From the hidden to the obvious: classification of primary and secondary school student suicides using cluster analysis.

BACKGROUND: Suicide is one of the leading causes of death in children and youth. Using a sample of fatal suicides among school-aged students in Hong Kong, this study aimed to demonstrate how the classification of children and adolescent suicides into distinct subgroups using cluster analysis can alert us to the heterogeneous nature of the student suicide population and increase our understanding of multidimensional underlying causes.  METHODS: Deaths by suicide of Hong Kong primary and secondary school students occurring between 2013-16 were identified. Reports were acquired from the Coroner's Court, Police Force, and Education Bureau in Hong Kong. Information about students' sociodemographic characteristics, suicide circumstances, stressors, and risk factors was extracted and organized for analysis. Based on the indicated stressors (school, family, close relationship, social challenge, finance, risk behaviour, suicide exposure, others) and risk factors (health and mental health, history of self-harm, suicidality, and psychological maladjustment), cluster analysis was conducted to derive distinct profiles of student suicides. RESULTS: A four-cluster solution was found. Patterns of stressors, risk factors, background characteristics and suicide circumstances within each cluster were examined. Four distinct and meaningful profiles of student suicides were characterised as "school distress", "hidden", "family and relationship", and "numerous issues". CONCLUSIONS: Findings highlighted the need to approach student suicides in meaningfully differentiated ways. Gathering suicide report data and generating evidence that advances our knowledge of student suicide profiles are important steps towards early identification and intervention.

LEAP: Using machine learning to support variant classification in a clinical setting.

Advances in genome sequencing have led to a tremendous increase in the discovery of novel missense variants, but evidence for determining clinical significance can be limited or conflicting. Here, we present Learning from Evidence to Assess Pathogenicity (LEAP), a machine learning model that utilizes a variety of feature categories to classify variants, and achieves high performance in multiple genes and different health conditions. Feature categories include functional predictions, splice predictions, population frequencies, conservation scores, protein domain data, and clinical observation data such as personal and family history and covariant information. L2-regularized logistic regression and random forest classification models were trained on missense variants detected and classified during the course of routine clinical testing at Color Genomics (14,226 variants from 24 cancer-related genes and 5,398 variants from 30 cardiovascular-related genes). Using 10-fold cross-validated predictions, the logistic regression model achieved an area under the receiver operating characteristic curve (AUROC) of 97.8% (cancer) and 98.8% (cardiovascular), while the random forest model achieved 98.3% (cancer) and 98.6% (cardiovascular). We demonstrate generalizability to different genes by validating predictions on genes withheld from training (96.8% AUROC). High accuracy and broad applicability make LEAP effective in the clinical setting as a high-throughput quality control layer.

Assessment of blind predictions of the clinical significance of BRCA1 and BRCA2 variants.

Testing for variation in BRCA1 and BRCA2 (commonly referred to as BRCA1/2), has emerged as a standard clinical practice and is helping countless women better understand and manage their heritable risk of breast and ovarian cancer. Yet the increased rate of BRCA1/2 testing has led to an increasing number of Variants of Uncertain Significance (VUS), and the rate of VUS discovery currently outpaces the rate of clinical variant interpretation. Computational prediction is a key component of the variant interpretation pipeline. In the CAGI5 ENIGMA Challenge, six prediction teams submitted predictions on 326 newly-interpreted variants from the ENIGMA Consortium. By evaluating these predictions against the new interpretations, we have gained a number of insights on the state of the art of variant prediction and specific steps to further advance this state of the art.

Congenital fibrosis of extraocular muscle type 1A due to KIF21A mutation: first case report from Hong Kong.

With the advancement of ophthalmological genetics, the molecular basis for more and more eye diseases can be elucidated. Congenital fibrosis of extraocular muscle (CFEOM) is an example. It is characterised by a congenital non-progressive restrictive ophthalmoplegia and ptosis. It is an autosomal dominant disease, caused by mutations of the KIF21A gene. With positive family history and typical ophthalmological findings, mutational analysis of KIF21A gene should be performed, not only to confirming the diagnosis, but also to offer a prognosis, for genetic counselling, and the possibility of prenatal diagnosis. Here we report the first KIF21A mutation associated with CFEOM1A in Hong Kong.

Effects of Community-Based Caring Contact in Reducing Thwarted Belongingness Among Postdischarge Young Adults With Self-Harm: Randomized Controlled Trial.

BACKGROUND: For patients with self-harm behaviors, the urge to hurt themselves persists after hospital discharge, leading to costly readmissions and even death. Hence, postdischarge intervention programs that reduce self-harm behavior among patients should be part of a cogent community mental health care policy. OBJECTIVE: We aimed to determine whether a combination of a self-help mobile app and volunteer support could complement treatment as usual (TAU) to reduce the risk of suicide among these patients. METHODS: We conducted a pragmatic randomized controlled trial on discharged patients aged between 18 and 45 years with self-harm episodes/suicide attempts, all of whom were recruited from 4 hospital emergency departments in Hong Kong. Participants were randomly assigned to one of three groups: (1) mobile app + TAU ("apps"), (2) mobile app + volunteer support + TAU ("volunteers"), or (3) TAU only as the control group ("TAU"). They were asked to submit a mobile app-based questionnaire during 4 measurement time points at monthly intervals. RESULTS: A total of 40 participants were recruited. Blending volunteer care with a preprogrammed mobile app was found to be effective in improving service compliance. Drawing upon the interpersonal-psychological theory of suicide, our findings suggested that a reduction in perceived burdensomeness and thwarted belongingness through community-based caring contact are linked to improvement in hopelessness, albeit a transient one, and suicide risk. CONCLUSIONS: A combination of volunteer care with a self-help mobile app as a strategy for strengthening the continuity of care can be cautiously implemented for discharged patients at risk of self-harm during the transition from the hospital to a community setting. TRIAL REGISTRATION: ClinicalTrials.gov NCT03081078; https://clinicaltrials.gov/study/NCT03081078.

Short- and long-term effects of a community-based suicide prevention program: A Hong Kong experience.

BACKGROUND: A multidisciplinary, multilayer, community-based suicide prevention program (2008-2012) was implemented in the Eastern District, Hong Kong. This article documents the program and reports on short- and longer-term program evaluation. METHODS: Characteristics and rates of self-harm/suicidal behaviors and suicide deaths by age group and gender in the Eastern District before, during, and after the intervention were calculated and compared with the rest of Hong Kong, using Kruskal-Wallis and chi-squared tests, and Jonckheere-Terpstra and Cochran-Mantel-Haenszel tests for trend analyses. RESULTS: The program impacts varied by age and gender subgroups. Suicide rates in the Eastern District were lower compared to the rest of Hong Kong during the intervention period. They slowly rebounded after the intervention ceased; nevertheless, they remained lower than the rest of Hong Kong until 2016. The rates of self-harm continuously dropped and remained lower than the rest of Hong Kong. During the intervention period in the Eastern District, the age of people who died by suicide increased; more deaths occurred from jumping and fewer by charcoal burning. CONCLUSIONS: The program coincided with the lowered self-harm and suicide rates after the implementation. Some of the strategies need to be rebooted or routinely and continuously implemented to ensure the sustainability.

An evaluation of the long-term sustainability of suicide prevention programs in an offshore Island.

INTRODUCTION: In 2002, a suicide prevention program for visitors was implemented in Cheung Chau, an offshore island with 21,000 residents and many visitors. This research revisited the intervention and evaluated its long-term effectiveness. Insights are provided into implementing a sustainable intervention. METHODS: Suicide death records (2007-2017) of Cheung Chau residents and visitors were retrieved. Information related to suicide method and sociodemographic characteristics of people who died by suicide were explored. Bivariate analyses were conducted to examine differences between visitors and residents; visitors and residents who died in Cheung Chau; and residents who died in or outside Cheung Chau. RESULTS: After post-implementation period, suicide rate for visitors and residents increased. Visitors were generally younger than the residents with a different distribution of type of housing. Most of the visitors killed themselves by charcoal burning, and nearly half of the residents used hanging. CONCLUSION: Long-term evaluation and monitoring of suicide prevention programs and sustained efforts are crucial to ensure program success. The prevention program in Cheung Chau was initially effective, but its long-term effects wore off. Both the mental health needs of visitors and residents should be addressed. Refinement of the current program and sustainable efforts are required for ensuring long-term success.

Bioengineered optogenetic model of human neuromuscular junction.

Functional human tissues engineered from patient-specific induced pluripotent stem cells (hiPSCs) hold great promise for investigating the progression, mechanisms, and treatment of musculoskeletal diseases in a controlled and systematic manner. For example, bioengineered models of innervated human skeletal muscle could be used to identify novel therapeutic targets and treatments for patients with complex central and peripheral nervous system disorders. There is a need to develop standardized and objective quantitative methods for engineering and using these complex tissues, in order increase their robustness, reproducibility, and predictiveness across users. Here we describe a standardized method for engineering an isogenic, patient specific human neuromuscular junction (NMJ) that allows for automated quantification of NMJ function to diagnose disease using a small sample of blood serum and evaluate new therapeutic modalities. By combining tissue engineering, optogenetics, microfabrication, optoelectronics and video processing, we created a novel platform for the precise investigation of the development and degeneration of human NMJ. We demonstrate the utility of this platform for the detection and diagnosis of myasthenia gravis, an antibody-mediated autoimmune disease that disrupts the NMJ function.

A Community-Based Response to a Suicide Cluster.

Background: A 45-month community-based suicide prevention program was launched in response to the emergence of a suicide cluster in 2010 in Hong Kong. Aims: This study aimed to evaluate the effectiveness of the program, document the implementation and outcomes of the project, and identify factors that contribute to the outcomes. Method: The program was delivered following the five key components of the public health approach: (a) community consensus building; (b) surveillance and monitoring; (c) development of coordinated action strategies; (d) interventions development and implementation at the universal, selective, and indicated levels; and (d) program evaluation. Results: A significant decreasing trend of suicide was found in the study site during the intervention period, whereas no changes were found in the three control sites. Spatial analysis also showed that the suicide cluster subsided after the intervention. Three impacts and one challenge of the program were identified from the qualitative feedback of the program stakeholders. Limitations: More investigations should be made to assess the sustainability of this community-based suicide prevention effort in the long run. Conclusion: A community-based suicide prevention program was successfully implemented to address the suicide cluster. A reduction in the suicide rate was observed after the intervention.

Polygenic background modifies penetrance of monogenic variants for tier 1 genomic conditions.

Genetic variation can predispose to disease both through (i) monogenic risk variants that disrupt a physiologic pathway with large effect on disease and (ii) polygenic risk that involves many variants of small effect in different pathways. Few studies have explored the interplay between monogenic and polygenic risk. Here, we study 80,928 individuals to examine whether polygenic background can modify penetrance of disease in tier 1 genomic conditions - familial hypercholesterolemia, hereditary breast and ovarian cancer, and Lynch syndrome. Among carriers of a monogenic risk variant, we estimate substantial gradients in disease risk based on polygenic background - the probability of disease by age 75 years ranged from 17% to 78% for coronary artery disease, 13% to 76% for breast cancer, and 11% to 80% for colon cancer. We propose that accounting for polygenic background is likely to increase accuracy of risk estimation for individuals who inherit a monogenic risk variant.

SIRAC: Supervised Identification of Regions of Aberration in aCGH datasets.

BACKGROUND: Array comparative genome hybridization (aCGH) provides information about genomic aberrations. Alterations in the DNA copy number may cause the cell to malfunction, leading to cancer. Therefore, the identification of DNA amplifications or deletions across tumors may reveal key genes involved in cancer and improve our understanding of the underlying biological processes associated with the disease. RESULTS: We propose a supervised algorithm for the analysis of aCGH data and the identification of regions of chromosomal alteration (SIRAC). We first determine the DNA-probes that are important to distinguish the classes of interest, and then evaluate in a systematic and robust scheme if these relevant DNA-probes are closely located, i.e. form a region of amplification/deletion. SIRAC does not need any preprocessing of the aCGH datasets, and requires only few, intuitive parameters. CONCLUSION: We illustrate the features of the algorithm with the use of a simple artificial dataset. The results on two breast cancer datasets show promising outcomes that are in agreement with previous findings, but SIRAC better pinpoints the dissimilarities between the classes of interest.

A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets.

BACKGROUND: Gene selection is an important step when building predictors of disease state based on gene expression data. Gene selection generally improves performance and identifies a relevant subset of genes. Many univariate and multivariate gene selection approaches have been proposed. Frequently the claim is made that genes are co-regulated (due to pathway dependencies) and that multivariate approaches are therefore per definition more desirable than univariate selection approaches. Based on the published performances of all these approaches a fair comparison of the available results can not be made. This mainly stems from two factors. First, the results are often biased, since the validation set is in one way or another involved in training the predictor, resulting in optimistically biased performance estimates. Second, the published results are often based on a small number of relatively simple datasets. Consequently no generally applicable conclusions can be drawn. RESULTS: In this study we adopted an unbiased protocol to perform a fair comparison of frequently used multivariate and univariate gene selection techniques, in combination with a ränge of classifiers. Our conclusions are based on seven gene expression datasets, across several cancer types. CONCLUSION: Our experiments illustrate that, contrary to several previous studies, in five of the seven datasets univariate selection approaches yield consistently better results than multivariate approaches. The simplest multivariate selection approach, the Top Scoring method, achieves the best results on the remaining two datasets. We conclude that the correlation structures, if present, are difficult to extract due to the small number of samples, and that consequently, overly-complex gene selection algorithms that attempt to extract these structures are prone to overtraining.

A Pilot Study on the Efficacy of Volunteer Mentorship for Young Adults With Self-Harm Behaviors Using a Quasi-Experimental Design.

BACKGROUND: Studies have shown that postdischarge care for self-harm patients is effective in reducing repeated suicidal behaviors. Little is known about whether volunteer support can help reduce self-harm repetition and improve psychosocial well-being. AIM: This study investigated the efficacy of volunteer support in preventing repetition of self-harm. METHOD: This study used a quasi-experimental design by assigning self-harm patients admitted to the emergency departments to an intervention group with volunteer support and treatment as usual (TAU) for 9 months and to a control group of TAU. Outcome measures include repetition of self-harm, suicidal ideation, hopelessness, and level of depressive and anxiety symptoms. RESULTS: A total of 74 cases were recruited (38 participants; 36 controls). There were no significant differences in age, gender, and clinical condition between the two groups at the baseline. The intervention group showed significant improvements in hopelessness and depressive symptoms. However, the number of cases of suicide ideation and of repetition of self-harm episodes was similar for both groups at the postintervention period. CONCLUSION: Postdischarge care provided by volunteers showed significant improvement in hopelessness and depression. Volunteers have been commonly involved in suicide prevention services. Further research using rigorous methods is recommended for improving service quality in the long term.

Integration of DNA copy number alterations and prognostic gene expression signatures in breast cancer patients.

PURPOSE: Several prognostic gene expression profiles have been identified in breast cancer. In spite of this progress in prognostic classification, the underlying mechanisms that drive these gene expression patterns remain unknown. Specific genomic alterations, such as copy number alterations, are an important factor in tumor development and progression and are also associated with changes in gene expression. EXPERIMENTAL DESIGN: We carried out array comparative genomic hybridization in 68 human breast carcinomas for which gene expression and clinical data were available. We used a two-class supervised algorithm, Supervised Identification of Regions of Aberration in aCGH data sets, for the identification of regions of chromosomal alterations that are associated with specific sample labeling. Using gene expression data from the same tumors, we identified genes in the altered regions for which the expression level is significantly correlated with the copy number and validated our results in public available data sets. RESULTS: Specific chromosomal aberrations are related to clinicopathologic characteristics and prognostic gene expression signatures. The previously identified poor prognosis, 70-gene expression signature is associated with the gain of 3q26.33-27.1, 8q22.1-24.21, and 17q24.3-25.1; the 70-gene good prognosis profile is associated with the loss at 16q12.1-13 and 16q22.1-24.1; basal-like tumors are associated with the gain of 6p12.3-23, 8q24.21-22, and 10p12.33-14 and losses at 4p15.31, 5q12.3-13.1, 5q33.1, 10q23.33, 12q13.13-3, 15q15.1, and 15q21.1; HER2+ breast show amplification at 17q11.1-12 and 17q21.31-23.2 (including HER2 gene). CONCLUSIONS: There is a strong correlation between the different gene expression signatures and underlying genomic changes. These findings help to establish a link between genomic changes and gene expression signatures, enabling a better understanding of the tumor biology that causes poor prognosis.