Effects of Ageing, Oestrogen Level and Altered Dietary Loading on Rat Mandibular Cartilage-A Polarised Light Microscopy Study.

Objective: The mandibular condylar cartilage (MCC) of rats was examined with polarised light microscopy (PLM) to study the effects of ageing, oestrogen level, and altered dietary loading on the structure of the MCC.

Materials And Methods: 96 Sprague-Dawley rats were separated into 12 groups based on their age (5 months [young] and 14 months [old]), oestrogen status (ovariectomised [OVX], non-ovariectomised [non-OVX]), and diet (hard, normal, or soft). The MCC specimens were examined using PLM to evaluate the orientation and retardation of collagen fibrils.

OLSIA: Open Lumbar Spine Image Analysis - A 3D Slicer Extension for Segmentation, Grading, and Intervertebral Disc Height Index with Multi-Dataset Validation.

Study Design: Retrospective and cross-sectional study.

Objective: The study aims to develop an open software for lumbar spine image analysis enabling no-code approach to lumbar spine segmentation, grading, and intervertebral disc height index (DHI) calculations with robust evaluation of the application on six external datasets from diverse geographical regions.

Summary Of Data: The datasets used include NFBC1966 (Finland), HKDDC (Hong Kong), TwinsUK (UK), CETIR (Spain), NCSD (Hungary), SPIDER (Netherlands), and Mendeley (global).

Effects of a multicomponent exercise regimen on subchondral bone and cartilage in postmenopausal women with knee osteoarthritis: protocol for a randomized controlled trial.

Background: Knee osteoarthritis (KOA) is considered a whole-joint disease that is amenable to prevention and treatment in the early stages. Exercise is among the core treatment recommendations for KOA and it has been suggested that optimal exercise regimens should improve aerobic capacity and knee extensor strength. Subchondral bone and articular cartilage are functionally paired, and information on the responses of these tissues to exercise may help in the development of efficacious and feasible exercise regimens that can potentially improve bone and cartilage properties.

Robust Radiomic Signatures of Intervertebral Disc Degeneration from MRI.

Study Design: A retrospective analysis.

Objective: The aim of this study was to identify a robust radiomic signature from deep learning segmentations for intervertebral disc (IVD) degeneration classification.

Summary Of Data: Low back pain (LBP) is the most common musculoskeletal symptom worldwide and IVD degeneration is an important contributing factor.

Total radius BMD correlates with the hip and lumbar spine BMD among post-menopausal patients with fragility wrist fracture in a machine learning model.

Unlabelled: Osteoporosis screening should be systematic in the group of over 50-year-old females with a radius fracture. We tested a phantom combined with machine learning model and studied osteoporosis-related variables. This machine learning model for screening osteoporosis using plain radiographs requires further investigation in larger cohorts to assess its potential as a replacement for DXA measurements in settings where DXA is not available.

The effect of contouring instruments on immediate quality and porosity of direct restorations.

Objective: The study aims to evaluate the effect of contouring instruments on the porosity and immediate quality of direct dental restorations.

Materials And Methods: Fifteen human molars with 30 Class II and 10 Class V cavities were restored by five voluntary dentists using three contouring instruments (conventional steel, silicone-tipped and diamond-like carbon coated-instruments) and three filling materials (Admira Fusion, Filtek Supreme XTE and Fuji II LC). The restorations were evaluated for immediate quality, porosity and number of pores using stereomicroscope and micro-computed tomography.

Structural knee MRI findings are already frequent in a general population-based birth cohort at 33 years of age.

Objective: To evaluate the incidence and severity of knee magnetic resonance imaging (MRI) findings and their associated lifestyle and health factors in a relatively healthy subset of a general population-based birth cohort.

Design: The study population (n = 288, 61.1% females, mean age 33.

Diagnostic performance of attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy for detecting osteoarthritis and rheumatoid arthritis from blood serum.

Osteoarthritis (OA) and rheumatoid arthritis (RA) are the two most common rheumatic diseases worldwide, causing pain and disability. Both conditions are highly heterogeneous, and their onset occurs insidiously with non-specific symptoms, so they are not always distinguishable from other arthritis during the initial stages. This makes early diagnosis difficult and resource-demanding in clinical environments.

Kellgren-Lawrence grading of knee osteoarthritis using deep learning: Diagnostic performance with external dataset and comparison with four readers.

Objective: To evaluate the performance of a deep learning (DL) model in an external dataset to assess radiographic knee osteoarthritis using Kellgren-Lawrence (KL) grades against versatile human readers.

Materials And Methods: Two-hundred-eight knee anteroposterior conventional radiographs (CRs) were included in this retrospective study. Four readers (three radiologists, one orthopedic surgeon) assessed the KL grades and consensus grade was derived as the mean of these.

End-to-End Prediction of Knee Osteoarthritis Progression With Multimodal Transformers.

Knee Osteoarthritis (KOA) is a prevalent chronic musculoskeletal condition with no currently available treatment. Predicting its progression is difficult due to its varied manifestation. Recent studies highlight the potential of using multimodal data and Deep Learning (DL) for prediction, though evidence is still emerging.

A customizable wireless potentiostat for assessing Ni(OH) decorated vertically aligned MoS thin films for electrochemical sensing of dopamine.

In this study, we show that on-chip grown, vertically aligned MoS films that are decorated with Ni(OH) catalyst are suitable materials to be applied as working electrodes in electrochemical sensing. The constructed sensors display a highly repeatable response to dopamine, used as a model analyte, in a large dynamic range from 1 μM to 1 mM with a theoretical detection limit of 0.1 μM.

Machine Learning Approaches for the Fusion of Near-Infrared, Mid-Infrared, and Raman Data to Identify Cartilage Degradation in Human Osteochondral Plugs.

Vibrational spectroscopy methods such as mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopies have been shown to have great potential for in vivo biomedical applications, such as arthroscopic evaluation of joint injuries and degeneration. Considering that these techniques provide complementary chemical information, in this study, we hypothesized that combining the MIR, NIR, and Raman data from human osteochondral samples can improve the detection of cartilage degradation. This study evaluated 272 osteochondral samples from 18 human knee joins, comprising both healthy and damaged tissue according to the reference Osteoarthritis Research Society International grading system.

Age and anatomical region-related differences in vascularization of the porcine meniscus using microcomputed tomography imaging.

Meniscal lesions in vascularized regions are known to regenerate while lack of vascular supply leads to poor healing. Here, we developed and validated a novel methodology for three-dimensional structural analysis of meniscal vascular structures with high-resolution microcomputed tomography (µCT). We collected porcine medial menisci from 10 neonatal (not-developed meniscus, n-) and 10 adults (fully developed meniscus, a-).

Clinical Super-Resolution Computed Tomography of Bone Microstructure: Application in Musculoskeletal and Dental Imaging.

Purpose: Clinical cone-beam computed tomography (CBCT) devices are limited to imaging features of half a millimeter in size and cannot quantify the tissue microstructure. We demonstrate a robust deep-learning method for enhancing clinical CT images, only requiring a limited set of easy-to-acquire training data.

Methods: Knee tissue from five cadavers and six total knee replacement patients, and 14 teeth from eight patients were scanned using laboratory CT as training data for the developed super-resolution (SR) technique.

Deep learning based detection of osteophytes in radiographs and magnetic resonance imagings of the knee using 2D and 3D morphology.

In this study, we investigated the discriminative capacity of knee morphology in automatic detection of osteophytes defined by the Osteoarthritis Research Society International atlas, using X-ray and magnetic resonance imaging (MRI) data. For the X-ray analysis, we developed a deep learning (DL) based model to segment femur and tibia. In case of MRIs, we utilized previously validated segmentations of femur, tibia, corresponding cartilage tissues, and menisci.

Semiautomatic Assessment of Facet Tropism From Lumbar Spine MRI Using Deep Learning: A Northern Finland Birth Cohort Study.

Study Design: This is a retrospective, cross-sectional, population-based study that automatically measured the facet joint (FJ) angles from T2-weighted axial magnetic resonance imagings (MRIs) of the lumbar spine using deep learning (DL).

Objective: This work aimed to introduce a semiautomatic framework that measures the FJ angles using DL and study facet tropism (FT) in a large Finnish population-based cohort.

Summary Of Data: T2-weighted axial MRIs of the lumbar spine (L3/4 through L5/S1) for (n=1288) in the NFBC1966 Finnish population-based cohort were used for this study.

Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting From Multimodal Data.

Deep neural networks are often applied to medical images to automate the problem of medical diagnosis. However, a more clinically relevant question that practitioners usually face is how to predict the future trajectory of a disease. Current methods for prognosis or disease trajectory forecasting often require domain knowledge and are complicated to apply.

Magnetic-Responsive Carbon Nanotubes Composite Scaffolds for Chondrogenic Tissue Engineering.

The demand for engineered scaffolds capable of delivering multiple cues to cells continues to grow as the interplay between cell fate with microenvironmental and external cues is revealed. Emphasis has been given to develop stimuli-responsive scaffolds. These scaffolds are designed to sense an external stimulus triggering a specific response (e.

Fourier-transform infrared study on effects of ageing, oestrogen level and altered dietary loading on rat mandibular condylar cartilage.

Objective: Mandibular condylar cartilage (MCC) of the rat was examined with the Fourier-transform infrared (FITR) spectroscopic imaging to study the effects of ageing, oestrogen level and altered dietary loading on the structure of MCC.

Materials And Methods: The Sprague-Dawley rats (n = 96) aged 5 and 14 months were divided into 12 subgroups according to age, oestrogen status (ovariectomized [OVX], non-ovariectomized [non-OVX)]) and diet (hard, normal, soft). Specimens of the MCC were examined with FTIR spectroscopic imaging to quantify the distribution of collagens and proteoglycans.

Characterisation of Cartilage Damage via Fusing Mid-Infrared, Near-Infrared, and Raman Spectroscopic Data.

Mid-infrared spectroscopy (MIR), near-infrared spectroscopy (NIR), and Raman spectroscopy are all well-established analytical techniques in biomedical applications. Since they provide complementary chemical information, we aimed to determine whether combining them amplifies their strengths and mitigates their weaknesses. This study investigates the feasibility of the fusion of MIR, NIR, and Raman spectroscopic data for characterising articular cartilage integrity.