Gaming disorder has become a global concern and it could have a variety of health and social consequences. The trauma model has been applied to the understanding of different types of addictions as behavioral addictions can sometimes be conceptualized as self-soothing strategies to avoid trauma-related stressors or triggers. However, much less is known about the relationship between trauma exposure and gaming disorder.

To inform prevention and intervention strategies and to facilitate further research, we conducted the first scoping review to explore and summarize the literature on the relationship between trauma and gaming disorder.

A systematic search was conducted on the Web of Science, Scopus and ProQuest. We looked for original studies published in English that included a measure of trauma exposure and a measure of gaming disorder symptoms, as well as quantitative data regarding the relationship between trauma exposure and gaming disorder.

The initial search generated 412 articles, of which 15 met the inclusion criteria. All of them were cross-sectional studies, recruiting participants from both clinical and non-clinical populations. Twelve of them (80%) reported significant correlations between trauma exposure and the severity of gaming disorder symptoms (r = 0.18 to 0.46, p < 0.010). Several potential mediators, including depressive symptoms and dissociative experiences, have been identified. One study found that parental monitoring moderated the relationship between trauma and gaming disorder symptoms. No studies reported the prevalence of trauma or trauma-related symptoms among people with gaming disorder.

There is some evidence supporting the association between trauma and gaming disorder, at small to medium effect sizes. Future studies should investigate the mediators and moderators underlying the relationship between trauma and gaming disorder. The longitudinal relationship between trauma exposure and the development of gaming disorder should be clarified. A trauma-informed approach may be a helpful strategy to alleviate gaming disorder symptoms.

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Major Depressive Disorder (MDD) stands as a prevalent psychiatric condition within the general population. Despite extensive research efforts, the identification of definitive diagnostic biomarkers for depressive disorders remains elusive. Currently, machine learning methods are gaining prominence in the diagnosis of medical illnesses.

This study aims to construct a machine learning-based prediction model for Major Depressive Disorder (MDD) by harnessing diffusion tensor imaging (DTI) data.

The DTI datasets comprising MDD (N=83) and Healthy Control (N=70) groups were procured from the cohort study of Anxiety and Depression conducted at the National Center for Mental Health in South Korea. A machine learning method using a decision tree algorithm was employed to select relevant brain regions and establish a robust diagnostic model. Features associated with white matter (WM) tracts were chosen through recursive feature elimination.

Demographic characteristics, including age, sex, and handedness, displayed no significant differences between the MDD and Healthy Control groups. However, the total score of the Beck Depression Inventory was notably higher in individuals with MDD compared to Healthy Controls. A diagnostic model was crafted using the decision tree algorithms to distinguish between the two groups. The model demonstrated the following classification performance metrics: accuracy (65.6% ± 8.5), sensitivity (66.6% ± 12.5), and specificity (64.7% ± 13.6). Furthermore, through recursive feature elimination, specific neuroanatomical features tied to brain structures such as the inferior cerebellar peduncle, posterior thalamic radiation, cingulum (hippocampus), uncinate fasciculus, and tapetum were identified.

Despite of limited performance of classification, a machine learning-based approach could provide insights into the development of a diagnostic model for MDD using neuroimaging data. Furthermore, these features, derived from DTI-derived data, may have implications for understanding the neural underpinnings of major depressive disorder.

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New prevalence and time trend data from various Asian countries show that Eating Disorders (ED) are increasingly common in Asia. (Youl-Ri Kim. Int J Eat Disord. Dec 2020). A recent study estimating the prevalence of ED in Singapore found an alarming 6.2% screened positive for a clinical ED diagnosis, 19.5% were screened to be at high risk, and estimated the point prevalence of Anorexia Nervosa (AN) to be 0.9%. (Chua SN et al. Int J Eat Disord. Jan 2021).

The ED unit in Singapore General Hospital (SGH) was set up in 2003 as a National Treatment Programme for patients with ED.

Two local studies have been published to date on the demographics and clinical profile of patients with AN. The first study examined 126 patients from 1994 – 2002 (HY Lee et al. Singapore Med J 2005; 46(6): 275-281). The second study reported on 271 cases from our SGH ED unit from 2003-2010 (Kuek et al. SIngapore Med J 2015; 56(6): 324-328). There have been no further studies in the last decade.

1. 1. Study the demographics and clinical profile of patients who presented with AN to our ED unit from 2011-2022

2. 2. Compare our data with the 2 previous studies and examine for any changes and trends in the past 30 years.

We conducted a review of the ED unit new case registry at SGH from 2011-2022. A total of 910 patients were diagnosed with AN at presentation. The data was analysed with approval from the hospital instituitional review board.

A total of 910 cases presented with AN over 12 years. Comparing with the 2 previous studies, the number of new cases each year has continued to increase from <15 in the 1990s to hit a peak of 109 per year in 2022. 94% were females, with a mean presenting age of 19. 79.2% were Chinese, 5.2% were Indians and 2.9% were of Malay ethnicity. The Malay population continue to be under-represented whereas other ethnic groups continue to be over-represented, increasing from 3.2% to 7% in the previous studies to 11.1%. Referrals were mainly from tertiary healthcare intuitions accounting for 41.4% of cases. Self-referrals have decreased over the last decade whereas referrals from primary care has increased. The mean presenting body mass index (BMI) was 15.9. Compared to a previous study, there was a significant increase in presenting BMI (15.9+/- 0.78 vs 14.4 +/- 1.77, p value 0.0074).

The number of new cases of AN has seen an almost 10-fold increase in the last 30years. The Malay ethnicity continues to be under-represented – more research is needed if they are somehow culturally protected or if they are not coming forth for treatment. Majority of referrals are from tertiary healthcare institutions but referrals from primary care have increased, reflecting a possible increase in awareness amongst primary care doctors. The mean presenting BMI has increased – hopefully reflecting an increase in ED awareness such that patients are coming forward earlier for treatment.

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Real-time reverse-transcriptase polymerase chain reaction (RT-PCR) has been the gold standard for diagnosing coronavirus disease 2019 (COVID-19) but has a lag time for the results. An effective prediction algorithm for infectious COVID-19, utilized at the emergency department (ED), may reduce the risk of healthcare-associated COVID-19.

To develop a prototypic prediction model for infectious COVID-19 at the time of presentation to the ED.

Retrospective cohort study of all adult patients admitted to Singapore General Hospital (SGH) through ED between March 15, 2020, and December 31, 2022, with admission of COVID-19 RT-PCR results. Two prediction models were developed and evaluated using area under the curve (AUC) of receiver operating characteristics (ROC) to identify infectious COVID-19 patients (cycle threshold (Ct) of <25).

Total of 78,687 patients were admitted to SGH through ED during study period. 6,132 of them tested severe acute respiratory coronavirus 2 positive on RT-PCR. Nearly 70% (4,226 of 6,132) of the patients had infectious COVID-19 (Ct<25). Model that included demographics, clinical history, symptom and laboratory variables had AUROC of 0.85 with sensitivity and specificity of 80.0% & 72.1% respectively. When antigen rapid test results at ED were available and added to the model for a subset of the study population, AUROC reached 0.97 with sensitivity and specificity of 95.0% and 92.8% respectively. Both models maintained respective sensitivity and specificity results when applied to validation data.

Clinical predictive models based on available information at ED can be utilized for identification of infectious COVID-19 patients and may enhance infection prevention efforts.

Population-wide restrictions during the COVID-19 pandemic may create barriers to mental health diagnosis. This study aims to examine changes in the number of incident cases and the incidence rates of mental health diagnoses during the COVID-19 pandemic.

By using electronic health records from France, Germany, Italy, South Korea and the UK and claims data from the US, this study conducted interrupted time-series analyses to compare the monthly incident cases and the incidence of depressive disorders, anxiety disorders, alcohol misuse or dependence, substance misuse or dependence, bipolar disorders, personality disorders and psychoses diagnoses before (January 2017 to February 2020) and after (April 2020 to the latest available date of each database [up to November 2021]) the introduction of COVID-related restrictions.

A total of 629,712,954 individuals were enrolled across nine databases. Following the introduction of restrictions, an immediate decline was observed in the number of incident cases of all mental health diagnoses in the US (rate ratios (RRs) ranged from 0.005 to 0.677) and in the incidence of all conditions in France, Germany, Italy and the US (RRs ranged from 0.002 to 0.422). In the UK, significant reductions were only observed in common mental illnesses. The number of incident cases and the incidence began to return to or exceed pre-pandemic levels in most countries from mid-2020 through 2021.

Healthcare providers should be prepared to deliver service adaptations to mitigate burdens directly or indirectly caused by delays in the diagnosis and treatment of mental health conditions.

Observer rating scales are necessary to evaluate the risk of suicide because individuals at risk for suicide are often unwilling to seek help on their own. Reliability and validity were evaluated for the newly developed Suicide Screening Questionnaire-Observer Rating (SSQ-OR).

Preliminary items were assessed by 251 experts online and 25 questions were selected. 328 individuals at high-risk and 661 controls from 12 Crisis Response Centers and 5 university counseling centers were recruited to complete SSQ-OR, Beck Scale for Suicide Ideation (BSSI) and Patient Health Questionnaire-9 (PHQ-9). In a 6 months follow-up, we reached out to 176 participants to ask whether they had experienced a suicidal thought, plan, or attempt since the baseline assessment. Cronbach's 慣, Mann-Whitney U test, Spearman's correlation, factor analyses, Receiver operating characteristic (ROC) analysis and logistic regression analysis were used to verify the SSQ-OR.

Structural validity was supported by a two-factor solution using exploratory and confirmatory factor analyses. Excellent model fit indices for the two-factor structure using exploratory factor analysis were confirmed (RMSEA = 0.033, TLI = 0.980, CFI = 0.983). The SSQ-OR demonstrated strong internal consistency. The concurrent validity based on the correlations with other self-reported indicators of suicidal potential-BSSI and PHQ-9- revealed substantial relationships. The high-risk group was effectively characterized by a cut-off point of 4, with a sensitivity of 0.73 and a specificity of 0.79. The SSQ-OR scores were significant predictors of suicidal thoughts and behaviors within 6 months.

The SSQ-OR exhibits sound psychometric properties, and could be used as a complement to a self-report or clinical-administered scale to screen suicide risk comprehensively.

Excessive and persistent fear of clusters of holes, also known as trypophobia, has been suggested to reflect cortical hyperexcitability and may be associated with mental health risks. No study, however, has yet examined these associations in representative epidemiological samples.

To examine the prevalence of trypophobia in a population-representative youth sample, its association with mental health and functioning, and its interaction with external stress.

A total of 2065 young people were consecutively recruited from a household-based epidemiological youth mental health study in Hong Kong. Trypophobia, symptoms of anxiety, depression and stress, and exposure to personal stressors were assessed. Logistic regression was used to assess the relationships between trypophobia and mental health. Potential additive and interaction effects of trypophobia and high stress exposure on mental health were also tested.

The prevalence of trypophobia was 17.6%. Trypophobia was significantly associated with severe symptoms of anxiety (odds ratio (OR) = 1.83, 95% CI = 1.32–2.53), depression (OR = 1.78, 95% CI = 1.24–2.56) and stress (OR = 1.68, 95% CI = 1.11–2.53), even when accounting for sociodemographic factors, personal and family psychiatric history, resilience and stress exposure. Dose–response relationships were observed, and trypophobia significantly potentiated the effects of stress exposure on symptom outcomes, particularly for depressive symptoms. Those with trypophobia also showed significantly poorer functioning across domains and poorer health-related quality of life.

Screening for trypophobia in young people may facilitate early risk detection and intervention, particularly among those with recent stress exposure. Nevertheless, the generally small effect sizes suggest that other factors have more prominent roles in determining recent mental health outcomes in population-based samples; these should be explored in future work.

Self-compassion (SC) describes an emotionally positive attitude extended toward ourselves when we suffer, consisting of three main components; self-kindness, common humanity, and mindfulness (Germer & Neff, 2013). SC entails being warm and understanding towards ourselves when encountering pain or personal shortcomings, rather than ignoring them or flagellating ourselves with self-criticism. SC also involves recognizing that suffering and failure are part of the shared human experience rather than isolating. In addition, SC requires taking a mindful approach to one’s feelings and thoughts, without judgment of them.

Self-compassion (SC) involves taking an emotionally positive attitude towards oneself when suffering. Although SC has positive effects on mental well-being as well as a protective role in preventing depression and anxiety in healthy individuals, few studies on white matter (WM) microstructures in neuroimaging studies of SC has been studied.

Magnetic resonance imaging data were acquired from 71 healthy participants with measured levels of SC and its six subscales. Mirroring network as WM regions of interest were analyzed using tract-based spatial statistics (TBSS). After the WM regions associated with SC were extracted, exploratory correlation analysis with the self-forgiveness scale, the coping scale, and the world health organization quality of life scale abbreviated version was performed.

We found that self-compassion scale (SCS) total scores were negatively correlated with the fractional anisotropy (FA) values of the superior longitudinal fasciculus (SLF) in healthy individuals. The self-kindness and mindfulness subscale scores of SCS were also negatively correlated with FA values of the same regions. The FA values of SLF related to SC were found to be negatively correlated with the total scores of self-forgiveness scale, and self-control coping strategy and confrontation coping strategy.

Our findings suggest that levels of SC and its self-kindness and mindfulness components may be negatively associated with DMN-related WM microstructures in healthy individuals. These less WM microstructures may be associated with positive personal attitudes, such as self-forgiveness, self-control and active confrontational strategies.

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Subclinical or subthreshold social anxiety (SSA) is associated with significant burden. Up to 20% of general population report subclinical social anxiety symptoms, which can change individual social, work functioning.

However, neural mechanisms of SSA have not been fully investigated in healthy individual yet. This study aimed to examine the relationship between gray matter volumes (GMVs) and SSA.

We enrolled a total of 57 healthy individuals with SSA. The General Anxiety Disorder-7 (GAD-7), Beck Depression Inventory-II (BDI-II), Beck Anxiety Inventory (BAI), and Albany Panic and Phobia Scale (APPQ) were evaluated. Freesurfer was applied to investigated the relationship between SSA and GMVs. Multiple regression models with age, sex, and total intracranial volume as covariates were performed. Pearson correlation analyses also investigated the exploratory correlations between the GMVs of the SSA-related regions and other psychological characteristics among healthy individuals.

Freesurfer voxel-wise correlational analyses showed a significant negative correlation between the SA scores of APPQ and gray matter volumes (GMVs) in the fusiform gyrus (FG). In addition, the GMVs in the FG were significantly negatively associated with the total GAD-7, BDI-II, BAI, and APPQ scores. Performance anxiety was significantly correlated with posterior cingulate gyrus, parahippocampal gyrus and fusiform gyrus.

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Our findings suggest that healthy individuals with SSA showed decreased GMVs in the FG and the GMVs of FG were associated with general anxiety and depression symptomatology.

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Patients with borderline personality disorder are often a challenge to the mental health system. Psychiatrists see people with BPD as manipulative, difficult to manage, annoying, unlikely to arouse sympathy, clinicians hold negative attitude towards personality disorder.

As the next generation of doctors, medical students’ perception of patients with personality disorder (PD) is critical.

Yet a systematic review of the literature shows this has not been studied.

The study aims to identify :

1) the understanding and perception of medical students about PD

2) factors that may relate to this knowledge and perception.

A focus group discussion (FGD) was conducted with eight medical students in their sixth year at Amoud University, Somaliland.

A case vignette of a patient with typical Borderline PD symptoms was presented to stimulate discussion. Barts Explanatory Model Inventory (BEMI) was used to explore the issue.

The FGD was conducted via MS teams, recorded, transcribed, translated and thematically analysed

The Medical students showed reasonably accurate knowledge regarding Borderline PD, recognising features of unstable mood, impulsiveness, and emptiness. Of note half the participants believed religious intervention would be helpful “I believe in Islam. So,basically so to some degree it could be managed in certain religious centers”. Importantly, medical students, when asked to divest of their professional identity, and to describe their personal views associated PD with madness.

The views of PD as ‘madness’ and that religious intervention has a role have important implications for training and service development.

The importance of a culturally sensitive training to Medical students regarding PD to match local cultural and religious views, and consideration of development of health services which are sensitive to religious practice is highlighted.

We recommend including social and cultural implications in the training of medical students to better prepare them for the complexity of managing PD.

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Childhood adversities (CAs) predict heightened risks of posttraumatic stress disorder (PTSD) and major depressive episode (MDE) among people exposed to adult traumatic events. Identifying which CAs put individuals at greatest risk for these adverse posttraumatic neuropsychiatric sequelae (APNS) is important for targeting prevention interventions.

Data came from n = 999 patients ages 18–75 presenting to 29 U.S. emergency departments after a motor vehicle collision (MVC) and followed for 3 months, the amount of time traditionally used to define chronic PTSD, in the Advancing Understanding of Recovery After Trauma (AURORA) study. Six CA types were self-reported at baseline: physical abuse, sexual abuse, emotional abuse, physical neglect, emotional neglect and bullying. Both dichotomous measures of ever experiencing each CA type and numeric measures of exposure frequency were included in the analysis. Risk ratios (RRs) of these CA measures as well as complex interactions among these measures were examined as predictors of APNS 3 months post-MVC. APNS was defined as meeting self-reported criteria for either PTSD based on the PTSD Checklist for DSM-5 and/or MDE based on the PROMIS Depression Short-Form 8b. We controlled for pre-MVC lifetime histories of PTSD and MDE. We also examined mediating effects through peritraumatic symptoms assessed in the emergency department and PTSD and MDE assessed in 2-week and 8-week follow-up surveys. Analyses were carried out with robust Poisson regression models.

Most participants (90.9%) reported at least rarely having experienced some CA. Ever experiencing each CA other than emotional neglect was univariably associated with 3-month APNS (RRs = 1.31–1.60). Each CA frequency was also univariably associated with 3-month APNS (RRs = 1.65–2.45). In multivariable models, joint associations of CAs with 3-month APNS were additive, with frequency of emotional abuse (RR = 2.03; 95% CI = 1.43–2.87) and bullying (RR = 1.44; 95% CI = 0.99–2.10) being the strongest predictors. Control variable analyses found that these associations were largely explained by pre-MVC histories of PTSD and MDE.

Although individuals who experience frequent emotional abuse and bullying in childhood have a heightened risk of experiencing APNS after an adult MVC, these associations are largely mediated by prior histories of PTSD and MDE.