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38 results for “in vitro methods”
JRC COVID-19 In Vitro Diagnostic Devices and Test Methods Database
<p>SUMMARY</p> <p>The <em>JRC COVID-19 In Vitro Diagnostic Devices and Test Methods Database</em>, aimed to collect in a single place all publicly available information on performance of CE-marked <em>in vitro</em> diagnostic medical devices (IVDs) as well as <em>in house</em> laboratory-developed devices and related test methods for COVID-19, is here presented. The database, manually curated and regularly updated, has been developed as a follow-up to the Communication from the European Commission “Guidelines on <em>in vitro</em> diagnostic tests and their performance” of 15 April 2020 and is freely accessible at <a href="https://covid-19-diagnostics.jrc.ec.europa.eu/">https://covid-19-diagnostics.jrc.ec.europa.eu/</a>.</p>
Fig. S2. E in Comparison of in vitro methods to inhibit growth of a virulent strain of Batrachochytrium dendrobatidis (Longcore, Pessier, and Nichols 1999)
Fig. S2. E. coli-violacein transformations. Violacein gene transformations of E. coli. (A) Example of NEB5-alpha-pJP1000 colony growth after 24 h post heat shock transformation. Each week, transformants were passed by re-streaking onto fresh agar and incubated at 37 °C for 24 h. (B) NEB5-alpha-pPSXvio+, (C) NEB5-alpha-pPSXvio++.
Fig. 3 in Comparison of in vitro methods to inhibit growth of a virulent strain of Batrachochytrium dendrobatidis (Longcore, Pessier, and Nichols 1999)
Fig. 3. Microscopic images (40x) of Bd challenge assays. (A) Microbacterium, (B) Micrococcaceae, and (C) recombinant E. coli- vio+. Vertical arrows point to the Bd lawn and horizontal arrows point to the left side of the bacterial streak. Absence of Bd lawn within field of view indicates the inhibitory effect from the bacterial streak. Approximate diameter of field of view ~0.5 mm.
Fig. S1. Toad skin swab cultures. Culture plates from A in Comparison of in vitro methods to inhibit growth of a virulent strain of Batrachochytrium dendrobatidis (Longcore, Pessier, and Nichols 1999)
Fig. S1. Toad skin swab cultures. Culture plates from A. boreas skin swabs after three days incubation at 25 °C.
Fig. 1. The Kruskal-Wallis rank sum test for the boxplot returned a in Comparison of in vitro methods to inhibit growth of a virulent strain of Batrachochytrium dendrobatidis (Longcore, Pessier, and Nichols 1999)
Fig. 1. The Kruskal-Wallis rank sum test for the boxplot returned a chi-squared value = 2, degrees of freedom (df) = 2, and P = 0.3679. The Wilcoxon rank sum test for the positive control and the highest concentration of amphotericin B returned W = 9, P = 0.1. These analyses show that the alternative hypothesis is true and assumes that there is information in the magnitudes and signs of the differences between paired observations, because the mean (location shift) is not equal to 0. Observed differences in mean cell count between the three groups (0.0, 1.6, and 3.2 µg/mL) are not statistically significant (P> 0.05).
Fig. 2 in Comparison of in vitro methods to inhibit growth of a virulent strain of Batrachochytrium dendrobatidis (Longcore, Pessier, and Nichols 1999)
Fig. 2. (A) and (B) Duplicate Bd negative controls, (C) E. coli-JP1000 (left bacterial streak) and Bacillus sp. (right bacterial streak), (D) E. coli-JP1000 displaying a clear ZOI surrounding the bacterial streak, (E) E. coli control.
Induced pluripotent stem cell-derived cardiomyocyte in vitro models: tissue fabrication protocols, assessment methods, and quantitative maturation metrics for benchmarking progress
<p>The advent of human induced pluripotent stem cells (hiPSCs) and techniques to differentiate cardiomyocytes from them has opened a viable path to creating <em>in vitro</em> models of normal and diseased hearts, accelerating more predictive drug screening and therapeutic strategies for cardiac pathologies. Currently, hiPSC-derived cardiomyocytes (hiPSC-CMs) are more similar to fetal than adult cardiomyocytes, leading many in the field to explore approaches to enhance cell and tissue maturation. There are over 2,000 studies utilizing hiPSC-CMs in models composed of various combinations of cell and extracellular matrix components, using a plethora of differentiation protocols, culture formats, and methods for quantifying cardiomyocyte function. To assess the current state of this rapidly growing area, we systematically analyzed 300 studies using hiPSC-CM models for their selection of hiPSC lines, hiPSC-CM differentiation protocols, types of <em>in vitro </em>models, maturation techniques, and metrics used to assess cardiomyocyte functionality and maturity. Here, we provide the data compiled from our analysis of these papers so others in the field can utilize it to inform their research.</p> <p>Based on this analysis, we highlight the diversity of, and current trends in, <em>in vitro</em> model designs and highlight the most common and promising practices for functional assessments. We further analyzed outputs spanning structural maturity, contractile function, electrophysiology, and gene expression and note field-wide improvements over time. Finally, we observe that a persistent lack of coordination amongst investigators is limiting the field's ability to benchmark and advance hiPSC-CM function against previous studies. We discuss opportunities to collectively pursue the common goal of hiPSC-CM model development, maturation, and assessment that we believe are critical to drive the entire community forward in engineering mature cardiac tissue.</p>
A systematic assessment of deep learning methods for drug response prediction: from in-vitro to clinical application
<p>https://github.com/LihongLab/Suppl-data-Benchmark</p> <p>## GDSC dataset</p> <p>**Table S3.** GDSC gene expression profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**Table S4.** GDSC gene mutation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**Table S5.** GDSC copy number variation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**Table S6.** GDSC drug response data for 966 cancer cell lines and 282 drugs in the form of the natural logarithm of the IC50 readout. The first column shows the cell line name and tissue collection site, the second column shows the drug name, and the third column shows the drug response readout.</p> <p> </p> <p>**Table S7.** GDSC annotations for 282 drugs include drug name, PubChem CID, PubChem canonical SMILES, Rdkit canonical SMILES, Target Pathway, standard deviation, bimodality coefficient and density coverage.</p> <p>## TCGA dataset</p> <p>**Table S8.** TCGA gene expression profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**Table S9.** TCGA gene mutation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**Table S10.** TCGA copy number variation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**Table S11.** TCGA clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, the fourth column shows the cancer type, and the last column shows the clinical label as responder or non-responder.</p>
A systematic assessment of deep learning methods for drug response prediction: From in vitro to clinical applications
<p>## GDSC dataset</p> <p>**GDSC_EXP.csv** GDSC gene expression profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**GDSC_MUT.csv** GDSC gene mutation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**GDSC_CNV.csv** GDSC copy number variation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**GDSC_DR.csv** GDSC drug response data for 966 cancer cell lines and 282 drugs in the form of the natural logarithm of the IC50 readout. The first column shows the cell line name and tissue collection site, the second column shows the drug name, and the third column shows the drug response readout.</p> <p> </p> <p>**GDSC_DrugAnnotation.csv** GDSC annotations for 282 drugs include drug name, PubChem CID, PubChem canonical SMILES, Rdkit canonical SMILES, Target Pathway, standard deviation, bimodality coefficient and density coverage.</p> <p>## TCGA dataset</p> <p>**TCGA_EXP.csv** TCGA gene expression profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**TCGA_MUT.csv** TCGA gene mutation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**TCGA_CNV.csv** TCGA copy number variation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**TCGA_DR.csv** TCGA clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, the fourth column shows the cancer type, and the last column shows the clinical label as responder or non-responder.</p> <p>## PMID17185464 (Bortezomib) dataset</p> <p>**PMID17185464_EXP.csv** Bortezomib clinical trial gene expression profiles, where each column represents a patient in the form of patient ID, and each row represents a gene in the form of the HGNC symbol.</p> <p>**PMID17185464_DR.csv** Bortezomib clinical trial clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, and the last column shows the clinical label as responder or non-responder (NR: Non-responder, R: Responder).</p>
Combined in vitro and cell-based selection display method producing specific binders against IL-9 receptor in high yields
<p>The FEBS Journal FJ-22-0728</p> <p>Combined in vitro and cell-based selection display method producing specific binders against IL-9 receptor in high yields</p> <p>Maroš Huličiak, Lada Biedermanová, Daniel Berdár, Štěpán Herynek, Lucie Kolářová,<br> Jakub Tomala, Pavel Mikulecký, and Bohdan Schneider</p> <p><em>Institute of Biotechnology of the Czech Academy of Sciences,<br> Průmyslová 595, 252 50 Vestec, Czech Republic</em></p> <p>Correspondence: Bohdan Schneider, Institute of Biotechnology of the Czech Academy of Sciences,<br> BIOCEV, CZ-252 50 Vestec, Czech Republic,<br> Tel: +420 728 303 566, e-mail: bohdan.schneider@ibt.cas.cz</p> <p>Abstract</p> <p>We combined cell-free ribosome display and cell-based yeast display selection to build specific protein binders to the extracellular domain of the human interleukin 9 receptor alpha (IL-9Rα). The target, IL-9Rα, is the receptor involved in the signaling pathway of IL-9, a pro-inflammatory cytokine medically important for its involvement in respiratory diseases. The successive use of modified protocols of ribosome and yeast displays allowed us to combine their strengths - the virtually infinite selection power of ribosome display and the production of (mostly) properly folded and soluble proteins in yeast display. The described experimental protocol is optimized to produce binders highly specific to the target, including selectivity to common proteins such as BSA, and proteins potentially competing for the binder such as receptors of other cytokines. The binders were trained from DNA libraries of two protein scaffolds called 57aBi and 57bBi developed in our laboratory. We show that the described unconventional combination of ribosome and yeast displays is effective in developing selective small protein binders to the medically relevant molecular target.</p>
Induced pluripotent stem cell-derived cardiomyocyte in vitro models: tissue fabrication protocols, assessment methods, and quantitative maturation metrics for benchmarking progress
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Assessment of four in vitro phenotypic biofilm detection methods in relation to antimicrobial resistance in aerobic clinical bacterial isolates
<p><strong>Introduction</strong>: The lack of standardized methods for detecting biofilms continues to pose a challenge to microbiological diagnostics since biofilm-mediated infections induce persistent and recurrent infections in humans that often defy treatment with common antibiotics. This study aimed to evaluate diagnostic parameters of four in vitro phenotypic biofilm detection assays in relation to antimicrobial resistance in aerobic clinical bacterial isolates.</p> <p><strong>Methods</strong>: In this cross-sectional study, bacterial strains from clinical samples were isolated and identified following the standard microbiological guidelines. The antibiotic resistance profile was assessed through the Kirby-Bauer disc diffusion method. Biofilm formation was detected by gold standard tissue culture plate method (TCPM), tube method (TM), Congo red agar (CRA), and modified Congo red agar (MCRA). Statistical analyses were performed using SPSS version 17.0, with a significant association considered at p<0.05.</p> <p><strong>Result</strong>: Among the total isolates (n=226), TCPM detected 140 (61.95%) biofilm producers, with CoNS (9/9) (p<0.001) as the predominant biofilm former. When compared to TCPM, TM (n=119) (p<0.001) showed 90.8% sensitivity and 70.1% specificity, CRA (n=88) (p=0.123) showed 68.2% sensitivity and 42% specificity, and MCRA (n=86) (p=0.442) showed 65.1% sensitivity and 40% specificity. Juxtaposed to CRA, colonies formed on MCRA developed more intense black pigmentation from 24 to 96 hours. There were 77 multi-drug-resistant (MDR)-biofilm formers and 39 extensively drug-resistant (XDR)-biofilm formers, with 100% resistance to ampicillin and ceftazidime, respectively.</p> <p><strong>Conclusion</strong>: It is suggested that TM be used for biofilm detection. Unlike MCRA, black pigmentation in colonies formed on CRA declined with time. MDR- and XDR-biofilm formers were frequent among the clinical isolates.</p>
Effects of Anesthetic Methods on Hepatic Cancer Cell Function in Vitro
ClinicalTrials.gov study NCT04510935. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Methods for Fertility Preservation: Impact of Vitrification on in Vitro Matured Oocytes
ClinicalTrials.gov study NCT03680937. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Self-operated Endo-vaginal Telemonitoring (SOET), an Economic and Patient-empowered Method for Ovarian Stimulation for In-vitro Fertilization (IVF)
ClinicalTrials.gov study NCT01781143. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Assessment of four in vitro phenotypic biofilm detection methods in relation to antimicrobial resistance in aerobic clinical bacterial isolates
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Figure 2 from: Logoyda L (2020) Efficient validated method of HPLC to determine amlodipine in combinated dosage form containing amlodipine, enalapril and bisoprolol and in vitro dissolution studies with in vitro/ in vivo correlation. Pharmacia 67(2): 55-61. https://doi.org/10.3897/pharmacia.67.e48220
Figure 2 Representative chromatogram of amlodipine in combinated tablets (1- peak of bisoprolol, 2- peak of enalapril, 3 – peak of amlodipine).
Raw in vitro screening data and R scripts for: A Bayesian method for population-wide cardiotoxicity hazard and risk characterization using an in vitro human model
<p>Human induced pluripotent stem cell (iPSC)-derived cardiomyocytes are an established model for testing potential chemical hazards. Inter-individual variability in toxicodynamic sensitivity has also been demonstrated <i>in vitro</i>; however, quantitative characterization of the population-wide variability has not been fully explored. We sought to develop a method to address this gap by combining a population-based iPSC-derived cardiomyocyte model with Bayesian concentration-response modeling. A total of 136 compounds, including 44 pharmaceuticals and 82 environmental chemicals, were tested in iPSC-derived cardiomyocytes from 43 non-diseased humans. Hierarchical Bayesian population concentration-response modeling was conducted for five phenotypes reflecting cardiomyocyte function or viability. Toxicodynamic variability was quantified through the derivation of chemical- and phenotype-specific variability factors (TDVF). Toxicokinetic modeling was used for probabilistic <i>in vitro</i>-to-<i>in vivo </i>extrapolation in order to derive population-wide margins of safety (MOS) for pharmaceuticals and margins of exposure (MOE) for environmental chemicals. Pharmaceuticals were found to be active across all phenotypes. Over half of tested environmental chemicals showed activity in at least one phenotype, most commonly positive chronotropy. TDVF estimates for the functional phenotypes were greater than those for cell viability, usually exceeding the generally-assumed default of ~3. Population variability-based MOS for pharmaceuticals were correctly predicted to be relatively narrow, between 10-100; however, MOE for environmental chemicals, based on population exposure estimates, generally exceeded 1000, suggesting they pose little risk at general population exposures even to sensitive sub populations. This study represents a first of its kind human <i>in vitro</i> model that can be used to characterize toxicodynamic population variability in cardiotoxic risk.</p>
Data from: Quantification of the activity of detoxifying enzymes in terrestrial invertebrates: optimization, evaluation and use of in vitro and ex vivo methods
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Raw in vitro screening data and R scripts for: A Bayesian method for population-wide cardiotoxicity hazard and risk characterization using an in vitro human model
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.