We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
PURITAN MEDICAL

Download Mobile App




AI-Powered Imaging Enables Faster Lung Disease Treatment

By LabMedica International staff writers
Posted on 24 Jun 2025

Idiopathic pulmonary fibrosis (IPF) is a chronic and incurable lung disease that causes progressive scarring of lung tissue, severely impairing a person’s ability to breathe. More...

Current treatments can only slow disease progression and do not reverse the fibrotic damage. The unpredictable nature of IPF and lack of effective therapeutic strategies make it a difficult disease to study and treat. Now, a newly developed deep learning algorithm offers a powerful approach to interpreting disease data and identifying potential treatments, marking a significant step forward in understanding IPF and other complex diseases.

The tool, known as UNAGI (unified in-silico cellular dynamics and drug screening framework), was developed by researchers at Yale School of Medicine (New Haven, CT, USA) in collaboration with other institutions. It is a deep generative neural network designed to uncover disease-specific biological patterns and propose drug candidates with minimal human oversight. UNAGI works by analyzing vast datasets to model disease progression at a cellular level. Using sequencing data from 230,000 cells, the AI system builds virtual representations of cells and their disease states.

What sets UNAGI apart from other models is its disease-informed design—it not only identifies genes and regulatory networks involved in disease progression but also integrates this information back into the model for continuous refinement. This embedded iterative learning allows the tool to autonomously interpret new data and test different drugs, eliminating the need for manual re-training. The system also pulls from a database of thousands of drugs with known mechanisms of action to predict which compounds might be effective for a specific disease.

UNAGI was initially trained using IPF data collected from lung tissue samples obtained during transplant surgeries. The tissue was sliced and analyzed to represent different stages of disease, enabling the Yale team to catalogue gene expression in single cells and create a pulmonary fibrosis single-cell atlas. The model identified relevant regulatory networks and classified disease stages, then screened thousands of drugs to identify eight with potential anti-fibrotic effects. One of these was nifedipine, a calcium channel blocker commonly used for hypertension, which UNAGI flagged as a potential treatment for IPF. Laboratory validation using IPF-modeled lung tissue slices confirmed UNAGI’s prediction—nifedipine successfully inhibited scar tissue formation.

This research, published in Nature Biomedical Engineering, underscores the transformative potential of merging AI with single-cell sequencing to unlock new avenues for treating IPF and other progressive diseases. UNAGI’s ability to analyze complex datasets, model disease trajectories, and autonomously identify therapeutic candidates could reshape clinical research by accelerating the discovery of effective treatments. Although initially applied to IPF, the tool has also shown promise in analyzing other disease states, including aging and COVID-19, suggesting its wide applicability across biomedical research and drug development.

Related Links:
Yale School of Medicine


Gold Member
Flocked Fiber Swabs
Puritan® Patented HydraFlock®
3-Part Differential Hematology Analyzer
Swelab Alfa Plus Sampler
New
Specimen Radiography System
TrueView 200 Pro
New
Automated PCR Setup
ESTREAM
Read the full article by registering today, it's FREE! It's Free!
Register now for FREE to LabMedica.com and get access to news and events that shape the world of Clinical Laboratory Medicine.
  • Free digital version edition of LabMedica International sent by email on regular basis
  • Free print version of LabMedica International magazine (available only outside USA and Canada).
  • Free and unlimited access to back issues of LabMedica International in digital format
  • Free LabMedica International Newsletter sent every week containing the latest news
  • Free breaking news sent via email
  • Free access to Events Calendar
  • Free access to LinkXpress new product services
  • REGISTRATION IS FREE AND EASY!
Click here to Register








DIASOURCE (A Biovendor Company)

Channels

Molecular Diagnostics

view channel
Image: Combining rapid diagnostic tests with conventional serology proves to be a useful strategy for diagnosing Chagas disease (Courtesy of Adobe Stock)

Rapid Tests for Chagas Disease Improves Diagnostic Access

Chagas disease, caused by the parasite Trypanosoma cruzi, affects between six and seven million people across the Americas. It is primarily transmitted by insect vectors and remains largely underdiagnosed,... Read more

Hematology

view channel
Image: CitoCBC is the world first cartridge-based CBC to be granted CLIA Waived status by FDA (Photo courtesy of CytoChip)

Disposable Cartridge-Based Test Delivers Rapid and Accurate CBC Results

Complete Blood Count (CBC) is one of the most commonly ordered lab tests, crucial for diagnosing diseases, monitoring therapies, and conducting routine health screenings. However, more than 90% of physician... Read more

Immunology

view channel
Image: How the predictive test works (Photo courtesy of QMUL)

World’s First Clinical Test Predicts Best Rheumatoid Arthritis Treatment

Rheumatoid arthritis (RA) is a chronic condition affecting 1 in 100 people in the UK today, causing the immune system to attack its joints. Unlike osteoarthritis, which is caused by wear and tear, RA can... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.