Sensitive detection of voltage transients using differential intensity surface plasmon resonance system

This paper describes theoretical and experimental study of the fundamentals of using surface plasmon resonance (SPR) for label-free detection of voltage. Plasmonic voltage sensing relies on the capacitive properties of metal-electrolyte interface that are governed by electrostatic interactions between charge carriers in both phases. Externally-applied voltage leads to changes in the free electron density in the surface of the metal, shifting the SPR position. The study shows the effects of the applied voltage on the shape of the SPR curve. It also provides a comparison between the theoretical and experimental response to the applied voltage. The response is presented in a universal term that can be used to assess the voltage sensitivity of different SPR instruments. Finally, it demonstrates the capacity of the SPR system in resolving dynamic voltage signals; a detection limit of 10mV with a temporal resolution of 5ms is achievable. These findings pave the way for the use of SPR systems in the detection of electrical activity of biological cells

Allelic frequency and genotypes of prion protein at codon 136 and 171 in Iranian Ghezel sheep breeds

PrP genotypes at codons 136 and 171 in 120 Iranian Ghezel sheep breeds were studied using allele-specific PCR amplification and compared with the well-known sheep breeds in North America, the United States and Europe. The frequency of V allele and VV genotype at codon 136 of Ghezel sheep breed was significantly lower than AA and AV. At codon 171, the frequency of allele H was significantly lower than Q and R. Despite the similarities of PrP genotypes at codons 136 and 171 between Iranian Ghezel sheep breeds and some of the studied breeds, significant differences were found with others. Planning of effective breeding control and successful eradication of susceptible genotypes in Iranian Ghezel sheep breeds will not be possible unless the susceptibility of various genotypes in Ghezel sheep breeds to natural or experimental scrapie has been elucidated

Atherosclerosis Imaging Quantitative Computed Tomography (AI-QCT) to guide referral to invasive coronary angiography in the randomized controlled CONSERVE trial

Aims: We compared diagnostic performance, costs, and association with major adverse cardiovascular events (MACE) of clinical coronary computed tomography angiography (CCTA) interpretation versus semiautomated approach that use artificial intelligence and machine learning for atherosclerosis imaging-quantitative computed tomography (AI-QCT) for patients being referred for nonemergent invasive coronary angiography (ICA). Methods: CCTA data from individuals enrolled into the randomized controlled Computed Tomographic Angiography for Selective Cardiac Catheterization trial for an American College of Cardiology (ACC)/American Heart Association (AHA) guideline indication for ICA were analyzed. Site interpretation of CCTAs were compared to those analyzed by a cloud-based software (Cleerly, Inc.) that performs AI-QCT for stenosis determination, coronary vascular measurements and quantification and characterization of atherosclerotic plaque. CCTA interpretation and AI-QCT guided findings were related to MACE at 1-year follow-up. Results: Seven hundred forty-seven stable patients (60 ± 12.2 years, 49% women) were included. Using AI-QCT, 9% of patients had no CAD compared with 34% for clinical CCTA interpretation. Application of AI-QCT to identify obstructive coronary stenosis at the ≥50% and ≥70% threshold would have reduced ICA by 87% and 95%, respectively. Clinical outcomes for patients without AI-QCT-identified obstructive stenosis was excellent; for 78% of patients with maximum stenosis < 50%, no cardiovascular death or acute myocardial infarction occurred. When applying an AI-QCT referral management approach to avoid ICA in patients with <50% or <70% stenosis, overall costs were reduced by 26% and 34%, respectively. Conclusions: In stable patients referred for ACC/AHA guideline-indicated nonemergent ICA, application of artificial intelligence and machine learning for AI-QCT can significantly reduce ICA rates and costs with no change in 1-year MACE

Coronary CTA With AI-QCT Interpretation: Comparison With Myocardial Perfusion Imaging for Detection of Obstructive Stenosis UsingInvasive Angiography as Reference Standard

Deep learning frameworks have been applied to interpretation of coronary CTA performed for coronary artery disease (CAD) evaluation. To compare the diagnostic performance of myocardial perfusion imaging (MPI) and coronary CTA with artificial intelligence-quantitative CT (AI-QCT) interpretation for detection of obstructive CAD on invasive angiography, and to assess downstream impact of including coronary CTA with AI-QCT in diagnostic algorithms. This study entailed a retrospective post-hoc analysis of the derivation cohort of the prospective 23-center CREDENENCE trial. The study included 301 patients [mean age 64.4±10.2 years; 88 female, 213 male] recruited from 2014 to 2017 with stable symptoms of myocardial ischemia referred for nonemergent invasive angiography. Patients underwent coronary CTA and MPI before angiography with quantitative coronary angiography (QCA) measurements and fractional flow reserve (FFR). CTA examinations were analyzed using an FDA-cleared cloud-based software that performs AI-QCT for stenosis determination. Diagnostic performance was evaluated. Diagnostic algorithms were compared. Among 102 patients with no ischemia on MPI, AI-QCT identified obstructive (≥50%) stenosis in 54%, including severe (≥70%) stenosis in 20%. Among 199 patients with ischemia on MPI, AI-QCT identified non-obstructive (1-49%) stenosis in 23%. AI-QCT had significantly higher AUC (all p\u3c.001) than MPI for predicting ≥50% stenosis by QCA (0.88 vs 0.66), ≥70% stenosis by QCA (0.92 vs 0.81), and FFR \u3c0.80 (0.90 vs 0.71). AI-QCT ≥50% and ischemia on stress MPI had sensitivity of 95% versus 74% and specificity of 63% versus 43% for detecting ≥50% stenosis by QCA measurement. Compared with performing MPI in all patients and those showing ischemia undergoing invasive angiography, a scenario of performing coronary CTA with AI-QCT in all patients and those showing ≥70% stenosis undergoing invasive angiography would reduce invasive angiography utilization by 39%; a scenario of performing MPI in all patients and those showing ischemia undergoing coronary CTA with AI-QCT and those with ≥70% stenosis on AI-QCT undergoing invasive angiography would reduce invasive angiography utilization by 49%. Coronary CTA with AI-QCT had higher diagnostic performance than MPI for detecting obstructive CAD. A diagnostic algorithm incorporating AI-QCT could substantially reduce unnecessary downstream invasive testing. ClinicalTrials.gov NCT02173275

Coronary CTA With AI-QCT Interpretation: Comparison With Myocardial Perfusion Imaging for Detection of Obstructive Stenosis Using Invasive Angiography as Reference Standard

BACKGROUND. Deep learning frameworks have been applied to interpretation of coronary CTA performed for coronary artery disease (CAD) evaluation. OBJECTIVE. The purpose of our study was to compare the diagnostic performance of myocardial perfusion imaging (MPI) and coronary CTA with artificial intelligence quantitative CT (AI-QCT) interpretation for detection of obstructive CAD on invasive angiography and to assess the downstream impact of including coronary CTA with AI-QCT in diagnostic algorithms. METHODS. This study entailed a retrospective post hoc analysis of the derivation cohort of the prospective 23-center Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia (CREDENCE) trial. The study included 301 patients (88 women and 213 men; mean age, 64.4 ± 10.2 [SD] years) recruited from May 2014 to May 2017 with stable symptoms of myocardial ischemia referred for nonemergent invasive angiography. Patients underwent coronary CTA and MPI before angiography with quantitative coronary angiography (QCA) measurements and fractional flow reserve (FFR). CTA examinations were analyzed using an FDA-cleared cloud-based software platform that performs AI-QCT for stenosis determination. Diagnostic performance was evaluated. Diagnostic algorithms were compared. RESULTS. Among 102 patients with no ischemia on MPI, AI-QCT identified obstructive (≥ 50%) stenosis in 54% of patients, including severe (≥ 70%) stenosis in 20%. Among 199 patients with ischemia on MPI, AI-QCT identified nonobstructive (1-49%) stenosis in 23%. AI-QCT had significantly higher AUC (all p < .001) than MPI for predicting ≥ 50% stenosis by QCA (0.88 vs 0.66), ≥ 70% stenosis by QCA (0.92 vs 0.81), and FFR < 0.80 (0.90 vs 0.71). An AI-QCT result of ≥ 50% stenosis and ischemia on stress MPI had sensitivity of 95% versus 74% and specificity of 63% versus 43% for detecting ≥ 50% stenosis by QCA measurement. Compared with performing MPI in all patients and those showing ischemia undergoing invasive angiography, a scenario of performing coronary CTA with AIQCT in all patients and those showing ≥ 70% stenosis undergoing invasive angiography would reduce invasive angiography utilization by 39%; a scenario of performing MPI in all patients and those showing ischemia undergoing coronary CTA with AI-QCT and those with ≥ 70% stenosis on AI-QCT undergoing invasive angiography would reduce invasive angiography utilization by 49%. CONCLUSION. Coronary CTA with AI-QCT had higher diagnostic performance than MPI for detecting obstructive CAD. CLINICAL IMPACT. A diagnostic algorithm incorporating AI-QCT could substantially reduce unnecessary downstream invasive testing and costs