Notes

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002). Although NaïveVax avidity lagged behind that of RecoVax for most of the follow-up periods, NaïveVax did reach similar avidity by ~6-months post-D1. These data suggest that one vaccine dose elicits maximal antibody response in RecoVax and may be sufficient. Also, despite decreasing levels in TAb and SNAb overtime, long-term avidity maybe a measure worth evaluating and possibly correlating to vaccine efficacy.Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the cause of coronavirus disease 2019 (COVID-19). Little is known about the interplay between pre-existing immunity towards endemic seasonal coronaviruses and the development of a SARS-CoV-2-specific IgG response. We investigated the kinetics, breadth, magnitude and level of cross-reactivity of IgG antibodies against SARS-CoV-2 and heterologous seasonal and epidemic coronaviruses at the clonal level in mild and severe COVID-19 patients and disease control patients. Antibody reactivity towards nucleocapsid and spike antigens was assessed and correlated to SARS-CoV-2 neutralization. COVID-19 patients mounted a mostly type-specific SARS-CoV-2 response. Additionally, IgG clones directed against seasonal coronavirus were boosted in patients with severe COVID-19. These boosted clones showed limited cross-reactivity and did not neutralize SARS-CoV-2. These findings support a boost of poorly protective coronavirus-specific antibodies in COVID-19 patients that correlates with disease severity, revealing original antigenic sin.
Cardiac injury is common and associated with poor clinical outcomes in COVID-19. Data are lacking whether high-dose intravenous vitamin C (HIVC) could help to ameliorate myocardial injury in the pandemic.

The retrospective cohort study included consecutive severe and critically ill COVID-19 patients with cardiac injury receiving symptomatic supportive treatments alone or together with HIVC. Troponin I and inflammatory markers were collected at admission and day 21 during hospitalization from the electronic medical records.

The patients (n = 113) were categorized into the ameliorated cardiac injury (ACI) group (n = 70) and the non-ameliorated cardiac injury (NACI) group (n = 43). Overall, fifty-one (45.1%) patients were administered with HIVC, the percentages of patients with HIVC were higher in the ACI group than those in the NACI group. Logistic regression analysis revealed that HIVC was independently associated with the improvement of myocardial injury. Further analysis showed that inflammatory markers levels significantly decreased at day 21 during hospitalization in patients with HIVC therapy compared to those administered with symptomatic supportive treatments alone. Meanwhile, similar results were also observed regarding changes in inflammatory markers levels from baseline to day 21 during hospitalization in the patients treated with HIVC.

HIVC can ameliorate cardiac injury through alleviating hyperinflammation in severe and critically ill patients with COVID-19.
HIVC can ameliorate cardiac injury through alleviating hyperinflammation in severe and critically ill patients with COVID-19.The use of machine learning to develop intelligent software tools for the interpretation of radiology images has gained widespread attention in recent years. The development, deployment, and eventual adoption of these models in clinical practice, however, remains fraught with challenges. In this paper, we propose a list of key considerations that machine learning researchers must recognize and address to make their models accurate, robust, and usable in practice. We discuss insufficient training data, decentralized data sets, high cost of annotations, ambiguous ground truth, imbalance in class representation, asymmetric misclassification costs, relevant performance metrics, generalization of models to unseen data sets, model decay, adversarial attacks, explainability, fairness and bias, and clinical validation. We describe each consideration and identify the techniques used to address it. Although these techniques have been discussed in prior research, by freshly examining them in the context of medical imaging and compiling them in the form of a laundry list, we hope to make them more accessible to researchers, software developers, radiologists, and other stakeholders.
Neurological complications including cognitive impairment persist among people with HIV on antiretrovirals; however, cognitive screening is not routinely conducted in HIV clinics.

Our objective for this study was 3-fold (1) to determine the feasibility of implementing an iPad-based cognitive impairment screener among adults seeking HIV care, (2) to examine the psychometric properties of the tool, and (3) to examine predictors of cognitive impairment using the tool.

A convenience sample of participants completed Brain Baseline Assessment of Cognition and Everyday Functioning (BRACE), which included (1) Trail Making Test Part A, measuring psychomotor speed; (2) Trail Making Test Part B, measuring set-shifting; (3) Stroop Color, measuring processing speed; and (4) the Visual-Spatial Learning Test. Global neuropsychological function was estimated as mean T score performance on the 4 outcomes. Impairment on each test or for the global mean was defined as a T score ≤40. Subgroups of participants repeated the also shown to have good psychometric properties. This easy-to-use tool in clinical settings may facilitate the care needs of people with HIV as cognitive impairment continues to remain a concern in people with HIV.
Diabetes has placed heavy social and economic burdens on society and families worldwide. Insufficient knowledge and training of frontline medical staff, such as nurses, interns, and residents, may lead to an increase in acute and chronic complications among patients with diabetes. However, interns have insufficient knowledge about diabetes management. The factors that affect interns' current level of diabetes-related knowledge are still unclear. Therefore, understanding the behavioral intentions of interns is essential to supporting the development and promotion of the use of virtual simulation teaching applications.

This study aimed to identify the determinants of nursing interns' intentions to use simulation-based education applications.

From December 1, 2020, to February 28, 2021, the web-based survey tool Sojump (Changsha Xingxin Information Technology Co) was used to survey nursing interns in hospitals across China. Two survey links were sent to 37 partner schools in 23 major cities in China, and tr, they have high requirements regarding this teaching method. Conducting high-quality randomized controlled trials and designing applications that are suitable for the needs of different nurse trainees will increase students' interest in learning and help improve diabetes knowledge among nursing interns.Electronic patient-reported outcome (ePRO) systems for symptom monitoring in patients with cancer have shown quality of life and survival benefits in controlled trials. They are beginning to be used in routine oncology practice. Many software developers provide software solutions for clinicians, but how should clinicians decide which system to use? We propose a synthesis of the main questions regarding the effectiveness, safety, and functionality of an ePRO system that a clinician should ask software providers to assist in the selection of a software product in order to obtain the best value tools for their patients and their practice.
Tobacco smoking is one of the biggest public health threats. Smartphone apps offer new promising opportunities for supporting smoking cessation in real time. This randomized controlled trial investigated the effectiveness of an app that encourages individuals to quit smoking with the help of a social network member (buddy) in daily life.

The objective of this study is to test the effectiveness of the SmokeFree buddy app compared with a control group with self-reported smoking abstinence and carbon monoxide (CO)-verified smoking abstinence as primary outcomes and self-reports of smoked cigarettes per day (CPD) as a secondary outcome.

A total of 162 adults who smoked participated in this single-blind, two-arm, parallel-group, intensive longitudinal randomized controlled trial. Around a self-set quit date (ie, 7 days before the self-set quit date and 20 days after) and 6 months later, participants of the intervention and control groups reported on daily smoking abstinence and CPD in end-of-day diaries. ABT199 Dainot have beneficial effects on smoking abstinence over and above the self-monitoring control condition. Future studies should examine whether and what support processes can be effectively stimulated and how app use can be improved to better achieve this goal.

ISRCTN Registry 11154315; https//www.isrctn.com/ISRCTN11154315.

RR2-10.1186/s12889-019-7723-z.
RR2-10.1186/s12889-019-7723-z.
An increasing number of mobile health (mHealth) apps are becoming available for download and use on mobile devices. Even with the increase in availability and use of mHealth apps, there has still not been a lot of research into understanding the intention to use this kind of apps.

The purpose of this study was to investigate a technology acceptance model (TAM) that has been specially designed for primary health care applications.

The proposed model is an extension of the TAM, and was empirically tested using data obtained from a survey of mHealth app users (n=310). The research analyzed 2 additional external factors promotion of health and health benefits. Data were analyzed with a PLS-SEM software and confirmed that gender moderates the adoption of mHealth apps in Spain. The explanatory capacity (R
for behavioral intention to use) of the proposed model was 76.4%. Likewise, the relationships of the external constructs of the extended TAM were found to be significant.

The results show the importance ct on PEOU (R
=40.9%), while promotion of health (β=.865, t
=29.943, P<.001) significantly influenced health benefits (R
=74.7%).

mHealth apps could be used to predict the behavior of patients in the face of recommendations to prevent pandemics, such as COVID-19 or SARS, and to track users' symptoms while they stay at home. Gender is a determining factor that influences the intention to use mHealth apps, so perhaps different interfaces and utilities could be designed according to gender.
mHealth apps could be used to predict the behavior of patients in the face of recommendations to prevent pandemics, such as COVID-19 or SARS, and to track users' symptoms while they stay at home. Gender is a determining factor that influences the intention to use mHealth apps, so perhaps different interfaces and utilities could be designed according to gender.
The vaccination uptake rates of the human papillomavirus (HPV) vaccine remain low despite the fact that the effectiveness of HPV vaccines has been established for more than a decade. Vaccine hesitancy is in part due to false information about HPV vaccines on social media. Combating false HPV vaccine information is a reasonable step to addressing vaccine hesitancy.

Given the substantial harm of false HPV vaccine information, there is an urgent need to identify false social media messages before it goes viral. The goal of the study is to develop a systematic and generalizable approach to identifying false HPV vaccine information on social media.

This study used machine learning and natural language processing to develop a series of classification models and causality mining methods to identify and examine true and false HPV vaccine-related information on Twitter.

We found that the convolutional neural network model outperformed all other models in identifying tweets containing false HPV vaccine-related information (F score=91.
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