The effect of fibrin sealant on bioactive glass S53P4 particles – pH impact and dissolution characteristics in vitro

Fibrin glue, a two-component tissue adhesive, has a range of clinical indications. Bioactive glass (BG) S53P4 has been approved for clinical use in several craniomaxillofacial and orthopedic applications. Although sometimes used simultaneously, there is no data available regarding the possible interaction of these two biocompatible substances. In this in vitro study, using a BG particle concentration of 4 mg/ml, a 0.4 unit pH increment (p<0.001) was observed in simulated body fluid (SBF) after a 7-day incubation period. The addition of fibrin glue (0.13 g, SD 0.04; or 3.7 mg/ml) on top of the BG particles raised further the pH by 0.5 units (p<0.001). The difference between these groups was statistically significant (p=0.008). With a BG concentration of 25 mg/ml and a fibrin glue concentration of 18 mg/ml during a 14-day incubation period, a pH increment of 0.6 units and SBF ion concentration change of Ca, K, Mg, Na, P and Si ions was seen. Moreover, a penetration depth between 4 and 6 mm was observed when fibrin glue was applied on top of a bed of BG particles. Conclusions: Fibrin glue is not likely to have a distracting effect on BG-induced pH increase of the SBF although it might delay early BG surface reactions based on ion concentration measurements. Fibrin glue penetrated to the interparticle space to some extent, binding the particles together for easy clinical use of BG. </p

Near-Infrared Spectroscopy Enables Arthroscopic Histologic Grading of Human Knee Articular Cartilage

Purpose: To develop the means to estimate cartilage histologic grades and proteoglycan content in ex vivo arthroscopy using near-infrared spectroscopy (NIRS). Methods: In this experimental study, arthroscopic NIR spectral measurements were performed on both knees of 9 human cadavers, followed by osteochondral block extraction and in vitro measurements: reacquisition of spectra and reference measurements (proteoglycan content, and three histologic scores). A hybrid model, combining principal component analysis and linear mixed-effects model (PCA-LME), was trained for each reference to investigate its relationship with in vitro NIR spectra. The performance of the PCA-LME model was validated with ex vivo spectra before and after the exclusion of outlying spectra. Model performance was evaluated based on Spearman rank correlation (ρ) and root-mean-square error (RMSE). Results: The PCA-LME models performed well (independent test: average ρ = 0.668, RMSE = 0.892, P < .001) in the prediction of the reference measurements based on in vitro data. The performance on ex vivo arthroscopic data was poorer but improved substantially after outlier exclusion (independent test: average ρ = 0.462 to 0.614, RMSE = 1.078 to 0.950, P = .019 to .008). Conclusions: NIRS is capable of nondestructive evaluation of cartilage integrity (i.e., histologic scores and proteoglycan content) under similar conditions as in clinical arthroscopy. Clinical Relevance: There are clear clinical benefits to the accurate assessment of cartilage lesions in arthroscopy. Visual grading is the current standard of care. However, optical techniques, such as NIRS, may provide a more objective assessment of cartilage damage.publishedVersionPeer reviewe

Near infrared spectroscopic evaluation of biochemical and crimp properties of knee joint ligaments and patellar tendon

Knee ligaments and tendons play an important role in stabilizing and controlling the motions of the knee. Injuries to the ligaments can lead to abnormal mechanical loading of the other supporting tissues (e.g., cartilage and meniscus) and even osteoarthritis. While the condition of knee ligaments can be examined during arthroscopic repair procedures, the arthroscopic evaluation suffers from subjectivity and poor repeatability. Near infrared spectroscopy (NIRS) is capable of non-destructively quantifying the composition and structure of collagen-rich connective tissues, such as articular cartilage and meniscus. Despite the similarities, NIRS-based evaluation of ligament composition has not been previously attempted. In this study, ligaments and patellar tendon of ten bovine stifle joints were measured with NIRS, followed by chemical and histological reference analysis. The relationship between the reference properties of the tissue and NIR spectra was investigated using partial least squares regression. NIRS was found to be sensitive towards the water (R2CV = .65) and collagen (R2CV = .57) contents, while elastin, proteoglycans, and the internal crimp structure remained undetectable. As collagen largely determines the mechanical response of ligaments, we conclude that NIRS demonstrates potential for quantitative evaluation of knee ligaments.publishedVersionPeer reviewe

Visible and Near-Infrared Spectroscopy Enables Differentiation of Normal and Early Osteoarthritic Human Knee Joint Articular Cartilage

Osteoarthritis degenerates cartilage and impairs joint function. Early intervention opportunities are missed as current diagnostic methods are insensitive to early tissue degeneration. We investigated the capability of visible light-near-infrared spectroscopy (Vis-NIRS) to differentiate normal human cartilage from early osteoarthritic one. Vis-NIRS spectra, biomechanical properties and the state of osteoarthritis (OARSI grade) were quantified from osteochondral samples harvested from different anatomical sites of human cadaver knees. Two support vector machines (SVM) classifiers were developed based on the Vis-NIRS spectra and OARSI scores. The first classifier was designed to distinguish normal (OARSI: 0–1) from general osteoarthritic cartilage (OARSI: 2–5) to check the general suitability of the approach yielding an average accuracy of 75% (AUC = 0.77). Then, the second classifier was designed to distinguish normal from early osteoarthritic cartilage (OARSI: 2–3) yielding an average accuracy of 71% (AUC = 0.73). Important wavelength regions for differentiating normal from early osteoarthritic cartilage were related to collagen organization (wavelength region: 400–600 nm), collagen content (1000–1300 nm) and proteoglycan content (1600–1850 nm). The findings suggest that Vis-NIRS allows objective differentiation of normal and early osteoarthritic tissue, e.g., during arthroscopic repair surgeries.Peer reviewe

Machine learning augmented near-infrared spectroscopy: In vivo follow-up of cartilage defects

OBJECTIVE: To assess the potential of near-infrared spectroscopy (NIRS) for in vivo arthroscopic monitoring of cartilage defects. METHOD: Sharp and blunt cartilage grooves were induced in the radiocarpal and intercarpal joints of Shetland ponies and monitored at baseline (0 weeks) and at three follow-up time points (11, 23, and 39 weeks) by measuring near-infrared spectra in vivo at and around the grooves. The animals were sacrificed after 39 weeks and the joints were harvested. Spectra were reacquired ex vivo to ensure reliability of in vivo measurements and for reference analyses. Additionally, cartilage thickness and instantaneous modulus were determined via computed tomography and mechanical testing, respectively. The relationship between the ex vivo spectra and cartilage reference properties was determined using convolutional neural network. RESULTS: For the independent test, the trained networks yielded significant correlations for cartilage thickness (ρ=0.473) and instantaneous modulus (ρ=0.498). These networks were used to predict the reference properties at baseline and follow-ups. In the radiocarpal joint, cartilage thickness increased significantly with both groove types after baseline and remained swollen. Additionally, at 39 weeks, a significant difference was observed in cartilage thickness between controls and sharp grooves. For the instantaneous modulus, significant decrease was observed with both groove types in the radiocarpal joint from baseline to 23 and 39 weeks. CONCLUSION: NIRS combined with machine learning enabled determination of cartilage properties in vivo, thereby providing longitudinal evaluation of post-intervention injury development. Additionally, radiocarpal joints demonstrated more vulnerability to cartilage degeneration after damage than intercarpal joints

Dual-contrast computed tomography enables detection of equine posttraumatic osteoarthritis in vitro

To prevent the progression of posttraumatic osteoarthritis, assessment of cartilage composition is critical for effective treatment planning. Posttraumatic changes include proteoglycan (PG) loss and elevated water content. Quantitative dual-energy computed tomography (QDECT) provides a means to diagnose these changes. Here, we determine the potential of QDECT to evaluate tissue quality surrounding cartilage lesions in an equine model, hypothesizing that QDECT allows detection of posttraumatic degeneration by providing quantitative information on PG and water contents based on the partitions of cationic and nonionic agents in a contrast mixture. Posttraumatic osteoarthritic samples were obtained from a cartilage repair study in which full-thickness chondral defects were created surgically in both stifles of seven Shetland ponies. Control samples were collected from three nonoperated ponies. The experimental (n = 14) and control samples (n = 6) were immersed in the contrast agent mixture and the distributions of the agents were determined at various diffusion time points. As a reference, equilibrium moduli, dynamic moduli, and PG content were measured. Significant differences (p < 0.05) in partitions between the experimental and control samples were demonstrated with cationic contrast agent at 30 min, 60 min, and 20 h, and with non-ionic agent at 60 and 120 min. Significant Spearman's rank correlations were obtained at 20 and 24 h (rho = 0.482-0.693) between the partition of cationic contrast agent, cartilage biomechanical properties, and PG content. QDECT enables evaluation of posttraumatic changes surrounding a lesion and quantification of PG content, thus advancing the diagnostics of the extent and severity of cartilage injuries

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

Publisher Copyright: © 2022, The Author(s).We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12–16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI’s magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.Peer reviewe

Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation

Reference panels from the 1000 Genomes (1000G) Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency ≥0.5% across European ancestry populations. Within the European Network for Genetic and Genomic Epidemiology (ENGAGE) Consortium, we have undertaken the fi

Формирование эмоциональной культуры как компонента инновационной культуры студентов

Homozygosity has long been associated with rare, often devastating, Mendelian disorders1 and Darwin was one of the first to recognise that inbreeding reduces evolutionary fitness2. However, the effect of the more distant parental relatedness common in modern human populations is less well understood. Genomic data now allow us to investigate the effects of homozygosity on traits of public health importance by observing contiguous homozygous segments (runs of homozygosity, ROH), which are inferred to be homozygous along their complete length. Given the low levels of genome-wide homozygosity prevalent in most human populations, information is required on very large numbers of people to provide sufficient power3,4. Here we use ROH to study 16 health-related quantitative traits in 354,224 individuals from 102 cohorts and find statistically significant associations between summed runs of homozygosity (SROH) and four complex traits: height, forced expiratory lung volume in 1 second (FEV1), general cognitive ability (g) and educational attainment (nominal p<1 × 10−300, 2.1 × 10−6, 2.5 × 10−10, 1.8 × 10−10). In each case increased homozygosity was associated with decreased trait value, equivalent to the offspring of first cousins being 1.2 cm shorter and having 10 months less education. Similar effect sizes were found across four continental groups and populations with different degrees of genome-wide homozygosity, providing convincing evidence for the first time that homozygosity, rather than confounding, directly contributes to phenotypic variance. Contrary to earlier reports in substantially smaller samples5,6, no evidence was seen of an influence of genome-wide homozygosity on blood pressure and low density lipoprotein (LDL) cholesterol, or ten other cardio-metabolic traits. Since directional dominance is predicted for traits under directional evolutionary selection7, this study provides evidence that increased stature and cognitive function have been positively selected in human evolution, whereas many important risk factors for late-onset complex diseases may not have been

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57