Using polyacrylamide gel electrophoresis the distribution of Hp serumtypes as well astheir inheritance in a Chinese population in Shanghai area was investigated.Among 1,231 unrelated blood donors from the Shanghai Blood Center,the distribution of Hp phenotypeswere Hpl-1,9.91％,Hp2-1,35.34％,Hp2-2,53.94％ and HpO,0.81％respectively.The gene frequencies of Hp~1,Hp~2,and Hp~0 were 0. 2575,0.6650,and0.0778 respectively.Some variants described by other authors were also determined;amongthose variants a new phenotype K-1 was found,which has never been reportedelsewhere.The inheritance of Hp of 96 family members from twenty Chinese pedigreeswas also adapted to the theory of Smithies and Walker.However,the anomalousinheritance of HpO in four families investigated could only be reasonably interpreted bythe presence of a‘silent’gene Hp~0,because there was no evidence of illegitimacy in thesegregation data of genetic markers in other blood group systems.

To explore the feasibility and efficacy of artificial neural network for differentiating high-grade glioma and low-grade glioma using image information.
 Methods: A total of 130 glioma patients with confirmed pathological diagnosis were selected retrospectively from 2012 to 2017. Forty one imaging features were extracted from each subjects based on 2-dimension magnetic resonance T1 weighted imaging with contrast-enhancement. An artificial neural network model was created and optimized according to the performance of feature selection. The training dataset was randomly selected half of the whole dataset, and the other half dataset was used to verify the performance of the neural network for glioma grading. The training-verification process was repeated for 100 times and the performance was averaged.
 Results: A total of 5 imaging features were selected as the ultimate input features for the neural network. The mean accuracy of the neural network for glioma grading was 90.32%, with a mean sensitivity at 87.86% and a mean specificity at 92.49%. The area under the curve of receiver operating characteristic curve was 0.9486.
 Conclusion: As a technique of artificial intelligence, neural network can reach a relatively high accuracy for the grading of glioma and provide a non-invasive and promising computer-aided diagnostic process for the pre-operative grading of glioma.
Brain Neoplasms ; diagnostic imaging ; pathology ; Glioma ; diagnostic imaging ; pathology ; Humans ; Magnetic Resonance Imaging ; Neoplasm Grading ; Neural Networks, Computer ; ROC Curve ; Retrospective Studies ; Sensitivity and Specificity