Tardive dyskinesia (TD) is a medication-induced hyperkinetic movement disorder, generally manifested as involuntary spasms or choreiform movements of the tongue, lower face and jaw, and limbs (lasting at least several weeks). It occurs after using neuroleptic medication for at least several months, causing irreversible injuries to the nerve system and significantly impacting patients′ daily function. This review summarizes recent research progress regarding TD risk factors, possible pathogenesis, clinical characteristics, assessment and identification, and treatment and management approaches. The goal is to raise awareness of TD to archive early prevention and identification, standardize treatment, and improve patients′ prognosis and quality of life.

Objective:The aim of this study was to explore the molecular mechanisms of sulforaphane in the treatment of autism spectrum disorders (ASD), identify metabolomic biomarkers associated with efficacy and construct efficacy prediction models.Methods:Forty children with ASD who were treated in Second Xiangya Hospital of Central South University and Guangzhou Huiai Hospital were recruited from August 2016 to May 2019. The patients were randomly allocated into sulforaphane treatment group ( n=26) and placebo group ( n=14). The OSU Autism Rating Scale-DSM-Ⅳ (OARS-4) was used to assess the change in clinical symptoms of children with ASD at baseline, week 4, week 8 and week 12 of treatment. A generalized linear mixed model was used to compare the differences in OARS-4 scale scores between groups and time. Plasma samples were collected from patients before and after treatment for untargeted metabolomic detection using ultra performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS). Differential metabolites were screened using ANOVA-component analysis, and metabolic pathway analysis was performed. Then, spearman correlation analysis was performed to find differential metabolites significantly associated with the efficacy of sulforaphane treatment, and finally Fisher′s discriminant analysis was used to screen for efficacy predictors. Result:After 12 weeks of treatment, the clinical symptoms improvement was significantly better in the sulforaphane group than in the placebo group ( F=14.11, P<0.001). There were differences in a total of 201 metabolites between the two groups, which were mainly significantly enriched in glycerophospholipid metabolism and primary bile acid biosynthesis pathways. Spearman′s correlation analysis showed that taurine, phosphatidylserine and lysophosphatidylserine were significantly positively associated with symptom changes in patients with ASD ( r=0.643, 0.401, 0.414, P<0.05 or 0.001), while lysophosphatidylethanolamine, sphingomyelin and triglyceride metabolites were significantly negatively associated with symptom changes ( r=-0.481--0.392, all P<0.05). Among them, sphingomyelin (d35∶1) and taurine entered the Fisher′s discriminant analysis model, which the accuracy of efficacy prediction was 84.6%(22/26). Conclusions:The molecular mechanism of sulforaphane in improving ASD related clinical symptoms may be related to cell membrane phospholipid metabolism. Sphingomyelin (d35∶1) and taurine may be possible predictors on the efficacy of sulforaphane in the treatment of ASD.

Tardive dyskinesia (TD) is a medication-induced hyperkinetic movement disorder, generally manifested as involuntary spasms or choreiform movements of the tongue, lower face and jaw, and limbs (lasting at least several weeks). It occurs after using neuroleptic medication for at least several months, causing irreversible injuries to the nerve system and significantly impacting patients′ daily function. This review summarizes recent research progress regarding TD risk factors, possible pathogenesis, clinical characteristics, assessment and identification, and treatment and management approaches. The goal is to raise awareness of TD to archive early prevention and identification, standardize treatment, and improve patients′ prognosis and quality of life.

Objective:The aim of this study was to explore the molecular mechanisms of sulforaphane in the treatment of autism spectrum disorders (ASD), identify metabolomic biomarkers associated with efficacy and construct efficacy prediction models.Methods:Forty children with ASD who were treated in Second Xiangya Hospital of Central South University and Guangzhou Huiai Hospital were recruited from August 2016 to May 2019. The patients were randomly allocated into sulforaphane treatment group ( n=26) and placebo group ( n=14). The OSU Autism Rating Scale-DSM-Ⅳ (OARS-4) was used to assess the change in clinical symptoms of children with ASD at baseline, week 4, week 8 and week 12 of treatment. A generalized linear mixed model was used to compare the differences in OARS-4 scale scores between groups and time. Plasma samples were collected from patients before and after treatment for untargeted metabolomic detection using ultra performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS). Differential metabolites were screened using ANOVA-component analysis, and metabolic pathway analysis was performed. Then, spearman correlation analysis was performed to find differential metabolites significantly associated with the efficacy of sulforaphane treatment, and finally Fisher′s discriminant analysis was used to screen for efficacy predictors. Result:After 12 weeks of treatment, the clinical symptoms improvement was significantly better in the sulforaphane group than in the placebo group ( F=14.11, P<0.001). There were differences in a total of 201 metabolites between the two groups, which were mainly significantly enriched in glycerophospholipid metabolism and primary bile acid biosynthesis pathways. Spearman′s correlation analysis showed that taurine, phosphatidylserine and lysophosphatidylserine were significantly positively associated with symptom changes in patients with ASD ( r=0.643, 0.401, 0.414, P<0.05 or 0.001), while lysophosphatidylethanolamine, sphingomyelin and triglyceride metabolites were significantly negatively associated with symptom changes ( r=-0.481--0.392, all P<0.05). Among them, sphingomyelin (d35∶1) and taurine entered the Fisher′s discriminant analysis model, which the accuracy of efficacy prediction was 84.6%(22/26). Conclusions:The molecular mechanism of sulforaphane in improving ASD related clinical symptoms may be related to cell membrane phospholipid metabolism. Sphingomyelin (d35∶1) and taurine may be possible predictors on the efficacy of sulforaphane in the treatment of ASD.

Objective: This study aimed to evaluate the consistency or stability of mental disorders diagnosed in the psychiatry ward setting, investigate factors associated with consistency, and observe the disease distribution over the decade. Methods: A total of 20,359 psychiatric inpatients were included in this retrospective study from June 2011 to December 2020. Diagnoses from the first admission to discharge were evaluated to determine the diagnostic consistency during hospitalization. Readmissions were selected as the subgroup, whose first and last discharge diagnoses were compared to analyze the relatively long-term diagnostic stability. Demographic and clinical characteristics were collected to identify predictors of diagnostic discrepancy. Results: From 2011–2020, the hospitalization rate decreased from 42.7% to 20.7% for schizophrenia and grew from 13.3% to 23.8% for depression. Diagnoses were retained by 92.6% of patients at their first discharge diagnosis, ranging from 100% for disorders of psychological development to 16.3% for unspecified mental disorders. About 33.9% of diagnostic conversions were to bipolar disorder in patients having inconsistent diagnoses. However, among rehospitalizations, the diagnostic stability notably dropped to 71.3%. For rehospitalizations, mood disorders and schizophrenia spectrum disorders were relatively stable diagnoses categories, with 72.6% to 76.7% of patients receiving the same diagnosis, although results of specified diagnoses within these categories ranged from 5.9% to 91.0%. Except for mood disorders and schizophrenia spectrum disorders, the diagnoses of all other categories were below 70%. Long lengths of hospitalization and old age were associated with short-term diagnosis alterations. Conclusion Longitudinal follow-up and integration of multiple aspects of information are essential for accurate diagnosis.

Objective:By comparing the changes of brain differentiation and neuronal markers of brain organoids in different periods, this study aims to clarify the effect and possible mechanisms of sulforaphane on the development of brain organoids.Methods:Brain organoids differentiated from human induced pluripotent stem cells treated with sulforaphane were used in this study. The experimental group was treated with sulforaphane at different concentrations (1 μmol/L, 2 μmol/L, 5 μmol/L), and the control group was a blank group, with 20 organoids in each group. Immunofluorescence and hematoxylin-eosin staining (HE) were used to observe and compare the expression characteristics of brain markers and neuronal markers in different periods of the two groups after sulforaphane treatment. Ribonucleic Acid (RNA) sequencing analysis of differentially expressed genes, functional enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted. Real-time Quantitative Reverse Transcriptase-mediated Polymerase Chain Reaction (qRT-PCR) was performed to further screen and verify the potential genes or possible targets that may be involved in sulforaphane in promoting the growth of brain organoids.Results:The sex determining region Y-box 2(SOX2), Forebrain protein forkhead box G1 (FOXG1), prefrontal cortex autism susceptibility candidate 2 (AUST2), and paired box 6 (PAX6) increased significantly in the brain organoids after 40 days of treatment with sulforaphane; the expression of neuron marker proteins like β-tubulin (TUJ1), neuronal nuclei (NeuN), recombinant microtubule associated protein 2 (MAP2) and T-brain-1 (TBR1) in the brain organoids also showed significant increase after 70 days of treatment with sulforaphane. RNA sequencing analysis revealed that compared with the control group, 105 up-regulated genes and 512 down-regulated genes were identified in the sulforaphane treatment group, which, in total, are 617 genes. qRT-PCR verified that the transcription levels of differentially expressed genes (SFTPC,AKR1C3,CXCR6,PRAP1,TMC8,GPR182,F2RL2,KCNJ10) are basically consistent with the sequencing results.Conclusion:AKR1C3,KCNJ10 and other genes may be involved in sulforaphane to promote the brain organoid prefrontal development and neuronal differentiation. This research helps to provide new experimental evidence that sulforaphane improves the cognition and symptoms of patients with mental illness and neurodegenerative diseases.

Objective:By comparing the changes of brain differentiation and neuronal markers of brain organoids in different periods, this study aims to clarify the effect and possible mechanisms of sulforaphane on the development of brain organoids.Methods:Brain organoids differentiated from human induced pluripotent stem cells treated with sulforaphane were used in this study. The experimental group was treated with sulforaphane at different concentrations (1 μmol/L, 2 μmol/L, 5 μmol/L), and the control group was a blank group, with 20 organoids in each group. Immunofluorescence and hematoxylin-eosin staining (HE) were used to observe and compare the expression characteristics of brain markers and neuronal markers in different periods of the two groups after sulforaphane treatment. Ribonucleic Acid (RNA) sequencing analysis of differentially expressed genes, functional enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted. Real-time Quantitative Reverse Transcriptase-mediated Polymerase Chain Reaction (qRT-PCR) was performed to further screen and verify the potential genes or possible targets that may be involved in sulforaphane in promoting the growth of brain organoids.Results:The sex determining region Y-box 2(SOX2), Forebrain protein forkhead box G1 (FOXG1), prefrontal cortex autism susceptibility candidate 2 (AUST2), and paired box 6 (PAX6) increased significantly in the brain organoids after 40 days of treatment with sulforaphane; the expression of neuron marker proteins like β-tubulin (TUJ1), neuronal nuclei (NeuN), recombinant microtubule associated protein 2 (MAP2) and T-brain-1 (TBR1) in the brain organoids also showed significant increase after 70 days of treatment with sulforaphane. RNA sequencing analysis revealed that compared with the control group, 105 up-regulated genes and 512 down-regulated genes were identified in the sulforaphane treatment group, which, in total, are 617 genes. qRT-PCR verified that the transcription levels of differentially expressed genes (SFTPC,AKR1C3,CXCR6,PRAP1,TMC8,GPR182,F2RL2,KCNJ10) are basically consistent with the sequencing results.Conclusion:AKR1C3,KCNJ10 and other genes may be involved in sulforaphane to promote the brain organoid prefrontal development and neuronal differentiation. This research helps to provide new experimental evidence that sulforaphane improves the cognition and symptoms of patients with mental illness and neurodegenerative diseases.

Second-generation antipsychotic medications are widely used to treat patients with schizophrenia and other mental disorders, however, second-generation antipsychotic medications could cause metabolic side effects such as weight gain, hyperglycemia, dyslipidemia and insulin resistance. The mechanisms that cause metabolic side effects is still unclear. Until now, there is still a lack of effective treatments of metabolic side effects caused by the second-generation antipsychotic medications. In recent years, many studies have showed that gut microbiota may play an important role. This review focused on the mechanisms of gut microbiota in metabolic side effects caused by the second-generation antipsychotic medications, and the role of probiotics and prebiotics in the prevention or treatment of metabolic side effects.

Second-generation antipsychotic medications are widely used to treat patients with schizophrenia and other mental disorders, however, second-generation antipsychotic medications could cause metabolic side effects such as weight gain, hyperglycemia, dyslipidemia and insulin resistance. The mechanisms that cause metabolic side effects is still unclear. Until now, there is still a lack of effective treatments of metabolic side effects caused by the second-generation antipsychotic medications. In recent years, many studies have showed that gut microbiota may play an important role. This review focused on the mechanisms of gut microbiota in metabolic side effects caused by the second-generation antipsychotic medications, and the role of probiotics and prebiotics in the prevention or treatment of metabolic side effects.

Antipsychotic medication is the primary treatment for schizophrenia, which is effective on ameliorating positive symptoms and can reduce the risk of recurrence, but it has limited efficacy for negative symptoms and cognitive dysfunction. The negative symptoms and cognitive dysfunction seriously affects the life quality and social function for the patients with schizophrenia. Currently, there is plenty evidence that antipsychotic drugs combined with adjuvant therapy drugs can effectively improve the negative symptoms and cognitive dysfunction. These drugs include anti-oxidants, nicotinic acetylcholine receptors and neuro-inflammatory drugs (anti-inflammatory drugs, minocycline), which show potential clinical effects.
Anti-Inflammatory Agents/therapeutic use* ; Antipsychotic Agents/therapeutic use* ; Cognitive Dysfunction/etiology* ; Humans ; Minocycline/therapeutic use* ; Schizophrenia/drug therapy*