Early Diagnosis Of Osteoporosis Research Articles

Hybrid Optimization Model Integrating Gradient Descent and Stochastic Descent for Enhanced Osteoporosis and Osteopenia Recognition

Osteoporosis and osteopenia, prevalent bone diseases affecting millions of people globally, necessitate accurate early diagnosis for effective treatment and fracture prevention. This paper proposes a novel hybrid optimization algorithm tailored for classifying these conditions based on Bone Mineral Density (BMD) measurements. The algorithm, a customized Mini-Batch Gradient Descent (MBGD), blends the advantages of Gradient Descent (GD) and Stochastic Gradient Descent (SGD), addressing specific needs for osteoporosis and osteopenia classification. Utilizing a dataset comprising BMD measurements and clinical risk factors from the Osteoporotic Fractures in Men (MrOS), Study of Osteoporotic Fractures (SOF), and Fracture Risk Assessment (FRAX), the model achieves an impressive accuracy of 99.01%. The proposed model outperforms existing methods, demonstrating superior accuracy compared to the accuracy obtained in Gradient Descent of 97.26%, Stochastic Gradient Descent of 97.23%, and other optimization algorithms such as Adam of 96.45% and the RMSprop of 96.23%. This hybrid model presents a robust framework for early diagnosis of Osteoporosis and osteopenia, and hence there is an enhancement in quality of life.

413 Evaluation of Osteoporosis in the Lumbar Spine in Multiple Myeloma Patients Using 18F-NaF PET/CT

INTRODUCTION: Multiple myeloma (MM) is a plasma cell malignancy that structurally damages and weakens the bone, most commonly in the spine, by inducing osteoporosis, osteolytic lesions and pathological fractures. Detecting molecular changes in bone metabolism, such as reduced osteoblastic activity and bone remodeling associated with MM, would allow for early diagnosis of osteoporosis in the lumbar spine and reduce risk for surgical spinal interventions. One such imaging candidate is 18F-NaF PET/CT, a molecular imaging technique sensitive to bone formation. METHODS: The FULIMA clinical trial (NCT02187731) was conducted with 70 newly diagnosed MM patients, with exclusion criteria leaving 33 patients for analysis (9 females, 24 males; mean age 66.6y). Patients’ trabecular body of the L1-L4 vertebrae was analyzed using 18F-NaF PET/CT scans to calculate and evaluate the linear associations among mean standardized NaF uptake value (SUVmean), mean Hounsfield unit (HU) values, and age. Receiver-operating characteristic (ROC) curve analysis with area under the curve (AUC) using the Wilson/Brown method assessed the predictive value of NaF uptake via SUVmean for osteoporosis determined by HU threshold. RESULTS: There was significant positive correlation between HU value and SUVmean (r = 0.64, p < 0.0001) and negative correlations between age and both HU value (r = -0.59, p < 0.0003) and SUVmean (r = -0.53, p = 0.002). Additionally, on average, there was a 29.1% difference in the NaF uptake of MM patients with osteoporosis (SUVmean = 7.0 ± 1.48) compared to normal MM patients (SUVmean = 4.96 ± 1.46). Predictive value of NaF per AUC analysis was 0.857 with p = 0.002. CONCLUSIONS: 18F-NaF PET/CT detects the decreased osteoblastic activity of osteoporosis in the lumbar spine with high precision and accuracy in MM patients, facilitating timely interventions and reducing risk for invasive procedures.

Enhancing 3D DNA Walker-Induced CRISPR/Cas12a Technology for Highly Sensitive Detection of ExomicroRNA Associated with Osteoporosis.

Exosomal microRNAs (exomiRNAs) have emerged as promising biomarkers for the early clinical diagnosis of osteoporosis. However, their limited abundance and short length in peripheral blood present significant challenges for the accurate detection of exomiRNAs. Herein, we have designed and implemented an efficacious fluorescence-based biosensor for the highly sensitive detection of exomiRNA associated with osteoporosis, leveraging the enhancing 3D DNA walker-induced CRISPR/Cas12a technology. The engineered DNA walker is capable of efficiently transforming target exomiRNA into amplifying DNA strands, thereby enhancing the sensitivity of the developed biosensor. Concurrently, the liberated DNA strands serve as activators to trigger Cas12a trans-cleavage activity, culminating in a significantly amplified fluorescent signal for the highly sensitive detection of exomiRNA-214. Under optimal conditions, the devised technology demonstrated the capacity to detect target exomiRNA-214 at concentrations as low as 20.42 fM, encompassing a wide linear range extending from 50.0 fM to 10.0 nM. Moreover, the fluorescence-based biosensor could accurately differentiate between healthy individuals and osteoporosis patients via the detection of exomiRNA-214, which was in agreement with RT-qPCR results. As such, this biosensing technology offers promise as a valuable tool for the early diagnosis of osteoporosis.

Discovery of potential biomarkers for osteoporosis using LC/GC-MS metabolomic methods.

For early diagnosis of osteoporosis (OP), plasma metabolomics of OP was studied by untargeted LC/GC-MS in a Chinese elderly population to find possible diagnostic biomarkers. A total of 379 Chinese community-dwelling older adults aged ≥65 years were recruited for this study. The BMD of the calcaneus was measured using quantitative ultrasound (QUS), and a T value ≤-2.5 was defined as OP. Twenty-nine men and 47 women with OP were screened, and 29 men and 36 women were matched according to age and BMI as normal controls using propensity matching. Plasma from these participants was first analyzed by untargeted LC/GC-MS, followed by FC and P values to screen for differential metabolites and heatmaps and box plots to differentiate metabolites between groups. Finally, metabolic pathway enrichment analysis of differential metabolites was performed based on KEGG, and pathways with P ≤ 0.05 were selected as enrichment pathways. We screened metabolites with FC>1.2 or FC<1/1.2 and P<0.05 and found 33 differential metabolites in elderly men and 30 differential metabolites in elderly women that could be potential biomarkers for OP. 2-Aminomuconic acid semialdehyde (AUC=0.72, 95% CI 0.582-0.857, P=0.004) is highly likely to be a biomarker for screening OP in older men. Tetradecanedioic acid (AUC=0.70, 95% CI 0.575-0.818, P=0.004) is highly likely to be a biomarker for screening OP in older women. These findings can be applied to clinical work through further validation studies. This study also shows that metabolomic analysis has great potential for application in the early diagnosis and recurrence monitoring of OP in elderly individuals.

Label-free quantitative proteomics in serum reveals candidate biomarkers associated with low bone mineral density in Mexican postmenopausal women.

Postmenopausal osteoporosis is a public health problem leading to an increased risk of fractures, negatively impacting women's health. The absence of sensitive and specific biomarkers for early detection of osteoporosis represents a substantial challenge for improving patient management. Herein, we aimed to identify potential candidate proteins associated with low bone mineral density (BMD) in postmenopausal women from the Mexican population. Serum samples from postmenopausal women (40 with normal BMD, 40 with osteopenia (OS), and 20 with osteoporosis (OP)) were analyzed by label-free LC-MS/MS quantitative proteomics. Proteome profiling revealed significant differences between the OS and OP groups compared to individuals with normal BMD. A quantitative comparison of proteins between groups indicated 454 differentially expressed proteins (DEPs). Compared to normal BMD, 14 and 214 DEPs were found in OS and OP groups, respectively, while 226 DEPs were identified between OS and OP groups. The protein-protein interaction and enrichment analysis of DEPs were closely linked to the bone mineral content, skeletal morphology, and immune response activation. Based on their role in bone metabolism, a panel of 12 candidate biomarkers was selected, of which 1 DEP (RYR1) was found upregulated in the OS and OP groups, 8 DEPs (APOA1, SHBG, FETB, MASP1, PTK2B, KNG1, GSN, and B2M) were upregulated in OP and 3 DEPs (APOA2, RYR3, and HBD) were downregulated in OS or OP. The proteomic analysis described here may help discover new and potentially non-invasive biomarkers for the early diagnosis of osteoporosis in postmenopausal women.

Cortical bone properties assessment using axially transmitted low frequency (&amp;lt;500 kHz) guided waves

The early diagnosis of osteoporosis through bone quality assessment has been extensively studied in the past decade. Research in axial transmission using ultrasonic guided waves has shown the method to be sensitive to intrinsic properties of long cortical bone. The aim of this work is, therefore, to show the capability of low frequency guided waves to enable the inversion of dispersion curves into bone properties. The proposed inversion scheme relies on dispersion curves simulated using the semi-analytical iso-geometric analysis (SAIGA) method. The model used in simulation comprised a bone phantom plate with a layer of soft tissue attached to the top surface in accordance with experimental bone phantom plates. A proprietary axial transmission multielement ultrasonic transducer specifically designed to excite ultrasonic guided waves under 500 kHz was used for measurements. Acquired data were processed using the 2-D Fast Fourier Transform to extract dispersion curves. The mechanical properties of the bone phantom plates were obtained by minimizing the difference between the experimental and simulated dispersion curves. The inversion was based on the dispersive properties of ultrasonic guided waves as well as their amplitudes. Results show a difference around 5% between the mechanical properties found with the SAIGA based inversion and those provided by the manufacturer of the plates.

Application and prospect of metabolomics in the early diagnosis of osteoporosis: a narrative review.

This paper reviews the application of metabolomics in the early diagnosis of osteoporosis in recent years. The authors searched electronic databases for the keywords "metabolomics", "osteoporosis" and "biomarkers", then analyzed the relationship between functional markers and osteoporosis using categorical summarization. Lipid metabolism, amino acid metabolism and energy metabolism are closely related to osteoporosis development and can become early diagnostic markers of the condition. However, the existing studies in metabolomics suffer from varying application methods, difficulty in identifying isomers, small study cohortsand insufficient research on metabolic mechanisms. Consequently, it is important for future research to focus on broadening and standardizing the scope of the application of metabolomics. High-quality studies on a large scale should also be conducted while promoting the early diagnosis of osteoporosis in a more precise, comprehensive and sensitive manner.

Osteoporosis screening using machine learning and electromagnetic waves

Osteoporosis is a disease characterized by impairment of bone microarchitecture that causes high socioeconomic impacts in the world because of fractures and hospitalizations. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing the disease, access to DXA in developing countries is still limited due to its high cost, being present only in specialized hospitals. In this paper, we analyze the performance of Osseus, a low-cost portable device based on electromagnetic waves that measures the attenuation of the signal that crosses the medial phalanx of a patient’s middle finger and was developed for osteoporosis screening. The analysis is carried out by predicting changes in bone mineral density using Osseus measurements and additional common risk factors used as input features to a set of supervised classification models, while the results from DXA are taken as target (real) values during the training of the machine learning algorithms. The dataset consisted of 505 patients who underwent osteoporosis screening with both devices (DXA and Osseus), of whom 21.8% were healthy and 78.2% had low bone mineral density or osteoporosis. A cross-validation with k-fold = 5 was considered in model training, while 20% of the whole dataset was used for testing. The obtained performance of the best model (Random Forest) presented a sensitivity of 0.853, a specificity of 0.879, and an F1 of 0.859. Since the Random Forest (RF) algorithm allows some interpretability of its results (through the impurity check), we were able to identify the most important variables in the classification of osteoporosis. The results showed that the most important variables were age, body mass index, and the signal attenuation provided by Osseus. The RF model, when used together with Osseus measurements, is effective in screening patients and facilitates the early diagnosis of osteoporosis. The main advantages of such early screening are the reduction of costs associated with exams, surgeries, treatments, and hospitalizations, as well as improved quality of life for patients.

Application of machine learning algorithms to predict osteoporosis in postmenopausal women with type 2 diabetes mellitus.

The screening and diagnosis of osteoporosis in patients with type 2 diabetes mellitus (T2DM) based on bone mineral density remains challenging because of the limited availability and accessibility of dual-energy X-ray absorptiometry. We aimed to develop and validate models to predict the risk of osteoporosis in postmenopausal women with T2DM based on machine learning (ML) algorithms. This retrospective study included 303 postmenopausal women with T2DM. To develop prediction models for osteoporosis, we applied nine ML algorithms combined with demographic, clinical, and laboratory data. The least absolute shrinkage and selection operator were used to perform feature selection. We used the bootstrap resampling technique for model training and validation. To test the performance of the models, we calculated indices including the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, calibration curve, and decision curve analysis. Furthermore, we conducted fivefold cross-validation for parameter optimization and model validation. Feature importance was assessed using the SHapley additive explanation (SHAP). We identified 10 independent predictors as the most valuable features. An AUROC of 0.616-1.000 was observed for nine ML algorithms. The extreme gradient boosting (XGBoost) model exhibited the best performance, outperforming conventional risk assessment tools and registering 0.993 in the training set, 0.798 in the validation set, and 0.786 in the test set for fivefold cross-validation. Using SHAP, we found that the explanatory variables contributed to the model and their relationship with osteoporosis occurrence. Furthermore, we developed a user-friendly tool for calculating the risk of osteoporosis. With the integration of demographic and clinical risk factors, ML algorithms can accurately predict osteoporosis. The XGBoost model showed ideal performance. With the incorporation of these models in the clinic, patients may benefit from early osteoporosis diagnosis and treatment.

Use of the Osteoporosis Self-Assessment Tool as a Screening Tool for Osteoporosis in Saudi Postmenopausal Women.

Osteoporosis is commonly referred to as the "silent disease," as bone loss is gradual and asymptomatic. In older women and men, osteoporosis can lead to increased bone fragility, thus increasing the risk of fractures. These fractures are associated with healthcare costs, physical disabilities, impaired quality of life, and mortality. Therefore, the study's main objective was to assess the applicability of the osteoporosis self-assessment tool (OST) in predicting osteoporosis in Saudi postmenopausal women who are 60 years of age and older and to give a thorough understanding of how such a method can aid in the early diagnosis of osteoporosis in Saudi Arabia and give physicians enough time to treat it. Methods: This study was done at King Abdulaziz Medical City, Riyadh, Saudi Arabia, where we included postmenopausal Saudi women 60 years of age and older who took the bone mineral density (BMD) test in the family medicine department. The approximate target population in this group, between the years 2016 and 2022, was 2969 patients. All data was taken from the BestCare database at King Abdulaziz Medical City in Riyadh. Data weretyped in an Excel sheet (Redmond, USA), then transferred to the R Studio software. The data collection method was chart review, so no informed consent was needed from patients. Names and medical record numbers were not stored. Results: The study included 2969 participants. According to the bone mineral density (BMD) T score results, 490 participants (16.5%) were normal, 1746 participants (58.8%) had osteopenia, and 733 participants (24.7%) suffered from osteoporosis. BMD T scores for normal, osteopenia, and osteoporosis participants were -0.6 (-0.9, -.3), -1.8 (-2.1), and -3 (-3.5, -2.7), consecutively. Estimated OSTI scores for those patients were 2 (0, 4), 1 (-2, 3), and -1 (-4, 1), consecutively. According to the OSTI score for normal participants, 4.29% were classified as being at high risk of osteoporosis. A high risk of osteoporosis was identified in 0.74% of those with osteopenia. 27.83% of osteoporosis patients were classified as being at high risk of osteoporosis. To differentiate normal individuals from those with osteopenia, the cutoff value with optimal sensitivity was 3.5. At such a cutoff value, the test sensitivity was 81.04%. To differentiate normal participants from those with osteoporosis, the cutoff value with optimal sensitivity was 2.5. At such a cutoff value, the test sensitivity was 86.49%. To differentiate osteopenia from osteoporosis patients, the cutoff threshold with optimal sensitivity was 1.5. At such a threshold, sensitivity was 78.44%. Conclusion: OSTA is a simple and validated tool that can identify subjects at increased risk of osteoporosis. Its use could facilitate a more cost-effective use of BMD; by avoiding measurements in low-risk groups.

A functional polymorphism at the miR-25-3p binding site in the 3′-untranslated region of the S1PR1 gene decreases the risk of osteoporosis in Chinese postmenopausal women

The low bone mineral density due to abnormally activated osteoclasts can induce bone disorders such as osteopenia and osteoporosis. MiR-25-3p modulates sphingosine-1-phosphate receptor 1 (S1PR1) expression to enhance osteoclast motivation. The association between miR-25 rs41274221 polymorphism and osteoporosis susceptibility is unknown. Herein, 300 patients with osteoporosis and 320 healthy controls were genotyped using polymerase chain reaction and Sanger sequencing to explore the role of miR-25 rs41274221 polymorphism in osteoporosis. The odds ratios (ORs) and 95% confidence intervals (CIs) were determined using logistic regression analysis. Additionally, this study also investigated the effect of miR-25 rs41274221 polymorphism on the expression of miR-25 and its target gene S1PR1 through luciferase reporter gene, qRT-PCR, and Western blot. The rs41274221 in miR-25 was found to be positively associated with osteoporosis and can be viewed as a protective factor. Furthermore, miR-25 rs41274221 polymorphism decreased the risk of osteoporosis among smokers. The TargetScan database predictions and dual-luciferase activity demonstrated that S1PR1 may be the target gene of miR-25. MiR-25 downregulated the S1PR1 mRNA and protein expressions. Patients with the AA genotype of rs41274221 polymorphism showed higher S1PR1 expression compared with the GG genotype. In conclusion, miR-25 rs41274221 polymorphism decreases the risk of osteoporosis through modifying the binding with S1PR1 and may serve as a novel biomarker for early diagnosis of osteoporosis.

The association of osteoporosis and geriatric syndromes in the elderly: data from the Russian epidemiological study EVKALIPT

SummaryOsteoporosis is associated with almost all geriatric syndromes (GSs), and the occurrence of osteoporosis in patients over 65 years of age increases by 1.2–2.5 times. Early diagnosis of osteoporosis and GSs is very important. Additional programs should be adopted by the state to introduce information about the possibilities of working with elderly patients.PurposeTo analyze associations of osteoporosis with geriatric syndromes in patients aged 65 years and older in the Russian Federation.MethodsA total of 4308 patients (30% men) aged 65–107 years were examined and distributed into 3 age groups (65–74 years, 75–84 years, and 85 years and older). All patients underwent a comprehensive geriatric assessment. In the “Falls and risk of falls” module, the number and circumstances of falls over the previous year were analyzed, as well as the history of fractures. The presence of osteoporosis was determined based on medical records. Physical examination included anthropometric measurements and standard enquiry, short physical performance battery (SPPB), dynamometry, measurement of gait velocity, Mini-Cog test, and orthostatic test.ResultsA total of 507 patients (11.8%) had evidence of osteoporosis; indications of low-energy fractures in history were recorded in 739 (17.3%) patients. Patients with osteoporosis were older, shorter, and predominantly women; had a lower body weight and a higher Charlson comorbidity index; and took more drugs. Patients with osteoporosis had lower gait velocity, hand grip strength, Barthel index value, and scores of the Lawton instrumental activities of daily living scale, the MNA (Mini Nutritional Assessment) short-form, and the SPPB. Osteoporosis is associated with almost all geriatric syndromes (GSs), and the occurrence of osteoporosis in patients over 65 years of age increases by 1.2–2.5 times.ConclusionsOsteoporosis is associated with almost all GSs. The association of osteoporosis with advanced GSs aggravates the condition of these patients. Early diagnosis of osteoporosis and GSs is very important. Additional programs should be adopted by the state to introduce information about the possibilities of working with elderly patients: early detection and correction of osteoporosis.

Effect of a Contrast Agent on Bone Mineral Density Measurement in the Spine and Hip Using QCT-Conversion Factor Recommendation.

Osteoporosis causes an increased fracture risk. Clinically, osteoporosis is diagnosed late, usually after the first fracture occurs. This emphasizes the need for an early diagnosis of osteoporosis. However, computed tomography (CT) as routinely used for polytrauma scans cannot be used in the form of quantitative computed tomography (QCT) diagnosis because QCT can only be applied natively, i.e., without any contrast agent application. Here, we tested whether and how contrast agent application could be used for bone densitometry measurements. Bone mineral density (BMD) was determined by QCT in the spine region of patients with and without the contrast agent Imeron 350. Corresponding scans were performed in the hip region to evaluate possible location-specific differences. Measurements with and without contrast agent administration between spine and hip bones indicate that the corresponding BMD values were reproducibly different between spine and hips, indicating that Imeron 350 application has a location-specific effect. We determined location-specific conversion factors that allow us then to determine the BMD values relevant for osteoporosis diagnosis. Results show that contrast administration cannot be used directly for CT diagnostics because the agent significantly alters BMD values. However, location-specific conversion factors can be established, which are likely to depend on additional parameters such as the weight and corresponding BMI of the patient.

Evaluation of bone mineral density and trabecular bone score for diagnosis of osteoporosis in Iranian diabetic patients

Introduction: Type 2 diabetes mellitus (T2DM) increases the risk of bone fractures. Objectives: This study aimed to examine the use of trabecular bone score (TBS) and BMD to select the best diagnostic tool for osteoporosis caused by type 2 diabetes. Patients and Methods: One hundred and four patients (52 individuals with type 2 diabetes and 52 ones without diabetes) aged at most 50 years were enrolled in a cross-sectional study that was conducted with dual-energy X-ray absorptiometry (DXA) images of patients referred to Khorshid and Al-Zahra hospitals, Isfahan, Iran. Results: Lumbosacral bone mineral density (BMD) was significantly lower in the diabetic group than in the control group (0.76 versus 0.82, P=0.041). The mean lumbosacral TBS was significantly lower in the diabetic group than in the controls (1.24 versus 1.36, P=0.001). Therefore, the lumbosacral TBS provided a more reliable indicator than lumbosacral BMD to discriminate between controls and women with T2DM. Conclusion: The TBS is recommended for early diagnosis of osteoporosis in diabetic patients because of its independence from BMD-related parameters. The TBS can capture a larger portion of the bone deterioration in women with T2DM that cannot be detected using methods based solely on BMD.

Prediction of osteoporosis using radiomics analysis derived from single source dual energy CT

BackgroundWith the aging population of society, the incidence rate of osteoporosis is increasing year by year. Early diagnosis of osteoporosis plays a significant role in the progress of disease prevention. As newly developed technology, computed tomography (CT) radiomics could discover radiomic features difficult to recognize visually, providing convenient, comprehensive and accurate osteoporosis diagnosis. This study aimed to develop and validate a clinical-radiomics model based on the monochromatic imaging of single source dual-energy CT for osteoporosis prediction.MethodsOne hundred sixty-four participants who underwent both single source dual-energy CT and quantitative computed tomography (QCT) lumbar-spine examination were enrolled in a study cohort including training datasets (n = 114 [30 osteoporosis and 84 non-osteoporosis]) and validation datasets (n = 50 [12 osteoporosis and 38 non-osteoporosis]). One hundred seven radiomics features were extracted from 70-keV monochromatic CT images. With QCT as the reference standard, a radiomics signature was built by using least absolute shrinkage and selection operator (LASSO) regression on the basis of reproducible features. A clinical-radiomics model was constructed by incorporating the radiomics signature and a significant clinical predictor (age) using multivariate logistic regression analysis. Model performance was assessed by its calibration, discrimination and clinical usefulness.ResultsThe radiomics signature comprised 14 selected features and showed good calibration and discrimination in both training and validation cohorts. The clinical-radiomics model, which incorporated the radiomics signature and a significant clinical predictor (age), also showed good discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.938 (95% confidence interval, 0.903–0.952) in the training cohort and an AUC of 0.988 (95% confidence interval, 0.967–0.998) in the validation cohort, and good calibration. The clinical-radiomics model stratified participants into groups with osteoporosis and non-osteoporosis with an accuracy of 94.0% in the validation cohort. Decision curve analysis (DCA) demonstrated that the radiomics signature and the clinical-radiomics model were clinically useful.ConclusionsThe clinical-radiomics model incorporating the radiomics signature and a clinical parameter had a good ability to predict osteoporosis based on dual-energy CT monoenergetic imaging.

Detection of defects in cellular solids using highly nonlinear solitary waves: a numerical study of the proximal femur.

This study investigates the suitability of a relatively new non-destructive evaluation (NDE) technique for the detection of non-visible defects in cellular solids using highly nonlinear solitary waves (HNSWs) in a one-dimensional granular chain. Specifically, the HNSW-based NDE approach is employed to identify the existence of micro-fractures in trabecular bone within the femoral neck (FN) and the intertrochanteric (IT) region of the proximal femur which are fracture-prone sites due to their relatively low bone density, particularly in osteoporosis patients. The availability of a HNSW-based bone quality assessment tool could not only help in early diagnosis of osteoporosis but also affect surgical decisions and improve clinical outcomes in joint replacement surgeries which motivated this study. To obtain a realistic representation of the trabecular microstructure, high-resolution finite-element (FE) models of the FN and the IT region are first constructed using a topology optimization-based bone reconstruction scheme. Then, artificial defects in the form of fractured ligaments are generated in the FN and IT models by selectively disconnecting various struts within the trabecular network. Using the FE models as the inspection medium, hybrid discrete-element/finite-element (DE/FE) simulations are performed to examine the interaction of the HNSWs with the cellular bone samples through two different inspection modes, i.e., inspection via direct contact with the sample and indirect contact through an adequately chosen face sheet inserted between the cellular sample and the granular chain. The delays and amplitudes of the HNSWs are used to estimate the effective elastic moduli of the cellular samples and these estimates were found to be reasonably accurate only in case the face sheet was applied. For the latter case, it was shown that the HNSW-based modulus estimates can be used as indicators for defect detection, allowing to discern between pristine and damaged cellular solids. These results suggest that HNSW-based NDE is a reliable and cost-effective technique for the identification of defects in cellular solids, and is expected to find applications in various fields, such as non-invasive screening of bone diseases and fractures, or damage detection in additively manufactured cellular structures.

Role of G protein-associated membrane receptors in the pathogenesis of osteoporosis

Osteoporosis is a chronic disease characterized by a pathological change in bone tissue, excessive fragility and a decrease in bone strength as a result of the predominance of bone resorption processes over the process of bone formation. This disease manifests in the form of low-traumatic fractures occurring in result of falling from a height of one’s height, or with little physical exertion. Osteoporosis could result in the fractures of the vertebral bodies and bones of the peripheral skeleton, causing a high level of disability. Of note, postmenopausal osteoporosis develops in women against the background of the cessation of the menstrual cycle. Here we overview the role of G-protein-associated membrane receptors (GPCR family) in the pathogenesis of this disease and the prospects for finding targets among these receptors for the diagnosis and treatment of osteoporosis. Malformations in the genes encoding GPCR lead to impaired bone tissue remodeling. Exploring the functions of GPCR family members is critical to the search for predisposition markers of bone tissue dysfunction and could improve the early diagnosis of osteoporosis. Studies on a model of osteoblasts differentiated from hIPSCs obtained from patients with osteoporosis associated with mutations in the genes of the GPCR family will allow a deeper understanding of the molecular nature of osteoporosis and the identification of new targets for osteoporosis treatment.

Effect of bone degradation on axially transmitted low frequency (&amp;lt;500 kHz) ultrasonic guided waves

The early diagnosis of osteoporosis through bone quality assessment has been extensively studied in the past few years. Research in axial transmission in long cortical bone using ultrasonic guided waves was shown to be sensitive to variations of the bone properties such as the geometry and the mechanical properties. The aim of this presentation is, therefore, to simulate a complex bone-like geometry using 3D finite elements (FE) in order to investigate the influence of the cortical thickness mechanical properties degradation. Three parameters were investigated: (1) the thickness of the periosteal region, (2) the degradation of the endosteal region, and (3) the position of the transition zone from periosteal to endosteal regions. Moreover, the influence of soft tissue was investigated on a plate-like structure using semi-analytical iso-geometric analysis method (SAIGA). Two cortical bone phantom plates with different mechanical properties covered with a soft tissue mimicking material were used to perform axial transmission measurement. The dispersion curves of the propagating modes were experimentally measured and then were compared to the solutions obtained with the SAIGA method. The results showed the possibility to retrieve the properties of the bone phantom plates through an inversion of the dispersion curves.

Serum biomarker-based osteoporosis risk prediction and the systemic effects of Trifolium pratense ethanolic extract in a postmenopausal model

BackgroundRecent years, a soaring number of marketed Trifolium pratense (red clover) extract products have denoted that a rising number of consumers are turning to natural alternatives to manage postmenopausal symptoms. T. pratense ethanolic extract (TPEE) showed immense potential for their uses in the treatment of menopause complications including osteoporosis and hormone dependent diseases. Early diagnosis of osteoporosis can increase the chance of efficient treatment and reduce fracture risks. Currently, the most common diagnosis of osteoporosis is performed by using dual-energy x-ray absorptiometry (DXA). However, the major limitation of DXA is that it is inaccessible and expensive in rural areas to be used for primary care inspection. Hence, serum biomarkers can serve as a meaningful and accessible data for osteoporosis diagnosis.MethodsThe present study systematically elucidated the anti-osteoporosis and estrogenic activities of TPEE in ovariectomized (OVX) rats by evaluating the bone microstructure, uterus index, serum and bone biomarkers, and osteoblastic and osteoclastic gene expression. Leverage on a pool of serum biomarkers obtained from this study, recursive feature elimination with a cross-validation method (RFECV) was used to select useful biomarkers for osteoporosis prediction. Then, using the key features extracted, we employed five classification algorithms: extreme gradient boosting (XGBoost), random forest, support vector machine, artificial neural network, and decision tree to predict the bone quality in terms of T-score.ResultsTPEE treatments down-regulated nuclear factor kappa-B ligand, alkaline phosphatase, and up-regulated estrogen receptor β gene expression. Additionally, reduced serum C-terminal telopeptides of type 1 collagen level and improvement in the estrogen dependent characteristics of the uterus on the lining of the lumen were observed in the TPEE intervention group. Among the tested classifiers, XGBoost stood out as the best performing classification model with the highest F1-score and lowest standard deviation.ConclusionsThe present study demonstrates that TPEE treatment showed therapeutic benefits in the prevention of osteoporosis at the transcriptional level and maintained the estrogen dependent characteristics of the uterus. Our study revealed that, in the case of limited number of features, RFECV paired with XGBoost model could serve as a powerful tool to readily evaluate and diagnose postmenopausal osteoporosis.

Osteoporosis Pre-Screening Using Ensemble Machine Learning in Postmenopausal Korean Women.

As osteoporosis is a degenerative disease related to postmenopausal aging, early diagnosis is vital. This study used data from the Korea National Health and Nutrition Examination Surveys to predict a patient’s risk of osteoporosis using machine learning algorithms. Data from 1431 postmenopausal women aged 40–69 years were used, including 20 features affecting osteoporosis, chosen by feature importance and recursive feature elimination. Random Forest (RF), AdaBoost, and Gradient Boosting (GBM) machine learning algorithms were each used to train three models: A, checkup features; B, survey features; and C, both checkup and survey features, respectively. Of the three models, Model C generated the best outcomes with an accuracy of 0.832 for RF, 0.849 for AdaBoost, and 0.829 for GBM. Its area under the receiver operating characteristic curve (AUROC) was 0.919 for RF, 0.921 for AdaBoost, and 0.908 for GBM. By utilizing multiple feature selection methods, the ensemble models of this study achieved excellent results with an AUROC score of 0.921 with AdaBoost, which is 0.1–0.2 higher than those of the best performing models from recent studies. Our model can be further improved as a practical medical tool for the early diagnosis of osteoporosis after menopause.