Tailor-Made Heterocharged Covalent Organic Framework Membrane for Efficient Ion Separation.

Pore size sieving, Donnan exclusion, and their combined effects seriously affect ion separation of membrane processes. However, traditional polymer-based membranes face some challenges in precisely controlling both charge distribution and pore size on the membrane surface, which hinders the ion separation performance, such as heavy metal ion removal. Herein, the heterocharged covalent organic framework (COF) membrane is reported by assembling two kinds of ionic COF nanosheets with opposite charges and different pore sizes. By manipulating the stacking quantity and sequence of two kinds of nanosheets, the impact of membrane surface charge and pore size on the separation performance of monovalent and multivalent ions is investigated. For the separation of anions, the effect of pore size sieving is dominant, while for the separation of cations, the effect of Donnan exclusion is dominant. The heterocharged TpEBr/TpPa-SO3H membrane with a positively charged upper layer and a negatively charged bottom layer exhibits excellent rejection of multivalent anions and cations (Ni2+, Cd2+, Cr2+, CrO4 2-, SeO3 2-, etc). The strategy provides not only high-performance COF membranes for ion separation but also an inspiration for the engineering of heterocharged membranes.

Once-daily versus divided dosing regimens of clozapine: A cross-sectional study in Singapore.

INTRODUCTION: Clozapine is recognized as the gold standard medication for treatment-resistant schizophrenia. Despite the general recommendation of administering in a divided dosing regimen, clozapine is often prescribed once daily at night in clinical practice. This study aims to compare patient characteristics, psychiatric symptoms, side effects, and plasma concentration of clozapine between once-daily dosing and divided dosing regimens. METHODS: This cross-sectional study included 159 participants with treatment-resistant schizophrenia or schizoaffective disorder. Participant's demographic information, anthropometric data, and medical history were collected. Their psychiatric symptoms, cognition, functioning, and side effects were evaluated. RESULTS: Once-daily dosing regimen was associated with younger age and competitive employment. Lower clinical symptom severity, better functioning and cognitive performance were observed in the once-daily dosing group. Lower daily dose of clozapine, trough plasma concentrations of clozapine and norclozapine were also significantly associated with once-daily dosing regimen. CONCLUSION: The study results support once-daily dosing of clozapine as a viable option to selected patients in clinical practice, as no association of severe symptoms or side effects were associated with once-daily dosing regimen. More studies are needed to examine the relationship between clinical outcomes and clozapine dosing regimen.

Identifying psychiatric manifestations in schizophrenia and depression from audio-visual behavioural indicators through a machine-learning approach.

Schizophrenia (SCZ) and depression (MDD) are two chronic mental disorders that seriously affect the quality of life of millions of people worldwide. We aim to develop machine-learning methods with objective linguistic, speech, facial, and motor behavioral cues to reliably predict the severity of psychopathology or cognitive function, and distinguish diagnosis groups. We collected and analyzed the speech, facial expressions, and body movement recordings of 228 participants (103 SCZ, 50 MDD, and 75 healthy controls) from two separate studies. We created an ensemble machine-learning pipeline and achieved a balanced accuracy of 75.3% for classifying the total score of negative symptoms, 75.6% for the composite score of cognitive deficits, and 73.6% for the total score of general psychiatric symptoms in the mixed sample containing all three diagnostic groups. The proposed system is also able to differentiate between MDD and SCZ with a balanced accuracy of 84.7% and differentiate patients with SCZ or MDD from healthy controls with a balanced accuracy of 82.3%. These results suggest that machine-learning models leveraging audio-visual characteristics can help diagnose, assess, and monitor patients with schizophrenia and depression.

Individual and combined effects of cannabidiol and Δ9-tetrahydrocannabinol on striato-cortical connectivity in the human brain.

BACKGROUND: Cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC) are the two major constituents of cannabis with contrasting mechanisms of action. THC is the major psychoactive, addiction-promoting, and psychotomimetic compound, while CBD may have opposite effects. The brain effects of these drugs alone and in combination are poorly understood. In particular, the striatum is implicated in the pathophysiology of several psychiatric disorders, but it is unclear how THC and CBD influence striato-cortical connectivity. AIMS: To examine effects of THC, CBD, and THC + CBD on functional connectivity of striatal sub-divisions (associative, limbic and sensorimotor). METHOD: Resting-state functional Magnetic Resonance Imaging (fMRI) was used across two within-subjects, placebo-controlled, double-blind studies, with a unified analysis approach. RESULTS: Study 1 (N = 17; inhaled cannabis containing 8 mg THC, 8 mg THC + 10 mg CBD or placebo) showed strong disruptive effects of both THC and THC + CBD on connectivity in the associative and sensorimotor networks, but a specific effect of THC in the limbic striatum network which was not present in the THC + CBD condition. In Study 2 (N = 23, oral 600 mg CBD, placebo), CBD increased connectivity in the associative network, but produced only relatively minor disruptions in the limbic and sensorimotor networks. OUTCOMES: THC strongly disrupts striato-cortical networks, but this effect is mitigated by co-administration of CBD in the limbic striatum network. Oral CBD administered has a more complex effect profile of relative increases and decreases in connectivity. The insula emerges as a key region affected by cannabinoid-induced changes in functional connectivity, with potential implications for understanding cannabis-related disorders, and the development of cannabinoid therapeutics.

The Impact of Negative Symptoms and Neurocognition on Functioning in MDD and Schizophrenia.

Introduction: Negative symptoms, neurocognitive deficits and functional impairment are prevalent in individuals with major depressive disorder (MDD) and schizophrenia (SCZ). However, unlike neurocognitive deficits, little is known about the role of negative symptoms toward functioning in individuals with MDD. On the other hand, both factors are well-studied in individuals with SCZ. Thus, this study aimed to examine the contributions of negative symptoms and neurocognitive impairments in functioning in individuals with MDD, compared to individuals with SCZ. Methods: Participants included 50 individuals with MDD, 49 individuals with SCZ and 49 healthy controls. The following measures were administered-Negative Symptom Assessment (NSA-16), Brief Assessment of Cognition in Schizophrenia (BACS), Patient Health Questionnaire (PHQ-9), and MIRECC-Global Assessment of Functioning (MIRECC-GAF) to evaluate negative symptoms, neurocognition, depressive symptoms, and functioning respectively. Results: Both MDD and SCZ groups had significantly more severe negative symptoms, depressive symptoms, and poorer functioning than healthy controls. Individuals with SCZ performed significantly poorer on the BACS than the other two groups. Both negative symptoms and neurocognition were significantly correlated with social and occupational functioning in SCZ. Motivation subdomain of the negative symptoms was significantly correlated with occupational functioning, while depressive symptoms correlated with functioning in MDD. Conclusion: Both negative symptoms and neurocognitive deficits appear to play differential roles on individual domains of functioning between MDD and SCZ. Future longitudinal studies with larger sample sizes should be done for a better understanding about the associations between the factors and functioning.

Comparative study of emotion recognition and theory of mind between major depressive disorder and schizophrenia.

BACKGROUND: Social cognition as a transdiagnostic construct between major depressive disorder (MDD) and schizophrenia (SCZ) is not well understood. This may be attributed to the variability of social cognitive measures indexing the same construct. This study aims to compare emotion recognition and theory of mind domains, known to be impaired in SCZ, between MDD and SCZ. METHODS: Three groups of participants (NTotal = 150) were enrolled in this study: MDD (n = 51), SCZ (n = 50) and healthy controls (HC; n = 49). Emotion recognition was assessed on the Bell Lysaker Emotion Recognition Task (BLERT) and Penn Emotion Recognition Task (ER40); theory of mind was measured on The Awareness of Social Inference Test (TASIT). Mixed ANCOVAs were utilised to compare social cognitive performance across the groups. RESULTS: SCZ performed poorer in all 3 social cognition tasks compared to both MDD and HC. No statistically significant difference in social cognitive performance was observed between MDD and HC. CONCLUSIONS: This study serves as an effort towards employing the same standardised social cognitive measures for direct comparison of performance patterns across diagnostic groups. Future work is needed to extend this in larger samples of different illness severity and diagnostic categories.

Predicting Real-World Functioning in Schizophrenia: The Relative Contributions of Neurocognition, Functional Capacity, and Negative Symptoms.

Neurocognition and functional capacity are commonly reported predictors of real-world functioning in schizophrenia. However, the additional impact of negative symptoms, specifically its subdomains, i.e., diminished expression (DE) and avolition-apathy (AA), on real-world functioning remains unclear. The current study assessed 58 individuals with schizophrenia. Neurocognition was assessed with the Brief Assessment of Cognition in Schizophrenia, functional capacity with the UCSD Performance-based Skills Assessment (UPSA-B), and negative symptoms with the Negative Symptom Assessment-16. Real-world functioning was assessed with the Multnomah Community Ability Scale (MCAS) with employment status as an additional objective outcome. Hierarchical regressions and sequential logistic regressions were used to examine the associations between the variables of interest. The results show that global negative symptoms contribute substantial additional variance in predicting MCAS and employment status above and beyond the variance accounted for by neurocognition and functional capacity. In addition, both AA and DE predict the MCAS after controlling for cognition and functional capacity. Only AA accounts for additional variance in employment status beyond that by UPSA-B. In summary, negative symptoms contribute substantial additional variance in predicting both real-world functioning and employment outcomes after accounting for neurocognition and functional capacity. Our findings emphasize both DE and AA as important treatment targets in functional recovery for people with schizophrenia.

Evaluating the utility of digital phenotyping to predict health outcomes in schizophrenia: protocol for the HOPE-S observational study.

INTRODUCTION: The course of schizophrenia illness is characterised by recurrent relapses which are associated with adverse clinical outcomes such as treatment-resistance, functional and cognitive decline. Early identification is essential and relapse prevention remains a primary treatment goal for long-term management of schizophrenia. With the ubiquity of devices such as smartphones, objective digital biomarkers can be harnessed and may offer alternative means for symptom monitoring and relapse prediction. The acceptability of digital sensors (smartphone and wrist-wearable device) and the association between the captured digital data with clinical and health outcomes in individuals with schizophrenia will be examined. METHODS AND ANALYSIS: In this study, we aim to recruit 100 individuals with schizophrenia spectrum disorders who are recently discharged from the Institute of Mental Health (IMH), Singapore. Participants are followed up for 6 months, where digital, clinical, cognitive and functioning data are collected while health utilisation data are obtained at the 6 month and 1 year timepoint from study enrolment. Associations between digital, clinical and health outcomes data will be examined. A data-driven machine learning approach will be used to develop prediction algorithms to detect clinically significant outcomes. Study findings will inform the design, data collection procedures and protocol of future interventional randomised controlled trial, testing the effectiveness of digital phenotyping in clinical management of individuals with schizophrenia spectrum disorders. ETHICS AND DISSEMINATION: Ethics approval has been granted by the National Healthcare Group (NHG) Domain Specific Review Board (DSRB Reference no.: 2019/00720). The results will be published in peer-reviewed journals and presented at conferences. TRIAL REGISTRATION NUMBER: NCT04230590.

Large-scale evaluation of the Positive and Negative Syndrome Scale (PANSS) symptom architecture in schizophrenia.

Although the Positive and Negative Syndrome Scale (PANSS) is widely utilized in schizophrenia research, variability in specific item loading exist, hindering reproducibility and generalizability of findings across schizophrenia samples. We aim to establish a common PANSS factor structure from a large multi-ethnic sample and validate it against a meta-analysis of existing PANSS models. Schizophrenia participants (N = 3511) included in the current study were part of the Singapore Translational and Clinical Research Program (STCRP) and the Clinical Antipsychotic Trials for Intervention Effectiveness (CATIE). Exploratory Factor Analysis (EFA) was conducted to identify the factor structure of PANSS and validated with a meta-analysis (N = 16,171) of existing PANSS models. Temporal stability of the PANSS model and generalizability to individuals at ultra-high risk (UHR) of psychosis were evaluated. A five-factor solution best fit the PANSS data. These were the i) Positive, ii) Negative, iii) Cognitive/disorganization, iv) Depression/anxiety and v) Hostility factors. Convergence of PANSS symptom architecture between EFA model and meta-analysis was observed. Modest longitudinal reliability was observed. The schizophrenia derived PANSS factor model fit the UHR population, but not vice versa. We found that two other domains, Social Amotivation (SA) and Diminished Expression (DE), were nested within the negative symptoms factor. Here, we report one of the largest transethnic factorial structures of PANSS symptom domains (N = 19,682). Evidence reported here serves as crucial consolidation of a common PANSS structure that could aid in furthering our understanding of schizophrenia.

Automated Verbal and Non-verbal Speech Analysis of Interviews of Individuals with Schizophrenia and Depression.

Schizophrenia and depression are the two most common mental disorders associated with negative symptoms that contribute to poor functioning and quality of life for millions of patients globally. This study is part of a larger research project. The overall aim of the project is to develop an automated objective pipeline that aids clinical diagnosis and provides more insights into symptoms of mental illnesses. In our previous work, we have analyzed non-verbal cues and linguistic cues of individuals with schizophrenia. In this study, we extend our work to include participants with depression. Powered by natural language processing techniques, we extract verbal features, both dictionary-based and vector-based, from participants' interviews that were automatically transcribed. We also extracted conversational, phonatory, articulatory and prosodic features from the interviews to understand the conversational and acoustic characteristics of schizophrenia and depression. Combining these features, we applied ensemble learning with leave-one-out cross-validation to classify healthy controls, schizophrenic and depressive patients, achieving an accuracy of 69%-75% in paired classification. From those same features, we also predict the subjective Negative Symptoms Assessment 16 scores of patients with schizophrenia or depression, yielding an accuracy of 90.5% for NSA2 but lower accuracy for other NSA indices. Our analysis also revealed significant linguistic and non-verbal differences that are potentially symptomatic of schizophrenia and depression respectively.

Non-verbal speech cues as objective measures for negative symptoms in patients with schizophrenia.

Negative symptoms in schizophrenia are associated with significant burden and possess little to no robust treatments in clinical practice today. One key obstacle impeding the development of better treatment methods is the lack of an objective measure. Since negative symptoms almost always adversely affect speech production in patients, speech dysfunction have been considered as a viable objective measure. However, researchers have mostly focused on the verbal aspects of speech, with scant attention to the non-verbal cues in speech. In this paper, we have explored non-verbal speech cues as objective measures of negative symptoms of schizophrenia. We collected an interview corpus of 54 subjects with schizophrenia and 26 healthy controls. In order to validate the non-verbal speech cues, we computed the correlation between these cues and the NSA-16 ratings assigned by expert clinicians. Significant correlations were obtained between these non-verbal speech cues and certain NSA indicators. For instance, the correlation between Turn Duration and Restricted Speech is -0.5, Response time and NSA Communication is 0.4, therefore indicating that poor communication is reflected in the objective measures, thus validating our claims. Moreover, certain NSA indices can be classified into observable and non-observable classes from the non-verbal speech cues by means of supervised classification methods. In particular the accuracy for Restricted speech quantity and Prolonged response time are 80% and 70% respectively. We were also able to classify healthy and patients using non-verbal speech features with 81.3% accuracy.

Factor structure of the positive and negative syndrome scale (PANSS) in people at ultra high risk (UHR) for psychosis.

INTRODUCTION: The Positive and Negative Syndrome Scale (PANSS), a comprehensive psychopathology assessment scale used in the evaluation of psychopathology in schizophrenia, is also often used in the Ultra-High-Risk (UHR) population. This paper examined the dimensional structure of the PANSS in a UHR sample. METHODS: A total of 168 individuals assessed to be at UHR for psychosis on the Comprehensive Assessment of At-Risk Mental States (CAARMS) were evaluated on the PANSS, Calgary Depression Scale for Schizophrenia (CDSS), Beck Anxiety Inventory (BAI), Brief Assessment of Cognition in Schizophrenia (BACS), and Global Assessment of Functioning (GAF). Exploratory factor analysis (EFA) of the PANSS was performed to identify the factorial structure. Convergent validity was explored with the CAARMS, CDSS, BAI and BACS. RESULTS: EFA of the PANSS yielded five symptom factors - Positive, Negative, Cognition/Disorganization, Anxiety/Depression, and Hostility. This 5-factor solution showed good convergent validity with the CAARMS composite score, CDSS, BAI, and BACS. Positive, Negative and Anxiety/Depression factors were associated with functioning. CONCLUSION: The reported PANSS factor structure may serve to improve the understanding and measurement of clinical symptom dimensions manifested in people with UHR for future research and clinical setting.

Montreal Cognitive Assessment as a screening instrument for cognitive impairments in schizophrenia.

BACKGROUND: Cognitive impairment is one of the core features of schizophrenia. For its evaluation, current clinical practice relies on detailed neuropsychological batteries which require trained testers and considerable amount of time to administer. Therefore, a brief and reliable screening tool for identification of overall cognitive impairment prior to a detailed comprehensive neurocognitive assessment is needed in a busy clinical setting. This study evaluates the clinical utility of the Montreal Cognitive Assessment (MoCA) in detecting cognitive impairments in schizophrenia and its relationship with functional outcome and demographic characters. METHODS: The MoCA, the Brief Assessment of Cognition in Schizophrenia (BACS), and the Brief UCSD Performance-based Skills Assessment (UPSA-B) were administered to 64 patients with schizophrenia. Mild and severe cognitive impairments were defined as BACS Z-score (calculated with the age and gender adjustments using previously published local norm data) of one or two standard deviations below the mean, respectively. RESULTS: The results showed that the MoCA was significantly correlated with BACS (r=.61, p<.001) and sensitive to detect both mild (AUC=0.82, p<.001) and severe (AUC=0.81, p<.001) cognitive impairments in schizophrenia. The MoCA was significantly correlated with UPSA-B score (r=.51, p<.001), and accounted for significant additional variance in UPSA-B score beyond the BACS. CONCLUSION: These findings indicate that MoCA is a useful bedside cognitive screening instrument for people with schizophrenia.

Longitudinal Cognitive Changes in Young Individuals at Ultrahigh Risk for Psychosis.

Importance: Cognitive deficits are a key feature of risk for psychosis. Longitudinal changes in cognitive architecture may be associated with the social and occupational functioning in young people. Objectives: To examine longitudinal profiles of cognition in individuals at ultrahigh risk (UHR) for psychosis, compared with healthy controls, and to investigate the association of cognition with functioning. Design, Setting, and Participants: This study has a multiple-group prospective design completed in 24 months and was conducted from January 1, 2009, to November 11, 2012, as part of the Longitudinal Youth at-Risk Study conducted in Singapore. Participants either were recruited from psychiatric outpatient clinics, educational institutions, and community mental health agencies or self-referred. Follow-up assessments were performed every 6 months for 2 years or until conversion to psychosis. Individuals with medical causes for psychosis, current illicit substance use, or color blindness were excluded. Data analysis was conducted from June 2014 to May 2018. Main Outcomes and Measures: Neuropsychological, perceptual, and social cognitive tasks; semi-structured interviews, and the Structured Clinical Interview for DSM-IV Axis I disorders were administered every 6 months. The UHR status of nonconverters, converters, remitters, and nonremitters was monitored. Cognitive domain scores and functioning were investigated longitudinally. Results: In total, 384 healthy controls and 173 UHR individuals between ages 14 and 29 years were evaluated prospectively. Of the 384 healthy controls, 153 (39.8%) were female and 231 (60.2%) were male with a mean (SD) age of 21.69 (3.26) years. Of the 173 individuals at UHR for psychosis, 56 (32.4%) were female and 117 (67.6%) were male with a mean (SD) age of 21.27 (3.52) years). After 24 months of follow-up, 383 healthy controls (99.7%) and 122 individuals at UHR for psychosis (70.5%) remained. Baseline cognitive deficits were associated with psychosis conversion later (mean odds ratio [OR], 1.66; combined 95% CI, 1.08-2.83; P = .04) and nonremission of UHR status (mean OR, 1.67; combined 95% CI, 1.09-2.95; P = .04). Five cognitive components-social cognition, attention, verbal fluency, general cognitive function, and perception-were obtained from principal components analysis. Longitudinal component structure change was observed in general cognitive function (maximum vertical deviation = 0.59; χ2 = 8.03; P = .01). Group-by-time interaction on general cognitive function (F = 12.23; η2 = 0.047; P < .001) and perception (F = 8.33; η2 = 0.032; P < .001) was present. Changes in attention (F = 5.65; η2 = 0.013; P = .02) and general cognitive function (F = 7.18; η2 = 0.014; P = .01) accounted for longitudinal changes in social and occupational functioning. Conclusions and Relevance: Individuals in this study who met the UHR criteria appeared to demonstrate cognitive deficits, and those whose UHR status remitted were seen to recover cognitively. Cognition appeared as poor in nonremitters and appeared to be associated with poor functional outcome. This study suggests that cognitive dimensions are sensitive to the identification of young individuals at risk for psychosis and to the longitudinal course of those at highest risk.

Effects of torrefaction and densification on switchgrass pyrolysis products.

The pyrolysis behaviors of four types of pretreated switchgrass (torrefied at 230 and 270°C, densification, and torrefaction at 270°C followed by densification) were studied at three temperatures (500, 600, 700°C) using a pyroprobe attached to a gas chromatogram mass spectroscopy (Py-GC/MS). The torrefaction of switchgrass improved its oxygen to carbon ratio and energy content. Contents of anhydrous sugars and phenols in pyrolysis products of torrefied switchgrass were higher than those in pyrolysis products of raw switchgrass. As the torrefaction temperature increased from 230 to 270°C, the contents of anhydrous sugars and phenols in pyrolysis products increased whereas content of guaiacols decreased. High pyrolysis temperature (600 and 700°C as compared to 500°C) enhanced decomposition of lignin and anhydrous sugars, leading to increase in phenols, aromatics and furans. Densification enhanced depolymerization of cellulose and hemicellulose during pyrolysis.

Combustion behaviours of tobacco stem in a thermogravimetric analyser and a pilot-scale fluidized bed reactor.

Despite its abundant supply, tobacco stem has not been exploited as an energy source in large scale. This study investigates the combustion behaviours of tobacco stem in a thermogravimetric analyser (TGA) and a pilot-scale fluidized bed (FB). Combustion characteristics, including ignition and burnout index, and combustion reaction kinetics were studied. Experiments in the FB investigated the effects of different operating conditions, such as primary air flow, secondary air flow and feeding rates, on the bed temperature profiles and combustion efficiency. Two kinds of bed materials cinder and silica sand were used in FB and the effect of bed materials on agglomeration was studied. The results indicated that tobacco stem combustion worked well in the FB. When operation condition was properly set, the tobacco stem combustion efficiency reached 94%. In addition, compared to silica sand, cinder could inhibit agglomeration during combustion because of its high aluminium content.

The effects of subliminal anger and sadness primes on agency appraisals.

Two studies examined whether appraisals can be differentially affected by subliminal anger and sadness primes. Participants from Singapore (Experiment 1) and China (Experiment 2) were exposed to either subliminal angry faces or subliminal sad faces. Supporting appraisal theories of emotions, participants exposed to subliminal angry faces were more likely to appraise negative events as caused by other people and those exposed to subliminal sad faces were more likely to appraise the same events as caused by situational factors. The results provide the first evidence for subliminal emotion-specific cognitive effects. They show that cognitive functions such as appraisals can be affected by subliminal emotional stimuli of the same valence.