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Background: Multicentric approaches are widely used in clinical trials to assess the generalizability of findings, however, they are novel in laboratory-based experimentation. It is unclear how multilaboratory studies may differ in conduct and results from single lab studies. Here, we synthesized the characteristics of these studies and quantitatively compared their outcomes to those generated by single laboratory studies.
Methods: MEDLINE and Embase were systematically searched. Screening and data extractions were completed in duplicate by independent reviewers. Multilaboratory studies investigating interventions using in vivo animal models were included. Study characteristics were extracted. Systematic searches were then performed to identify single lab studies matched by intervention and disease. Difference in standardized mean differences (DSMD) was then calculated across studies to assess differences in effect estimates based on study design (>0 indicates larger effects in single lab studies).
Results: Sixteen multilaboratory studies met inclusion criteria and were matched to 100 single lab studies. The multicenter study design was applied across a diverse range of diseases, including stroke, traumatic brain injury, myocardial infarction, and diabetes. The median number of centers was four (range 2-6) and the median sample size was 111 (range 23-384) with rodents most frequently used. Multilaboratory studies adhered to practices that reduce the risk of bias significantly more often than single lab studies. Multilaboratory studies also demonstrated significantly smaller effect sizes than single lab studies (DSMD 0.72 [95% confidence interval 0.43-1]).
Conclusions: Multilaboratory studies demonstrate trends that have been well recognized in clinical research (i.e. smaller treatment effects with multicentric evaluation and greater rigor in study design). This approach may provide a method to robustly assess interventions and the generalizability of findings between laboratories.
Funding: uOttawa Junior Clinical Research Chair; The Ottawa Hospital Anesthesia Alternate Funds Association; Canadian Anesthesia Research Foundation; Government of Ontario Queen Elizabeth II Graduate Scholarship in Science and Technology.
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http://dx.doi.org/10.7554/eLife.76300 | DOI Listing |
PLoS Biol
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Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.
Inter-laboratory replicability is crucial yet challenging in microbiome research. Leveraging microbiomes to promote soil health and plant growth requires understanding underlying molecular mechanisms using reproducible experimental systems. In a global collaborative effort involving five laboratories, we aimed to help advance reproducibility in microbiome studies by testing our ability to replicate synthetic community assembly experiments.
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Luoyang R&D Center of Technology, SINOPEC Engineering (Group) Co., Ltd, Luoyang 471003, China. Electronic address:
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August 2025
Division of Applied Regulatory Science, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA.
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July 2025
Department of Mechanical Engineering and Materials Science, University of Pittsburgh, 3700 O'Hara St., Pittsburgh, PA 15261 USA.
Unlabelled: Surface performance is critically influenced by topography in virtually all real-world applications. The current standard practice is to describe topography using one of a few industry-standard parameters. The most commonly reported number is a, the average absolute deviation of the height from the mean line (at some, not necessarily known or specified, lateral length scale).
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Department of Chemistry & Biochemistry, University of North Carolina at Greensboro, Greensboro, North Carolina 27402, United States.
Annotation is the process of assigning features in mass spectrometry metabolomics data sets to putative chemical structures or "analytes." The purpose of this study was to identify challenges in the annotation of untargeted mass spectrometry metabolomics datasets and suggest strategies to overcome them. Toward this goal, we analyzed an extract of the plant ashwagandha () using liquid chromatography-mass spectrometry on two different platforms (an Orbitrap and Q-ToF) with various acquisition modes.
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