Improved rapid diagnostic tests to detect syphilis and yaws: a systematic review and meta-analysis.

BACKGROUND: Current rapid tests for syphilis and yaws can detect treponemal and non-treponemal antibodies. We aimed to critically appraise the literature for rapid diagnostic tests (RDTs) which can better distinguish an active infection of syphilis or yaws. METHODS: We conducted a systematic review and meta-analysis, searching five databases between January 2010 and October 2021 (with an update in July 2022). A generalised linear mixed model was used to conduct a bivariate meta-analysis for the pooled sensitivity and specificity. Heterogeneity was assessed using the I2 statistic. We used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) to assess the risk of bias and Grading of Recommendations, Assessment, Development and Evaluations (GRADE) to evaluate the certainty of evidence. RESULTS: We included 17 studies for meta-analyses. For syphilis, the pooled sensitivity and specificity of the treponemal component were 0.93 (95% CI: 0.86 to 0.97) and 0.98 (95% CI: 0.96 to 0.99), respectively. For the non-treponemal component, the pooled sensitivity and specificity were 0.90 (95% CI: 0.82 to 0.95) and 0.97 (95% CI: 0.92 to 0.99), respectively. For yaws, the pooled sensitivity and specificity of the treponemal component were 0.86 (95% CI: 0.66 to 0.95) and 0.97 (95% CI: 0.94 to 0.99), respectively. For the non-treponemal component, the pooled sensitivity and specificity were 0.80 (95% CI: 0.55 to 0.93) and 0.96 (95% CI: 0.92 to 0.98), respectively. CONCLUSIONS: RDTs that can differentiate between active and previously treated infections could optimise management by providing same-day treatment and reducing unnecessary treatment. PROSPERO REGISTRATION NUMBER: CRD42021279587

Exercise training as S-Klotho protein stimulator in sedentary healthy adults: Rationale, design, and methodology

Aims: The secreted form of the α-Klotho gene (S-Klotho), which is considered a powerful biomarker of longevity, makes it an attractive target as an anti-ageing therapy against functional decline, sarcopenic obesity, metabolic and cardiovascular diseases, osteoporosis, and neurodegenerative disorders. The S-Klotho plasma levels could be related to physical exercise inasmuch physical exercise is involved in physiological pathways that regulate the SKlotho plasma levels. FIT-AGEING will determine the effect of different training modalities on the S-Klotho plasma levels (primary outcome) in sedentary healthy adults. FIT-AGEING will also investigate the physiological consequences of activating the klotho gene (secondary outcomes). Methods: FIT-AGEING will recruit 80 sedentary, healthy adults (50% women) aged 45–65 years old. Eligible participants will be randomly assigned to a non-exercise group, i.e. the control group, (n=20), a physical activity recommendation from World Health Organization group (n=20), a high intensity interval training group (n=20), and a whole-body electromyostimulation group (n=20). The laboratory measurements will be taken at the baseline and 12 weeks later including the S-Klotho plasma levels, physical fitness (cardiorespiratory fitness, muscular strength), body composition, basal metabolic rate, heart rate variability, maximal fat oxidation, health blood biomarkers, free-living physical activity, sleep habits, reaction time, cognitive variables, and health-related questionnaires. We will also obtain dietary habits data and cardiovascular disease risk factors.The study is supported by the Spanish Ministry of Education (FPU14/04172 and FPU15/03960). The study was partially supported by the University of Granada, Plan Propio de Investigación 2016, Excellence actions: Units of Excellence; Unit of Excellence on Exercise and Health (UCEES)

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

A comparative study of two orthologous gene identification methods on synteny block inference / Chow Kingsley

A synteny block is a set of orthologous genes that share the same relative ordering on the chromosomes of two species. Synteny analysis at the genome scale is a powerful means of identifying orthologs in a set of genomes of interest for downstream phylogenetic studies. OrthoCluster is a data mining tool for inferring synteny blocks among multiple genomes. Before using OrthoCluster to infer synteny blocks, orthologous gene relationships between the species of interest have to be identified first. In this study, we evaluated the effects of two different orthologous gene identification methods: InParanoid and ad hoc BLAST, on the number, size and content of synteny blocks returned by OrthoCluster using the genomes of Oryza sativa and Arabidopsis thaliana. Results show that InParanoid identified 22 124 orthologous relationships while ad hoc BLAST identified 14 928. Subsequently, OrthoCluster identified 942 conserved synteny blocks that contain no mismatches using input from InParanoid. These synteny blocks cover 1234 genes (5.97 Mb) in O. sativa and 1403 genes (2.76 Mb) in A. thaliana, respectively. With input from ad hoc BLAST, OrthoCluster detected just 314 conserved synteny blocks, which cover 427 genes (2.3 Mb) in O. sativa and 435 genes (1.1 Mb) in A. thaliana. Allowing mismatches within a synteny block, OrthoCluster identified 1510 nonconserved synteny blocks from InParanoid input, which cover 3509 genes (25.1 Mb) in O. sativa and 3648 genes (9.06 Mb) in A. thaliana. Only 589 non-conserved synteny blocks were detected using ad hoc BLAST input, with 1335 genes (8.22 Mb) in O. sativa and 1257 genes (3.32 Mb) in A. thaliana

Comparison of biometrics industry between Malaysia and India: an overview

The amount of information being churned out by the field of biology has jumped manifold and now requires the extensive use of computers of the management of this information. The field of bioinformatics that addresses this need of biology has become an industry in its own right with the pharmaceutical and biotechnology industries being dependent on it for their growth. The bursting of the dotcom bubble in 2000 saw investors and venture capitalists flocking to the biotechnology industry in general and bioinformatics in particular. This work gives an analytical comparison of the bioinformatics industry in Malaysia and India. We examined government policy, education and economic aspects that are faced by the industry of each country. We also examined the difference in the development for the Bioinformatics industry between each country

Lipidomics in Understanding Pathophysiology and Pharmacologic Effects in Inflammatory Diseases: Considerations for Drug Development

The lipidome has a broad range of biological and signaling functions, including serving as a structural scaffold for membranes and initiating and resolving inflammation. To investigate the biological activity of phospholipids and their bioactive metabolites, precise analytical techniques are necessary to identify specific lipids and quantify their levels. Simultaneous quantification of a set of lipids can be achieved using high sensitivity mass spectrometry (MS) techniques, whose technological advancements have significantly improved over the last decade. This has unlocked the power of metabolomics/lipidomics allowing the dynamic characterization of metabolic systems. Lipidomics is a subset of metabolomics for multianalyte identification and quantification of endogenous lipids and their metabolites. Lipidomics-based technology has the potential to drive novel biomarker discovery and therapeutic development programs; however, appropriate standards have not been established for the field. Standardization would improve lipidomic analyses and accelerate the development of innovative therapies. This review aims to summarize considerations for lipidomic study designs including instrumentation, sample stabilization, data validation, and data analysis. In addition, this review highlights how lipidomics can be applied to biomarker discovery and drug mechanism dissection in various inflammatory diseases including cardiovascular disease, neurodegeneration, lung disease, and autoimmune disease