized COVID-19 patients and calls for attention to patients with high CAC. Supplementary Information: The online version contains supplementary material available at 10.1007/s42058-021-00072-4.

Barriers and Facilitators Associated With Vaccine Acceptance and Uptake Among Pregnant Women in High Income Countries: A Mini-Review.

Vaccination during pregnancy is a safe and effective intervention to protect women from potentially severe consequences of influenza and reduce risk of influenza and pertussis in their infants. However, coverage remains variable. In this mini-review we update findings from a 2015 systematic review to describe results from recent studies in high income countries on the uptake of influenza and pertussis vaccination in pregnancy, reasons for vaccine hesitancy and barriers to increasing uptake, from maternal and healthcare provider (HCP) perspectives. Studies reported highly variable uptake (from 0% to 78%). A main facilitator for uptake among pregnant women was receiving a recommendation from their HCP. However, studies showed that HCP awareness of guidelines did not consistently translate into them recommending vaccines to pregnant women. Safety concerns are a well-established barrier to uptake/coverage of maternal immunization; 7%-52% of unvaccinated women gave safety concerns as a reason but these were also present in vaccinated women. Knowledge/awareness gaps among pregnant women and lack of confidence among HCPs to discuss vaccination were both important barriers. Several studies indicated that midwives were more likely to express safety concerns than other HCPs, and less likely to recommend vaccination to pregnant women. Women who perceived the risk of infection to be low were less likely to accept vaccination in several studies, along with women with prior vaccine refusal. Findings highlight the importance of further research to explore context-specific barriers to vaccination in pregnancy, which may include lack of vaccine confidence among pregnant woman and HCPs, and policy and structural factors.

Blocking Effect of Demethylzeylasteral on the Interaction between Human ACE2 Protein and SARS-CoV-2 RBD Protein Discovered Using SPR Technology.

The novel coronavirus disease (2019-nCoV) has been affecting global health since the end of 2019, and there is no sign that the epidemic is abating. Targeting the interaction between the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and the human angiotensin-converting enzyme 2 (ACE2) receptor is a promising therapeutic strategy. In this study, surface plasmon resonance (SPR) was used as the primary method to screen a library of 960 compounds. A compound 02B05 (demethylzeylasteral, CAS number: 107316-88-1) that had high affinities for S-RBD and ACE2 was discovered, and binding affinities (KD, µM) of 02B05-ACE2 and 02B05-S-RBD were 1.736 and 1.039 µM, respectively. The results of a competition experiment showed that 02B05 could effectively block the binding of S-RBD to ACE2 protein. Furthermore, pseudovirus infection assay revealed that 02B05 could inhibit entry of SARS-CoV-2 pseudovirus into 293T cells to a certain extent at nontoxic concentration. The compoundobtained in this study serve as references for the design of drugs which have potential in the treatment of COVID-19 and can thus accelerate the process of developing effective drugs to treat SARS-CoV-2 infections.

Blocking Effect of Demethylzeylasteral on the Interaction between Human ACE2 Protein and SARS-CoV-2 RBD Protein Discovered Using SPR Technology

The novel coronavirus disease (2019-nCoV) has been affecting global health since the end of 2019, and there is no sign that the epidemic is abating. Targeting the interaction between the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and the human angiotensin-converting enzyme 2 (ACE2) receptor is a promising therapeutic strategy. In this study, surface plasmon resonance (SPR) was used as the primary method to screen a library of 960 compounds. A compound 02B05 (demethylzeylasteral, CAS number: 107316-88-1) that had high affinities for S-RBD and ACE2 was discovered, and binding affinities (KD, μM) of 02B05-ACE2 and 02B05-S-RBD were 1.736 and 1.039 μM, respectively. The results of a competition experiment showed that 02B05 could effectively block the binding of S-RBD to ACE2 protein. Furthermore, pseudovirus infection assay revealed that 02B05 could inhibit entry of SARS-CoV-2 pseudovirus into 293T cells to a certain extent at nontoxic concentration. The compoundobtained in this study serve as references for the design of drugs which have potential in the treatment of COVID-19 and can thus accelerate the process of developing effective drugs to treat SARS-CoV-2 infections.

Differential diagnosis of coronavirus disease 2019 from community-acquired-pneumonia by computed tomography scan and follow-up.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) is currently the most serious infectious disease in the world. An accurate diagnosis of this disease in the clinic is very important. This study aims to improve the differential ability of computed tomography (CT) to diagnose COVID-19 and other community-acquired pneumonias (CAPs) and evaluate the short-term prognosis of these patients. METHODS: The clinical and imaging data of 165 COVID-19 and 118 CAP patients diagnosed in seven hospitals in Anhui Province, China from January 21 to February 28, 2020 were retrospectively analysed. The CT manifestations of the two groups were recorded and compared. A correlation analysis was used to examine the relationship between COVID-19 and age, size of lung lesions, number of involved lobes, and CT findings of patients. The factors that were helpful in diagnosing the two groups of patients were identified based on specificity and sensitivity. RESULTS: The typical CT findings of COVID-19 are simple ground-glass opacities (GGO), GGO with consolidation or grid-like changes. The sensitivity and specificity of the combination of age, white blood cell count, and ground-glass opacity in the diagnosis of COVID-19 were 92.7 and 66.1%, respectively. Pulmonary consolidation, fibrous cords, and bronchial wall thickening were used as indicators to exclude COVID-19. The sensitivity and specificity of the combination of these findings were 78.0 and 63.6%, respectively. The follow-up results showed that 67.8% (112/165) of COVID-19 patients had abnormal changes in their lung parameters, and the severity of the pulmonary sequelae of patients over 60 years of age worsened with age. CONCLUSIONS: Age, white blood cell count and ground-glass opacity have high accuracy in the early diagnosis of COVID-19 and the differential diagnosis from CAP. Patients aged over 60 years with COVID-19 have a poor prognosis. This result provides certain significant guidance for the diagnosis and treatment of new coronavirus pneumonia.

CT manifestations of coronavirus disease-2019: A retrospective analysis of 73 cases by disease severity.

PURPOSE: To report CT features of coronavirus disease-2019 (COVID-19) in patients with various disease severity. METHODS: The CT manifestations and clinical data of 73 patients with COVID-19 were retrospectively collected in 6 hospitals from Jan 21 to Feb 3, 2020. We analyzed the initial and follow-up CT features of patients with disease severity, according to the Guidelines for the Diagnosis and Treatment of New Coronavirus Pneumonia. RESULTS: Six patients (8%) were diagnosed as mild type pneumonia; these patients had no obvious abnormal CT findings or manifested mild changes of lung infection. All 43 patients (59 %) with common type presented unique or multiple ground-glass opacities (GGO) in the periphery of the lungs, with or without interlobular septal thickening. In the 21 patients (29 %) with severe type, extensive GGO and pulmonary consolidation were found in 16 cases (16/21, 76 %) and 5 cases (24 %), respectively. An extensive "white lung", with atelectasis and pleural effusion were found in critical type patients (3, 4%). On the resolutive phase of the disease, CT abnormalities showed complete resolution, or demonstrated residual linear opacities. CONCLUSIONS: Different CT features are seen according to disease severity, which can help COVID-19 stratification.