Analysis of risk factors of Omicron infection and antibody response in people with HIV: a multi-center cross-sectional study (preprint)

[Purpose]Analyse the factors that influence the rate of Omicron infection, symptom severity, symptom duration and antibody levels in PLWH, in particular the role of vaccination. [Methods]We randomly investigated the Omicron infection status in 338 PLWH at multiple centers and examined their WT and Omicron specific antibodies, separately. [Results]Age was the only factor that affected the rate of Omicron infection, severity of symptoms, and duration of symptoms in PLWH. Infection, number of vaccination, ART regimen and time all affect antibody levels.With the increase of number of vaccination, both WT and BA.5 specific antibodies were gradually increased, and the overall antibody levels of PLWH with hybrid immunity were higher than those of PLWH with only vaccine immune. [Conclusions]The prevalence of Omicron is higher and symptoms is more severe in PLWH younger than 40 years, but the duration of symptoms is shorter in PLWH younger than 40 years. Although vaccination does not reduce the infection rate of SARS-CoV-2, it can significantly not only enhance the antibody level against the original strain, but also expand the antibody response against the newly emerged virus variant strain. What’s more, although breakthrough infections still occur, antibody levels can be significantly increased after hybrid immunization. For PLWH at high risk of infection, booster vaccination may be beneficial for them to cope with SARS-CoV-2 normalized infection.

Epidemiological and Clinical Characteristics of Measles in Jinan, Shandong Province, China, from 1991 to 2022.

Objective: This study aimed to analyze the epidemiological and clinical characteristics of measles in Jinan, Shandong, China, over a 32-year stage to facilitate measles prevention in the future. Methods: Data on measles cases from 1991 to 2022 were obtained from the public health department and medical records of patients at Shandong Public Health Clinical Center. Retrospective analysis was conducted on the distribution of measles cases in different years, months, and age groups, and observation of the differences in clinical manifestations and complications among different age groups. Results: From January 1991 to December 2022, 7531 measles cases were recorded at Shandong Public Health Clinical Center. During the 32-year period, there were two outbreaks of measles in 2008 and 2016, respectively. During the COVID-19 pandemic period from 2020 to 2022, the number of cases reached the lowest point in the past 30 years. The number and percentage of cases in the 0-1y groups was significantly higher than in other age groups, and 97.75% patients in this group did not receive measles vaccine. Complications such as pneumonia and myocarditis appeared more frequent in patients under 12 years of age, but liver function damage is more common in adult patients. Conclusion: Although the measles epidemic has been greatly controlled since the use of measles vaccine, intermittent outbreaks still exist, so there is still a long way to go to eliminate measles. The proportion of infants under the age of 1 without measles vaccine and adults over 24 years old accounts for nearly 80% of the total. This group of people should be of concern, and feasible measures should be designated to protect these susceptible populations.

Exploring the interior of 3D endoluminal lesions in the air spaces by a novel electronic biopsy technique: A preliminary study of endoluminal colon lesions.

To explore the interior of a lesion in a 3D endoluminal view, this study investigates the application of an 'electronic biopsy' (EB) technique to computed tomographic colonography (CTC) for further differentiation and 2D image correlation of endoluminal lesions in the air spaces. A retrospective study of sixty-two various endoluminal lesions from thirty patients (13 males, 17 females; age range, 31 to 90 years) was approved by our institutional review board and evaluated. The endoluminal lesions were segmented using gray-level threshold and reconstructed into isosurfaces using a marching cube algorithm. EB allows users to interactively erode and apply grey-level mapping (GM) to the surface of the region of interest (ROI) in 3D CTC. Radiologists conducted the clinical evaluation, and the resulting data were analyzed. EB significantly improves 3D gray-level presentation for evaluating the surface and inside of endoluminal lesions over that of SR, GM or target GM (TGM) (P < 0.01) with preservation of the 3D spatial effect. Moreover, 3D to 2D image correlation were achieved in any layer of the lesion using EB as did GM/TGM on the surface. The specificity and diagnostic accuracy of EB are significantly greater than those of SR (P < 0.01). These performance can be better further with GM/TGM and reach the best with EB (specificity, 89.3-92.9%; accuracy, 95.2-96.8%). EB can be used in CTC to improve the differentiation of endoluminal lesions. EB increases 3D to 2D image correlations of the lesions on or beneath the lesion surface.

Machine learning approach for predicting inhalation injury in patients with burns.

BACKGROUND: The coronavirus disease pandemic has had a tangible impact on bronchoscopy for burn inpatients due to isolation and triage measures. We utilised the machine-learning approach to identify risk factors for predicting mild and severe inhalation injury and whether patients with burns experienced inhalation injury. We also examined the ability of two dichotomous models to predict clinical outcomes including mortality, pneumonia, and duration of hospitalisation. METHODS: A retrospective 14-year single-centre dataset of 341 intubated patients with burns with suspected inhalation injury was established. The medical data on day one of admission and bronchoscopy-diagnosed inhalation injury grade were compiled using a gradient boosting-based machine-learning algorithm to create two prediction models: model 1, mild vs. severe inhalation injury; and model 2, no inhalation injury vs. inhalation injury. RESULTS: The area under the curve (AUC) for model 1 was 0·883, indicating excellent discrimination. The AUC for model 2 was 0·862, indicating acceptable discrimination. In model 1, the incidence of pneumonia (P < 0·001) and mortality rate (P < 0·001), but not duration of hospitalisation (P = 0·1052), were significantly higher in patients with severe inhalation injury. In model 2, the incidence of pneumonia (P < 0·001), mortality (P < 0·001), and duration of hospitalisation (P = 0·021) were significantly higher in patients with inhalation injury. CONCLUSIONS: We developed the first machine-learning tool for differentiating between mild and severe inhalation injury, and the absence/presence of inhalation injury in patients with burns, which is helpful when bronchoscopy is not available immediately. The dichotomous classification predicted by both models was associated with the clinical outcomes.

Machine Learning Approach for Predicting Inhalation Injury in Patients with Burns

Background The coronavirus disease pandemic has had a tangible impact on bronchoscopy for burn inpatients due to isolation and triage measures. We utilised the machine-learning approach to identify risk factors for predicting mild and severe inhalation injury (INH) and whether patients with burns experienced INH. We also examined the ability of two dichotomous models to predict clinical outcomes including mortality, pneumonia, and duration of hospitalisation. Methods A retrospective 14-year single-centre dataset of 341 intubated patients with burns with suspected INH was established. The medical data on day one of admission and bronchoscopy-diagnosed INH grade were compiled using a gradient boosting-based machine-learning algorithm to create two prediction models: model 1, mild vs. severe INH;and model 2, no INH vs. INH. Results The area under the curve (AUC) for model 1 was 0·883, indicating excellent discrimination. The AUC for model 2 was 0·862, indicating acceptable discrimination. In model 1, the incidence of pneumonia (P<0·001) and mortality rate (P<0·001), but not duration of hospitalisation (P=0·1052), were significantly higher in patients with severe INH. In model 2, the incidence of pneumonia (P<0·001), mortality (P<0·001), and duration of hospitalisation (P=0·021) were significantly higher in patients with INH. Conclusions We developed the first machine-learning tool for differentiating between mild and severe INH, and the absence/presence of INH in patients with burns, which is helpful when bronchoscopy is not available immediately. The dichotomous classification predicted by both models was associated with the clinical outcomes.

New COVID-19 challenges and response strategies adopted by a national suicide prevention hotline: A qualitative study.

Crisis helplines provide important support for vulnerable individuals during the COVID-19 pandemic, which may also impact the helplines. We explored the challenges that the pandemic brought to Taiwan's national suicide prevention hotline and the hotline's responses. We interviewed 14 hotline workers and conducted data analysis using the framework method. The pandemic posed two new challenges to the hotline: potential service interruption and the adjustment of perceived role among hotline workers. The hotline's well-formulated response plan helped it sustain its services during the pandemic, although the workers also experienced stress and frustration resulted from role ambiguity. Our data highlighted the hotline workers' need for accurate COVID-19 information, relevant training, and timely support.

Exploration of COVID-19 Pandemic Prevention Behaviors among Healthcare Workers.

Since the outbreak of COVID-19, the pandemic has become an important topic of global public health. To reduce the rapid spread of the pandemic, compliance with preventive behaviors has become one of the important guidelines from the World Health Organization (WHO). Healthcare workers stand on the frontline for pandemic prevention, and preventive behaviors are essential measures to protect their health and safety. The purpose of this study was to propose an integrative model that explained and predicted COVID-19 preventive behaviors among healthcare workers. The study integrated workplace safety climate and the health belief model (HBM) to verify the impact of workplace safety climate and health belief factors on the safety attitude, safety compliance, and safety satisfaction of healthcare workers performing COVID-19 pandemic prevention behaviors. A cross-sectional study was conducted from March to August 2021 with a self-administered online questionnaire. The sample of the study was drawn from healthcare workers of a famous medical institution in Taipei City as research subjects. After collecting 273 valid questionnaires and verifying them through the analysis of structural equation modeling (SEM), the findings revealed that workplace safety climate had an impact on health belief factors, and then health belief factors had impacts on safety attitudes. In addition, safety attitude affected safety compliance, while safety compliance further affected safety satisfaction. The study showed that workplace safety climate can strengthen healthcare workers' health beliefs and further affect their safety attitudes, safety compliance, and safety satisfaction. The study attempted to propose a model of healthcare workers' pandemic prevention behaviors as a reference for medical facility administrators in real practice.

Framing the virus: The political, economic, biomedical and social understandings of the COVID-19 in Taiwan.

The purpose of this paper is to study how people use texts and languages to interpret or make sense of the COVID-19 pandemic. We draw on the theoretical literature of framing perspectives to formulate our arguments that consider the virus a socially constructed reality. We use Taiwan as an empirical case study, using topic modeling analysis of newspaper articles. Our findings show that the language of the COVID-19 coverage combines the four frames of political evaluation, economic impact, biomedical science and social life in varying proportions. These frames are subject to changes in pandemic conditions. Implications for theory and practice are presented.

Efficacy of a multicomponent exercise training program intervention in community-dwelling older adults during the COVID-19 pandemic: A cluster randomized controlled trial.

The coronavirus disease 2019 (COVID-19) pandemic caused changes in lifestyle for older adults such as reduced physical activity and community participation. Community activity centers were randomly assigned to the intervention (n = 82) or control arm (n = 85). The intervention comprised one 60 min group exercise session per week in weeks 1-8 and an online home exercise program in weeks 9-16. Physical activity, physical performance, and prefrailty rates were assessed at baseline and 16 weeks. At 16 weeks, compared to the control arm, the intervention arm exhibited improved (p < 0.05) leisure-time physical activity (phi = 0.571), vigorous physical activity (phi = 0.534), and moderate-vigorous physical activity (phi = 0.344); prefrailty rates (phi = 0.179); and short physical performance battery results (η2p = 0.113). The intervention thus effectively improved physical activity levels, physical performance, and prefrailty rates in community-dwelling older adults during the COVID-19 pandemic.

RESEARCH ON THE APPLICATION OF BIG DATA IN HEALTHCARE

According to a report (from 2017 to 2020) conducted by Taiwan's Health Promotion Administration, the proportion of adults suffering from hypertension, hyperlipidemia and hyperglycemia were 23.52%, 22.7% and 9.74% respectively (see Fig. 1). Through the intelligent application of clinical big data, real-time analysis and information technology, doctors can make informed decisions, reduce possible medical errors, enhance the efficiency and quality of medical services, and further improve the overall efficiency of healthcare system (Zeadally, et al., 2019). Due to its ever-growing nature, the required data analysis must take atypical approaches and involve specific requirements for data collection, data preprocessing, and data analysis. [...]many algorithms designed for big data analysis have been developed (Mohamed, et al., 2020). (3) Data Preprocessing The data should be consistent and can be processed in a posterior analysis before proceeding to data analysis. [...]these metadata must be preprocessed in order to fulfill the purposes including but not limited to denoising, filtering data, compiling data, and simplifying data (Alonso-Betanzos & Bolon Canedo 2018).

The Impact of the COVID-19 Pandemic on Calls to a National Suicide Prevention Hotline in Taiwan: An Analysis of Time Trend and Characteristics of Calls.

We investigated the impact of the COVID-19 pandemic on call volumes and call characteristics using data from a national crisis helpline. Data were extracted for 215,066 calls to Taiwan's national suicide prevention hotline (January 2018-May 2020). We used negative binomial regression to investigate changes in the weekly number of calls during the early period of the COVID-19 outbreak (January 21, 2020-May 25, 2020), relative to that expected according to the pre-pandemic trend. The call characteristics during the pandemic period (February 18, 2020-May 31, 2020) were compared between COVID-19 related vs unrelated calls. Higher-than-expected call volumes started from the 6th week of the pandemic and reached a peak in the 14th week, which was 38% (rate ratio = 1.38, 95% confidence interval 1.26-1.51) higher than that expected based on the pre-pandemic trend. The higher-than-expected call volumes were mainly attributable to higher-than-expected calls from non-suicidal and male callers. Calls in which COVID-19 was mentioned (13.2%) were more likely to be from male and first-time callers, occur outside 12 am-6 am, last less than 5 min, and were less likely to be from callers who had previous suicide attempts, recent suicidal ideation or suicide plans or actions than COVID-19 unrelated calls. Callers who made COVID-19 related calls were more likely to request information than other callers. Crisis helplines should strategically adapt to the increased need and callers' specific concerns related to the outbreak.

"I Can't See an End in Sight." How the COVID-19 Pandemic May Influence Suicide Risk.

Background: The COVID-19 pandemic and its consequences may affect population mental health and suicide risk. Aims: To explore the experiences among suicidal individuals who made calls to a suicide prevention hotline and to identify factors and psychological responses that may influence suicide risk. Method: We identified 60 eligible recorded calls to Taiwan's suicide prevention hotline (January 23, 2020-May 31, 2020) and analyzed the transcripts using a framework analysis. Results: We identified three themes: (a) effects of the COVID-19 pandemic on society (impacts on local economies, the fear of contagion, and disruptions caused by outbreak control measures); (b) stress experienced by callers, including increased challenges (financial burden, restricted freedom of movement, interpersonal conflicts, feelings of uncertainty, and education/career interruption) and reduced support (reduced access to health services and social support); and (c) the callers' psychological responses to stress, including anxiety, sleep disturbance, depression, loneliness, hopelessness, and entrapment, which may increase suicide risk. Limitations: Only the experiences among those who sought help by calling the hotline during the early months of the pandemic in 2020 were explored. Conclusion: Our findings revealed the potential process underlying the impact of the COVID-19 pandemic on suicide risk and have implications for prevention and intervention strategies.

The predictive performance of current termination-of-resuscitation rules in patients following out-of-hospital cardiac arrest in Asian countries: A cross-sectional multicentre study.

BACKGROUND: Termination-of-resuscitation rules (TORRs) in out-of-hospital cardiac arrest (OHCA) patients have been applied in western countries; in Asia, two TORRs were developed and have not been externally validated widely. We aimed to externally validate the TORRs using the registry of Pan-Asian Resuscitation Outcomes Study (PAROS). METHODS: PAROS enrolled 66,780 OHCA patients in seven Asian countries from 1 January 2009 to 31 December 2012. The American Heart Association-Basic Life Support and AHA-ALS (AHA-BLS), AHA-Advanced Life Support (AHA-ALS), Goto, and Shibahashi TORRs were selected. The diagnostic test characteristics and area under the receiver operating characteristic curve (AUC) were calculated. We further determined the most suitable TORR in Asia and analysed the variable differences between subgroups. RESULTS: We included 55,064 patients in the final analysis. The sensitivity, specificity, negative predictive value, positive predictive value, and AUC, respectively, for AHA-BLS, AHA-ALS, Goto, Shibashi TORRs were 79.0%, 80.0%, 19.6%, 98.5%, and 0.80; 48.6%, 88.3%, 9.8%, 98.5%, and 0.60; 53.8%, 91.4%, 11.2%, 99.0%, and 0.73; and 35.0%, 94.2%, 8.4%, 99.0%, and 0.65. In countries using the Goto TORR with PPV<99%, OHCA patients were younger, had more males, a higher rate of shockable rhythm, witnessed collapse, pre-hospital defibrillation, and survival to discharge, compared with countries using the Goto TORR with PPV ≥99%. CONCLUSIONS: There was no single TORR fit for all Asian countries. The Goto TORR can be considered the most suitable; however, a high predictive performance with PPV ≥99% was not achieved in three countries using it (Korea, Malaysia, and Taiwan).

Suicide numbers during the first 9-15 months of the COVID-19 pandemic compared with pre-existing trends: An interrupted time series analysis in 33 countries.

Background: Predicted increases in suicide were not generally observed in the early months of the COVID-19 pandemic. However, the picture may be changing and patterns might vary across demographic groups. We aimed to provide a timely, granular picture of the pandemic's impact on suicides globally. Methods: We identified suicide data from official public-sector sources for countries/areas-within-countries, searching websites and academic literature and contacting data custodians and authors as necessary. We sent our first data request on 22nd June 2021 and stopped collecting data on 31st October 2021. We used interrupted time series (ITS) analyses to model the association between the pandemic's emergence and total suicides and suicides by sex-, age- and sex-by-age in each country/area-within-country. We compared the observed and expected numbers of suicides in the pandemic's first nine and first 10-15 months and used meta-regression to explore sources of variation. Findings: We sourced data from 33 countries (24 high-income, six upper-middle-income, three lower-middle-income; 25 with whole-country data, 12 with data for area(s)-within-the-country, four with both). There was no evidence of greater-than-expected numbers of suicides in the majority of countries/areas-within-countries in any analysis; more commonly, there was evidence of lower-than-expected numbers. Certain sex, age and sex-by-age groups stood out as potentially concerning, but these were not consistent across countries/areas-within-countries. In the meta-regression, different patterns were not explained by countries' COVID-19 mortality rate, stringency of public health response, economic support level, or presence of a national suicide prevention strategy. Nor were they explained by countries' income level, although the meta-regression only included data from high-income and upper-middle-income countries, and there were suggestions from the ITS analyses that lower-middle-income countries fared less well. Interpretation: Although there are some countries/areas-within-countries where overall suicide numbers and numbers for certain sex- and age-based groups are greater-than-expected, these countries/areas-within-countries are in the minority. Any upward movement in suicide numbers in any place or group is concerning, and we need to remain alert to and respond to changes as the pandemic and its mental health and economic consequences continue. Funding: None.

Encoding protein dynamic information in graph representation for functional residue identification

Summary Recent advances in protein function prediction exploit graph-based deep learning approaches to correlate the structural and topological features of proteins with their molecular functions. However, proteins in vivo are not static but dynamic molecules that alter conformation for functional purposes. Here, we apply normal mode analysis to native protein conformations and augment protein graphs by connecting edges between dynamically correlated residue pairs. In the multilabel function classification task, our method demonstrates a remarkable performance gain based on this dynamics-informed representation. The proposed graph neural network, ProDAR, increases the interpretability and generalizability of residue-level annotations and robustly reflects structural nuance in proteins. We elucidate the importance of dynamic information in graph representation by comparing class activation maps for hMTH1, nitrophorin, and SARS-CoV-2 receptor binding domain. Our model successfully learns the dynamic fingerprints of proteins and pinpoints the residues of functional impacts, with vast untapped potential for broad biotechnology and pharmaceutical applications.

Remdesivir and Mortality in Patients With Coronavirus Disease 2019.

BACKGROUND: The impact of remdesivir (RDV) on mortality rates in coronavirus disease 2019 (COVID-19) is controversial, and the mortality effect in subgroups of baseline disease severity has been incompletely explored. The purpose of this study was to assess the association of RDV with mortality rates in patients with COVID-19. METHODS: In this retrospective cohort study we compared persons receiving RDV with those receiving best supportive care (BSC). Patients hospitalized between 28 February and 28 May 2020 with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 infection were included with the development of COVID-19 pneumonia on chest radiography and hypoxia requiring supplemental oxygen or oxygen saturation ≤94% with room air. The primary outcome was overall survival, assessed with time-dependent Cox proportional hazards regression and multivariable adjustment, including calendar time, baseline patient characteristics, corticosteroid use, and random effects for hospital. RESULTS: A total of 1138 patients were enrolled, including 286 who received RDV and 852 treated with BSC, 400 of whom received hydroxychloroquine. Corticosteroids were used in 20.4% of the cohort (12.6% in RDV and 23% in BSC). Comparing persons receiving RDV with those receiving BSC, the hazard ratio (95% confidence interval) for death was 0.46 (.31-.69) in the univariate model (P < .001) and 0.60 (.40-.90) in the risk-adjusted model (P = .01). In the subgroup of persons with baseline use of low-flow oxygen, the hazard ratio (95% confidence interval) for death in RDV compared with BSC was 0.63 (.39-1.00; P = .049). CONCLUSION: Treatment with RDV was associated with lower mortality rates than BSC. These findings remain the same in the subgroup with baseline use of low-flow oxygen.