Adolescents presenting with a first psychotic episode often have a long history of pediatric treatment. However, there is insufficient evidence of children’s subclinical characteristics in non-psychiatric settings. To address this issue, we retrospectively studied schizophrenia spectrum disorder (SSD) patients to identify characteristic patterns of subclinical psychological, behavioral, and physical problems in childhood. In the previous study, we had developed the child psychosis-risk screening system (CPSS) that incorporates this pattern as a risk evaluation algorithm (Hamasaki et al. BMC Psychiatry 2021; 21, 57).

In this present cross-sectional study, we evaluated the specificity of the CPSS to identify the risk of psychosis in pediatric and psychiatric patients and determine its discriminatory power and cutoff values.

To identify the risk of developing psychosis in pediatric and psychiatric outpatients, we evaluated data from 336 patients aged 6–18 years selected for the present study using the CPSS. We defined six major diagnostic categories i.e., Neurodevelopmental Disorders, SSD, Depressive Disorders, Anxiety Disorders (including Obsessive-Compulsive Disorder), Somatic Symptom Disorders, and Others to examine the specificity of the CPSS variance in diagnosis. We analyzed the receiver operating characteristic (ROC) curve using the onset of schizophrenia spectrum as the outcome and determined the discriminatory power and cutoff values of CPSS.

We found significant differences in CPSS variance among the diagnostic categories (Kruskal–Wallis test; p<0.001), especially between SSD and neurodevelopmental disorders (Bonferroni method; p=0.001). Similarly, significant differences were identified in variance when comparing the CPSS for each neurodevelopmental disorder category and SSD, particularly between SSD and attention deficit hyperactivity disorder (ADHD) (Bonferroni method; p<0.001) and SSD and autism spectrum disorder (ASD) (Bonferroni method; p=0.004). CPSS showed sufficient discriminatory power for SSD diagnosis (area under the ROC curve=0.853 [95% confidence interval: 0.774–0.931]). The cutoff value for the risk of SSD was determined to be 3.94, achieving the best mean of the sum of sensitivity (90.9%) and specificity (84.0%). 18.3% of patients (12.5% pediatric and 29.1% psychiatric) were identified as risk groups above the cutoff value.

These results suggest that CPSS can be applied in pediatric clinical practice not only for early detection and risk identification of psychosis but also for differentiation from neurodevelopmental disorders. If early identification of psychosis risk in pediatrics becomes possible, discussions regarding effective prevention during the critical period of psychosis will become increasingly important.

None Declared

In our previous study, we have developed the Child Psychosis-risk Screening System (CPSS), which incorporates psychological and behavioral characteristics of childhood into an algorithm, based on a retrospective survey.

In this study, we actually tried to evaluate the risk of psychosis in pediatric and psychiatric outpatients using the CPSS.

We conducted an epidemiological study of 323 outpatients aged 6-18 years visiting pediatric and psychiatric departments using CBCL and clinical data (sex, age, winter birth, chief complaint, diagnosis, abuse, bullying, hikikomori). ROC analysis was used to assess the accuracy of CPSS predictions. Cross-sectional logistic regression analysis was performed on the clinical data to identify factors associated with risk groups exceeding the cutoff value.

The results of the ROC analysis showed that the AUC (Area under the ROC Curve) was 80.3%, indicating that the CPSS has Moderate accuracy. The cutoff value was 98.11% (sensitivity: 0.857, specificity: 0.835), and 18% of the subjects were identified as risk groups above this value. Cross-sectional logistic regression analysis showed that schizophrenia diagnosis, no abuse, winter birth, and hikikomori were associated with the risk group, with respective odds ratios of 22.88, 10.76, 1.91, and 1.37.

The results of this study suggest that the CPSS can be applied to pediatric practice for early detection of risk for psychosis. The risk group is also present among pediatric patients with physical chief complaints. The factors suggested to be associated with risk groups may reflect the factors acting on the critical period of psychosis onset and the dynamic state.

No significant relationships.

Children in a prodromal state manifesting as truancy or social isolation (hikikomori) often complain of problems that are physical in nature and are subject to significant changes. We developed the Child Psychosis-Risk Screening System (CPSS) that incorporates childhood psycho-behavioral characteristics revealed through a retrospective survey of schizophrenia patients into its algorithm.

Our research aimed to test the risk identification of pediatric and psychiatric clinic outpatients using the CPSS.

We conducted an epidemiological study involving 204 outpatients between the ages of 6 and 14 years who had been examined at a pediatric or psychiatric clinic using the CBCL and clinical data from medical charts. Logistic regression analysis and T-tests were performed using each clinical data variable to clarify the risk of the CPSS calculated from the CBCL data and contributing factors.

The results of the logistic regression analysis demonstrated that the diagnostic category (physical illness or DSM-5 diagnosis) and chief complaint did not contribute to differentiate between the high-risk and low-risk groups. Meanwhile, the environmental factors of “abuse” and “social isolation” did contribute to the discrimination of the two groups.

The fact that the diagnostic category during childhood does not contribute to the discrimination of the high- risk group warrants attention. It is possible that the high-risk group only had a latent endophenotype that had not yet manifested during this period. The factors suggested to have an association with the high-risk group may be reflecting activators and the dynamic state of the critical period for psychosis.

No significant relationships.

Altered levels of phenylalanine and its metabolites in blood and cerebrospinal fluid have previously been reported in schizophrenia. This study attempted to examine whether phenylalanine kinetics is altered in schizophrenia using the 13C-phenylalanine breath test (13C-PBT).

Subjects were 20 patients with schizophrenia and the same number of controls. 13C-phenylalanine was administered and then 13CO2 concentration in breath was monitored for 120 minutes. The Δ 13CO2 at each collecting time, the maximal Δ 13CO2 (Cmax), the time to reach Cmax (Tmax), the area under the curve of time course of Δ13CO2 (AUC), the cumulative recovery rate (CRR) at each collecting time of the 13C-PBT were calculated for each subject.

Body weight (BW) and diagnostic status were significant predictors for Cmax. BW, age and diagnostic status were significant predictors for AUC and CRR at 120 minutes (CRR0-120). A repeated measures ANCOVA controlling for age and BW revealed a different pattern of change in CRR over time between the patients and controls and that Δ13CO2 in schizophrenia were lower than that in healthy control at all sampling point during 120 min, with an overall significant differences between healthy control and schizophrenia. The ANCOVA controlling for age and BW, showed that Cmax, AUC and CRR0-120 were significantly lower in schizophrenics than in controls.

Our data indicate the different change of Δ13CO2 and CRR over time and the decreased Cmax, AUC and CRR0-120 of 13C-PBT in schizophrenia patients compared to healthy controls, suggesting the altered phenylalanine kinetics in schizophrenia.

Smoking rates in schizotypic individuals are shown to be elevated, as in patients with schizophrenia, although findings on the association of smoking with different symptomatology of schizotypy have been mixed. Moreover, possible moderating effects of schizotypy on the relationship between smoking and cognition have not been well documented.

The Schizotypal Personality Questionnaire (SPQ) and the full version of the Wechsler Memory Scale-Revised (WMS-R) were administered to 501 healthy adults. Subjects were divided into smokers (n = 85) and non-smokers (n = 416) based on the presence/absence of current smoking.

The analysis of covariance (ANCOVA) on the three factor scores as well as the total score of the SPQ, controlling for age and gender, revealed that cognitive-perceptual factor was significantly associated with an increased rate of smoking (P = 0.048). The ANCOVA on the WMS-R indices, with smoking group as a fixed factor and age, gender and total SPQ score as covariates, revealed that the schizotypy-by-smoking interaction was significant for attention/working memory (P = 0.029).

Positive schizotypy may be associated with more smoking. Schizotypy and smoking could interact with each other to negatively affect attention/working memory.

It has been reported that cognitive functioning in major depressive disorder (MDD) can be affected by various factors, such as symptom severity, personality dimensions and stress hormone activity. However, the relative role of each is largely unknown.

Seventy-six non-remitted patients with MDD were recruited. Symptomatology was assessed by the 21-item version of the Hamilton Rating Scale for Depression and the Hopkins Symptom Checklist (HSCL). Personality was assessed by the Temperament and Character Inventory (TCI). Neurocognitive functions, including verbal and visual memory, delayed recall and attention/working memory were measured by the full version of the Wechsler Memory Scale-Revised. Neuroendocrine function was determined by the reactivity of cortisol and dehydroepiandrosterone-sulfate (DHEAS) to the combined dexamethasone/corticotropin releasing hormone test. To quantify cognitive impairments in patients, age-, sex- and education- matched 187 healthy controls were also recruited and administered the same neuropsychological test.

MDD patients performed significantly worse than controls on visual memory and delayed recall. A stepwise multiple regression analysis predicting performance of each cognitive domain from five HSCL dimensions, seven TCI dimensions and hormonal variables, controlling for age, gender and education, revealed that higher cooperativeness was the only significant predictor towards better verbal memory, that less somatization symptoms and lower self-directedness were significant predictors towards better visual memory, and that lower age, less anxiety symptoms and lower DHEAS levels after dexamethasone administration were significant predictors towards better delayed recall.

Besides symptomatology, some personality dimensions and neuroendocrine function may, at least partly independently, contribute to memory impairment in MDD.

Recent studies have shown that it is important to understand the brain mechanism specifically by focusing on the common and unique functional connectivity in each disorder including depression.

To specify the biomarker of major depressive disorder (MDD), we applied the sparse machine learning algorithm to classify several types of affective disorders using the resting state fMRI data collected in multiple sites, and this study shows the results of depression as a part of those results.

The aim of this study is to understand some specific pattern of functional connectivity in MDD, which would support diagnosis of depression and development of focused and personalized treatments in the future.

The neuroimaging data from patients with major depressive disorder (MDD, n = 100) and healthy control adults (HC: n = 100) from multiple sites were used for the training dataset. A completely separate dataset (n = 16) was kept aside for testing. After all preprocessing of fMRI data, based on one hundred and forty anatomical region of interests (ROIs), 9730 functional connectivities during resting states were prepared as the input of the sparse machine-learning algorithm.

As results, 20 functional connectivities were selected with the classification performance of Accuracy: 83.0% (Sensitivity: 81.0%, Specificity: 85.0%). The test data, which was completely separate from the training data, showed the performance accuracy of 83.3%.

The selected functional connectivities based on the sparse machine learning algorithm included the brain regions which have been associated with depression.

The authors have not supplied their declaration of competing interest.

This study aimed to investigate the function of tissue plasminogen activator in the olfactory epithelium of mice following neural injury.

Transmission electron microscopy was used to study the changes in the morphology of the olfactory epithelium 1–7 days after surgical ablation of the olfactory bulb (bulbectomy).

Prior to bulbectomy, a uniformly fine material was observed within some regions of the olfactory epithelium of mice deficient in tissue plasminogen activator. At 2–3 days after bulbectomy, there were degenerative changes in the olfactory epithelium. At 5–7 days after bulbectomy, we noted drastic differences in olfactory epithelium morphology between mice deficient in tissue plasminogen activator and wild-type mice (comparisons were made using findings from a previous study). The microvilli seemed to be normal and olfactory vesicles and receptor neuron dendrites were largely intact in the olfactory epithelium of mice deficient in tissue plasminogen activator.

The tissue plasminogen activator plasmin system may inhibit the regeneration of the olfactory epithelium in the early stages following neural injury.