Multimodal Sensing for Depression Risk Detection: Integrating Audio, Video, and Text Data.

Depression is a major psychological disorder with a growing impact worldwide. Traditional methods for detecting the risk of depression, predominantly reliant on psychiatric evaluations and self-assessment questionnaires, are often criticized for their inefficiency and lack of objectivity. Advancements in deep learning have paved the way for innovations in depression risk detection methods that fuse multimodal data. This paper introduces a novel framework, the Audio, Video, and Text Fusion-Three Branch Network (AVTF-TBN), designed to amalgamate auditory, visual, and textual cues for a comprehensive analysis of depression risk. Our approach encompasses three dedicated branches-Audio Branch, Video Branch, and Text Branch-each responsible for extracting salient features from the corresponding modality. These features are subsequently fused through a multimodal fusion (MMF) module, yielding a robust feature vector that feeds into a predictive modeling layer. To further our research, we devised an emotion elicitation paradigm based on two distinct tasks-reading and interviewing-implemented to gather a rich, sensor-based depression risk detection dataset. The sensory equipment, such as cameras, captures subtle facial expressions and vocal characteristics essential for our analysis. The research thoroughly investigates the data generated by varying emotional stimuli and evaluates the contribution of different tasks to emotion evocation. During the experiment, the AVTF-TBN model has the best performance when the data from the two tasks are simultaneously used for detection, where the F1 Score is 0.78, Precision is 0.76, and Recall is 0.81. Our experimental results confirm the validity of the paradigm and demonstrate the efficacy of the AVTF-TBN model in detecting depression risk, showcasing the crucial role of sensor-based data in mental health detection.

Beyond canonical PROTAC: biological targeted protein degradation (bioTPD).

Targeted protein degradation (TPD) is an emerging therapeutic strategy with the potential to modulate disease-associated proteins that have previously been considered undruggable, by employing the host destruction machinery. The exploration and discovery of cellular degradation pathways, including but not limited to proteasomes and lysosome pathways as well as their degraders, is an area of active research. Since the concept of proteolysis-targeting chimeras (PROTACs) was introduced in 2001, the paradigm of TPD has been greatly expanded and moved from academia to industry for clinical translation, with small-molecule TPD being particularly represented. As an indispensable part of TPD, biological TPD (bioTPD) technologies including peptide-, fusion protein-, antibody-, nucleic acid-based bioTPD and others have also emerged and undergone significant advancement in recent years, demonstrating unique and promising activities beyond those of conventional small-molecule TPD. In this review, we provide an overview of recent advances in bioTPD technologies, summarize their compositional features and potential applications, and briefly discuss their drawbacks. Moreover, we present some strategies to improve the delivery efficacy of bioTPD, addressing their challenges in further clinical development.

[Survey on mental disorders among registered residents and non-registered residents in Shenzhen].

OBJECTIVE: To study the prevalence and distribution of mental disorders among registered and non-registered residents in Shenzhen. METHODS: An epidemiological survey on mental disorders were carried out in Shenzhen by stratified multi-stage randomized sampling method; 7134 respondents were assessed through face-to-face interview, using the WHO standardized version on World Mental Health (WMH) Survey Initiative of the Composite International Diagnostic Interview (CIDI3.1). RESULTS: (1) The weighting prevalence of mental disorders was 21.87%. The prevalence of non-registered residents was significantly higher than that of the registered residents (22.34% vs. 19.99%; OR=1.15, 95%CI: 1.03-1.29; P<0.05) and the prevalence of females was significantly higher than that of males (22.68% vs. 19.67%; OR= 1.20, 95%CI: 1.07-1.34; P<0.05). The weighting prevalence of mood disorders, anxiety disorders and psychoses were 9.62%, 14.45% and 1.40%, respectively. (2) The weighting twelve-month incidence of mental disorders was 13.42%. The incidence of non-registered residents was significantly higher than that of the registered residents (13.80% vs. 11.90%; OR=1.19, 95%CI: 1.03-1.36; P<0.05). (3)The co-morbidity rate between mental disorders was 35.76%. (4)The prevalence and severity of mental disorders were associated with sex, household situation of registration, marital status, education, economic condition and occupation status. CONCLUSION: Mental disorders have become common diseases and serious public health problem in Shenzhen, with non-registered residents and females deserve more attention.