Myocardial interstitial fibrosis in the era of precision medicine. Biomarker-based phenotyping for a personalized treatment

The matrix metalloproteinases (MMPs) play a key role during cardiac remodeling. The aim of the study was to investigate the changes in collagenous proteins and MMPs in the model of non-ischemic, anthracycline-induced chronic cardiomyopathy in rabbits using both biochemical and histological approaches. The study was carried out in three groups of Chinchilla male rabbits: 1) daunorubicin (3 mg/kg, once weekly for 10 weeks), 2) control (saline in the same schedule), 3) daunorubicin with the cardioprotectant dexrazoxane (60 mg/kg, before each daunorubicin). Morphological changes in the myocardium of daunorubicin-treated animals were characterized by focal myocardial interstitial fibrosis of different intensity. The subsequent proliferation of the fibrotic tissue was marked by an increased content of both collagen types I and III, which resulted in their typical coexpression in the majority of bundles of fibers forming either smaller or larger scars. Biochemical analysis showed a significantly increased concentration of hydroxyproline, mainly in the pepsin-insoluble fraction of collagenous proteins, in the daunorubicin-treated group (1.42±0.12 mg/g) as compared with the control (1.03±0.04 mg/g) and dexrazoxane (1.07±0.07 mg/g) groups. Dexrazoxane co-administration remarkably reduced the cardiotoxic effects of daunorubicin to the extent comparable with the controls in all evaluated parameters. Using zymography, it was possible to detect only a gelatinolytic band corresponding to MMP-2 (MMP-9 activity was not detectable). However, no significant changes in MMP-2 activity were determined between individual groups. Immunohistochemical analysis revealed increased MMP-2 expression in both cardiomyocytes and fibroblasts. Thus, this study has revealed specific alterations in the collagen network in chronic anthracycline cardiotoxicity in relationship to the expression and activity of major MMPs.

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A quantum-enhanced precision medicine application to support data-driven clinical decisions for the personalized treatment of advanced knee osteoarthritis: development and preliminary validation of precisionKNEE QNN

10.1101/2021.12.13.21267704 ◽

2021 ◽

Author(s):

Stefano Olgiati ◽

Nima Heidari ◽

Davide Meloni ◽

Federico Pirovano ◽

Ali Noorani ◽

...

Keyword(s):

Machine Learning ◽

Computational Complexity ◽

Knee Osteoarthritis ◽

Precision Medicine ◽

Real World ◽

Data Driven ◽

Personalized Treatment ◽

Validation Dataset ◽

Clinical Decisions ◽

Quantum Machine Learning

Background Quantum computing (QC) and quantum machine learning (QML) are promising experimental technologies which can improve precision medicine applications by reducing the computational complexity of algorithms driven by big, unstructured, real-world data. The clinical problem of knee osteoarthritis is that, although some novel therapies are safe and effective, the response is variable, and defining the characteristics of an individual who will respond remains a challenge. In this paper we tested a quantum neural network (QNN) application to support precision data-driven clinical decisions to select personalized treatments for advanced knee osteoarthritis. Methods Following patients consent and Research Ethics Committee approval, we collected clinico-demographic data before and after the treatment from 170 patients eligible for knee arthroplasty (Kellgren-Lawrence grade ≥ 3, OKS ≤ 27, Age ≥ 64 and idiopathic aetiology of arthritis) treated over a 2 year period with a single injection of microfragmented fat. Gender classes were balanced (76 M, 94 F) to mitigate gender bias. A patient with an improvement ≥ 7 OKS has been considered a Responder. We trained our QNN Classifier on a randomly selected training subset of 113 patients to classify responders from non-responders (73 R, 40 NR) in pain and function at 1 year. Outliers were hidden from the training dataset but not from the validation set. Results We tested our QNN Classifier on a randomly selected test subset of 57 patients (34 R, 23 NR) including outliers. The No Information Rate was equal to 0.59. Our application correctly classified 28 Responders out of 34 and 6 non-Responders out of 23 (Sensitivity = 0.82, Specificity = 0.26, F1 Statistic= 0.71). The Positive (LR+) and Negative (LR-) Likelihood Ratios were respectively 1.11 and 0.68. The Diagnostic Odds Ratio (DOR) was equal to 2. Conclusions Preliminary results on a small validation dataset show that quantum machine learning applied to data-driven clinical decisions for the personalized treatment of advanced knee osteoarthritis is a promising technology to reduce computational complexity and improve prognostic performance. Our results need further research validation with larger, real-world unstructured datasets, and clinical validation with an AI Clinical Trial to test model efficacy, safety, clinical significance and relevance at a public health level.

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