Knee meniscal extrusion in a largely non-osteoarthritic cohort: association with greater loss of cartilage volume

We conducted a longitudinal study (duration 2 years), including 294 individuals (mean age 45 years, 58% female), in order to examine associations between meniscal extrusion, knee structure, radiographic changes and risk factors for osteoarthritis (OA) in a largely non-osteoarthritic cohort. Meniscal extrusion, tibiofemoral cartilage defect score and cartilage volume, and tibial plateau bone area were determined using T1-weighted fat-saturated magnetic resonance imaging. At baseline the presence of medial meniscal extrusion was significantly associated with body mass index (odds ratio [OR] per kg/m2 = 1.13, 95% confidence interval [CI] = 1.02–1.25), past knee injury (positive versus negative history: OR = 3.73, 95% CI = 1.16–11.97), medial tibial bone area (OR per cm2 = 1.37, 95% CI = 1.02–1.85), and osteophytes (OR per grade = 4.89, 95% CI = 1.59–15.02). Two-year longitudinal data revealed that medial meniscal extrusion at baseline was associated with a greater rate of loss of medial tibiofemoral cartilage volume (extrusion versus no extrusion: -1.4%/year; P < 0.05) and greater risk for increased medial femoral cartilage defects (OR = 2.59, 95% CI = 1.14–5.86) and lateral tibial cartilage defects (OR = 2.64, 95% CI = 1.03–6.76). However, the latter two associations became nonsignificant after adjustment for tibial bone area and osteophytes. This study suggests that increasing body mass index and bone size, past knee injury, and osteophytes may be causally related to meniscal extrusion. Most importantly, meniscal extrusion at baseline is associated with greater loss of knee cartilage over 2 years, and this seems to be mediated mostly by subchondral bone changes, suggesting extrusion represents one pathway between bone expansion and cartilage loss

Single nucleotide polymorphism genes and mitochondrial DNA haplogroups as biomarkers for early prediction of knee osteoarthritis structural progressors: use of supervised machine learning classifiers

[Abstract] Background. Knee osteoarthritis is the most prevalent chronic musculoskeletal debilitating disease. Current treatments are only symptomatic, and to improve this, we need a robust prediction model to stratify patients at an early stage according to the risk of joint structure disease progression. Some genetic factors, including single nucleotide polymorphism (SNP) genes and mitochondrial (mt)DNA haplogroups/clusters, have been linked to this disease. For the first time, we aim to determine, by using machine learning, whether some SNP genes and mtDNA haplogroups/clusters alone or combined could predict early knee osteoarthritis structural progressors. Methods. Participants (901) were first classified for the probability of being structural progressors. Genotyping included SNP genes TP63, FTO, GNL3, DUS4L, GDF5, SUPT3H, MCF2L, and TGFA; mtDNA haplogroups H, J, T, Uk, and others; and clusters HV, TJ, KU, and C-others. They were considered for prediction with major risk factors of osteoarthritis, namely, age and body mass index (BMI). Seven supervised machine learning methodologies were evaluated. The support vector machine was used to generate gender-based models. The best input combination was assessed using sensitivity and synergy analyses. Validation was performed using tenfold cross-validation and an external cohort (TASOAC). Results. From 277 models, two were defined. Both used age and BMI in addition for the first one of the SNP genes TP63, DUS4L, GDF5, and FTO with an accuracy of 85.0%; the second profits from the association of mtDNA haplogroups and SNP genes FTO and SUPT3H with 82.5% accuracy. The highest impact was associated with the haplogroup H, the presence of CT alleles for rs8044769 at FTO, and the absence of AA for rs10948172 at SUPT3H. Validation accuracy with the cross-validation (about 95%) and the external cohort (90.5%, 85.7%, respectively) was excellent for both models. Conclusions. This study introduces a novel source of decision support in precision medicine in which, for the first time, two models were developed consisting of (i) age, BMI, TP63, DUS4L, GDF5, and FTO and (ii) the optimum one as it has one less variable: age, BMI, mtDNA haplogroup, FTO, and SUPT3H. Such a framework is translational and would benefit patients at risk of structural progressive knee osteoarthritis

Deformable two-dimensional photonic crystal slab for cavity optomechanics

We have designed photonic crystal suspended membranes with optimized optical and mechanical properties for cavity optomechanics. Such resonators sustain vibration modes in the megahertz range with quality factors of a few thousand. Thanks to a two-dimensional square lattice of holes, their reflectivity at normal incidence at 1064 nm reaches values as high as 95%. These two features, combined with the very low mass of the membrane, open the way to the use of such periodic structures as deformable end-mirrors in Fabry-Perot cavities for the investigation of cavity optomechanical effect

Assessing the robustness of Antarctic temperature reconstructions over the past 2 millennia using pseudoproxy and data assimilation experiments

The Antarctic temperature changes over the past millennia remain more uncertain than in many other continental regions. This has several origins: (1) the number of high-resolution ice cores is small, in particular on the East Antarctic plateau and in some coastal areas in East Antarctica; (2) the short and spatially sparse instrumental records limit the calibration period for reconstructions and the assessment of the methodologies; (3) the link between isotope records from ice cores and local climate is usually complex and dependent on the spatial scales and timescales investigated. Here, we use climate model results, pseudoproxy experiments and data assimilation experiments to assess the potential for reconstructing the Antarctic temperature over the last 2 millennia based on a new database of stable oxygen isotopes in ice cores compiled in the framework of Antarctica2k (Stenni et al.,). The well-known covariance between δ 18 O and temperature is reproduced in the two isotope-enabled models used (ECHAM5/MPI-OM and ECHAM5-wiso), but is generally weak over the different Antarctic regions, limiting the skill of the reconstructions. Furthermore, the strength of the link displays large variations over the past millennium, further affecting the potential skill of temperature reconstructions based on statistical methods which rely on the assumption that the last decades are a good estimate for longer temperature reconstructions. Using a data assimilation technique allows, in theory, for changes in the δ 18 O-temperature link through time and space to be taken into account. Pseudoproxy experiments confirm the benefits of using data assimilation methods instead of statistical methods that provide reconstructions with unrealistic variances in some Antarctic subregions. They also confirm that the relatively weak link between both variables leads to a limited potential for reconstructing temperature based on δ 18 O. However, the reconstruction skill is higher and more uniform among reconstruction methods when the reconstruction target is the Antarctic as a whole rather than smaller Antarctic subregions. This consistency between the methods at the large scale is also observed when reconstructing temperature based on the real δ 18 O regional composites of Stenni et al. (2017). In this case, temperature reconstructions based on data assimilation confirm the long-term cooling over Antarctica during the last millennium, and the later onset of anthropogenic warming compared with the simulations without data assimilation, which is especially visible in West Antarctica. Data assimilation also allows for models and direct observations to be reconciled by reproducing the east-west contrast in the recent temperature trends. This recent warming pattern is likely mostly driven by internal variability given the large spread of individual Paleoclimate Modelling Intercomparison Project (PMIP)/Coupled Model Intercomparison Project (CMIP) model realizations in simulating it. As in the pseudoproxy framework, the reconstruction methods perform differently at the subregional scale, especially in terms of the variance of the time series produced. While the potential benefits of using a data assimilation method instead of a statistical method have been highlighted in a pseudoproxy framework, the instrumental series are too short to confirm this in a realistic setup