Dioxin-like compounds are a class of persistent organic pollutants(POPs).Because of their high toxicity, stability and bioaccumulation, they have serious effects on human health and environment.Studies on the toxicity of dioxins have involved in many aspects, such as reproduction toxicity, immune toxicity, carcinogenicity, endocrine toxicity, and so on.However, few studies of the effects on intelligence had been done,and its potential mechanism had not been reported till now.In this review, the authors tried to generalize the relationship between dioxins and intelligence based on animal behavioral tests and population investigations and hypothesize its potential mechanisms.

Objective To explore the influencing factor of duration of untreated psychosis(DUP) and the relationship between duration of untreated psychosis and treatment outcome in epidemiological first-episode schizophrenia. Methods 912 medical records of the first episode schizophrenia patients were recruited in the study for epidemiological survey . The general medical data, clinical diagnosis, and treatment outcome were collected. DUP was determined refer to symptom onset for schizophrenia (SOS) scale. Patients were divided into short DUP and long DUP groups according to the median values of DUP values. Results Between short DUP and long DUP groups, the difference in marriage was significant ( P < 0. 05). The unmarried patients in the short DUP group (63.7% ) was higher than the long DUP group (52.6% ), the variance was significant (λ2 =5.990, P<0.05). The divorce in the short DUP group (0.6% ) was lower than the long DUP group (4.4% ) , the variance was significant (λ2 =5.079, P<0.01).The simple schizophrenia in the short DUP group (1.5%) was lower than the long DUP group (4. 1% ) , the variance was significant (λ2 =5.868, P<0.05). The treatment outcomes between the two groups had a significant variance ( λ2 =36.093, P < 0.01) , and the Ridit analysis of treatment outcomes between the two groups showed significant difference ( u = 5.183, P<0.01). The marriage,age of onset and clinical diagnosis were significantly correlated with DUP in multiple factor Logistic regression analysis. Conclusion The marriage,age of onset and clinical diagnosis are significantly correlated with DUP in the first episode schizophrenia patients,the longer of DUP and the worse of treatment outcome.

Hemangioendothelioma, Epithelioid ; pathology ; Humans

Objective: To determine the basal metabolic rate (BMR) of young adults aged between 20-29 years old in Changsha. Methods: We recruited volunteers to join in our research project from April to May, 2015. All recruited volunteers must meet the inclusion criteria: aged 20-29 years old, height between 164-180 centimeters in males and 154-167 centimeters in females, in good health condition, and with no habit of regular physical exercise in last year. Finally, 81 qualified volunteers were selected as research objects, including 43 males and 38 females. The BMR, resting lying metabolism rate and resting sitting metabolism rate of the subjects were detected, and the determined BMR was compared with the calculated results: from the adjusted Schofield equation. Results The BMR, resting lying metabolism rate and resting sitting metabolism rate among males were (166.10±22.09), (174.22±24.56), and (179.54±23.35) kJ·m^-2·h^-1, respectively, which were all higher than those among females were (137.70±20.04), (149.79±19.25), and (167.78±26.02) kJ·m^-2·h^-1, respectively, (P<0.001). The BMR of males and females calculated from the adjusted Schofield equation were (160.83±3.93), and (140.29±4.18) kJ·m^-2·h^-1, respectively, and there was no significantly statistical difference found between the determined BMR and the calculated results from Schofield equation (adjusted) classified by sex, all P values >0.05. Conclusion The BMR of young adults aged 20-29 years old in Changsha was in the national average level, and the adjusted Schofield equation displayed fine accuracy in predicting BMR of young adults aged 20-29 years old in Changsha.

Objective:To assess the practical value of visual scores of magnetic resonance imaging(MRI)features in the diagnosis and classification of dementia with Lewy bodies(DLB).Methods:In this study, 102 DLB patients were prospectively recruited, with 102 cognitively normal elderly people as the normal control group(NC).All included subjects underwent MRI examinations and neuropsychological assessments.Based on the clinical dementia rating(CDR)scale, DLB patients were divided into a mild(CDR=1.0), a moderate(CDR=2.0)and a severe(CDR=3.0)group.The results of MRI were scored visually and the rating scales included medial temporal lobe atrophy(MTA), global cortical atrophy-frontal subscale(GCA-F), posterior cortical atrophy(PCA), white matter lesions(the Fazekas scale), cerebral microbleeds(CMBs), and the Evans Index(EI).Statistical differences were compared between the DLB and NC groups and between DLB patients with different degrees of cognitive impairment.Results:In terms of neuropsychology, the Mini-Mental State Examination(MMSE) score of the DLB group[16.0(11.0, 21.0)]was statistically significantly lower than that of the NC group[29.0(28.0, 30.0)]( Z=-12.31, P<0.001), the Montreal Cognitive Assessment(MoCA)score of the DLB group[9.5(6.0, 15.0)]was statistically significantly lower than that of the NC group[28.0(27.0, 29.0)]( Z=-12.40, P<0.001), and the Activities of Daily Living(ADL)score of the DLB group[32.0(23.8, 40.0)]was statistically significantly higher than that of the NC group[20.0(20.0, 20.0)]( Z=-11.98, P<0.001).The scores of all MRI visual assessment scales in DLB patients were statistically significantly higher than those in the NC group( P<0.001).There were significant differences in MTA scores between DLB patients with different degrees of cognitive impairment( P0<0.001).The MTA score of the mild group[1.0(1.0, 1.0)]was statistically significantly lower than that of the moderate group[2.0(1.0, 2.0)]( P1<0.001, P2<0.001); The MTA score of the moderate group[2.0(1.0, 2.0)]was statistically significantly lower than that of the severe group[2.0(2.0, 3.0)]( P1=0.003, P2=0.010). Conclusions:This study has for the first time after comprehensively evaluated the value of various visual scores in DLB diagnosis, MTA can be used to help diagnose DLB and distinguish the severity of DLB, providing a new supplemental tool for clinical diagnosis.

BACKGROUND:Fibromyalgia syndrome,as a common rheumatic disease,is related to central sensitization and immune abnormalities.However,the specific mechanism has not been elucidated,and there is a lack of specific diagnostic markers.Exploring the possible pathogenesis of this disease has important clinical significance. OBJECTIVE:To screen the potential diagnostic marker genes of fibromyalgia syndrome and analyze the possible immune infiltration characteristics based on bioinformatics methods,such as weighted gene co-expression network analysis(WGCNA),and machine learning. METHODS:Gene expression profiles in peripheral serum of fibromyalgia syndrome patients and healthy controls were obtained from the gene expression omnibus(GEO)database.The differentially co-expressed genes were screened in the expression profile by differential analysis and WGCNA analysis.Least absolute shrinkage and selection operator(LASSO)and support vector machine-recursive feature elimination(SVM-RFE)machine learning algorithm were further used to identify hub biomarkers,and draw receiver operating characteristic curve(ROC)to evaluate the accuracy of diagnosing fibromyalgia syndrome.Finally,single sample gene set enrichment analysis(ssGSEA)and gene set enrichment analysis(GSEA)were used to evaluate the immune cell infiltration and pathway enrichment in patients with fibromyalgia syndrome. RESULTS AND CONCLUSION:Eight down-regulated differentially expressed genes(DEGs)were obtained after differential analysis of the GSE67311 dataset according to the conditions of log2|(FC)|>0 and P<0.05.After WGCNA analysis,497 genes were included in the module(MEdarkviolet)with the highest positive correlation(r=0.22,P=0.04),and 19 genes were included in the module(MEsalmon2)with the highest negative correlation(r=-0.41,P=6×10-5).After intersecting DEGs and the module genes of WGCNA,seven genes were obtained.Four genes were screened out by LASSO regression algorithm and five genes were screened out by SVM-RFE machine learning algorithm.After the intersection of the two,three core genes were identified,which were germinal center associated signaling and motility like,integrin beta-8,and carboxypeptidase A3.The areas under the ROC curve of the three core genes were 0.744,0.739,and 0.734,respectively,indicating that they have good diagnostic value and can be used as biomarkers for fibromyalgia syndrome.The results of immune infiltration analysis showed that memory B cells,CD56 bright NK cells,and mast cells were significantly down-regulated in patients with fibromyalgia syndrome compared with the control group(P<0.05),and were significantly positively correlated with the above three biomarkers(P<0.05).The enrichment analysis suggested that there were nine fibromyalgia syndrome enrichment pathways,mainly related to olfactory transduction pathway,neuroactive ligand-receptor interaction,and infection pathway.The above results showed that the occurrence and development of fibromyalgia syndrome are related to the involvement of multiple genes,abnormal immune regulation,and multiple pathways imbalance.However,the interactions between these genes and immune cells,as well as their relationships with various pathways need to be further investigated.