Machine learning and bioinformatic analysis of brain and blood mRNA profiles in major depressive disorder: A case-control study.

This study analyzed gene expression messenger RNA data, from cases with major depressive disorder (MDD) and controls, using supervised machine learning (ML). We built on the methodology of prior studies to obtain more generalizable/reproducible results. First, we obtained a classifier trained on gene expression data from the dorsolateral prefrontal cortex of post-mortem MDD cases (n = 126) and controls (n = 103). An average area-under-the-receiver-operating-characteristics-curve (AUC) from 10-fold cross-validation of 0.72 was noted, compared to an average AUC of 0.55 for a baseline classifier (p = .0048). The classifier achieved an AUC of 0.76 on a previously unused testing-set. We also performed external validation using DLPFC gene expression values from an independent cohort of matched MDD cases (n = 29) and controls (n = 29), obtained from Affymetrix microarray (vs. Illumina microarray for the original cohort) (AUC: 0.62). We highlighted gene sets differentially expressed in MDD that were enriched for genes identified by the ML algorithm. Next, we assessed the ML classification performance in blood-based microarray gene expression data from MDD cases (n = 1,581) and controls (n = 369). We observed a mean AUC of 0.64 on 10-fold cross-validation, which was significantly above baseline (p = .0020). Similar performance was observed on the testing-set (AUC: 0.61). Finally, we analyzed the classification performance in covariates subgroups. We identified an interesting interaction between smoking and recall performance in MDD case prediction (58% accurate predictions in cases who are smokers vs. 43% accurate predictions in cases who are non-smokers). Overall, our results suggest that ML in combination with gene expression data and covariates could further our understanding of the pathophysiology in MDD.

Improvement of Microstomia in Scleroderma after Carbon Dioxide Laser Treatment.

Limited mouth opening (LMO) is a frequent complication of systemic sclerosis (SS). Its management is complex and there are limited treatment options. We report four patients with SS and severe LMO [interincisal distance (IID) <30 mm] treated with pulsed carbon dioxide (CO2) laser. Pulsed CO2 laser treatment of the white lips was performed after all patients had signed a written informed consent in the absence of alternative treatment. Treatment was carried out under locoregional anaesthesia using a Sharplan 30C CO2 laser in the Silk Touch® resurfacing mode. One to three laser sessions were performed at intervals of 8-12 months between sessions. Assessments were performed at 3 and 12 months with measurement of the IID using a ruler, calculation of the Mouth Handicap in Systemic Sclerosis (MHISS) scale and global evaluation by the patients. Adverse events were also reported. In all four patients, an improvement in IID occurred 3 months after the first session with a mean gain of +5 mm (range: 2-7). At 12 months, a mean gain of +8.5 mm (range: 7-10) in IID was observed. The MHISS score decreased by a mean of •14 (range: 11-17). All patients showed improvement of lip flexibility or mouth opening, allowing better phonation and mastication and easier dental care. Adverse effects were transient erythema and/or dyschromia. CO2 laser appears to be effective and well tolerated in the improvement of LMO in SS.