Search Results (349)

This study aims to characterize short-wave infrared (SWIR) reflectance spectra at cranial (at the scalp overlying the frontal cortex and the temporal bone window) and extracranial (biceps and triceps) sites in patients with multiple sclerosis (MS) and age-/sex-matched controls. We sought to identify the diagnostic accuracy of wavelength-specific patterns in distinguishing MS from normal controls and spectral markers associated with disability (e.g., Expanded Disability Status Scale scores). To achieve these objectives, we employed a multi-site SWIR spectroscopy acquisition protocol that included measurements from traditional cranial locations as well as extracranial reference sites. Advanced spectral analysis techniques, including wavelength-dependent absorption modeling and machine learning-based classification, were applied to differentiate MS-related hemodynamic changes from normal physiological variability. Classification models achieved perfect performance (accuracy = 1.00), and cortical site regression models showed strong predictive power (EDSS: R²_CV = 0.980; FSS: R²_CV = 0.939). Variable Importance in Projection (VIP) analysis highlighted key wavelengths as potential spectral biomarkers. This approach allowed us to explore novel biomarkers of neural and systemic impairment in MS, paving the way for potential clinical applications of SWIR spectroscopy in disease monitoring and management. In conclusion, spectral analysis revealed distinct wavelength-specific patterns collected from cranial and extracranial sites reflecting biochemical and structural differences between patients with MS and normal subjects. These differences are driven by underlying physiological changes, including myelin integrity, neuronal density, oxidative stress, and water content fluctuations in the brain or muscles. This study shows that portable spectral devices may contribute to bedside individuation and monitoring of neural diseases, offering a cost-effective alternative to repeated imaging. Full article

The management of adult spinal deformity (ASD) has evolved dramatically over the past century, transitioning from external bracing and in situ fusion to complex, technology-driven surgical interventions. This review traces the historical development of spinal deformity correction and highlights contemporary enabling technologies that are redefining the surgical landscape. Advances in stereoradiographic imaging now allow for precise, low-dose three-dimensional assessment of spinopelvic parameters and segmental bone density, facilitating individualized surgical planning. Robotic assistance and intraoperative navigation improve the accuracy and safety of instrumentation, while patient-specific rods and interbody implants enhance biomechanical conformity and alignment precision. Machine learning and predictive modeling tools have emerged as valuable adjuncts for risk stratification, surgical planning, and outcome forecasting. Minimally invasive deformity correction strategies, including anterior column realignment and circumferential minimally invasive surgery (cMIS), have demonstrated equivalent clinical and radiographic outcomes to traditional open surgery with reduced perioperative morbidity in select patients. Despite these advancements, complications such as proximal junctional kyphosis and failure remain prevalent. Adjunctive strategies—including ligamentous tethering, modified proximal fixation, and vertebral cement augmentation—offer promising preventive potential. Collectively, these innovations signal a paradigm shift toward precision spine surgery, characterized by data-informed decision-making, individualized construct design, and improved patient-centered outcomes in spinal deformity care. Full article

Background/Objectives: Myelofibrosis (MF) is a myeloproliferative neoplasm characterized by the replacement of healthy bone marrow (BM) with malignant and fibrotic tissue. In a healthy state, bone marrow is composed of approximately 60–70% fat cells, which are replaced as disease progresses. Proton density fat fraction (PDFF), a non-invasive and quantitative MRI metric, enables analysis of BM architecture by measuring the percentage of fat versus cells in the environment. Our objective is to investigate variance in quantitative PDFF-MRI values over time as a marker of disease progression and response to treatment. Methods: We analyzed existing data from three cohorts of mice: two groups with MF that failed to respond to therapy with approved drugs for MF (ruxolitinib, fedratinib), investigational compounds (navitoclax, balixafortide), or vehicle and monitored over time by MRI; the third group consisted of healthy controls imaged at a single time point. Using in-house MATLAB programs, we performed a voxel-wise analysis of PDFF values in lower extremity bone marrow, specifically comparing the variance of each voxel within and among mice. Results: Our findings revealed a significant difference in PDFF values between healthy and diseased BM. With progressive disease non-responsive to therapy, the expansion of hematopoietic cells in BM nearly completely replaced normal fat, as determined by a markedly reduced PDFF and notable reduction in the variance in PDFF values in bone marrow over time. Conclusions: This study validated our hypothesis that the variance in PDFF in BM decreases with disease progression, indicating pathologic expansion of hematopoietic cells. We can conclude that disease progression can be tracked by a decrease in PDFF values. Analyzing variance in PDFF may improve the assessment of disease progression in pre-clinical models and ultimately patients with MF. Full article

Background: Short-stem hip replacements are designed to provide improved load distribution and to mimic natural biomechanics. The interplay between implant design, positioning, and resulting bone biomechanics in individual patients remains underexplored, and the relationship between radiographically assessed bone remodeling around short stems and biomechanical predictions has not been previously reported. Methods: This study evaluated three short-stem hip implant designs: Proxima, Collo-MIS, and Minima. Postoperative bone remodeling patterns were analyzed, categorizing remodeling as bone gain, bone loss, or no observable activity, with changes tracked over time. Patient-specific biomechanical models were generated from 6-week postoperative radiographs. Finite element simulations incorporated body weight and gluteal muscle forces to estimate stress and strain distributions within the proximal femur. Strain energy was then applied to a mechanostat-based remodeling algorithm to predict bone remodeling patterns. These biomechanical predictions were compared to observed radiographic remodeling at 2 years post-surgery. A validated biomechanical model was further used to simulate different postoperative positions of the three types of stems. Results: No differences in bone remodeling patterns were observed among the three short-stem designs. Computational modeling demonstrated a statistically significant correlation between predicted remodeling and radiographic measurements at 2 years (p < 0.001). Proxima stems showed a tendency towards increased cortical bone loading under pronounced varus or valgus position in comparison to other two stems, although this observation requires further validation. Conclusions: This exploratory study demonstrates the feasibility of using biomechanical modeling to estimate bone remodeling around short-stem hip implants based on early postoperative radiographs. While the results are promising, they should be interpreted with caution due to the limited cohort size. The proposed modeling approach may offer clinical value in evaluating implant behavior and informing patient-specific treatment strategies. However, further research with larger populations is necessary to refine and validate these predictive tools. Full article

Background/Objectives: Ewing sarcoma (ES), a highly aggressive bone and soft tissue cancer occurring in children and young adults, is defined by the ETS fusion oncoprotein EWS::FLI1. Although event-free survival rates remain high in ES patients with localized disease, those with metastatic or relapsed disease face poor long-term survival odds. Topoisomerase 1 (TOP1) inhibitors are commonly used therapeutics in ES relapse regimens. Methods: In this work, we used a genome-wide CRISPR knockout library screen to identify the deletion of the TOP1 gene as a mechanism for resistance to topoisomerase 1 inhibitors. Using isogenic cell line models, we performed a high-throughput small-molecule screen to discover a small molecule, GNF-7, which had an IC50 that was 10-fold lower in TOP1-deficient cells when compared to the wild-type cells. Results: The characterization of GNF-7 demonstrated the molecule was highly active in the inhibition of CSK, p38α, EphA2, Lyn, and ZAK and specifically downregulated genes induced by the EWS::FLI1 fusion oncoprotein. Conclusions: Together, these results suggest that GNF-7 or small molecules with a similar kinase profile could be effective treatments for ES patients in combination with TOP1 inhibitors or for those patients who have developed resistance to TOP1 inhibitors. Full article

Systemic lupus erythematosus (SLE) is a chronic inflammatory autoimmune disease characterized by tissue damage in multiple organs caused by autoantibodies and the resulting immune complexes. One possible way for complement system contribution to onset of autoimmune disorder could be realized by the impairment of C1q-mediated apoptotic clearance as part of human homeostasis. The capacity of C1q to bind early apoptotic cells could be decreased or even lost in the presence of anti-C1q antibodies. A monoclonal anti-idiotypic single-chain (scFv) antibody was selected from the phage library Griffin1” to recognize anti-C1q autoantibodies, purified from sera of lupus nephritis patients. Lupus-prone MRL/lpr mice were injected weekly with scFv A1 fragment-binding anti-C1q antibodies. The number of in vitro and ex vivo studies with collected cells, sera, and organs from the treated animals was performed. scFv treatment changed the percentage of different B-, T-, and NK-cell subpopulations as well as plasma cells and plasmablasts in the spleen and bone marrow. An increase in the levels of splenocyte proliferation, anti-C1q antibodies, and the number of plasma cells producing anti-dsDNA and anti-C1q antibodies were also observed in scFv-treated animals. High levels of proteinuria and hematuria combined with unstable levels of IL10 and IFNγ promote the development of severe lupus and shorten the survival of treated MRL/lpr mice. Therapy with the scFv A1 antibody resulted in BCR recognition on the surface of anti-C1q-specific B-cells and had a disease progression effect, enhancing lupus symptoms in the MRL/lpr mouse model of SLE. Full article

Objectives: To quantify inpatient palliative care use over time and to test whether patient or hospital characteristics represent determinants of inpatient palliative care use in patients with metastatic penile cancer. Methods: Relying on the National Inpatient Sample database (2006–2019), we identified 1017 metastatic penile cancer patients. Estimated annual percentage change analyses and multivariable logistic regression models addressing inpatient palliative care use were fitted. Results: Of 1017 metastatic penile cancer patients, 139 (13.7%) received inpatient palliative care. Over time, the proportion of inpatient palliative care use per year increased from 6.5% in 2006 to 17.8% in 2019 (estimated annual percentage change +6.7%; p = 0.001). In the multivariable logistic regression models, contemporary study years (odds ratio [OR] 1.80; p = 0.003), the presence of bone metastases (OR 1.90; p = 0.002) and the presence of brain metastases (OR 2.60; p = 0.013) independently predicted higher inpatient palliative care use. Conversely, distant lymph node metastases independently predicted lower inpatient palliative care use (OR 0.58; p = 0.022). Finally, hospital admission in the South (OR 2.42; p = 0.007) and in the Northeast (OR 2.34; p = 0.015) was associated with higher inpatient palliative care use than hospital admission in the Midwest. Conclusions: In metastatic penile cancer patients, the proportions of inpatient palliative care use were low but have increased over time. Unfortunately, some geographical regions are more refractory to inpatient palliative care use than others. Finally, specific patient characteristics such as bone metastases and brain metastases represent independent predictors of higher inpatient palliative care use. Full article

Peri-implantitis (PI) presents a growing challenge in implant dentistry, with regenerative surgical approaches often incorporating barrier membranes despite the uncertainty of their clinical value. This systematic review and meta-analysis of in vivo studies aimed to evaluate the efficacy of barrier membranes in the reconstructive surgical treatment of PI. A comprehensive electronic search was performed in PubMed, Scopus, Google Scholar, and the Cochrane Library, covering studies published from 1990 to 2024. The protocol followed PRISMA guidelines and was registered in PROSPERO (CRD42025625417). Eligible studies included in vivo investigations comparing regenerative procedures with and without membrane use, with a minimum follow-up of 6 months and at least 10 implants per study. Risk of bias (RoB) was assessed using the Cochrane RoB tool. The meta-analysis was conducted using a random-effects model and included 15 studies comprising 560 patients. Although not consistently statistically significant, the findings suggested that membrane use may offer enhanced outcomes in terms of probing pocket depth (PPD) reduction and marginal bone level (MLB) gain. The evidence was limited by high clinical heterogeneity, variability in outcome definitions, and short follow-up durations. While membranes are commonly utilized, current evidence does not justify their routine use. Further well-designed, long-term clinical trials are needed to establish specific indications and optimize treatment strategies. Full article

Background: TNBC patients respond poorly to chemotherapy, leading to high mortality rates and a worsening prognosis. Here, we investigated the effect of M-I on TNBC tumor growth suppression and its potential mechanisms. Methods: Signaling pathways were analyzed to study the effect of M-I on TNBC cells (human MDA-MB-231 and mouse 4T1). We used orthotopic mouse models to examine the anti-tumor efficacy of M-I. Tumor volume and the status of tumor-associated macrophages (TAMs) were assessed by qRT-PCR or FACS analysis. Results: We found a significant dose- and time-dependent inhibition of TNBC cell proliferation following treatment with M-I. Cell cycle analysis revealed a shortened S phase in M-I-treated cells and downregulation of AURKA, PLK1, CDC25c, CDK1, and cyclinB1. Furthermore, M-I treatment reduced the expression of pSTAT3, cyclinD1, and c-Myc in TNBC cells. To evaluate the anti-tumor efficacy of M-I, we employed orthotopic TNBC mouse models and observed a significant reduction in tumor growth without measurable toxicity. Next, we analyzed RNA from control and M-I-treated tumors to further assess the status of TAMs and observed a significant decrease in M2-like macrophages in the M-I-treated group. Immortalized bone marrow-derived mouse macrophages (iMacs) exposed to conditioned media (CM) of TNBC cells with or without M-I treatment indicated that the M-I treated CM of TNBC cells significantly reduce the M2phenotype in iMacs. Mechanistically, we found that M-I specifically targets the IL-4/MAPK signaling axis to reduce immunosuppressive M2 macrophage polarization. Conclusions: Our study reveals a novel mechanism by which M-I inhibits TNBC cell proliferation by regulating intracellular signaling and altering TAMs in the tumor microenvironment and highlights its potential as a promising candidate for TNBC therapy. Full article

Background/Objectives: This study evaluates whether combining ⁶⁸Ga-PSMA-11-PET/CT derived imaging biomarkers with clinical risk factors improves the prediction of early biochemical recurrence (eBCR) or clinical progress in patients with high-risk prostate cancer (PCa) after primary treatment, using machine learning (ML) models. Methods: We analyzed data from 93 high-risk PCa patients who underwent ⁶⁸Ga-PSMA-11 PET/CT and received primary treatment at a single center. Two predictive models were developed: a logistic regression (LR) model and an ML derived probabilistic graphical model (PGM) based on a naïve Bayes framework. Both models were compared against each other and against the CAPRA risk score. The models’ input variables were selected based on statistical analysis and domain expertise including a literature review and expert input. A decision tree was derived from the PGM to translate its probabilistic reasoning into a transparent classifier. Results: The five key input variables were as follows: binarized CAPRA score, maximal intraprostatic PSMA uptake intensity (SUVmax), presence of bone metastases, nodal involvement at common iliac bifurcation, and seminal vesicle infiltration. The PGM achieved superior predictive performance with a balanced accuracy of 0.73, sensitivity of 0.60, and specificity of 0.86, substantially outperforming both the LR (balanced accuracy: 0.50, sensitivity: 0.00, specificity: 1.00) and CAPRA (balanced accuracy: 0.59, sensitivity: 0.20, specificity: 0.99). The decision tree provided an explainable classifier with CAPRA as a primary branch node, followed by SUVmax and specific PET-detected tumor sites. Conclusions: Integrating ⁶⁸Ga-PSMA-11 imaging biomarkers with clinical parameters, such as CAPRA, significantly improves models to predict progression in patients with high-risk PCa undergoing primary treatment. The PGM offers superior balanced accuracy and enables risk stratification that may guide personalized treatment decisions. Full article

Musculoskeletal (MSK) disorders remain a major global cause of disability, with diagnostic complexity arising from their heterogeneous presentation and multifactorial pathophysiology. Recent advances across imaging modalities, molecular biomarkers, artificial intelligence applications, and point-of-care technologies are fundamentally reshaping musculoskeletal diagnostics. This review offers a novel synthesis by unifying recent innovations across multiple diagnostic imaging modalities, such as CT, MRI, and ultrasound, with emerging biochemical, genetic, and digital technologies. While existing reviews typically focus on advances within a single modality or for specific MSK conditions, this paper integrates a broad spectrum of developments to highlight how use of multimodal diagnostic strategies in combination can improve disease detection, stratification, and clinical decision-making in real-world settings. Technological developments in imaging, including photon-counting detector computed tomography, quantitative magnetic resonance imaging, and four-dimensional computed tomography, have enhanced the ability to visualize structural and dynamic musculoskeletal abnormalities with greater precision. Molecular imaging and biochemical markers such as CTX-II (C-terminal cross-linked telopeptides of type II collagen) and PINP (procollagen type I N-propeptide) provide early, objective indicators of tissue degeneration and bone turnover, while genetic and epigenetic profiling can elucidate individual patterns of susceptibility. Point-of-care ultrasound and portable diagnostic devices have expanded real-time imaging and functional assessment capabilities across diverse clinical settings. Artificial intelligence and machine learning algorithms now automate image interpretation, predict clinical outcomes, and enhance clinical decision support, complementing conventional clinical evaluations. Wearable sensors and mobile health technologies extend continuous monitoring beyond traditional healthcare environments, generating real-world data critical for dynamic disease management. However, standardization of diagnostic protocols, rigorous validation of novel methodologies, and thoughtful integration of multimodal data remain essential for translating technological advances into improved patient outcomes. Despite these advances, several key limitations constrain widespread clinical adoption. Imaging modalities lack standardized acquisition protocols and reference values, making cross-site comparison and clinical interpretation difficult. AI-driven diagnostic tools often suffer from limited external validation and transparency (“black-box” models), impacting clinicians’ trust and hindering regulatory approval. Molecular markers like CTX-II and PINP, though promising, show variability due to diurnal fluctuations and comorbid conditions, complicating their use in routine monitoring. Integration of multimodal data, especially across imaging, omics, and wearable devices, remains technically and logistically complex, requiring robust data infrastructure and informatics expertise not yet widely available in MSK clinical practice. Furthermore, reimbursement models have not caught up with many of these innovations, limiting access in resource-constrained healthcare settings. As these fields converge, musculoskeletal diagnostics methods are poised to evolve into a more precise, personalized, and patient-centered discipline, driving meaningful improvements in musculoskeletal health worldwide. Full article

Multiple myeloma is a hematologic malignancy characterized by the clonal proliferation of plasma cells within the bone marrow. The tumor microenvironment plays a crucial role in multiple myeloma pathogenesis, progression, and drug resistance. Traditional two-dimensional cell culture models have been instrumental in multiple myeloma research. However, they fail to recapitulate the complex in vivo bone marrow microenvironment, leading to limited predictive value for clinical outcomes. Three-dimensional cell culture models emerged as more physiologically relevant systems, offering enhanced insights into multiple myeloma biology. Scaffold-based systems (e.g., hydrogels, collagen, and Matrigel), scaffold-free spheroids, and bioprinted models have been developed to simulate the bone marrow microenvironment, incorporating key components like mesenchymal stromal cells, osteoblasts, endothelial cells, and immune cells. These models enable the functional assessment of cell adhesion-mediated drug resistance, cytokine signaling networks, and hypoxia-induced adaptations, which are often lost in 2D cultures. Moreover, 3D platforms demonstrated improved predictive value in preclinical drug screening, facilitating the evaluation of novel agents and combination therapies in a setting that better mimics the in vivo tumor context. Hence, 3D cultures represent a pivotal step toward bridging the gap between basic myeloma research and translational applications, supporting the development of more effective and patient-specific therapies. Full article

Objectives: The aim of this study was to develop AI-based predictive models to assess the risk of osteoporosis in postmenopausal women using panoramic radiographs (OPTs). Methods: A total of 301 panoramic radiographs (OPTs) from postmenopausal women were collected and labeled based on DXA-assessed bone mineral density. Of these, 245 OPTs from the Hospital of San Giovanni Rotondo were used for model training and internal testing, while 56 OPTs from the University of Parma served as an external validation set. A mandibular region of interest (ROI) was defined on each image. Predictive models were developed using classical radiomics, deep radiomics, and convolutional neural networks (CNNs), evaluated based on AUC, accuracy, sensitivity, and specificity. Results: Among the tested approaches, classical radiomics showed limited predictive ability (AUC = 0.514), whereas deep radiomics using DenseNet-121 features combined with logistic regression achieved the best performance in this group (AUC = 0.722). For end-to-end CNNs, ResNet-50 using a hybrid feature extraction strategy achieved the highest AUC in external validation (AUC = 0.786), with a sensitivity of 90.5%. While internal testing yielded high performance metrics, external validation revealed reduced generalizability, highlighting the challenges of translating AI models into clinical practice. Conclusions: AI-based models show potential for opportunistic osteoporosis screening from OPT images. Although the results are promising, particularly those obtained with deep radiomics and transfer learning strategies, further refinement and validation in larger and more diverse populations are essential before clinical application. These models could support the early, non-invasive identification of at-risk patients, complementing current diagnostic pathways. Full article

This study aimed to demonstrate the Convolutional Neural Network (CNN) algorithm’s efficiency in detecting alveolar bone loss using panoramic radiographs. The comparison was evaluated among 1874 pictures retrieved from an institution, from which the training set included 953 showing bone loss and 921 normal cases. A confusion matrix was performed for statistical analysis. The CNN method correctly identified 92 out of 100 bone loss cases and 89 out of 100 healthy cases. The model showed a sensitivity of 0.8327, a specificity of 0.8683, a precision of 0.8918, an accuracy of 0.8927, and an F1 score of 0.8615 in detecting bone loss. This study concluded that a faster CNN model may be used as an adjuvant technique to diagnose periodontal disease and alveolar bone loss using dental panoramic radiography images, thereby minimizing diagnostic effort, and saving assessment time. However, the execution of precisely detecting periodontal cases by fully automated AI models using panoramic radiographs appears imminent and needs clinical periodontal evaluation for definitive diagnosis. The suitability of this approach is supported by the sensitivity, specificity, accuracy, and F-measure, which showed satisfactory performance for classifying cases. Based on population and periodontal disease burden standpoint, the use of AI in diagnosing periodontal diseases may serve as an excellent surveillance method to classify alveolar bone loss. Monitoring a periodontal patient after treatment needs a wide area to cover by AI-based diagnostic modality. With AI as the future of dentistry, performance-based clinical usage of CNN models demands confirmed practical application by dentists. Full article

Objectives: This study investigated the potential of Hounsfield unit (HU) values obtained from computed tomography (CT) scans as predictors of mechanical complications (MCs) in patients undergoing long-segment spinal fusion involving the pelvis. Additionally, it identified a threshold HU value associated with an increased risk of MCs. Methods: We conducted a retrospective, multicenter review of patients who underwent long-segment spinal fusion involving the pelvis, with a minimum follow-up period of two years. Patients were categorized based on the presence or absence of postoperative MCs. Both preoperative and postoperative radiographic parameters were analyzed, and HU values were quantified from CT images. Logistic regression modeling was used to identify independent risk factors for MCs. Results: Among 129 patients, 33 (25.6%) developed MCs, including proximal and distal junctional failures, rod fractures, and cases necessitating re-operation. The HU values were significantly lower in the MC group, whereas conventional bone mineral density (BMD) measurements showed no significant difference. Global alignment parameters, such as the sagittal vertical axis (SVA) and global tilt (GT), were consistently higher in patients with MCs. Receiver operating characteristic analysis identified 131 HU as the optimal cut-off, yielding a sensitivity of 56.4% and a specificity of 69.7%. Multivariate analysis confirmed that lower HU values were independently associated with the occurrence of MCs. Conclusions: Lower HU values and larger radiological global alignment parameters are significant predictors of MCs in patients undergoing surgery for adult spinal deformity. These findings underscore the importance of CT-based quantitative assessments in preoperative planning. Full article