Machine learning for prediction of schizophrenia based on identifying the primary and interaction effects of minor physical anomalies.

In the neurodevelopmental model of schizophrenia, minor physical anomalies (MPAs) are considered neurodevelopmental markers of schizophrenia. To date, there has been no research to evaluate the interaction between MPAs. Our study built and used a machine learning model to predict the risk of schizophrenia based on measurements of MPA items and to investigate the potential primary and interaction effects of MPAs. The study included 470 patients with schizophrenia and 354 healthy controls. The models used are classical statistical model, Logistic Regression (LR), and machine leaning models, Decision Tree (DT) and Random Forest (RF). We also plotted two-dimensional scatter diagrams and three-dimensional linear/quadratic discriminant analysis (LDA/QDA) graphs for comparison with the DT dendritic structure. We found that RF had the highest predictive power for schizophrenia (Full-training AUC = 0.97 and 5-fold cross-validation AUC = 0.75). We identified several primary MPAs, such as the mouth region, high palate, furrowed tongue, skull height and mouth width. Quantitative MPA analysis indicated that the higher skull height and the narrower mouth width, the higher the risk of schizophrenia. In the interaction, we further identified that skull height and mouth width, furrowed tongue and skull height, high palate and skull height, and high palate and furrowed tongue, showed significant two-item interactions with schizophrenia. A weak three-item interaction was found between high palate, skull height, and mouth width. In conclusion, we found that the two machine learning methods showed good predictive ability in assessing the risk of schizophrenia using the primary and interaction effects of MPAs.

Thematic maps with scatter and 4-quadrant plots in R to identity dominant entities on schizophrenia in psychiatry since 2017: Bibliometric analysis.

BACKGROUND: Schizophrenia affects more than 21 million people worldwide. There have been a number of articles published in the literature regarding schizophrenia. It is unclear which authors contributed the most to the field of schizophrenia. This study examines which article entities (affiliated countries, institutes, journals, and authors) earn the most research achievements (RAs) and whether keywords in articles are associated with the number of article citations. METHODS: As of August 25, 2022, 20,606 abstracts published on schizophrenia in psychiatry since 2017 were retrieved from the WoS core collection (WoSCC). RAs were measured using the category, JIF, authorship, and L-index (CJAL) score. The follower-leading cluster algorithm (FLCA) was used to examine clusters of keywords associated with core concepts of research. There were 7 types of visualizations used to report the study results, including Sankey diagrams, choropleth maps, scatter charts, radar plots, and cluster plots. A hypothesis was examined that the mean number of citations for keywords could predict the number of citations for 100 top-cited articles(T100SCHZ). RESULTS: The results indicate that the US (18861), Kings College London (U.S. (2572), Psychiatry (14603), and Kolanu Nithin (Australia) (9.88) had the highest CJAL scores in countries, institutes, departments, and authors, respectively. The journal of Schizophrenia Res had higher citations (19,017), counts (1681), and mean citations (11.31) in journals. There was a significant correlation between article citations and weighted keywords (F = 1471.74; P < .001). CONCLUSION: Seven visualizations were presented to report the study results, particularly with thematic maps using scatter and 4-quadrant plots produced in R programming language. We recommend that more future bibliographical studies utilize CAJL scores and thematic maps to report their findings, not restrict themselves solely to schizophrenia in psychiatry as done in this study.

DNA methylation signature aberration as potential biomarkers in treatment-resistant schizophrenia: Constructing a methylation risk score using a machine learning method.

Treatment-resistant schizophrenia (TRS) is defined as a non-response to at least two trials of antipsychotic medication with an adequate dose and duration. We aimed to evaluate the discriminant abilities of DNA methylation probes and methylation risk score between treatment-resistant schizophrenia and non-treatment-resistant schizophrenia. This study recruited 96 schizophrenia patients (TRS and non-TRS) and 56 healthy controls (HC). Participants were divided into a discovery set and a validation set. In the discovery set, we conducted genome-wide methylation analysis (human MethylationEPIC 850K BeadChip) on the subject's blood DNA and discriminated significant methylation signatures, then verified these methylation signatures in the validation set. Based on genome-wide scans of TRS versus non-TRS, thirteen differentially methylated probes were identified at FDR <0.05 and >20% differences in DNA methylation ß-values. Next, we selected six probes within gene coding regions (LOC404266, LOXL2, CERK, CHMP7, and SLC17A9) to conduct verification in the validation set using quantitative methylation-specific PCR (qMSP). These six methylation probes showed satisfactory discrimination between TRS patients and non-TRS patients, with an AUC ranging from 0.83 to 0.92, accuracy ranging from 77.8% to 87.3%, sensitivity ranging from 80% to 90%, and specificity ranging from 65.6% to 85%. This methylation risk score model showed satisfactory discrimination between TRS patients and non-TRS patients, with an accuracy of 88.3%. These findings support that methylation signatures may be used as an indicator of TRS vulnerability and provide a model for the clinical use of methylation to identify TRS.

The use of radar plots with the Yk-index to identify which authors contributed the most to the journal of Medicine in 2020 and 2021: A bibliometric analysis.

BACKGROUND: A consensus exists that the first author and corresponding author make the most contribution to the publication of an article. The Y-index has been proposed to assess the scientific achievements of authors, institutions, and countries/regions (AIC/R for short) based on the number of first-author publications (FPs) and corresponding-author publications (RPs). Nonetheless, the Y-index is defined in terms of count and radian (represented by j and h) instead of using the relative radius and angle degree to simplify understanding. In the literature, a method for drawing radar diagrams online with the Y-index is also lacking. This study was conducted to enhance the Y-index with an additional relative radius denoted by k and the angle degree represented by h* (named Yk-index), include easy-to-use features (e.g., copying and pasting) for the delivery of the online Radar-Yk, and identify which one of AIC/R contributed the most to a scientific journal. METHODS: From the Web of Science (WoS) database, we downloaded 9498 abstracts of articles published in the journal of Medicine (Baltimore) in 2020 and 2021. Three visual representations were used, including a Sankey diagram, a choropleth map, and a radar diagram, to identify the characteristics of contributions by AIC/R to Medicine (Baltimore) using the Yk-index (j, k, h*). A demonstration of Rada-Yk with easy-to-use features was given using the copy-and-paste technique. RESULTS: We found that Qiu Chen (China), Sichuan University (China), China, and South Korea (based on regions, e.g., provinces/metropolitan areas in China) were the most productive AIC/R, with their Yk equal to 27,715, 12415.1, and 2045, respectively; a total of 85.6% of the published articles in Medicine (Baltimore) came from the 3 countries (China, South Korea, and Japan); and this method of drawing the Radar-Yk online was provided and successfully demonstrated. CONCLUSION: A breakthrough was achieved by developing the online Radar-Yk to show the most contributions to Medicine (Baltimore). Visualization of Radar-Yk could be replicated for future academic research and applications on other topics in future bibliographical studies.

Profiling antibody signature of schizophrenia by Escherichia coli proteome microarrays.

Schizophrenia (SZ) is influenced by genetic and environmental factors, and associated with chronic neuroinflammation. If the symptoms express after adolescence, environmental impacts are more substantial, and the disease is defined as adult-onset schizophrenia (AOS). Effects of environmental factors on antibody responses such as Escherichia coli (E. coli) to immunoglobulin G (IgG) and immunoglobulin M (IgM) might increase the severity of symptoms in SZ via the gut-brain axis. The purpose of this study is to reveal antibody profiles of SZ against bacterial protein antigens. We analyzed the IgG and IgM antibodies using E. coli proteome microarrays from 80 SZ patients and 40 healthy controls (HC). Using support vector machine to select panels of proteins differentiating between groups and conducted enrichment analysis for those proteins. We identified that the groL, pldA, yjjU, livG, and ftsE can classify IgGs in AOS vs HC achieved accuracy of 0.7. The protein yjjU, livG and ftsE can form the best combination panel to classify IgG in AOS vs HC with accuracy of 0.8. The enrichment results are highly related to ABC (ATP binding cassette) transporter in the protein domain and cellular component. We further found that the human ATP binding cassette subfamily b member 1 (ABCB1) autoantibody level in AOS is significantly higher than in HC. The findings suggest that AOS had different immunoglobulin production compared to early-onset schizophrenia (EOS) and HC. We also identified potential antibody biomarkers of AOS and found their antigens are enriched in ABC transporter related domains, including human ABCB1 protein.

Development and validation of a web-based prediction tool on minor physical anomalies for schizophrenia.

In support of the neurodevelopmental model of schizophrenia, minor physical anomalies (MPAs) have been suggested as biomarkers and potential pathophysiological significance for schizophrenia. However, an integrated, clinically useful tool that used qualitative and quantitative MPAs to visualize and predict schizophrenia risk while characterizing the degree of importance of MPA items was lacking. We recruited a training set and a validation set, including 463 schizophrenia patients and 281 healthy controls to conduct logistic regression and the least absolute shrinkage and selection operator (Lasso) regression to select the best parameters of MPAs and constructed nomograms. Two nomograms were built to show the weights of these predictors. In the logistic regression model, 11 out of a total of 68 parameters were identified as the best MPA items for distinguishing between patients with schizophrenia and controls, including hair whorls, epicanthus, adherent ear lobes, high palate, furrowed tongue, hyperconvex fingernails, a large gap between first and second toes, skull height, nasal width, mouth width, and palate width. The Lasso regression model included the same variables of the logistic regression model, except for nasal width, and further included two items (interpupillary distance and soft ears) to assess the risk of schizophrenia. The results of the validation dataset verified the efficacy of the nomograms with the area under the curve 0.84 and 0.85 in the logistic regression model and lasso regression model, respectively. This study provides an easy-to-use tool based on validated risk models of schizophrenia and reflects a divergence in development between schizophrenia patients and healthy controls ( https://www.szprediction.net/ ).

Chronic Pain Increases the Risk of Dementia: A Nationwide Population-Based Cohort Study.

BACKGROUND: Chronic pain (CP) may increase the risk of cognitive impairment; however, the association between CP and dementia is still unclear. OBJECTIVES: Therefore, we conducted this study to clarify the association between CP and dementia. STUDY DESIGN: Retrospective cohort study. SETTINGS: Nationwide population based. METHODS: This study recruited 27,792 patients (>= 50 years) with CP from the Taiwan National Health Insurance Research Database between January 1, 2000, and December 31, 2015, as the study cohort. The comparison cohort consists of patients without CP who were matched 1:1 for age, gender, and index date with the study cohort. A comparison of the risk of dementia between the two cohorts was performed by following up until 2015. RESULTS: The prevalence of CP was 13.4% in the population aged >= 50 years. Patients with CP had a higher risk of dementia than those without CP (adjusted hazard ratio [AHR]: 1.21; 95% confidence interval [CI]: 1.15-1.26). Compared with the other age subgroups, the 50-64 years age group with CP had the highest risk of dementia (AHR: 1.28; 95% CI: 1.14-1.43). The impact of CP on the increased risk of dementia was more prominent in the younger age subgroup and decreased with aging. The increased risk of dementia in patients with CP was persistent, even following up for more than 5 years (AHR: 1.19; 95% CI: 1.12-1.26). LIMITATIONS: Using "analgesics use at least 3 months" as the surrogate criteria of CP may underestimate the diagnosis of CP. CONCLUSIONS: CP was associated with a higher risk of dementia, especially in the 50-64 years age group. Early treatment of CP for the prevention of dementia is suggested.

Neurodevelopment regulators miR-137 and miR-34 family as biomarkers for early and adult onset schizophrenia.

Early-onset schizophrenia (EOS) may have stronger familial aggregation and a more severe outcome than adult-onset schizophrenia (AOS). MicroRNA (miRNA) takes on dual roles as a genetic and epigenetic modulator, which may mediate the influence of genetic risk. Neurological soft signs (NSS) are neurological abnormalities that may be intermediate phenotypes or endophenotypes for schizophrenia. Our previous study found poorer performance on NSS tests from patients with EOS and their unaffected first-degree relatives. Thus, we aimed to identify a set of aberrant neurodevelopmental-related miRNAs that could serve as potential biomarkers for EOS or schizophrenia with NSS. This study included 215 schizophrenia patients (104 EOS and 111 AOS), 72 unaffected first-degree relatives, 31 patients with bipolar disorder, and 100 healthy controls. Differential expression analysis revealed that miR-137, miR-34b, and miR-34c were significantly up-regulated in patients with schizophrenia and their unaffected first-degree relatives compared to healthy controls. Receiver operating characteristic (ROC) analysis showed that the miR-137 expression signature could be used to discriminate between patients with EOS and healthy controls (AUC = 0.911). Additionally, miR-34b had the highest ability to discriminate between EOS and AOS (AUC = 0.810), which may indicate different aetiological pathways to disease onset. Moreover, miR-137 dysregulation was correlated with almost all NSS subscales (i.e., sensory integration, motor sequencing, etc.) and, when EOS patients with NSS, miR-137 expression discriminated these patients from healthy controls to a greater extent (AUC = 0.957). These findings support the potential for neurodevelopmental-related miRNAs to be used as indicators of vulnerability to EOS.

Alterations of plasma cytokine biomarkers for identifying age at onset of schizophrenia with neurological soft signs.

Several studies have been suggested that immunity plays a part in neurodevelopment and schizophrenia pathogenesis. Early age of onset in schizophrenia is associated with genetic factors which affect neurodevelopment. This study aims to identify immune abnormalities associated with neurodevelopmental impairments in early-onset schizophrenia (EOS) and adult-onset schizophrenia (AOS) patients. We determined the plasma levels of six cytokines (IL-1ß, IL-4, IL-6, IL-10, IL-12 and TNF-α) in schizophrenia patients and healthy controls. Measurements included neurological soft signs (NSS) to distinguish and subgroup those with neurodevelopmental impairments. The study included 210 schizophrenia patients, which were divided into 84 EOS and 126 AOS patients, as well as 122 healthy controls. We observed significant differences in levels of IL-4, IL-6 and IL-10 between EOS and AOS patients. The results demonstrated the area under ROC curve (AUC) of the IL-4 in EOS and healthy controls was 0.81. Moreover, these results indicated that AUC of the IL-4 and the combination of IL-4, IL-6 and IL-12 in EOS with NSS and healthy controls were 0.91 and 0.95. These cytokines are altered in EOS and schizophrenia patients with neurodevelopmental impairments and demonstrated good classification abilities. These findings manifested that both pro- and anti-inflammatory cytokines are contributed to the clinical and pathophysiological features of schizophrenia. Future works are expected to explore potential genetic effectors and predictors as well as therapeutic directions in personalized medicine for early-onset schizophrenia.

Risk Model Assessment in Early-Onset and Adult-Onset Schizophrenia Using Neurological Soft Signs.

Age at onset is one of the most important clinical features of schizophrenia that could indicate greater genetic loadings. Neurological soft signs (NSS) are considered as a potential endophenotype for schizophrenia. However, the association between NSS and different age-onset schizophrenia still remains unclear. We aimed to compare risk model in patients with early-onset schizophrenia (EOS) and adult-onset schizophrenia (AOS) with NSS. This study included 262 schizophrenia patients, 177 unaffected first-degree relatives and 243 healthy controls. We estimated the discriminant abilities of NSS models for early-onset schizophrenia (onset age < 20) and adult-onset schizophrenia (onset age ≥ 20) using three data mining methods: artificial neural networks (ANN), decision trees (DT) and logistic regression (LR). We then assessed the magnitude of NSS performance in EOS and AOS families. For the four NSS subscales, the NSS performance were greater in EOS and AOS families compared with healthy individuals. More interestingly, there were significant differences found between patients' families and control group in the four subscales of NSS. These findings support the potential for neurodevelopmental markers to be used as schizophrenia vulnerability indicators. The NSS models had higher discriminant abilities for EOS than for AOS. NSS were more accurate in distinguishing EOS patients from healthy controls compared to AOS patients. Our results support the neurodevelopmental hypothesis that EOS has poorer performance of NSS than AOS. Hence, poorer NSS performance may be imply trait-related NSS feature in EOS.

Comparing Affiliate Stigma Between Family Caregivers of People With Different Severe Mental Illness in Taiwan.

The family caregivers of people with mental illness may internalize the public stereotypes into the affiliate stigma (i.e., the self-stigma of family members). This study aimed to compare the affiliate stigma across schizophrenia, bipolar disorder, and major depressive disorder, and to investigate potential factors associated with affiliate stigma. Each caregiver of family members with schizophrenia (n = 215), bipolar disorder (n = 85), and major depressive disorder (n = 159) completed the Affiliate Stigma Scale, Rosenberg Self-Esteem Scale, Caregiver Burden Inventory, Taiwanese Depression Questionnaire, and Beck Anxiety Inventory. After controlling for potential confounders, the hierarchical regression models showed that caregivers of a family member with schizophrenia had a higher level of affiliate stigma than those of bipolar disorder (ß = -0.109; p < 0.05) and major depressive disorder (ß = -0.230; p < 0.001). Self-esteem, developmental burden, and emotional burden were significant factors for affiliate stigma. The affiliate stigma of caregivers is associated with their self-esteem, caregiver burden, and by the diagnosis.

Improving risk assessment and familial aggregation of age at onset in schizophrenia using minor physical anomalies and craniofacial measures.

Age at onset is the most important feature of schizophrenia that could indicate its origin. Minor physical anomalies (MPAs) characterize potential marker indices of disturbances in early neurodevelopment. However, the association between MPAs and age at onset of schizophrenia is still unclear. We aimed to compare risk assessment and familial aggregation in patients with early-onset schizophrenia (EOS) and adult-onset schizophrenia (AOS) with MPAs and craniofacial measures.We estimated the risk assessment of MPAs among patients with EOS (n = 68), patients with AOS (n = 183), nonpsychotic relatives (n = 147), and healthy controls (n = 241) using 3 data-mining algorithms. In addition, we assessed the magnitude of familial aggregation of MPAs with respect to the age at onset of schizophrenia.The performance of EOS was superior to that of AOS, with discrimination accuracies of 89% and 76%, respectively. Combined MPA scores as the risk assessment were significantly higher in all schizophrenia subgroups and the nonpsychotic relatives of EOS patients than in the healthy controls. The recurrence risk ratio for familial aggregation of the MPA scores of EOS families (odds ratio 9.27) was substantially higher than that of AOS families (odds ratio 2.47).The results highlight that EOS improves risk assessment and has a severe magnitude of familial aggregation of MPAs. These findings indicate that EOS might result from a stronger genetic susceptibility to neurodevelopmental deficits.

SLC6A2 variants may predict remission from major depression after venlafaxine treatment in Han Chinese population.

OBJECTIVE: Venlafaxine, an antidepressant of the serotonin-norepinephrine reuptake inhibitor (SNRI) type, is used to treat patients with major depressive disorder (MDD). Much evidence suggests that genetic polymorphisms may modulate serotonergic and noradrenergic function, thereby affecting the treatment efficacy of venlafaxine. The aim of this study was to examine whether polymorphisms in the norepinephrine transporter gene (SLC6A2) associate with remission after venlafaxine treatment for MDD. METHOD: An 8-week naturalistic treatment study with venlafaxine was carried out in 243 Han Chinese patients with MDD. The patients were screened for seven single-nucleotide polymorphisms of the SLC6A2 gene. Of the enrolled patients, 161 completed the 8-week treatment. The 21-item Hamilton Depression Rating Scale (HDRS) was used to assess the improvement of depressive symptoms in each subject from baseline to the endpoint. For better presentation of time-course change of remission status, a Cox regression analysis for remission incidence during the 8-week treatment was conducted. RESULTS: Between remitters and non-remitters, significant differences in genotype frequencies were observed in five of the investigated SLC6A2 variants (rs28386840, rs1532701, rs40434, rs13333066, rs187714). GCG haplotype (rs40434 - rs13333066 - rs187714) in the SLC6A2 gene showed a association with non-remission. A Cox regression analysis for remission incidence during the 8-week treatment course significantly depends on SLC6A2 variants (rs28386840, rs40434, and rs187714). CONCLUSION: Our results suggest that the variation of the SLC6A2 gene is associated with treatment remission after venlafaxine in patients with MDD.

Patients' acceptance towards a web-based personal health record system: an empirical study in Taiwan.

The health care sector has become increasingly interested in developing personal health record (PHR) systems as an Internet-based telehealthcare implementation to improve the quality and decrease the cost of care. However, the factors that influence patients' intention to use PHR systems remain unclear. Based on physicians' therapeutic expertise, we implemented a web-based infertile PHR system and proposed an extended Technology Acceptance Model (TAM) that integrates the physician-patient relationship (PPR) construct into TAM's original perceived ease of use (PEOU) and perceived usefulness (PU) constructs to explore which factors will influence the behavioral intentions (BI) of infertile patients to use the PHR. From ninety participants from a medical center, 50 valid responses to a self-rating questionnaire were collected, yielding a response rate of 55.56%. The partial least squares (PLS) technique was used to assess the causal relationships that were hypothesized in the extended model. The results indicate that infertile patients expressed a moderately high intention to use the PHR system. The PPR and PU of patients had significant effects on their BI to use PHR, whereas the PEOU indirectly affected the patients' BI through the PU. This investigation confirms that PPR can have a critical role in shaping patients' perceptions of the use of healthcare information technologies. Hence, we suggest that hospitals should promote the potential usefulness of PHR and improve the quality of the physician-patient relationship to increase patients' intention of using PHR.

Adjunctive low-dose aripiprazole with standard-dose sertraline in treating fresh major depressive disorder: a randomized, double-blind, controlled study.

OBJECTIVES: Second-generation (atypical) antipsychotics have been accepted as an adjunctive medication in patients with treatment-resistant depression. This clinical trial evaluated the efficacy and safety of low-dose aripiprazole combined with regular-dose sertraline for acute major depressive episode in non-treatment-resistant depression outpatients. METHODS: The study patients were 18- to 65-year-old outpatients fulfilling the criteria of major depressive disorder in Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. The patients were randomly assigned to 2 groups: one with sertraline 50 mg/d plus aripiprazole 2.5 mg/d and the other with sertraline 50 mg/d plus placebo. After baseline assessment, the subjects were followed up at weeks 1, 2, 4, 6, and 10. The primary efficacy was the score change of the 17-item Hamilton Rating Scale for Depression (HAM-D17), and secondary efficacies were the score of Short Form 36 Health Survey, Clinical Global Impressions-Severity, and Clinical Global Impressions-Improvement. This study also monitored patients for movement disorder using Simpson-Angus Scale and Barnes Akathisia Rating Scale. RESULTS: Twenty-one patients were assigned to the aripiprazole group and 20 to the placebo group. Because of high dropout rate, only data of the first 4 weeks were analyzed. The aripiprazole group exhibited significantly better efficacy than the placebo group in mean total score changes of HAM-D17 from the baseline to weeks 1, 2, and 4. The item "work and social activities" of HAM-D17 showed significant improvement at week 2, and the item "somatic symptoms (GI)" showed significant improvement at week 1. The aripiprazole group exhibited significant improvement in "social role function" section of Short Form 36 Health Survey at week 4. The mean total score of Clinical Global Impressions-Severity showed marginally significant improvement in the aripiprazole group. In Clinical Global Impressions-Improvement, patients in the aripiprazole group had scores of less than 2 (much improved) at weeks 2 and 4, and the scores of the placebo group were greater than 2.4 (indicating a minimal improvement). No patients had akathisia during the trial period. CONCLUSIONS: The primitive data showed that adjunctive low-dose aripiprazole could augment the efficacy of regular-dose sertraline in fresh major depressive disorder. A large-scale study is needed to confirm this finding.

Ultra-low-dose naloxone restores the antinociceptive effect of morphine and suppresses spinal neuroinflammation in PTX-treated rats.

The aim of the present study was to examine the effect of ultra-low-dose naloxone on pertussis toxin (PTX)-induced thermal hyperalgesia in rats and its underlying mechanisms. Male Wistar rats, implanted with an intrathecal catheter with or without a microdialysis probe, received a single intrathecal injection of PTX (1 microg in 5 microl saline). Four days after PTX injection, they were randomly given a different dose of naloxone (either 15 microg or 15 ng in 5 microl saline), followed by a morphine injection (10 microg in 5 microl saline) after 30 min. The results found that PTX injection induced thermal hyperalgesia and increasing excitatory amino acid (EAA; L-glutamate and L-aspartate) concentration in the spinal CSF dialysates. Ultra-low-dose naloxone not only preserved the antinociceptive effect of morphine but also suppressed the PTX-evoked EAA release as well. Moreover, ultra-low-dose naloxone plus morphine administration inhibited the downregulation of L-glutamate transporters (GTs) and the L-glutamate-metabolizing enzyme glutamine synthetase (GS), and, moreover, inhibited microglial activation and suppressed cytokine expression in PTX-treated rat spinal cords. These results show that ultra-low-dose naloxone preserves the antinociceptive effect of morphine in PTX-treated rats. The mechanisms include (a) inhibition of pro-inflammatory cytokine expression, (b) attenuation of PTX-evoked EAA release, and (c) reversion of the downregulation of GT expression.

Psychiatric consultation record retrieval using scenario-based representation and multilevel mixture model.

Psychiatric consultation record retrieval attempts to help people to efficiently and effectively locate the consultation records relevant to their depressive problems. Consultation records can also make people aware that they are not alone, because many individuals have suffered from the same or similar problems. Additionally, people can understand how to alleviate their depressive symptoms according to recommendations from health professionals. To achieve this goal, this paper proposes the use of a scenario-based representation, i.e., a symptom-based structural representation, to capture the depressive symptoms and their semantic relations, such as cause-effect and temporal relations, for understanding users' queries clearly. The symptoms and relations are identified from semantic mining and analysis of consultation records. The multilevel mixture model is adopted to estimate the relevance of queries and consultation records based on the structural information. Experimental results show that the proposed approach achieves higher precision than does a term-based flat representation. An experiment is also conducted to examine the effect of error propagation resulting from incorrect identification of symptoms and relations. Experimental results demonstrate that combining different approaches can improve the retrieval robustness.