Patient-Specific Numerical Simulations of Coronary Artery Hemodynamics and Biomechanics: A Pathway to Clinical Use.

PURPOSE: Numerical models that simulate the behaviors of the coronary arteries have been greatly improved by the addition of fluid-structure interaction (FSI) methods. Although computationally demanding, FSI models account for the movement of the arterial wall and more adequately describe the biomechanical conditions at and within the arterial wall. This offers greater physiological relevance over Computational Fluid Dynamics (CFD) models, which assume the walls do not move or deform. Numerical simulations of patient-specific cases have been greatly bolstered by the use of imaging modalities such as Computed Tomography Angiography (CTA), Magnetic Resonance Imaging (MRI), Optical Coherence Tomography (OCT), and Intravascular Ultrasound (IVUS) to reconstruct accurate 2D and 3D representations of artery geometries. The goal of this study was to conduct a comprehensive review on CFD and FSI models on coronary arteries, and evaluate their translational potential. METHODS: This paper reviewed recent work on patient-specific numerical simulations of coronary arteries that describe the biomechanical conditions associated with atherosclerosis using CFD and FSI models. Imaging modality for geometry collection and clinical applications were also discussed. RESULTS: Numerical models using CFD and FSI approaches are commonly used to study biomechanics within the vasculature. At high temporal and spatial resolution (compared to most cardiac imaging modalities), these numerical models can generate large amount of biomechanics data. CONCLUSIONS: Physiologically relevant FSI models can more accurately describe atherosclerosis pathogenesis, and help to translate biomechanical assessment to clinical evaluation.

Lifestyle Changes Reduced Estimated White Matter Hyperintensities Based on Retinal Image Analysis.

This study evaluates if there is an association between lifestyle changes and the risk of small vessel disease (SVD) as measured by cerebral white matter hyperintensities (WMH) estimated by the automatic retinal image analysis (ARIA) method. We recruited 274 individuals into a community cohort study. Subjects were assessed at baseline and annually with the Health-Promoting Lifestyle Profile II Questionnaire (HPLP-II) and underwent a simple physical assessment. Retinal images were taken using a non-mydriatic digital fundus camera to evaluate the level of WMH estimated by ARIA (ARIA-WMH) to measure the risk of small vessel disease. We calculated the changes from baseline to one year for the six domains of HPLP-II and analysed the relationship with the ARIA-WMH change. A total of 193 (70%) participants completed both the HPLP-II and ARIA-WMH assessments. The mean age was 59.1 ± 9.4 years, and 76.2% (147) were women. HPLP-II was moderate (Baseline, 138.96 ± 20.93; One-year, 141.97 ± 21.85). We observed a significant difference in ARIA-WMH change between diabetes and non-diabetes subjects (0.03 vs. -0.008, respectively, p = 0.03). A multivariate analysis model showed a significant interaction between the health responsibility (HR) domain and diabetes (p = 0.005). For non-diabetes subgroups, those with improvement in the HR domain had significantly decreased in ARIA-WMH than those without HR improvement (-0.04 vs. 0.02, respectively, p = 0.003). The physical activity domain was negatively related to the change in ARIA-WMH (p = 0.02). In conclusion, this study confirms that there is a significant association between lifestyle changes and ARIA-WMH. Furthermore, increasing health responsibility for non-diabetes subjects reduces the risk of having severe white matter hyperintensities.

Machine-learning method for localization of cerebral white matter hyperintensities in healthy adults based on retinal images.

Retinal vessels are known to be associated with various cardiovascular and cerebrovascular disease outcomes. Recent research has shown significant correlations between retinal characteristics and the presence of cerebral small vessel disease as measured by white matter hyperintensities from cerebral magnetic resonance imaging. Early detection of age-related white matter changes using retinal images is potentially helpful for population screening and allow early behavioural and lifestyle intervention. This study investigates the ability of the machine-learning method for the localization of brain white matter hyperintensities. All subjects were age 65 or above without any history of stroke and dementia and recruited from local community centres and community networks. Subjects with known retinal disease or disease influencing vessel structure in colour retina images were excluded. All subjects received MRI on the brain, and age-related white matter changes grading was determined from MRI as the primary endpoint. The presence of age-related white matter changes on each of the six brain regions was also studied. Retinal images were captured using a fundus camera, and the analysis was done based on a machine-learning approach. A total of 240 subjects are included in the study. The analysis of various brain regions included the left and right sides of frontal lobes, parietal-occipital lobes and basal ganglia. Our results suggested that data from both eyes are essential for detecting age-related white matter changes in the brain regions, but the retinal parameters useful for estimation of the probability of age-related white matter changes in each of the brain regions may differ for different locations. Using a classification and regression tree approach, we also found that at least three significant heterogeneous subgroups of subjects were identified to be essential for the localization of age-related white matter changes. Namely those with age-related white matter changes in the right frontal lobe, those without age-related white matter changes in the right frontal lobe but with age-related white matter changes in the left parietal-occipital lobe, and the rest of the subjects. Outcomes such as risks of severe grading of age-related white matter changes and the proportion of hypertension were significantly related to these subgroups. Our study showed that automatic retinal image analysis is a convenient and non-invasive screening tool for detecting age-related white matter changes and cerebral small vessel disease with good overall performance. The localization analysis for various brain regions shows that the classification models on each of the six brain regions can be done, and it opens up potential future clinical application.

A machine learning approach for retinal images analysis as an objective screening method for children with autism spectrum disorder.

BACKGROUND: Autism spectrum disorder (ASD) is characterised by many of features including problem in social interactions, different ways of learning, some children showing a keen interest in specific subjects, inclination to routines, challenges in typical communication, and particular ways of processing sensory information. Early intervention and suitable supports for these children may make a significant contribution to their development. However, considerable difficulties have been encountered in the screening and diagnosis of ASD. The literature has indicated that certain retinal features are significantly associated with ASD. In this study, we investigated the use of machine learning approaches on retinal images to further enhance the classification accuracy. METHODS: Forty-six ASD participants were recruited from three special needs schools and 24 normal control were recruited from the community. Among them, 23 age-gender matched ASD and normal control participant-pairs were constructed for the primary analysis. All retinal images were captured using a nonmydriatic fundus camera. Automatic retinal image analysis (ARIA) methodology applying machine-learning technology was used to optimise the information of the retina to develop a classification model for ASD. The model's validity was then assessed using a 10-fold cross-validation approach to assess its validity. FINDINGS: The sensitivity and specificity were 95.7% (95% CI 76.0%, 99.8%) and 91.3% (95% CI 70.5%, 98.5%) respectively. The area under the ROC curve was 0.974 (95% CI 0.934, 1.000); however, it was noted that the specificity for female participants might not be as high as that for male participants. INTERPRETATION: Because ARIA is a fully automatic cloud-based algorithm and relies only on retinal images, it can be used as a risk assessment tool for ASD screening. Further diagnosis and confirmation can then be made by professionals, and potential treatment may be provided at a relatively early stage.

Retinal image analytics detects white matter hyperintensities in healthy adults.

Objective: We investigated whether an automatic retinal image analysis (ARIA) incorporating machine learning approach can identify asymptomatic older adults harboring high burden of white matter hyperintensities (WMH) using MRI as gold standard. Methods: In this cross-sectional study, we evaluated 180 community-dwelling, stroke-, and dementia-free healthy subjects and performed ARIA by acquiring a nonmydriatic retinal fundus image. The primary outcome was the diagnostic performance of ARIA in detecting significant WMH on MRI brain, defined as age-related white matter changes (ARWMC) grade ≥2. We analyzed both clinical variables and retinal characteristics using logistic regression analysis. We developed a machine learning network model with ARIA to estimate WMH and its classification. Results: All 180 subjects completed MRI and ARIA. The mean age was 70.3 ± 4.5 years, 70 (39%) were male. Risk factor profiles were: 106 (59%) hypertension, 31 (17%) diabetes, and 47 (26%) hyperlipidemia. Severe WMH (global ARWMC grade ≥2) was found in 56 (31%) subjects. The performance for detecting severe WMH with sensitivity (SN) 0.929 (95% CI from 0.819 to 0.977) and specificity (SP) 0.984 (95% CI from 0.937 to 0.997) was excellent. There was a good correlation between WMH volume (log-transformed) obtained from MRI versus those estimated from retinal images using ARIA with a correlation coefficient of 0.897 (95% CI from 0.864 to 0.922). Interpretation: We developed a robust algorithm to automatically evaluate retinal fundus image that can identify subjects with high WMH burden. Further community-based prospective studies should be performed for early screening of population at risk of cerebral small vessel disease.

Estimation of clinical parameters of chronic kidney disease by exhaled breath full-scan mass spectrometry data and iterative PCA with intensity screening algorithm.

Breath mass spectrometry is a useful tool for identifying important compounds associated with health. However, there have been few studies that have explored human exhaled breath by full-scan mass spectrometry as a non-invasive method for medical diagnosis, which may be attributed to the difficulties resulting from multicollinearity and small sample sizes relative to a large number of product ions. In this study, breath samples from 54 chronic kidney disease patients were analyzed by selected ion flow tube mass spectrometry in the full-scan mode. With the signal intensities of product ions, we developed a novel and robust algorithm, iterative PCA with intensity screening (IPS), to build linear models for estimating important clinical parameters of chronic kidney disease. It has been shown that IPS provided good estimations in cross-validated samples, and furthermore the identified product ions could have direct medical relevance to the disease. The study demonstrated the potential of quantitative breath analysis using mass spectrometry for medical diagnosis, and the importance of applying appropriate statistical tools to unveil the rich information in this type of data.

Use of a least absolute shrinkage and selection operator (LASSO) model to selected ion flow tube mass spectrometry (SIFT-MS) analysis of exhaled breath to predict the efficacy of dialysis: a pilot study.

Selected ion flow tube-mass spectrometry (SIFT-MS) provides rapid, non-invasive measurements of a full-mass scan of volatile compounds in exhaled breath. Although various studies have suggested that breath metabolites may be indicators of human disease status, many of these studies have included few breath samples and large numbers of compounds, limiting their power to detect significant metabolites. This study employed a least absolute shrinkage and selective operator (LASSO) approach to SIFT-MS data of breath samples to preliminarily evaluate the ability of exhaled breath findings to monitor the efficacy of dialysis in hemodialysis patients. A process of model building and validation showed that blood creatinine and urea concentrations could be accurately predicted by LASSO-selected masses. Using various precursors, the LASSO models were able to predict creatinine and urea concentrations with high adjusted R-square (>80%) values. The correlation between actual concentrations and concentrations predicted by the LASSO model (using precursor H3O+) was high (Pearson correlation coefficient = 0.96). Moreover, use of full mass scan data provided a better prediction than compounds from selected ion mode. These findings warrant further investigations in larger patient cohorts. By employing a more powerful statistical approach to predict disease outcomes, breath analysis using SIFT-MS technology could be applicable in future to daily medical diagnoses.

Randomized Phase II Study of the X-linked Inhibitor of Apoptosis (XIAP) Antisense AEG35156 in Combination With Sorafenib in Patients With Advanced Hepatocellular Carcinoma (HCC).

OBJECTIVES: This multicenter, randomized, open-label, phase II trial evaluated the efficacy and safety of AEG35156 in addition to sorafenib in patients with advanced hepatocellular carcinoma (HCC), as compared with sorafenib alone. METHODS: Eligible patients were randomly assigned in a 2:1 ratio to receive AEG35156 (300 mg weekly intravenous infusion) in combination with sorafenib (400 mg twice daily orally) or sorafenib alone. The primary endpoint was progression-free survival (PFS). Other endpoints include overall survival (OS), objective response rates (ORR), and safety profile. RESULTS: A total of 51 patients were enrolled; of them, 48 were evaluable. At a median follow-up of 16.2 months, the median PFS and OS were 4.0 months (95% CI, 1.2-4.1) and 6.5 months (95% CI, 3.9-11.5) for combination arm, and 2.6 (95% CI, 1.2-5.4) and 5.4 months (95% CI, 4.3-11.2) for sorafenib arm. Patients who had the study treatment interrupted or had dose modifications according to protocol did significantly better, in terms of PFS and OS, than those who had no dose reduction in combination arm and those in sorafenib arm. The ORR based on Choi and RECIST criteria were 16.1% and 9.7% in combination arm, respectively. The ORR was 0 in control arm. One drug-related serious adverse event of hypersensitivity occurred in the combination arm, whereas 2 gastrointestinal serious adverse events in the sorafenib arm. CONCLUSION: AEG35156 in combination with sorafenib showed additional activity in terms of ORR and was well tolerated. The benefit on PFS is moderate but more apparent in the dose-reduced subgroups.