A novel machine learning approach to shorten depression risk assessment for convenient uses.

BACKGROUND: Depression is a mental disorder affecting many people worldwide which has been exacerbated by the current pandemic. There is an urgent need for a reliable yet short scale for individuals to self-assess the risk of depression conveniently and rapidly on a regular basis. METHODS: We obtained a dataset of responses to the Depression, Anxiety, and Stress questionnaire (DASS-42) from a large sample of individuals worldwide (N = 31,715). With this dataset, important items from the questionnaire were extracted by applying feature selection techniques. Then, using the most important features, various machine learning algorithms were trained, tested, and validated in predicting depression status. RESULTS: This study revealed that only seven items are needed to predict depression status with at least 90 % accuracy of the original full scale. This can be achieved through the Stacked Generalization Ensemble learning method of multiple models. The trained machine learning models from the best algorithm were then implemented as an online Depression Rapid Assessment tool, which allows the user to evaluate their current depression status conveniently and quickly (about 1 min). LIMITATIONS: The sample size of the present study is still relatively small and has biases toward certain demographics (e.g., mostly young, Asian, and female). Further, memory issues with Stacked Generalization Ensemble prevent it from being trained in the same way as the other algorithm. CONCLUSION: It is possible to produce very short assessments that approximate the accuracy of the full scale for convenient and rapid self-assessment of depression risks.

A Novel Approach for Developing Efficient and Convenient Short Assessments to Approximate a Long Assessment.

This paper describes a novel Long to Short approach that uses machine learning to develop efficient and convenient short assessments to approximate a long assessment. This approach is applicable to any assessments used to assess people's behaviors, opinions, attitudes, mental and physical states, traits, aptitudes, abilities, and mastery of a subject matter. We demonstrated the Long to Short approach on the Depression Anxiety Stress Scale (DASS-42) for assessing anxiety levels in adults. We first obtained data for the original assessment from a large sample of participants. We then derived the total scores from participants' responses to all items of the long assessment as the ground truths. Next, we used feature selection techniques to select participants' responses to a subset of items of the long assessment to predict the ground truths accurately. We then trained machine learning models that uses the minimal number of items needed to achieve the prediction accuracy similar to that when the responses to all items of the whole long assessment are used. We generated all possible combinations of minimal number of items to create multiple short assessments of similar predictive accuracies for use if the short assessment is to be done repeatedly. Finally, we implemented the short anxiety assessments in a web application for convenient use with any future participant of the assessment.

RTN4B-mediated suppression of Sirtuin 2 activity ameliorates ß-amyloid pathology and cognitive impairment in Alzheimer's disease mouse model.

Sirtuin 2 (SIRT2) is an NAD+ dependent deacetylase that is the most abundant sirtuin protein in the brain. Accumulating evidence revealed the role of SIRT2 in a wide range of biological processes and age-related diseases. However, the pivotal mechanism of SIRT2 played in Alzheimer's disease (AD) remains unknown. Here, we report that pharmacological inactivation of SIRT2 has a beneficial effect in AD. The deacetylase inhibitor of SIRT2 rescued the cognitive impairment in amyloid precursor protein/presenilin 1 transgenic mouse (APP/PS1 mouse), and the BACE1 cleavage was weakened to reduce the ß-amyloid (Aß) production in the hippocampus. Moreover, we firstly identified that Reticulon 4B (RTN4B) played a crucial role between SIRT2/BACE1 regulation in AD. RTN4B, as a deacetylation substrate for SIRT2, the deacetylation by SIRT2 drived the ubiquitination and degradation of RTN4B and then the disturbed RTN4B interacted with and influenced the expression of BACE1. When we overexpressed RTN4B in neurons of the hippocampus in the AD mouse model, the abnormal Aß accumulation and cognitive impairment were ameliorated, consistent with the results of SIRT2 inhibition in vivo. Moreover, we showed that the regulatory effect of SIRT2 on BACE1 is dependent on RTN4B. When RTN4B was knocked down, the effects of SIRT2 inhibition on the BACE1 level, Aß pathology, and AD-liked behaviors were also blocked. Collectively, we provide evidence that SIRT2 may be a potential target for AD; the new found SIRT2/RTN4B/BACE1 pathological pathway is one of the critical mechanisms for the improvement of SIRT2 on AD.