Postmortem Interval Estimation Using Protein Chip Technology Combined with Multivariate Analysis Methods / 法医学杂志

OBJECTIVES@#To estimate postmortem interval (PMI) by analyzing the protein changes in skeletal muscle tissues with the protein chip technology combined with multivariate analysis methods.@*METHODS@#Rats were sacrificed for cervical dislocation and placed at 16 ℃. Water-soluble proteins in skeletal muscles were extracted at 10 time points (0 d, 1 d, 2 d, 3 d, 4 d, 5 d, 6 d, 7 d, 8 d and 9 d) after death. Protein expression profile data with relative molecular mass of 14 000-230 000 were obtained. Principal component analysis (PCA) and orthogonal partial least squares (OPLS) were used for data analysis. Fisher discriminant model and back propagation (BP) neural network model were constructed to classify and preliminarily estimate the PMI. In addition, the protein expression profiles data of human skeletal muscles at different time points after death were collected, and the relationship between them and PMI was analyzed by heat map and cluster analysis.@*RESULTS@#The protein peak of rat skeletal muscle changed with PMI. The result of PCA combined with OPLS discriminant analysis showed statistical significance in groups with different time points (P<0.05) except 6 d, 7 d and 8 d after death. By Fisher discriminant analysis, the accuracy of internal cross-validation was 71.4% and the accuracy of external validation was 66.7%. The BP neural network model classification and preliminary estimation results showed the accuracy of internal cross-validation was 98.2%, and the accuracy of external validation was 95.8%. There was a significant difference in protein expression between 4 d and 25 h after death by the cluster analysis of the human skeletal muscle samples.@*CONCLUSIONS@#The protein chip technology can quickly, accurately and repeatedly obtain water-soluble protein expression profiles in rats' and human skeletal muscles with the relative molecular mass of 14 000-230 000 at different time points postmortem. The establishment of multiple PMI estimation models based on multivariate analysis can provide a new idea and method for PMI estimation.

Value of discriminant analysis for screening of clonorchiasis sinensis / 中国血吸虫病防治杂志

Objective To build a discriminant function of clonorchiasis sinensis using stepwise discriminant analysis, so as to investigate the feasibility of discriminant analysis for clonorchiasis sinensis screening. Methods Ten villages in Mulan County of Harbin City were sampled as the study sites using a cluster random sampling method. The fecal samples were collected from the permanent residents in the study sites and detected for Clonorchis sinensis eggs using the modified Kato-Katz smear technique. A questionnaire of clonorchiasis sinensis was designed. All data were entered into Epidata 3.1, and a discriminant analysis was performed using the software SPSS version 15.0. Variable were screened using the stepwise discriminant analysis, and the discriminant function was built using the Fisher’s discriminant analysis method. The effectiveness of the discriminant function for clonorchiasis sinensis screening was evaluated by comparison with the modified Kato-Katz smear method. Results Eight variables with statistical significance were included to build the discriminant function, including chronic cholecystitis, cholangitis, gender, eating raw fish, abdominal distension, ethnicity, abdominal pain and age, and the correction rate of the discriminant function was 88.75% to identify clonorchiasis sinensis. Conclusions The discriminant function of clonorchiasis sinensis can be rapidly and simply built based on the strong data processing and analysis capability of the SPSS software, which is rapid to screen clonorchiasis sinensis in Harbin City. Such a function has a high discriminant analysis capability, and provides insights into the establishment of rapid screening of clonorchiasis sinensis in other endemic areas.

Single trial classification of motor imagery electroencephalogram based on Fisher criterion / 生物医学工程学杂志

In order to realize brain-computer interface (BCI), optimal features of single trail motor imagery electroencephalogram (EEG) were extracted and classified. Mu rhythm of EEG was obtained by preprocessing, and the features were optimized by spatial filtering, which are estimated from a set of data by method of common spatial pattern. Classification decision can be made by Fisher criterion, and classification performance can be evaluated by cross validation and receiver operating characteristic (ROC) curve. Optimal feature dimension determination projected by spatial filter was discussed deeply in cross-validation way. The experimental results show that the high discriminate accuracy can be guaranteed, meanwhile the program running speed is improved. Motor imagery intention classification based on optimized EEG feature provides difference of states and simplifies the recognition processing, which offers a new method for the research of intention recognition.

The diagnostic value of four serum tumor markers for gastric cancer based on pattern recognition techniques* / 重庆医学

Objective To evaluate the diagnostic value of serum tumors CA72-4,CA242,CA19-9 and carcino-embryonic antigen(CEA)in patients with gastric cancer based on pattern recognition techniques.Methods Data of serum concentrations of CA72-4,CA242,CA19-9 and CEA of 212 patients with gastric cancer,116 patients with benign gastric disease and 117 healthy subjects were retrospectively analyzed;and the diagnostic performance of each tumor marker,four tumor markers based principle component analysis(PCA),decision tree,PCA-decision tree and the fisher discriminant analysis models were established.Results CA242 had the best diagnostic effect on gastric cancer,and the area under the ROC curve(AUC)was 0.841(95％CI:0.804-0.877).PCA model showed that the serum levels of four tumor markers in patients with gastric cancer were significantly different from those in benign and healthy patients,and obvious metabolic disorders of serum with four tumor markers were found among the patients with gastric cancer.The diagnosis accuracy of the decision tree,PCA-decision tree and the Fisher discriminant analysis models for gastric cancer patients was 58.6％,65.5％and 58.6％respectively,and for non-gastric cancer patients(benign gastric diseases and healthy controls)was 94.7％,99.4％and 97.6％.And the prediction accuracy of the decision tree,PCA-decision tree and the fisher discriminant analysis models for gastric cancer patients was 65.7％,77.6％and 73.1％,and for non-gastric cancer patients was 87.5％,96.9％and 96.9％,respectively.Conclusion The PCA-decision tree model of serum CA72-4,CA242,CA19-9 and CEA might be helpful for the diagnosis and prediction of patients with gastric cancer.

Soil changes in a subtropical seasonal forest chronosequences in the south of Brazil / Mudanças no solo em cronossequências da floresta estacional subtropical no sul do Brasil

ABSTRACT:The abandonment of field crops and the vegetation recovery in exhausted soils have been a recently studied subject as a way to assess the forest role on the soil recovering. The aim of this study was assess changes in the chemical(14 variables) and grain size (sand, silt, and clay) soil features in four forests chronosequences grew over abandoned field crops in the edge of the Brazilian Southern plateau, Rio Grande do Sul State. There were selected 25 forests aging from 5 to >100 years old in areas of slopes and highlands where samples of Leptosols and Regosols were collected at 15cm in depth. The Fisher's Discriminant Analysis showed that some variable groups of soils can distinguish significantly the soils under different forest ages. Six chemical features of soil fertility were the best monitoring indicators of areas impacted by agriculture. Changes in soil did not occur in a linear way towards time.

RESUMO:O abandono de terras agrícolas e a recuperação da vegetação e dos solos exauridos têm sido um tema recentemente investigado como forma de avaliar o papel da floresta na recuperação do solo. O objetivo deste estudo foi verificar as mudanças nas propriedades químicas (14 variáveis) e granulométricos (areia, silte e argila) em quatro cronossequências florestais originadas após o cultivo agrícola no rebordo do Planalto Meridional, sul do Brasil. Foram selecionadas 25 florestas com idades variando de 5 a >100 anos, localizadas em áreas de encostas e patamares onde amostras de Neossolos Litólicos e Regolíticos Eutróficos foram coletados a 15cm de profundidade. A análise discriminante de Fisher demonstrou que alguns grupos de variáveis dos solos podem distinguir significativamente os solos sob as diferentes idades florestais. Seis atributos químicos relacionados com a fertilidade do solo poderiam ser considerados os melhores indicadores de monitoramento das áreas impactadas pela agricultura. As mudanças no solo não ocorreram de forma linear ao longo do tempo.

Fisher discriminant analysis of ultrasonography index in diagnosing characters of ovarian neoplasms / 中国医学影像技术

Objective To construct Fisher discrminant functions with index of ultrasonography. Methods A total of 48 non-neoplastic ovarian cysts, 137 benign and 120 malignant ovarian tumors were enrolled in this study. Taking ultrasonographic parameters and Doppler blood flow signals as differential diagnosis variable, a diagnosis model was developed using stepwise discriminant analysis. Then a projection and territorial map were drew and the diagnostic ability of the model was verified with substitution method and jackknife. Results ①Univariate analysis indicated that ovarian cysts volume, end-diastolic blood flow velocity (V_(ED)), mean blood flow velocity (V_m), resistance index (RI), pulse index (PI), physical property, echo, shape, boundary, ascites and blood flow signal have statistical difference among the three kinds of ovarian cysts. ②Stepwise discriminant analysis showed that volume, resistance index, physical property, shape and boundary are the independent prognostic variables. The two Fisher discriminant functions were as following: Function 1=0.002volume-4.793 RI+0.468physical property+0.862shape+0.901boundary-4.076, Function 2=0.005volume-1.480 RI+0.851physical property-0.291shape+0.443boundary+0.524. ③The projective positions of three kinds of ovarian cysts at 2D coordinates were clear. ④The sensibility and specificity of mode for diagnosis non-neoplastic ovarian cysts, benign and malignant ovarian tumors was 91.67%, 88.32% and 93.33% with substitution method, and was 91.67%, 86.13% and 93.33% with jackknife method. Conclusion Cysts volume, RI, physical property, shape and boundary are the significant differential prognostic variables. Fisher discriminant analysis can provide a reliable prognostic model for ovarian cysts.

The value of Fisher discriminant analysis in the diagnosis of ectopic pregnancy / 中国综合临床

Objective To discuss the value of Fisher discriminant analysis of serum progesterone and the growing rate of β-human chorionic gonadotropin in the prediction of early ectopic pregnancy. Methods 66 patients with ectopic pregnancy (11 cases were successfully treated expectantly and 55 cases were treated surgically including 40 cases of rupture of fallopian tube and 15 cases of tubal abortion) and 55 patients with intrauterine pregnancy and 50 patients with threatened abortion were chosen. Serum progesterone,β-HCG,48 hβ-HCG and the 48 h growing rate of β-HCG in each group were measured and a Fisher discriminant analysis was used. Results The serum progester-one was (30.27± 18.20) nmol/L in ectopic pregnancy group,( 108.44±23.27 ) nmol/L in intrauterine pregnancy group and (91.68±34.90) nmol/L in threatened abortion group. The first β-HCG was ( 3767.63 ± 3530.38 ) U/L in ectopie pregnancy group,(29 028.65 ± 10 874.01 )U/L in intrauterine pregnancy group and (13 457.47±16 367.65)U/L in threatened abortion group. The second β-HCG was (4349.24±3536.22)U/L in ectopic pregnancygroup,(56 139.46 ± 23 296.87 ) U/L in intrauterine pregnancy group and (23 270.63 ± 23 811.68 ) U/L in threat-ened abortion group. The growing rate of β-HCG ( β-HCG/the first serum β-HCG) was 1.29 ± 0.28 in ectopic preg-nancy group,1.93 ± 0.36 in intrauterine pregnancy group and 1.97±0.28 in threatened abortion group. There was significant difference in serum progesterone,the first β-HCG and the second β-HCG as well as the growing rate of β-HCG among the groups(P<0.05 or <0.01). Fisher discriminant analysis of combing progesterone and the growing rate of β-HCG were connected with diagnosis of ectopic pregnancy,however,the only one serum β-HCG was not con-nected with diagnosis of ectopic pregnancy. 98.5% of ectopic pregnancy,65.6% of intrauterine pregnancy and 64.0% of threatened abortion were correctly classified in the Fisher discfiminant analysis,with overall correct rate of 77.8%. Conclusion Fisher discriminant analysis of combing progesterone and the growing rate of β-HCG can bet-ter predict the early ectopic pregnancy.