The role of surfactant proteins A and D (SP-A and SP-D) in lung clearance and translocation to secondary organs of inhaled nanoparticles was investigated by exposing SP-A and SP-D knockout (AKO and DKO) and wild type (WT) mice nose-only for 3 hours to an aerosol of 20 nm gold nanoparticles (AuNPs). Animals were euthanised at 0-, 1-, 7- and 28-days post-exposure. Analysis by inductively coupled plasma mass spectrometry (ICP-MS) of the liver and kidneys showed that extrapulmonary translocation was below the limits of detection. Imaging of the lungs by laser ablation ICP-MS confirmed the homogenous distribution of AuNPs. Coherent anti-Stokes Raman Scattering, Second Harmonic Generation and Two-Photon Fluorescence imaging were applied for semi-quantitative analysis of the uptake of AuNPs by alveolar macrophages and found uptake increased with time post-exposure, peaking after 7 days, and with the largest increase in uptake being in WT mice. Single particle ICP-MS allowed particle counting and sizing of AuNPs in the lungs showing that particle agglomeration following deposition within the lung was greater for the wildtype than the knockout models, indicating a role for SP-A and SP-D in agglomeration, however, any effect of this on overall lung clearance was minimal. For all groups, the Au (mass) lung burden initial clearance half-time was approximately 20-25 d, however, the AuNP (particle number) lung burden clearance half-time was shorter at approximately 10 days. In general terms, differences between the results for the three models were limited, indicating the preferential clearance of smaller particles from the lung.

Osteoarthritis (OA) is a complex disease of cartilage characterised by joint pain, functional limitation, and reduced quality of life with affected joint movement leading to pain and limited mobility. Current methods to diagnose OA are predominantly limited to X-ray, MRI and invasive joint fluid analysis, all of which lack chemical or molecular specificity and are limited to detection of the disease at later stages. A rapid minimally invasive and non-destructive approach to disease diagnosis is a critical unmet need. Label-free techniques such as Raman Spectroscopy (RS), Coherent anti-Stokes Raman scattering (CARS), Second Harmonic Generation (SHG) and Two Photon Fluorescence (TPF) are increasingly being used to characterise cartilage tissue. However, current studies are based on whole tissue analysis and do not consider the different and structurally distinct layers in cartilage. In this work, we use Raman spectroscopy to obtain signatures from the superficial (top) and deep (bottom) layer of healthy and osteoarthritic cartilage samples from 64 patients (19 control and 45 OA). Spectra were acquired both in the 'fingerprint' region from 700 to 1720 cm and high-frequency stretching region from 2500 to 3300 cm. Principal component and linear discriminant analysis was used to identify the peaks that contributed significantly to classification accuracy of the different samples. The most pronounced differences were observed at the proline (855 cm and 921 cm) and hydroxyproline (877 cm and 938 cm), sulphated glycosaminoglycan (sGAG) (1064 cm and 1380 cm) frequencies for both control and OA as well as the 1245 cm and 1272 cm, 1320 cm and 1345 cm, 1451 cm collagen modes were altered in OA samples, consistent with expected collagen structural changes. Classification accuracy based on Raman fingerprint spectral analysis of superficial and deep layer cartilage for controls was found to be 97% and 93% on using individual/all spectra and, 100% and 95% on using mean spectra per patient, respectively. OA diseased cartilage was classified with an accuracy of 88% and 84% for individual/all spectra, and 96% and 95% for mean spectra per patient based on analysis of the superficial and the deep layers, respectively. Raman spectra from the C-H stretching region (2500-3300 cm) resulted in high classification accuracy for identification of different layers and OA diseased cartilage but low accuracy for controls. Differential changes in superficial and deep layer cartilage signatures were observed with age (under 60 and over 60 years), in contrast, less significant differences were observed with gender. Prominent chemical changes in the different layers of cartilage were preliminarily imaged using CARS, SHG and TPF. Cell clustering was observed in OA together with differences in pericellular matrix and collagen structure in the superficial and the deep layers correlating with the Raman spectral analysis. The current study demonstrates the potential of Raman Spectroscopy and multimodal imaging to interrogate cartilage tissue and provides insight into the chemical and structural composition of its different layers with significant implications for OA diagnosis for an increasing aging demographic.

Raman spectroscopy is a rapid method for analysing the molecular composition of biological material. However, noise contamination in the spectral data necessitates careful pre-processing prior to analysis. Here we propose an end-to-end Convolutional Neural Network to automatically learn an optimal combination of pre-processing strategies, for the classification of Raman spectra of superficial and deep layers of cartilage harvested from 45 Osteoarthritis and 19 Osteoporosis (Healthy controls) patients. Using 6-fold cross-validation, the Multi-Convolutional Neural Network achieves comparable or improved classification accuracy against the best-performing Convolutional Neural Network applied to either the raw or pre-processed spectra. We utilised Integrated Gradients to identify the contributing features (Raman signatures) in the network decision process, showing they are biologically relevant. Using these features, we compared Artificial Neural Networks, Decision Trees and Support Vector Machines for the feature selection task. Results show that training on fewer than 3 and 300 features, respectively, for the disease classification and layer assignment task provide performance comparable to the best-performing CNN-based network applied to the full dataset. Our approach, incorporating multi-channel input and Integrated Gradients, can potentially facilitate the clinical translation of Raman spectroscopy-based diagnosis without the need for laborious manual pre-processing and feature selection.

Osteoarthritis (OA) is the most common degenerative joint disease, presented as wearing down of articular cartilage and resulting in pain and limited mobility for 1 in 10 adults in the UK [Osteoarthr. Cartil.28(6), 792 (2020)10.1016/j.joca.2020.03.004]. There is an unmet need for patient friendly paradigms for clinical assessment that do not use ionizing radiation (CT), exogenous contrast enhancing dyes (MRI), and biopsy. Hence, techniques that use non-destructive, near- and shortwave infrared light (NIR, SWIR) may be ideal for providing label-free, deep tissue interrogation. This study demonstrates multimodal "spectromics", low-level abstraction data fusion of non-destructive NIR Raman scattering spectroscopy and NIR-SWIR absorption spectroscopy, providing an enhanced, interpretable "fingerprint" for diagnosis of OA in human cartilage. This is proposed as method level innovation applicable to both arthro- or endoscopic (minimally invasive) or potential exoscopic (non-invasive) optical approaches. Samples were excised from femoral heads post hip arthroplasty from OA patients (n = 13) and age-matched control (osteoporosis) patients (n = 14). Under multivariate statistical analysis and supervised machine learning, tissue was classified to high precision: 100% segregation of tissue classes (using 10 principal components), and a classification accuracy of 95% (control) and 80% (OA), using the combined vibrational data. There was a marked performance improvement (5 to 6-fold for multivariate analysis) using the spectromics fingerprint compared to results obtained from solely Raman or NIR-SWIR data. Furthermore, clinically relevant tissue components were identified through discriminatory spectral features - spectromics biomarkers - allowing interpretable feedback from the enhanced fingerprint. In summary, spectromics provides comprehensive information for early OA detection and disease stratification, imperative for effective intervention in treating the degenerative onset disease for an aging demographic. This novel and elegant approach for data fusion is compatible with various NIR-SWIR optical devices that will allow deep non-destructive penetration.

Low-grade chronic inflammation is associated with many age-related conditions. Non-invasive methods to monitor low-grade chronic inflammation may improve the management of older people at risk of poorer outcomes. This longitudinal cohort study has determined baseline inflammation using neopterin volatility in monthly urine samples of 45 independent older adults (aged 65-75 years). Measurement of neopterin, an inflammatory metabolite, enabled stratification of individuals into risk categories based on how often in a 12-month period their neopterin level was raised. Hearing was measured (pure-tone audiometry) at baseline, 1 year and 3 years of the study. Results show that those in the highest risk category (neopterin raised greater than 50% of the time) saw greater deterioration, particularly in high-frequency, hearing. A one-way Welch's ANOVA showed a significant difference between the risk categories for change in high-frequency hearing (W (3, 19.6) = 9.164, p = 0.0005). Despite the study size and duration individuals in the highest risk category were more than twice as likely to have an additional age-related morbidity than those in the lowest risk category. We conclude that volatility of neopterin in urine may enable stratification of those at greatest risk of progression of hearing loss.