Risperidone In Situ Microimplants (ISM®), a novel long-acting injectable (LAI) antipsychotic, rapidly achieves therapeutic plasma levels, with a significant first plasma peak occurring at 48 hours. This rapid onset of action, without the need for oral supplementation or loading doses, offers a promising new approach for effective management of acute symptoms (Walling et al. Drug Des Dev Ther 2021;15 4371-4382).

To evaluate the efficacy and safety of risperidone ISM (R-ISM) in the treatment of acute manic episodes with psychotic symptoms, focusing on symptom change over time.

This preliminary and retrospective study included 15 inpatients with schizoaffective disorder who were started on R-ISM during a manic episode. RISM was administered after 6 days of oral risperidone. We retrospectively examined the Young Mania Rating Scale (YMRS) scores obtained in routine clinical practice at baseline (Dx), on the day of inpatient admission, on the day of injection (D0) and then 24 hours (D1), 48 hours (D2), 7 days (D8) and 28 days (D28) after the injection.

A statistically significant improvement in the total YMRS score was observed as early as the day of injection, with the median score decreasing from 37 [IQR: 4.5] at baseline to 27 [IQR: 2.5] at D0 and further to 20 [IQR: 4] at 24 hours after the injection 1 (D1) (p < 0.01). The improvement remained statistically significant at all assessment time points, reaching 11 [IQR: 1.5] at day 28 (D28) (see Image 1).

Single-item analysis showed rapid and significant improvement across all YMRS items, in particular the following symptoms (see Image 2):

• - Irritability: Significantly decreased from a score of 6 [IQR: 0] at baseline to 3 [IQR: 0.5] at D1 and to 1 [IQR: 0.5] at D28 (p < 0.01).

• - Disruptive-Aggressive Behavior: Significantly decreased from a score of 3 [IQR: 2] at baseline to 1 [IQR: 1] at D1 and to 0 [IQR: 1] at D28 (p < 0.01).

• - Sleep Disturbances: Significantly decreased from a score of 3 [IQR: 1] at baseline to 0 [IQR: 0.5] at D28 (p < 0.01).

• - Speech (Rate and Amount): Significantly decreased from a score of 4 [IQR: 1] at baseline to 1 [IQR: 0] at D28 (p < 0.01).

• - Content (Delusions; Hallucinations): Significantly decreased from a score of 6 [IQR: 0.5] at baseline to 3 [IQR: 1] at D28 (p < 0.01).

• - Insight: Significantly decreased from a score of 3 [IQR: 2] at baseline to 2 [IQR: 1] at D28 (p<0.01).

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This preliminary and retrospective study suggests the possible efficacy of risperidone ISM (approved for schizophrenia) for acute manic episodes. However, due to the retrospective design of the study, the small sample size, and the presence of concomitant treatments, the results are primarily exploratory and no conclusions can be drawn until prospective, randomized, placebo-controlled trials are conducted.

None Declared

Artificial intelligence (AI) language models are increasingly accessible tools that offer potential support in mental health care. Despite their promise in revolutionizing mental health care through symptom assessment and treatment suggestions, concerns about their validity, accuracy, ethical considerations, and risk management persist. This study evaluates the clinical reasoning capabilities of two leading AI language models in assessing a clinical case vignette of Major Depressive Disorder (MDD).

To evaluate the diagnostic accuracy, risk assessment proficiency, and quality of treatment recommendations provided by ChatGPT and Claude when applied to a standardised clinical vignette of a case of MDD.

A clinical vignette describing a 50-year-old male patient exhibiting symptoms consistent with MDD was presented to both ChatGPT 4o and Claude 3.5 Sonnet. The patient had significant cardiac disease, leading to unemployment, social withdrawal, and passive suicidal ideation. Both AI models were asked five identical questions regarding: (1) diagnosis, (2) severity assessment, (3) first-line treatment recommendations, (4) optimal antidepressant selection, and (5) suicide risk evaluation. Two psychiatrists independently reviewed the responses for accuracy, comprehensiveness, and alignment with established guidelines and evidence-based treatment for depression with comorbid cardiac disease.

Both AI models correctly diagnosed MDD and accurately recognized the severity of the case due to the presence of suicidal ideation and significant functional impairment. Both offered comprehensive treatment recommendations, including pharmacotherapy and psychotherapy, and specifically suggested Sertraline as the antidepressant of choice due to its favourable cardiac safety profile. Both models assessed the patient as having a moderate to high suicide risk and provided a reasonably thorough analysis of risk and protective factors. However, limitations were noted in their ability to incorporate individualized patient nuances and psychosocial factors fully.

ChatGPT 4o and Claude 3.5 Sonnet demonstrated significant capabilities in clinical reasoning, providing diagnoses and treatment recommendations that align with best clinical practices. Their responses were largely accurate and comprehensive, indicating potential utility as supportive tools for healthcare professionals. AI models may assist non-specialists in preliminary assessment and management but are not substitutes for professional psychiatric evaluation. Caution is advised in relying on AI for clinical decision making, and further refinement is necessary to enhance their ability to integrate patients-centered care and adherence to ethical guidelines, to mitigate risks associated with self-diagnosis and inappropriate treatment.

None Declared

Eating disorders (ED) can sometimes present with psychotic symptoms, including delusions and cenesthetic or auditory hallucinations. In a minority of patients, these symptoms may stem from an underlying psychotic disorder, such as schizophrenia, which is more common in males (Bou Khalil R, Hachem D, Richa S. (2011). Eating disorders and schizophrenia in male patients: a review. Eat Weight Disord, 16(3), 150-6). Therefore, early detection and intervention are critical in cases where EDs are accompanied by prodromal or attenuated psychotic symptoms.

To present the clinical case of a 14-year-old male with an unspecified eating disorder and high-risk mental state for psychosish

To highlight the importance of early identification and intervention in eating disorders with psychotic features.

A Pubmed database was used to collect information about psycothic symptoms in EDs, using the terms ‘eating disorder’, ‘pyscosis’ and ‘high risk mental state’.

We present the following clinical case:

A 14-year-old spanish male of Bolivian descent. The patient exhibited a two-year history of food restriction, vigorous exercise, social isolation, and absenteeism from school. Detailed clinical evaluations were performed, documenting his physical, psychological, and behavioral symptoms. The patient’s diagnosis was reevaluated based on emerging psychotic symptoms during hospitalization.

The patient reported intense distress about perceived fat accumulation in his face and trunk, which he believed diminished immediately after exercise. He engaged in excessive physical activity, including jumping rope for at least two hours multiple times a day, and swimming against currents. He also experienced episodes of binge eating followed by purging and compensatory exercise. Social withdrawal, emotional blunting, disorganized biological rhythms, and soliloquies were observed during his admission. Based on these findings, his diagnosis was revised to an unspecified ED with a high-risk mental state for psychosis.

Psychotic symptoms, particularly in restrictive anorexia, can arise during the course of an ED, with malnutrition acting as both a cause and sustaining factor by exacerbating serotonin-dopamine dysregulation (Sarró, S (2018). Those courageous boys: 73 years after the Minnesota starvation experiment. A psychiatrist’s view. Neurosciences and History, 6(1), 28-37). While these symptoms may result from malnutrition, they could also signal the onset of a primary psychotic disorder, with males at higher risk (3.6%). Early detection of attenuated or prodromal psychotic symptoms is essential, and regular reevaluation is recommended, especially during the first months of follow-up, to prevent short-, medium-, and long-term complications.

None Declared

The detection of electrophysiological markers to differentiate patients with suicide attempts could support the efforts of clinicians to provide proper diagnosis and treatment for them, and finally reduce the suicide mortality rate.

The objective of the current study was to investigate and compare cortical functional networks from resting-state electroencephalogram in patients with suicide attempts and suicidal ideation using source-level weighted network analysis. We also examined the possibility of network features serving as biomarkers by differentiation between suicide attempts and suicidal ideation using machine learning techniques.

This study investigated source-level cortical functional networks using resting-state electroencephalography in drug-naïve depressed patients with suicide attempt and suicidal ideation. Electroencephalogram was recorded in 55 patients with suicide attempts and in 54 patients with suicidal ideation. Graph-theory-based source-level weighted functional networks were assessed using strength, clustering coefficient (CC), and path length (PL) in seven frequency bands. Finally, we applied machine learning to differentiate between the two groups using source-level network features.

There were significant differences in the three global level indices of the high alpha band. The strength and CC of the high alpha band were significantly lower in patients with suicide attempts than in those with suicide attempts ideation. In contrast, the PL of the high alpha band was significantly higher in patients with suicide attempts than in those with suicide ideation. No significant differences in the other frequency bands were between the two groups.

The nodal CCs of patients with suicide attempts were significantly lower than those of patients with suicide ideation in most regions, except for three of the 68 regions.

The best classification performance between suicide attempts and suicide ideation was an accuracy of 73.39%, a sensitivity of 76.36%, and a specificity of 70.37% with 17 features. Three of these features were global-level network indices (strength, CC, and PL) in the high alpha band; the other 14 features were high alpha band nodal level CCs in limbic, frontal, temporal, parietal,and occipital area.

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This is the first study to use electroencephalogram source level network measures, revealing that network indices for high alpha band could be potential biomarkers to distinguish betweensuicide attempts and suicide ideation in patients with depression. Moreover, our study evaluated the electroencephalogram signals immediately after fatal suicide attempts in a relatively large number of un-medicated patients.

None Declared

The differential diagnosis between Major Depressive Disorder (MDD) and Bipolar Disorder (BD) heavily relies on clinical observation. However, the two disorders often show similar symptomatologic profiles, leading to high misdiagnosis rates. Reliable biomarkers are therefore crucial to accurately discriminate between MDD and BD and provide better treatments. In this regard, Machine Learning (ML) could represent a turning point in the field precision psychiatry, given its capability of making single-subject level predictions.

In the present work, we aimed at providing a biomarker-based differential diagnosis between MDD and BD. To that end, we implemented: i) a structural MRI-based ML model; ii) a combined ML model, trained on MRI data and Polygenic Risk Scores (PRS) for different psychiatric disorders.

168 depressed patients (73 MDD, 95 BD) were recruited at the IRCCS San Raffaele Scientific Institute. All patients underwent T1-weighted and Diffusion Tensor Imaging scans. Voxel-Based Morphometry (VBM) measures were extracted with Computational Anatomy Toolbox 12 (CAT12). Fractional Anisotropy (FA), Axial Diffusivity (AD), Mean Diffusivity (MD), and Radial Diffusivity (RD) were extracted with Tract-Based Spatial Statistics (TBSS). PRS for MDD, BD, Schizophrenia, Attention Deficit/Hyperactivity Disorder, Anorexia Nervosa and Autism were computed for a subsample of 155 patients (67 MDD; 88 BD) through Infinium PsychArray 24 BeadChip. We trained a Multiple Kernel Learning (MKL) algorithm with voxel-wise VBM and DTI features, subsequently combining them with the extracted PRS.

The neuroimaging model achieved a Balanced Accuracy (BA) of 71.65% and an Area Under the Curve (AUC) of 0.77 (85.44% sensitivity, 57.86% specificity). All the features contributed to the prediction, with AD (63%) and MD (26%) as the most predictive. Adding PRS to neuroimaging resulted in an improved performance, reaching 74.18% BA and 0.77 AUC (90.97% sensitivity, 57.38% specificity). The most predictive features of the neuroimaging-PRS model were MD (56%) and AD (27%).

Structural MRI discriminated between MDD and BD, and adding PRS to neuroimaging features improved the performance of the ML model. These results highlight the predictive power of structural neuroimaging for the differential diagnosis between MDD and BD, as well as prompting multimodal classifiers as a promising tool in the context of precision psychiatry.

None Declared

As a complementary or stand-alone treatment, sport and exercise therapy (SET) can have a therapeutic effect on the symptoms of mental illness as well as having a therapeutic or preventive effect on physical comorbidities. Therefore, treatment guidelines recommend the integration of exercise therapy as a complementary approach in a multimodal treatment. In a clinical inpatient setting SET are supervised by specialised professionals and conducted in an individual or group setting. Low level of participation in SET during treatment points to the need for research into influencing factors. One study suggests, that SET during mental health treatment increases the likelihood of meeting the physical activity recommendations.

The purpose of the study is to investigate the extent to which SET can increase patients’ levels of physical activity during inpatient treatment and, in particular, promote a physically active lifestyle after inpatient treatment, thereby supporting long-term stabilisation. In order to gain further insight into the factors that influence participation in SET during treatment, this study analyses both intrapersonal and interindividual factors.

Patients (age ≥ 18 years, all genders) in partial- or full-time inpatient treatment at a psychotherapeutic and psychosomatic specialist clinic in Lower Saxony, Germany are examined by online self-report questionnaire. It’s a longitudinal study design with 3 time points (start of treatment, end of treatment, 12 weeks after the end of treatment). Patients participate in SET as part of their treatment. Physical activity in minutes per week and the therapeutic alliance between exercise therapists and patients are measured. In addition, self-efficacy expectations, sport- and exercise-related self-concordance and the subjectively perceived effectiveness of SET are assessed as further factors influencing physical activity.

The results of the inferential-static data analysis will be reported.

Based on the results, possible implications for the focus of SET and the role of exercise therapists are discussed. Conclusions based on motivational aspects of maintaining a physically active lifestyle after the end of treatment are considered.

None Declared

A resilience-based approach should be more integrated in order to get a greater understanding of the psychopathological patterns and derive prevention or intervention implications from this (Kalisch et al. Nat Hum Behav 2019; 1(11) 784-790). In subthreshold psychopathology, so far there is a growing body of research focusing on potential risk and protective factors while most of these studies are following an isolated focus on either of those factors. Or are using statistical methods that are not often considered the dynamic interplay of those variables (Pereira-Morales et al. J Ment Health 2019; 28(2) 153-160; Schäfer et al. Transl Psychiatry 2023; 13(1) 328). Scruitinizing the dynamic patterns enables the network approach. Mental disorders can be conceived as a complex network, involving a dynamic interplay between symptoms and protective factors (Boorsboom et al. Nat Rev Methods Primers 2021; 1:58).

This study investigates the role of risk and protective factors in relation to subthreshold psychosis like-experience symptoms (schizotypy, mistrust and anomalous perceptual experience) in a network structure.

This cross-sectional analysis of the prospective longitudinal ZInEP Epidemiology Survey included n = 632 participants (general population), aged 20-41 years. Dynamic relationships between potential risk factors (child hood trauma, maladaptive coping, self-stigma, perceived stress, chronic stress), potntial protective factors (adaptive coping, self-efficacy, optimism, self-confidence, self-control, spirituality) and psychopathology (schizotypy, mistrust, anomalous perceptual experience) are investigated using network analysis at baseline.

• • negative association of schizotypy with optimism and self-control

• • negative association between mistrust and self-control

• • positive association of schizotypy with perceived and chronic stress, maladaptive coping and childhood trauma

• • perceived stress highly negatively assiociated with optimism and self-efficacy

• • maladaptive coping as a bridge from potential protective factors to perceived and chronic stress and schizotypy

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• • optimism and self-control as protective factors for schizotypy and mistrust

• • perceived and chronic stress, maladaptive coping and childhood trauma as risk factors associated with all psychopathological symptoms

• • protective factors might have more an indirect impact over risk factors on symptoms

• • interventions for optimism and self-control might reduce stress

None Declared

Pharmacogenomic testing is a cutting-edge precision medicine tool that analyzes genetic variations influencing drug metabolism. By assessing an individual’s unique genetic profile, this testing enables the personalization of treatment strategies, improving therapeutic outcomes, and enhancing patient care. Integrating pharmacogenomic testing into clinical practice holds great promise for improving the efficiency and effectiveness of mental health care delivery. In this case, a 17-year-old patient presented with a severe case of obsessive-compulsive disorder showed no response to treatment with sertraline (250mg). Sertraline is metabolized into N-desmethylsertraline through multiple pathways, including CYP3A4, CYP2C19, CYP2B6, and other CYP enzymes, with pharmacokinetic studies identifying CYP2C19 as the primary metabolic pathway.

The patient had a poor response to pharmacological treatments previosly used, our aim was to determine the possible involvement of patient specific responses to treatments based on his pharmacogenetic proffile.

A blood sample was submitted for pharmacogenetic testing. This analysis includes genes involved in the metabolism of sertraline (CYP2C19, CYP3A4, and, to a lesser extent, CYP2B6 and CYP2D6) as well as other pharmacogenes associated with the metabolism and response to psychiatric medications, including HTR2A, OPRM1, COMT, and DRD2.

Gene | Genotype | Inferred Phenotype

• - CYP2C19 | *1/*1 | Normal Metabolizer

• - CYP2B6 | *1/*1 | Normal Metabolizer

• - CYP2D6 | *3/*4 | Poor Metabolizer

• - CYP3A4 | *1/*1 | Normal Metabolizer

• - OPRM1 | AA | Normal Genotype

• - HTR2A (rs7997012 A>G) | GG | Normal Genotype

• - HTR2A (rs6311 G>A) | GA | Heterozygous

• - DRD2A (rs1799732 G>-) | GG | Normal Genotype

• - DRD2A (rs1799978 A>G) | TT | Normal Genotype

The patient did not exhibit clinically significant alterations in the metabolism of sertraline (CYP2C19, CYP3A4 and CYP2B6). However, the lack of response to treatment should be further investigated, factors such as potential drug interactions, and other variables including age, renal function, and liver function should be considered. In contrast, the patient has notable alterations in CYP2D6 and HTR2A, which could be important for guiding future treatment decisions. Variants in HTR2A can significantly influence a patient’s response to antidepressants, particularly selective serotonin reuptake inhibitors (SSRIs), specific polymorphisms in HTR2A, such as rs7997012 and rs6311 have been associated with differences in treatment outcomes, side effects, and remission rates. Has a CYP2D6 poor metabolizer, this patient may be at risk for higher drug levels and increased side effects when taking medications such as venlafaxine, fluoxetine, paroxetine (SSRIs), haloperidol, and risperidone.

None Declared

Earlier age at menarche has been associated with an increased risk of affective disorders, but also a later onset of schizophrenia in women, indicating a complex relationship between hormonal changes and mental health. This interplay highlights the importance of understanding how pubertal timing and estrogen exposure can influence both mood disorders and psychotic conditions throughout a person’s life.

Our study aimed to explore the relationship between age at menarche and severity of clinical presentation of patients pertaining to mood-psychosis continuum.

The study group consisted of a total of 109 female patients, 71 diagnosed with bipolar disorder and 38 diagnosed with schizophrenia. The dimensional assessment of psychosis and affective symptoms was done using the Schizo-Bipolar Scale (SBS), together with the severity of the symptom domains measured by the Brief Psychiatric Rating Scale (BPRS).

Age at menarche significantly correlated to score on SBS scale (r= 0.257,p=0.028), after controlling for confounders (age and body mass index). Other clinical characteristics or psychometric properties were not related to age at menarche in the group of BD and SCH patients perceived as a unified group, possibly pertaining to one continuum.

Pubertal timing may play a role in the severity of symptoms associated with bipolar disorder and schizophrenia. Further research is needed to elucidate the specific pathways linking age at menarche to symptom severity and to explore the potential implications for early intervention and treatment strategies targeting the interplay between hormonal factors and mental health outcomes in individuals with bipolar disorder and schizophrenia.

None Declared

Artificial intelligence (AI) is transforming psychiatric training and education by enhancing diagnostic accuracy, improving therapeutic decision-making, and personalizing learning experiences for trainees. AI-driven simulations, virtual patients, and natural language processing (NLP)-based assessments allow for more effective skill development in psychiatric diagnosis and psychotherapy. Machine learning models provide evidence-based guidance, reinforcing clinical reasoning and treatment strategies. Ethical considerations, including patient confidentiality and bias mitigation, remain central to AI implementation in training. This session explores the latest advancements in AI-driven psychiatric education, discussing practical applications, challenges, and future directions for integrating AI into clinical training programs.

AI, psychiatry, education, machine learning, clinical training

1. Ahmed, M., & Rush, A. J. (2023). Artificial intelligence in psychiatry: Current applications and future directions. Journal of Psychiatric Research, 157, 106-121.

2. Ryu, S., & Kim, H. (2022). AI-based learning tools for medical education: A systematic review. Medical Teacher, 44(5), 512-520.

3. Luxton, D. D. (2021). Ethical implications of AI in mental health care. Journal of Ethics in Mental Health, 12, 1-14.

None Declared

The management of elderly patients receiving polypharmacy presents significant clinical challenges due to age-related physiological changes, the prevalence of chronic conditions, and the potential for drug-drug interactions and adverse drug reactions (ADRs). Blood level assessments of medications provide a critical tool for optimizing pharmacotherapy in this population. Here we present the benefits of therapeutic drug monitoring (TDM) and pharmacokinetic assessments in the personalized management of elderly patients with polypharmacy. Regular monitoring of blood drug concentrations can identify drug-drug interactions, related toxicity risks, and adherence issues, thereby informing dosage adjustments to achieve optimal therapeutic outcomes.

G. Schoretsanitis Consultant of: Dexcel, Saladax, HLS Therapeutics, Speakers bureau of: Saladax, HLS Therapeutics, Lundbeck, Thermo Fisher.

Migrant people may constitute a vulnerable population with an increased risk of suicide-related behaviour due to the accumulation of multiple risk factors, such as migration-related stress, the history of traumatic experiences and socioeconomic situation in the country of immigration.

To study the prevalence of suicide attempts from migrant population in hospital emergency departments. Moreover, it aimed to study suicide-related outcomes, according to migration status.

Data from 754 patients (73.1% female; m= 40.23, sd= 15.72) with a recent suicide attempt from 10 Spanish hospitals were included. Assessment protocols were delivered within the 15 days after the index attempt. Suicide-related outcomes, clinical and sociodemographic factors were assessed by administering a wide range of clinical tools (C-SSRS, MINI, BIS-21, BSI, ACSS-FAD, CTQ).

One in four patients was foreign-born, mostly being from Latin American countries (74% of foreign-born patients). Foreign-born patients were younger, higher psychopathology symptom severity, child trauma scores (Figure 1), than their counterparts (p < .01). Higher proportion of employed people and lower amount of people receiving pension benefits, were found in the foreign-born group. No between-group differences were observed regarding suicide-related outcomes. Finally index attempt in foreign-born group was featured by using more lethal methods (p < .05) (Figure 2).

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Significant proportion of attempts attended in clinical settings may come from migrant people, mainly featured by child trauma history. Attempts from migrant populations may be featured by more lethal methods. Health care provision adjustment becomes mandatory to meet migrant people needs in current times.

None Declared

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social communication and interaction, as well as restrictive and repetitive stereotyped behaviors, with considerable variation in cognitive and adaptive functioning. Accordingly, behavioral symptoms observed in daily life are likely to vary across individuals.

The present study aimed to explore the heterogeneity in intellectual abilities and behavioral issues among individuals with ASD using Latent Profile Analysis (LPA). This study was conducted between 2020 and 2021, with approval from the Institutional Review Board (IRB) of SNUH.

A cross-sectional analysis was conducted on 66 children (ages 6-18) diagnosed with ASD. The following psychometric instruments were used: Autism Diagnostic Observation Schedule-2 (ADOS-2), Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV), Child Behavior Checklist 6-18 (CBCL 6-18), Social Responsiveness Scale (SRS).

As the 3-profile solution returned a significant BLRT value and model entropy was estimated to be 0.93, the LPA of the WISC-IV indices and ADOS-2 comparison scores revealed three profiles that were clinically meaningful and balanced in group sizes: Profile 1 (“Higher ASD score with intellectual disability”; ADOS = 6.25, VCI = 55.16, PRI = 52.34, WMI = 53.47, PSI = 50.31), Profile 2 (“Higher ASD score with borderline intelligence”; ADOS = 5.76, VCI = 78.59, PRI = 80.65, WMI = 80.82, PSI = 67.12), and Profile 3 (“Lower ASD score with above-average intelligence”; ADOS = 4.41, VCI = 105.53, PRI = 108.41, WMI = 106.35, PSI = 89.41). On the SRS subscales, Profile 3 showed significantly lower scores in Social Cognition, Social Communication, and Social Motivation compared to Profile 1 (F(2, 63) = 10.45, p <.001; F(2, 63) = 5.24, p < .01; F(2, 63) = 8.75, p < .001). Additionally, on the CBCL syndrome subscales, Profile 3 showed significantly lower problem behaviors in Withdrawal/Depression, Social Immaturity, and Attention Problems (F(2, 63) = 4.57, p <.05; F(2, 63) = 5.07, p < .01; F(2, 63) = 4.19, p < .01).

In the present study, Latent Profile Analysis (LPA) using IQ and ADOS scores identified three distinct profiles within children with ASD. The findings suggest that while high-functioning ASD has traditionally been defined by an IQ threshold of 70–75, further distinction between borderline and above-average intelligence ASD groups may be warranted. Furthermore, targeted interventions addressing negative emotions, such as depression, may be indicated for the ASD group with intellectual disability.

Y. K. Lim Financial Support for Research from:, Financial Support for Research from: This research was supported by the National Research Foundation (NRF) funded by the Korean Government (MSIT) (RS-2024-00397737), and co-funded by the National IT Industry Promotion Agency(NIPA), an agency under the MSIT and with the support of the Daegu Digital Innovation Promotion Agency (DIP), the organization under the Daegu Metropolitan Government., E. Chung: None Declared, B. N. Kim: None Declared

Schizophrenia is a severe mental condition marked by a progressive onset of symptoms. Early evaluation and proper management are necessary for improving long-term outcomes and reducing the disorder’s severity. Early detection of prodromal symptoms and the prompt initiation of treatment can substantially influence the evolution of the condition, resulting in improved prognoses and a better quality of life.

The paper examines methods for recognizing early indicators of schizophrenia and evaluates the effects of early intervention. The emphasis encompasses comprehending the prevalent prodromal symptoms linked to schizophrenia, assessing diverse early detection techniques, and analysing the advantages of prompt intervention on long-term results.

A comprehensive examination of existing literature and clinical investigations was performed to identify and delineate prevalent prodromal symptoms of schizophrenia, including social disengagement, cognitive impairments, and atypical thought processes. The assessment examined various early detection instruments, encompassing structured clinician interviews, self-report questionnaires, and neuroimaging methodologies. Furthermore, data from longitudinal studies was examined to ascertain how early intervention may impact the disorder’s course and enhance patient outcomes.

The review realized multiple significant prodromal signs, including social isolation and cognitive impairments. Multiple early detection instruments, including structured interviews and neuroimaging, proved helpful in identifying persons at elevated risk for developing schizophrenia. Timely intervention measures, integrating pharmacological therapies and psychosocial assistance, correlated with a substantial decrease in symptom severity and improved long-term results.

The management of schizophrenia necessitates early identification and intervention. The severity of the disorder and the prognosis can be significantly reduced by recognizing prodromal symptoms and administering effective treatment. In order to enhance recovery and mitigate the effects of schizophrenia on individuals and their families, clinicians should prioritize early detection and early treatment.

None Declared

There is a growing evidence that a presence of mild behavioral impairment (MBI) in geriatric patients with mild cognitive impairment (MCI) increases the risk of Alzheimer disease. However, neurobiological patterns underling such additional risk remains unclear.

We aimed to investigate structural cortical patterns in MCI patients that differentiate converters from non-converters to Alzheimer disease (AD) and to explore correlations of such patterns with mild behavioral impairment.

Thirty five right-handed geriatric patients with amnestic type of MCI (aMCI) were followed up during the period of 11.3±6.9 months and divided into converters to AD (n=11, mean age 74.6±7.5, 10 females) and non-converters (n=24, mean age 72.6±8.1, 18 females). Patients and matched healthy controls (n=17, mean age 72.3±7.2 years, 14 females) underwent structural 3T MRI at baseline. MRI images were processed via FreeSurfer 6.0 to quantify gray matter thickness for 68 cortical areas according to Desikan atlas. Cognitive status was assessed using the Montreal Cognitive Assessment (MoCA) scale, and the severity of mild behavioral impairment was assessed using the MBI-C (Mild Behavioral Impairment Checklist) at baseline.

Cortical thickness in the left inferior parietal lobe (IPL) and left middle temporal gyrus (MTG) were decreased in converters compared to both non-converters (IPL: F(1,30)=12.8, p=0.0012, Cohen’s d=−1.30; MTG: F(1,30)=12.8, p=0.0012; Cohen’s d=−1.30) and healthy controls (IPL: F(1,24)=11.4, p=0.0025, Cohen’s d=−1.33; MTG: F(1,24)=8.3, p=0.008; Cohen’s d=−1.15) (Image 1A,B).

Converters also showed larger baseline MBI-C scores compared to non-converters (13.8±12.0 vs 7.8±6.6; GML t=2.1, p=0.045) and no difference in baseline MoCA (21.8±4.0 vs 23.2±2.8; GML t=−1.4, p=0.19).

Baseline MBI-C scores in the whole aMCI group correlated negatively with cortical thickness both in the left IPL (R=−0.53, p=0.0011) and left MTG (R=−0.48, p=0.004) (Image 1C). No correlations between MoCA scores and cortical thickness were observed.

Image 1. A: Clusters of decreased cortical thickness according to atlas of Desikan et al. (2006) in converters compared to non-converters and healthy controls. B: Box-plots of cortical thickness in the left inferior parietal lobe and left middle temporal gyrus. C: Scatter plots of MBI-C scores and gray matter thickness in the left inferior parietal lobe and left middle temporal gyrus in the whole aMCI group (n = 35).

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The findings suggest that the decreased baseline cortical thickness in the left inferior parietal lobe and middle temporal gyrus with associated greater severity of mild behavioral impairment could be potential marker of increased risk of conversion to AD in aMCI patients.

The study was supported by RSF grant 24-15-00220

None Declared

Screening children for the potential development of psychosis is challenging, resulting in a suboptimal healthcare transition from pediatrics to psychiatry.

In this prospective study, we aimed to evaluate the predictive ability of the Child Psychosis-Risk Screening System (CPSS) for schizophrenia spectrum disorder (SSD) by observing the outcomes of pediatric and psychiatric outpatients.

A total of 478 outpatients aged 6–18 years visiting the pediatric and psychiatric departments of university and community hospitals were enrolled in this study. Assessments included the Child Behavior Checklist (CBCL) and clinical data (sex, age, birth month, chief complaint, diagnosis, abuse, bullying, and withdrawal). The CPSS calculated the risk of developing SSD using eight CBCL subscale scores. The presence of SSD was confirmed after one year. Receiver operating characteristic (ROC) curve analysis was used to evaluate the accuracy of the CPSS in predicting SSD onset. Light-gradient boosting machine (LightGBM) learning algorithm was used to calculate the importance of each clinical data point for SSD onset prediction.

ROC analysis demonstrated that CPSS showed adequate predictive power for determining SSD onset (area under the curve = 0.902, 95% confidence interval: 0.866–0.939). LightGBM revealed that the importance of the CPSS risk% in predicting SSD onset exceeded other variables, including the CBCL Thought Problems subscale.

The CPSS demonstrated adequate predictive power for SSD development and may serve as an objective adjunctive diagnostic method to screen children at risk for SSD who require early psychiatric referral. As a simple screening system utilizing common clinical practice CBCL data, we advocate CPSS implementation in pediatric-to-psychiatric healthcare.

None Declared

Menopause causes physiological, cognitive, and psychological changes in women and negatively affects women’s quality of life.

This study aimed to examine the effects of cognitive behavioral approach-based intervention on the cognitive situation, quality of life, anxiety, depression, and stress levels experienced by women after menopause with mixed-method research.

The research was carried out between March 2022 and August 2023 as a mixed-method research consisting of three phases (quantitative, qualitative, and intervention). Eighty women (experiment=27, control=53) attended in the quantitative phase. Quantitative data were collected before & after intervention by The Sociodemographic Data Form, The Montreal Cognitive Assessment Test, The Menopause-Specific Quality of Life Scale, and The Depression Anxiety Stress Scale. In the intervention phase, a six-session, cognitive behavioral approach-based nursing intervention was conducted with five groups using online Zoom. Hermeneutic phenomenology design was used in the qualitative phase. Qualitative data were collected online, via Zoom platform, and through three focus group interviews. Qualitative data were evaluated by the Thematic Analysis method. In the analysis of quantitative data, descriptive statistics, Independent Samples Groups t-test, Mann-Whitney U Test, and Wilcoxon Test were used.

There was no statistically significant difference between intervention and control groups in terms of sociodemographic characteristics, age, age of onset of menstruation, and menopause. Post-intervention cognitive scores (Z=-3.936, p=0.001) and psychosocial quality of life scores (Z=-2.771, p=0.006) of women who were in the intervention were higher than their pretest scores. There was no statistically significant difference in the post-intervention mean scores between groups in terms of other variables (p>0.05). The themes were loss, stigma, loneliness, not being understood, aging, loss of health, sexuality, acceptance, self-awareness, and coping ability. Women’s perceptions of menopause changed mostly functionally after the intervention study.

The research findings showed that Cognitive Behavioral Intervention had some curative effects on women’s cognitive changes and psychosocial changes they experienced during menopause. Nurses working with menopausal women can use Cognitive Behavioral approaches to manage the changes brought about by menopause effectively.

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Postnatal anxiety (PNA) in recent years, has become increasingly recognized as an important issue, as it affects a substantial number of women. Data from non-perinatal populations indicate that insomnia has a bidirectional association with anxiety.

Here we aimed to explore the association between insomnia in third trimester and PNA.

We analyzed data from the hospital’s birth records and questionnaire responses from pregnancy week 32 and postnatal week 4 (n=225). Postnatal anxiety symptoms were measured using the Beck Anxiety Inventory (BAI). Anxiety disorder measurements were based on questions from the Mini-International Neuropsychiatric Interview. Insomnia was measured using the Insomnia Severity Index.

Among postnatal women, 8.7 % reported symptoms of at least one anxiety disorder. The observed prevalence of obsessive-compulsive disorder after delivery was 3.7%, and for social anxiety disorder 2%. Multiple regression analysis, with adjustment for several psychosocial and reproductive variables, indicated that insomnia in third trimester was significantly associated with postpartum anxiety symptoms.

Our results suggest that anxiety disorders are prevalent in postnatal period. Healthcare professionals should be aware that women with insomnia in third trimester may have an increased risk of postnatal anxiety disorders.

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In 1760, Charles Bonnet, a Genoese naturalist and philosopher, described the case of his grandfather, who experienced vivid, elaborate, and recurrent visual hallucinations and who also suffered from visual impairment. Bonnet himself later developed visual impairment and experienced similar symptoms. Since then, there have been multiple reports and cases in the European literature regarding this syndrome.

Auditory Charles-Bonnet syndrome describes a rare condition presenting with sensorineural hearing loss, which can result in auditory-musical hallucinations in the absence of an acoustic stimulus. It has been reported in patients with diseases such as psychiatric disorders and organic brain diseases. However, the most common are idiopathic musical hallucinations that occur along with deafness in elderly people. Musical hallucinations that accompany hearing loss may reflect impaired brain function.

We present the case of a 84-year-old woman with a long-standing history of depression, who also presents mild bilateral pantonal sensorineural hearing loss with associated subjective tinnitus, without other associated somatic and/or psychiatric symptoms. In addition, a CT study of the head was performed which revealed severe fronto-temporal cortical atrophy.

The treatment remains the subject of extensive research. Some authors have reported that hearing aids, antiepileptic drugs, benzodiazepines and antipsychotics can alleviate musical hallucination, which in the case of our patient was eradicated, so the contribution of this case could enrich the current bibliography.

This is unfrecuently presentation of Charles Bonnet symdrom.

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