Measuring routine childhood vaccination coverage in 204 countries and territories, 1980-2019 : a systematic analysis for the Global Burden of Disease Study 2020, Release 1

Background Measuring routine childhood vaccination is crucial to inform global vaccine policies and programme implementation, and to track progress towards targets set by the Global Vaccine Action Plan (GVAP) and Immunization Agenda 2030. Robust estimates of routine vaccine coverage are needed to identify past successes and persistent vulnerabilities. Drawing from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2020, Release 1, we did a systematic analysis of global, regional, and national vaccine coverage trends using a statistical framework, by vaccine and over time. Methods For this analysis we collated 55 326 country-specific, cohort-specific, year-specific, vaccine-specific, and dosespecific observations of routine childhood vaccination coverage between 1980 and 2019. Using spatiotemporal Gaussian process regression, we produced location-specific and year-specific estimates of 11 routine childhood vaccine coverage indicators for 204 countries and territories from 1980 to 2019, adjusting for biases in countryreported data and reflecting reported stockouts and supply disruptions. We analysed global and regional trends in coverage and numbers of zero-dose children (defined as those who never received a diphtheria-tetanus-pertussis [DTP] vaccine dose), progress towards GVAP targets, and the relationship between vaccine coverage and sociodemographic development. Findings By 2019, global coverage of third-dose DTP (DTP3; 81.6% [95% uncertainty interval 80.4-82 .7]) more than doubled from levels estimated in 1980 (39.9% [37.5-42.1]), as did global coverage of the first-dose measles-containing vaccine (MCV1; from 38.5% [35.4-41.3] in 1980 to 83.6% [82.3-84.8] in 2019). Third- dose polio vaccine (Pol3) coverage also increased, from 42.6% (41.4-44.1) in 1980 to 79.8% (78.4-81.1) in 2019, and global coverage of newer vaccines increased rapidly between 2000 and 2019. The global number of zero-dose children fell by nearly 75% between 1980 and 2019, from 56.8 million (52.6-60. 9) to 14.5 million (13.4-15.9). However, over the past decade, global vaccine coverage broadly plateaued; 94 countries and territories recorded decreasing DTP3 coverage since 2010. Only 11 countries and territories were estimated to have reached the national GVAP target of at least 90% coverage for all assessed vaccines in 2019. Interpretation After achieving large gains in childhood vaccine coverage worldwide, in much of the world this progress was stalled or reversed from 2010 to 2019. These findings underscore the importance of revisiting routine immunisation strategies and programmatic approaches, recentring service delivery around equity and underserved populations. Strengthening vaccine data and monitoring systems is crucial to these pursuits, now and through to 2030, to ensure that all children have access to, and can benefit from, lifesaving vaccines. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.Peer reviewe

A random search based effective algorithm for pairwise test data generation

Testing is a very important task to build error free software. As the resources and time to market is limited for a software product, it is impossible to perform exhaustive test i.e., to test all combinations of input data. To reduce the number of test cases in an acceptable level, it is preferable to use higher interaction level (t way, where t ≥ 2). Pairwise (2-way or t = 2) interaction can find most of the software faults. This paper proposes an effective random search based pairwise test data generation algorithm named R2Way to optimize the number of test cases. Java program has been used to test the performance of the algorithm. The algorithm is able to support both uniform and non-uniform values effectively with performance better than the existing algorithms/tools in terms of number of generated test cases and time consumption

EasyA: Easy and effective way to generate pairwise test data

Testing is a very important task to build error free software. As the resources and time to market is limited for a software product, it is impossible to perform exhaustive test i.e., to test all combinations of input data. To reduce the number of test cases in an acceptable level, it is preferable to use higher interaction level (t way, where t = 2). Pairwise (2- way or t = 2) interaction can find most of the software faults. This paper proposes a matrix based calculation for pairwise test data generation algorithm named EasyA to optimize the number of test cases. Java program has been used to test the performance of the algorithm. The performance is better than the existing algorithms/tools in terms of number of generated test cases and time consumption

Human Catestatin Alters Gut Microbiota Composition in Mice

The mammalian intestinal tract is heavily colonized with a dense, complex, and diversified microbial populations. In healthy individuals, an array of epithelial antimicrobial agents is secreted in the gut to aid intestinal homeostasis. Enterochromaffin cells (EC) in the intestinal epithelium are a major source of chromogranin A (CgA), which is a pro-hormone and can be cleaved into many bioactive peptides that include catestatin (CST). This study was carried out to evaluate the possible impact of CST on gut microbiota in vivo using a mouse model. The CST (Human CgA352−372) or normal saline was intrarectally administered in C57BL/6 male mice for 6 days and then sacrificed. Feces and colonic mucosa tissue samples were collected, DNA was extracted, the V4 region of bacterial 16S rRNA gene was amplified and subjected to MiSeq Illumina sequencing. The α-diversity was calculated using Chao 1 and β-diversity was determined using QIIME. Differences at the genus level were determined using partial least square discriminant analysis (PLS-DA). Phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) was used to predict functional capacity of bacterial community. CST treatment did not modify bacterial richness in fecal and colonic mucosa-associated microbiota; however, treatment significantly modified bacterial community composition between the groups. Also, CST-treated mice had a significantly lower relative abundance of Firmicutes and higher abundance of Bacteroidetes, observed only in fecal samples. However, at lower phylogenetic levels, PLS-DA analysis revealed that some bacterial taxa were significantly associated with the CST-treated mice in both fecal and colonic mucosa samples. In addition, differences in predicted microbial functional pathways in both fecal and colonic mucosa samples were detected. The results support the hypothesis that CST treatment modulates gut microbiota composition under non-pathophysiological conditions, however, the result of this study needs to be further validated in a larger experiment. The data may open new avenues for the development of a potential new line of antimicrobial peptides and their use as therapeutic agents to treat several inflammatory conditions of the gastrointestinal tract, such as inflammatory bowel disease (IBD), inflammatory bowel syndrome (IBS), or other health conditions

Using a deep learning method and data from two-dimensional (2D) marker-less video-based images for walking speed classification

Human body measurement data related to walking can characterize functional move ment and thereby become an important tool for health assessment. Single-camera-captured two dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. In this study, we propose five ratio-based body measurement data that can be extracted from 2D images and can be used to classify three walking speeds (i.e., slow, normal, and fast) using a deep learning-based bidirectional long short-term memory classification model. The results showed that average classification accuracies of 88.08% and 79.18% could be achieved in indoor and outdoor environments, respectively. Additionally, the proposed ratio-based body measurement data are independent of body-worn garments and not susceptible to changes in the distance between the walking individual and camera. As a simple but efficient technique, the proposed walking speed classification has great potential to be employed in clinics and aged care homes

Stability of Reference Genes for Messenger RNA Quantification by Real-Time PCR in Mouse Dextran Sodium Sulfate Experimental Colitis - Fig 8

<p><b>Effect of reference gene selection on the relative expression of colonic TNF-α (A) and IL-1β (B).</b> Target gene expression was normalized against the 13 reference genes using comparative the ΔΔCt method. Significant differences between control and inflamed colon were seen only with the stable reference genes. Student’s t-test was used to compare the groups. Data is presented as the mean ± SD (n = 6/group).</p

Central Muscarinic Cholinergic Activation Alters Interaction between Splenic Dendritic Cell and CD4⁺CD25^- T Cells in Experimental Colitis

<div><p>Background</p><p>The cholinergic anti-inflammatory pathway (CAP) is based on vagus nerve (VN) activity that regulates macrophage and dendritic cell responses in the spleen through alpha-7 nicotinic acetylcholine receptor (a7nAChR) signaling. Inflammatory bowel disease (IBD) patients present dysautonomia with decreased vagus nerve activity, dendritic cell and T cell over-activation. The aim of this study was to investigate whether central activation of the CAP alters the function of dendritic cells (DCs) and sequential CD4⁺/CD25⁻T cell activation in the context of experimental colitis.</p><p>Methods</p><p>The dinitrobenzene sulfonic acid model of experimental colitis in C57BL/6 mice was used. Central, intracerebroventricular infusion of the M1 muscarinic acetylcholine receptor agonist McN-A-343 was used to activate CAP and vagus nerve and/or splenic nerve transection were performed. In addition, the role of α7nAChR signaling and the NF-kB pathway was studied. Serum amyloid protein (SAP)-A, colonic tissue cytokines, IL-12p70 and IL-23 in isolated splenic DCs, and cytokines levels in DC-CD4⁺CD25⁻T cell co-culture were determined.</p><p>Results</p><p>McN-A-343 treatment reduced colonic inflammation associated with decreased pro-inflammatory Th1/Th17 colonic and splenic cytokine secretion. Splenic DCs cytokine release was modulated through α7nAChR and the NF-kB signaling pathways. Cholinergic activation resulted in decreased CD4⁺CD25⁻T cell priming. The anti-inflammatory efficacy of central cholinergic activation was abolished in mice with vagotomy or splenic neurectomy.</p><p>Conclusions</p><p>Suppression of splenic immune cell activation and altered interaction between DCs and T cells are important aspects of the beneficial effect of brain activation of the CAP in experimental colitis. These findings may lead to improved therapeutic strategies in the treatment of IBD.</p></div

Range of quantification/threshold cycle (Ct) values of the candidate reference genes.

<p>A scatter dot plot displays the mean value and standard deviation in all colon samples (n = 12).</p