Telepathology consultation for frozen section diagnosis in China

Huang, Yingxin; Liang, Yan; Wang, Qi; Li, Dazhou; Ma, Lili; Guo, Lili; Tang, Maolin; Liu, Guanglong; Yan, Qianwen; Shen, Lan; Tong, Guihui; Jing, Zhiliang; Zhang, Yan; Deng, Yaotang

doi:10.1186/s13000-018-0705-0

Cited by 28 publications

(19 citation statements)

References 20 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The results, with slightly different composition of large and small bdEVs, suggested that not all small RNAs are uniformly loaded from cells into EVs. Consistent with other reports 13,[38][39][40][41][42] , we also found that fragments of rRNA and tRNA, were more abundant than miRNAs in EVs, even without employing ligation-dependent sequencing library preparation 43 . Although some publications have reported a higher miRNA proportion in EVs 44, 45 than in cells, our results suggested that mapped miRNA reads and diversity gradually decreased from brain homogenate to large EVs and then small EVs.…”

Section: Discussionsupporting

confidence: 92%

Influence of species and processing parameters on recovery and content of brain tissue-derived extracellular vesicles

Huang

Cheng

Turchinovich

et al. 2020

Preprint

View full text Add to dashboard Cite

Extracellular vesicles (EVs) are involved in a wide range of physiological and pathological processes by shuttling material out of and between cells. Tissue EVs may thus lend insights into disease mechanisms and also betray disease when released into easily accessed biological fluids.Since brain-derived EVs (bdEVs) and their cargo may serve as biomarkers of neurodegenerative diseases, we evaluated modifications to a published, rigorous protocol for separation of EVs from brain tissue and studied effects of processing variables on quantitative and qualitative outcomes.To this end, size exclusion chromatography (SEC) and gradient density ultracentrifugation were compared as final separation steps in protocols involving stepped ultracentrifugation. bdEVs were separated from brain tissues of human, macaque, and mouse. Effects of tissue perfusion and a model of post-mortem interval (PMI) before final bdEV separation were probed.MISEV2018-compliant EV characterization was performed, and both small RNA and protein profiling were done. We conclude that the modified, SEC-employing protocol achieves EV separation efficiency roughly similar to a protocol using gradient density ultracentrifugation, while decreasing operator time and, potentially, variability. The protocol appears to yield bdEVs of higher purity for human tissues compared with those of macaque and, especially, mouse, suggesting opportunities for optimization. Where possible, perfusion should be performed in animal models. The interval between death/tissue storage/processing and final bdEV separation can also affect bdEV populations and composition and should thus be recorded for rigorous reporting. Finally, different types of EVs obtained through the modified method reported herein display characteristic RNA and protein content that hint at biomarker potential. To conclude, this study finds that the automatable and increasingly employed technique of SEC can be applied to tissue EV separation, and also reveals more about the importance of species-specific and technical considerations when working with tissue EVs. These results are expected to enhance the use of bdEVs in revealing and understanding brain disease. Results Protocol comparison: bdEV separationThe tissue processing and bdEV separation protocol previously published by several members of the author team (Vella et al., JEV, 2017) 13 was followed through the 2,000 x g centrifugation step ( Figure 1). After enrichment of large EVs (lEVs) by filtration and 10,000 x g centrifugation, supernatant was subjected to SDGU (as previously published) or SEC. Where indicated, SEC fractions were then concentrated by UC or ultrafiltration (UF) (Figure 1). Comparison of bdEV particle count, morphology, and protein markers in different fractions of SDGU and SEC: human and mouse tissueParticle yield per 100 mg tissue input (human or mouse) was determined by nanoparticle tracking analysis (NTA). Particle yield was highest for F2 from SDGU and F7-10 from SEC+UC. Particle concentration was below the reliable range of meas...

show abstract

Section: Discussionsupporting

confidence: 92%

Influence of species and processing parameters on recovery and content of brain tissue-derived extracellular vesicles

Huang

Cheng

Turchinovich

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Recently, based on the development of modern computer and electronic systems, especially the advent of whole-slide imaging (WSI) technology, which enabled digitization of histological slides, the field of digital pathology has greatly evolved [ 17 ]. The remote pathology analysis of histological slides using digital image transmission, namely telepathology, is getting increasingly used in routine practice [ 18 , 19 ], demonstrating a great potential to solve the problem of uneven distribution of medical resources. What’s more, traditional glass slides are fragile and hard to preserve for long, and the discoloration of tissue slides is inevitable [ 20 ], even for some rare and precious histological sections.…”

Section: The Development Of Pathologymentioning

confidence: 99%

Transpathology: molecular imaging-based pathology

Tian

Jin

et al. 2021

Eur J Nucl Med Mol Imaging

View full text Add to dashboard Cite

Pathology is the medical specialty concerned with the study of the disease nature and causes, playing a key role in bridging basic researches and clinical medicine. In the course of development, pathology has significantly expanded our understanding of disease, and exerted enormous impact on the management of patients. However, challenges facing pathology, the inherent invasiveness of pathological practice and the persistent concerns on the sample representativeness, constitute its limitations. Molecular imaging is a noninvasive technique to visualize, characterize, and measure biological processes at the molecular level in living subjects. With the continuous development of equipment and probes, molecular imaging has enabled an increasingly precise evaluation of pathophysiological changes. A new pathophysiology visualization system based on molecular imaging is forming and shows the great potential to reform the pathological practice. Several improvements in “trans-,” including trans-scale, transparency, and translation, would be driven by this new kind of pathological practice. Pathological changes could be evaluated in a trans-scale imaging mode; tissues could be transparentized to better present the underlying pathophysiological information; and the translational processes of basic research to the clinical practice would be better facilitated. Thus, transpathology would greatly facilitate in deciphering the pathophysiological events in a multiscale perspective, and supporting the precision medicine in the future.

show abstract

“…Full remote diagnosis by scanning slides has become a reality with WSIs that capture an entire slide at high magnification, and the rise of 5G technology is expected to accelerate the use of WSIs in remote diagnosis. As it was pointed out above, currently, there are a few operating remote diagnostic networks in various areas of the world (12)(13)(14). This trend will accelerate, allowing institutions to develop remote consultation systems that include outside expert pathologists more easily.…”

Section: Advances In Digital Pathologymentioning

confidence: 99%

A narrative review of digital pathology and artificial intelligence: focusing on lung cancer

Sakamoto

Furukawa

Lami

et al. 2020

Transl Lung Cancer Res

View full text Add to dashboard Cite

The emergence of whole slide imaging technology allows for pathology diagnosis on a computer screen. The applications of digital pathology are expanding, from supporting remote institutes suffering from a shortage of pathologists to routine use in daily diagnosis including that of lung cancer. Through practice and research large archival databases of digital pathology images have been developed that will facilitate the development of artificial intelligence (AI) methods for image analysis. Currently, several AI applications have been reported in the field of lung cancer; these include the segmentation of carcinoma foci, detection of lymph node metastasis, counting of tumor cells, and prediction of gene mutations. Although the integration of AI algorithms into clinical practice remains a significant challenge, we have implemented tumor cell count for genetic analysis, a helpful application for routine use. Our experience suggests that pathologists often overestimate the contents of tumor cells, and the use of AI-based analysis increases the accuracy and makes the tasks less tedious. However, there are several difficulties encountered in the practical use of AI in clinical diagnosis. These include the lack of sufficient annotated data for the development and validation of AI systems, the explainability of black box AI models, such as those based on deep learning that offer the most promising performance, and the difficulty in defining the ground truth data for training and validation owing to inherent ambiguity in most applications. All of these together present significant challenges in the development and clinical translation of AI methods in the practice of pathology. Additional research on these problems will help in resolving the barriers to the clinical use of AI. Helping pathologists in developing knowledge of the working and limitations of AI will benefit the use of AI in both diagnostics and research.

show abstract

Telepathology consultation for frozen section diagnosis in China

Cited by 28 publications

References 20 publications

Influence of species and processing parameters on recovery and content of brain tissue-derived extracellular vesicles

Influence of species and processing parameters on recovery and content of brain tissue-derived extracellular vesicles

Transpathology: molecular imaging-based pathology

A narrative review of digital pathology and artificial intelligence: focusing on lung cancer

Contact Info

Product

Resources

About