Back

Assessing the role of collagen to improve understanding of disease pathophysiology in patients with acute recurrent tonsillitis

Polland, K.; Clapperton, M.; Kunanandam, T.; Florea, C. D.; Douglas, C. M.; McConnell, G.

2025-07-10 biophysics
10.1101/2025.07.07.663453 bioRxiv
Show abstract

Acute recurrent tonsillitis (ART) and obstructive sleep apnoea (OSA) are the most common indications for tonsillectomy worldwide however the disease pathophysiology is poorly understood. Acute recurrent tonsillitis causes repeated episodes of inflammation within the tonsils. Repeated episodes of inflammation can lead to scarring of tissue due to an overproduction of extracellular matrix proteins such as collagen, which in return can cause decreased immunological function. In this study we assess collagen prevalence within ART and OSA tonsils with the aim of better understanding the underlying disease pathophysiology. By performing label-free second harmonic generation (SHG) imaging of tonsil tissue from patients with ART and OSA it is possible to enable measurement of the relative abundance of immature and mature type I collagen within ex vivo paediatric palatine tonsils. We have demonstrated that there is a significant difference in the mean area fraction of immature type I collagen with a higher relative fraction in patients with ART compared to those with OSA (p = 0.0059). However, there was no statistically significant difference in the mean area fraction of mature type I collagen in patients with ART and OSA (p = 0.61). Spatial analysis demonstrated that there was a significantly fraction in tonsillar tissue from patients with ART of immature type I collagen in the epithelia dominant regions (p = 0.0074) and the interior regions (p = 0.0023). Our results provide new information regarding these two common paediatric diseases, giving us an improved insight into the disease pathophysiology.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.