Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees

BACKGROUND: Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann–Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression. RESULTS: The DT models exhibited the lowest predictive power and the poorest extrapolation when applied to the test samples. The ANN models displayed the best predictive power and showed the best extrapolation. The 21 most-associated genes, as determined by integration of each model, were analyzed using Cox regression with a 3.53-fold (95% CI: 2.24-5.58) increased risk of breast cancer five-year recurrence… CONCLUSIONS: The 21 selected genes can predict breast cancer recurrence. Among these genes, CCNB1, PLK1 and TOP2A are in the cell cycle G2/M DNA damage checkpoint pathway. Oncologists can offer the genetic information for patients when understanding the gene expression profiles on breast cancer recurrence

Effectiveness of Augmented and Virtual Reality-Based Interventions in Improving Knowledge, Attitudes, Empathy and Stigma Regarding People with Mental Illnesses—A Scoping Review

Interventions adopting augmented and virtual reality (AR/VR) modalities allow participants to explore and experience realistic scenarios, making them useful psycho-educational tools for mental illnesses. This scoping review aims to evaluate the effectiveness of AR/VR interventions in improving (1) knowledge, (2) attitudes, (3) empathy and (4) stigma regarding people with mental illnesses. Literature on published studies in English up till April 2022 was searched within several databases. Sixteen articles were included. The majority of studies were conducted in the West (93.8%), within undergraduates (68.8%) but also amongst high school students, patients, caregivers, public including online community, and covered conditions including psychotic illnesses, dementia, anxiety and depression. A preponderance of these included studies which employed AR/VR based interventions observed improvements in knowledge (66.7%), attitudes (62.5%), empathy (100%) and reduction of stigma (71.4%) pertaining to people with mental illnesses. In the context of relatively limited studies, extant AR/VR based interventions could potentially improve knowledge, attitudes, empathy and decrease stigma regarding people with mental illness. Further research needs to be conducted in larger and more diverse samples to investigate the relatively beneficial effects of different AR/VR modalities and the durability of observed improvements of relevant outcomes of interests over time for different mental conditions

THE ONLINE HOPE INTERVENTION ON MENTAL HEALTH LITERACY AMONG YOUTHS - A RANDOMISED CONTROLLED TRIAL

Ph.DDOCTOR OF PHILOSOPHY (NUR

Effectiveness of Augmented and Virtual Reality-Based Interventions in Improving Knowledge, Attitudes, Empathy and Stigma Regarding People with Mental Illnesses—A Scoping Review

Interventions adopting augmented and virtual reality (AR/VR) modalities allow participants to explore and experience realistic scenarios, making them useful psycho-educational tools for mental illnesses. This scoping review aims to evaluate the effectiveness of AR/VR interventions in improving (1) knowledge, (2) attitudes, (3) empathy and (4) stigma regarding people with mental illnesses. Literature on published studies in English up till April 2022 was searched within several databases. Sixteen articles were included. The majority of studies were conducted in the West (93.8%), within undergraduates (68.8%) but also amongst high school students, patients, caregivers, public including online community, and covered conditions including psychotic illnesses, dementia, anxiety and depression. A preponderance of these included studies which employed AR/VR based interventions observed improvements in knowledge (66.7%), attitudes (62.5%), empathy (100%) and reduction of stigma (71.4%) pertaining to people with mental illnesses. In the context of relatively limited studies, extant AR/VR based interventions could potentially improve knowledge, attitudes, empathy and decrease stigma regarding people with mental illness. Further research needs to be conducted in larger and more diverse samples to investigate the relatively beneficial effects of different AR/VR modalities and the durability of observed improvements of relevant outcomes of interests over time for different mental conditions

Effectiveness of artificial intelligence methods in personalized aggression risk prediction within inpatient psychiatric treatment settings - a systematic review

Aggression risk assessments are vital to prevent injuries and morbidities amongst patients and staff in psychiatric settings. More recent studies have harnessed artificial intelligence (AI) methods such as machine learning algorithms to determine factors associated with aggression in psychiatric treatment settings. In this review, using Cooper's five-stage review framework, we aimed to evaluate the: (1) predictive accuracy, and (2) clinical variables associated with AI-based aggression risk prediction amongst psychiatric inpatients. Databases including PubMed, Cochrane, Scopus, PsycINFO, CINAHL were searched for relevant articles until April 2022. The eight included studies were independently evaluated using critical appraisal tools for systematic review developed by Joanna Briggs Institute. Most of the studies (87.5%) examined health records in predicting aggression and reported acceptable to excellent accuracy with specific machine learning algorithms employed (area under curve range 0.75-0.87). No particular machine learning algorithm outperformed the others consistently across studies (area under curve range 0.61-0.87). Relevant factors identified with aggression related to demographic and social profile, past aggression, forensic history, other psychiatric history, psychopathology, challenging behaviors and management domains. The limited extant studies have highlighted a potential role for the use of AI methods to clarify factors associated with aggression in psychiatric inpatient treatment settings.Published versionThe study was funded by West Region, Institute of Mental Health

Impact of the HOPE intervention on mental health literacy, psychological well-being and stress levels amongst university undergraduates: a randomised controlled trial

Mental health literacy (MHL) promotes mental health among youths. We aimed to evaluate the effectiveness of the newly developed HOPE intervention in improving depression literacy, anxiety literacy, psychological well-being, and reducing personal stigma and stress levels amongst young adults at a university in Singapore. After two pilot studies, we conducted a randomised controlled trial (RCT) and recruited 174 participants aged 18-24 years old through social media platforms. The HOPE intervention group received four online sessions over two weeks and the control group received online inspirational quotes. Study outcomes were measured with self-reported questionnaires and they were assessed at baseline, post-intervention, and two-month follow-up (ClinicalTrials.gov: NCT04266119). Compared with the control arm, the intervention group was associated with increased depression and anxiety literacy levels at post-intervention and two-month follow-up. In addition, personal stigma for depression was reduced at the post-intervention juncture. However, there were no statistically significant changes in the ratings of psychological well-being and stress levels between the two groups. Longitudinal studies with larger sample sizes are warranted to replicate and extend the extant findings.Published versionThis work was supported by the National Youth Fund (grant number: NYF/Jul19/01); and the Nursing Graduate Research Student Project Fund (grant number: N/A)

Gene Expression Profiling of Colorectal Tumors and Normal Mucosa by Microarrays Meta-Analysis Using Prediction Analysis of Microarray, Artificial Neural Network, Classification, and Regression Trees

Background. Microarray technology shows great potential but previous studies were limited by small number of samples in the colorectal cancer (CRC) research. The aims of this study are to investigate gene expression profile of CRCs by pooling cDNA microarrays using PAM, ANN, and decision trees (CART and C5.0). Methods. Pooled 16 datasets contained 88 normal mucosal tissues and 1186 CRCs. PAM was performed to identify significant expressed genes in CRCs and models of PAM, ANN, CART, and C5.0 were constructed for screening candidate genes via ranking gene order of significances. Results. The first screening identified 55 genes. The test accuracy of each model was over 0.97 averagely. Less than eight genes achieve excellent classification accuracy. Combining the results of four models, we found the top eight differential genes in CRCs; suppressor genes, CA7, SPIB, GUCA2B, AQP8, IL6R and CWH43; oncogenes, SPP1 and TCN1. Genes of higher significances showed lower variation in rank ordering by different methods. Conclusion. We adopted a two-tier genetic screen, which not only reduced the number of candidate genes but also yielded good accuracy (nearly 100%). This method can be applied to future studies. Among the top eight genes, CA7, TCN1, and CWH43 have not been reported to be related to CRC

Gene Expression Profiling of Colorectal Tumors and Normal Mucosa by Microarrays Meta-Analysis Using Prediction Analysis of Microarray, Artificial Neural Network, Classification, and Regression Trees

Background. Microarray technology shows great potential but previous studies were limited by small number of samples in the colorectal cancer (CRC) research. The aims of this study are to investigate gene expression profile of CRCs by pooling cDNA microarrays using PAM, ANN, and decision trees (CART and C5.0). Methods. Pooled 16 datasets contained 88 normal mucosal tissues and 1186 CRCs. PAM was performed to identify significant expressed genes in CRCs and models of PAM, ANN, CART, and C5.0 were constructed for screening candidate genes via ranking gene order of significances. Results. The first screening identified 55 genes. The test accuracy of each model was over 0.97 averagely. Less than eight genes achieve excellent classification accuracy. Combining the results of four models, we found the top eight differential genes in CRCs; suppressor genes, CA7, SPIB, GUCA2B, AQP8, IL6R and CWH43; oncogenes, SPP1 and TCN1. Genes of higher significances showed lower variation in rank ordering by different methods. Conclusion. We adopted a two-tier genetic screen, which not only reduced the number of candidate genes but also yielded good accuracy (nearly 100%). This method can be applied to future studies. Among the top eight genes, CA7, TCN1, and CWH43 have not been reported to be related to CRC