Heterogeneous Glycation of Cancellous Bone and Its Association with Bone Quality and Fragility

Non-enzymatic glycation (NEG) and enzymatic biochemical processes create crosslinks that modify the extracellular matrix (ECM) and affect the turnover of bone tissue. Because NEG affects turnover and turnover at the local level affects microarchitecture and formation and removal of microdamage, we hypothesized that NEG in cancellous bone is heterogeneous and accounts partly for the contribution of microarchitecture and microdamage on bone fragility. Human trabecular bone cores from 23 donors were subjected to compression tests. Mechanically tested cores as well as an additional 19 cores were stained with lead-uranyl acetate and imaged to determine microarchitecture and measure microdamage. Post-yield mechanical properties were measured and damaged trabeculae were extracted from a subset of specimens and characterized for the morphology of induced microdamage. Tested specimens and extracted trabeculae were quantified for enzymatic and non-enzymatic crosslink content using a colorimetric assay and Ultra-high Performance Liquid Chromatography (UPLC). Results show that an increase in enzymatic crosslinks was beneficial for bone where they were associated with increased toughness and decreased microdamage. Conversely, bone with increased NEG required less strain to reach failure and were less tough. NEG heterogeneously modified trabecular microarchitecture where high amounts of NEG crosslinks were found in trabecular rods and with the mechanically deleterious form of microdamage (linear microcracks). The extent of NEG in tibial cancellous bone was the dominant predictor of bone fragility and was associated with changes in microarchitecture and microdamage

Robust knowledge graph completion with stacked convolutions and a student re-ranking network

Knowledge Graph (KG) completion research usually focuses on densely connected benchmark datasets that are not representative of real KGs. We curate two KG datasets that include biomedical and encyclopedic knowledge and use an existing commonsense KG dataset to explore KG completion in the more realistic setting where dense connectivity is not guaranteed. We develop a deep convolutional network that utilizes textual entity representations and demonstrate that our model outperforms recent KG completion methods in this challenging setting. We find that our model's performance improvements stem primarily from its robustness to sparsity. We then distill the knowledge from the convolutional network into a student network that re-ranks promising candidate entities. This re-ranking stage leads to further improvements in performance and demonstrates the effectiveness of entity re-ranking for KG completion

Improving broad-coverage medical entity linking with semantic type prediction and large-scale datasets

Objectives Biomedical natural language processing tools are increasingly being applied for broad-coverage information extraction—extracting medical information of all types in a scientific document or a clinical note. In such broad-coverage settings, linking mentions of medical concepts to standardized vocabularies requires choosing the best candidate concepts from large inventories covering dozens of types. This study presents a novel semantic type prediction module for biomedical NLP pipelines and two automatically-constructed, large-scale datasets with broad coverage of semantic types. Methods We experiment with five off-the-shelf biomedical NLP toolkits on four benchmark datasets for medical information extraction from scientific literature and clinical notes. All toolkits adopt a staged approach of mention detection followed by two stages of medical entity linking: (1) generating a list of candidate concepts, and (2) picking the best concept among them. We introduce a semantic type prediction module to alleviate the problem of overgeneration of candidate concepts by filtering out irrelevant candidate concepts based on the predicted semantic type of a mention. We present MedType, a fully modular semantic type prediction model which we integrate into the existing NLP toolkits. To address the dearth of broad-coverage training data for medical information extraction, we further present WikiMed and PubMedDS, two large-scale datasets for medical entity linking. Results Semantic type filtering improves medical entity linking performance across all toolkits and datasets, often by several percentage points of F-1. Further, pretraining MedType on our novel datasets achieves state-of-the-art performance for semantic type prediction in biomedical text. Conclusions Semantic type prediction is a key part of building accurate NLP pipelines for broad-coverage information extraction from biomedical text. We make our source code and novel datasets publicly available to foster reproducible research

Microarchitecture Influences Microdamage Accumulation in Human Vertebral Trabecular Bone

It has been suggested that accumulation of microdamage with age contributes to skeletal fragility. However, data on the age-related increase in microdamage and the association between microdamage and trabecular microarchitecture in human vertebral cancellous bone are limited. We quantified microdamage in cancellous bone from human lumbar (L2) vertebral bodies obtained from 23 donors 54–93 yr of age (8 men and 15 women). Damage was measured using histologic techniques of sequential labeling with chelating agents and was related to 3D microarchitecture, as assessed by high-resolution μCT. There were no significant differences between sexes, although women tended to have a higher microcrack density (Cr.Dn) than men. Cr.Dn increased exponentially with age (r = 0.65, p < 0.001) and was correlated with bone volume fraction (BV/TV; r = −0.55; p < 0.01), trabecular number (Tb.N; r = −0.56 p = 0.008), structure model index (SMI; r = 0.59; p = 0.005), and trabecular separation (Tb.Sp; r = 0.59; p < 0.009). All architecture parameters were strongly correlated with each other and with BV/TV. Stepwise regression showed that SMI was the best predictor of microdamage, explaining 35% of the variance in Cr.Dn and 20% of the variance in diffuse damage accumulation. In addition, microcrack length was significantly greater in the highest versus lowest tertiles of SMI. In conclusion, in human vertebral cancellous bone, microdamage increases with age and is associated with low BV/TV and a rod-like trabecular architecture

The free moment in walking and its change with foot rotation angle

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Tibial Loading Increases Osteogenic Gene Expression and Cortical Bone Volume in Mature and Middle-Aged Mice

There are conflicting data on whether age reduces the response of the skeleton to mechanical stimuli. We examined this question in female BALB/c mice of different ages, ranging from young to middle-aged (2, 4, 7, 12 months). We first assessed markers of bone turnover in control (non-loaded) mice. Serum osteocalcin and CTX declined significantly from 2 to 4 months (p<0.001). There were similar age-related declines in tibial mRNA expression of osteoblast- and osteoclast-related genes, most notably in late osteoblast/matrix genes. For example, Col1a1 expression declined 90% from 2 to 7 months (p<0.001). We then assessed tibial responses to mechanical loading using age-specific forces to produce similar peak strains (−1300 µε endocortical; −2350 µε periosteal). Axial tibial compression was applied to the right leg for 60 cycles/day on alternate days for 1 or 6 weeks. qPCR after 1 week revealed no effect of loading in young (2-month) mice, but significant increases in osteoblast/matrix genes in older mice. For example, in 12-month old mice Col1a1 was increased 6-fold in loaded tibias vs. controls (p = 0.001). In vivo microCT after 6 weeks revealed that loaded tibias in each age group had greater cortical bone volume (BV) than contralateral control tibias (p<0.05), due to relative periosteal expansion. The loading-induced increase in cortical BV was greatest in 4-month old mice (+13%; p<0.05 vs. other ages). In summary, non-loaded female BALB/c mice exhibit an age-related decline in measures related to bone formation. Yet when subjected to tibial compression, mice from 2–12 months have an increase in cortical bone volume. Older mice respond with an upregulation of osteoblast/matrix genes, which increase to levels comparable to young mice. We conclude that mechanical loading of the tibia is anabolic for cortical bone in young and middle-aged female BALB/c mice

Bone Loss in Diabetes: Use of Antidiabetic Thiazolidinediones and Secondary Osteoporosis

Clinical evidence indicates that bone status is affected in patients with type 2 diabetes mellitus (T2DM). Regardless of normal or even high bone mineral density, T2DM patients have increased risk of fractures. One class of antidiabetic drugs, thiazolidinediones (TZDs), causes bone loss and further increases facture risk, placing TZDs in the category of drugs causing secondary osteoporosis. Risk factors for development of TZD-induced secondary osteoporosis are gender (women), age (elderly), and duration of treatment. TZDs exert their antidiabetic effects by activating peroxisome proliferator-activated receptor-γ (PPAR-γ) nuclear receptor, which controls glucose and fatty acid metabolism. In bone, PPAR-γ controls differentiation of cells of mesenchymal and hematopoietic lineages. PPAR-γ activation with TZDs leads to unbalanced bone remodeling: bone resorption increases and bone formation decreases. Laboratory research evidence points toward a possible separation of unwanted effects of PPAR-γ on bone from its beneficial antidiabetic effects by using selective PPAR-γ modulators. This review also discusses potential pharmacologic means to protect bone from detrimental effects of clinically used TZDs (pioglitazone and rosiglitazone) by using combinational therapy with approved antiosteoporotic drugs, or by using lower doses of TZDs in combination with other antidiabetic therapy. We also suggest a possible orthopedic complication, not yet supported by clinical studies, of delayed fracture healing in T2DM patients on TZD therapy