Methods: Adult chemotherapy-naïve patients with confirmed prostate adenocarcinoma, Eastern Cooperative Oncology Group (ECOG) performance status (PS) grade 0-1, ongoing androgen deprivation (serum testosterone <50 ng/dL) with prostate specific antigen (PSA) or radiographic progression were randomized to receive abiraterone acetate (1000 mg, QD) + prednisone (5 mg, BID) or placebo + prednisone (5 mg, BID), until disease progression, unacceptable toxicity or consent withdrawal. Primary endpoint was improvements in time to PSA progression (TTPP).

METHODS: This was a follow-up study of participants recruited in the Malaysian HIV & Aging study (MHIVA) from 2014 to 2016 at the University Malaya Medical Centre (n = 336). PLWH on suppressive antiretroviral therapy (ART) for a minimum of 12 months were invited to participate. At study entry, all participants underwent screening for diabetes (DM), hypertension (HTN) and dyslipidaemia; and completed assessments using the depression, anxiety and stress scale (DASS-21). Screening results were recorded in medical charts and clinical management provided as per standard of care. A subsequent review of medical records was performed at 24 months following study completion among participants who remained on active follow-up. Treatment pathways for NCD treatment and psychiatric referrals were assessed based on local practice guidelines to construct the care cascade.

RESULTS: A total of 329 participants (median age = 43 years, 83% male, 100% on ART) completed follow-up at 24 months. The prevalence of diabetes was 13%, dyslipidaemia 88% and hypertension 44%, whereas 23% presented with severe/extremely severe symptoms of depression, anxiety and/or stress. More than 50% of participants with dyslipidaemia and hypertension were not diagnosed until study screening, whereas over 80% with prevalent psychiatric symptoms were not previously recognized clinically. Suboptimal control of fasting lipids, sugar and blood pressure were found in the majority of participants despite optimal HIV treatment outcomes maintained over this same period. Only 32% of participants with severe/extremely severe mental health symptoms received psychiatric referrals and 83% of these attended their psychiatry clinic appointments.

MATERIALS AND METHODS: We propose a mixed-method study of mental health assessment that combines psychological questionnaires with facial emotion analysis to comprehensively evaluate the mental health of students on a large scale. The Depression Anxiety and Stress Scale-21(DASS-21) is used for the psychological questionnaire. The facial emotion recognition model is implemented by transfer learning based on neural networks, and the model is pre-trained using FER2013 and CFEE datasets. Among them, the FER2013 dataset consists of 48 × 48-pixel face gray images, a total of 35,887 face images. The CFEE dataset contains 950,000 facial images with annotated action units (au). Using a random sampling strategy, we sent online questionnaires to 400 college students and received 374 responses, and the response rate was 93.5%. After pre-processing, 350 results were available, including 187 male and 153 female students. First, the facial emotion data of students were collected in an online questionnaire test. Then, a pre-trained model was used for emotion recognition. Finally, the online psychological questionnaire scores and the facial emotion recognition model scores were collated to give a comprehensive psychological evaluation score.

RESULTS: The experimental results of the facial emotion recognition model proposed to show that its classification results are broadly consistent with the mental health survey results. This model can be used to improve efficiency. In particular, the accuracy of the facial emotion recognition model proposed in this paper is higher than that of the general mental health model, which only uses the traditional single questionnaire. Furthermore, the absolute errors of this study in the three symptoms of depression, anxiety, and stress are lower than other mental health survey results and are only 0.8%, 8.1%, 3.5%, and 1.8%, respectively.