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1,055 results for “stenosis”
Hydrogen sulfide release via the ACE inhibitor Zofenopril prevents intimal hyperplasia in human vein segments and in a mouse model of carotid artery stenosis
<p>The current strategies to reduce intimal hyperplasia (IH) principally rely on local drug delivery, in endovascular approach. The oral angiotensin converting enzyme inhibitor (ACEi) Zofenopril has additional effects compared to other non-sulfyhydrated ACEi to prevent intimal hyperplasia and restenosis. Given the number of patients treated with ACEi worldwide, these findings call for further prospective clinical trials to test the benefits of sulfhydrated ACEi over classic ACEi for the prevention of restenosis in hypertensive patients.</p> <p>Abstract</p> <p>Objectives</p> <p>Hypertension is a major risk factor for intimal hyperplasia (IH) and restenosis following vascular and endovascular interventions. Pre-clinical studies suggest that hydrogen sulfide (H2S), an endogenous gasotransmitter, limits restenosis. While there is no clinically available pure H2S releasing compound, the sulfhydryl-containing angiotensin-converting enzyme inhibitor Zofenopril is a source of H2S. Here, we hypothesized that Zofenopril, due to H2S release, would be superior to other non-sulfhydryl containing angiotensin converting enzyme inhibitor (ACEi), in reducing intimal hyperplasia in the context of hypertension.</p> <p>Materials</p> <p>Spontaneously hypertensive male Cx40 deleted mice (Cx40-/-) or WT littermates were randomly treated with Enalapril 20 mg (Mepha Pharma) or Zofenopril 30 mg (Mylan SA). Discarded human vein segments and primary human smooth muscle cells (SMC) were treated with the active compound Enalaprilat or Zofenoprilat.</p> <p>Methods</p> <p>IH was evaluated in mice 28 days after focal carotid artery stenosis surgery and in human vein segments cultured for 7 days ex vivo. Human primary smooth muscle cell (SMC) proliferation and migration were studied in vitro.</p> <p>Results</p> <p>Compared to control animals (intima/media thickness=2.3±0.33), Enalapril reduced IH in Cx40-/- hypertensive mice by 30% (1.7±0.35; p=0.037), while Zofenopril abrogated IH (0.4±0.16; p<.0015 vs. Ctrl and p>0.99 vs. sham-operated Cx40-/-mice). In WT normotensive mice, enalapril had no effect (0.9665±0.2 in control vs 1.140±0.27; p>.99), while Zofenopril also abrogated IH (0.1623±0.07, p<.008 vs. Ctrl and p>0.99 vs. sham-operated WT mice). Zofenoprilat, but not Enalaprilat, also prevented intimal hyperplasia in human veins segments ex vivo. The effect of Zofenopril on carotid and SMC correlated with reduced SMC proliferation and migration. Zofenoprilat inhibited the MAPK and mTOR pathways in SMC and human vein segments.</p> <p>Conclusion</p> <p>Zofenopril provides extra beneficial effects compared to non-sulfhydryl ACEi to reduce SMC proliferation and restenosis, even in normotensive animals. These findings may hold broad clinical implications for patients suffering from vascular occlusive diseases and hypertension.</p>
Figs. 1-3 in Nuevos datos sobre Stenosis oteroi Español, 1981 (Coleoptera: Tenebrionidae) en Galicia
Figs. 1-3.- Stenosis oteroi, ejemplar macho de Baroña (Porto do Son). 1.- Vista dorsal. 2.- Vista ventral. 3.- Edeago.
Fig. 21 in Nuevos datos sobre Stenosis oteroi Español, 1981 (Coleoptera: Tenebrionidae) en Galicia
Fig. 21.- Piedra con individuos de Stenosis oteroi y nido de la hormiga Pheidole pallidula (Baroña, Porto do Son).
In-vitro dataset for classification and regression of stenosis: dependence on heart rate, waveform and location
<p><strong>Background</strong></p> <p>This data supplements the paper "Classification and regression of stenosis using an in-vitro pulse wave dataset:<br> dependence on heart rate, waveform and location". It was created at Technische Hochschule Mittelhessen (THM) in Germany and uploaded to Zenodo. Please cite the paper (<a href="https://doi.org/10.1016/j.compbiomed.2022.106224">https://doi.org/10.1016/j.compbiomed.2022.106224</a>) and the Zenodo doi when using this dataset.</p> <p><strong>General description / Dataset structure</strong></p> <p>Each mat-File describes a different measurement (details can be found in the paper). There are 17 pressure signals for different positions, one flow sensor close to the stenosis location and one monitor signal of the proportional valve use to control the input curve. Total duration of each signal is 60s with a sampling rate of 1000 Hz. Each mat-file contains a header structure with metadata and struct array for signals of each sensor. Signals in each mat-File are aligned with respect to a common time axis, but this is not guaranteed between different measurements/files. We did our best to make the beginnings end endings align as close as possible (by removing buffer artefacts and aligning the input signal of the monitor), however algorithms should not rely on a global time axis. This similar to patient measurements without an ekg, this does also not share a global time axis comparable among patients.</p> <p>The file format can either be loaded directly in Matlab or in Python with scipy's loadmat function.</p> <p>The data is structure first by stenosis "state" (or location) then by heart rate and then by heart waveform. The stenosis "states" can devided in 1 subset of 10 folders created for regression and 6 created for classification. Excerpt of the folder structure:</p> <ul> <li>No Stenosis <ul> <li>HR 50 <ul> <li>WaveForm1.mat</li> <li>WaveForm2.mat</li> <li>...</li> </ul> </li> <li>HR 55 <ul> <li>...</li> </ul> </li> <li>...</li> </ul> </li> <li>Regression - Stenosis at Pos01 <ul> <li>HR 50 <ul> <li>...</li> </ul> </li> <li>...</li> </ul> </li> <li>...</li> </ul> <p>The tools also available at this page help with traversing this folder structure and are available for Python and Matlab.</p> <p><strong>Data Fields of each file</strong></p> <table> <caption>headerStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>id</td> <td>internal database id</td> </tr> <tr> <td>name</td> <td>stenosis location</td> </tr> <tr> <td>rate</td> <td>sampling rate in Hz</td> </tr> <tr> <td>description</td> <td>definition of automatic parameter sweep range</td> </tr> <tr> <td>configuration</td> <td>concrete parameters of the trapezoidal input curve (offset and amplitude in mmHg, ascend times and descend times and smoothing window in a fraction the time period (1.2s))</td> </tr> </tbody> </table> <p> </p> <table> <caption>signalStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>nodeId</td> <td>corresponds to numbered nodes at which the sensor is placed, the corresponding location can be found in the technical paper describing the MACSim simulator (node numbering, not sensor numbers) or in the software SISCA in the example database.</td> </tr> <tr> <td>type</td> <td>'p' ... pressure or 'q' ... flow</td> </tr> <tr> <td>data</td> <td>double array, time series of each sensor, unit mmHg for type 'p' and ml/s for type 'q'</td> </tr> <tr> <td>anatomicalPosition</td> <td>name of the corresponding anatomical position</td> </tr> </tbody> </table> <p><strong>Tools:</strong></p> <p>This Tools should make it easier to load the dataset. The usage is documented in the respective code files.</p> <p>Code for the publication is available here:<br> https://gitlab.com/agbernhard.lse.thm/publication_macsim_machinelearning<br> </p> <p> </p> <p> </p>
CardSort data for treatment features and goals for aortic stenosis
<p>Background: Guidelines recommend including the patient's values and preferences when choosing treatment for severe aortic stenosis (sAS). However, little is known about what matters most to patients as they develop treatment preferences. Our objective was to identify, prioritize, and organize patient-reported goals and features of treatment for sAS.</p> <p>Results: 51 adults with sAS and 3 caregivers with experience choosing treatment (age 36-92 years) were included. Participants were referred from multiple health centers across the U.S. and online. Eight nominal group meetings generated 32 unique treatment goals and 46 treatment features, which were grouped into 10 clusters of goals and 11 clusters of features. The most important clusters were: 1) trust in the healthcare team, 2) having good information about options, and 3) long-term outlook. Other clusters addressed the need for and urgency of treatment, being independent and active, overall health, quality of life, family and friends, recovery, homecare, and the process of decision-making.</p> <p>Conclusions: These patient-reported items addressed the impact of the treatment decision on the lives of patients and their families from the time of decision-making through recovery, homecare, and beyond. Many attributes had not been previously reported for sAS. The goals and features that patients' value, and the relative importance that they attach to them, differ from those reported in clinical trials and vary substantially from one individual to another. These findings are being used to design a shared decision-making tool to help patients and their clinicians choose a treatment that aligns with the patients' priorities.</p>
Figs. 20 in Nuevos datos sobre Stenosis oteroi Español, 1981 (Coleoptera: Tenebrionidae) en Galicia
Figs. 20.- Hábitat de Stenosis oteroi en la playa de Area Maior (Muros).
Figs. 7-8 in Nuevos datos sobre Stenosis oteroi Español, 1981 (Coleoptera: Tenebrionidae) en Galicia
Figs. 7-8.- Stenosis oteroi.
Fig. 18 in Nuevos datos sobre Stenosis oteroi Español, 1981 (Coleoptera: Tenebrionidae) en Galicia
Fig. 18.- Stenosis oteroi depositado en el MCNB.
The associations of gene-gene interactions among the 6 variants in 3 genes related to inflammation and endothelial function with carotid stenosis were performed by the GMDR approach
<p><span>The associations of gene-gene interactions among the 6 variants in 3 genes related to inflammation and endothelial function with carotid stenosis were performed by the GMDR approach. The best model for carotid stenosis including <em>ITGA2</em> rs4865756 and <em>HABP2</em> rs7923349 scored 8/10 for cross-validation consistency and 10/10 for sign testing. </span></p>
Efficacy of Acupuncture for Lumbar Spinal Stenosis
ClinicalTrials.gov study NCT03784729. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Trial of Cilostazol in Symptomatic Intracranial Arterial Stenosis II
ClinicalTrials.gov study NCT00130039. IPD Sharing: Not stated. Countries: 4. Publications: 5.
JENAVALVE AS EFS TRIAL: Pericardial TAVR Aortic Stenosis Study
ClinicalTrials.gov study NCT02732691. IPD Sharing: NO. Countries: 4. Publications: 4.
Acute Hemodynamic Effects of Sildenafil in Patients With Severe Aortic Stenosis
ClinicalTrials.gov study NCT01060020. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Minimally Invasive Decompression and Fusion Versus Open for Degenerative Lumbar Stenosis
ClinicalTrials.gov study NCT04594980. IPD Sharing: YES. Countries: 1. Publications: 1.
Safety and Efficacy Continued Access Study of the Medtronic CoreValve® System in the Treatment of Symptomatic Severe Aortic Stenosis in Very High Risk Subjects and High Risk Subjects Who Need Aortic V
ClinicalTrials.gov study NCT01531374. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Asymptomatic Carotid Stenosis: Cognitive Function and Plaque Correlates
ClinicalTrials.gov study NCT01353196. IPD Sharing: NO. Countries: 1. Publications: 1.
Stenting vs. Aggressive Medical Management for Preventing Recurrent Stroke in Intracranial Stenosis
ClinicalTrials.gov study NCT00576693. IPD Sharing: Not stated. Countries: 1. Publications: 16.
FUnctional diagnoSIs of corONary Stenosis (FUSION)
ClinicalTrials.gov study NCT04356027. IPD Sharing: NO. Countries: 1. Publications: 1.
Physiologic Assessment of Coronary Stenosis Following PCI
ClinicalTrials.gov study NCT03084367. IPD Sharing: Not stated. Countries: 3. Publications: 2.
Correlation of Auscultatory Severity of Aortic Stenosis With Trans Thoracic Echocardiography
ClinicalTrials.gov study NCT01605669. IPD Sharing: NO. Countries: 1. Publications: 4.
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