摘要： IntroductionPeople from backgrounds that are economically/socially disadvantaged experienced disproportionately high COVID-19 death rates and had lower vaccination rates. Effective outreach strategies for increasing vaccine uptake during the pandemic are not fully known. Among patients receiving care at a Federally Qualified Health Center, we tested whether community engaged digitally-enabled outreach from a trusted clinician messenger increased COVID vaccine *** design, setting, and participantsA 3-parallel-arm randomized controlled trial with a hybrid effectiveness-implementation design was conducted among patients >= 18 years old on study enrollment during 2021 with 1,650 assigned in 3:10:20 ratio; 2,328 were later selected for two subsequent implementation *** April 13 to June 10, 2021, patients were proactively sent a text-messaging invitation to make an appointment for vaccination as part of the routine practice with a link to frequently asked questions (Arm 1, n = 150) with added personalized clinician recommendation alone (Arm 2, n = 500) or with enabled 2-way SMS messaging feature (Arm 3, n = 1,000). Further implementation used messaging addressing vaccine hesitancy (n = 1,323) or adverse reactions to vaccines (n = 1,005).Main outcomes and measuresThe primary outcome was the completion of the first SARS-Cov-2 vaccine dose determined at 14, 30 and 90 days after *** 1,650 patients in effectiveness Arms, 61% was female. Vaccination rates for Arms 1, 2, and 3, were 6% (n = 9), 5.4% (n = 27) and 3.3% (n = 33) at 14 days, and 11.5% (n = 17), 11.6% (n = 58), and 8.5% (n = 85) at 90 days, respectively, which were similar in pairwise comparisons. At 90 days, vaccination rates were similar across the two implementation rounds (3.9% vs. 3.6%) and were similar to the rate (3.3%) among patients who were not selected for intervention arms or implementation rounds (n = 8,671).ConclusionsDigitally-enabled outreach that included

关键词： HIV-RELATED STIGMA   HIV/AIDS STIGMA   AGINCOURT HEALTH   WOMEN   POPULATION   GENDER  

摘要： Background Infectious disease-related stigma is a pervasive global issue that impedes disease control efforts by increasing reluctance to seek treatment or engagement in prevention behaviors for fear of ostracism. Despite this, there is limited research on COVID-19 stigma in Africa, specifically rural South Africa, which has faced infectious disease-related stigma throughout the HIV epidemic. Methods Population-based surveys were conducted among 1,662 adults living in the Agincourt Health and Socio-Demographic Surveillance System (AHDSS) area in Mpumalanga, South Africa, in August-October 2020 and August-October 2021. Surveys measured anticipated COVID-19-related stigma from low to high levels. Changes in stigma between surveys were compared using Wilcoxon ranked sign tests, and log-binomial models estimated the association between socio-demographic factors and anticipated stigma at both intervals. Qualitative interviews were conducted in 2022 among 31 adults who completed the original surveys, and the data were analyzed thematically to describe anticipated, perceived, and enacted stigma. Results Anticipated stigma significantly decreased from the first to the second survey (p-value:<0.0001). Stigma was significantly higher among older age groups. In 2020, those less knowledgeable about COVID-19 were 2.24 times as likely to have higher levels of anticipated stigma compared to those who were more knowledgeable (RR:2.24, 95% CI: 1.90,2.64). Fear of being stigmatized influenced willingness to disclose infection. Participants perceived COVID-19 stigma as similar to HIV/AIDS stigma, but concern and fear reduced over time, with differences observed across generations and sexes. For some, fear of death and mistrust of others endorsed enacting stigma toward others. Conclusion While COVID-19 stigma decreased over time in rural South Africa, different forms of stigma persisted and influenced participants' willingness to reveal their COVID-19 infection status. Given South Afri

关键词： Acinetobacter baumannii   Molecular epidemiology   Resistance mechanisms   ST540   Tn2009   RESISTANCE   DISSEMINATION   TIGECYCLINE   SEQUENCE  

摘要： Background The wide spread of carbapenem-resistance clones of Acinetobacter baumannii has made it a global public problem. Some studies have shown that the prevalence of Acinetobacter baumannii clones can change over time. However, few studies with respect to the change of epidemiological clones in Acinetobacter baumannii during Corona Virus Disease 2019 (COVID-19) were reported. This study aims to investigate the molecular epidemiology and resistance mechanisms of Acinetobacter baumannii during COVID-19. Results A total of 95 non-replicated Acinetobacter baumannii isolates were enrolled in this study, of which 60.0% (n = 57) were identified as carbapenem-resistant Acinetobacter baumannii (CRAB). The positive rate of the blaOXA-23 gene in CRAB isolates was 100%. A total of 28 Oxford sequence types (STs) were identified, of which the most prevalent STs were ST540 (n = 13, 13.7%), ST469 (n = 13, 13.7%), ST373 (n = 8, 8.4%), ST938 (n = 7, 7.4%) and ST208 (n = 6, 6.3%). Differently, the most widespread clone of Acinetobacter baumannii in China during COVID-19 was ST208 (22.1%). Further study of multidrug-resistant ST540 showed that all of them were carrying blaOXA-23, blaOXA-66, blaADC-25 and blaTEM-1D, simultaneously, and first detected Tn2009 in ST540. The blaOXA-23 gene was located on transposons Tn2006 or Tn2009. In addition, the ST540 strain also contains a drug-resistant plasmid with msr(E), armA, sul1 and mph(E) genes. Conclusion The prevalent clones of Acinetobacter baumannii in our organization have changed during COVID-19, which was different from that of China. ST540 strains which carried multiple drug-resistant mobile elements was spreading, indicating that it is essential to strengthen the molecular epidemiology of Acinetobacter baumannii.

关键词： Sleep disorders   Nurses   Prevalence   COVID-19   NIGHT-SHIFT   DURATION   FATIGUE   HEALTH   INDEX  

摘要： The COVID-19 pandemic has significantly impacted the working conditions of nurses, leading to a detrimental effect on their sleep quality. This cross-sectional study aimed to investigate the prevalence of poor sleep quality and its associated factors among nurses working in COVID-19 wards in Kermanshah, Iran. A total of 97 nurses were selected through simple random sampling from COVID-19 wards. Data was collected using a demographic information sheet and the Pittsburgh Sleep Quality Index. Descriptive and inferential statistical methods, including chi-square and multiple logistic regression, were used for data analysis. The results showed that 74.2% (n = 72) of the nurses experienced poor sleep quality. Significant associations were found between poor sleep quality and work experience (p = 0.045) as well as the type of work shift (p = 0.001). However, no significant relationships were observed between poor sleep quality and factors such as age, sex, body mass index, overtime hours per month, physical activity, or underlying diseases. The high prevalence of poor sleep quality among nurses working in COVID-19 wards underscores the necessity of implementing targeted interventions to address this issue. In this regard, in addition to periodic shift schedule changes and reductions in working hours, it is necessary to adopt purposeful measures to improve working conditions and enhance the physical and mental health of nurses. These measures may include providing sufficient human resources to reduce the workload and fatigue of nurses, appropriate scheduling of working hours, and the implementation of stress management programs.

关键词： COVID-19   vaccine   disparities   outreach   research   prevention   Puerto Rico  

摘要： In September 2020, the National Institutes of Health acted in response to the COVID-19 pandemic, recognizing the critical need to combat misinformation, particularly in communities disproportionately affected by the crisis. The Community Engagement Alliance (CEAL) emerged as an initiative dedicated to fostering reliable, science-based information, diversity, and inclusion; aiming to implement effective strategies to mitigate the spread of COVID-19 nationwide. One of the teams participating in this initiative is Puerto Rico-CEAL (PR-CEAL). Our whose goal was to raise awareness about the coronavirus disease and advance research, mainly focusing on vulnerable and underserved populations. This concept paper seeks to outline PR-CEAL's infrastructure during its initial two cycles, providing insights into the research and community engagement activities designed to enhance prevention, counter misinformation, and foster awareness and uptake of COVID-19 vaccines. Ultimately, our objective is to reflect on the strengths and challenges encountered thus far as we endeavor to sustain this robust infrastructure, addressing ongoing public health issues with a forward-looking approach.

关键词： acute respiratory distress syndrome   biomarkers   COVID-19 management   dexamethasone   inflammation   mortality   pneumonia   randomized controlled trial  

摘要： Introduction and objectives Corticosteroids are among the drugs demonstrating a mortality benefit for coronavirus disease 2019 (COVID-19). The RECOVERY trial highlighted that dexamethasone reduced 28-day mortality for hospitalized COVID-19 patients requiring either supplemental oxygen or mechanical ventilation. It is noted that approximately 30% of COVID-19 patients, initially presenting with mild symptoms, will advance to acute respiratory distress syndrome (ARDS), especially those with detectable laboratory markers of inflammation indicative of disease progression. Our research aimed to explore the efficacy of dexamethasone in preventing the progression to ARDS in patients hospitalized with COVID-19 pneumonia who do not yet require additional oxygen but are at high risk of developing ARDS, potentially leading to a reduction in *** In this multicenter, randomized, controlled trial, we evaluated the impact of dexamethasone on adult patients diagnosed with COVID-19 pneumonia who did not need supplementary oxygen at admission but were identified as having risk factors for ARDS. The risk of ARDS was determined based on specific criteria: elevated lactate dehydrogenase levels over 245 U/L, C-reactive protein levels exceeding 100 mg/L, and a lymphocyte count below 0.80 x 109/L. Participants were randomly allocated to either receive dexamethasone or the standard care. The primary endpoints included the incidence of moderate or severe ARDS and all-cause mortality within 30 days *** One hundred twenty-six patients were randomized. Among them, 41 were female (30.8%), with a mean age of 48.8 +/- 14.4 years. Ten patients in the dexamethasone group (17.2%) and ten patients in the control group (14.7%) developed moderate ARDS with no significant differences. Mechanical ventilation was required in six patients (4.7%), with four in the treatment group and two in the control group. There were no deaths during hospitalization or during follow-u

关键词： COVID-19   Deep learning   Severity   ViT  

摘要： In this research, we developed a two-stage deep learning (DL) model using Vision Transformer (ViT) to detect COVID-19 and assess its severity from thoracic CT images. In the first stage, we utilized a pre-trained ViT model (ViT_B/32) and a custom CNN model to classify CT images as COVID-19 or non-COVID-19. The ViT model achieved superior performance with a fivefold cross-validated accuracy of 99.7%, compared to the custom CNN's 98%. In the second stage, we employed a ViT-based U-Net model (Vision Transformer for Biomedical Image Segmentation, VITBIS) to segment lung and infection regions in COVID-19 positive CT images, determining the infection severity. This model uses transformers with attention mechanisms in both the encoder and decoder. The lung segmentation network achieved an Intersection Over Union (IOU) of 95.8% and a sensitivity of 99.67%, while the lesion segmentation network attained an IOU of 94% and a sensitivity of 98.3%. Accurate COVID-19 Detection: ViT-based models outperform CNNs in detecting COVID-19 from CT scans, achieving 99% *** Severity Classification: Utilizing ViT for segmentation, the proposed DL approach accurately classifies infection severity *** Improvements: Larger datasets and ground truth severity labels could enhance model performance and simplify tasks.

关键词： Sentiment analysis   Natural language processing (NLP)   Deep learning (DL)   Recurrent neural networks (RNN)   Long short-term memory (LSTM)   Gated recurrent units (GRUs)   SENTIMENT ANALYSIS   NUMBER   MODEL  

摘要： In March 2020, the whole world suffered from the coronavirus pandemic. This virus is a sort of virus that comes in many forms, some of which may kill. It mainly affects the human respiratory system. The development and search for COVID-19 vaccines became the global goal to stop the spread of the deadly disease. By the end of 2020, the first set of immunizations started to become available. Some countries began their immunization campaigns early. Meanwhile, others awaited the outcome of a successful trial. This research explores classifying users' hesitation or confidence about COVID-19 immunizations. To determine the sentiment of tweets related to vaccines, we collected tweets in Arabic related to various vaccines. After collecting the tweets, we have done pre-possessing using natural language processing (NLP) techniques. After that, we developed a hybrid approach for data annotation to detect the polarity of data. We used a hybrid data annotation utilizing three different lexicons. Finally, many machine learning (ML) and deep learning (DL) methods such as Multinomial Na & iuml;ve Bayes (MNB), logistic regression (LR), support vector machine (SVM), long short-term memory (LSTM), combined Gated Recurrent Unit (GRU), conventional neural network and combinations of CNN and LSTM and their hybrid versions were used and compared. Experimental results revealed that the proposed hybrid annotation method outperformed the conventional one in predicting the confidence or hesitation of people regarding COVID-19 vaccines. The maximum accuracy achieved was 98.1% using the hybrid CNN-GRU with a hybrid approach to data annotation.

关键词： FLIGHT-INITIATION DISTANCE   HUMAN DISTURBANCE   ESCAPE BEHAVIOR   ANTIPREDATOR BEHAVIOR   R PACKAGE   MOBILITY   FEAR   HABITUATION   HYPOTHESIS   DIVERSITY  

摘要： The coronavirus disease 2019 (COVID-19) pandemic and respective shutdowns dramatically altered human activities, potentially changing human pressures on urban-dwelling animals. Here, we use such COVID-19-induced variation in human presence to evaluate, across multiple temporal scales, how urban birds from five countries changed their tolerance towards humans, measured as escape distance. We collected 6369 escape responses for 147 species and found that human numbers in parks at a given hour, day, week or year (before and during shutdowns) had a little effect on birds' escape distances. All effects centered around zero, except for the actual human numbers during escape trial (hourly scale) that correlated negatively, albeit weakly, with escape distance. The results were similar across countries and most species. Our results highlight the resilience of birds to changes in human numbers on multiple temporal scales, the complexities of linking animal fear responses to human behavior, and the challenge of quantifying both simultaneously in situ. A study on urban birds' tolerance towards humans highlights their resilience to changes in human numbers, induced by COVID-19 shutdowns, across multiple temporal scales.