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Background Bone healing process includes four phases: inflammatory response, soft callus formation, hard callus development, and remodeling. Mechanobiological models have been used to investigate the role of various mechanical and biological factors on bone healing. However, the effects of initial healing phase, which includes the inflammatory stage, the granulation tissue formation, and the initial callus formation during the first few days post-fracture, are generally neglected in such studies. Methods In this study, we developed a finite-element-based model to simulate different levels of diffusion coefficient for mesenchymal stem cell (MSC) migration, Young’s modulus of granulation tissue, callus thickness and interfragmentary gap size to understand the modulatory effects of these initial phase parameters on bone healing. Results The results quantified how faster MSC migration, stiffer granulation tissue, thicker callus, and smaller interfragmentary gap enhanced healing to some extent. However, after a certain threshold, a state of saturation was reached for MSC migration rate, granulation tissue stiffness, and callus thickness. Therefore, a parametric study was performed to verify that the callus formed at the initial phase, in agreement with experimental observations, has an ideal range of geometry and material properties to have the most efficient healing time. Conclusions Findings from this paper quantified the effects of the initial healing phase on healing outcome to better understand the biological and mechanobiological mechanisms and their utilization in the design and optimization of treatment strategies. It is also demonstrated through a simulation that for fractures, where bone segments are in close proximity, callus development is not required. This finding is consistent with the concepts of primary and secondary bone healing.

Background Insurance status is important as medical expenses may decrease the likelihood of follow-up after musculoskeletal trauma, especially for low-income populations. However, it is unknown what insurance factors are associated with follow-up care. In this study, we assessed the association between insurance plan benefits, the end of the post-surgical global period, and follow-up after musculoskeletal injury. Methods This is a retrospective cohort study of 394 patients with isolated extremity fractures who were treated at three level-I trauma centers over four months in 2018. Paired t-tests were utilized to assess the likelihood of follow-up in relation to the 90-day post-surgical global period. Regression analysis was used to assess factors associated with the likelihood of follow-up. Supervised machine learning algorithms were used to develop predictive models of follow-up after the post-surgical global period. Results Our final analysis included 328 patients. Likelihood of follow-up did not significantly change while within the post-surgical global period. When comparing follow-up within and outside of the post-surgical global period, there was a 20.1% decrease in follow-up between the 6-weeks and 6-month time points (68.3% versus 48.2%, respectively; p  < 0.0001). Medicaid insurance compared to Medicare (OR 0.27, 95% confidence interval (CI) = [0.09, 0.84], p  = 0.02) was a predictor of decreased likelihood of follow-up at 6-months post-operatively. Conclusions Our study demonstrates a statistically significant decrease in follow-up for orthopaedic trauma patients after the post-surgical global period, particularly for patients with Medicaid or Private insurance.

Osteoporosis is the most common chronic metabolic bone disease worldwide. Vertebral compression fracture (VCF) is the most common type of osteoporotic fracture. Approximately 700,000 osteoporotic VCFs are diagnosed annually in the USA alone, resulting in an annual economic burden of ~$13.8B. With an aging population, the rate of osteoporotic VCFs and their associated burdens are expected to rise. Those burdens include pain, functional impairment, and increased medical expenditure. Therefore, it is of utmost importance to develop an analytical tool to aid in the identification of VCFs. Computed Tomography (CT) imaging is commonly used to detect occult injuries. Unlike the existing VCF detection approaches based on CT, the standard clinical criteria for determining VCF relies on the shape of vertebrae, such as loss of vertebral body height. We developed a novel automated vertebrae localization, segmentation, and osteoporotic VCF detection pipeline for CT scans using state-of-the-art deep learning models to bridge this gap. To do so, we employed a publicly available dataset of spine CT scans with 325 scans annotated for segmentation, 126 of which also graded for VCF (81 with VCFs and 45 without VCFs). Our approach attained 96% sensitivity and 81% specificity in detecting VCF at the vertebral-level, and 100% accuracy at the subject-level, outperforming deep learning counterparts tested for VCF detection without segmentation. Crucially, we showed that adding predicted vertebrae segments as inputs significantly improved VCF detection at both vertebral and subject levels by up to 14% Sensitivity and 20% Specificity (p-value = 0.028).

IntroductionAmong the various hip fracture predictors explored to date, modifiable risk factors warrant special consideration, since they present promising targets for preventative measures. This systematic review and meta-analysis aims to assess various modifiable risk factors.Material and methodsWe searched four online databases in September 2017. We included studies that reported on modifiable lifestyle risk factors for sustaining fragility hip fractures. The quality of the included studies was assessed using the Newcastle–Ottawa Scale (NOS).The inclusion criteria consisted of (1) adult patients with osteoporotic hip fracture, (2) original study, (3) availability of full text articles in English, and (4) report of a modifiable lifestyle risk factor.ResultsThirty-five studies, containing 1,508,366 subjects in total, were included in this study. The modifiable risk factors that were significantly associated with an increased risk of hip fracture were the following: weight < 58 kg (128 lbs) (pooled OR 4.01, 95% CI 1.62–9.90), underweight body mass index (BMI) (< 18.5) (pooled OR 2.83, 95% CI 1.82–4.39), consumption of ≥ 3 cups of coffee daily (pooled OR 2.27, 95% CI 1.04–4.97), inactivity (pooled OR 2.14, 95% CI 1.21–3.77), weight loss (pooled OR 1.88, 95% CI 1.32–2.68), consumption of ≥ 27 g (approx. > 2 standard drinks) alcohol per day (pooled OR 1.54, 95% CI 1.12–2.13), and being a current smoker (pooled OR 1.50, 95% CI 1.22–1.85). Conversely, two factors were significantly associated with a decreased risk of hip fracture: obese BMI (> 30) (pooled OR 0.58, 95% CI 0.34–0.99) and habitual tea drinking (pooled OR 0.72, 95% CI 0.66–0.80).ConclusionModifiable factors may be utilized clinically to provide more effective lifestyle interventions for at risk populations. We found that low weight and underweight BMI carried the highest risk, followed by high coffee consumption, inactivity, weight loss, and high daily alcohol consumption.

Traditional antibiotic therapy has encountered significant challenges for clinical treatment of infections for multiple reasons, including antimicrobial resistance (AMR) and poor efficacy against biofilms, demanding research into alternative therapeutic agents. Because of their unique antimicrobial mechanisms as well as their target specificity, diversity, exponential self-amplification, and anti-biofilm activity, combined with recent advances in genomics and synthetic biology, bacteriophages have attracted increased interest as potential alternatives or therapeutic adjuncts to antibiotics. However, obstacles such as phage-host specificity, bacterial resistance, and the selection of optimal phages, amongst other factors, impede clinical adoption of phage therapy. Here, machine learning (ML) and artificial intelligence (AI) tools have the opportunity to revolutionize phage therapy by enhancing scalability, efficiency and precision of these therapies. This article highlights potential key applications of ML/AI in the study, development and deployment of phage therapy.

Arthrofibrosis, or rigid contracture of major articular joints, is a significant morbidity of many neurodegenerative disorders. The pathogenesis depends on the mechanism and severity of the precipitating neuromuscular disorder. Most neuromuscular disorders, whether spastic or hypotonic, culminate in decreased joint range of motion. Limited range of motion precipitates a cascade of pathophysiological changes in the muscle-tendon unit, the joint capsule, and the articular cartilage. Resulting joint contractures limit functional mobility, posing both physical and psychosocial burdens to patients, economic burdens on the healthcare system, and lost productivity to society. This article reviews the pathophysiology of arthrofibrosis in the setting of neuromuscular disorders. We describe current non-surgical and surgical interventions for treating arthrofibrosis of commonly affected joints. In addition, we preview several promising modalities under development to ameliorate arthrofibrosis non-surgically and discuss limitations in the field of arthrofibrosis secondary to neuromuscular disorders.

Background Nonunion following treatment of supracondylar femur fractures with lateral locked plates (LLP) has been reported to be as high as 21 %. Implant related and surgeon-controlled variables have been postulated to contribute to nonunion by modulating fracture-fixation construct stiffness. The purpose of this study is to evaluate the effect of surgeon-controlled factors on stiffness when treating supracondylar femur fractures with LLPs: Does plate length affect construct stiffness given the same plate material, fracture working length and type of screws? Does screw type (bicortical locking versus bicortical nonlocking or unicortical locking) and number of screws affect construct stiffness given the same material, fracture working length, and plate length? Does fracture working length affect construct stiffness given the same plate material, length and type of screws? Does plate material (titanium versus stainless steel) affect construct stiffness given the same fracture working length, plate length, type and number of screws? Methods Mechanical study of simulated supracondylar femur fractures treated with LLPs of varying lengths, screw types, fractureworking lenghts, and plate/screw material. Overall construct stiffness was evaluated using an Instron hydraulic testing apparatus. Results Stiffness was 15 % higher comparing 13-hole to the 5-hole plates (995 N/mm849N vs. /mm, p  = 0.003). The use of bicortical nonlocking screws decreased overall construct stiffness by 18 % compared to bicortical locking screws (808 N/mm vs. 995 N/mm, p  = 0.0001). The type of screw (unicortical locking vs. bicortical locking) and the number of screws in the diaphysis (3 vs. 10) did not appear to significantly influence construct stiffness ( p  = 0.76, p  = 0.24). Similarly, fracture working length (5.4 cm vs. 9.4 cm, p  = 0.24), and implant type (titanium vs. stainless steel, p  = 0.12) did also not appear to effect stiffness. Discussion Using shorter plates and using bicortical nonlocking screws (vs. bicortical locking screws) reduced overall construct stiffness. Using more screws, using unicortical locking screws, increasing fracture working length and varying plate material (titanium vs. stainless steel) does not appear to significantly alter construct stiffness. Surgeons can adjust plate length and screw types to affect overall fracture-fixation construct stiffness; however, the optimal stiffness to promote healing remains unknown.

Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence projected to increase by 240–310% by 2050. Hip fractures are predominantly diagnosed by radiologist review of radiographs. In this study, we developed a deep learning model by extending the VarifocalNet Feature Pyramid Network (FPN) for detection and localization of proximal femur fractures from plain radiography with clinically relevant metrics. We used a dataset of 823 hip radiographs of 150 subjects with proximal femur fractures and 362 controls to develop and evaluate the deep learning model. Our model attained 0.94 specificity and 0.95 sensitivity in fracture detection over the diverse imaging dataset. We compared the performance of our model against five benchmark FPN models, demonstrating 6–14% sensitivity and 1–9% accuracy improvement. In addition, we demonstrated that our model outperforms a state-of-the-art transformer model based on DINO network by 17% sensitivity and 5% accuracy, while taking half the time on average to process a radiograph. The developed model can aid radiologists and support on-premise integration with hospital cloud services to enable automatic, opportunistic screening for hip fractures.

Arthrofibrosis is a prevalent condition affecting greater than 5% of the general population and leads to a painful decrease in joint range of motion (ROM) and loss of independence due to pathologic accumulation of periarticular scar tissue. Current treatment options are limited in effectiveness and do not address the underlying cause of the condition: accumulation of fibrotic collagenous tissue. Herein, the naturally occurring peptide hormone relaxin-2 is administered for the treatment of adhesive capsulitis (frozen shoulder) and to restore glenohumeral ROM in shoulder arthrofibrosis. Recombinant human relaxin-2 down-regulates type I collagen and α smooth muscle actin production and increases intracellular cAMP concentration in human fibroblast-like synoviocytes, consistent with a mechanism of extracellular matrix degradation and remodeling. Pharmacokinetic profiling of a bolus administration into the glenohumeral joint space reveals the brief systemic and intraarticular (IA) half-lives of relaxin-2: 0.96 h and 0.62 h, respectively. Furthermore, using an established, immobilization murine model of shoulder arthrofibrosis, multiple IA injections of human relaxin-2 significantly improve ROM, returning it to baseline measurements collected before limb immobilization. This is in contrast to single IA (sIA) or multiple i.v. (mIV) injections of relaxin-2 with which the ROM remains constrained. The histological hallmarks of contracture (e.g., fibrotic adhesions and reduced joint space) are absent in the animals treated with multiple IA injections of relaxin-2 compared with the untreated control and the sIA- and mIV-treated animals. As these findings show, local delivery of relaxin- 2 is an innovative treatment of shoulder arthrofibrosis.

Spine disorders can cause severe functional limitations, including back pain, decreased pulmonary function, and increased mortality risk. Plain radiography is the first-line imaging modality to diagnose suspected spine disorders. Nevertheless, radiographical appearance is not always sufficient due to highly variable patient and imaging parameters, which can lead to misdiagnosis or delayed diagnosis. Employing an accurate automated detection model can alleviate the workload of clinical experts, thereby reducing human errors, facilitating earlier detection, and improving diagnostic accuracy. To this end, deep learning-based computer-aided diagnosis (CAD) tools have significantly outperformed the accuracy of traditional CAD software. Motivated by these observations, we proposed a deep learning-based approach for end-to-end detection and localization of spine disorders from plain radiographs. In doing so, we took the first steps in employing state-of-the-art transformer networks to differentiate images of multiple spine disorders from healthy counterparts and localize the identified disorders, focusing on vertebral compression fractures (VCF) and spondylolisthesis due to their high prevalence and potential severity. The VCF dataset comprised 337 images, with VCFs collected from 138 subjects and 624 normal images collected from 337 subjects. The spondylolisthesis dataset comprised 413 images, with spondylolisthesis collected from 336 subjects and 782 normal images collected from 413 subjects. Transformer-based models exhibited 0.97 Area Under the Receiver Operating Characteristic Curve (AUC) in VCF detection and 0.95 AUC in spondylolisthesis detection. Further, transformers demonstrated significant performance improvements against existing end-to-end approaches by 4–14% AUC (p-values < 10−13) for VCF detection and by 14–20% AUC (p-values < 10−9) for spondylolisthesis detection.