The Effects Of Triphenylphosphate and Recorcinolbis(Diphenylphosphate) on the Thermal Degradation Of Polycarbonate in Air

The thermal degradation of polycarbonate/triphenylphosphate (PC/TPP) and PC/resocinolbis(diphenylphosphate) (PC/RDP) in air has been studied using TGA/FTIR and GC/MS. In PC/phosphate blends, the phosphate stabilizes the carbonate group of polycarbonate from alcoholysis between the alcohol products of polycarbonate degradation and the carbonate linkage. Thus, the evolution of bisphenol A, which is mainly produced via hydrolysis/alcoholysis of the carbonate linkage, is significantly reduced, while, the evolution of various alkylphenols and diarylcarbonates increases. The bonds that are broken first in the thermal degradation of both the carbonate and isopropylidene linkages of polycarbonate are the weakest bonds in each, when a phosphate is present. Triphenylphosphate and resocinolbis(diphenyl-phosphate), even though they exhibit a significant difference in their volatilization temperature, appear to play a similar role in the degradation pathway of polycarbonate

The Thermal degradation of Bisphenol A Polycarbonate in Air

The thermal degradation of polycarbonate in air was studied as a function of mass loss using TGA/FTIR, GC/MS and LC/MS. In the main degradation region, 480–560 °C, the assigned structures of smaller molecules and linear molecules that evolved in air were very similar to those obtained from the degradation in nitrogen; the degradation of polycarbonate follows chain scission of the isopropylidene linkage, in agreement with the bond dissociation energies, and hydrolysis/alcoholysis of carbonate linkage. Compared to the degradation in nitrogen, some differences were observed primarily in the beginning stage of degradation. Oxygen may facilitate branching as well as radical formation via the formation of peroxides. These peroxides undergo further dissociations and combinations, producing aldehydes, ketones and some branched structures, mainly in the beginning stage of degradation. It is speculated that the intermediate char formed in the beginning due to branching reactions of peroxide interferes with the mass transfer through the surface of degrading polycarbonate in the main degradation. Thus, even though the mass loss begins earlier in air, a slower mass loss rate is observed

A TGA/FTIR and Mass Spectral Study on the Thermal Degradation of Bisphenol A Polycarbonate

The thermal degradation of polycarbonate under nitrogen was studied using TGA/FTIR, GC/MS and LC/MS as a function of mass loss. The gases evolved during degradation were inspected by in situ FTIR and then the evolved products were collected and analysed using FTIR, GC–MS and LC–MS. The structures of the evolved products are assigned on the basis of FTIR and GC/MS results. The main thermal degradation pathways follow chain scission of the isopropylidene linkage, and hydrolysis/alcoholysis and rearrangement of carbonate linkages. In the case of chain scission, it was proposed that methyl scission of isopropylidene occurs first, according to the bond dissociation energies. The presence of carbonate structures, 1,1′-bis(4-hydroxyl phenyl) ethane and bisphenol A in significant amounts, supports the view that chain scission and hydrolysis/alcoholysis are the main degradation pathways for the formation of the evolved products

Meridianin C inhibits the growth of YD-10B human tongue cancer cells through macropinocytosis and the down-regulation of Dickkopf-related protein-3

Meridianin C is a marine natural product known for its anti‐cancer activity. At present, the anti‐tumour effects of meridianin C on oral squamous cell carcinoma are unknown. Here, we investigated the effect of meridianin C on the proliferation of four different human tongue cancer cells, YD‐8, YD‐10B, YD‐38 and HSC‐3. Among the cells tested, meridianin C most strongly reduced the growth of YD‐10B cells; the most aggressive and tumorigenic of the cell lines tested. Strikingly, meridianin C induced a significant accumulation of macropinosomes in the YD‐10B cells; confirmed by the microscopic and TEM analysis as well as the entry of FITC‐dextran, which was sensitive to the macropinocytosis inhibitor amiloride. SEM data also revealed abundant long and thin membrane extensions that resemble lamellipodia on the surface of YD‐10B cells treated with meridianin C, pointing out that meridianin C‐induced macropinosomes was the result of macropinocytosis. In addition, meridianin C reduced cellular levels of Dickkopf‐related protein‐3 (DKK‐3), a known negative regulator of macropinocytosis. A role for DKK‐3 in regulating macropinocytosis in the YD‐10B cells was confirmed by siRNA knockdown of endogenous DKK‐3, which led to a partial accumulation of vacuoles and a reduction in cell proliferation, and by exogenous DKK‐3 overexpression, which resulted in a considerable inhibition of the meridianin C‐induced vacuole formation and decrease in cell survival. In summary, this is the first study reporting meridianin C has novel anti‐proliferative effects via macropinocytosis in the highly tumorigenic YD‐10B cell line and the effects are mediated in part through down‐regulation of DKK‐3

A CREDENCE Trial Substudy

Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.OBJECTIVES: The study compared the performance for detection and grading of coronary stenoses using artificial intelligence-enabled quantitative coronary computed tomography angiography (AI-QCT) analyses to core lab-interpreted coronary computed tomography angiography (CTA), core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR). BACKGROUND: Clinical reads of coronary CTA, especially by less experienced readers, may result in overestimation of coronary artery disease stenosis severity compared with expert interpretation. AI-based solutions applied to coronary CTA may overcome these limitations. METHODS: Coronary CTA, FFR, and QCA data from 303 stable patients (64 ± 10 years of age, 71% male) from the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia) trial were retrospectively analyzed using an Food and Drug Administration-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. RESULTS: Disease prevalence was high, with 32.0%, 35.0%, 21.0%, and 13.0% demonstrating ≥50% stenosis in 0, 1, 2, and 3 coronary vessel territories, respectively. Average AI-QCT analysis time was 10.3 ± 2.7 minutes. AI-QCT evaluation demonstrated per-patient sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 94%, 68%, 81%, 90%, and 84%, respectively, for ≥50% stenosis, and of 94%, 82%, 69%, 97%, and 86%, respectively, for detection of ≥70% stenosis. There was high correlation between stenosis detected on AI-QCT evaluation vs QCA on a per-vessel and per-patient basis (intraclass correlation coefficient = 0.73 and 0.73, respectively; P < 0.001 for both). False positive AI-QCT findings were noted in in 62 of 848 (7.3%) vessels (stenosis of ≥70% by AI-QCT and QCA of <70%); however, 41 (66.1%) of these had an FFR of <0.8. CONCLUSIONS: A novel AI-based evaluation of coronary CTA enables rapid and accurate identification and exclusion of high-grade stenosis and with close agreement to blinded, core lab-interpreted quantitative coronary angiography. (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia [CREDENCE]; NCT02173275).proofepub_ahead_of_prin

The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography

Publisher Copyright: © 2022 The AuthorsObjectives: To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis. Background: CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters. Methods: CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm's diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI). Results: Within the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had 400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters. Conclusion: The diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables. Condensed abstract: An AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient's BMI or heart rate at time of scan affect the software's diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters.publishersversionpublishe

Antileishmanial High-Throughput Drug Screening Reveals Drug Candidates with New Scaffolds

Drugs currently available for leishmaniasis treatment often show parasite resistance, highly toxic side effects and prohibitive costs commonly incompatible with patients from the tropical endemic countries. In this sense, there is an urgent need for new drugs as a treatment solution for this neglected disease. Here we show the development and implementation of an automated high-throughput viability screening assay for the discovery of new drugs against Leishmania. Assay validation was done with Leishmania promastigote forms, including the screening of 4,000 compounds with known pharmacological properties. In an attempt to find new compounds with leishmanicidal properties, 26,500 structurally diverse chemical compounds were screened. A cut-off of 70% growth inhibition in the primary screening led to the identification of 567 active compounds. Cellular toxicity and selectivity were responsible for the exclusion of 78% of the pre-selected compounds. The activity of the remaining 124 compounds was confirmed against the intramacrophagic amastigote form of the parasite. In vitro microsomal stability and cytochrome P450 (CYP) inhibition of the two most active compounds from this screening effort were assessed to obtain preliminary information on their metabolism in the host. The HTS approach employed here resulted in the discovery of two new antileishmanial compounds, bringing promising candidates to the leishmaniasis drug discovery pipeline

Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence

Objective: The study evaluates the relationship of coronary stenosis, atherosclerotic plaque characteristics (APCs) and age using artificial intelligence enabled quantitative coronary computed tomographic angiography (AI-QCT). Methods: This is a post-hoc analysis of data from 303 subjects enrolled in the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial who were referred for invasive coronary angiography and subsequently underwent coronary computed tomographic angiography (CCTA). In this study, a blinded core laboratory analysing quantitative coronary angiography images classified lesions as obstructive (≥50%) or non-obstructive (\u3c50%) while AI software quantified APCs including plaque volume (PV), low-density non-calcified plaque (LD-NCP), non-calcified plaque (NCP), calcified plaque (CP), lesion length on a per-patient and per-lesion basis based on CCTA imaging. Plaque measurements were normalised for vessel volume and reported as % percent atheroma volume (%PAV) for all relevant plaque components. Data were subsequently stratified by age \u3c65 and ≥65 years. Results: The cohort was 64.4±10.2 years and 29% women. Overall, patients \u3e65 had more PV and CP than patients \u3c65. On a lesion level, patients \u3e65 had more CP than younger patients in both obstructive (29.2 mm3 vs 48.2 mm3; p\u3c0.04) and non-obstructive lesions (22.1 mm3 vs 49.4 mm3; p\u3c0.004) while younger patients had more %PAV (LD-NCP) (1.5% vs 0.7%; p\u3c0.038). Younger patients had more PV, LD-NCP, NCP and lesion lengths in obstructive compared with non-obstructive lesions. There were no differences observed between lesion types in older patients. Conclusion: AI-QCT identifies a unique APC signature that differs by age and degree of stenosis and provides a foundation for AI-guided age-based approaches to atherosclerosis identification, prevention and treatment

The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography

Objectives: To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis. Background: CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters. Methods: CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm\u27s diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI). Results: Within the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had \u3c50% stenosis in all vessel territories evaluated by QCA. Average AI analysis time was 10.3 ± 2.7 min. On a per vessel basis, there were significant differences only in sensitivity for ≥50% stenosis based on contrast type (iso-osmolar 70.0% vs non isoosmolar 92.1% p = 0.0345) and iodine concentration (\u3c350 mg/ml 70.0%, 350-369 mg/ml 90.0%, 370-400 mg/ml 90.0%, \u3e400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters. Conclusion: The diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables. Condensed abstract: An AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient\u27s BMI or heart rate at time of scan affect the software\u27s diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters