Dataset on equine cartilage near infrared spectra, composition, and functional properties.
Jaakko K SarinJari TorniainenMithilesh PrakashLassi RieppoIsaac O AfaraJuha TöyräsPublished in: Scientific data (2019)
Near infrared (NIR) spectroscopy is a well-established technique that is widely employed in agriculture, chemometrics, and pharmaceutical engineering. Recently, the technique has shown potential in clinical orthopaedic applications, for example, assisting in the diagnosis of various knee-related diseases (e.g., osteoarthritis) and their pathologies. NIR spectroscopy (NIRS) could be especially useful for determining the integrity and condition of articular cartilage, as the current arthroscopic diagnostics is subjective and unreliable. In this work, we present an extensive dataset of NIRS measurements for evaluating the condition, mechanical properties, structure, and composition of equine articular cartilage. The dataset contains NIRS measurements from 869 different locations across the articular surfaces of five equine fetlock joints. A comprehensive library of reference values for each measurement location is also provided, including results from a mechanical indentation testing, digital densitometry imaging, polarized light microscopy, and Fourier transform infrared spectroscopy. The published data can either be used as a model of human cartilage or to advance equine veterinary research.
Keyphrases
- high resolution
- single molecule
- photodynamic therapy
- endothelial cells
- fluorescence imaging
- total knee arthroplasty
- knee osteoarthritis
- drug release
- climate change
- extracellular matrix
- rheumatoid arthritis
- fluorescent probe
- electronic health record
- atomic force microscopy
- high throughput
- anterior cruciate ligament reconstruction
- optical coherence tomography
- mass spectrometry
- randomized controlled trial
- drug delivery
- systematic review
- big data
- machine learning
- density functional theory
- deep learning
- induced pluripotent stem cells
- cystic fibrosis
- artificial intelligence
- pseudomonas aeruginosa
- data analysis