Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review

The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™-aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized.

Cardiovascular disease/stroke risk stratification in deep learning framework: a review

The global mortality rate is known to be the highest due to cardiovascular disease (CVD). Thus, preventive, and early CVD risk identification in a non-invasive manner is vital as healthcare cost is increasing day by day. Conventional methods for risk prediction of CVD lack robustness due to the non-linear relationship between risk factors and cardiovascular events in multi-ethnic cohorts. Few recently proposed machine learning-based risk stratification reviews without deep learning (DL) integration. The proposed study focuses on CVD risk stratification by the use of techniques mainly solo deep learning (SDL) and hybrid deep learning (HDL). Using a PRISMA model, 286 DL-based CVD studies were selected and analyzed. The databases included were Science Direct, IEEE Xplore, PubMed, and Google Scholar. This review is focused on different SDL and HDL architectures, their characteristics, applications, scientific and clinical validation, along with plaque tissue characterization for CVD/stroke risk stratification. Since signal processing methods are also crucial, the study further briefly presented Electrocardiogram (ECG)-based solutions. Finally, the study presented the risk due to bias in AI systems. The risk of bias tools used were (I) ranking method (RBS), (II) region-based map (RBM), (III) radial bias area (RBA), (IV) prediction model risk of bias assessment tool (PROBAST), and (V) risk of bias in non-randomized studies-of interventions (ROBINS-I). The surrogate carotid ultrasound image was mostly used in the UNet-based DL framework for arterial wall segmentation. Ground truth (GT) selection is vital for reducing the risk of bias (RoB) for CVD risk stratification. It was observed that the convolutional neural network (CNN) algorithms were widely used since the feature extraction process was automated. The ensemble-based DL techniques for risk stratification in CVD are likely to supersede the SDL and HDL paradigms. Due to the reliability, high accuracy, and faster execution on dedicated hardware, these DL methods for CVD risk assessment are powerful and promising. The risk of bias in DL methods can be best reduced by considering multicentre data collection and clinical evaluation.

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.

Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0

BACKGROUND

COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, wiliest high performance on lung segmentation and lesion localization.

METHOD

ology: The proposed study uses multicenter ∼9,000 CT slices from two different nations, namely, CroMed from Croatia (80 patients, experimental data), and NovMed from Italy (72 patients, validation data). We hypothesize that by using pruning and evolutionary optimization algorithms, the size of the AI models can be reduced significantly, ensuring optimal performance. Eight different pruning techniques (i) differential evolution (DE), (ii) genetic algorithm (GA), (iii) particle swarm optimization algorithm (PSO), and (iv) whale optimization algorithm (WO) in two deep learning frameworks (i) Fully connected network (FCN) and (ii) SegNet were designed. COVLIAS 2.0 was validated using "Unseen NovMed" and benchmarked against MedSeg. Statistical tests for stability and reliability were also conducted.

RESULTS

Pruning algorithms (i) FCN-DE, (ii) FCN-GA, (iii) FCN-PSO, and (iv) FCN-WO showed improvement in storage by 92.4%, 95.3%, 98.7%, and 99.8% respectively when compared against solo FCN, and (v) SegNet-DE, (vi) SegNet-GA, (vii) SegNet-PSO, and (viii) SegNet-WO showed improvement by 97.1%, 97.9%, 98.8%, and 99.2% respectively when compared against solo SegNet. AUC > 0.94 (p < 0.0001) on CroMed and > 0.86 (p < 0.0001) on NovMed data set for all eight EA model. PAI <0.25 s per image. DenseNet-121-based Grad-CAM heatmaps showed validation on glass ground opacity lesions.

CONCLUSIONS

Eight PAI networks that were successfully validated are five times faster, storage efficient, and could be used in clinical settings.

COVLIAS 1.0Lesion vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans

Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models—namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet—were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests—namely, the Mann−Whitney test, paired t-test, and Wilcoxon test—demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test.

Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction-A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review

PURPOSE

The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients.

METHODS

Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification.

SUMMARY

We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.

Cardiovascular/Stroke Risk Stratification in Parkinson's Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review

Parkinson’s disease (PD) is a severe, incurable, and costly condition leading to heart failure. The link between PD and cardiovascular disease (CVD) is not available, leading to controversies and poor prognosis. Artificial Intelligence (AI) has already shown promise for CVD/stroke risk stratification. However, due to a lack of sample size, comorbidity, insufficient validation, clinical examination, and a lack of big data configuration, there have been no well-explained bias-free AI investigations to establish the CVD/Stroke risk stratification in the PD framework. The study has two objectives: (i) to establish a solid link between PD and CVD/stroke; and (ii) to use the AI paradigm to examine a well-defined CVD/stroke risk stratification in the PD framework. The PRISMA search strategy selected 223 studies for CVD/stroke risk, of which 54 and 44 studies were related to the link between PD-CVD, and PD-stroke, respectively, 59 studies for joint PD-CVD-Stroke framework, and 66 studies were only for the early PD diagnosis without CVD/stroke link. Sequential biological links were used for establishing the hypothesis. For AI design, PD risk factors as covariates along with CVD/stroke as the gold standard were used for predicting the CVD/stroke risk. The most fundamental cause of CVD/stroke damage due to PD is cardiac autonomic dysfunction due to neurodegeneration that leads to heart failure and its edema, and this validated our hypothesis. Finally, we present the novel AI solutions for CVD/stroke risk prediction in the PD framework. The study also recommends strategies for removing the bias in AI for CVD/stroke risk prediction using the PD framework.

Peroxidase-like activity of hemoglobin-based hybrid materials against different substrates and their enhanced application for H2O2 detection

ABSTRACT. Organic-inorganic hybrid nanoflowers method with unique properties are preferred than conventional immobilization methods for the past decade. Hereemoglobin-based hybrid material (HbNFs@Cu) was synthesized under different experimental conditions (pH 5.0-9.0 and 0.01-0.50 mgmL-1 of hemoglobin) obtaining a material size of 9-10 µm. The encapsulation percentage and weight yield of HbNFs@Cu were determined as 100% and 6.7%, respectively. The peroxidase-like activities of the material against different substrates (ABTS and Guaiacol) were compared to free hemoglobin. The HbNFs@Cu hybrid structure exhibited Vmax of 3.6995 EU/mg and a Michaelis-Menten constant (KM) of 0.1357 mM/mL. The HbNFs@Cu hybrid material was then used to catalyze the oxidation of a peroxidase substrate ABTS to the pigmented product, which provided a colorimetric and spectrophotometric detection of H2O2. The linear operating range, detectable colorimetrically as H2O2 sensor, is 0.005-0.0042 mM, while the linear operating range, detectable spectrometically, is 0.003-0.0042 mM. The limits of detection of colorimetric and spectrophotometric sensors were 0.005 mM and 0.003 mM, respectively. Collectively, these results showed that HbNFs@Cu can be used as colorimetric biosensor for H2O2 in potential applications such as pharmaceutical food, biomedical, environmental, and industrial.                     KEY WORDS: Hydrogen peroxide, Hemoglobin, Hybrid Material, Colorimetric assay   Bull. Chem. Soc. Ethiop. 2021, 35(3), 537-550.  DOI: https://dx.doi.org/10.4314/bcse.v35i3.6

Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson's Disease: A Narrative Review

Background and Motivation: Diagnosis of Parkinson's disease (PD) is often based on medical attention and clinical signs. It is subjective and does not have a good prognosis. Artificial Intelligence (AI) has played a promising role in the diagnosis of PD. However, it introduces bias due to lack of sample size, poor validation, clinical evaluation, and lack of big data configuration. The purpose of this study is to compute the risk of bias (RoB) automatically.

METHOD

The PRISMA search strategy was adopted to select the best 39 AI studies out of 85 PD studies closely associated with early diagnosis PD. The studies were used to compute 30 AI attributes (based on 6 AI clusters), using AP(ai)Bias 1.0 (AtheroPointTM, Roseville, CA, USA), and the mean aggregate score was computed. The studies were ranked and two cutoffs (Moderate-Low (ML) and High-Moderate (MH)) were determined to segregate the studies into three bins: low-, moderate-, and high-bias.

RESULT

The ML and HM cutoffs were 3.50 and 2.33, respectively, which constituted 7, 13, and 6 for low-, moderate-, and high-bias studies. The best and worst architectures were "deep learning with sketches as outcomes" and "machine learning with Electroencephalography," respectively. We recommend (i) the usage of power analysis in big data framework, (ii) that it must undergo scientific validation using unseen AI models, and (iii) that it should be taken towards clinical evaluation for reliability and stability tests.

CONCLUSION

The AI is a vital component for the diagnosis of early PD and the recommendations must be followed to lower the RoB.

COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts

(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 AtheroPoint^TM, 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.

Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review

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.

Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

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.

COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models

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.

Centennial Review: History and husbandry recommendations for raising Pekin ducks in research or commercial production

By some accounts, ducks were domesticated between 400 and 10,000 yr ago and have been a growing portion of the poultry industry for decades. Ducks specifically, and waterfowl in general, have unique health, housing, nutrition and welfare concerns compared to their galliform counterparts. Although there have been many research publications in regards to health, nutrition, behavior, and welfare of ducks there have been very few reviews to provide an overview of these numerous studies, and only one text has attempted to review all aspects of the duck industry, from breeders to meat ducks. This review covers incubation, hatching, housing, welfare, nutrition, and euthanasia and highlights the needs for additional research at all levels of duck production. The purpose of this review is to provide guidelines to raise and house ducks for research as specifically related to industry practices.

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most.

AB0447 PREVALENCE OF PRIMARY BILIARY CIRRHOSIS IN SYSTEMIC SCLEROSIS AND SJÖGREN’S SYNDROME OVER TIME: A SYSTEMATIC REVIEW

Background:

Primary biliary cirrhosis (PBC) is a rare slowly progressive autoimmune disease characterized by inflammatory destruction and fibrosis of intrahepatic bile ducts. It is known to coexist together with rheumatological conditions such as Sjögren’s syndrome (SS) and systemic sclerosis (SSc). There is a wide range in reported prevalence of disease overlap with these entities; however, the exact prevalence rates remain unclear.

Objectives:

The objectives were to determine the prevalence of: 1) PBC in patients with SS and SSc (and the subsets of limited cutaneous SSc (lcSSc) and diffuse cutaneous SSc (dcSSc)), 2) SSc and SS in patients with PBC, and 3) to analyze changes in frequency over time. SSc occurs in 3/10,000 and PBC in 4-40/10,000 but these rare autoimmune diseases are known to coexist together. We speculated that there could be more cases diagnosed due to increasing availability of standardized antibody tests such as ANA, centromere antibodies, ENA and mitochondrial antibodies.

Methods:

A systematic review of the literature was performed using Medline, EMBASE, CINAHL, and the Cochrane Library databases up till June 16, 2020. Only full text articles in the English language with at least 40 patients were included. Cohorts, case series, cross-sectional studies, correspondences and registries with reported prevalence rates of both PBC in patients with SS and SSc as well as SSc and SS in patients with PBC were included. Data on frequency of co-existent diseases was studied by year of publication to determine if prevalence changed over time using linear regression. We used the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist to assess the quality of the studies.

Results:

Of 2876 citations identified, 67 were included in the analysis (n=33 for PBC, 15 for SSc, 18 for SS and 1 for SSc/SS). STROBE checklist scores ranged from 7-21. The prevalence of PBC was 5% in patients with SSc. Within the subsets, the prevalence of PBC in lcSSc was 8% and in dcSSc was 1%. In patients with SS, the prevalence of PBC was 4%. The prevalence of SSc overall in those with PBC was 5% and, within the subsets was 6% in lcSSc and 0% in dcSSc. The prevalence of SS in PBC was 18%. There was also no significant association between year of publication and prevalence. There was a lack of standardized definitions so misclassification may have occurred.

Conclusion:

PBC is increased in SSc but mostly in the lcSSc subset. SS in PBC is common at nearly 1 in 5. Over the years, there was no change in the prevalence of PBC in SSc indicating stability over time.

Acknowledgements:

Meagan Stanley, Western University Librarian.

Disclosure of Interests:

None declared.

POS0462 ALCOHOL AND INFLAMMATORY ARTHRITIS DISEASE ACTIVITY: PERSPECTIVES FROM A 979-PATIENT COHORT WITH SYSTEMATIC REVIEW AND META-ANALYSIS

Background:

The effect of alcohol on disease activity in inflammatory arthritis remains poorly understood. Studies of alcohol and the incidence or risk of inflammatory arthritis are conflicting [1,2]. Alcohol does downregulate pro-inflammatory cytokines and may therefore reduce immune-mediated disease activity.

Objectives:

This study investigates the relationship between alcohol consumption and disease activity in our inflammatory arthritis patient cohort and performs a systematic review with meta-analysis.

Methods:

Cohort Study Design and data analysis

Patients attending a rheumatology clinic between 2010-2020 were prospectively followed. Information on demographics, alcohol use, smoking habits, and disease outcome measures were collected. Statistical analysis included univariate and multivariate linear and binary logistic regressions, Mann Whitney-U tests, and one-way ANOVA with Tukey’s HSD.

Meta-analysis

EMBASE, Pubmed, the Cochrane library, and Web of Science were searched. Studies reporting on alcohol consumption and disease activity in a cohort of RA patients were included for further investigation. Forest plots were generated from 95% confidence intervals of extracted data using mean differences. Linear regression was used to determine correlations between alcohol and antibody status, gender, and smoking status.

Results:

Cohort Study

Of the 979 analysed patients, 62% had RA, 26.7% had PsA, and 11.2% had AS. Mean DAS28-CRP in RA and PsA at one year was 2.96 ± 1.39, and 64.2% of patients were in remission (DAS28-CRP ≤ 2.6 or BASDAI ≤ 4). Both male gender and risky drinking (>15 units of weekly alcohol) were both significantly associated with remission. Compared to women, men had an odds ratio of 1.78 [1.04, 2.52] (p=0.034) for any alcohol consumption and 6.9 [4.7, 9.1] (p=0.001) for drinking at least 15 weekly drinks. when adjusted for gender, there was no significant association between alcohol and disease activity. Yet, when adjusted for alcohol consumption, gender still influenced disease activity.

Meta-analysis

The search identified 4126 citations of which 14 were included. The pooled mean difference in DAS28 (95% CI) was 0.34 (0.24,0.44) (p<10-5) between non-drinkers and drinkers, 0.33 (0.05,0.62) (p=0.02) between non-drinkers and heavy drinkers, and 0 (-0.3,0.3) (p=0.98). between low- and high-risk drinkers. There was a significant difference in the mean difference of HAQ assessments between those who drink alcohol compared to those who do not (0.3 (0.18,0.41), p<10-5). There was no significant correlation between drinking and gender, smoking status, or antibody positivity.

Conclusion:

While it appears that alcohol is linked to remission in inflammatory arthritis, this association is lost when adjusted for gender. Men with inflammatory arthritis drink significantly more than women and men generally have less severe disease activity. However, the meta-analysis suggests alcohol consumption is associated with lower disease activity and self-reported health assessment in rheumatoid arthritis.

References:

[1]Bae S-C, Lee YH. Alcohol intake and risk of rheumatoid arthritis: a Mendelian randomization study. Z Rheumatol 2019;78:791–6. doi:10.1007/s00393-018-0537-z

[2]Scott IC, Tan R, Stahl D, et al. The protective effect of alcohol on developing rheumatoid arthritis: a systematic review and meta-analysis. Rheumatology (Oxford) 2013;52:856–67. doi:10.1093/rheumatology/kes376

Figure 1.

Mean differences in DAS28 between drinking groups. A between non-drinkers and drinkers. B between non-drinkers and high-risk drinkers. C between low-risk and high-risk drinkers.

Disclosure of Interests:

None declared

POS0625 ASSOCIATIONS OF REMISSION AND PERSISTENCE OF BIOLOGICS AT 1 AND 12 YEARS

Background:

Biologic therapies have greatly improved outcomes in rheumatoid arthritis (RA) and psoriatic arthritis (PsA). Yet, our ability to predict long-term remission and persistence or continuation of therapy remains limited.

Objectives:

To compare RA and PsA outcomes at 1 and 12 years after commencing biologic DMARDs and to identify predictors of remission and persistence of therapy.

Methods:

RA and PsA patients were prospectively recruited from a biologic clinic. Outcomes on commencing therapy, at 1 year and 12 years were reviewed. Demographics, medications, morning stiffness, patient global health score, tender and swollen joint counts, antibody status, CRP and HAQ were collected. Outcomes at 1 and 12 years are reported and predictors of EULAR-defined remission (DAS28-CRP < 2.6) and biologic persistence are examined with univariate and multivariate analysis.

Results:

A total of 403 patients (274 RA and 129 PsA) were analysed. PsA patients were more likely to be male, in full-time employment and have completed higher education. PsA had higher remission rates than RA at both 1 year (60.3% versus 34.5%, p < 0.001) and 12 years (91.3% versus 60.6%, p < 0.001). This difference persisted when patients were matched for baseline disease activity (p < 0.001). Biologic continuation rates were high for RA and PsA at 1 year (49.6% versus 58.9%) and 12 years (38.2% versus 52.3%). In PsA, patients starting on etanercept had lower CRP at 12 years (p = 0.041). Multivariate analysis showed 1-year continuation [OR 4.28 (1.28–14.38)] and 1-year low-disease activity [OR 3.90 (95% CI 1.05–14.53)] was predictive of a 12-year persistence. Persistence with initial biologic at 12 years [OR 4.98 (95% CI 1.83–13.56)] and male gender [OR 4.48 (95% CI 1.25–16.01)] predicted 12 year remission.

Conclusion:

This is the first real world data to show better response to biologic therapy in PsA compared to RA at 12 years. Long-term persistence with initial biologic agent was high and predicted by biologic persistence and low-disease activity at 1 year. Interestingly, PsA patients had higher levels of employment, educational attainment, and long-term remission rates compared to RA patients.

Disclosure of Interests:

Kieran Murray Grant/research support from: Bresnihan Molloy and Newman Fellowships, Matthew Turk: None declared, Yousef Alammari: None declared, Francis Young: None declared, Phil Gallagher: None declared, Tajvur Parveen Saber: None declared, Ursula Fearon: None declared, Douglas Veale: None declared

POS1216 SYMPTOM RATES, ATTITUDES AND MEDICATION ADHERENCE OF RHEUMATIC AND MUSCULOSKELETAL DISEASE PATIENTS DURING THE SARS-CoV2 PANDEMIC

Background:

SARS-CoV2 has caused over two million deaths globally. The relationship between rheumatic and musculoskeletal disease (RMDs), immunosuppressive medications and COVID-19 is unclear.

Objectives:

This study explores the rates of COVID-19 symptoms and positive tests, DMARD adherence and attitudes to virtual clinics. amongst RMD patients.

Methods:

An online population survey was disseminated via the Arthritis Ireland website and social media channels.

Results:

There were 1381 respondents with RMD, 74.8% were on immunosuppressive medication. COVID-19 symptoms were reported by 3.7% of respondents of which 0.46% tested positive, no different from the general population at that timepoint. The frequency of COVID-19 symptoms was higher for respondents with spondyloarthropathy [odds ratio (OR) 2.06, 95% CI: 1.14, 3.70] and lower in those on immunosuppressive medication (OR 0.48, 95% CI: 0.27, 0.88), and those compliant with health authority (HSE) guidance (OR 0.47, 95% CI: 0.25, 0.89). Adherence to RMD medications was reported in 84.1%, with 57.1% using health authority guidelines for information on medication use. Importantly, adherence rates were higher amongst those who cited guidelines (89.3% vs 79.9%, P <0.001), and conversely lower in those with COVID-19 symptoms (64.0% vs 85.1%, P =0.009). Finally, the use of virtual clinics was supported by 70.4% of respondents.

Conclusion:

The rate of COVID-19 positivity in RMD patients was similar to the general population. COVID-19 symptoms were lower amongst respondents on immunosuppressive medication and those adherent to medication guidelines. Respondents were supportive of HSE advice and virtual rheumatology clinics.

Disclosure of Interests:

Kieran Murray Grant/research support from: Bresnihan Molloy and Newman fellowships, Sean Quinn: None declared, Matthew Turk: None declared, Anna O’Rourke: None declared, Eamonn Molloy: None declared, Lorraine O’Neill: None declared, Anne Barbara Mongey: None declared, Ursula Fearon: None declared, Douglas Veale: None declared.

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings.

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.

COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review

Artificial intelligence (AI) has penetrated the field of medicine, particularly the field of radiology. Since its emergence, the highly virulent coronavirus disease 2019 (COVID-19) has infected over 10 million people, leading to over 500,000 deaths as of July 1st, 2020. Since the outbreak began, almost 28,000 articles about COVID-19 have been published (https://pubmed.ncbi.nlm.nih.gov); however, few have explored the role of imaging and artificial intelligence in COVID-19 patients-specifically, those with comorbidities. This paper begins by presenting the four pathways that can lead to heart and brain injuries following a COVID-19 infection. Our survey also offers insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. Such symptoms include myocardial injury, hypoxia, plaque rupture, arrhythmias, venous thromboembolism, coronary thrombosis, encephalitis, ischemia, inflammation, and lung injury. At its core, this study considers the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection. Image-based AI is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis.

3-D optimized classification and characterization artificial intelligence paradigm for cardiovascular/stroke risk stratification using carotid ultrasound-based delineated plaque: Atheromatic™ 2.0

BACKGROUND AND PURPOSE

Atherosclerotic plaque tissue rupture is one of the leading causes of strokes. Early carotid plaque monitoring can help reduce cardiovascular morbidity and mortality. Manual ultrasound plaque classification and characterization methods are time-consuming and can be imprecise due to significant variations in tissue characteristics. We report a novel artificial intelligence (AI)-based plaque tissue classification and characterization system.

METHODS

We hypothesize that symptomatic plaque is hypoechoic due to its large lipid core and minimal collagen, as well as its heterogeneous makeup. Meanwhile, asymptomatic plaque is hyperechoic due to its small lipid core, abundant collagen, and the fact that it is often calcified. We designed a computer-aided diagnosis (CADx) system consisting of three kinds of deep learning (DL) classification paradigms: Deep Convolutional Neural Network (DCNN), Visual Geometric Group-16 (VGG16), and transfer learning, (tCNN). DCNN was 3-D optimized by varying the number of CNN layers and data augmentation frameworks. The DL systems were benchmarked against four types of machine learning (ML) classification systems, and the CADx system was characterized using two novel strategies consisting of DL mean feature strength (MFS) and a bispectrum model using higher-order spectra.

RESULTS

After balancing symptomatic and asymptomatic plaque classes, a five-fold augmentation process was applied, yielding 1000 carotid scans in each class. Then, using a K10 protocol (trained to test the ratio of 90%-10%), tCNN and DCNN yielded accuracy (area under the curve (AUC)) pairs of 83.33%, 0.833 (p < 0.0001) and 95.66%, 0.956 (p < 0.0001), respectively. DCNN was superior to ML by 7.01%. As part of the characterization process, the MFS of the symptomatic plaque was found to be higher compared to the asymptomatic plaque by 17.5% (p < 0.0001). A similar pattern was seen in the bispectrum, which was higher for symptomatic plaque by 5.4% (p < 0.0001). It took <2 s to perform the online CADx process on a supercomputer.

CONCLUSIONS

The performance order of the three AI systems was DCNN > tCNN > ML. Bispectrum-based on higher-order spectra proved a powerful paradigm for plaque tissue characterization. Overall, the AI-based systems offer a powerful solution for plaque tissue classification and characterization.

Ultrasound-based stroke/cardiovascular risk stratification using Framingham Risk Score and ASCVD Risk Score based on "Integrated Vascular Age" instead of "Chronological Age": a multi-ethnic study of Asian Indian, Caucasian, and Japanese cohorts

Background

Vascular age (VA) has recently emerged for CVD risk assessment and can either be computed using conventional risk factors (CRF) or by using carotid intima-media thickness (cIMT) derived from carotid ultrasound (CUS). This study investigates a novel method of integrating both CRF and cIMT for estimating VA [so-called integrated VA (IVA)]. Further, the study analyzes and compares CVD/stroke risk using the Framingham Risk Score (FRS)-based risk calculator when adapting IVA against VA.

Methods

The system follows a four-step process: (I) VA using cIMT based using linear-regression (LR) model and its coefficients; (II) VA prediction using ten CRF using a multivariate linear regression (MLR)-based model with gender adjustment; (III) coefficients from the LR-based model and MLR-based model are combined using a linear model to predict the final IVA; (IV) the final step consists of FRS-based risk stratification with IVA as inputs and benchmarked against FRS using conventional method of CA. Area-under-the-curve (AUC) is computed using IVA and benchmarked against CA while taking the response variable as a standardized combination of cIMT and glycated hemoglobin.

Results

The study recruited 648 patients, 202 were Japanese, 314 were Asian Indian, and 132 were Caucasians. Both left and right common carotid arteries (CCA) of all the population were scanned, thus a total of 1,287 ultrasound scans. The 10-year FRS using IVA reported higher AUC (AUC =0.78) compared with 10-year FRS using CA (AUC =0.66) by ~18%.

Conclusions

IVA is an efficient biomarker for risk stratifications for patients in routine practice.

FRI0545 A META-ANALYSIS OF GIANT CELL ARTERITIS TEMPORALLY AND ACROSS REGIONS

Background:

Giant cell arteritis (GCA) is an immune-mediated disease of the large vessels, and occurs in adults over 50 years old1. It is the most commonly seen form of chronic vasculitis and is associated with significant rates of morbidity2. This meta-analysis examines the geographical and temporal epidemiology of GCA, including incidence, prevalence and mortality.

Objectives:

To identify changes in incidence rate, prevalence, and mortality rate over time

To compare these rates between geographic regions around the world

Methods:

A systematic review of the English literature was conducted using the EMBase, Scopus and PubMed databases. Articles were included if they were cohort or cross-sectional studies with 50 or more patients with GCA and reported on population, location and time-frame parameters. Articles on mortality were included if they compared mortality to age and gender matched population. Review articles, case-control studies and case series were excluded. Two reviewers extracted data and a third verified inclusion of studies. Study quality was assessed by using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist. Mortality rate was standardized across cohorts to deaths per 1000 people per year.

Results:

Of the 3569 citations identified by the literature search, 107 were included in analysis. The pooled incidence of GCA internationally was 10.00 [9.22, 10.78] cases per 100 000 people over 50 years old (Figure). This incidence was highest in Scandinavia 21.57 [18.90, 24.23], followed by North and South America 10.89 [8.78, 13.00], Europe 7.26 [6.05, 8.47], and Oceania 7.85 [1.48,17.19]. Nine studies reported prevalence. Pooled prevalence from these 9 was 51.74 [42.04,61.43] cases per 100 000 people over 50 years old. Overall, pooled mortality was 20.44 [17.84,23.03] deaths/1000 per year. Mortality had a generally decreasing trend over the years of publication.

Conclusion:

The incidence of GCA varies regionally almost 3-fold. Likely genetic and environmental factors may explain this trend. Incidence and prevalence are important for tracking the efficacy and side effects of current therapies, as well as planning for the costs of biologic treatment.

References:

[1] Floris A, Piga M, Cauli A, Salvarani C, Mathieu A. Polymyalgia rheumatica: an autoinflammatory disorder?. RMD Open. 2018;4(1):e000694. Published 2018 Jun 4. doi:10.1136/rmdopen-2018-000694

[2] Crow RW, Katz BJ, Warner JE, et al. Giant cell arteritis and mortality. J Gerontol A Biol Sci Med Sci. 2009;64(3):365–369. doi:10.1093/gero na/gln030

Acknowledgments:

Both Daniel Semenov and Katherine Li equally contributed and sharing first authorship

Funding in part was from the Canadian Rheumatology Association summer studentship

Disclosure of Interests:

Daniel Semenov: None declared, Katherine Li: None declared, Matthew Turk: None declared, Janet Pope Grant/research support from: AbbVie, Bristol-Myers Squibb, Eli Lilly & Company, Merck, Roche, Seattle Genetics, UCB, Consultant of: AbbVie, Actelion, Amgen, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, Eicos Sciences, Eli Lilly & Company, Emerald, Gilead Sciences, Inc., Janssen, Merck, Novartis, Pfizer, Roche, Sandoz, Sanofi, UCB, Speakers bureau: UCB

FRI0130 A SYSTEMATIC REVIEW OF NATURAL SUPPLEMENTS IN THE TREATMENT OF RHEUMATOID ARTHRITIS

Background:

Rheumatoid arthritis (RA) is a chronic autoimmune condition affecting almost 1% of the general population (1). Pharmacological management has been the mainstay of treatment for RA and includes DMARDs and biologics. Despite these therapies, anywhere from 28-90% of patients with RA use complementary and alternative medicine (2). These non-pharmacological therapies range from dietary interventions to supplements to nonprescription therapies.

Objectives:

To determine the efficacy of non-pharmacological, orally-ingested interventions on clinically-relevant endpoints in patients with rheumatoid arthritis.

Methods:

We systematically reviewed EMBASE and MEDLINE electronical databases from inception until Feb 23, 2019 for relevant articles. Only randomized controlled trials (RCTs) which assessed oral, non-pharmacological interventions (e.g. diets, vitamins, oils, herbal remedies, fatty acids, supplements, etc.) in adult patients with RA, that presented clinically-relevant outcomes (defined as pain, fatigue, disability, joint counts, and/or disease indices) were included.

Clinical outcome data was extracted by two independent authors as difference from baseline measurement. Therapies with at least 3 RCTs which presented data on the same clinical outcome were meta-analyzed using a pooled random effects model using RevMan 5.

Results:

A total of 4423 unique articles were independently assessed by two authors, of which 72 articles met our inclusion criteria. Thirteen different interventions were studied more than once, and six interventions had clinical outcomes reported in at least 3 trials. However, only vitamin D and fatty acids met criteria for meta-analysis.

Pooled random effects models suggested vitamin D supplementation improved HAQ scores from baseline (mean difference = -0.10, 95% confidence interval (CI) = -0.17 to -0.02; p=0.01) but had no effect on DAS28 scores (Table 1).

Table 1.

Mean differences from baseline of various clinical outcomes in RA patients taking vitamin D or fatty acid supplementation compared to control group.

Clinical OutcomeTotal PatientsMean Difference (95% CI)P-valueVitamin DHAQ573-0.10 (-0.17 to -0.02)0.01DAS28174-0.30 (-0.71 to 0.11)0.15Fatty AcidsTJC661-2.05 (-2.83 to -1.27)0.04SJC582-0.35 (-0.96 to 0.26)0.26RAI234-1.82 (-4.69 to 1.05)0.21Pain756-0.61 (-1.02 to -0.20)0.004Patient Global484-0.26 (-0.59 to 0.07)0.12Physician Global382-1.08 (-1.98 to -0.18)0.02HAQ277-0.13 (-0.18 to -0.09)<0.001DAS28543-0.19 (-0.36 to -0.01)0.03

Fatty acid supplementation improved total joint counts, pain, physician global assessment scores, HAQ, and DAS28 from baseline (Table 1). There were significantly more patients who achieved ACR20 criteria (Relative Risk Ratio = 2.73, 95% CI 1.62-4.58; p<0.001) (Figure 1).

Figure 1.

Forest plot of studies in which RA patients taking fatty acids achieved ACR20 criteria.https://account-congress.eular.org/Modules/Abstract/Submission/summary.aspx

Conclusion:

From our meta-analysis, vitamin D and fatty acids supplementation showed statistically significant improvement in some clinical outcomes in patients with RA; however, the degree of improvement is unlikely to be clinically significant.

Overall, many trials were of low quality and had high risks of bias including inadequate reporting of data. Further clinical trials that are well-designed and fully powered are still needed to confirm the efficacy of many supplements and diets in RA.

References:

[1]Myasoedova E, Crowson CS, Kremers HM, Therneau TM, Gabriel SE. Is the incidence of rheumatoid arthritis rising?: results from Olmsted County, Minnesota, 1955-2007.Arthritis Rheum. 2010;62(6):1576–1582. doi:10.1002/art.27425

[2]Efthimiou P, Kukar M, Mackenzie CR. Complementary and alternative medicine in rheumatoid arthritis: no longer the last resort!.HSS J.2010;6(1):108–111. doi:10.1007/s11420-009-9133-8

Disclosure of Interests:

Yideng Liu: None declared, Janet Pope Grant/research support from: AbbVie, Bristol-Myers Squibb, Eli Lilly & Company, Merck, Roche, Seattle Genetics, UCB, Consultant of: AbbVie, Actelion, Amgen, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, Eicos Sciences, Eli Lilly & Company, Emerald, Gilead Sciences, Inc., Janssen, Merck, Novartis, Pfizer, Roche, Sandoz, Sanofi, UCB, Speakers bureau: UCB, Matthew Turk: None declared

Integration of estimated glomerular filtration rate biomarker in image-based cardiovascular disease/stroke risk calculator: a south Asian-Indian diabetes cohort with moderate chronic kidney disease

BACKGROUND

Recently, a 10-year image-based integrated calculator (called AtheroEdge Composite Risk Score-AECRS1.0) was developed which combines conventional cardiovascular risk factors (CCVRF) with image phenotypes derived from carotid ultrasound (CUS). Such calculators did not include chronic kidney disease (CKD)-based biomarker called estimated glomerular filtration rate (eGFR). The novelty of this study is to design and develop an advanced integrated version called-AECRS2.0 that combines eGFR with image phenotypes to compute the composite risk score. Furthermore, AECRS2.0 was benchmarked against QRISK3 which considers eGFR for risk assessment.

METHODS

The method consists of three major steps: 1) five, current CUS image phenotypes (CUSIP) measurements using AtheroEdge system (AtheroPoint, CA, USA) consisting of: average carotid intima-media thickness (cIMTave), maximum cIMT (cIMTmax), minimum cIMT (cIMTmin), variability in cIMT (cIMTV), and total plaque area (TPA); 2) five, 10-year CUSIP measurements by combining these current five CUSIP with 11 CCVRF (age, ethnicity, gender, body mass index, systolic blood pressure, smoking, carotid artery type, hemoglobin, low-density lipoprotein cholesterol, total cholesterol, and eGFR); 3) AECRS2.0 risk score computation and its comparison to QRISK3 using area-under-the-curve (AUC).

RESULTS

South Asian-Indian 339 patients were retrospectively analyzed by acquiring their left/right common carotid arteries (678 CUS, mean age: 54.25±9.84 years; 75.22% males; 93.51% diabetic with HbA1c ≥6.5%; and mean eGFR 73.84±20.91 mL/min/1.73m²). The proposed AECRS2.0 reported higher AUC (AUC=0.89, P<0.001) compared to QRISK3 (AUC=0.51, P<0.001) by ~74% in CKD patients.

CONCLUSIONS

An integrated calculator AECRS2.0 can be used to assess the 10-year CVD/stroke risk in patients suffering from CKD. AECRS2.0 was much superior to QRISK3.

Morphological Carotid Plaque Area Is Associated With Glomerular Filtration Rate: A Study of South Asian Indian Patients With Diabetes and Chronic Kidney Disease

We evaluated the association between automatically measured carotid total plaque area (TPA) and the estimated glomerular filtration rate (eGFR), a biomarker of chronic kidney disease (CKD). Automated average carotid intima-media thickness (cIMTave) and TPA measurements in carotid ultrasound (CUS) were performed using AtheroEdge (AtheroPoint). Pearson correlation coefficient (CC) was then computed between the TPA and eGFR for (1) males versus females, (2) diabetic versus nondiabetic patients, and (3) between the left and right carotid artery. Overall, 339 South Asian Indian patients with either type 2 diabetes mellitus (T2DM) or CKD, or hypertension (stage 1 or stage 2) were retrospectively analyzed by acquiring cIMTave and TPA measurements of their left and right common carotid arteries (CCA; total CUS: 678, mean age: 54.2 ± 9.8 years; 75.2% males; 93.5% with T2DM). The CC between TPA and eGFR for different scenarios were (1) for males and females -0.25 (P < .001) and -0.35 (P < .001), respectively; (2) for T2DM and non-T2DM -0.26 (P < .001) and -0.49 (P = .02), respectively, and (3) for left and right CCA -0.25 (P < .001) and -0.23 (P < .001), respectively. Automated TPA is an equally reliable biomarker compared with cIMTave for patients with CKD (with or without T2DM) with subclinical atherosclerosis.

Low-cost preventive screening using carotid ultrasound in patients with diabetes

Diabetes and atherosclerosis are the predominant causes of stroke and cardiovascular disease (CVD) both in low- and high-income countries. This is due to the lack of appropriate medical care or high medical costs. Low-cost 10-year preventive screening can be used for deciding an effective therapy to reduce the effects of atherosclerosis in diabetes patients. American College of Cardiology (ACC)/American Heart Association (AHA) recommended the use of 10-year risk calculators, before advising therapy. Conventional risk calculators are suboptimal in certain groups of patients because their stratification depends on (a) current blood biomarkers and (b) clinical phenotypes, such as age, hypertension, ethnicity, and sex. The focus of this review is on risk assessment using innovative composite risk scores that use conventional blood biomarkers combined with vascular image-based phenotypes. AtheroEdge™ tool is beneficial for low-moderate to high-moderate and low-risk to high-risk patients for the current and 10-year risk assessment that outperforms conventional risk calculators. The preventive screening tool that combines the image-based phenotypes with conventional risk factors can improve the 10-year cardiovascular/stroke risk assessment.

Global perspective on carotid intima-media thickness and plaque: should the current measurement guidelines be revisited?

Carotid intima-media thickness (cIMT) and carotid plaque (CP) currently act as risk predictors for CVD/Stroke risk assessment. Over 2000 articles have been published that cover either use cIMT/CP or alterations of cIMT/CP and additional image-based phenotypes to associate cIMT related markers with CVD/Stroke risk. These articles have shown variable results, which likely reflect a lack of standardization in the tools for measurement, risk stratification, and risk assessment. Guidelines for cIMT/CP measurement are influenced by major factors like the atherosclerosis disease itself, conventional risk factors, 10-year measurement tools, types of CVD/Stroke risk calculators, incomplete validation of measurement tools, and the fast pace of computer technology advancements. This review discusses the following major points: 1) the American Society of Echocardiography and Mannheim guidelines for cIMT/CP measurements; 2) forces that influence the guidelines; and 3) calculators for risk stratification and assessment under the influence of advanced intelligence methods. The review also presents the knowledge-based learning strategies such as machine and deep learning which may play a future role in CVD/stroke risk assessment. We conclude that both machine learning and non-machine learning strategies will flourish for current and 10-year CVD/Stroke risk prediction as long as they integrate image-based phenotypes with conventional risk factors.

A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes

Background

Most cardiovascular (CV)/stroke risk calculators using the integration of carotid ultrasound image-based phenotypes (CUSIP) with conventional risk factors (CRF) have shown improved risk stratification compared with either method. However such approaches have not yet leveraged the potential of machine learning (ML). Most intelligent ML strategies use follow-ups for the endpoints but are costly and time-intensive. We introduce an integrated ML system using stenosis as an endpoint for training and determine whether such a system can lead to superior performance compared with the conventional ML system.

Methods

The ML-based algorithm consists of an offline and online system. The offline system extracts 47 features which comprised of 13 CRF and 34 CUSIP. Principal component analysis (PCA) was used to select the most significant features. These offline features were then trained using the event-equivalent gold standard (consisting of percentage stenosis) using a random forest (RF) classifier framework to generate training coefficients. The online system then transforms the PCA-based test features using offline trained coefficients to predict the risk labels on test subjects. The above ML system determines the area under the curve (AUC) using a 10-fold cross-validation paradigm. The above system so-called "AtheroRisk-Integrated" was compared against "AtheroRisk-Conventional", where only 13 CRF were considered in a feature set.

Results

Left and right common carotid arteries of 202 Japanese patients (Toho University, Japan) were retrospectively examined to obtain 395 ultrasound scans. AtheroRisk-Integrated system [AUC =0.80, P<0.0001, 95% confidence interval (CI): 0.77 to 0.84] showed an improvement of ~18% against AtheroRisk-Conventional ML (AUC =0.68, P<0.0001, 95% CI: 0.64 to 0.72).

Conclusions

ML-based integrated model with the event-equivalent gold standard as percentage stenosis is powerful and offers low cost and high performance CV/stroke risk assessment.

Wilson's disease: A new perspective review on its genetics, diagnosis and treatment

Wilson's disease (WD) is an autosomal recessive disorder which is caused by poor excretion of copper in mammalian cells. In this review, various issues such as effective characterization of ATP7B genes, scope of gene network topology in genetic analysis, pattern recognition using different computing approaches and fusion possibilities in imaging and genetic dataset are discussed vividly. We categorized this study into three major sections: (A) WD genetics, (B) diagnosis guidelines and (3) treatment possibilities. We addressed the scope of advanced mathematical modelling paradigms for understanding common genetic sequences and dominating WD imaging biomarkers. We have also discussed current state-of-the-art software models for genetic sequencing. Further, we hypothesized that involvement of machine and deep learning techniques in the context of WD genetics and image processing for precise classification of WD. These computing procedures signify changing roles of various data transformation techniques with respect to supervised and unsupervised learning models.

A Special Report on Changing Trends in Preventive Stroke/Cardiovascular Risk Assessment Via B-Mode Ultrasonography

PURPOSE OF REVIEW

Cardiovascular disease (CVD) and stroke risk assessment have been largely based on the success of traditional statistically derived risk calculators such as Pooled Cohort Risk Score or Framingham Risk Score. However, over the last decade, automated computational paradigms such as machine learning (ML) and deep learning (DL) techniques have penetrated into a variety of medical domains including CVD/stroke risk assessment. This review is mainly focused on the changing trends in CVD/stroke risk assessment and its stratification from statistical-based models to ML-based paradigms using non-invasive carotid ultrasonography.

RECENT FINDINGS

In this review, ML-based strategies are categorized into two types: non-image (or conventional ML-based) and image-based (or integrated ML-based). The success of conventional (non-image-based) ML-based algorithms lies in the different data-driven patterns or features which are used to train the ML systems. Typically these features are the patients' demographics, serum biomarkers, and multiple clinical parameters. The integrated (image-based) ML-based algorithms integrate the features derived from the ultrasound scans of the arterial walls (such as morphological measurements) with conventional risk factors in ML frameworks. Even though the review covers ML-based system designs for carotid and coronary ultrasonography, the main focus of the review is on CVD/stroke risk scores based on carotid ultrasound. There are two key conclusions from this review: (i) fusion of image-based features with conventional cardiovascular risk factors can lead to more accurate CVD/stroke risk stratification; (ii) the ability to handle multiple sources of information in big data framework using artificial intelligence-based paradigms (such as ML and DL) is likely to be the future in preventive CVD/stroke risk assessment.

Ranking of stroke and cardiovascular risk factors for an optimal risk calculator design: Logistic regression approach

PURPOSE

Conventional cardiovascular risk factors (CCVRFs) and carotid ultrasound image-based phenotypes (CUSIP) are independently associated with long-term risk of cardiovascular (CV) disease. In this study, 26 cardiovascular risk (CVR) factors which consisted of a combination of CCVRFs and CUSIP together were ranked. Further, an optimal risk calculator using AtheroEdge composite risk score (AECRS1.0) was designed and benchmarked against seven conventional CV risk (CVR) calculators.

METHODS

Two types of ranking were performed: (i) ranking of 26 CVR factors and (ii) ranking of eight types of 10-year risk calculators. In the first case, multivariate logistic regression was used to compute the odds ratio (OR) and in the second, receiver operating characteristic curves were used to evaluate the performance of eight types of CVR calculators using SPSS23.0 and MEDCALC12.0 with validation against STATA15.0.

RESULTS

The left and right common carotid arteries (CCA) of 202 Japanese patients were examined to obtain 404 ultrasound scans. CUSIP ranked in the top 50% of the 26 covariates. Intima-media thickness variability (IMTV) and IMTV_10yr were the most influential carotid phenotypes for left CCA (OR = 250, P < 0.0001 and OR = 207, P < 0.0001 respectively) and right CCA (OR = 1614, P < 0.0001 and OR = 626, P < 0.0001 respectively). However, for the mean CCA, AECRS1.0 and AECRS1.0_10yr reported the most highly significant OR among all the CVR factors (OR = 1.073, P < 0.0001 and OR = 1.104, P < 0.0001). AECRS1.0_10yr also reported highest area-under-the-curve (AUC = 0.904, P < 0.0001) compared to seven types of conventional calculators. Age and glycated haemoglobin reported highest OR (1.96, P < 0.0001 and 1.05, P = 0.012) among all other CCVRFs.

CONCLUSION

AECRS1.0_10yr demonstrated the best performance due to presence of CUSIP and ranked at the first place with highest AUC.

Performance evaluation of 10-year ultrasound image-based stroke/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: A diabetic study

MOTIVATION

AtheroEdge Composite Risk Score (AECRS1.0_10yr) is an integrated stroke/cardiovascular risk calculator that was recently developed and computes the 10-year risk of carotid image phenotypes by integrating conventional cardiovascular risk factors (CCVRFs). It is therefore important to understand how closely AECRS1.0_10yr is associated with the ten other currently available conventional cardiovascular risk calculators (CCVRCs).

METHODS

The Institutional Review Board of Toho University approved the examination of the left/right common carotid arteries of 202 Japanese patients. Step 1 consists of measurement of AECRS1.0_10yr, given current image phenotypes and CCVRFs. Step 2 consists of computing the risk score using ten different CCVRCs given CCVR factors: QRISK3, Framingham Risk Score (FRS), United Kingdom Prospective Diabetes Study (UKPDS) 56, UKPDS60, Reynolds Risk Score (RRS), Pooled cohort Risk Score (PCRS or ASCVD), Systematic Coronary Risk Evaluation (SCORE), Prospective Cardiovascular Munster Study (PROCAM) calculator, NIPPON, and World Health Organization (WHO) risk. Step 3 consists of computing the closeness factor between AECRS1.0_10yr and ten CCVRCs using cumulative ranking index derived using eight different statistically derived metrics.

RESULTS

AECRS1.0_10yr reported the highest area-under-the-curve (0.927;P < 0.001) among all the risk calculators. The top three CCVRCs closest to AECRS1.0_10yr were QRISK3, FRS, and UKPDS60 with cumulative ranking scores of 2.1, 3.0, and 3.8, respectively.

CONCLUSION

AECRS1.0_10yr produced the largest AUC due to the integration of image-based phenotypes with CCVR factors, and ranked at first place with the highest AUC. Cumulative ranking of ten CCVRCs demonstrated that QRISK3 was the closest calculator to AECRS1.0_10yr, which is also consistent with the industry trend.

Cerebral Small Vessel Disease: A Review Focusing on Pathophysiology, Biomarkers, and Machine Learning Strategies

Cerebral small vessel disease (cSVD) has a crucial role in lacunar stroke and brain hemorrhages and is a leading cause of cognitive decline and functional loss in elderly patients. Based on underlying pathophysiology, cSVD can be subdivided into amyloidal and non-amyloidal subtypes. Genetic factors of cSVD play a pivotal role in terms of unraveling molecular mechanism. An important pathophysiological mechanism of cSVD is blood-brain barrier leakage and endothelium dysfunction which gives a clue in identification of the disease through circulating biological markers. Detection of cSVD is routinely carried out by key neuroimaging markers including white matter hyperintensities, lacunes, small subcortical infarcts, perivascular spaces, cerebral microbleeds, and brain atrophy. Application of neural networking, machine learning and deep learning in image processing have increased significantly for correct severity of cSVD. A linkage between cSVD and other neurological disorder, such as Alzheimer's and Parkinson's disease and non-cerebral disease, has also been investigated recently. This review draws a broad picture of cSVD, aiming to inculcate new insights into its pathogenesis and biomarkers. It also focuses on the role of deep machine strategies and other dimensions of cSVD by linking it with several cerebral and non-cerebral diseases as well as recent advances in the field to achieve sensitive detection, effective prevention and disease management.

Comparison of the mechanical properties of platelet-rich fibrin and ankaferd blood stopper-loaded platelet-rich fibrin

Background and Aim

Platelet-rich fibrin (PRF) can be named as a natural fibrin-based biomaterial favorable to increasing vascularization and able to guide epithelial cell migration to its surface. The membrane has a significant positive effect on protecting open wounds and accelerating healing. Similar to PRF Ankaferd Blood Stopper (ABS) also has positive effects on wound healing. The aim of this study was to detect if we can improve known physical properties of PRF combining with ABS. This idea was based on the known mechanism of ABS in forming protein network without damaging any blood cells. Materials and Methods: A total of 25 adult rabbits used for collecting 5-7 ml of blood passively with the help of winged blood collection needle to the test tube. Collected samples were centrifuged at 3000 rpm for 10 min. Two similar samples obtained from each animal and one of the samples was placed in 20% ABS 80% saline solution for 5 min. Mechanical properties of the membrane samples were measured using Universal Testing Machine. Results: There is the statistically significant difference between PRF and ABS added PRF in elongation/mm (dL) and elongation/% at break values. Maximum force (fMax) and modulus values did not show any statistically significant differences.

Conclusion

ABS loaded PRF causes better physical properties. This combination seems to exhibit superior performance when used as a membrane barrier solely. Advanced studies can be done on biological properties of ABS loaded PRF, especially on tissue healing.

Histo-cytochemistry and scanning electron microscopy for studying spatial and temporal extraction of metabolites induced by ultrasound. Towards chain detexturation mechanism

There are more than 1300 articles in scientific literature dealing with positive impacts of Ultrasound-Assisted Extraction (UAE) such as reduction of extraction time, diminution of solvent and energy used, enhancement in yield and even selectivity, intensification of diffusion, and eliminating wastes. This present study aims to understand what are the mechanism(s) behind these positive impacts which will help to design a decision tool for UAE of natural products. Different microscopic observations (Scanning Electron Microscopy (SEM), Environmental Scanning Electron Microscopy (e-SEM), Cyto-histochemistry) have been used for spacial and temporal localization of metabolites in rosemary leaves, which is one of the most studied and most important plant for its antioxidant metabolites used in food industry, during conventional and ultrasound extraction. The study permits to highlight that ultrasound impacted rosemary leaves not by a single or different mechanisms in function of ultrasound power, as described by previous studies, but by a chain detexturation mechanism in a special order: local erosion, shear forces, sonoporation, fragmentation, capillary effect, and detexturation. These detexturation impacts followed a special order during ultrasound treatment leading at the end to the total detexturation of rosemary leaves. These mechanisms and detexturation impacts were identified in glandular trichomes, non-glandular-trichomes and the layer adaxial and abaxial cuticle. Modelling metabolites diffusion phenomenon during conventional and ultrasound extraction with the second Fick's law allowed the estimation of diffusivities and solvent penetration into the inner tissues and in meantime to accelerate the release of valuable metabolites.

Transformation of Face Transplants: Volumetric and Morphologic Graft Changes Resemble Aging After Facial Allotransplantation

Facial allotransplantation restores normal anatomy to severely disfigured faces. Although >30 such operations performed worldwide have yielded promising short-term results, data on long-term outcomes remain scarce. Three full-face transplant recipients were followed for 40 months. Severe changes in volume and composition of the facial allografts were noted. Data from computed tomography performed 6, 18 and 36 months after transplantation were processed to separate allograft from recipient tissues and further into bone, fat and nonfat soft tissues. Skin and muscle biopsies underwent diagnostic evaluation. All three facial allografts sustained significant volume loss (mean 19.55%) between 6 and 36 months after transplant. Bone and nonfat soft tissue volumes decreased significantly over time (17.22% between months 6 and 18 and 25.56% between months 6 and 36, respectively), whereas fat did not. Histological evaluations showed atrophy of muscle fibers. Volumetric and morphometric changes in facial allografts have not been reported previously. The transformation of facial allografts in this study resembled aging through volume loss but differed substantially from regular aging. These findings have implications for risk-benefit assessment, donor selection and measures counteracting muscle and bone atrophy. Superior long-term outcomes of facial allotransplantation will be crucial to advance toward future clinical routine.

Characteristics of Cerebral Hemodynamics in Patients with Ischemic Leukoaraiosis and New Ultrasound Indices of Ischemic Leukoaraiosis

OBJECTIVE

The diagnosis of ischemic leukoaraiosis (ILA) is based on head magnetic resonance imaging (MRI) and exclusion of other causes of white matter hyperintensities (WMHs). Recent studies have shown increased arterial stiffness and diminished carotid flow in ILA patients. So far, there are very little data on intracerebral hemodynamic parameters in ILA. Due to the specific structure of the intracranial arteries, our aim was to investigate intracerebral hemodynamic parameters in ILA patients and, possibly, to find a reliable ultrasound index of combined intra- and extracranial cerebral arteries.

METHODS

We compared different hemodynamic parameters in the middle cerebral artery (MCA) and local carotid stiffness parameters in 53 ILA patients to 40 gender and risk factor-matched controls with normal head MRI. The ILA diagnosis was based on head MRI and exclusion of other causes of WMH. In addition, we introduced new ischemic leukoariosis indices (ILAi) that are ratios of carotid stiffness parameters and MCA mean blood flow velocity. The diagnostic significance of ILAi for the prediction of ILA was analyzed.

RESULTS

We found significantly lower diastolic, systolic, and mean MCA blood flow velocities and increased carotid stiffness in the ILA group (P ≤ .05). All ILAi significantly differed between the groups (P < .05), were significantly associated with ILA (P < .01), and were sensitive and specific for predicting ILA (P < .05).

CONCLUSION

MCA blood flow velocities in ILA patients are lower compared to risk factor-matched controls. A combination of lower velocities and increased carotid stiffness represented as ILAi could have a potential diagnostic value for ILA.

Ratio between carotid artery stiffness and blood flow - a new ultrasound index of ischemic leukoaraiosis

Background

Ischemic leukoaraiosis (ILA) is associated with cognitive decline and aging. Its pathophysiology is believed to be ischemic in origin due to its association with cerebrovascular risk factors and similarity in location to lacunar infarctions. ILA diagnosis is still based on magnetic resonance imaging (MRI) as well as exclusion of other causes of white matter hyperintensities. So far, there are no known confirming diagnostic tests of ILA. Ultrasound studies have recently shown increased large artery stiffness, increased cerebrovascular resistance, and lower cerebral blood flow in patients with ILA. Increased arterial stiffness and decreased blood flow could have a synergistic effect, and their ratio could be a useful diagnostic index of ILA.

Methods

In this post hoc analysis, we introduced new ILA indices (ILAi) that are ratios of the carotid stiffness parameters (pulse wave velocity beta [PWVβ], pressure–strain elasticity modulus [Ep], β index), and diastolic and mean blood flows in the internal carotid artery: Q-ICAd and Q-ICAm. We compared the ILAi of 52 patients with ILA and 44 sex- and risk factor-matched controls with normal MRI of the head. ILA diagnosis was based on MRI and exclusion of other causes of white matter hyperintensities. The diagnostic significance of ILAi for the prediction of ILA was analyzed.

Results

All ILAi significantly differed between the groups; the most significant were PWVβ/Q-ICAd (ILA group: 1.96±0.64 vs control group: 1.56±0.40, P=0.001) and PWVβ/Q-ICAm (ILA group: 1.13±0.32 vs control group: 0.94±0.25, P=0.003). All ILAi were significantly associated with ILA (P<0.01) and were significant independent predictors of ILA. All ILAi were also sensitive and specific for predicting ILA (area under the curve: 0.632–0.683, P<0.05).

Conclusion

The new ultrasound indices significantly differed between patients with ILA and the control group and were significant predictors of ILA. A combination of lower carotid blood flow and increased carotid stiffness represented as ILAi probably has a diagnostic value in patients with ILA.

Antenna-type radiofrequency generator in nanoparticle-mediated hyperthermia

This study covers the employment an antenna-type RF generator modulus at varying powers for different nanoparticle types to evaluate viability, apoptosis and necrosis of L-929 fibroblast and MCF-7 breast cancer cell lines.

Carotid Arterial Hemodynamic in Ischemic Levkoaraiosis Suggests Hypoperfusion Mechanism

BACKGROUND

Leukoaraiosis (ILA) is believed to be ischaemic in origin due to its similar location as that of lacunar infarctions and its association with cerebrovascular risk factors. However, its pathophysiology is not well understood. The ischaemic injuries may be a result of increased pulsatility or cerebral hypo-perfusion. We used carotid duplex ultrasound to prove that the underlying mechanism is hypo-perfusion.

METHODS

We compared 55 ILA patients to 44 risk factor-matched controls with normal magnetic resonance imaging (MRI) of the head. ILA diagnosis was based on MRI and was further categorised according to the Fazekas scale. We measured carotid artery blood flow velocity and diameter and calculated carotid blood flow and resistance indexes.

RESULTS

Blood flow velocities and blood flows were significantly lower in the ILA group, including diastolic, systolic and mean pressures (p ≤ 0.05). The resistance indices were higher in the ILA group, but the differences were not statistically significant. All the velocities and blood flows showed a decreasing trend with higher Fazekas score, whereas resistance indexes showed an increasing trend.

CONCLUSIONS

Lower blood flow and higher resistance of carotid arteries are consistent with the hypo-perfusion theory of ILA. Carotid ultrasound could have a diagnostic and prognostic role in ILA patients.

Ultrasound diagnosis of carotid artery stiffness in patients with ischemic leukoaraiosis

The pathophysiology of ischemic leukoaraiosis (ILA) is unknown. It was recently found that ILA patients have increased aortic stiffness. Carotid stiffness is a more specific parameter and could have value as a non-invasive diagnostic value for ILA. Therefore, using color-coded duplex sonography, we compared local carotid stiffness parameters of 59 patients with ILA with those of 45 well-matched controls. The diagnosis of ILA was based on exclusion of other causes of white matter changes seen on magnetic resonance imaging. Pulse wave velocity β (PWVβ, m/s), pressure-strain elasticity modulus (Ep, kPa), β index and augmentation index (Aix, %) values were higher and arterial compliance (AC, mm(2)/kPa) values were lower in the ILA group; however, only Ep and PWVβ reached statistical significance (p ≤ 0.05). β, Ep and PWVβ exhibited an increasing trend with higher Fazekas score, though only Ep reached significance (p = 0.05). The main conclusion was that Ep and PWVβ could have a diagnostic role in patients with ILA.

Abstract P3-05-01: Molecular subtyping improves stratification of patients into diagnostically more meaningful risk groups

Background: Microarray-based gene expression profiling demonstrated that breast cancer is a heterogeneous group of different diseases characterized by distinct molecular aberrations, rather than one disease. Improved understanding of the molecular phenotypes of the disease has already shown prognostic and predictive value and, when prospectively applied, could have dramatic implications in establishing a more personalized approach to the management of early-stage breast cancer. Combined use of MammaPrint and a molecular subtyping profile (BluePrint) identifies disease subgroups with marked differences in long-term outcome and response to neo-adjuvant therapy [Glück et al. ASCO 2012]. The aim of this study was to evaluate the implication of accurate molecular subtyping using MammaPrint and BluePrint in women with early-stage breast cancer treated at US Institutions following National Comprehensive Cancer Network (NCCN) standard guidelines.

Methods: 208 frozen tumor samples from consecutive BC patients (TI-III, N0-Ib) were obtained from 2 US centers. Median age at diagnosis was 56 years (range 28–89 years). Between 1992 and 2010 patients were treated either with breast-conserving therapy or mastectomy with axillary lymph node dissection followed by systemic adjuvant therapy when indicated. Sixty-three percent of patients received adjuvant endocrine therapy (ET), 58% received adjuvant chemotherapy (CT) and 32% received both. Hormone Receptor (HR) and HER2 status were assessed by immunohistochemistry (IHC) and fluorescent in-situ hybridization (FISH), following standard guidelines. Median follow-up was 11.3 years. Survival was assessed for patient groups according to local pathological assessment and compared with molecular classification of patients (centrally assessed full genome expression at Agendia laboratory).

Results: Standard HR and HER2 status assessment revealed that 57% of all tumors examined were luminal-like (ER/PR positive, HER2 negative), 20% HER2 positive and 24% triple negative. Molecular classification demonstrated discordance in the following clinical groups: 16 out of 41 patients previously identified as HER2 positive were reclassified as Luminal-type, with 97% 5-year distant metastases-free survival (DMFS) for Luminal A (MammaPrint Low Risk/Luminal-type) and 98% for Luminal B (MammaPrint High Risk/Luminal-type). Ten patients identified with clinical triple-negative tumors were reclassified with molecular subtyping as HER2 positive (n = 6) and Luminal-type (n = 4). Of the patients classified with BluePrint Basal-type tumors, 58 (28%) had a 5-year DMFS of 82% (81% received adjuvant CT). Of those with HER2-type tumors, 25 (12%) had a 5-year DMFS of 76% (88% received adjuvant CT without trastuzumab). Discordant cases are being centrally re-assessed for ER, PR and HER2.

Conclusions: This retrospective study showed that Molecular Subtyping with BluePrint and MammaPrint leads to clinically significant prognostic and molecular stratification. The use of MammaPrint and BluePrint in the management of patients with primary breast cancer should be considered for a more accurate selection of adjuvant therapies in this era of personalized medicine.

Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P3-05-01.

A New Biased Discriminant Analysis Using Composite Vectors for Eye Detection

We propose a new biased discriminant analysis (BDA) using composite vectors for eye detection. A composite vector consists of several pixels inside a window on an image. The covariance of composite vectors is obtained from their inner product and can be considered as a generalization of the covariance of pixels. The proposed composite BDA (C-BDA) method is a BDA using the covariance of composite vectors. We construct a hybrid cascade detector for eye detection, using Haar-like features in the earlier stages and composite features obtained from C-BDA in the later stages. The proposed detector runs in real time; its execution time is 5.5 ms on a typical PC. The experimental results for the CMU PIE database and our own real-world data set show that the proposed detector provides robust performance to several kinds of variations such as facial pose, illumination, eyeglasses, and partial occlusion. On the whole, the detection rate per pair of eyes is 98.0% for the 3604 face images of the CMU PIE database and 95.1% for the 2331 face images of the real-world data set. In particular, it provides a 99.7% detection rate for the 2120 CMU PIE images without glasses. Face recognition performance is also investigated using the eye coordinates from the proposed detector. The recognition results for the real-world data set show that the proposed detector gives similar performance to the method using manually located eye coordinates, showing that the accuracy of the proposed eye detector is comparable with that of the ground-truth data.

A large-scale outbreak of trichinellosis caused by Trichinella britovi in Turkey

An outbreak of trichinellosis occurred in Izmir, Turkey, between January and March 2004. The outbreak was caused by the consumption of raw meat balls made of beef deceptively mixed with pork infected with Trichinella britovi. A total of 1098 people who had consumed this food either in 14 restaurants or from the street vendors located in three different neighbourhoods, consulted six different healthcare centres with a wide range of clinical signs and symptoms. Of them, 418 (38.1%) patients fulfilled the criteria for the diagnosis of acute trichinellosis. The most commonly observed signs and symptoms were myalgia (89.2%), arthralgia (69.9%) and eyelid (67%) and facial oedema (65.8%). High levels of creatinine kinase (69.3%) and lactate dehydrogenase (93.8%) with leucocytosis (>10 000/mm(3), 58.9%) and eosinophilia (>1000/mm(3), 60.5%) were the most prominent laboratory findings. All, but 13 of these patients were treated with mebendazole or albendazole. Based on the physicians' assessments of disease severity, 78 (19%) patients were additionally given prednisolone in whom a significantly more rapid recovery of clinical signs and symptoms (e.g. fever, myalgia, facial and eyelid oedema) was observed, with a rapid improvement in leucocytosis, eosinophilia and muscle enzymes, compared with those, who had not received corticosteroids (P < 0.05). Beef illegally mixed with pork of unknown origin, by a wholesale butcher who had sold this product to restaurants and street vendors at a lower price than the prevailing market price of beef, was the cause of this large-scale outbreak in a country with a predominantly Muslim population.