Hasil untuk "Diseases of the musculoskeletal system"

Akshay S. Chaudhari, Zhongnan Fang, Feliks Kogan et al.

PurposeTo develop a super‐resolution technique using convolutional neural networks for generating thin‐slice knee MR images from thicker input slices, and compare this method with alternative through‐plane interpolation methods.MethodsWe implemented a 3D convolutional neural network entitled DeepResolve to learn residual‐based transformations between high‐resolution thin‐slice images and lower‐resolution thick‐slice images at the same center locations. DeepResolve was trained using 124 double echo in steady‐state (DESS) data sets with 0.7‐mm slice thickness and tested on 17 patients. Ground‐truth images were compared with DeepResolve, clinically used tricubic interpolation, and Fourier interpolation methods, along with state‐of‐the‐art single‐image sparse‐coding super‐resolution. Comparisons were performed using structural similarity, peak SNR, and RMS error image quality metrics for a multitude of thin‐slice downsampling factors. Two musculoskeletal radiologists ranked the 3 data sets and reviewed the diagnostic quality of the DeepResolve, tricubic interpolation, and ground‐truth images for sharpness, contrast, artifacts, SNR, and overall diagnostic quality. Mann‐Whitney U tests evaluated differences among the quantitative image metrics, reader scores, and rankings. Cohen's Kappa (κ) evaluated interreader reliability.ResultsDeepResolve had significantly better structural similarity, peak SNR, and RMS error than tricubic interpolation, Fourier interpolation, and sparse‐coding super‐resolution for all downsampling factors (p < .05, except 4 × and 8 × sparse‐coding super‐resolution downsampling factors). In the reader study, DeepResolve significantly outperformed (p < .01) tricubic interpolation in all image quality categories and overall image ranking. Both readers had substantial scoring agreement (κ = 0.73).ConclusionDeepResolve was capable of resolving high‐resolution thin‐slice knee MRI from lower‐resolution thicker slices, achieving superior quantitative and qualitative diagnostic performance to both conventionally used and state‐of‐the‐art methods.

Wenqian Xu, Xiaotao Huang, Jinsong Liu et al.

Abstract Background The accuracy of prosthesis placement was crucial to the clinical efficacy after primary total knee arthroplasty (TKA). Some patients’ X-ray after TKA showed that the coronal and patellar axial parameters of the prosthesis position were within the acceptable range, but the clinical efficacy was still not as expected, which may be correlated to the abnormal sagittal parameters of postoperative prosthesis placement. Therefore, this study was designed to analyze the relevant factors of sagittal parameters on clinical efficacy after TKA and build a clinical prediction model. Method A retrospective analysis was conducted to collect patients who underwent primary TKA with osteoarthritis of knee joint from the First Affiliated Hospital of Kunming Medical University from September 2017 to September 2024. X-ray imaging and PACS imaging system were used to collect the coronal, patella axial and sagittal parameters from the anteroposterior, lateral and axial radiographs of the knee joint. According to the inclusion criteria, coronal parameters [Hip–Knee–Ankle HKA (177.8° ± 0.8°)] and patellar parameters [sulcus angle SA (135° ± 10°)], patellar tilt angle (11° ± 2.5°) within the normal range were collected. Sagittal parameters including femoral parameters [lateral femoral component angle (LFCA), femoral prosthesis flexion angle (FPFA), posterior condylar offset (PCO), Anterior–posteior dimension (ACP), posterior condylar offset ratio (PCOR), anterior femoral notching (AFN)], tibial parameters [lateral tibial component angle (LTCA), posterior tibial slope (PTS)] and patellar parameters [blackburne–peel index (B–P index), patella thickness, patella length)]. Furthermore, Forgotten Joint Score (FJS-12), Kujala patellofemoral score (KPS) and Hospital for Special Surgery Knee Score (HSS) were used to evaluate the prognosis of patients. Spearman coefficient was used to analyze the correlation between sagittal knee parameters with HSS and Kujala score. Univariate and multivariate logistic regression methods were used to investigate the risk and protect factors of Forgotten Joint Score (FJS-12) and build a clinical predictive model by R language. Result A total of 188 patients were collected, including 25 males and 163 females, with an average age of 64.8 years. PTS (P < 0.05, OR = 0.2), PCO (P < 0.05, OR = 0.2) and PCOR (P < 0.05, OR = 0.2) were considered positively correlated with HSS, but patellar thickness (P < 0.05, OR = − 0.2) and B–P index (P < 0.05, OR = − 0.2) was negatively correlated with HSS. However, only B–P index was negative with KPS. In addition, PTS (P < 0.05), PCO (P < 0.05), PCOR (P < 0.05), B–P index (P < 0.01) and patella thickness (P < 0.05) were independently associated with FJS-12. Moreover, clinical prediction model showed that PTS ≥ 5.5°, PCO ≥ 31.2 mm, B–P index < 1 and patella thickness < 16.6 mm were the optimal parameter for patients to achieve satisfactory postoperative status after TKA. Conclusions The prognosis of patients after TKA was influenced by sagittal parameters, including PCO, PCOR, PTS, B–P index and patellar thickness, which should be paid close attention by clinicians when placing prostheses.

Diego A. Alonso-Aubin, Ester Jiménez-Ormeño, César Gallo-Salazar et al.

<b>Background</b>: CrossFit^® has gained widespread popularity as a high-intensity training modality, yet evidence describing neuromuscular performance characteristics in this population remains limited. This study aimed to evaluate isometric and ballistic strength profiles in trained CrossFit^® athletes and to identify sex-based differences in absolute and relative neuromuscular performance. <b>Methods</b>: Seventy-two athletes participated (41 males and 31 females) participated in the study, completing two maximal isometric mid-thigh pull (IMTP) tests and three countermovement jump (CMJ) tests within a single testing session. Assessments were conducted using a dual force plate system (Hawkin Dynamics, Westbrook, ME, USA). <b>Results</b>: In the IMTP, males exhibited substantially higher absolute isometric force outputs, including peak force (3059 ± 576 vs. 1899 ± 324 N; <i>p</i> < 0.001) and relative peak force (36.34 ± 6.74 vs. 30.99 ± 4.41 N/kg; <i>p</i> < 0.001). Rates of force development were also greater in males for both early (0–50 ms: 7665 ± 5420 vs. 4001 ± 3021 N/s; <i>p</i> < 0.001) and late phases (0–250 ms: 5350 ± 1832 vs. 3035 ± 886 N/s; <i>p</i> < 0.001). However, no significant sex differences were detected in time to peak force (2.31 ± 1.27 vs. 1.94 ± 1.04 s) or dynamic strength index (0.72 ± 0.12 vs. 0.73 ± 0.12 a.u.). In ballistic performance using CMJ, males achieved higher jump height (0.33 ± 0.07 vs. 0.23 ± 0.05 m; <i>p</i> < 0.001), jump momentum (215 ± 27.9 vs. 131 ± 19.1 kg·m/s; <i>p</i> < 0.001), and modified reactive strength index (0.46 ± 0.11 vs. 0.32 ± 0.08 a.u.; <i>p</i> < 0.001). Relative propulsive and braking forces were also moderately greater in males. Notably, sex differences were reduced when variables were normalized to body mass or peak force, indicating comparable relative neuromuscular function across sexes. <b>Conclusions</b>: These findings provide descriptive neuromuscular performance data for CrossFit^® athletes and show that sex-based differences primarily reflect disparities in absolute force-production capacity rather than intrinsic neuromuscular efficiency. Such insights may support more precise, evidence-informed, and sex-specific training prescriptions to optimize performance.

Md. Ehsanul Haque, Md. Saymon Hosen Polash, Rakib Hasan Ovi et al.

Grapes are among the most economically and culturally significant fruits on a global scale, and table grapes and wine are produced in significant quantities in Europe and Asia. The production and quality of grapes are significantly impacted by grape diseases such as Bacterial Rot, Downy Mildew, and Powdery Mildew. Consequently, the sustainable management of a vineyard necessitates the early and precise identification of these diseases. Current automated methods, particularly those that are based on the YOLO framework, are often computationally costly and lack interpretability that makes them unsuitable for real-world scenarios. This study proposes grape leaf disease classification using Optimized DenseNet 121. Domain-specific preprocessing and extensive connectivity reveal disease-relevant characteristics, including veins, edges, and lesions. An extensive comparison with baseline CNN models, including ResNet18, VGG16, AlexNet, and SqueezeNet, demonstrates that the proposed model exhibits superior performance. It achieves an accuracy of 99.27%, an F1 score of 99.28%, a specificity of 99.71%, and a Kappa of 98.86%, with an inference time of 9 seconds. The cross-validation findings show a mean accuracy of 99.12%, indicating strength and generalizability across all classes. We also employ Grad-CAM to highlight disease-related regions to guarantee the model is highlighting physiologically relevant aspects and increase transparency and confidence. Model optimization reduces processing requirements for real-time deployment, while transfer learning ensures consistency on smaller and unbalanced samples. An effective architecture, domain-specific preprocessing, and interpretable outputs make the proposed framework scalable, precise, and computationally inexpensive for detecting grape leaf diseases.

Yelena Sánchez Cantos, Amada Barcia Cansino

La dermatomiositis con anticuerpos contra la proteína 5 asociada a la diferenciación del melanoma (DM-MDA5) es un subtipo de dermatomiositis con un mal pronóstico que se presenta típicamente como manifestaciones cutáneas y enfermedad pulmonar intersticial de rápida progresión (EPI-RP). Las características clínicas y de laboratorio más destacadas de la DM-MDA5 son erupciones cutáneas distintivas, enfermedad pulmonar intersticial de rápida progresión, linfopenia periférica y niveles elevados de ferritina sérica. La infección concomitante es una complicación frecuente de la DM-MDA5. La evaluación adecuada de los pacientes requiere el conocimiento de la heterogeneidad de la enfermedad y la variabilidad del curso clínico. Los pacientes varones con DM-MDA5 presentan un mayor riesgo de EPI-RP, tasas de mortalidad elevadas y un tiempo de supervivencia general reducido en comparación con sus contrapartes femeninas, y la positividad de anti-Ro52 puede ser un factor pronóstico desfavorable para estos pacientes. Reportamos una paciente femenina con lesiones cutáneas tipo ulceras y enfermedad intersticial rápidamente progresiva sin síntomas respiratorios y sin debilidad muscular ni elevación de enzimas musculares con anticuerpo positivo MDA5, anti Ro52 positivo y ferritina elevada. La paciente recibió tratamiento con corticoides, micofenolato mofetilo y rituximab para alcanzar la remisión clínica.

Aditya Raj, Golrokh Mirzaei

Imaging and genomic data offer distinct and rich features, and their integration can unveil new insights into the complex landscape of diseases. In this study, we present a novel approach utilizing radiogenomic data including structural MRI images and gene expression data, for Alzheimer's disease detection. Our framework introduces a novel heterogeneous bipartite graph representation learning featuring two distinct node types: genes and images. The network can effectively classify Alzheimer's disease (AD) into three distinct stages:AD, Mild Cognitive Impairment (MCI), and Cognitive Normal (CN) classes, utilizing a small dataset. Additionally, it identified which genes play a significant role in each of these classification groups. We evaluate the performance of our approach using metrics including classification accuracy, recall, precision, and F1 score. The proposed technique holds potential for extending to radiogenomic-based classification to other diseases.

Jianshu She, Wenhao Zheng, Zhengzhong Liu et al.

The computational complexity of large language model (LLM) inference significantly constrains their deployment efficiency on edge devices. In contrast, small language models offer faster decoding and lower resource consumption but often suffer from degraded response quality and heightened susceptibility to hallucinations. To address this trade-off, collaborative decoding, in which a large model assists in generating critical tokens, has emerged as a promising solution. This paradigm leverages the strengths of both model types by enabling high-quality inference through selective intervention of the large model, while maintaining the speed and efficiency of the smaller model. In this work, we present a novel collaborative decoding inference system that allows small models to perform on-device inference while selectively consulting a cloud-based large model for critical token generation. Remarkably, the system achieves a 60% performance gain on CommonsenseQA using only a 0.5B model on an M1 MacBook, with under 7% of tokens generation uploaded to the large model in the cloud.

Li-Chin Chen, Ji-Tian Sheu, Yuh-Jue Chuang

Demographic attributes are universally present in electronic health records. They are the most widespread information across populations and diseases, and serve as vital predictors in clinical risk stratification and treatment decisions. Despite their significance, these attributes are often treated as auxiliaries in model design, with limited attention being paid to learning their representations. This study explored the development of a General Demographic Pre-trained (GDP) model as a foundational model tailored to demographic attributes, focusing on age and gender. The model is pre-trained and evaluated using datasets with diverse diseases and populations compositions from different geographic regions. The composition of GDP architecture was explored through examining combinations of ordering approaches and encoding methods to transform tabular demographic inputs into effective latent embeddings. Results demonstrate the feasibility of GDP to generalize across task, diseases, and populations. In detailed composition, the sequential ordering substantially improves model performance in discrimination, calibration, and the corresponding information gain at each decision tree split, particularly in diseases where age and gender contribute significantly to risk stratification. Even in datasets where demographic attributes hold relatively low predictive value, GDP enhances the representational importance, increasing their influence in downstream gradient boosting models. The findings suggest that foundation models for tabular demographic attributes offer a promising direction for improving predictive performance in healthcare applications.

Clara Tourbez, Claude Semay, Cyrille Chevalier

When particles interact via two-body short-range central potential wells, binding can occur for some critical values of the coupling constants. Using the envelope theory, upper bounds for critical coupling constants are computed for quantum nonrelativistic systems containing identical particles and systems containing identical particles plus a different one.

Jennifer S. Hanberg, Faith Selzer, Elena Losina et al.

Tanja Kraus, Catharina Chiari

Hip ultrasound, according to Graf, is a standardized sonographic method for the detection of developmental dysplasia of the hip (DDH) during the neonatal period. Graf established his method during the 1980s in his home country Austria. It was implemented in the Austrian Mother-Child Health Passport in 1992. Since then it served as a general screening method. The aim of this paper is to present the effects of general hip ultrasound screening in Austria by reviewing and analysing the literature of Austrian authors. This article described how the method was further developed and which prerequisites are currently required for a correct diagnosis. Moreover, it reports about the education in ultrasound screening according to Graf in Austria.

Jürgen Braun, Kirsten Karberg, Denis Poddubnyy

A 76-year-old male patient who has been suffering from psoriatic arthritis (PsA) for 15 years was diagnosed with chronic lymphocytic leukemia (CLL) 18 months ago. He has been treated him with a Bruton’s tyrosine kinase (BTK) inhibitor (ibrutinib) at a dose of 420 mg once daily (q.d.) for his CLL. For about two years, he received a quite successful treatment with methotrexate and the subcutaneously administered tumor necrosis factor (TNF) inhibitor (adalimumab) for his PsA, until his plaque psoriasis worsened. He consulted us when the severity of his skin condition necessitated a change in his treatment regimen. In the following discussion, we explore treatment options for this clinical scenario, with a particular focus on managing PsA in the context of CLL as a comorbidity. Additionally, we report on the initial phase of treatment with an anti-interleukin-23 (IL-23) inhibitor (guselkumab), specifically targeting his aggravated psoriasis.

P. Krebs, M. Nägele, P. Fomina et al.

Objective: A prototype infrared attenuated total reflection (IR-ATR) laser spectroscopic system designed for in vivo classification of human cartilage tissue according to its histological health status during arthroscopic surgery is presented. Prior to real-world in vivo applications, this so-called osteoarthritis (OA) scanner has been tested at in vitro conditions revealing the challenges associated with complex sample matrices and the accordingly obtained sparse spectral datasets. Methods: In vitro studies on human knee cartilage samples at different contact pressures (i.e., 0.2–0.5 ​MPa) allowed recording cartilage degeneration characteristic IR signatures comparable to in vivo conditions with high temporal resolution. Afterwards, the cartilage samples were assessed based on the clinically acknowledged osteoarthritis cartilage histopathology assessment (OARSI) system and correlated with the obtained sparse IR data. Results: Amide and carbohydrate signal behavior was observed to be almost identical between the obtained sparse IR data and previously measured FTIR data used for sparse partial least squares discriminant analysis (SPLSDA) to identify the spectral regions relevant to cartilage condition. Contact pressures between 0.3 and 0.4 ​MPa seem to provide the best sparse IR spectra for cylindrical (d ​= ​3 ​mm) probe tips. Conclusion: Laser-irradiating IR-ATR spectroscopy is a promising analytical technique for future arthroscopic applications to differentiate healthy and osteoarthritic cartilage tissue. However, this study also revealed that the flexible connection between the laser-based analyzer and the arthroscopic ATR-probe via IR-transparent fiberoptic cables may affect the robustness of the obtained IR data and requires further improvements.

Luciano Rossi, Ignacio Tanoira, Rodrigo Brandariz et al.

Xiao-Bing Xiang, Kai-Yin Gao, Wei-Wei Zhang et al.

Abstract Objective Despite advancements in spinal metastasis surgery techniques and the rapid development of multidisciplinary treatment models, we aimed to explore the clinical efficacy of spinal metastasis surgery performed by a combined NOMS decision system-utilizing multidisciplinary team and Revised Tokuhashi scoring system, compared with the Revised Tokuhashi scoring system. Methods Clinical data from 102 patients with spinal metastases who underwent surgery at three affiliated hospitals of Zunyi Medical University from December 2017 to June 2022 were analysed. The patients were randomly assigned to two groups: 52 patients in the treatment group involving the combined NOMS decision system-utilizing multidisciplinary team and Revised Tokuhashi scoring system (i.e., the combined group), and 50 patients in the treatment group involving the Revised Tokuhashi scoring system only (i.e., the revised TSS-only group). Moreover, there were no statistically significant differences in preoperative general data or indicators between the two groups. Intraoperative and postoperative complications, average hospital stay, mortality rate, and follow-up observation indicators, including the visual analogue scale (VAS) score for pain, Eastern Cooperative Oncology Group (ECOG) performance status, Karnofsky Performance Status (KPS) score, negative psychological assessment score (using the Self-Rating Anxiety Scale, [SAS]), and neurological function recovery score (Frankel functional classification) were compared between the two groups. Results All 102 patients successfully completed surgery and were discharged. The follow-up period ranged from 12 to 24 months, with an average of (13.2 ± 2.4) months. The patients in the combined group experienced fewer complications such as surgical wound infections 3 patients(5.77%), intraoperative massive haemorrhage 2 patients(3.85%), cerebrospinal fluid leakage 2 patients(3.85%), deep vein thrombosis 4 patients(7.69%),and neurological damage 1 patient(1.92%), than patients in the revised TSS-only group (wound infections,11 patients(22%); intraoperative massive haemorrhage, 8 patients(16%);cerebrospinal fluid leakage,5 patients(10%);deep vein thrombosis,13 patients (26%); neurological damage,2 patients (4%). Significant differences were found between the two groups in terms of surgical wound infections, intraoperative massive haemorrhage, and deep vein thrombosis (P < 0.05). The average postoperative hospital stay in the combined group (7.94 ± 0.28 days) was significantly shorter than that in the revised TSS-only group (10.33 ± 0.30 days) (P < 0.05). Long-term follow-up (1 month, 3 months, 6 months, and 1 year postoperatively) revealed better clinical outcomes in the combined group than in the revised TSS-only group in terms of VAS scores, overall KPS%, neurological function status Frankel classification, ECOG performance status, and SAS scores.(P < 0.05). Conclusion A multidisciplinary team using the NOMS combined with the Revised Tokuhashi scoring system for spinal metastasis surgery showed better clinical efficacy than the sole use of the Revised Tokuhashi scoring system. This personalized, precise, and rational treatment significantly improves patient quality of life, shortens hospital stay, reduces intraoperative and postoperative complications, and lowers mortality rates.

Claudia Baran, Shanice Belgacem, Mathilde Paillet et al.

Active commuting (AC) may have the potential to prevent the incidence of cardiovascular disease (CVD). However, the evidence for a correlation between AC and the risk of CVD remains uncertain. The current study thoroughly and qualitatively summarized research on the relationship between AC and the risk of CVD disease. From conception through December 2022, researchers explored four databases (PubMed, PEDro, Cochrane, and Bibliothèque Nationale of Luxembourg [BnL]) for observational studies. The initial findings of the search yielded 1042 references. This systematic review includes five papers with 491,352 participants between 16 and 85 years old, with 5 to 20 years of follow-up period. The exposure variable was the mode of transportation used to commute on a typical day (walking, cycling, mixed mode, driving, or taking public transportation). The primary outcome measures were incident CVD, fatal and non-fatal (e.g., ischemic heart disease (IHD), ischemic stroke (IS), hemorrhagic stroke (HS) events, and coronary heart disease (CHD). Despite methodological variability, the current evidence supports AC as a preventive measure for the development of CVD. Future research is needed to standardize methodologies and promote policies for public health and environmental sustainability.

Youcef Ferdi

The advances in computer vision made possible by deep learning technology are increasingly being used in precision agriculture to automate the detection and classification of plant diseases. Symptoms of plant diseases are often seen on their leaves. The leaf images in existing datasets have been collected either under controlled conditions or in the field. The majority of previous studies have focused on identifying leaf diseases using images captured in controlled laboratory settings, often achieving high performance. However, methods aimed at detecting and classifying leaf diseases in field images have generally exhibited lower performance. The objective of this study is to evaluate the impact of a data augmentation approach that involves removing complex backgrounds from leaf images on the classification performance of apple leaf diseases in images captured under real world conditions. To achieve this objective, the lightweight pre-trained MobileNetV2 deep learning model was fine-tuned and subsequently used to evaluate the impact of expanding the training dataset with background-removed images on classification performance. Experimental results show that this augmentation strategy enhances classification accuracy. Specifically, using the Adam optimizer, the proposed method achieved a classification accuracy of 98.71% on the Plant Pathology database, representing an approximately 3% improvement and outperforming state-of-the-art methods. This demonstrates the effectiveness of background removal as a data augmentation technique for improving the robustness of disease classification models in real-world conditions.

Daniel A. M. Villela

The emergence or adaptation of pathogens may lead to epidemics, highlighting the need for a thorough understanding of pathogen evolution. The tradeoff hypothesis suggests that virulence evolves to reach an optimal transmission intensity relative to the mortality caused by the disease. This study introduces a mathematical model that incorporates key factors such as recovery times and mortality rates, focusing on the diminishing effects of parasite growth on transmission, with a focus on vector-borne diseases. The analysis reveals conditions under which heightened virulence occurs in hosts, indicating that these factors can support vector-host transmission of a pathogen, even if the host-only component is insufficient for sustainable transmission. This insight helps explain the significant presence of pathogens with high fatality rates, such as those in vector-borne diseases. The findings underscore an elevated risk for future outbreaks involving such diseases. Enhanced surveillance of mortality rates and techniques to monitor pathogen evolution are vital to effectively control future epidemics. This study provides essential insights for epidemic preparedness and highlights the need for ongoing research into pathogen evolution.

Christof Naumzik, Alice Kongsted, Werner Vach et al.

Clinical data informs the personalization of health care with a potential for more effective disease management. In practice, this is achieved by subgrouping, whereby clusters with similar patient characteristics are identified and then receive customized treatment plans with the goal of targeting subgroup-specific disease dynamics. In this paper, we propose a novel mixture hidden Markov model for subgrouping patient trajectories from chronic diseases. Our model is probabilistic and carefully designed to capture different trajectory phases of chronic diseases (i.e., "severe", "moderate", and "mild") through tailored latent states. We demonstrate our subgrouping framework based on a longitudinal study across 847 patients with non-specific low back pain. Here, our subgrouping framework identifies 8 subgroups. Further, we show that our subgrouping framework outperforms common baselines in terms of cluster validity indices. Finally, we discuss the applicability of the model to other chronic and long-lasting diseases.

Ananya Nandy, Ron C. M. Helderman, Santosh Thapa et al.

AbstractBone formation is a highly energy-demanding process that can be impacted by metabolic disorders. Glucose has been considered the principal substrate for osteoblasts, although fatty acids are also important for osteoblast function. Here, we report that osteoblasts can derive energy from endogenous fatty acids stored in lipid droplets via lipolysis and that this process is critical for bone formation. As such, we demonstrate that osteoblasts accumulate lipid droplets that are highly dynamic and provide the molecular mechanism by which they serve as a fuel source for energy generation during osteoblast maturation. Inhibiting cytoplasmic lipolysis leads to both an increase in lipid droplet size in osteoblasts and an impairment in osteoblast function. The fatty acids released by lipolysis from these lipid droplets become critical for cellular energy production as cellular energetics shifts towards oxidative phosphorylation during nutrient-depleted conditions. In vivo, conditional deletion of the ATGL-encoding gene Pnpla2 in osteoblast progenitor cells reduces cortical and trabecular bone parameters and alters skeletal lipid metabolism. Collectively, our data demonstrate that osteoblasts store fatty acids in the form of lipid droplets, which are released via lipolysis to support cellular bioenergetic status when nutrients are limited. Perturbations in this process result in impairment of bone formation, specifically reducing ATP production and overall osteoblast function.