Automated classification of depression from structural brain measures across two independent community‐based cohorts

Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well‐phenotyped community‐based group of current MDD cases with clinical interview‐based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, ‘STRADL’). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types—SVM, penalised logistic regression or decision tree—either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population‐based sample with self‐reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses—remitted MDD in STRADL, and lifetime‐experienced MDD in UK Biobank. The highest cross‐validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self‐reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime‐experienced MDD (52.68–60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.     

The Transient Receptor Potential Vanilloid 1 Antagonist Capsazepine Improves the Impaired Lung Mechanics during Endotoxemia

Acute lung injury (ALI) caused by systemic inflammatory response remains a leading cause of morbidity and mortality in critically ill patients. Management of patients with sepsis is largely limited to supportive therapies, reflecting an incomplete understanding of the underlying pathophysiology. Furthermore, there have been limited advances in the treatments for ALI. In this study, lung function and a histological analysis were performed to evaluate the impact of transient receptor potential vanilloid‐1 receptor (TRPV1) antagonist (capsazepine; CPZ) on the lipopolysaccharide (LPS)‐induced lung injury in mice. For this, adult mice pre‐treated with CPZ or vehicle received intraperitoneal injections of LPS or saline and 24 hr after, the mice were anaesthetized, and lung mechanics was evaluated. The LPS‐challenged mice exhibited substantial mechanical impairment, characterized by increases in respiratory system resistance, respiratory system elastance, tissue damping and tissue elastance. The pre‐treatment with CPZ prevented the increase in respiratory system resistance and decreased the increase in tissue damping during endotoxemia. In addition, mice pre‐treated with CPZ had an attenuated lung injury evidenced by reduction on collapsed area of the lung parenchyma induced by LPS. This suggests that the TRPV1 antagonist capsazepine has a protective effect on lung mechanics in ALI during endotoxemia and that it may be a target for enhanced therapeutic efficacy in ALI.     

Vestibular Function Measurement Devices

Vestibular function laboratories utilize a multitude of diagnostic instruments to evaluate a dizzy patient. Caloric irrigators, oculomotor stimuli, and rotational chairs produce a stimulus whose accuracy is required for the patient response to be accurate. Careful attention to everything from cleanliness of equipment to threshold adjustments determine on a daily basis if patient data are going to be correct and useful. Instrumentation specifications that change with time such as speed and temperature must periodically be checked using calibrated instruments.     

Checkpoint-blocker-induced autoimmunity is associated with favourable outcome in metastatic melanoma and distinct T-cell expression profiles

Background Immune checkpoint blockers (ICBs) activate CD8⁺ T cells, eliciting both anti-cancer activity and immune-related adverse events (irAEs). The relationship of irAEs with baseline parameters and clinical outcome is unclear.Methods Retrospective evaluation of irAEs on survival was performed across primary (N = 144) and secondary (N = 211) independent cohorts of patients with metastatic melanoma receiving single agent (pembrolizumab/nivolumab—sICB) or combination (nivolumab and ipilimumab—cICB) checkpoint blockade. RNA from pre-treatment and post-treatment CD8⁺ T cells was sequenced and differential gene expression according to irAE development assessed.Results 58.3% of patients developed early irAEs and this was associated with longer progression-free (PFS) and overall survival (OS) across both cohorts (log-rank test, OS: P < 0.0001). Median survival for patients without irAEs was 16.6 months (95% CI: 10.9–33.4) versus not-reached (P = 2.8 × 10⁻⁶). Pre-treatment monocyte and neutrophil counts, but not BMI, were additional predictors of clinical outcome. Differential expression of numerous gene pathway members was observed in CD8⁺ T cells according to irAE development, and patients not developing irAEs demonstrating upregulated CXCR1 pre- and post-treatment.Conclusions Early irAE development post-ICB is associated with favourable survival in MM. Development of irAEs is coupled to expression of numerous gene pathways, suggesting irAE development in-part reflects baseline immune activation.Subject terms: Immunotherapy, Melanoma