Studies on [aipha]-D-Xylosidase from bacillus sp.

Thesis--University of Tsukuba, D.Agr.(A), no. 696, 1989. 7.3

A strong convergence theorem on solving common solutions for generalized equilibrium problems and fixed-point problems in Banach space

Abstract In this paper, the common solution problem (P1) of generalized equilibrium problems for a system of inverse-strongly monotone mappings and a system of bifunctions satisfying certain conditions, and the common fixed-point problem (P2) for a family of uniformly quasi-&#981;-asymptotically nonexpansive and locally uniformly Lipschitz continuous or uniformly H&#246;lder continuous mappings are proposed. A new iterative sequence is constructed by using the generalized projection and hybrid method, and a strong convergence theorem is proved on approximating a common solution of (P1) and (P2) in Banach space. 2000 MSC: 26B25, 40A05</p

Immunotargeting of collagenase on thrombus

In this study, we aimed to develop a thrombus-targeting delivery system of collagenase bound to a monoclonal antibody, and to investigate the thrombolysis of an immune-conjugate in vitro and in vivo as well as the targeting effect. We prepared the immunizing conjugation of collagenase by the 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide (EDCI) method. In order to conjugate collagenase and a monoclonal antibody, bovine serum albumin was used as a linker, increasing the number of collagenase molecules carried and keeping collagenase and the monoclonal antibody active. In vitro thrombolysis experiments showed that collagenase had a strong dissolving effect on collagen-embolus within 24 hours. We established a rabbit pulmonary embolism model to investigate the thrombolysis effect of collagenase and collagenase immunizing conjugation in vivo. Our results revealed a significant difference between collagenase and collagenase immunizing conjugation (P < 0.05). We also established a rabbit ear edge vein model to investigate the active target of collagenase immunizing conjugation. We found that collagenase immunizing conjugation had active targets, and had a strong ability to dissolve organized thrombi. In conclusion, the thrombus-targeting delivery system of collagenase we developed has active targeting effects on thrombi

Gleason Grade Group Prediction for Prostate Cancer Patients with MR Images Using Convolutional Neural Network

Purpose: Gleason Grading (GG) Grouping system is an important index in determining treatment plan or predicting outcome for prostate cancer patients. Unfortunately, currently GG Grouping results can only be obtained from biopsy-driven pathological tests. We aim to predict GG groups for PCa patients from multiparametric magnetic resonance images (mp-MRI). Methods: The challenges include data heterogeneity, small sample size and highly imbalanced distribution among different groups. A retrospective collection of 201 patients with 320 lesions from the SPIE-AAPM-NCI PROSTATEx Challenge (https://doi.org/10.7937/K9TCIA.2017.MURS5CL) was studied, among which only 98 patients with 110 lesions having GG available. And number of lesions from each group was 36, 39, 20, 8, and 7, respectively, for GG 1-5. We approached the challenging task by bridging though easier one of classifying 320 lesions into benign or malignant, and transferring learned knowledge to GG prediction on 110 lesions. During implementation, a four-convolutional neural network (CNN) was used for malignancy classification. To prevent over-fitting on small sample size, instead of fine-tuning on CNN, learned features were extracted and classified by weighted extreme learning machine (wELM), traditional classifier that assigned larger weight to samples from minority class.Image pre-processing included registration and normalization. Image rotation and scaling were also used to increase sample size and re-balance number of malignant and benign lesions. Results: The best combination of modalities as input to CNN was found to be T2W, apparent diffusion coefficient (ADC) and B-value maps (b=50 s/mm2). During phase 1 of CNN training, average and best results of (Sensitivity, Specificity, G-mean) over 10 folds were (0.53, 0.83, 0.65) and (1, 0.88, 0.91), respectively. Features from best performing model were extracted to represent each lesion, and those from the last convolutional layer were found constantly better than from all other layers (Table 1). This implies that semantic features regarding lesion information is more important than local and detailed features such as contrast change in GG prediction. Conclusion: This work has successfully tackled the challenging task of GG prediction from mp-MRI by bridging through an easier task and has combined feature extraction using deep learning model and small data classification using traditional classifier to benefit from both.https://scholarlycommons.henryford.com/merf2019basicsci/1003/thumbnail.jp