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ER Retrieval Pathway

ER Retrieval Pathway

In the secretory pathway, vesicles transport proteins from one cellular compartment to another in forward transport to deliver the protein to its correct location. Occasionally, misfolded proteins and incorrect proteins escape their original compartments, and a retrieval pathway is used to return the escaped proteins to their original compartment.
The ER uses many checkpoints to prevent the entry of incorrectly folded or a resident protein as cargo onto a transport vesicle. These mechanisms...

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Related Experiment Video

Updated: Jul 23, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Improving Information Extraction from Pathology Reports using Named Entity Recognition.

Ken G Zeng¹, Tarun Dutt², Jan Witowski²

¹New York University, New York, NY, USA.

Research Square

|July 18, 2023

Summary

This summary is machine-generated.

This study introduces a BERT-based named entity recognition (NER) system for extracting detailed diagnostic information from pathology reports. The enhanced model, using data augmentation, achieved high accuracy and generalizability across diverse medical datasets.

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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

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Published on: June 10, 2025

Area of Science:

Medical Informatics
Computational Linguistics
Artificial Intelligence in Healthcare

Background:

Pathology reports are crucial for medical research, containing comprehensive diagnostic data.
Existing natural language processing (NLP) methods for information extraction from these reports have limitations in granularity and generalizability.
Variations in language, negation, and human error in reports hinder accurate automated analysis.

Conclusions:

The developed NER system effectively extracts fine-grained diagnostic information from diverse pathology reports.
The approach offers potential for large-scale information extraction in medical and AI research.
The study highlights the effectiveness of BERT and data augmentation for robust medical NLP tasks.