Development and validation of a machine learning-based vocal predictive model for major depressive disorder.

BACKGROUND: Variations in speech intonation are known to be associated with changes in mental state over time. Behavioral vocal analysis is an algorithmic method of determining individuals' behavioral and emotional characteristics from their vocal patterns. It can provide biomarkers for use in psychiatric assessment and monitoring, especially when remote assessment is needed, such as in the COVID-19 pandemic. The objective of this study was to design and validate an effective prototype of automatic speech analysis based on algorithms for classifying the speech features related to MDD using a remote assessment system combining a mobile app for speech recording and central cloud processing for the prosodic vocal patterns. METHODS: Machine learning compared the vocal patterns of 40 patients diagnosed with MDD to the patterns of 104 non-clinical participants. The vocal patterns of 40 patients in the acute phase were also compared to 14 of these patients in the remission phase of MDD. RESULTS: A vocal depression predictive model was successfully generated. The vocal depression scores of MDD patients were significantly higher than the scores of the non-patient participants (p < 0.0001). The vocal depression scores of the MDD patients in the acute phase were significantly higher than in remission (p < 0.02). LIMITATIONS: The main limitation of this study is its relatively small sample size, since machine learning validity improves with big data. CONCLUSIONS: The computerized analysis of prosodic changes may be used to generate biomarkers for the early detection of MDD, remote monitoring, and the evaluation of responses to treatment.

Acetylation reduces SOX9 nuclear entry and ACAN gene transactivation in human chondrocytes.

Changes in the content of aggrecan, an essential proteoglycan of articular cartilage, have been implicated in the pathophysiology of osteoarthritis (OA), a prevalent age-related, degenerative joint disease. Here, we examined the effect of SOX9 acetylation on ACAN transactivation in the context of osteoarthritis. Primary chondrocytes freshly isolated from degenerated OA cartilage displayed lower levels of ACAN mRNA and higher levels of acetylated SOX9 compared with cells from intact regions of OA cartilage. Degenerated OA cartilage presented chondrocyte clusters bearing diffused immunostaining for SOX9 compared with intact cartilage regions. Primary human chondrocytes freshly isolated from OA knee joints were cultured in monolayer or in three-dimensional alginate microbeads (3D). SOX9 was hypo-acetylated in 3D cultures and displayed enhanced binding to a -10 kb ACAN enhancer, a result consistent with higher ACAN mRNA levels than in monolayer cultures. It also co-immunoprecipitated with SIRT1, a major deacetylase responsible for SOX9 deacetylation. Finally, immunofluorescence assays revealed increased nuclear localization of SOX9 in primary chondrocytes treated with the NAD SIRT1 cofactor, than in cells treated with a SIRT1 inhibitor. Inhibition of importin ß by importazole maintained SOX9 in the cytoplasm, even in the presence of NAD. Based on these data, we conclude that deacetylation promotes SOX9 nuclear translocation and hence its ability to activate ACAN.