Citations

Purpose: The majority of patients with end stage renal disease who require hemodialysis use a arteriovenous fistula (AVF) for their vascular access. However, only 40% of AVFs are clinically usable after 1 year. Arteriovenous fistulas fail because of venous neointimal hyperplasia (VNH) that develops in the proximal outflow vein. The immediate early gene, IEX-1 (IER-3), is increased in response to shear stress and hypoxia and both factors contribute to VNH. IEX-1 expression was increased in stenotic venous stenoses removed from patients with hemodialysis vascular access compared to controls. We hypothesized that reducing the expression of IEX-1 using a knockout mouse model with chronic kidney disease (CKD) and AVFs would have decreased VNH compared to wild type (WT) mice. Methods: CKD was induced by performing a nephrectomy. Four weeks later, an AVF was placed to connect the carotid artery to ipsilateral jugular vein. The mice were sacrificed 3 and 28 days after fistula creation and outflow veins were harvested for gene expression and histomorphometric analyses. Results: By day 3, the average gene expression of vascular endothelial growth factor-A (VEGF-A) was significantly lower in the IEX-1 KO vs. WT mice (average reduction: 40%, P<0.05). At day 28, the average lumen vessel area was increased significantly (average increase: 133%, P<0.05, IEX-1 KO vs. WT, respectively) while the average area of the neointima was significantly decreased (average reduction: 20%, IEX-1 KO vs. WT, respectively). Accompanied with this, there was a significant decrease in α-SMA staining and Ki-67 proliferation while the average TUNEL staining was significantly increased (all P<0.05, IEX-1 KO vs. WT). Finally, there was a significant decrease in the average staining intensity for VEGF-A and matrix metalloproteinase-9 (MMP-9) in the KO vs. WT mice (both, P<0.05). Conclusion: A reduction in IEX-1 gene expression results in positive vascular remodeling accompanied with a reduction in proliferation and α-SMA density mediated through a VEGF-A/MMP-9 pathway. Taken collectively, these findings indicate that reducing IEX-1 expression is protective for VNH formation and provide a rationale for use of anti-IEX-1 therapies aimed at prolonging AVF function.

Rationale: The role of cardiac natriuretic peptides (NPs) in peripheral arterial disease (PAD) or thromboangiitis obliterans (TA) is unknown. Recent studies in mice suggested that BNP may be produced outside the heart in the ischemic limbs contributing to peripheral angiogenesis. Objective: Determine if synthesis or consumption/degradation of NPs occur in the ischemic legs of patients with PAD or TA. Methods: 32 patients with critical limb ischemia (CLI) or claudication (PAD n=31; TA n=1; 20 males; mean age 72.9 years) underwent diagnostic angiography or revascularization. We determined differences of concentrations of BNP, NT-proBNP, and CNP in the blood of proximal veins and arteries of both legs. Concentrations differences were then compared between the more ischemic target and the contralateral self-control legs. Results: Values are means ± SD. P values <0.05 were considered significant. In the PAD group, in the target extremity, concentration step-ups of NT-proBNP (11±68; p=0.40) and CNP (0.45±6.22; p=0.71) were not significant from the arterial to venous blood. In the contralateral self-control legs, NT-proBNP and CNP concentrations drops were also not significant (-37±160, p=0.7; -0.89±4.09, p=0.23). Venous BNP concentrations were significantly lower than arterial both in the target (-66±153, p<0.001) and self-control legs (-59±107, p<0.001). BNP concentrations drops were greater in the CLI than claudication cases driving the overall significance. In the patient with TA, whose only identifiable PAD risk factor was smoking, arterial concentrations of NPs were generally low (NT-proBNP 81; BNP 46; CNP 8.2), but higher than venous concentrations (80; 41; 6.7). Conclusion: Significantly lower venous BNP concentrations suggest possible BNP consumption/degradation in the ischemic legs. Concentrations of NT-proBNP, that has a longer half-life than BNP, were more stable suggesting no significant local production or conversion to BNP. CNP concentrations were stable. Recognizing the angiogenic properties of NPs and lack of evidence of peripheral production, studies are warranted in humans with PAD to determine the potential angiogenic properties of exogenous NPs supplementation in what could be considered a local NPs deficiency state.

(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPointTM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland–Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment.

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

Key Points At 1 year after placement, 60% of hemodialysis arteriovenous fistulas (AVF) will develop venous neointimal hyperplasia (VNH) and subsequent venous stenosis (VS). Autologous adipose-derived mesenchymal stem cells may help reduce VS formation associated with hemodialysis AVF. There are no therapies available to prevent VS formation associated with hemodialysis AVF. Background Hemodialysis arteriovenous fistulas (AVFs) are the preferred vascular access for patients on hemodialysis. In the Hemodialysis Fistula Maturation Study, 44% of the patients achieved unassisted maturation of their fistula without needing an intervention. Venous neointimal hyperplasia (VNH) and subsequent venous stenosis are responsible for lack of maturation. There are no therapies that can prevent VNH/VS formation. The goal of this paper is to present the background, rationale, and trial design of an innovative phase 1/2 clinical study that is investigating the safety of autologous adipose-derived mesenchymal stem cells delivered locally to the adventitia of newly created upper extremity radiocephalic (RCF) or brachiocephalic fistula (BCF). Methods The rationale and preclinical studies used to obtain a physician-sponsored investigational new drug trial are discussed. The trial design and end points are discussed. Results This is an ongoing trial that will complete this year. Conclusion This is a phase 1/2 single-center, randomized trial that will investigate the safety and efficacy of autologous AMSCs in promoting maturation in new upper-extremity AVFs. Clinical Trial registration number : NCT02808208

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

Aim The objective was to examine the prevalence of atherosclerotic cardiovascular disease (ASCVD) and its risk factors among patients with RA with diabetes mellitus (RA-DM) and patients with RA without diabetes mellitus (RAwoDM), and to evaluate lipid and blood pressure (BP) goal attainment in RA-DM and RAwoDM in primary and secondary prevention. Methods The cohort was derived from the Survey of Cardiovascular Disease Risk Factors in Patients with Rheumatoid Arthritis from 53 centres/19 countries/3 continents during 2014–2019. We evaluated the prevalence of cardiovascular disease (CVD) among RA-DM and RAwoDM. The study population was divided into those with and without ASCVD, and within these groups we compared risk factors and CVD preventive treatment between RA-DM and RAwoDM. Results The study population comprised of 10 543 patients with RA, of whom 1381 (13%) had DM. ASCVD was present in 26.7% in RA-DM compared with 11.6% RAwoDM (p<0.001). The proportion of patients with a diagnosis of hypertension, hyperlipidaemia and use of lipid-lowering or antihypertensive agents was higher among RA-DM than RAwoDM (p<0.001 for all). The majority of patients with ASCVD did not reach the lipid goal of low-density lipoprotein cholesterol <1.8 mmol/L. The lipid goal attainment was statistically and clinically significantly higher in RA-DM compared with RAwoDM both for patients with and without ASCVD. The systolic BP target of <140 mm Hg was reached by the majority of patients, and there were no statistically nor clinically significant differences in attainment of BP targets between RA-DM and RAwoDM. Conclusion CVD preventive medication use and prevalence of ASCVD were higher in RA-DM than in RAwoDM, and lipid goals were also more frequently obtained in RA-DM. Lessons may be learnt from CVD prevention programmes in DM to clinically benefit patients with RA .

Abstract Aims To assess differences in estimated cardiovascular disease (CVD) risk among rheumatoid arthritis (RA) patients from different world regions and to evaluate the management and goal attainment of lipids and blood pressure (BP). Methods and results The survey of CVD risk factors in patients with RA was conducted in 14 503 patients from 19 countries during 2014–19. The treatment goal for BP was &lt;140/90 mmHg. CVD risk prediction and lipid goals were according to the 2016 European guidelines. Overall, 21% had a very high estimated risk of CVD, ranging from 5% in Mexico, 15% in Asia, 19% in Northern Europe, to 31% in Central and Eastern Europe and 30% in North America. Of the 52% with indication for lipid-lowering treatment (LLT), 44% were using LLT. The lipid goal attainment was 45% and 18% in the high and very high risk groups, respectively. Use of statins in monotherapy was 24%, while 1% used statins in combination with other LLT. Sixty-two per cent had hypertension and approximately half of these patients were at BP goal. The majority of the patients used antihypertensive treatment in monotherapy (24%), while 10% and 5% as a two- or three-drug combination. Conclusion We revealed considerable geographical differences in estimated CVD risk and preventive treatment. Low goal attainment for LLT was observed, and only half the patients obtained BP goal. Despite a high focus on the increased CVD risk in RA patients over the last decade, there is still substantial potential for improvement in CVD preventive measures.

The exact molecular mechanisms involved in hemodialysis arteriovenous fistula (AVF) failure caused by venous neointimal hyperplasia (VNH) are not clear. It has been observed that there is an accumulation of extracellular matrix and up regulation of pro-fibrotic genes accompanied with presence of fibroblasts, smooth muscle cells, and inflammatory cells in the stenotic veins. Previous studies have demonstrated that adventitial and medial fibroblasts have a pivotal role(s) in VNH formation. MicroRNA-21 (miR-21) contributes to fibroblast to myofibroblast differentiation and dysregulation of miR-21 plays a pathological role in failure of coronary artery bypass grafts. The aim of the present study was to determine the role of miR-21 in VNH associated with AVF. We assessed miR-21 expression using qRT-PCR in the outflow veins of AVFs compared to control (contralateral jugular veins) veins in the C57BL/6J mice with chronic kidney disease (CKD). MiR-21 expression was upregulated accompanied with down regulation of miR-21 target genes; PPAR-α, PTEN and TIMP-3. In addition, gene expression of fibroblast specific protein (FSP) -1, TGF (transforming growth factor) -β1, matrix metalloproteinases (MMP)-2, -9, collagen-I, and IV were significantly increased at day 7 after AVF creation. Immunohistochemistry revealed that there was a significant increase in proliferating cell index (Ki-67) and fibroblast index (FSP-1) in the outflow veins of AVFs. Hypoxia has been shown to increase fibroblast to myofibroblast differentiation and this is predicted to be an early step in VNH formation. Therefore we assessed miR-21 expression in hypoxic (1%O 2 ) mouse pulmonary vein fibroblasts compared to normoxic cells in vitro and it was found that miR-21and TGF-β1 significantly elevated with down regulation of miR-21 target genes PTEN and TIMP-3. Furthermore, miR-21 knockdown in hypoxic fibroblasts attenuated TGF-β1 expression with a significant upregulation of genes targeted by miR-21 compared to controls. Together these results indicate that upregulation of miR-21 expression may result in fibroblast to myofibroblast differentiation resulting in VNH formation.