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91 results for “raman spectroscopy”
Data from: Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case-control study with multicohort validation
<p class="AbstractSummary"><b>Background</b></p> <p class="AbstractSummary">Prostate cancer (PC) is the most frequently diagnosed cancer in North American men. Pathologists are in critical need of accurate biomarkers to characterize PC, particularly to confirm the presence of intraductal carcinoma of the prostate (IDC-P), an aggressive histopathological variant for which therapeutic options are now available. Our aim was to identify IDC-P with Raman micro-spectroscopy and machine learning technology following a protocol suitable for routine clinical histopathology laboratories.</p> <p class="AbstractSummary"><b>Methods and findings</b></p> <p class="AbstractSummary">We used Raman micro-spectroscopy to differentiate IDC-P from PC, as well as PC and IDC-P from benign tissue on formalin-fixed paraffin-embedded first-line radical prostatectomy specimens (embedded in tissue microarrays, TMAs) from 483 patients treated in three Canadian institutions between 1993 and 2013. The main measures were the presence or absence of IDC-P and of PC, regardless of the clinical outcomes. Most of the 483 patients were pT2 stage (44–69%), and pT3a (22–49%) was more frequent than pT3b (9–12%). After approval of the construction of the TMAs by local ethics review board, the diagnostic accuracy study was approved by the Centre hospitalier de l'Université de Montréal (CHUM) ethics review board. Briefly, two consecutive sections of each TMA block were cut. The first section was transferred onto a glass slide to perform immunohistochemistry with H&E counterstaining for cell identification. The second section was placed on an aluminum slide, dewaxed, and then used to acquire an average of 7 Raman spectra per specimen (between 4 and 24 Raman spectra, 4 acquisitions / TMA core). Raman spectra of each cell type were then analyzed to retrieve tissue-specific molecular information and to generate classification models using machine learning technology. <span>Models were trained and cross-validated using data from one institution. Accuracy, sensitivity and specificity were respectively of 87 ± 5%, 86 ± 6% and 89 ± 8% to differentiate PC from benign tissue, and of 95 ± 2%, 96 ± 4% and 94 ± 2% respectively to differentiate IDC-P from PC. The trained models were then tested on data from two independent institutions, reaching accuracies, sensitivities and specificities of 84 and 86%, 84 and 87%, and 81 and 82%, respectively</span><span> to diagnose PC, and of 85 and 91%, 85 and 88%, and 86 and 93% respectively for the identification of IDC-P.</span> IDC-P could further be differentiated from high-grade prostatic intraepithelial neoplasia (HGPIN), a pre-malignant intraductal proliferation which can be mistaken as IDC-P, with accuracies, sensitivities and specificities >95% in both training and testing cohorts. As we used stringent criteria to diagnose IDC-P, the main limitation of our study is the exclusion of borderline, difficult to classify lesions from our datasets.</p> <p class="AbstractSummary"><b>Conclusions</b></p> <p>In this study, we developed classification models for the analysis of Raman micro-spectroscopy data to differentiate IDC-P, PC and benign tissue, including HGPIN. Raman micro-spectroscopy could be a next-generation histopathological technique used to <span>reinforce the identification of high-risk PC patients and lead to more precise diagnosis of IDC-P.</span></p>
Supplementary data to 'Feedbacks and non-linearity of silicate glass alteration in hyperalkaline solution studied by in operando fluid-cell Raman spectroscopy'
<p>Additiona data to published article:</p> <p>Müller G., Fritzsche M. B. K., Dohmen L. and Geisler T. (2022) Feedbacks and non-linearity of silicate glass alteration in hyperalkaline solution studied by in operando fluid-cell Raman spectroscopy. Geochim. Cosmochim. Acta 329, 1–21.<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.gca.2022.05.013" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.gca.2022.05.013</span></span></a></p>
Monitoring Solid-Phase Reactions in Self-Assembled Monolayers by Surface-Enhanced Raman Spectroscopy
<p>Data underlying the figures in the publication “Monitoring Solid-Phase Reactions in Self-Assembled Monolayers by Surface-Enhanced Raman Spectroscopy”, published in <em>Angew. Chem. Int. Ed.,</em> <strong>2021</strong>, 60, 2–10<strong>.</strong></p> <p><a href="https://onlinelibrary.wiley.com/doi/full/10.1002/anie.202102319">https://onlinelibrary.wiley.com/doi/full/10.1002/anie.202102319</a></p> <p>Table of contents:</p> <p><strong>1. Figure 1C</strong>; Zip file containing the numerical data for <em>Figure 1C</em>.</p> <p>The data were obtained from optical numerical simulations using the software <em>Lumerical</em>. The parameters used for the simulations are described in the SI of the publication. The file “OCH04-015_0nm.txt” has been exported from the simulated solution. It includes the distribution of the electric field intensity (|E|^2) in x and y directions at the Au-air interface. The data were then plotted as the electromagnetic enhancement factor in log scale (log|E|^4) using the origin lab software (“OCH04-015.opju”.</p> <p><strong>2. Figure 1D, 1E, 1F</strong>; Zip file containing the numerical data for <em>Figures 1D, 1E</em> and <em>1F.</em></p> <p><strong>Figure 1D:</strong> 100 data files with the general file name:</p> <p>“OCH04-021_3_633nm_300lpermm_10perc_2x30s_300hole_100x_Yyy_Xxx.txt”</p> <p>The yy and xx are different numeric values for each file indicating the position in the 10 x 10 map. And:</p> <p>«OCH02-072_2_blankAu_2x30s_10perc_633nm_100x_01.txt” is the dataset of the orange dotted spectrum which was recorded on the planar Au surface.</p> <p>In all text files, there are two columns: The first one is the Raman shift in cm–1 and the second one the intensity in photon counts. The Raman spectroscopy data in the files starting with “OCH04-021…” were generated using the Horiba LabRAM Software and the baseline has already been subtracted using this software. The 100 spectra were plotted without further data smoothing (grey spectra) and the average spectrum (black) was generated by using the dedicated function in the Origin Lab software. The orange spectrum originates from «OCH02-072_2_blankAu_2x30s_10perc_633nm_100x_01.txt”. It was smoothed with 10 points using a Savitzky-Golay Filter in Origin Lab and the Baseline was subtracted.</p> <p><strong>Figure 1E:</strong> The Box Plot was generated using the 100 grey spectra from 1D and applying a Gaussian fit to the three peaks indicated in the figure and extracting the peak positions. Using these peak position data, the box plot was generated using the Origin Lab software.</p> <p><strong>Figure 1F:</strong> The contour plot was generated using the 100 grey spectra from 1D and applying a gaussian fit to the peak indicated in the figure description and extracting the peak heights. Using these peak height data, the contour plot was generated using the Origin Lab software.</p> <p><strong>3. Figure 2</strong>; Zip file containing the numerical data for <em>Figure 2</em>.</p> <p>In all text files, there are two columns: The first one is the Raman shift in cm<sup>–1</sup> and the second one the intensity in photon counts. The spectra were smoothed with 10 points using a Savitzky-Golay Filter in Origin Lab and the Baseline was subtracted. The y intensity was normalized so that the Si peak at approx. 950 cm<sup>–1</sup> had the same height. The Raman shift in x direction was shifted so that the Si peak at 300 cm<sup>–1</sup> was at the same position in each spectrum.</p> <p><strong>4. Figure 3A, 3C</strong>; Zip file containing the numerical data for <em>Figures 3A</em> and <em>3C</em>.</p> <p>In all text files, there are two columns: The first one is the Raman shift in cm–1 and the second one the intensity in photon counts. The spectra were smoothed with 8 points using a Savitzky-Golay Filter in Origin Lab and the Baseline was subtracted. The average of three spectra was calculated for the spectra with the same y description for the plotted spectra in 3A. Figure 3C was generated by applying a gaussian fit to the three peaks indicated in the figure in the original 12 data sets and extracting the peak heights. The average and standard deviation of the peak height data from the spectra with the same y description was then calculated to generate Figure 3C.</p> <p><strong>5. Figure 4A, 4B</strong>; Zip file containing the numerical data for <em>Figures 4A</em> and <em>4B</em>.</p> <p><strong>4A:</strong> In all text files, there are two columns: The first one is the Raman shift in cm<sup>–1</sup> and the second one the intensity in photon counts. The spectra were smoothed with 10 points using a Savitzky-Golay Filter in Origin Lab and the Baseline was subtracted. The y intensity was normalised so that the Si peak at approx. 950 cm<sup>–1</sup> had the same height. The Raman shift in x direction was shifted so that the Si peak at 300 cm<sup>–1</sup> was at the same position in each spectrum.</p> <p><strong>4B:</strong> The peak positions from <em>Figures 2</em> and <em>4A</em> were used to generate <em>Figure 4B</em>.</p>
Mechanically Tunable Lattice-Plasmon Resonances by Templated Self-Assembled Superlattices for Multi- Wavelength Surface-Enhanced Raman Spectroscopy
<p>Related publication: Charconnet, M; Kuttner, C; Plou, J; Garcia-Pomar, JL; Mihi, A; Liz-Marzan, LM; Seifert, A. Mechanically Tunable Lattice-Plasmon Resonances by Templated Self-Assembled Superlattices for Multi-Wavelength Surface-Enhanced Raman Spectroscopy. <em>Small Methods</em> 2021, 2100453. 10.1002/smtd.202100453</p>
Label-Free Identification of Exosomes Using Raman Spectroscopy and Machine Learning
<p>Figures and datasets used in the manuscript "Label-Free Identification of Exosomes Using Raman Spectroscopy and Machine Learning". Codes that produce the figures are also included.</p> <p> </p> <p> </p> <div> </div>
Label-free Raman Spectroscopy for Discrimination Between Breast Cancer Tumor and Adjacent Tissues After Neoadjuvant Treatment
ClinicalTrials.gov study NCT06394050. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Data from: Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case-control study with multicohort validation
Open the record for dataset details and reuse information.
Data from: Specificity and strain-typing capabilities of Nanorod Array-Surface Enhanced Raman Spectroscopy for Mycoplasma pneumoniae detection
Open the record for dataset details and reuse information.
Porous carbon nanowire array for surface-enhanced Raman spectroscopy
<p>Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for vibrational spectroscopy as it provides several orders of magnitude higher sensitivity than inherently weak spontaneous Raman scattering by exciting localized surface plasmon resonance (LSPR) on metal substrates. However, SERS can be unreliable for biomedical use since it sacrifices reproducibility, uniformity, biocompatibility, and durability due to its strong dependence on “hot spots”, large photothermal heat generation, and easy oxidization. Here we demonstrate the design, fabrication, and use of a metal-free (i.e., LSPR-free), topologically tailored nanostructure composed of porous carbon nanowires in an array as a SERS substrate to overcome all these problems. Specifically, it offers not only high signal enhancement (~10<sup>6</sup>) due to its strong broadband charge-transfer resonance, but also extraordinarily high reproducibility due to the absence of hot spots, high durability due to no oxidization, and high compatibility to biomolecules due to its fluorescence quenching capability.</p>
Research data supporting "In Vivo Biomolecular Imaging of Zebrafish Embryos using Confocal Raman Spectroscopy"
<p>Research raw data supporting Hogset et al., "In vivo biomolecular imaging of zebrafish embryos using confocal Raman spectroscopy", 2020, Nature Communications.</p>
Skin Fibrosis Analysis by Raman Spectroscopy in Systemic Sclerosis
ClinicalTrials.gov study NCT04996082. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Cardiovascular Risk Stratification on the Basis of Surface Enhanced Raman Spectroscopy
ClinicalTrials.gov study NCT06399328. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Sentry Study: Raman Spectroscopy on Ex Vivo Lungs
ClinicalTrials.gov study NCT05790226. IPD Sharing: NO. Countries: 1. Publications: 0.
An Innovative Method in SAliva Samples for the Early Differential Diagnosis of High-impact NeuroDegenerative Diseases Through Raman Spectroscopy
ClinicalTrials.gov study NCT06875739. IPD Sharing: Not stated. Countries: 1. Publications: 0.
In Vivo Raman Spectroscopy of Human Capillary Beds
ClinicalTrials.gov study NCT02333136. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Raman Spectroscopy Compared to Flow Cytometry
ClinicalTrials.gov study NCT06291428. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Multicenter, Prospective Clinical Study of the Serum Raman Spectroscopy Intelligent System for the Diagnosis of Prostate Cancer
ClinicalTrials.gov study NCT05854940. IPD Sharing: Not stated. Countries: 1. Publications: 0.
In Vivo Proof-of-principle Study of Raman Spectroscopy in Trial Participants With Bladder Tumours
ClinicalTrials.gov study NCT05124106. IPD Sharing: NO. Countries: 1. Publications: 0.
Raman Spectroscopy-Based Deep Learning Model for Early Pan-Cancer Early Diagnosis
ClinicalTrials.gov study NCT06822413. IPD Sharing: NO. Countries: 1. Publications: 0.
Raman Spectroscopy-Based Non-Invasive Blood Glucose Detection
ClinicalTrials.gov study NCT07311421. IPD Sharing: NO. Countries: 1. Publications: 0.
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.