An important factor for successful translational stroke research is study quality. Low-quality studies are at risk of biased results and effect overestimation, as has been intensely discussed for small animal stroke research. However, little is known about the methodological rigor and quality in large animal stroke models, which are becoming more frequently used in the field. Based on research in two databases, this systematic review surveys and analyses the methodological quality in large animal stroke research. Quality analysis was based on the Stroke Therapy Academic Industry Roundtable and the Animals in Research: Reporting In Vivo Experiments guidelines. Our analysis revealed that large animal models are utilized with similar shortcomings as small animal models. Moreover, translational benefits of large animal models may be limited due to lacking implementation of important quality criteria such as randomization, allocation concealment, and blinded assessment of outcome. On the other hand, an increase of study quality over time and a positive correlation between study quality and journal impact factor were identified. Based on the obtained findings, we derive recommendations for optimal study planning, conducting, and data analysis/reporting when using large animal stroke models to fully benefit from the translational advantages offered by these models.
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http://dx.doi.org/10.1177/0271678X20931062 | DOI Listing |
Cytotherapy
November 2024
Department of Translational and Precision Medicine, University of Rome, Rome, Italy. Electronic address:
Cellular and gene therapy (CGT) products have emerged as a popular approach in regenerative medicine, showing promise in treating various pancreatic and liver diseases in numerous clinical trials. Before these therapies can be tested in human clinical trials, it is essential to evaluate their safety and efficacy in relevant animal models. Such preclinical testing is often required to obtain regulatory approval for investigational new drugs.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
January 2025
Faculty of Geography, Lomonosov Moscow State University, 119991, Moscow, Russia.
The content of 39 metals and metalloids (MMs) in submicron road dust (PM fraction) was studied in the traffic zone, residential courtyards with parking lots, and on pedestrian roads in parks in Moscow. The geochemical profiles of PM vary slightly between different types of roads and courtyards but differ significantly from those in parks. In Moscow, compared to other cities worldwide, submicron road dust contains less As, Sb, Mo, Cr, Cd, Sn, Tl, Ca, Rb, La, Y, U, but more Cu, Zn, Co, Fe, Mn, Ti, Zr, Al, V.
View Article and Find Full Text PDFCommun Biol
January 2025
Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO, USA.
Symbioses are major drivers of organismal diversification and phenotypic innovation. However, how long-term symbioses shape whole genome evolution in metazoans is still underexplored. Here, we use a giant clam (Tridacna maxima) genome to demonstrate how symbiosis has left complex signatures in an animal's genome.
View Article and Find Full Text PDFSci Rep
January 2025
Dipartimento di Medicina Veterinaria e delle Produzioni Animali, Università degli Studi di Napoli Federico II, Naples, Italy.
BPV1, BPV2, BPV13, and BPV14 are all genotypes of bovine delta papillomaviruses (δPV), of which the first three cause infections in horses and are associated with equine sarcoids. However, BPV14 infection has never been reported in equine species. In this study, we examined 58 fresh and thawed commercial semen samples from healthy stallions.
View Article and Find Full Text PDFNat Commun
January 2025
Key Laboratory of Healthy Mariculture for the East China Sea, Ministry of Agriculture and Rural Affairs & Fisheries college, Jimei University, Xiamen, Fujian, People's Republic of China.
Deep phenotyping can enhance the power of genetic analysis, including genome-wide association studies (GWAS), but the occurrence of missing phenotypes compromises the potential of such resources. Although many phenotypic imputation methods have been developed, the accurate imputation of millions of individuals remains challenging. In the present study, we have developed a multi-phenotype imputation method based on mixed fast random forest (PIXANT) by leveraging efficient machine learning (ML)-based algorithms.
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