Two New Identification Methods for Encephalitozoon cuniculi on Tissue Section

[ Objective] The paper aimed to search new identification methods of Encephalitozoon cuniculi on tissue sections. [ Method] Using improved Gram staining method and methyl green pyronin staining method, the pathological sections of sick rabbits were stained and identified. [ Result] The pathological changes in brain tissue could be clearly observed on sections, but parasites were not examined in pathological brain tissues stained by common staining method. When the pathological section was stained by improved Gram staining method, the pathological changes in brain tissue were not ouly stained very clearly, but blue parasites were also found in brain tissues. The parasites in epithelioid cells were stained into purple ones by methyl green pyronin staining method. [ Conclusion] The im- proved Gram staining method and methyl green pyronin staining method performed good staining effects of E. cuniculi in pathological sections, which were conducive to rapid diagnosis of encephalitozoonosis in rabbit.

Value of MRI diffusion weighted imaging in localization of prostate cancer with whole-mount step section pathology

Objective To evaluate the value of MRI diffusion weighted imaging in localization of prostate cancer with whole-mount step section pathology. Methods We treated 36 patients using laparoscopic radical prostatectomy from Oct. 2009 to Jun. 2010. Patients who did not have an MRL /DWI examination or a surgical history of pros-

Long-term Toxicity of Maxing Erchen Zhike Granules

[Objectives]To study the long-term toxicity of Maxing Erchen Zhike granules to rats after intragastric administration,so as to provide reference for its preclinical safety evaluation.[Methods]Total 80 rats were randomly and evenly divided into high-dose group(1.2 mL/100 g,120 g/kg),middle-dose group(96.0 g/kg),low-dose group(72.0 g/kg)and blank control group.The rats in the treatment groups were administered with corresponding doses of Maxing Erchen Zhike granules,and those in the blank control group were given with equal-amount normal saline.The administration lasted for 30 consecutive days.During the experiment,the rats'feed intake,activity,feces and other conditions and toxicity reactions were observed every day.After 24 h of the last administration,12 rats(half male and half female)were randomly selected from each group.Each of the rats was anesthetized with 10%chloral hydrate solution(0.3 mL/100 g)through intraperitoneal injection and subjected to abdominal aorta blood collection(two tubes)for hematological examination and blood biochemical examination(serum).Then,the main organs of the rats were weighed,and pathological examinations were performed.After that,the main organs were weighed and pathological examination was performed.The remaining rats in each group were discontinued and observed for 14 d.On the 15th d,they were subjected to the same treatment,and the body weight,organ coefficients,hematological indices,blood biochemical indices and pathological indices were examined.[Results]After 30 d of administration,there was no abnormality in the appearance and behavior of the animals.There was no significant difference in the daily consumption of feed among the groups,and there was no special case of weight gain.Among the blood biochemical indices,the ALB and ALT levels of each administration group were significantly different from those of the blank control group(P<0.05).The results of histopathological examination show that there was one case of interstitial pneumonia in each of the high-dose group,middle-dose group and blank control group.After 14 d that the administration was stopped,one case of focal myocarditis appeared in the high-dose group,and one case of interstitial pneumonia appeared in the middle-dose group.[Conclusions]Maxing Erchen Zhike granules are safe to be administered to rats at 100 times the clinical dose.and there should be no safety hazards clinically when used at conventional doses.

Artificial intelligence recognition of pathological T stage and tumor invasion in rectal cancer based on large panoramic pathological sections

Background The incidence of colorectal cancer is increasing worldwide,and it currently ranks third among all cancers.Moreover,pathological diagnosis is becoming increasingly arduous.Artificial intelligence has demon-strated the ability to fully excavate image features and assist doctors in making decisions.Large panoramic patho-logical sections contain considerable amounts of pathological information.In this study,we used large panoramic pathological sections to establish a deep learning model to assist pathologists in identifying cancerous areas on whole-slide images of rectal cancer,as well as for T staging and prognostic analysis.Methods We collected 126 cases of primary rectal cancer from the Affiliated Hospital of Qingdao University West Coast Hospital District(internal dataset)and 42 cases from Shinan and Laoshan Hospital District(external dataset)that had tissue surgically removed from January to September 2019.After sectioning,staining,and scanning,a total of 2350 hematoxylin-eosin-stained whole-slide images were obtained.The patients in the internal dataset were randomly divided into a training cohort(n=88)and a test cohort(n=38)at a ratio of 7:3.We chose DeepLabV3+and ResNet50 as target models for our experiment.We used the Dice similarity coefficient,accuracy,sensitivity,specificity,receiver operating characteristic(ROC)curve,and area under the curve(AUC)to evaluate the performance of the artificial intelligence platform in the test set and validation set.Finally,we followed up patients and examined their prognosis and short-term survival to corroborate the value of T-staging investigations.Results In the test set,the accuracy of image segmentation was 95.8%,the Dice coefficient was 0.92,the accuracy of automatic T-staging recognition was 86%,and the ROC AUC value was 0.93.In the validation set,the accuracy of image segmentation was 95.3%,the Dice coefficient was 0.90,the accuracy of automatic classification was 85%,the ROC AUC value was 0.92,and the image analysis time was 0.2 s.There was a difference in survival in patients with local recurrence or distant metastasis as the outcome at follow-up.Univariate analysis showed that T stage,N stage,preoperative carcinoembryonic antigen(CEA)level,and tumor location were risk factors for postoperative recurrence or metastasis in patients with rectal cancer.When these factors were included in a multivariate analysis,only preoperative CEA level and N stage showed significant differences.Conclusion The deep convolutional neural networks we have establish can assist clinicians in making decisions of T-stage judgment and improve diagnostic efficiency.Using large panoramic pathological sections enables better judgment of the condition of tumors and accurate pathological diagnoses,which has certain clinical application value.

Development and application of a detection platform for colorectal cancer tumor sprouting pathological characteristics based on artificial intelligence

Objective Tumor sprouting can reflect independent risk factors for tumor malignancy and a poor clinical progno-sis.However,there are significant differences and difficulties associated with manually identifying tumor sprout-ing.This study used the Faster region convolutional neural network(RCNN)model to build a colorectal cancer tumor sprouting artificial intelligence recognition framework based on pathological sections to automatically identify the budding area to assist in the clinical diagnosis and treatment of colorectal cancer.Methods We retrospectively collected 100 surgical pathological sections of colorectal cancer from January 2019 to October 2019.The pathologists used LabelImg software to identify tumor buds and to count their numbers.Finally,1,000 images were screened,and the total number of tumor buds was approximately 3,226;the images were randomly divided into a training set and a test set at a ratio of 6:4.After the images in the training set were manually identified,the identified buds in the 600 images were used to train the Faster RCNN identification model.After the establishment of the artificial intelligence identification detection platform,400 images in the test set were used to test the identification detection system to identify and predict the area and number of tumor buds.Finally,by comparing the results of the Faster RCNN system and the identification information of pathologists,the performance of the artificial intelligence automatic detection platform was evaluated to determine the area and number of tumor sprouting in the pathological sections of the colorectal cancers to achieve an auxiliary diagnosis and to suggest appropriate treatment.The selected performance indicators include accuracy,precision,specificity,etc.ROC(receiver operator characteristic)and AUC(area under the curve)were used to quantify the performance of the system to automatically identify tumor budding areas and numbers.Results The AUC of the receiver operating characteristic curve of the artificial intelligence detection and identi-fication system was 0.96,the image diagnosis accuracy rate was 0.89,the precision was 0.855,the sensitivity was 0.94,the specificity was 0.83,and the negative predictive value was 0.933.After 400 test sets,pathological image verification showed that there were 356 images with the same positive budding area count,and the difference between the positive area count and the manual detection count in the remaining images was less than 3.The detection system based on tumor budding recognition in pathological sections is comparable to that of patholo-gists’accuracy;however,it took significantly less time(0.03±0.01)s for the pathologist(13±5)s to diagnose the sections with the assistance of the AI model.Conclusion This system can accurately and quickly identify the tumor sprouting area in the pathological sections of colorectal cancer and count their numbers,which greatly improves the diagnostic efficacy,and effectively avoids the need for confirmation by different pathologists.The use of the AI reduces the burden of pathologists in reading sections and it has a certain clinical diagnosis and treatment value.