Object This study attempted to explore the effects of vaccination on disease severity and the factors for viral clearance and hospitalization in omicron-infected patients. Methods The clinical manifestations of 3,265 Omicron-infected patients (BA.2 lineage variant; the Omicron group) were compared with those of 226 Delta-infected patients (the Delta group). A Multi-class logistic regression model was employed to analyze the impacts of vaccination doses and intervals on disease severity; a logistic regression model to evaluate the risk factors for hospitalization; R 4.1.2 data analysis to investigate the factors for time for nucleic acid negativization (NAN). Results Compared with the Delta group, the Omicron group reported a fast transmission, mild symptoms, and lower severity incidence, and a significant inverse correlation of vaccination dose with clinical severity (OR: 0.803, 95%CI: 0.742-0.868, p<0.001). Of the 7 or 5 categories of vaccination status, the risk of severity significantly decreased only at ≥21 days after three doses (OR: 0.618, 95% CI: 0.475-0.803, p<0.001; OR: 0.627, 95% CI: 0.482-0.815, p<0.001, respectively). The Omicron group also reported underlying illness as an independent factor for hospitalization, sore throat as a protective factor, and much shorter time for NAN [15 (12,19) vs. 16 (12,22), p<0.05]. NAN was associated positively with age, female gender, fever, cough, and disease severity, but negatively with vaccination doses. Conclusion Booster vaccination should be advocated for COVID-19 pandemic-related control and prevention policies and adequate precautions should be taken for patients with underlying conditions.

The effects of vaccination on the disease severity and factors for viral clearance and hospitalization in Omicron-infected patients: A retrospective observational cohort study from recent regional outbreaks in China

https://doi.org/10.1016/j.virol.2022.09.003

Development of a thermostable SARS-CoV-2 variant-based bivalent protein vaccine with cross-neutralizing potency against Omicron subvariants

Data on immunogenicity, immune response persistency, and safety of COVID-19 boosters in patients with comorbidities are limited. Therefore, we aimed to evaluate three different boosters’ immunogenicity and safety in individuals with at least one underlying disease (UD) (obesity, hypertension, and diabetes mellitus) with healthy ones (HC) who were primed with two doses of the BBIBP-CorV vaccine and received a booster shot of the same priming vaccine or protein subunit vaccines, PastoCovac Plus or PastoCovac. One hundred and forty subjects including sixty-three ones with a comorbidity and seventy-seven healthy ones were enrolled. The presence of SARS-CoV-2 antibodies was assessed before the booster injection and 28, 60, 90, and 180 days after it. Moreover, the adverse events (AEs) were recorded on days 7 and 21 postbooster shot for evaluating safety outcomes. Significantly increased titers of antispike, antiRBD, and neutralizing antibodies were observed in both UD and HC groups 28 days after the booster dose. Nevertheless, the titer of antispike IgG and anti-RBD IgG was lower in the UD group compared to the HC group. The long-term assessment regarding persistence of humoral immune responses showed that the induced antibodies were detectable up to 180 days postbooster shots though with a declined titer in both groups with no significant differences (p > 0.05). Furthermore, no significant difference in antibody levels was observed between each UD subgroup and the HC group, except for neutralizing antibodies in the hypertension subgroup. PastoCovac Plus and PastoCovac boosters induced a higher fold rise in antibodies in UD individuals than BBIBP-CorV booster recipients. No serious AEs after the booster injection were recorded. The overall incidence of AEs after the booster injection was higher in the UD group than the HC group among whom the highest systemic rate of AEs was seen in the BBIBP-CorV booster recipients. In conclusion, administration of COVID-19 boosters could similarly induce robust and persistent humoral immune responses in individuals with or without UD primarily vaccinated with two doses of the BBIBP-CorV. Protein-based boosters with higher a higher fold rise in antibodies and lower AEs in individuals with comorbidities might be considered a better choice for these individuals.

A Comparative Study of Immunogenicity, Antibody Persistence, and Safety of Three Different COVID-19 Boosters between Individuals with Comorbidities and the Normal Population

Introduction It is well known that influenza and other respiratory viruses are wintertime-seasonal in temperate regions. However, respiratory disease seasonality in the tropics is less well understood. In this study, we aimed to characterise the seasonality of influenza-like illness (ILI) and influenza virus in Ho Chi Minh City, Vietnam. Methods We monitored the daily number of ILI patients in 89 outpatient clinics from January 2010 to December 2019. We collected nasal swabs and tested for influenza from a subset of clinics from May 2012 to December 2019. We used spectral analysis to describe the periodic signals in the system. We evaluated the contribution of these periodic signals to predicting ILI and influenza patterns through lognormal and gamma hurdle models. Results During 10 years of community surveillance, 66 799 ILI reports were collected covering 2.9 million patient visits; 2604 nasal swabs were collected, 559 of which were PCR-positive for influenza virus. Both annual and nonannual cycles were detected in the ILI time series, with the annual cycle showing 8.9% lower ILI activity (95% CI 8.8% to 9.0%) from February 24 to May 15. Nonannual cycles had substantial explanatory power for ILI trends (ΔAIC=183) compared with all annual covariates (ΔAIC=263) in lognormal regression. Near-annual signals were observed for PCR-confirmed influenza but were not consistent over time or across influenza (sub)types. The explanatory power of climate factors for ILI and influenza virus trends was weak. Conclusion Our study reveals a unique pattern of respiratory disease dynamics in a tropical setting influenced by both annual and nonannual drivers, with influenza dynamics showing near-annual periodicities. Timing of vaccination campaigns and hospital capacity planning may require a complex forecasting approach. 

pdf/a-combination-of-annual-and-nonannual-forces-drive-1l8exe05xz.pdf

A combination of annual and nonannual forces drive respiratory disease in the tropics

https://doi.org/10.1016/j.vaccine.2023.01.020

Safety of primary immunization using inactivated SARS-CoV-2 vaccine (CoronaVac®) among population aged 3 years and older in a large-scale use: A multi-center open-label study in China

Background: People living with chronic disease, particularly seniors older than 60 years old, are lagging behind in the national vaccination campaign in China due to uncertainty of safety and effectiveness. However, this special population made up of most severe symptom and death cases among infected patients and should be prioritized in vaccination program. In this retrospective study, we assessed the safety and immunogenicity of the CoronaVac inactivated vaccines in people with underlying medical conditions to address the vaccine hesitation in this special population. Methods: In this cohort study, volunteers aged 40 years and older, had received two doses of CoronaVac inactivated vaccines (3-5 weeks interval), been healthy or with at least one of the six diseases: coronary heart disease (CAD), hypertension, diabetes mellitus (DM), chronic respiratory disease (CRD), obesity and cancer, were recruited from 4 study sites in China. The primary safety outcome was the incidence of adverse events within 14 days after each dose of vaccination. The primary immunogenic outcome was geometric mean titer (GMT) of neutralizing antibodies to living SARS-CoV-2 virus at 14-28 days, 3 months, and 6 months after full two-dose vaccination. This study is registered with ChiCTR.org.cn (ChiCTR2200058281) and is active but no longer recruiting. Findings: Among 1,302 volunteers screened between Jul 5 and Dec 30, 2021, 969 were eligible and enrolled in our cohort, including 740 living with underlying medical conditions and 229 as healthy control. All of them formed the safety cohort. The overall incidence of adverse reactions was 150 (20.27%) of 740 in the comorbidities group versus 32 (13.97%) of 229 in the healthy group, with significant difference (P=0.0334). The difference was mainly contributed by fatigue and injection-site pain in some groups. Most adverse reactions were mild (Grade 1). We did not observe any serious adverse events related to vaccination. By day 14-28 post vaccination, the seroconversion rates and GMT of neutralizing antibody showed no significant difference between disease group and healthy group, except CAD group (P=0.03) and CRD group (P=0.04) showed slight reduction. By day 90, the neutralizing antibody GMTs were significantly reduced in each group, with no significant difference between diseases and healthy group. By day 180, the neutralizing antibody continued to decrease in each group, but with slower declination. Interpretation: For people living with chronic disease especially seniors older than 60 years, the CoronaVac vaccines are as safe as in healthy people. Although the immunogenicity is slightly different in subgroup of some diseases compared with that of the healthy population, the overall trend was consistent. Our findings highlight the evidence to address vaccine hesitancy for seniors and people living with chronic diseases. Funding: Yunnan Provincial Science and Technology Department (202102AA100051 and 202003AC100010, China), Sinovac Biotech Ltd (PRO-nCOV-4004).

Immunogenicity and safety of the CoronaVac inactivated SARS-CoV-2 vaccine in people with underlying medical conditions: a retrospective study

Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications. While much literature exists on non-medical images, the impacts on medical images, particularly chest X-rays (CXRs) are less understood. Addressing this gap, our study explores three deep model initialization techniques: Cold-start, Warm-start, and Shrink and Perturb start, focusing on adult and pediatric populations. We specifically focus on scenarios with periodically arriving data for training, thereby embracing the real-world scenarios of ongoing data influx and the need for model updates. We evaluate these models for generalizability against external adult and pediatric CXR datasets. We also propose novel ensemble methods: F-score-weighted Sequential Least-Squares Quadratic Programming (F-SLSQP) and Attention-Guided Ensembles with Learnable Fuzzy Softmax to aggregate weight parameters from multiple models to capitalize on their collective knowledge and complementary representations. We perform statistical significance tests with 95% confidence intervals and p-values to analyze model performance. Our evaluations indicate models initialized with ImageNet-pretrained weights demonstrate superior generalizability over randomly-initialized counterparts, contradicting some findings for non-medical images. Notably, ImageNet-pretrained models exhibit consistent performance during internal and external testing across different training scenarios. Weight-level ensembles of these models show significantly higher recall (p<0.05) during testing compared to individual models. Thus, our study accentuates the benefits of ImageNet-pretrained weight initialization, especially when used with weight-level ensembles, for creating robust and generalizable deep learning solutions.

/pdf/uncovering-the-effects-of-model-initialization-on-deep-model-1soo0s52.pdf

Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images

Background: It is well known that influenza and other respiratory viruses are wintertime-seasonal in temperate regions. However, respiratory disease seasonality in the tropics remains elusive. In this study, we aimed to characterize the seasonality of influenza-like illness (ILI) and influenza virus in Ho Chi Minh City (HCMC), Vietnam. Methods: We monitored the daily number of ILI patients in 89 outpatient clinics from January 2010 to December 2019. We collected nasal swabs and tested for influenza from a subset of clinics from May 2012 to December 2019. We used spectral analysis to describe the periodicities in the system. We evaluated the contribution of these periodicities to predicting ILI and influenza patterns through lognormal and gamma hurdle models. Findings: During ten years of community surveillance, 66,799 ILI reports were collected covering 2.9 million patient visits; 2604 nasal swabs were collected 559 of which were PCR-positive for influenza virus. Both annual and nonannual cycles were detected in the ILI time series, with the annual cycle showing 8.9% lower ILI activity (95% CI: 8.8%-9.0%) from February 24 to May 15. Nonannual cycles had substantial explanatory power for ILI trends ({Delta}AIC = 183) compared to all annual covariates ({Delta}AIC = 263). Near-annual signals were observed for PCR-confirmed influenza but were not consistent along in time or across influenza (sub)types. Interpretation: Our study reveals a unique pattern of respiratory disease dynamics in a tropical setting influenced by both annual and nonannual drivers. Timing of vaccination campaigns and hospital capacity planning may require a complex forecasting approach. Funding: National Institutes of Health, Wellcome Trust.

/pdf/a-combination-of-annual-and-nonannual-forces-drive-25lf6uo3.pdf

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

F. Yang

Author Tools

Chat about Author

Papers

Immunogenicity and safety of the CoronaVac inactivated SARS-CoV-2 vaccine in people with underlying medical conditions: a retrospective study

Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images

A combination of annual and nonannual forces drive respiratory disease in the tropics