Optical monitoring of inflammatory response in joint explant cultures using near infrared spectroscopy.

Authors

Nithin Sadeesh, Fatemeh Safari, Abolfazl Jalali, Jovana Zvicer, Arjen Gebraad, Ervin Nippolainen, Susanna Miettinen, Sibylle Grad, Zhen Li, Isaac O Afara

Published in

Osteoarthritis and cartilage. Oct 16, 2026. Epub Oct 16, 2026.

Abstract

To evaluate near-infrared (NIR) spectroscopy combined with machine learning (ML) for detecting and estimating inflammatory and matrix-related biomarkers released into the culture medium of an explant model.
Bovine osteochondral explants and synovial tissues were co-cultured under inflammatory factor-treated (IF-treated) conditions that mimic osteoarthritis (OA) and non-IF-treated conditions. The co-cultures were maintained for seven days with the culture medium collected and replaced daily. The samples were subjected to NIR spectral measurements (900-1700 nm), followed by measurement of sulphated glycosaminoglycans (sGAG), nitric oxide (NO), interleukin (IL)-6, and IL-8 released into the culture. Acquired spectra were preprocessed and analyzed for classification and estimation of biomarkers using ML-based models. Feature importance analyses were performed to identify key wavelength features that contribute to classification and prediction of biomarkers.
Biomarker release showed a substantial increase in the OA-like setup compared to the non-IF-treated setup. During classification, the IF-treated and non-IF-treated spectra showed distinct patterns, with noticeable differences in the 1440-1520 nm and 1600-1700 nm regions. Using these spectral variations, the best performing classifier achieved an accuracy of 95.3% in distinguishing OA-like from non-IF-treated samples. The regression analysis also showed good regression performance with R² values of 0.44 (sGAG), 0.48 (NO), 0.42 (IL-6), and 0.36 (IL-8).
This study showed that NIR spectroscopy combined with ML can detect and quantify OA-related spectral signatures in explant media, thereby demonstrating its potential as a non-destructive and rapid method for monitoring inflammatory activity in ex vivo OA models.

PMID:
42887672
Bibliographic data and abstract were imported from PubMed on 17 Oct 2026.

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