Effectiveness of inactive COVID-19 vaccines against severe illness in B.1.617.2 (Delta) variant-infected patients in Jiangsu, China

The SARS-CoV-2 B.1.617.2 (Delta) variant has caused a new surge in the number of COVID-19 cases. The effectiveness of vaccines against this variant is not fully understood. Using data from a recent large-scale outbreak of COVID-19 in China, we conducted a real-world study to explore the effect of inactivated vaccine immunization on the course of disease in patients infected with Delta variants. We recruited 476 confirmed cases over the age of 18, of which 42 were severe. After adjusting for age, gender, and comorbidities, patients who received two doses of inactivated vaccine (fully vaccinated) had an 88% reduced risk in progressing to the severe stage (adjusted OR: 0.12, 95% CI: 0.02- 0.45). However, this protective effect was not observed in patients who only received only one dose of the vaccine(adjusted OR: 1.11, 95% CI: 0.51- 2.36). The full immunization offered 100% protection from a severe illness among women. The effect of the vaccine was potentially affected by underlying medical conditions (OR: 0.26, 95% CI: 0.03-1.23). This is the largest real-world study confirming the effectiveness of inactive COVID-19 vaccines against severe illness in Delta variant-infected patients in Jiangsu, China.

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FAITH AND CHILD SURVIVAL: THE ROLE OF RELIGION IN CHILDHOOD IMMUNIZATION IN NIGERIA

Journal of Biosocial Science ◽

10.1017/s0021932008002861 ◽

2009 ◽

Vol 41 (1) ◽

pp. 57-76 ◽

Cited By ~ 33

Author(s):

DIDDY ANTAI

Keyword(s):

Religious Affiliation ◽

Child Survival ◽

Childhood Immunization ◽

Child Immunization ◽

Competing Hypotheses ◽

Using Data ◽

Role Of Religion ◽

Full Immunization ◽

Reduced Risk

SummaryThis study assessed the role of mother’s religious affiliation in child immunization status of surviving children 12 months of age and older in Nigeria, using data from the 2003 Nigeria Demographic and Health Survey (NDHS). Guided by two competing hypotheses – the ‘characteristics hypothesis’ and the ‘particularized theology hypothesis’ – variations in the risks of child immunization in Nigeria were examined using logistic regression analysis. The results indicate that religion plays a role in the risk of non-immunization; religion was not associated with the risk of partial immunization; however, religion was significantly associated with the reduced risk of full immunization.

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1588-P: Therapy Trends in Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽

10.2337/db20-1588-p ◽

2020 ◽

Vol 69 (Supplement 1) ◽

pp. 1588-P ◽

Cited By ~ 1

Author(s):

ROMIK GHOSH ◽

ASHOK K. DAS ◽

AMBRISH MITHAL ◽

SHASHANK JOSHI ◽

K.M. PRASANNA KUMAR ◽

...

Keyword(s):

Real World ◽

Large Scale ◽

Nationwide Study

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2258-PUB: Impact of Cardiovascular (CV) Risk Factors during the Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽

10.2337/db20-2258-pub ◽

2020 ◽

Vol 69 (Supplement 1) ◽

pp. 2258-PUB

Author(s):

ROMIK GHOSH ◽

ASHOK K. DAS ◽

SHASHANK JOSHI ◽

AMBRISH MITHAL ◽

K.M. PRASANNA KUMAR ◽

...

Keyword(s):

Risk Factors ◽

Real World ◽

Large Scale ◽

Nationwide Study

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Student Perceptions of Community: A Different Perspective

NASPA Journal ◽

10.2202/0027-6014.1064 ◽

1998 ◽

Vol 35 (4) ◽

Author(s):

Jackie Clark ◽

Joan Hirt

Keyword(s):

Student Perceptions ◽

Large Scale ◽

Research University ◽

Small Scale ◽

Social Environments ◽

Common Assumption ◽

Small Communities ◽

Public Research University ◽

The Common ◽

Using Data

The creation of small communities has been proposed as a way of enhancing the educational experience of students at large institutions. Using data from a survey of students living in large and small residences at a public research university, this study does not support the common assumption that small-scale social environments are more conducive to positive community life than large-scale social environments.

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The graph neural networking challenge

ACM SIGCOMM Computer Communication Review ◽

10.1145/3477482.3477485 ◽

2021 ◽

Vol 51 (3) ◽

pp. 9-16

Author(s):

José Suárez-Varela ◽

Miquel Ferriol-Galmés ◽

Albert López ◽

Paul Almasan ◽

Guillermo Bernárdez ◽

...

Keyword(s):

Machine Learning ◽

Computer Networks ◽

Real World ◽

Large Scale ◽

Lessons Learned ◽

Educational Resources ◽

Global Competition ◽

International Telecommunication Union ◽

International Telecommunication ◽

Broad Audience

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge", an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020". We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.

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Modeling Fused Filament Fabrication using Artificial Neural Networks

Production Engineering ◽

10.1007/s11740-021-01020-y ◽

2021 ◽

Author(s):

Paul Oehlmann ◽

Paul Osswald ◽

Juan Camilo Blanco ◽

Martin Friedrich ◽

Dominik Rietzel ◽

...

Keyword(s):

Real Time ◽

Large Scale ◽

Cost Effective ◽

Print Quality ◽

Ann Model ◽

Production Environment ◽

Fused Filament Fabrication ◽

Artificial Neural ◽

Using Data ◽

Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

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Nebula: ultra-efficient mapping-free structural variant genotyper

Nucleic Acids Research ◽

10.1093/nar/gkab025 ◽

2021 ◽

Author(s):

Parsoa Khorsand ◽

Fereydoun Hormozdiari

Keyword(s):

Large Scale ◽

Structural Variants ◽

Sequencing Technologies ◽

Generic Framework ◽

Common Genetic Variants ◽

Order Of Magnitude ◽

Complex Events ◽

Comparable Accuracy ◽

Using Data ◽

Computational Resources

Abstract Large scale catalogs of common genetic variants (including indels and structural variants) are being created using data from second and third generation whole-genome sequencing technologies. However, the genotyping of these variants in newly sequenced samples is a nontrivial task that requires extensive computational resources. Furthermore, current approaches are mostly limited to only specific types of variants and are generally prone to various errors and ambiguities when genotyping complex events. We are proposing an ultra-efficient approach for genotyping any type of structural variation that is not limited by the shortcomings and complexities of current mapping-based approaches. Our method Nebula utilizes the changes in the count of k-mers to predict the genotype of structural variants. We have shown that not only Nebula is an order of magnitude faster than mapping based approaches for genotyping structural variants, but also has comparable accuracy to state-of-the-art approaches. Furthermore, Nebula is a generic framework not limited to any specific type of event. Nebula is publicly available at https://github.com/Parsoa/Nebula.

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Assisting scalable diagnosis automatically via CT images in the combat against COVID-19

Scientific Reports ◽

10.1038/s41598-021-83424-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Bohan Liu ◽

Pan Liu ◽

Lutao Dai ◽

Yanlin Yang ◽

Peng Xie ◽

...

Keyword(s):

Large Scale ◽

Area Under The Curve ◽

Independent Set ◽

Reference Method ◽

Ct Images ◽

Loss Of Life ◽

Case Identification ◽

Reverse Transcription Pcr ◽

Using Data

AbstractThe pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

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Long-term effectiveness in patients previously treated with cladribine tablets: a real-world analysis of the Italian multiple sclerosis registry (CLARINET-MS)

Therapeutic Advances in Neurological Disorders ◽

10.1177/1756286420922685 ◽

2020 ◽

Vol 13 ◽

pp. 175628642092268 ◽

Cited By ~ 1

Author(s):

Francesco Patti ◽

Andrea Visconti ◽

Antonio Capacchione ◽

Sanjeev Roy ◽

Maria Trojano ◽

...

Keyword(s):

Multiple Sclerosis ◽

Real World ◽

Event Analysis ◽

Treatment Change ◽

Real World Data ◽

Indirect Measure ◽

Disease Modifying Drugs ◽

Disease Modifying ◽

Using Data

Background: The CLARINET-MS study assessed the long-term effectiveness of cladribine tablets by following patients with multiple sclerosis (MS) in Italy, using data from the Italian MS Registry. Methods: Real-world data (RWD) from Italian MS patients who participated in cladribine tablets randomised clinical trials (RCTs; CLARITY, CLARITY Extension, ONWARD or ORACLE-MS) across 17 MS centres were obtained from the Italian MS Registry. RWD were collected during a set observation period, spanning from the last dose of cladribine tablets during the RCT (defined as baseline) to the last visit date in the registry, treatment switch to other disease-modifying drugs, date of last Expanded Disability Status Scale recording or date of the last relapse (whichever occurred last). Time-to-event analysis was completed using the Kaplan–Meier (KM) method. Median duration and associated 95% confidence intervals (CI) were estimated from the model. Results: Time span under observation in the Italian MS Registry was 1–137 (median 80.3) months. In the total Italian patient population ( n = 80), the KM estimates for the probability of being relapse-free at 12, 36 and 60 months after the last dose of cladribine tablets were 84.8%, 66.2% and 57.2%, respectively. The corresponding probability of being progression-free at 60 months after the last dose was 63.7%. The KM estimate for the probability of not initiating another disease-modifying treatment at 60 months after the last dose of cladribine tablets was 28.1%, and the median time-to-treatment change was 32.1 (95% CI 15.5–39.5) months. Conclusion: CLARINET-MS provides an indirect measure of the long-term effectiveness of cladribine tablets. Over half of MS patients analysed did not relapse or experience disability progression during 60 months of follow-up from the last dose, suggesting that cladribine tablets remain effective in years 3 and 4 after short courses at the beginning of years 1 and 2.

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Integrative cBioPortal Analysis Revealed Molecular Mechanisms That Regulate EGFR-PI3K-AKT-mTOR Pathway in Diffuse Gliomas of the Brain

Cancers ◽

10.3390/cancers13133247 ◽

2021 ◽

Vol 13 (13) ◽

pp. 3247

Author(s):

Petar Brlek ◽

Anja Kafka ◽

Anja Bukovac ◽

Nives Pećina-Šlaus

Keyword(s):

Large Scale ◽

Molecular Mechanisms ◽

In Silico Analysis ◽

Total Sample ◽

Mtor Signaling ◽

Biological Behavior ◽

Mrna Transcription ◽

Diffuse Gliomas ◽

New Treatment ◽

Using Data

Diffuse gliomas are a heterogeneous group of tumors with aggressive biological behavior and a lack of effective treatment methods. Despite new molecular findings, the differences between pathohistological types still require better understanding. In this in silico analysis, we investigated AKT1, AKT2, AKT3, CHUK, GSK3β, EGFR, PTEN, and PIK3AP1 as participants of EGFR-PI3K-AKT-mTOR signaling using data from the publicly available cBioPortal platform. Integrative large-scale analyses investigated changes in copy number aberrations (CNA), methylation, mRNA transcription and protein expression within 751 samples of diffuse astrocytomas, anaplastic astrocytomas and glioblastomas. The study showed a significant percentage of CNA in PTEN (76%), PIK3AP1 and CHUK (75% each), EGFR (74%), AKT2 (39%), AKT1 (32%), AKT3 (19%) and GSK3β (18%) in the total sample. Comprehensive statistical analyses show how genomics and epigenomics affect the expression of examined genes differently across various pathohistological types and grades, suggesting that genes AKT3, CHUK and PTEN behave like tumor suppressors, while AKT1, AKT2, EGFR, and PIK3AP1 show oncogenic behavior and are involved in enhanced activity of the EGFR-PI3K-AKT-mTOR signaling pathway. Our findings contribute to the knowledge of the molecular differences between pathohistological types and ultimately offer the possibility of new treatment targets and personalized therapies in patients with diffuse gliomas.

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