Mental disorder may affect individual’s ability to operate the motor vehicle. Previous studies have found that patient’s negative emotions may trigger aggressive driving behaviors. Thus, efficiently evaluating the correlation between emotions and driving behaviors in individuals with mental disorders has been drawn emphasis.

To explore the related factors of fitness-to-drive of individuals with mental disorders, to determine the application value of traffic psychology scales in assessment for fitness-to-drive of individuals with mental disorders, and to help establish consummate and effective assessment systems.

One hundred individuals with mental disorders were enrolled as the patient group, and 100 healthy individuals were enrolled as the control group. Positive and Negative Syndrome Scale (PANSS) was used to assess the psychiatric symptoms of the patient group. Driver Profile of Mood States (DPOMS), Driver Anger Scale (DAS), and Driving Behavior Scale (DBS) were used to evaluate the performance during driving within two groups. T-test were used to compare the differences in each factor score of traffic psychology scales within two groups. Pearson’s correlation analysis was used to calculate the correlation between scores of PANSS and scores of traffic psychology scales of the patient group.

The patient group had significantly higher score of driving function deficit in DBS than the control group (t=2.48, P<0.05), but scores of hostile gestures, impolite driving, overly cautious behaviors in DBS and total score of DAS showed the opposite (P<0.05). Positive syndrome in PANSS was positively related to traffic congestion in DAS (r = 0.315, P < 0.05). Anger in DPOMS was positively related to driving function deficit (r = 0.488, P < 0.01) and hostile behaviors in DBS (r = 0.510, P < 0.01), whereas it was negatively related to overly cautious behaviors in DBS (r = -0.417, P < 0.05). Anxiety and depression were also related to some factors in DAS and DBS.

The study found the practical application value of DPOMS, DAS, and DBS in assessment for fitness-to-drive of individuals with mental disorders. Patient’s anger in specific traffic situations such as traffic congestion may be mainly related to their positive syndrome. Patient’s anger may be a trigger of aggressive driving behaviors, and other emotions such as anxiety and depression also play important roles. Patient’s aggressive driving behaviors may be attributed to the compounding of many negative emotions.

S. Wang: None Declared, X. Ling: None Declared, Q. Zhang: None Declared, H. Li Grant / Research support from: This study was supported by National Key R & D Program of China [grant number 2022YFC3302001], National Natural Science Foundation of China [grant number 81801881], Science and Technology Committee of Shanghai Municipality [grant numbers 20DZ1200300, 21DZ2270800, 19DZ2292700].

Violence is a major global health concern among patients with schizophrenia. However, the triggers of violent behavior remain unclear. In previous studies, familial risk factors are believed to be associated with mental disorders and violence. The relationship between parental bonding or childhood adversity and psychopathologic behavior (such as violence) has rarely been evaluated.

The study aimed to explore the relationship between violent behavior and childhood experience and to determine the role of the early child-parent bond in violence risk in patients with schizophrenia.

The study enrolled 287 patients with schizophrenia and 100 healthy controls. Patients were divided into 3 groups: patients with homicidal history (Group A), patients with violent behavior and without homicidal history (Group B) and patients without violent behavior (Group C). Childhood trauma questionnaire (CTQ), parental bonding instrument (PBI) and modified overt aggression scale (MOAS) were used to explore the violent behavior and childhood experience. All individuals participated voluntarily and provided informed consent. This study was approved by the ethics committee of the Academy of Forensic Science.

The findings indicated the proportion of males to be higher in the patient groups than in the healthy controls, especially in the group with homicidal history. Patients had a significantly higher prevalence of sexual abuse, emotional abuse and emotional neglect than the healthy controls. The emotional abuse and emotional neglect were found to be positively and negatively related to MOAS scores. Maternal over protection was found to be negatively related to the MOAS scores. On the CTQ subscales, emotional neglect was significantly associated with violence risk (OR=1.13, 95% CI=1.04–1.22). On the PBI subscales, maternal and paternal care (0.84, 0.74–0.94 and 1.30, 1.13–1.49) and over protection (1.18, 1.07–1.29 and 0.87, 0.81-0.95) were found to be significantly associated with violence risk. Maternal and paternal over protection were significantly associated with homicide risk (0.87, 0.78-0.97 and 1.10, 1.01-1.20).

The schizophrenia patients with violence might suffer lower paternal care and emotional abuse during the childhood. In terms of violence in schizophrenia patients, paternal over protection and maternal care might be a protective factor and emotional neglect, maternal over protection and paternal care might be a risk factor. In terms of homicide in schizophrenia patients, paternal over protection might be a risk factor and maternal over protection might be a protective factor. Therefore, childhood trauma and parental care and over protection could be a potential reference indicator for assessing violence risk in patients with schizophrenia.

X. Ling: None Declared, S. Wang: None Declared, N. Li: None Declared, Q. Zhang: None Declared, H. Li Grant / Research support from: This study was supported by National Key R & D Program of China [grant number 2022YFC3302001], National Natural Science Foundation of China [grant number 81801881], Science and Technology Committee of Shanghai Municipality [grant numbers 20DZ1200300, 21DZ2270800, 19DZ2292700].

Schizophrenia is a severe psychiatric disorder affecting 50% of patients intermittently and 20% chronically, with high unemployment rates (80-90%) and reduced life expectancy. Although genetic and neurodevelopmental factors are established non-modifiable risk factors, knowledge gaps persist regarding prevention strategies, particularly the combined impact of modifiable risk factors.

The aim of this study is to identify the modifiable risk factors and to estimate their joint effect on Schizophrenia.

We conducted an exposure-wide association study (EWAS) using the UK Biobank cohort to systematically evaluate 206 potentially modifiable factors associated with schizophrenia risk. The study population comprised individuals without schizophrenia at baseline, with diagnoses determined using ICD-10 criteria. We employed Cox proportional hazard regression models with Bonferroni correction (significance threshold: P<1.91×10-4) to identify significant factors. The identified factors were categorized into six domains: lifestyle, local environment, medical history, physical measures, psychosocial factors, and socioeconomic status (SES). Domain-specific, weighed, and standardized scores were calculated based on coefficients from Cox models, adjusting for covariates. Scores were stratified into tertiles (favorable, intermediate, unfavorable) for risk assessment. Population attributable fractions (PAFs) were calculated to quantify prevention potential.

The study cohort included 498,351 participants (54.45% female; mean age: 56.55 years) followed for a mean duration of 14.37 years, during which 1,345 participants developed schizophrenia. We identified 86 significant modifiable factors, with disability (HR 6.23, 95% CI 5.48-7.07), depression (HR 5.06, 95% CI 4.93-5.20), and anxiety disorders (HR 3.69, 95% CI 3.12-4.36) showing the strongest associations. Our analyses suggested that transitioning unfavorable profiles to intermediate and favorable status (Estimation 1) could prevent 59.6% of schizophrenia cases, while shifting both intermediate and unfavorable profiles to favorable (Estimation 2) could prevent 90.4% of cases. In Estimation 2, the preventive potential was highest for SES (18.0%), followed by medical history (17.5%), lifestyle factors (17.0%), psychosocial factors (14.3%), physical measures (12.8%), and local environment (10.8%).

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This analysis identifies multiple modifiable risk factors for schizophrenia, demonstrating substantial prevention potential through multi-domain interventions. Socioeconomic, medical, and lifestyle factors emerge as key targets for prevention strategies. The consistency of associations across genetic risk strata suggests interventions could be beneficial regardless of genetic predisposition, informing targeted prevention strategies and public health policies.

None Declared

Major depressive disorder (MDD) is a prevalent and disabling condition. Approximately 30-50% of patients do not respond to first-line medication or psychotherapy. Therefore, several studies have investigated the predictive potential of pretreatment severity rating or neuroimaging features to guide clinical approaches that can speed optimal treatment selection.

To evaluate the performance of 1) severity ratings (scores of Hamilton Depression/Anxiety Scale, illness duration, and sleep quality, etc.) and demographic characteristic and 2) brain magnetic resonance imaging (MRI) features in predicting treatment outcomes for MDD. Second, to assess performance variations among varied modalities and interventions in MRI studies.

We searched studies in PubMed, Embase, Web of Science, and Science Direct databases before March 22, 2023. We extracted a confusion matrix for prediction in each study. Separate meta-analyses were performed for clinical and MRI studies. The logarithm of diagnostic odds ratio [log(DOR)], sensitivity, and specificity were conducted using Reitsma’s random effect model. The area under curve (AUC) of summary receiver operating characteristic (SROC) curve was calculated.

Subgroup analyses were conducted in MRI studies based on modalities: resting-state functional MRI (rsfMRI), task-based fMRI (tbfMRI), and structural MRI (sMRI), and interventions: antidepressant (including selective serotonin reuptake inhibitors [SSRI]) and electroconvulsive therapy (ECT). Meta-regression was conducted 1) between clinical and MRI studies and 2) among modality or intervention subgroups in MRI studies.

We included ten studies used clinical features covering 6494 patients, yielded a log(DOR) of 1.42, AUC of 0.71, sensitivity of 0.61, and specificity of 0.74. In terms of MRI, 44 studies with 2623 patients were included, revealing an overall log(DOR) of 2.53. The AUC, sensitivity, and specificity were 0.89, 0.78, and 0.75.

Studies using MRI features had a higher sensitivity (0.89 vs. 0.61) in predicting treatment outcomes than clinical features (P < 0.001). RsfMRI had higher specificity (0.79 vs. 0.69) than tbfMRI subgroup (P = 0.01). No significant differences were found between sMRI and other modalities, nor between antidepressants (SSRIs and others) and ECT. Antidepressant studies primarily identified predictive imaging features in limbic and default mode networks, while ECT mainly focused on limbic network.

Our findings suggest a robust promise for pretreatment brain MRI features in predicting treatment outcomes in MDD, offering higher accuracy than clinical studies. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. For MRI studies, overlapping but distinct network level measures predicted outcomes for antidepressants and ECT.

None Declared

Obsessive-compulsive disorder (OCD) is a common psychiatric disorder. It is considered that dysregulation of cytokine levels is related to the pathophysiological mechanism of OCD. However, the results of previous studies on cytokine levels in OCD are inconsistent.

To perform a meta-analysis assessing cytokine levels in peripheral blood of OCD patients.

We searched in PubMed, Web of Science, and Embase from inception to March 31, 2023 for eligible studies. We conducted multivariate meta-analysis in combined proinflammatory cytokines (interleukin-6 [IL-6], IL-1β, IL-2, tumor necrosis factor-α [TNF-α], and interferon-γ [IFN-γ]) and combined anti-inflammatory cytokines (IL-10 and IL-4) respectively, and calculated the same meta-analysis in each cytokine. We also performed sensitivity analysis and publication bias tests, as well as subgroup analysis (i.e. different age groups, varied cytokine measurement methods, medication treated or naïve, and presence of psychiatric comorbidities) and meta-regression analysis (variables including patients’ sex ratio, age, age at symptom onset, illness duration, scores of Y-BOCS, family history of psychiatric disorders, and BMI).

17 original studies (13, 13, 10, 5, 4, 3, 2 studies for IL-6, TNF-α, IL-1β, IL-10, IL-2, IL-4, and IFN-γ, respectively), 573 patients (mean age, 25.2; 50.3% female) and 498 healthy controls (HC; mean age, 25.3; 51.4% female) were included. The results showed that the levels of combined pro- or anti-inflammatory cytokines and each signle cytokine were not significantly different between OCD patients and HC (all P>0.05), with significant heterogeneities in all analyses (I2 from 79.1% to 91.7%). We did not find between-group differences in cytokine levels in all subgroup analyses. Meta-regression analysis suggested that age at onset (P=0.0003) and family history (P=0.0062) might be the source of heterogeneity in TNF-α level. Sensitivity analysis confirmed that all results were stable, except for IL-4 where different cytokine measurement methods may be the contributing factor. Egger test did not find publication bias.

Our study showed no difference in cytokine levels between OCD patients and HC, but age at onset and family history may affect TNF-α level. Confounding factors such as age at onset, family history, and cytokine measurement methods should be controlled in future studies to further explore the immune mechanism of OCD.

None Declared

There’s large heterogeneity present in major depressive disorder (MDD) and controversial evidence on alterations of brain functional connectivity (FC), making it hard to elucidate the neurobiological basis of MDD. Subtyping is one promising solution to characterize this heterogeneity.

To identify neurophysiological subtypes of MDD based on FC derived from resting-state functional magnetic resonance imaging using large multisite data and investigate the differences in genetic mechanisms and neurotransmitter basis of FC alterations, and the differences of FC-related cognition between each subtype.

Consensus clustering of FC patterns was applied to a population of 829 MDD patients from REST-Meta-MDD database after data cleaning and image quality control. Gene transcriptomic data derived from Allen Human Brain Atlas and neurotransmitter receptor/transporter density data acquired by using neuromap toolbox were used to characterize the molecular mechanism underlying each FC-based subtype by identifying the gene set and neurotransmitters/transporters showing high spatial similarity with the profiles of FC alterations between each subtype and 770 healthy controls. The FC-related cognition in each subtype was also selected by lasso regression.

Two stable neurophysiological MDD subtypes were found and labeled as hypoconnectivity (n=527) and hyperconnectivity (n=299) characterized by the FC differences in each subtype relative to controls, respectively. The two subtypes did not differ in age, sex, and scores of Hamilton Depression/Anxiety Scale.

The genes related to FC alterations were enriched in ion transmembrane transport, synaptic transmission/organization, axon development, and regulation of neurotransmitter level for both subtypes, but specifically enriched in glial cell differentiation for hypoconnectivity subtype, while enriched in regulation of presynaptic membrane and regulation of neuron differentiation for hyperconnectivity subtype.

FC alterations were associated with the density of 5-HT2a receptor in both subtypes. For hyperconnectivity subtype, FC alterations were also correlated with the density of norepinephrine transporter, glutamate receptor, GABA receptor, 5-HT1b receptor, and cannabinoid receptor.

Both subtypes showed correlations between FC and categorization, motor inhibition, and localization. The FC in hypoconnectivity subtype correlated with response inhibition, selective attention, face recognition, sleep, empathy, expertise, uncertainty, and anticipation, while that was related to inference, speech perception, and reward anticipation in hyperconnectivity subtype.

Our findings suggested the presence of two neuroimaging subtypes of MDD characterized by hypo or hyper-connectivity. The two subtypes had both shared and distinct genetic mechanisms, neurotransmitter receptor/transporter profiles, and cognition types.

None Declared

Following limited clinical exposure during the coronavirus disease 2019 pandemic, a simulation-based platform aimed at providing a unique and safe learning tool was established. The aim was to improve the skills, knowledge and confidence of new ENT doctors.

The course was developed through 5 iterations over 28 months, moving from a half-day session to 2 full-day courses with more scenarios. Participant, faculty and local simulation team feedback drove course development. High-fidelity scenarios were provided, ranging from epistaxis to stridor, using technology including SimMan3 G mannequin, mask-Ed™ and nasendoscopy simulators.

Participant feedback consistently demonstrated that the knowledge and skills acquired enhanced preparedness for working in ENT, with impact being sustained in clinical practice.

Preparing healthcare professionals adequately is essential to enhancing patient safety. This simulation course has been effective in supporting new doctors in ENT and has subsequently been rolled out at a national level.

Major depressive disorder (MDD) is characterized by both clinical symptoms and cognitive deficits. Prior studies have typically examined either symptoms or cognition correlated with brain measures, thus causing a notable paucity of stable brain markers that capture the full characteristics of MDD. Brain controllability derived from newly proposed brain model integrating both metabolism (energy cost) and dynamics from a control perspective has been considered as a sensitive biomarker for characterizing brain function. Thus, identifying such a biomarker of controllability related to both symptoms and cognition may provide a promising state monitor of MDD.

To assess the associations between two multi-dimensional clinical (symptoms and cognition) and brain controllability data of MDD in an integrative model.

Sparse canonical correlation analysis (sCCA) was used to investigate the association between brain controllability at a network level and both clinical symptoms and cognition in 99 first-episode medication-naïve patients with MDD. The potential mediation effect of cognition on relationship between controllability and symptoms was also tested.

Average controllability was significantly correlated with both symptoms and cognition (rmean=0.54, PBonferroni=0.03). Average controllability of dorsal attention network (DAN) (r=0.46) and visual network (r=0.29) had the highest correlation with both symptoms and cognition. Among clinical variables, depressed mood (r=-0.23) , suicide(r=-0.25), work and activities(r=-0.27), gastrointestinal symptoms (r=-0.25) were significantly negatively associated with average controllability, while cognitive flexibility (r=0.29) was most strongly positively correlated with average controllability. Additionally, cognitive flexibility fully mediated the association between average controllability of DAN and depressed mood (indirect effect=-0.11, 95% CI [-0.18, -0.04], P=0.001) in MDD.

Brain average controllability was correlated with both clinical symptoms and cognition in first-episode medication-naïve patients with MDD. The results suggest that average controllability of DAN and visual network reached high associations with clinical variates in MDD, thus these brain features may serve as stable biomarkers to control the brain functional states transitions to be relevant to cognitions deficits and clinical symptoms of MDD. Additionally, altered average controllability of DAN in patients could induce impairment of cognitive flexibility, and thus cause severe depressed mood, indicating that controllability of DAN may be a potential intervention target for alleviating depressed mood through improving cognitive flexibility in MDD.

None Declared