Publications by authors named "Xiaohong Ma"

187 Publications

The interactions between childhood adversities and recent stress were associated with early-adulthood depression among Chinese undergraduate students.

Depress Anxiety 2021 Jul 22. Epub 2021 Jul 22.

Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China.

Background: It is widely acknowledged that childhood adversities (CAs) and recent stress are potential risk factors for adult depression. However, the mechanism(s) by which interactions of CAs with recent stress affect adult depression remain unclear.

Aims: To investigate the predictive association of the interaction among CAs and recent stress with early-adult depression.

Method: We conducted an annual survey of all freshmen for the period of 2016-2018 in a Chinese comprehensive university, with a sample size of 23,206. An online questionnaire including standardized self-report instruments was used to assess sociodemographic factors, childhood experiences of left-behind (CELB), and maltreatments (CEMTs) including beating (CEB), neglect (CEN), sexual abuse (CESA), recent stress, and current depression (measured by the 9-item Patient Health Questionnaire).

Results: The correlation of Individual CAs and recent stress was significant. In addition to their significant independent/direct incremental effects, all surveyed CAs were associated with increased severity of early-adult depression, and increased frequency of clinically significant depression (CSD), through significant associations with recent stress (mediation effect). History of CEMTs including CEB, CEN, and CESA significantly increased the effects of recent stress on depression (moderation effect).

Conclusions: Chinese undergraduate students reported frequent history of exposure to CAs, which increased the likelihood of depression in early adulthood, not only directly but also through the increasing the likelihood (mediation effect) and impact (moderation effect) of recent stress on depression. These novel findings may help to extend our understanding of environmental determinants of depression, and to guide further research, clinical practice, and policy in this context.
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http://dx.doi.org/10.1002/da.23201DOI Listing
July 2021

Integrin αβ-targeted MR molecular imaging of breast cancer in a xenograft mouse model.

Cancer Imaging 2021 Jun 29;21(1):44. Epub 2021 Jun 29.

Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.

Background: The motif RXDLXXL-based nanoprobes allow specific imaging of integrin αβ, a protein overexpressed during tumorigenesis and tumor progression of various tumors. We applied a novel RXDLXXL-coupled cyclic arginine-glycine-aspartate (RGD) nonapeptide conjugated with ultrasmall superparamagnetic iron oxide nanoparticles (referred to as cFK-9-USPIO) for the application of integrin αβ-targeted magnetic resonance (MR) molecular imaging for breast cancer.

Methods: A novel MR-targeted nanoprobe, cFK-9-USPIO, was synthesized by conjugating integrin αβ-targeted peptide cFK-9 to N-amino (-NH2)-modified USPIO nanoparticles via a dehydration esterification reaction. Integrin αβ-positive mouse breast cancer (4 T1) and integrin αβ negative human embryonic kidney 293 (HEK293) cell lines were incubated with cFK-9-AbFlour 647 (blocking group) or cFK-9-USPIO (experimental group), and subsequently imaged using laser scanning confocal microscopy (LSCM) and 3.0 Tesla magnetic resonance imaging (MRI) system. The affinity of cFK-9 targeting αβ was analyzed by calculating the mean fluorescent intensity in cells, and the nanoparticle targeting effect was measured by the reduction of T2 values in an in vitro MRI. The in vivo MRI capability of cFK-9-USPIO was investigated in 4 T1 xenograft mouse models. Binding of the targeted nanoparticles to αβ-positive 4 T1 tumors was determined by ex vivo histopathology.

Results: In vitro laser scanning confocal microscopy (LSCM) imaging showed that the difference in fluorescence intensity between the targeting and blocking groups of 4 T1 cells was significantly greater than that in HEK293 cells (P < 0.05). The in vitro MRI demonstrated a more remarkable T2 reduction in 4 T1 cells than in HEK293 cells (P < 0.001). The in vivo MRI of 4 T1 xenograft tumor-bearing nude mice showed significant T2 reduction in tumors compared to controls. Prussian blue staining further confirmed that αβ integrin-targeted nanoparticles were specifically accumulated in 4 T1 tumors and notably fewer nanoparticles were detected in 4 T1 tumors of mice injected with control USPIO and HEK293 tumors of mice administered cFK-9-USPIO.

Conclusions: Integrin αβ-targeted nanoparticles have great potential for use in the detection of αβ-overexpressed breast cancer with MR molecular imaging.
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http://dx.doi.org/10.1186/s40644-021-00411-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8244136PMC
June 2021

Circadian misalignment leads to changes in cortisol rhythms, blood biochemical variables and serum miRNA profiles.

Biochem Biophys Res Commun 2021 Aug 12;567:9-16. Epub 2021 Jun 12.

State Key Laboratory of Biocontrol, Key Laboratory of Gene Engineering of the Ministry of Education, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510006, China. Electronic address:

The circadian clock plays a critical role in synchronizing the inner molecular, metabolic and physiological processes to environmental cues that cycle with a period of 24 h. Non-24 h and shift schedules are commonly used in maritime operations, and both of which can disturb circadian rhythms. In this study, we first conducted an experiment in which the volunteers followed a 3-d rotary schedule with consecutive shift in sleep time (rotatory schedule), and analyzed the changes in salivary cortisol rhythms and blood variables. Next we conducted another experiment in which the volunteers followed an 8 h-on and 4-h off schedule (non-24-h schedule) to compare the changes in blood/serum variables. The rotatory schedule led to elevated levels of serum cortisol during the early stage, and the phase became delayed during the early and late stages. Interestingly, both of the schedules caused comprehensive changes in blood/serum biochemical variables and increased phosphate levels. Furthermore, transcriptomic analysis of the plasma miRNAs from the volunteers following the rotatory schedule identified a subset of serum miRNAs targeting genes involved in circadian rhythms, sleep homeostasis, phosphate transport and multiple important physiological processes. Overexpression of miRNAs targeting the phosphate transport associated genes, SLC20A1 and SLC20A2, showed altered expression due to rotary schedule resulted in attenuated cellular levels of phosphate, which might account for the changed levels in serum phosphate. These findings would further our understanding of the deleterious effects of shift schedules and help to optimize and enhance the performances and welfare of personnel working on similar schedules.
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http://dx.doi.org/10.1016/j.bbrc.2021.06.015DOI Listing
August 2021

Fatostatin reverses progesterone resistance by inhibiting the SREBP1-NF-κB pathway in endometrial carcinoma.

Cell Death Dis 2021 May 26;12(6):544. Epub 2021 May 26.

Department of Gynecology and Obstetrics, Qilu Hospital of Shandong University, 250012, Jinan, China.

Progesterone resistance can significantly restrict the efficacy of conservative treatment for patients with endometrial cancer who wish to preserve their fertility or those who suffer from advanced and recurrent cancer. SREBP1 is known to be involved in the occurrence and progression of endometrial cancer, although the precise mechanism involved remains unclear. In the present study, we carried out microarray analysis in progesterone-sensitive and progesterone-resistant cell lines and demonstrated that SREBP1 is related to progesterone resistance. Furthermore, we verified that SREBP1 is over-expressed in both drug-resistant tissues and cells. Functional studies further demonstrated that the inhibition of SREBP1 restored the sensitivity of endometrial cancer to progesterone both in vitro and in vivo, and that the over-expression of SREBP1 promoted resistance to progesterone. With regards to the mechanism involved, we found that SREBP1 promoted the proliferation of endometrial cancer cells and inhibited their apoptosis by activating the NF-κB pathway. To solve the problem of clinical application, we found that Fatostatin, an inhibitor of SREBP1, could increase the sensitivity of endometrial cancer to progesterone and reverse progesterone resistance by inhibiting SREBP1 both in vitro and in vivo. Our results highlight the important role of SREBP1 in progesterone resistance and suggest that the use of Fatostatin to target SREBP1 may represent a new method to solve progesterone resistance in patients with endometrial cancer.
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http://dx.doi.org/10.1038/s41419-021-03762-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8155186PMC
May 2021

Elevated expression of LPCAT1 predicts a poor prognosis and is correlated with the tumour microenvironment in endometrial cancer.

Cancer Cell Int 2021 May 20;21(1):269. Epub 2021 May 20.

Department of Gynecology and Obstetrics, Qilu Hospital, Cheeloo College of Medicine, Shandong University, 107 West Wenhua Rd, Jinan, 250012, Shandong, People's Republic of China.

Background: Endometrial cancer (EC) is one of the three malignant reproductive tumours that threaten women's lives and health. Glycerophospholipids (GPLs) are important bioactive lipids involved in various physiological and pathological processes, including cancer. Immune infiltration of the tumour microenvironment (TME) is positively associated with the overall survival in EC. Exploring GPL-related factors associated with the TME in endometrial cancer can aid in the prognosis of patients and provide new therapeutic targets.

Methods: Differentially expressed GPL-related genes were identified from TCGA-UCEC datasets and the Molecular Signatures Database (MSigDB). Univariate Cox regression analysis was used to select GPL-related genes with prognostic value. The Random forest algorithm, LASSO algorithm and PPI network were used to identify critical genes. ESTIMATEScore was calculated to identify genes associated with the TME. Then, differentiation analysis and survival analysis of LPCAT1 were performed based on TCGA datasets. GSE17025 and immunohistochemistry (IHC) verified the results of the differentiation analysis. An MTT assay was then conducted to determine the proliferation of EC cells. GO and KEGG enrichment analyses were performed to explore the underlying mechanism of LPCAT1. In addition, we used the ssGSEA algorithm to explore the correlation between LPCAT1 and cancer immune infiltrates.

Results: Twenty-three differentially expressed GPL-related genes were identified, and eleven prognostic genes were selected by univariate Cox regression analysis. Four significant genes were identified by two different algorithms and the PPI network. Only LPCAT1 was significantly correlated with the tumour microenvironment. Then, we found that LPCAT1 was highly expressed in tumour samples compared with that in normal tissues, and lower survival rates were observed in the groups with high LPCAT1 expression. Silencing of LPCAT1 inhibited the proliferation of EC cells. Moreover, the expression of LPCAT1 was positively correlated with the histologic grades and types. The ROC curve indicated that LPCAT1 had good prognostic accuracy. Receptor ligand activity, pattern specification process, regionalization, anterior/posterior pattern specification and salivary secretion pathways were enriched as potential targets of LPCAT1. By using the ssGSEA algorithm, fifteen kinds of tumor-infiltrating cells (TICs) were found to be correlated with LPCAT1 expression.

Conclusion: These findings suggested that LPCAT1 may act as a valuable prognostic biomarker and be correlated with immune infiltrates in endometrial cancer, which may provide novel therapy options for and improved treatment of EC.
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http://dx.doi.org/10.1186/s12935-021-01965-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8139085PMC
May 2021

Lower regional grey matter in alcohol use disorders: evidence from a voxel-based meta-analysis.

BMC Psychiatry 2021 05 11;21(1):247. Epub 2021 May 11.

Mental Health Center, West China Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China.

Background: Previous research using whole-brain neuroimaging techniques has revealed structural differences of grey matter (GM) in alcohol use disorder (AUD) patients. However, some of the findings diverge from other neuroimaging studies and require further replication. The quantity of relevant research has, thus far, been limited and the association between GM and abstinence duration of AUD patients has not yet been systematically reviewed.

Methods: The present research conducted a meta-analysis of voxel-based GM studies in AUD patients published before Jan 2021. The study utilised a whole brain-based d-mapping approach to explore GM changes in AUD patients, and further analysed the relationship between GM deficits, abstinence duration and individual differences.

Results: The current research included 23 studies with a sample size of 846 AUD patients and 878 controls. The d-mapping approach identified lower GM in brain regions including the right cingulate gyrus, right insula and left middle frontal gyrus in AUD patients compared to controls. Meta-regression analyses found increasing GM atrophy in the right insula associated with the longer mean abstinence duration of the samples in the studies in our analysis. GM atrophy was also found positively correlated with the mean age of the samples in the right insula, and positively correlated with male ratio in the left middle frontal gyrus.

Conclusions: GM atrophy was found in the cingulate gyrus and insula in AUD patients. These findings align with published meta-analyses, suggesting they are potential deficits for AUD patients. Abstinence duration, age and gender also affect GM atrophy in AUD patients. This research provides some evidence of the underlying neuroanatomical nature of AUD.
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http://dx.doi.org/10.1186/s12888-021-03244-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8111920PMC
May 2021

Subtypes of depression characterized by different cognitive decline and brain activity alterations.

J Psychiatr Res 2021 06 29;138:413-419. Epub 2021 Apr 29.

Psychiatric Laboratory and Mental Health Center, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China; West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China. Electronic address:

Depression is characterized by the heterogeneity in anti-depressant treatment response and clinical outcomes. Cognitive impairment may be one of the more practically important aspects of depression. A new approach was to identify neuropsychologically derived depression subtypes based on the trajectory of neuro-cognition such as intelligence quotient (IQ) change. We used a classical premorbid IQ prediction algorithm and then compared predicted premorbid IQ with current IQ. IQ change was used to delineate the patterns of neuropsychological heterogeneity within a large dataset consisting of 131 patients with major depressive disorder (MDD) and 165 healthy controls (HCs). Neurocognitive results from CANTAB and 3 T resting-state fMRI data were compared among the subgroups identified. IQ change heterogeneity identified two subgroups within the MDD group: preserved IQ (PIQ) and deteriorated IQ (DIQ) in MDD. The DIQ subgroup was marked by poorer functioning across multiple cognition domains, including increased impairments in motor speed, cognitive flexibility, and catastrophic thinking when compared to PIQ and HCs. Moreover, cognitive performance of patients with DIQ was correlated with IQ decline. Also, increased brain activity of anterior cingulate cortex and medial prefrontal cortex was found in DIQ but not in PIQ and HCs. IQ-based subgroups of depression may be differentially associated with the extent of neurocognitive impairment and brain activities, which suggests that classifying the cognitive heterogeneity associated with depression may provide a platform to better characterize the neurobiological underpinnings of the disease.
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http://dx.doi.org/10.1016/j.jpsychires.2021.04.023DOI Listing
June 2021

Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker.

Biol Psychiatry 2021 Jan 30. Epub 2021 Jan 30.

Centre for Population Neuroscience and Stratified Medicine, Institute for Science and Technology of Brain-Inspired Intelligence, Fudan University, Shanghai, China; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany.

Background: Dysfunctional reward processing is implicated in multiple mental disorders. Novelty seeking (NS) assesses preference for seeking novel experiences, which is linked to sensitivity to reward environmental cues.

Methods: A subset of 14-year-old adolescents (IMAGEN) with the top 20% ranked high-NS scores was used to identify high-NS-associated multimodal components by supervised fusion. These features were then used to longitudinally predict five different risk scales for the same and unseen subjects (an independent dataset of subjects at 19 years of age that was not used in predictive modeling training at 14 years of age) (within IMAGEN, n ≈1100) and even for the corresponding symptom scores of five types of patient cohorts (non-IMAGEN), including drinking (n = 313), smoking (n = 104), attention-deficit/hyperactivity disorder (n = 320), major depressive disorder (n = 81), and schizophrenia (n = 147), as well as to classify different patient groups with diagnostic labels.

Results: Multimodal biomarkers, including the prefrontal cortex, striatum, amygdala, and hippocampus, associated with high NS in 14-year-old adolescents were identified. The prediction models built on these features are able to longitudinally predict five different risk scales, including alcohol drinking, smoking, hyperactivity, depression, and psychosis for the same and unseen 19-year-old adolescents and even predict the corresponding symptom scores of five types of patient cohorts. Furthermore, the identified reward-related multimodal features can classify among attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia with an accuracy of 87.2%.

Conclusions: Adolescents with higher NS scores can be used to reveal brain alterations in the reward-related system, implicating potential higher risk for subsequent development of multiple disorders. The identified high-NS-associated multimodal reward-related signatures may serve as a transdiagnostic neuroimaging biomarker to predict disease risks or severity.
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http://dx.doi.org/10.1016/j.biopsych.2021.01.011DOI Listing
January 2021

Spatial Expression Pattern of ZNF391 Gene in the Brains of Patients With Schizophrenia, Bipolar Disorders or Major Depressive Disorder Identifies New Cross-Disorder Biotypes: A Trans-Diagnostic, Top-Down Approach.

Schizophr Bull 2021 Apr 3. Epub 2021 Apr 3.

Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China.

The results generated from large psychiatric genomic consortia show us some new vantage points to understand the pathophysiology of psychiatric disorders. We explored the potential of integrating the transcription output of the core gene underlying the commonality of psychiatric disorders with a clustering algorithm to redefine psychiatric disorders. Our results showed that an extended MHC region was associated with the common factor of schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) at the level of genomic significance, with rs7746199 (P = 4.905e-08), a cis-eQTL to the gene ZNF391, pinpointed as a potential causal variant driving the signals in the region. Gene expression pattern of ZNF391 in the brain led to the emergence of 3 biotypes, independent of disorder. The 3 biotypes performed significantly differently in working memory and demonstrated different gray matter volumes in the right inferior frontal orbital gyrus (RIFOG), with a partial causal pathway arising from ZNF391 to RIFOG to working memory. Our study illustrates the potential of a trans-diagnostic, top-down approach in understanding the commonality of psychiatric disorders.
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http://dx.doi.org/10.1093/schbul/sbaa167DOI Listing
April 2021

Conventional Machine Learning versus Deep Learning for Magnification Dependent Histopathological Breast Cancer Image Classification: A Comparative Study with Visual Explanation.

Diagnostics (Basel) 2021 Mar 16;11(3). Epub 2021 Mar 16.

Department of Computer Science and Math, Abbes Laghrour University, Khenchela 40000, Algeria.

Breast cancer is a serious threat to women. Many machine learning-based computer-aided diagnosis (CAD) methods have been proposed for the early diagnosis of breast cancer based on histopathological images. Even though many such classification methods achieved high accuracy, many of them lack the explanation of the classification process. In this paper, we compare the performance of conventional machine learning (CML) against deep learning (DL)-based methods. We also provide a visual interpretation for the task of classifying breast cancer in histopathological images. For CML-based methods, we extract a set of handcrafted features using three feature extractors and fuse them to get image representation that would act as an input to train five classical classifiers. For DL-based methods, we adopt the transfer learning approach to the well-known VGG-19 deep learning architecture, where its pre-trained version on the large scale ImageNet, is block-wise fine-tuned on histopathological images. The evaluation of the proposed methods is carried out on the publicly available BreaKHis dataset for the magnification dependent classification of benign and malignant breast cancer and their eight sub-classes, and a further validation on KIMIA Path960, a magnification-free histopathological dataset with 20 image classes, is also performed. After providing the classification results of CML and DL methods, and to better explain the difference in the classification performance, we visualize the learned features. For the DL-based method, we intuitively visualize the areas of interest of the best fine-tuned deep neural networks using attention maps to explain the decision-making process and improve the clinical interpretability of the proposed models. The visual explanation can inherently improve the pathologist's trust in automated DL methods as a credible and trustworthy support tool for breast cancer diagnosis. The achieved results show that DL methods outperform CML approaches where we reached an accuracy between 94.05% and 98.13% for the binary classification and between 76.77% and 88.95% for the eight-class classification, while for DL approaches, the accuracies range from 85.65% to 89.32% for the binary classification and from 63.55% to 69.69% for the eight-class classification.
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http://dx.doi.org/10.3390/diagnostics11030528DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8001768PMC
March 2021

A plasma metabolomics study suggests alteration of multiple metabolic pathways in patients with bipolar disorder.

Psychiatry Res 2021 May 18;299:113880. Epub 2021 Mar 18.

Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China. Electronic address:

Previous omics studies have greatly contributed to our knowledge of bipolar disorder. Metabolomics is a relatively new field of omics science that can provide complementary insight into data obtained from genomics, transcriptomics or proteomics analyses. In this study, we aimed to identify metabolic pathways associated with bipolar disorder. We performed a liquid chromatography-mass spectrometry-based study to identify plasma metabolic profiles in patients with bipolar disorder (N = 91) and healthy controls (N = 92). Multivariate features selection by sparse partial least square-discriminant analysis combined with metabolite set enrichment analysis were used to identify metabolites and biological pathways that discriminate patients with bipolar disorder from healthy controls. The results showed that eighty metabolites in the plasma were identified to discriminate patients with bipolar disorder from healthy controls, and nine metabolic pathways, i.e., (1) glycine and serine metabolism, (2) glutamate metabolism, (3) arginine and proline metabolism, (4) tyrosine metabolism, (5) catecholamine biosynthesis, (6) purine metabolism, (7) amino sugar metabolism, (8) ammonia recycling, and (9) carnitine synthesis, were identified to be altered in bipolar disorder compared to healthy controls. We conclude that the 80 metabolites and nine metabolic pathways identified might serve as biomarkers to distinguish bipolar disorder patients from healthy controls.
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http://dx.doi.org/10.1016/j.psychres.2021.113880DOI Listing
May 2021

LncRNA-ZXF1 regulates P21 expression in endometrioid endometrial carcinoma by managing ubiquitination-mediated degradation and miR-378a-3p/PCDHA3 axis.

Mol Oncol 2021 Mar 9. Epub 2021 Mar 9.

Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, China.

Long noncoding RNAs (lncRNAs) have a profound effect on biological processes in various malignancies. However, few studies have investigated their functions and specific mechanisms in endometrial cancer. In this study, we focused on the role and mechanism of lncRNA-ZXF1 in endometrial cancer. Bioinformatics and in viro and in vivo experiments were used to explore the expression and function of lncRNA-ZXF1. We identified lncRNA-ZXF1 altered the migration and invasion of endometrioid endometrial cancer (EEC) cells. Furthermore, our results suggest that lncRNA-ZXF1 regulates EEC cell proliferation. This regulation may be achieved by the lncRNA-ZXF1-mediated alteration in the expression of P21 through two mechanisms. One is that lncRNA-ZXF1 functions as a molecular sponge of miR-378a-3p to regulate PCDHA3 expression and then modulate the expression of P21. The other is that lncRNA-ZXF1 inhibits CDC20-mediated degradation of ubiquitination by directly binding to P21. To the best of our knowledge, this study is the first to explore lncRNA-ZXF1 functioning as a tumor-suppressing lncRNA in EEC. LncRNA-ZXF1 may become therapeutic, diagnostic, and prognostic indicator in the future.
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http://dx.doi.org/10.1002/1878-0261.12940DOI Listing
March 2021

Plasma metabolites were associated with spatial working memory in major depressive disorder.

Medicine (Baltimore) 2021 Feb;100(8):e24581

Psychiatric Laboratory and Mental Health Center.

Abstract: Major depressive disorder (MDD) is a common disease with both affective and cognitive disorders. Alterations in metabolic systems of MDD patients have been reported, but the underlying mechanisms still remains unclear. We sought to identify abnormal metabolites in MDD by metabolomics and to explore the association between differential metabolites and neurocognitive dysfunction.Plasma samples from 53 MDD patients and 83 sex-, gender-, BMI-matched healthy controls (HCs) were collected. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) system was then used to detect metabolites in those samples. Two different algorithms were applied to identify differential metabolites in 2 groups. Of the 136 participants, 35 MDD patients and 48 HCs had completed spatial working memory test. Spearman rank correlation coefficient was applied to explore the relationship between differential metabolites and working memory in these 2 groups.The top 5 metabolites which were found in sparse partial least squares-discriminant analysis (sPLS-DA) model and random forest (RF) model were the same, and significant difference was found in 3 metabolites between MDD and HCs, namely, gamma-glutamyl leucine, leucine-enkephalin, and valeric acid. In addition, MDD patients had higher scores in spatial working memory (SWM) between errors and total errors than HCs. Valeric acid was positively correlated with working memory in MDD group.Gamma-glutamyl leucine, leucine-enkephalin, and valeric acid were preliminarily proven to be decreased in MDD patients. In addition, MDD patients performed worse in working memory than HCs. Dysfunction in working memory of MDD individuals was associated with valeric acid.
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http://dx.doi.org/10.1097/MD.0000000000024581DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7909221PMC
February 2021

Association Analysis Between Catechol-O-Methyltransferase Expression and Cognitive Function in Patients with Schizophrenia, Bipolar Disorder, or Major Depression.

Neuropsychiatr Dis Treat 2021 22;17:567-574. Epub 2021 Feb 22.

The Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China.

Introduction: Schizophrenia, bipolar disorder (BD), and major depressive disorder are three common mental disorders. Although their diagnosis and treatment differ, they partially overlap.

Methods: To explore the similarities and characteristics of these three psychiatric diseases, an intelligence quotient (IQ) assessment was performed to evaluate cognitive deficits. Relative catechol-O-methyltransferase () expression in peripheral blood mononuclear cells was examined in all three groups compared with healthy controls (HCs).

Results: The results indicated that patients with any of the three psychiatric diseases presented IQ deficits, and that the first-episode schizophrenia (FES) group had even lower cognitive function than the other two groups. The relative expression decreased in the FES group and increased in the BD group compared with the HC group. The correlation analysis of expression level and IQ scores showed a positive correlation between relative expression and full-scale IQ in the HC group. However, this correlation disappeared in all three psychiatric diseases studied.

Conclusion: In conclusion, this cross-disease strategy provided important clues to explain lower IQ scores and dysregulated expression among three common mental illnesses.
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http://dx.doi.org/10.2147/NDT.S286102DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7910219PMC
February 2021

CNR1 may reverse progesterone-resistance of endometrial cancer through the ERK pathway.

Biochem Biophys Res Commun 2021 Apr 25;548:148-154. Epub 2021 Feb 25.

Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong Province, China. Electronic address:

Endocrine therapy is a promising treatment for endometrial cancer (EC) that preserves fertility, however, progesterone-resistance is currently the major challenges. The Cancer Genome Atlas (TCGA) database analysis showed that CNR1 was closely have a negative correlation with overall survival (OS) and relapse-free survival (RFS) in endometrial cancer. To explore the role of CNR1 in progesterone resistance and possible molecular regulation mechanism, we established stable progesterone-resistant cell lines (IshikawaPR) via progesterone tolerance of ordinary cancer cells (Ishikawa). The difference of CNR1 level in two cell lines was assessed by MTT, RT-PCR, Western blot, immunofluorescence. Then, lentiviruses constructed CNR1-knockdown with GV248 as the tool vector were used to transfect IshikwaPR cells, and the changes of biological behavior and progesterone sensitivity was verified respectively through plate cloning experiment, EdU assay, flow cytometry cycle analysis, transwell, Scratch test, etc. We founded after CNR1 was knocked down, the proliferative activity and ability to migrate of IshikawaPR cells decreased, progesterone-response sensitivity could be improved. Moreover, knockdown of CNR1 can also down-regulate ERK and NFκ B expression and activation. Furthermore, subcutaneous xenograft in nude mice was tested similarly in vivo. The above datas suggest that targeting CNR1 may reverse the progesterone resistance in endometrial cancer and may coordinate the role of ERK pathway activation.
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http://dx.doi.org/10.1016/j.bbrc.2021.02.038DOI Listing
April 2021

Effect and mechanism of the algicidal bacterium Sulfitobacter porphyrae ZFX1 on the mitigation of harmful algal blooms caused by Prorocentrum donghaiense.

Environ Pollut 2020 Aug 1;263(Pt A):114475. Epub 2020 Apr 1.

State Key Laboratory of Cellular Stress Biology, and School of Life Sciences, Xiamen University, Xiamen, Fujian, 361102, PR China; Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, Xiamen University, Xiamen, Fujian, 361102, PR China. Electronic address:

Sulfitobacter porphyrae ZFX1, isolated from surface seawater of the East China Sea during a Prorocentrum donghaiense bloom recession, exhibits high algicidal activity against P. donghaiense. To evaluate the algicidal effect of ZFX1, the algicidal mode and stability were investigated. The results showed that ZFX1 indirectly attacked algae by secreting algicidal compounds, and the algicidal activity of the ZFX1 supernatant was insensitive to different temperatures, light intensities and pH values (pH 3-12). To explore the algicidal mechanism of the ZFX1 supernatant, its effects on the morphological and ultrastructural alterations, photosynthetic capacity, reactive oxygen species (ROS) and antioxidative system of P. donghaiense were investigated. Scanning and transmission electron microscopy revealed that the ZFX1 supernatant destroyed the algal cell membrane structure and caused intracellular leakage. The decrease in the chlorophyll a content and the marked declines in both the photosynthetic efficiency (Fv/Fm) and the electron transport rate (rETR) indicated that the ZFX1 supernatant could damage the photosynthetic system of P. donghaiense. The excessive production of ROS in algal cells demonstrated the oxidative damage triggered by the ZFX1 supernatant. Although the antioxidant defense system of P. donghaiense was activated to scavenge excessive ROS, lipid oxidation occurred. The fatty acid composition profile indicated that the ZFX1 supernatant markedly increased the contents of two saturated fatty acids and a monounsaturated fatty acid and decreased the proportion of two polyunsaturated fatty acids, which resulted in lipids with a lower degree of unsaturation (DU). The decline in the DU decreased the lipid fluidity and rigidified the membrane system, and these effects destroyed the function of the membrane system and ultimately resulted in algal cell death. Therefore, ZFX1 probably plays a key role in mitigating P. donghaiense bloom by inducing lipid oxidation, decreasing the DU of lipids and ultimately destroying the membrane systems of algal cells.
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http://dx.doi.org/10.1016/j.envpol.2020.114475DOI Listing
August 2020

Valproate Reverses Mania-Like Behavior of Mouse and Alters Monoamine Neurotransmitters Metabolism in the Hippocampus.

Neuropsychiatr Dis Treat 2021 11;17:471-480. Epub 2021 Feb 11.

Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, People's Republic of China.

Background: Mice with a deletion at exon 19 of the circadian locomotor output cycles Kaput gene ( ) exhibit mania-like behavior and have been one of the most common animal models for bipolar disorder (BD). The predictive validity of the was investigated via studies using lithium previously. Determination of effects of other mood stabilizers on mouse would be helpful for better understanding of the mechanism underlined.

Methods: Wildtype (WT) and mice were treated with saline (n = 10 for WT and n=10 for ) or valproate (VPA) (n = 10 for WT and n=10 for ) for 10 days. The hyperactivity, anxiety-like behaviors and depression-like behaviors were tested. The concentration of monoamine neurotransmitters and their metabolites in the hippocampus of saline or VPA treated WT and mouse (n = 8 for each) were also determined.

Results: VPA can reverse hyperactivity, lower level of anxiety-like and depression-like behaviors of the mouse. mouse exhibited lower levels of serotonin (5-HT) and dopamine (DA) in right hippocampus compared to WT mouse. Chronic VPA treatment did not affect the levels of 5-HT and DA, but can reduce the level of levodopa (L-DOPA) in the right hippocampus of mouse.

Conclusion: Our results indicated that chronic VPA treatment can reverse the mania-like behaviors of the mouse and further consolidate the validity of the mouse as a model of BD. Monoamine neurotransmitters and their metabolites in the hippocampus are partly regulated by mutation of the gene or VPA treatment.
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http://dx.doi.org/10.2147/NDT.S293482DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7884953PMC
February 2021

Inferior frontal gyrus seed-based resting-state functional connectivity and sustained attention across manic/hypomanic, euthymic and depressive phases of bipolar disorder.

J Affect Disord 2021 03 6;282:930-938. Epub 2021 Jan 6.

Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, P R China; Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; Brain Research Center, West China Hospital of Sichuan University, Chengdu, China. Electronic address:

Objective: Seed-based resting-state functional connectivity (rs-FC) of inferior frontal gyrus (IFG), as well as sustained attention cognitive deficit are consistently reported to be impaired in bipolar disorders. However, whether these deficits exist across mood states and euthymic state are lacking. We compared rs-FC of IFG and sustained attention of bipolar patients in (hypo) mania, depression and euthymia, with controls. We also explored the interrelationships between clinical, cognitive, and imaging measurements.

Methods: Participants included 110 bipolar subjects: 46 manic/hypomanic, 35 euthymic, and 29 depressed, matched with 41 healthy controls (HCs) underwent structural magnetic resonance imaging (MRI) and resting-state functional MRI scans. Seed-based functional connectivity analyses were performed focused on bilateral IFG seeds. Clinical symptoms and sustained attention function were measured. Stepwise linear regression analysis was conducted to explore predictors of sustained attention measurements.

Results: Increased rs-FC between right IFG and bilateral frontal pole/superior frontal gyrus, precuneus, and posterior cingulate gyrus, as well as decreased rs-FC between right IFG and sensorimotor areas, anterior middle cingulate gyrus were found in all three bipolar subgroups compared with HCs. Impaired sustained attention measurement was found in bipolar manic/hypomanic and depressive subgroups compared with HCs. Linear regression analyses revealed a significant impact of the manic symptoms and psychotic symptoms on the performance of sustained attention task.

Conclusions: Our results revealed that IFG seed-based resting-state functional networks involved in emotion regulation and cognitive function were trait-like deficit in bipolar patients. Higher manic levels and psychotic symptoms were predictors of a worse sustained attention performance.
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http://dx.doi.org/10.1016/j.jad.2020.12.199DOI Listing
March 2021

Aberrant dynamic functional connectivity of default mode network predicts symptom severity in major depressive disorder.

Brain Connect 2021 Jan 29. Epub 2021 Jan 29.

Georgia Institute of Technology, 1372, Electrical and Computer Engineering , Atlanta, Georgia, United States.

Background: Major depressive disorder (MDD) is a complex mental disorder characterized by a persistent sad feeling and lack of interest. The default mode network (DMN) is a set of brain areas that is more active during rest and deactivate during a goal-oriented behavior. Recent studies have shown abnormal static functional connectivity in the DMN of MDD. In this work, we extend previous findings by evaluating dynamic functional connectivity (dFC) within the DMN subnodes in MDD.

Methods: We analyzed resting-state functional magnetic resonance imaging (rs-fMRI) data of 262 MDD patients and 277 healthy controls (HCs). We employed a sliding-window approach to estimate dFCs for seven subnodes of the DMN, including anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), and precuneus (PCu), followed by clustering the dFCs into five recurring brain states. Classification of MDD and HC subjects based on within-state FC was performed using a logistic regression classifier with elastic net regularization. Transition probabilities between dFC states were used to identify relationships between symptom severity and dFC data in MDD patients.

Results: By comparing state-specific FC between HC and MDD, a disrupted connectivity pattern was observed within DMN. In more detail, we found that the connectivity of ACC is stronger, and the connectivity between PCu and PCC is weaker in individuals with MDD than in those of HC subjects. In addition, MDD subjects showed a higher probability of transitioning from a state with weaker ACC connectivity to a state with stronger ACC connectivity, and this abnormality is associated with symptom severity. This study is the first attempt to study dFC of the DMN in MDD using a relatively large sample size. It provides novel evidence of abnormal time-varying DMN configuration in MDD and offers links to symptom severity in MDD subjects.
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http://dx.doi.org/10.1089/brain.2020.0748DOI Listing
January 2021

Lasting effects of residential mobility during childhood on psychopathology among Chinese University students.

BMC Psychiatry 2021 01 15;21(1):45. Epub 2021 Jan 15.

Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, No. 28 Dianxin South street, Chengdu, 610041, Sichuan, China.

Background: Residential mobility during childhood increases risk of psychopathology in adulthood and is a common experience among Chinese children. This study investigated associations between number and age of first move, etiological risk factors for psychopathology, and common mental disorders in adolescence and early adulthood.

Methods: The sample included 39,531 undergraduates (84.5% completion rate) age 15-34 years in their first year at a Chinese comprehensive university in annual cross-sectional surveys during 2014-2018. Common mental disorders measured using standardised self-report instruments. Data analysed using logistic regression models and interaction analysis.

Results: Half of all students experienced one or more moves of residence before age 15 years. Outcomes of Depression, Somatisation, Obsessive-compulsive disorder, Hallucinations and Delusions, and Suicide attempts showed dose-response relationships with increasing number of moves. Other etiological risk factors, including childhood disadvantage and maltreatment, showed similar dose response relationships but did not confound associations with mobility. We found interactions between reporting any move and being a left-behind child on depression and somatisation; number of moves and younger age at first move on depression, somatisation, suicide attempts and hallucinations and delusions.

Conclusions: Residential mobility in childhood is associated with psychopathology in adulthood and this association increases with increasing number of moves. Mobility is also associated with childhood disadvantage and maltreatment but associations with psychopathology are independent of these factors. Multiplicative effects were shown for multiple moves starting at a younger age and if the participant had been a left-behind child.
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http://dx.doi.org/10.1186/s12888-020-03018-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7811262PMC
January 2021

Chronic lithium exposure attenuates ketamine-induced mania-like behavior and c-Fos expression in the forebrain of mice.

Pharmacol Biochem Behav 2021 03 13;202:173108. Epub 2021 Jan 13.

Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China; Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangzhou, China. Electronic address:

Ketamine, a dissociative anaesthetic, has been used in the treatment of major depressive disorder (MDD) as a rapid acting antidepressant drug. Recent studies have shown that ketamine may increase the potential risk of treatment-induced mania in MDD patients. Lithium is a well-known mood stabilizer and has been widely used for the treatment of mania. It is not fully understood which forebrain regions are involved in ketamine- and lithium-induced expression of c-Fos. Therefore, our aim was to investigate the effect of chronic lithium treatment on mania-like behavior and c-Fos expression in the mouse forebrain activated by a single administration of ketamine. In the open field test, our results showed that ketamine significantly increased the total distance and total cumulative duration of movement in mice, while chronic lithium could attenuate these effects of ketamine. In addition, acute ketamine induced higher c-Fos expression in the lateral septal nucleus, hypothalamus, amygdala, and hippocampus of mice in the treatment group compared to those in the control group. However, chronic lithium inhibited the significant increase in c-Fos-immunoreactive neurons following acute ketamine administration in the dentate gyrus of the hippocampus, field CA1 of the hippocampus, dorsal subiculum, ventral subiculum, ventral subiculum, central amygdaloid nucleus and basolateral amygdaloid nucleus. In summary, our research shows that pretreatment with lithium moderates the effects of acute ketamine administration on mania-like behavior and c-Fos expression in the forebrain. These findings could be helpful in better understanding the episodes of mania related to ketamine treatment for MDD and bipolar disorder.
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http://dx.doi.org/10.1016/j.pbb.2021.173108DOI Listing
March 2021

Aberrant triple-network connectivity patterns discriminate biotypes of first-episode medication-naive schizophrenia in two large independent cohorts.

Neuropsychopharmacology 2021 07 6;46(8):1502-1509. Epub 2021 Jan 6.

Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan, China.

Schizophrenia is a complex disorder associated with aberrant brain functional connectivity. This study aims to demonstrate the relation of heterogeneous symptomatology in this disorder to distinct brain connectivity patterns within the triple-network model. The study sample comprised 300 first-episode antipsychotic-naive patients with schizophrenia (FES) and 301 healthy controls (HCs). At baseline, resting-state functional magnetic resonance imaging data were captured for each participant, and concomitant neurocognitive functions were evaluated outside the scanner. Clinical information of 49 FES in the discovery dataset were reevaluated at a 6-week follow-up. Differential features between FES and HCs were selected from triple-network connectivity profiles. Cutting-edge unsupervised machine learning algorithms were used to define patient subtypes. Clinical and cognitive variables were compared between patient subgroups. Two FES subgroups with differing triple-network connectivity profiles were identified in the discovery dataset and confirmed in an independent hold-out cohort. One patient subgroup appearing to have more severe clinical symptoms was distinguished by salience network (SN)-centered hypoconnectivity, which was associated with greater impairments in sustained attention. The other subgroup exhibited hyperconnectivity and manifested greater deficits in cognitive flexibility. The SN-centered hypoconnectivity subgroup had more persistent negative symptoms at the 6-week follow-up than the hyperconnectivity subgroup. The present study illustrates that clinically relevant cognitive subtypes of schizophrenia may be associated with distinct differences in connectivity in the triple-network model. This categorization may foster further analysis of the effects of therapy on these network connectivity patterns, which may help to guide therapeutic choices to effectively reach personalized treatment goals.
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http://dx.doi.org/10.1038/s41386-020-00926-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8208970PMC
July 2021

Biotypes of major depressive disorder: Neuroimaging evidence from resting-state default mode network patterns.

Neuroimage Clin 2020 28;28:102514. Epub 2020 Nov 28.

Department of Psychiatry, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China.

Background: Major depressive disorder (MDD) is heterogeneous disorder associated with aberrant functional connectivity within the default mode network (DMN). This study focused on data-driven identification and validation of potential DMN-pattern-based MDD subtypes to parse heterogeneity of the disorder.

Methods: The sample comprised 1397 participants including 690 patients with MDD and 707 healthy controls (HC) registered from multiple sites based on the REST-meta-MDD Project in China. Baseline resting-state functional magnetic resonance imaging (rs-fMRI) data was recorded for each participant. Discriminative features were selected from DMN between patients and HC. Patient subgroups were defined by K-means and principle component analysis in the multi-site datasets and validated in an independent single-site dataset. Statistical significance of resultant clustering were confirmed. Demographic and clinical variables were compared between identified patient subgroups.

Results: Two MDD subgroups with differing functional connectivity profiles of DMN were identified in the multi-site datasets, and relatively stable in different validation samples. The predominant dysfunctional connectivity profiles were detected among superior frontal cortex, ventral medial prefrontal cortex, posterior cingulate cortex and precuneus, whereas one subgroup exhibited increases of connectivity (hyperDMN MDD) and another subgroup showed decreases of connectivity (hypoDMN MDD). The hyperDMN subgroup in the discovery dataset had age-related severity of depressive symptoms. Patient subgroups had comparable demographic and clinical symptom variables.

Conclusions: Findings suggest the existence of two neural subtypes of MDD associated with different dysfunctional DMN connectivity patterns, which may provide useful evidence for parsing heterogeneity of depression and be valuable to inform the search for personalized treatment strategies.
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http://dx.doi.org/10.1016/j.nicl.2020.102514DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7724374PMC
June 2021

Sustained effects of left-behind experience during childhood on mental health in Chinese university undergraduates.

Eur Child Adolesc Psychiatry 2020 Oct 28. Epub 2020 Oct 28.

Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, No. 28 Dianxin South Street, Chengdu, 610041, Sichuan, China.

Rapid industrialization and urbanization in China have resulted in labor migrants leaving children behind. For left-behind children (LBC), disrupted parental attachment may increase the risk of psychiatric morbidity in adulthood. To investigate psychopathological consequences for university students who were LBC and to estimate the effects of one or both parents being migrants, the duration of left-behind experience, and parental absence during critical periods of growth on psychiatric morbidity. We conducted an annual survey of all freshmen at a Chinese university from 2014 to 2018. The questionnaire collected information on left-behind experiences and psychiatric morbidity using standardized self-report instruments. Regression coefficients derived from logistic regression were used to measure the associations among total time left behind, absence of one parent or both parents, age when left behind and psychopathological consequences. A total of 42,505 students were included. Students who were LBC had more psychopathology, including depression, anxiety, somatoform disorder, obsessive-compulsive disorder, self-reported suicide attempts and deliberate self-harm, than those who were not. Students for whom one or both parents were migrants showed a greater risk of psychiatric morbidity. The risk of psychiatric morbidity increased with the length of parental absence. Left-behind experience during childhood represents sustained impacts for university students into early adulthood. The higher prevalence of psychiatric morbidity in young adults who experienced the absence of one or both of their parents, especially in their early childhood, suggests that other factors besides attachment, such as protection from other risks, are important and that further research is necessary.
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http://dx.doi.org/10.1007/s00787-020-01666-6DOI Listing
October 2020

Longitudinal trajectory analysis of antipsychotic response in patients with schizophrenia: 6-week, randomised, open-label, multicentre clinical trial.

BJPsych Open 2020 Oct 22;6(6):e126. Epub 2020 Oct 22.

Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, China.

Background: Understanding the patterns of treatment response is critical for the treatment of patients with schizophrenia; one way to achieve this is through using a longitudinal dynamic process study design.

Aims: This study aims to explore the response trajectory of antipsychotics and compare the treatment responses of seven different antipsychotics over 6 weeks in patients with schizoprenia (trial registration: Chinese Clinical Trials Registry Identifier: ChiCTR-TRC-10000934).

Method: Data were collected from a multicentre, randomised open-label clinical trial. Patients were evaluated with the Positive and Negative Syndrome Scale (PANSS) at baseline and follow-up at weeks 2, 4 and 6. Trajectory groups were classified by the method of k-means cluster modelling for longitudinal data. Trajectory analyses were also employed for the seven antipsychotic groups.

Results: The early treatment response trajectories were classified into a high-trajectory group of better responders and a low-trajectory group of worse responders. The results of trajectory analysis showed differences compared with the classification method characterised by a 50% reduction in PANSS scores at week 6. A total of 349 patients were inconsistently grouped by the two methods, with a significant difference in the composition ratio of treatment response groups using these two methods (χ2 = 43.37, P < 0.001). There was no differential contribution of high- and low trajectories to different drugs (χ2 = 12.52, P = 0.051); olanzapine and risperidone, which had a larger proportion in the >50% reduction at week 6, performed better than aripiprazole, quetiapine, ziprasidone and perphenazine.

Conclusions: The trajectory analysis of treatment response to schizophrenia revealed two distinct trajectories. Comparing the treatment responses to different antipsychotics through longitudinal analysis may offer a new perspective for evaluating antipsychotics.
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http://dx.doi.org/10.1192/bjo.2020.105DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745240PMC
October 2020

Confirming diagnostic categories within a depression continuum: Testing extra-linearity of risk factors and a latent class analysis.

J Affect Disord 2021 01 7;279:183-190. Epub 2020 Oct 7.

Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China; West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Centre for Psychological Educational and Consultation, Sichuan University, Chengdu, China. Electronic address:

Background: Dimensions are recommended as replacements for diagnostic categories of depression, but clinicians continue to use categories. Categories are appropriate if major, underlying changes in symptom structure occur above a clinical cut-off on a depression continuum.

Methods: Cross-sectional surveys of Chinese undergraduates (n = 39,446) 2014-2018 measured self-reported depressive symptoms, associated psychopathology and etiological risk factors using standardised instruments. We created a continuum using PHQ-9 scores and tested linear and extra-linear contrasts in associated psychopathology, and etiology. We carried out latent class analyses (LCA).

Results: Most symptoms showed linear increase, but depressed mood, anhedonia, and suicidal ideation showed marked increase at the severe end of the continuum. There was extra-linear increase in associated psychotic symptoms, other psychopathology, age, low family income, chronic pain and physical illness, childhood physical and sexual abuse, and neglect. Four LCs corresponding to Melancholic, Severe melancholic, Non-melancholic, and Mild depression were confirmed, but only above a clinical cut-off along the continuum. Etiological risk factors did not differentiate between classes but showed overall dramatic increase in impact above threshold of clinical severity.

Limitations: Only one self-report instrument was used (PHQ-9) to measure depression and diagnoses were not validated by clinical interviews.

Conclusions: Categories are necessary to describe the dramatic changes in underlying structure and symptom associations above a clinical threshold of severity. These result from extra-linear impact of etiological risk factors at the severe end of the depression continuum. Although the study confirmed melancholic and non-melancholic subtypes, further investigation should investigate etiological factors that determine this subdivision.
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http://dx.doi.org/10.1016/j.jad.2020.10.010DOI Listing
January 2021

Aberrant Functional Network Connectivity Transition Probability in Major Depressive Disorder.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:1493-1496

Major depressive disorder (MDD) is a common and serious mental disorder characterized by a persistent negative feeling and tremendous sadness. In recent decades, several studies used functional network connectivity (FNC), estimated from resting state functional magnetic resonance imaging (fMRI), to investigate the biological signature of MDD. However, the majority of them have ignored the temporal change of brain interaction by focusing on static FNC (sFNC). Dynamic functional network connectivity (dFNC) that explores temporal patterns of functional connectivity (FC) might provide additional information to its static counterpart. In the current study, by applying k-means clustering on dFNC of MDD and healthy subjects (HCs), we estimated 5 different states. Next, we use the hidden Markov model as a potential biomarker to differentiate the dFNC pattern of MDD patients from HCs. Comparing MDD and HC subjects' hidden Markov model (HMM) features, we have highlighted the role of transition probabilities between states as potential biomarkers and identified that transition probability from a lightly- connected state to highly connected one reduces as symptom severity increases in MDD subjects.Index Terms- Major depressive disorder, Dynamic functional network connectivity, Machine learning, Resting- state functional magnetic resonance imaging, Hidden Markov model.
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http://dx.doi.org/10.1109/EMBC44109.2020.9175872DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8233065PMC
July 2020

Social support and clinical improvement in COVID-19 positive patients in China.

Nurs Outlook 2020 Nov - Dec;68(6):830-837. Epub 2020 Aug 24.

Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Mental Health Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, China. Electronic address:

Objectives: To explore the relationship between psychosocial support related factors and the mental health of COVID-19 positive patients.

Methods: This exploratory study of 35 COVID-19 positive patients were enrolled between February 1 to March 1, 2020. Sleep quality, depression, anxiety, and social support were measured and social support related data of participants were collected. Psychological intervention was administered and patients were followed two weeks post intervention. Linear regression was performed to explore the relationship between psychosocial risk factors and improvement of psychological symptoms.

Findings: Thirty-two individuals exhibited sleep, depressive and anxiety symptoms which improved post support intervention. At baseline, symptoms were associated with gender, severity of pneumonia, social support. Better social support at follow-up and improvement from COVID-19 predicted improvement in their psychological symptoms.

Discussion: This initial evidence from China may stress the importance of administering psychosocial intervention during the treatment of COVID-19 for better patient outcomes in other countries.
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http://dx.doi.org/10.1016/j.outlook.2020.08.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7444976PMC
December 2020

Molecular imaging and deep learning analysis of uMUC1 expression in response to chemotherapy in an orthotopic model of ovarian cancer.

Sci Rep 2020 09 10;10(1):14942. Epub 2020 Sep 10.

Precision Health Program, Michigan State University, East Lansing, MI, 48823, USA.

Artificial Intelligence (AI) algorithms including deep learning have recently demonstrated remarkable progress in image-recognition tasks. Here, we utilized AI for monitoring the expression of underglycosylated mucin 1 (uMUC1) tumor antigen, a biomarker for ovarian cancer progression and response to therapy, using contrast-enhanced in vivo imaging. This was done using a dual-modal (magnetic resonance and near infrared optical imaging) uMUC1-specific probe (termed MN-EPPT) consisted of iron-oxide magnetic nanoparticles (MN) conjugated to a uMUC1-specific peptide (EPPT) and labeled with a near-infrared fluorescent dye, Cy5.5. In vitro studies performed in uMUC1-expressing human ovarian cancer cell line SKOV3/Luc and control uMUC1 ES-2 cells showed preferential uptake on the probe by the high expressor (n = 3, p < .05). A decrease in MN-EPPT uptake by SKOV3/Luc cells in vitro due to uMUC1 downregulation after docetaxel therapy was paralleled by in vivo imaging studies that showed a reduction in probe accumulation in the docetaxel treated group (n = 5, p < .05). The imaging data were analyzed using deep learning-enabled segmentation and quantification of the tumor region of interest (ROI) from raw input MRI sequences by applying AI algorithms including a blend of Convolutional Neural Networks (CNN) and Fully Connected Neural Networks. We believe that the algorithms used in this study have the potential to improve studying and monitoring cancer progression, amongst other diseases.
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http://dx.doi.org/10.1038/s41598-020-71890-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7484755PMC
September 2020

Artificial Intelligence Analysis of Magnetic Particle Imaging for Islet Transplantation in a Mouse Model.

Mol Imaging Biol 2021 02 24;23(1):18-29. Epub 2020 Aug 24.

Precision Health Program, Michigan State University, 766 Service Road, Rm. 2020, East Lansing, MI, 48823, USA.

Purpose: Current approaches to quantification of magnetic particle imaging (MPI) for cell-based therapy are thwarted by the lack of reliable, standardized methods of segmenting the signal from background in images. This calls for the development of artificial intelligence (AI) systems for MPI analysis.

Procedures: We utilize a canonical algorithm in the domain of unsupervised machine learning, known as K-means++, to segment the regions of interest (ROI) of images and perform iron quantification analysis using a standard curve model. We generated in vitro, in vivo, and ex vivo data using islets and mouse models and applied the AI algorithm to gain insight into segmentation and iron prediction on these MPI data. In vitro models included imaging the VivoTrax-labeled islets in varying numbers. In vivo mouse models were generated through transplantation of increasing numbers of the labeled islets under the kidney capsule of mice. Ex vivo data were obtained from the MPI images of excised kidney grafts.

Results: The K-means++ algorithms segmented the ROI of in vitro phantoms with minimal noise. A linear correlation between the islet numbers and the increasing prediction of total iron value (TIV) in the islets was observed. Segmentation results of the ROI of the in vivo MPI scans showed that with increasing number of transplanted islets, the signal intensity increased with linear trend. Upon segmenting the ROI of ex vivo data, a linear trend was observed in which increasing intensity of the ROI yielded increasing TIV of the islets. Through statistical evaluation of the algorithm performance via intraclass correlation coefficient validation, we observed excellent performance of K-means++-based model on segmentation and quantification analysis of MPI data.

Conclusions: We have demonstrated the ability of the K-means++-based model to provide a standardized method of segmentation and quantification of MPI scans in an islet transplantation mouse model.
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http://dx.doi.org/10.1007/s11307-020-01533-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785569PMC
February 2021
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