Age-dependent genetic regulation of osteoarthritis: independent effects of immune system genes.

OBJECTIVES: Osteoarthritis (OA) is a joint disease with a heritable component. Genetic loci identified via genome-wide association studies (GWAS) account for an estimated 26.3% of the disease trait variance in humans. Currently, there is no method for predicting the onset or progression of OA. We describe the first use of the Collaborative Cross (CC), a powerful genetic resource, to investigate knee OA in mice, with follow-up targeted multi-omics analysis of homologous regions of the human genome. METHODS: We histologically screened 275 mice for knee OA and conducted quantitative trait locus (QTL) mapping in the complete cohort (> 8 months) and the younger onset sub-cohort (8-12 months). Multi-omic analysis of human genetic datasets was conducted to investigate significant loci. RESULTS: We observed a range of OA phenotypes. QTL mapping identified a genome-wide significant locus on mouse chromosome 19 containing Glis3, the human equivalent of which has been identified as associated with OA in recent GWAS. Mapping the younger onset sub-cohort identified a genome-wide significant locus on chromosome 17. Multi-omic analysis of the homologous region of the human genome (6p21.32) indicated the presence of pleiotropic effects on the expression of the HLA - DPB2 gene and knee OA development risk, potentially mediated through the effects on DNA methylation. CONCLUSIONS: The significant associations at the 6p21.32 locus in human datasets highlight the value of the CC model of spontaneous OA that we have developed and lend support for an immune role in the disease. Our results in mice also add to the accumulating evidence of a role for Glis3 in OA.

Wavelength weightings in machine learning for ovine joint tissue differentiation using diffuse reflectance spectroscopy (DRS).

Objective: To investigate the DRS of ovine joint tissue to determine the optimal optical wavelengths for tissue differentiation and relate these wavelengths to the biomolecular composition of tissues. In this study, we combine machine learning with DRS for tissue classification and then look further at the weighting matrix of the classifier to further understand the key differentiating features. Methods: Supervised machine learning was used to analyse DRS data. After normalising the data, dimension reduction was achieved through multiclass Fisher's linear discriminant analysis (Multiclass FLDA) and classified with linear discriminant analysis (LDA). The classifier was first run with all the tissue types and the wavelength range 190 nm - 1081 nm. We analysed the weighting matrix of the classifier and then ran the classifier again, the first time using the ten highest weighted wavelengths and the second using only the single highest. Our method was applied to a dataset containing ovine joint tissue including cartilage, cortical and subchondral bone, fat, ligament, meniscus, and muscle. Results: It achieved a classification accuracy of 100% using the wavelength 190 nm - 1081 nm (2048 attributes) with an accuracy of 90% being present for 10 attributes with the exception of those with comparable compositions such as ligament and meniscus. An accuracy greater than 70% was achieved using a single wavelength, with the same exceptions. Conclusion: Multiclass FLDA combined with LDA is a viable technique for tissue identification from DRS data. The majority of differentiating features existed within the wavelength ranges 370-470 and 800-1010 nm. Focusing on key spectral regions means that a spectrometer with a narrower range can potentially be used, with less computational power needed for subsequent analysis.

Machine learning classification of human joint tissue from diffuse reflectance spectroscopy data.

Objective: To assess if incorporation of DRS sensing into real-time robotic surgery systems has merit. DRS as a technology is relatively simple, cost-effective and provides a non-contact approach to tissue differentiation. Methods: Supervised machine learning analysis of diffuse reflectance spectra was performed to classify human joint tissue that was collected from surgical procedures. Results: We have used supervised machine learning in the classification of a DRS human joint tissue data set and achieved classification accuracy in excess of 99%. Sensitivity for the various classes were; cartilage 99.7%, subchondral 99.2%, meniscus 100% and cancellous 100%. Full wavelength range is required for maximum classification accuracy. The wavelength resolution must be larger than 8nm. A SNR better than 10:1 was required to achieve a classification accuracy greater than 50%. The 800-900nm wavelength range gave the greatest accuracy amongst those investigated Conclusion: DRS is a viable method for differentiating human joint tissue and has the potential to be incorporated into robotic orthopaedic surgery.

Patient Dissatisfaction Following Total Knee Arthroplasty: A Systematic Review of the Literature.

BACKGROUND: Dissatisfaction following total knee arthroplasty (TKA) is common. Approximately 20% of patients report dissatisfaction following primary TKA. This systematic literature review explores key factors affecting patient dissatisfaction following TKA. METHODS: Six literature databases published between 2005 and 1 January 2016 were searched using 3 key search phrases. Papers were included if the study investigated patient dissatisfaction in primary unilateral or bilateral TKA. Information from each article was categorized to the domains of socioeconomic, preoperative, intraoperative, and postoperative factors affecting patient dissatisfaction. RESULTS: This review found that patient dissatisfaction pertains to several key factors. Patient expectations prior to surgery, the degree of improvement in knee function, and pain relief following surgery were commonly cited in the literature. Fewer associations were found in the socioeconomic and surgical domains. CONCLUSION: Identifying who may be dissatisfied after their TKA is mystifying; however, we note several strategies that target factors whereby an association exists. Further research is needed to better quantify dissatisfaction, so that the causal links underpinning dissatisfaction can be more fully appreciated and strategies employed to target them.

A review of the physiological and histological effects of laser osteotomy.

Osteotomy is the surgical cutting of bone. Some obstacles to laser osteotomy have been melting, carbonisation and subsequent delayed healing. New cooled scanning techniques have resulted in effective bone cuts without the strong thermal side effects, which were observed by inappropriate irradiation techniques with continuous wave and long pulsed lasers. With these new techniques, osteotomy gaps histologically healed with new bone formation without any noticeable or minimum thermal damage. No significant cellular differences in bone healing between laser and mechanical osteotomies were noticed. Some studies even suggest that the healing rate may be enhanced following laser osteotomy compared to conventional mechanical osteotomy. Additional research is necessary to evaluate different laser types with appropriate laser setting variables to increase ablation rates, with control of depth, change in bone type and damage to adjacent soft tissue. Laser osteotomy has the potential to become incorporated into the armamentarium of bone surgery.

The anatomy of the pubic region revisited: implications for the pathogenesis and clinical management of chronic groin pain in athletes.

Chronic groin pain is a common complaint for athletes participating in sports that involve repetitive sprinting, kicking or twisting movements, such as Australian Rules football, soccer and ice hockey. It is frequently a multifactorial condition that presents a considerable challenge for the treating sports medicine practitioner. To better understand the pathogenesis of chronic groin pain in athletes, a precise anatomical knowledge of the pubic symphysis and surrounding soft tissues is required. Several alternative descriptions of pubic region structures have been proposed. Traditionally, chronic groin pain in athletes has been described in terms of discrete pathology requiring specific intervention. While this clinical reasoning may apply in some cases, a review of anatomical findings indicates the possibility of multiple pathologies coexisting in athletes with chronic groin pain. An appreciation of these alternative descriptions may assist sports medicine practitioners with diagnostic and clinical decision-making processes. The purpose of this literature review is to reappraise the anatomy of the pubic region, considering findings from cadaveric dissection and histology studies, as well as those from diagnostic imaging studies in athletes.