Neurovascular unit dysfunction with blood-brain barrier hyperpermeability contributes to major depressive disorder: a review of clinical and experimental evidence

About one-third of people with major depressive disorder (MDD) fail at least two antidepressant drug trials at 1 year. Together with clinical and experimental evidence indicating that the pathophysiology of MDD is multifactorial, this observation underscores the importance of elucidating mechanisms beyond monoaminergic dysregulation that can contribute to the genesis and persistence of MDD. Oxidative stress and neuroinflammation are mechanistically linked to the presence of neurovascular dysfunction with blood-brain barrier (BBB) hyperpermeability in selected neurological disorders, such as stroke, epilepsy, multiple sclerosis, traumatic brain injury, and Alzheimer’s disease. In contrast to other major psychiatric disorders, MDD is frequently comorbid with such neurological disorders and constitutes an independent risk factor for morbidity and mortality in disorders characterized by vascular endothelial dysfunction (cardiovascular disease and diabetes mellitus). Oxidative stress and neuroinflammation are implicated in the neurobiology of MDD. More recent evidence links neurovascular dysfunction with BBB hyperpermeability to MDD without neurological comorbidity. We review this emerging literature and present a theoretical integration between these abnormalities to those involving oxidative stress and neuroinflammation in MDD. We discuss our hypothesis that alterations in endothelial nitric oxide levels and endothelial nitric oxide synthase uncoupling are central mechanistic links in this regard. Understanding the contribution of neurovascular dysfunction with BBB hyperpermeability to the pathophysiology of MDD may help to identify novel therapeutic and preventative approaches

169. Inflammatory and oxidative stress are highly correlated in unmedicated major depression

Inflammation and oxidative stress have been implicated both separately and jointly in aging and the pathophysiology of many diseases. This study examines the relationship between inflammation and oxidative stress in controls and in unmedicated MDD patients (N=18/group) before and after antidepressant treatment. Fasting blood samples were assayed for F2-isoprostanes, IL-6, and IL-10. MDD subjects were then treated with sertraline for eight weeks, and blood measurements resampled. Across subjects, F2-isoprostanes were directly correlated with IL-6 (p<0.03) and the ratio of Il-6/IL-10 (p=0.009) and were inversely correlated with IL-10 (p<0.04). These correlations remained significant or near-significant in the MDD group alone (p’s=0.053, 0.02 and 0.04, respectively). In controls, F2-isoprostanes were significantly correlated with the IL-6/IL-10 ratio (p<0.001), but not with either cytokine alone. After treatment, F2-isoprostanes remained inversely correlated with IL-10 (P<0.04) but were not correlated with IL-6. Baseline HDRS ratings were unrelated to inflammatory and oxidative markers, but week 8 HDRS ratings were positively correlated with week 8 IL-6 (p<0.05) and IL-6/IL-10 (P=0.04). Antidepressant response was significantly related to decreases in the IL-6/ IL-10 ratio (P<0.02) and F2-isoprostanes (P=0.056). These preliminary data suggest that oxidation and inflammation are closely linked in unmedicated MDD, as also seen in a number of medical conditions. This relationship was only partially evident in healthy controls and in medicated MDD

Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning

Deep vein thrombosis (DVT) is a blood clot most commonly found in the leg, which can lead to fatal pulmonary embolism (PE). Compression ultrasound of the legs is the diagnostic gold standard, leading to a definitive diagnosis. However, many patients with possible symptoms are not found to have a DVT, resulting in long referral waiting times for patients and a large clinical burden for specialists. Thus, diagnosis at the point of care by non-specialists is desired. We collect images in a pre-clinical study and investigate a deep learning approach for the automatic interpretation of compression ultrasound images. Our method provides guidance for free-hand ultrasound and aids non-specialists in detecting DVT. We train a deep learning algorithm on ultrasound videos from 255 volunteers and evaluate on a sample size of 53 prospectively enrolled patients from an NHS DVT diagnostic clinic and 30 prospectively enrolled patients from a German DVT clinic. Algorithmic DVT diagnosis performance results in a sensitivity within a 95% CI range of (0.82, 0.94), specificity of (0.70, 0.82), a positive predictive value of (0.65, 0.89), and a negative predictive value of (0.99, 1.00) when compared to the clinical gold standard. To assess the potential benefits of this technology in healthcare we evaluate the entire clinical DVT decision algorithm and provide cost analysis when integrating our approach into diagnostic pathways for DVT. Our approach is estimated to generate a positive net monetary benefit at costs up to £72 to £175 per software-supported examination, assuming a willingness to pay of £20,000/QALY