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Introduction: Stem cell transplantation is one of the most promising strategies to improve healing in chronic wounds as systemic administration of endothelial progenitor cells (EPC) enhances healing by promoting neovascularization and homing though a high amount of cells is needed. In the following study, we analysed whether local application can reduce the number of EPC needed achieving the same beneficial effect on wound healing.
Material and Methods: Wound healing after local or systemic treatment with EPC was monitored in vivo by creating standardized wounds on the dorsum of hairless mice measuring wound closure every second day. Systemic group received 2 × 106 EPC i.v. and locally treated group 2 × 105 EPC, locally injected. As control PBS injection was performed the same way. Expression of CD31, VEGF, CD90 and, SDF-1α was analysed immunohistochemically for evaluation of neovascularisation and amelioration of homing.
Results: Local (7.1 ± 0.45 SD) as well as systemic (6.1 ± 0.23 SD) EPC transplantation led to a significant acceleration of wound closure compared to controls (PBS local: 9.7 ± 0.5 SD, PBS systemic 10.9 ± 0.38 SD). Systemic application enhanced CD31 expression on day 6 after wounding and local EPC on 6 and 9 in comparison to control. VEGF expression was not significantly affected. Systemic and local EPC treatment resulted in a significantly enhanced SDF-1α and CD90 expression on all days investigated.
Conclusion: Local as well as systemic EPC treatment enhances wound healing. Moreover, beneficial effects are obtained with a tenfold decrease number of EPC when applied locally. Thus, local EPC treatment might be more convenient way to enhance wound healing as number of progenitor cells is limited.
Background: The objective of the STREAM Trial was to evaluate the effect of simulation training on process times in acute stroke care.
Methods: The multicenter prospective interventional STREAM Trial was conducted between 10/2017 and 04/2019 at seven tertiary care neurocenters in Germany with a pre- and post-interventional observation phase. We recorded patient characteristics, acute stroke care process times, stroke team composition and simulation experience for consecutive direct-to-center patients receiving intravenous thrombolysis (IVT) and/or endovascular therapy (EVT). The intervention consisted of a composite intervention centered around stroke-specific in situ simulation training. Primary outcome measure was the ‘door-to-needle’ time (DTN) for IVT. Secondary outcome measures included process times of EVT and measures taken to streamline the pre-existing treatment algorithm.
Results: The effect of the STREAM intervention on the process times of all acute stroke operations was neutral. However, secondary analyses showed a DTN reduction of 5 min from 38 min pre-intervention (interquartile range [IQR] 25–43 min) to 33 min (IQR 23–39 min, p = 0.03) post-intervention achieved by simulation-experienced stroke teams. Concerning EVT, we found significantly shorter door-to-groin times in patients who were treated by teams with simulation experience as compared to simulation-naive teams in the post-interventional phase (−21 min, simulation-naive: 95 min, IQR 69–111 vs. simulation-experienced: 74 min, IQR 51–92, p = 0.04).
Conclusion: An intervention combining workflow refinement and simulation-based stroke team training has the potential to improve process times in acute stroke care.
TRIANNI mice carry an entire set of human immunoglobulin V region gene segments and are a powerful tool to rapidly isolate human monoclonal antibodies. After immunizing these mice with DNA encoding the spike protein of SARS-CoV-2 and boosting with spike protein, we identified 29 hybridoma antibodies that reacted with the SARS-CoV-2 spike protein. Nine antibodies neutralize SARS-CoV-2 infection at IC50 values in the subnanomolar range. ELISA-binding studies and DNA sequence analyses revealed one cluster of three clonally related neutralizing antibodies that target the receptor-binding domain and compete with the cellular receptor hACE2. A second cluster of six clonally related neutralizing antibodies bind to the N-terminal domain of the spike protein without competing with the binding of hACE2 or cluster 1 antibodies. SARS-CoV-2 mutants selected for resistance to an antibody from one cluster are still neutralized by an antibody from the other cluster. Antibodies from both clusters markedly reduced viral spread in mice transgenic for human ACE2 and protected the animals from SARS-CoV-2-induced weight loss. The two clusters of potent noncompeting SARS-CoV-2 neutralizing antibodies provide potential candidates for therapy and prophylaxis of COVID-19. The study further supports transgenic animals with a human immunoglobulin gene repertoire as a powerful platform in pandemic preparedness initiatives.
The emerging disciplines of lipidomics and metabolomics show great potential for the discovery of diagnostic biomarkers, but appropriate pre-analytical sample-handling procedures are critical because several analytes are prone to ex vivo distortions during sample collection. To test how the intermediate storage temperature and storage period of plasma samples from K3EDTA whole-blood collection tubes affect analyte concentrations, we assessed samples from non-fasting healthy volunteers (n = 9) for a broad spectrum of metabolites, including lipids and lipid mediators, using a well-established LC-MS-based platform. We used a fold change-based approach as a relative measure of analyte stability to evaluate 489 analytes, employing a combination of targeted LC-MS/MS and LC-HRMS screening. The concentrations of many analytes were found to be reliable, often justifying less strict sample handling; however, certain analytes were unstable, supporting the need for meticulous processing. We make four data-driven recommendations for sample-handling protocols with varying degrees of stringency, based on the maximum number of analytes and the feasibility of routine clinical implementation. These protocols also enable the simple evaluation of biomarker candidates based on their analyte-specific vulnerability to ex vivo distortions. In summary, pre-analytical sample handling has a major effect on the suitability of certain metabolites as biomarkers, including several lipids and lipid mediators. Our sample-handling recommendations will increase the reliability and quality of samples when such metabolites are necessary for routine clinical diagnosis.
Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects
(2023)
Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders.
The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results.
The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.
Investigators in the cognitive neurosciences have turned to Big Data to address persistent replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. While there is tremendous potential to advance science through open data sharing, these efforts unveil a host of new questions about how to integrate data arising from distinct sources and instruments. We focus on the most frequently assessed area of cognition - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated raw data from 53 studies from around the world which measured at least one of three distinct verbal learning tasks, totaling N = 10,505 healthy and brain-injured individuals. A mega analysis was conducted using empirical bayes harmonization to isolate and remove site effects, followed by linear models which adjusted for common covariates. After corrections, a continuous item response theory (IRT) model estimated each individual subject’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance by 37% while preserving covariate effects. The effects of age, sex, and education on scores were found to be highly consistent across memory tests. IRT methods for equating scores across AVLTs agreed with held-out data of dually-administered tests, and these tools are made available for free online. This work demonstrates that large-scale data sharing and harmonization initiatives can offer opportunities to address reproducibility and integration challenges across the behavioral sciences.