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73 results for “Aptamer”
DNA aptamers for detection of pyoverdine pf5
GEO Series GSE225440. synthetic construct. 1 samples. Type: Other.
Potential capacity of aptamers to trigger immune activation in human blood
GEO Series GSE46676. Homo sapiens. 21 samples. Type: Expression profiling by array.
Analytical Validation of Aptamer-Based Serum Vancomycin Monitoring Relative to Automated Immunoassays
<p>***ABSTRACT***</p> <p>_________________________</p> <p>The practice of monitoring therapeutic drug concentrations in patient biofluids can significantly improve clinical outcomes while simultaneously minimizing adverse side effects. A model example of this practice is vancomycin dosing in intensive care units. If dosed correctly, vancomycin can effectively treat methicillin-resistant staphylococcus aureus (MRSA) infections. However, it can also induce nephrotoxicity or fail to kill the bacteria if dosed too high or low, respectively. Although undeniably important to achieve effectiveness, therapeutic drug monitoring remains inconvenient in practice, due primarily to the lengthy process of sample collection, transport to a centralized facility, and analysis using costly instrumentation. Adding to this workflow is the possibility of backlogs at centralized clinical laboratories, which is not uncommon and may result in additional delays between biofluid sampling and concentration measurement, which can negatively affect clinical outcomes. Here we explore the possibility of using point-of-care, electrochemical aptamer-based (E-AB) sensors to minimize the time delay between biofluid sampling and drug measurement. Specifically, we conducted a clinical agreement study comparing the measurement outcomes of E-AB sensors to the benchmark automated competitive immunoassays for vancomycin monitoring in serum. Our results demonstrate that E-ABs are selective for free vancomycin—the active form of the drug—over total vancomycin. In contrast, competitive immunoassays measure total vancomycin, including both protein-bound and free drug. Accounting for these differences in a pilot study consisting of 85 clinical samples, we demonstrate that the E-AB vancomycin measurement achieved a 95% positive correlation rate with the benchmark immunoassays. Therefore, we conclude that E-AB sensors could provide clinically useful stratification of patient samples at trough sampling to guide effective vancomycin dose recommendations.</p> <p> </p> <p>***README FILE ***</p> <p>_________________________</p> <div>Project: Analytical Validation of Aptamer-Based Serum Vancomycin Monitoring Relative to Automated Immunoassays</div> <div>Date Accepted: December 5th, 2023</div> <div>DOI: </div> <div>Contact: Netz Arroyo, netzarroyo@jhmi.edu</div> <div> </div> <div>ORGANIZATION</div> <div> </div> <div>All files in this repository are sorted based on their corresponding figure in the associated manuscript</div> <div>and supplementary information document (both found at DOI above). Organization and files found in each figure folder </div> <div>are as follows:</div> <div> </div> <div>- 'Figure_1':</div> <div>- Final .svg image for Figure 1.</div> <div> </div> <div>- 'Figure_2':</div> <div>-"Figure_2.png"</div> <div>- PNG image of Figure 2. Figure panels were generated as detailed in the following entries. Figure was compiled in Adobe Illustrator. </div> <div>-"swv trace.svg"</div> <div>- SVG file for Panel C. Generated using Igor Pro v8 from data in file "SWV Traces.pxp".</div> <div>-"ON&OFF.svg"</div> <div>- SVG file for Panel D . Generated using Igor Pro v8 from data in file "Signal ON&OFF.pxp". </div> <div>-"ratio box whisker plot.svg"</div> <div>- SVG file for Panel E. Generated using Igor Pro v8. </div> <div>-"calibration.svg"</div> <div>- SVG file for Panel F. Generated using Igor Pro v8. </div> <div>-Data: "SWV Traces.xlsx"</div> <div>"SWV Traces.csv"</div> <div>"SWV Traces.pxp" </div> <div>Data was obtained from sheet "SWV curves" of Excel file "ZiO-JHU_March 2023_Data processing Updates" recieved 10:37 EST, June 2 2023, from Yu Liu. </div> <div>Data was used as recieved, and imported to Igor Pro v8 for plotting. Data in all files are identical, except for file format.</div> <div>-Data: "Signal ON&OFF.xlsx"</div> <div>"Signal ON&OFF.csv"</div> <div>"Signal ON&OFF.pxp" </div> <div>Data was obtained from sheet "signal-on&signal-off" of Excel file "ZiO-JHU_March 2023_Data processing Updates" recieved 10:37 EST, June 2 2023, from Yu Liu. </div> <div>Data was used as recieved, and imported to Igor Pro v8 for plotting. Data in all files are identical, except for file format. Regression to the Hill equation</div> <div>was performed in Igor Pro v8, constraining rate=1.</div> <div>-Data: "Dose response curve box plot.xlsx"</div> <div>"Dose response curve box plot.csv"</div> <div>"Dose response curve box plot.pxp" </div> <div>Data was obtained from sheet "Curve fitting_Ratio(SUM_MEA)" of Excel file "ZiO-JHU_March 2023_Data processing Updates" recieved 10:37 EST, June 2 2023, from Yu Liu. </div> <div>Data was used as recieved, and imported to Igor Pro v8 for plotting. Data in all files are identical, except for file format. Box and whisker plot was generated using</div> <div>Igor Pro v8.</div> <div>-Data: "Day1-Day6 Dose Response Curve.xlsx"</div> <div>"Day1-Day6 Dose Response Curve.csv"</div> <div>"Day1-Day6 Dose Response Curve.pxp" </div> <div>Data was obtained from sheet "Curve fitting_Ratio(SUM_MEA)" of Excel file "ZiO-JHU_March 2023_Data processing Updates" recieved 10:37 EST, June 2 2023, from Yu Liu. </div> <div>Data was used as recieved, and imported to Igor Pro v8 for plotting. Data in all files are identical, except for file format. Nonlinear regression to the Hill equation</div> <div>was generated using Igor Pro v8. No variables were constrained.</div> <div> </div> <div>-'Figure_3':</div> <div>- DATA FOR FIGURE 3 WAS OBTAINED FROM THE "Ratiometric response" SHEET OF THE FILE "Confidential_ZiO Health Stability Test Data" RECIEVED 12:08, NOVEMBER 8 2023, FROM FRANCESCA NAPOLI.</div> <div>- DATA FROM FIELDS "Average response to 12.5 mg/L for chips tested in specific week" AND "SD response to 12.5 mg/L for chips tested in specific week" (ROWS 39 AND 40) , WITH ASSOCIATED TIMEPOINT IDENTIFIERS, WERE EXTRACTED AND USED AS RECIEVED</div> <div>- IN ROWS 41 AND 42, DATA FROM THE 0.1 MG/L CONCENTRATION POINT (ROW 13) WAS EXCTRACTED AND PROCESSED IN THE SAME WAY AS "Average response to 12.5 mg/L for chips tested in specific week" AND "SD response to 12.5 mg/L for chips tested in specific week" (I.E, AVERAGE AND STANDARD DEVIATION OF THREE OR FOUR CHIPS PER WEEK)</div> <div>- TIMEPOINT IDENTIFIERS WERE CONVERTED TO NUMERIC AND GIVEN THE TITLE "Time" and UNIT "Weeks"</div> <div>- FIELD "Average response to 12.5 mg/L for chips tested in specific week" WAS TITLED "12.5 mg/L Sensor response ratio" AND GIVEN UNIT "360Hz/20Hz"</div> <div>- FIELD "SD response to 12.5 mg/L for chips tested in specific week" WAS TITLED "12.5 mg/L SD" AND GIVEN UNIT "360Hz/20Hz"</div> <div>- THE SAME WAS DONE FOR THE 0.1 MG/l CONCENTRATION DATA</div> <div>- DATA FILE USED FOR MAKING THE FIGURE IS "2023_11_08_Chip_Stability.pxp"</div> <div>- BOTH O.1 MG/L AND 12.5 MG/L WERE SUBJECTED TO LINEAR REGRESSION ANALYSIS IN IGOR V8</div> <div> </div> <div>-'Figure_4':</div> <div>- Final PNG image for Figure 4.</div> <div> </div> <div>-'Figure_5':</div> <div>- "2023_11_07_Fig5.png"</div> <div>- Final .PNG file used for Figure 5. Panels were arranged and Mean/StDev numbers were added in Adobe Illustrator.</div> <div>- "EAB_diff.svg"</div> <div>-.svg file used for panel A of Figure 5. Generated in Igor v8.</div> <div>- "EAB_pct_aveplot.svg"</div> <div>-.svg file used for panel C of Figure 5. Generated in Igor v8.</div> <div>- "ImA_diff.svg"</div> <div>-.svg file used for panel B of Figure 5. Generated in Igor v8.</div> <div>- "ImA_pct_aveplot.svg"</div> <div>-.svg file used for panel D of Figure 5. Generated in Igor v8.</div> <div>- Data: "2023_07_10_Differentials.xlsx"</div> <div>"2023_07_10_Differentials.csv"</div> <div>"2023_07_10_Differentials.pxp"</div> <div>Data was obtained from sheet "Predict con.c" of Excel file "ZiO-JHU_March 2023_Data processing Updates" recieved 10:37 EST, June 2 2023, from Yu Liu. </div> <div>Data was used as recieved, and imported to Igor Pro v8 for plotting. </div> <div>-'Figure_6':</div> <div>- "2023_07_12_Fig6.png"</div> <div>- Final .PNG file used for Figure 6. Panels were arranged and statistics valued added in Adobe Illustrator.</div> <div>- "serum.svg"</div> <div>-.svg file used for panel A of Figure 5. Generated in Igor v8 from Table 0.</div> <div>- "Filtrate smaller points.svg"</div> <div>-.svg file used for panel C of Figure 5. Generated in Igor v8 from Graph 0.</div> <div>- "Filtrate_insert.svg"</div> <div>-.svg file used for panel B of Figure 5. Generated in Igor v8 from Graph 5.</div> <div>- "Bland-Altman.svg"</div> <div>-.svg file used for panel D of Figure 5. Generated in Igor v8 from Graph 1.</div> <div>- "Bland-Altman insert.svg"</div> <div>-.svg file used for panel D of Figure 5. Generated in Igor v8 from Graph 1.</div> <div>- "Rplot0_png.png"</div> <div>-.png file of Bangdiwala plot. Generated in Rstudio by code mentioned below.</div> <div>- "Agreement plot code.txt"</div> <div>-Code used to generate Bangdiwala agreement plot using Rstudio. </div> <div>- Data: "Concentrations_all.xlsx"</div> <div>"Concentrations_all.csv"</div> <div>Data was obtained from sheet "Predict con.c" of Excel file "ZiO-JHU_March 2023_Data processing Updates" recieved 10:37 EST, June 2 2023, from Yu Liu. </div> <div>Data was used as recieved, and imported to Igor Pro v8 for plotting. </div> <div>- Data: "Bland-Altman.xlsx"</div> <div>"Bland-Altman.csv"</div> <div>Data was obtained from sheet "Bland–Altman Plot" of Excel file "ZiO-JHU_March 2023_Data processing Updates" recieved 10:37 EST, June 2 2023, from Yu Liu. </div> <div>Data was used as recieved, and imported to Igor Pro v8 for plotting. </div> <div>- Data: "ROC-input data for agreement plot.xlsx"</div> <div>"ROC-input data for agreement plot.csv"</div> <div>Data was recieved 12:30 EST, December 7 2023, from Francesca Napoli.</div> <div>- Data: "Fig6.pxp"</div> <div>Table 0: all samples from "Concentrations_all.xlsx"</div> <div>Table 1: average and difference values for EA-B filtrate samples vs immunoassay filtrate samples from "Bland-Altman.xlsx", as well as upper and lower 95% CI boundaries</div> <div>Table 2: samples sorted by concentration and filtered by immunoassay values <15mg/L and <10mg/L.</div> <div>Graph 0: plot of immunoassay vs E-AB serum concentration values from Table 0.</div> <div>Graph 1: plot of immunoassay vs E-AB filtrate concentration values from Table 0.</div> <div>Graph 2: plot of average vs difference values from Tablel 1.</div> <div>Graph 4: plot of filtered immunoassay vs E-AB filtrate concentration values <10mg/L from Table 3.</div> <div>Graph 5: plot of filtered immunoassay vs E-AB filtrate concentration values <15mg/L from Table 3.</div> <div> </div> <div> </div> <div>NOTES</div> <div> </div> <div>(1) Raw voltammetry data is the property of ZiO Health, and is therefore not available for public release. All data presented here were extracted from raw voltammetric traces by ZiO Health,</div> <div>and transfered to the Arroyo lab for further analysis. </div> <div> </div> <div>(2) RStudio is available at: https://posit.co/products/open-source/rstudio/</div> <div> </div> <div>(3) Igor Pro is available at: https://www.wavemetrics.com/</div> <div> </div>
Improved Calibration of Electrochemical Aptamer-Based Sensors
<p>Figure 2A raw data: titration of vancomycin into 37C rat blood</p>
Effect of nucleolin-binding aptamer AS1411 on gene expression in 3T3 cells
GEO Series GSE274745. Mus musculus. 11 samples. Type: Expression profiling by high throughput sequencing.
Aptamer-modified 5-fluorouracil encapsulated metal-organic framework nanodrugs for enhanced therapy of colorectal cancer via peroxidative induced cell death
GEO Series GSE315569. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
ssDNA aptamers against amyloid-β peptide as biomarker and inhibtor [SELEX]
GEO Series GSE124865. Homo sapiens. 3 samples. Type: Other.
An aptamer-based bionic spinal cord scaffold elicits robust neurogenesis in situ after spinal cord injury in rats
GEO Series GSE245409. Rattus norvegicus. 6 samples. Type: Expression profiling by high throughput sequencing.
Therapeutic circRNA aptamer alleviates PKR-associated osteoarthritis
GEO Series GSE287411. Homo sapiens. 4 samples. Type: Other.
Antibody-Free and Fast Profiling of Transcription Factor Binding Sites Using Aptamer-Guided Chromatin Cleavage with Sequencing (AptaChC-seq)
GEO Series GSE221869. Homo sapiens. 25 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
DNA aptamers that modulate biological activity of model neurons
GEO Series GSE278187. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
Coagulation Factor XIII-Binding Aptamers as Bivalent Ligands Targeting Fibrin and Activated Platelets in the Thrombus
GEO Series GSE163934. synthetic construct. 1 samples. Type: Other.
The raw images of Laser Confocal Microscopy experiments in the manuscript: Inert Pepper aptamer-mediated endogenous mRNA recognition and imaging in living cells
<p>The <strong>original imaging data</strong> folder contains the raw images of Laser confocal microscopy experiments in the manuscript: Inert Pepper aptamer-mediated endogenous mRNA recognition and imaging in living cells. <a href="https://doi.org/10.1093/nar/gkac368">https://doi.org/10.1093/nar/gkac368</a> </p>
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