Search Results (17)

Background/Objectives: Degenerative joint diseases (DJDs) involve intricate molecular disruptions within bone, cartilage, and synovial tissues, often preceding overt radiographic changes. These tissues exhibit complex biomolecular architectures and their degeneration leads to microstructural disorganization and inflammation that are challenging to detect with conventional imaging techniques. This review aims to synthesize recent advances in imaging, computational modeling, and sequencing technologies that enable high-resolution, non-invasive characterization of joint tissue health. Methods: We examined advanced modalities including high-resolution MRI (e.g., T1ρ, sodium MRI), quantitative and dual-energy CT (qCT, DECT), and ultrasound elastography, integrating them with radiomics, deep learning, and multi-scale modeling approaches. We also evaluated RNA-seq, spatial transcriptomics, and mass spectrometry-based proteomics for omics-guided imaging biomarker discovery. Results: Emerging technologies now permit detailed visualization of proteoglycan content, collagen integrity, mineralization patterns, and inflammatory microenvironments. Computational frameworks ranging from convolutional neural networks to finite element and agent-based models enhance diagnostic granularity. Multi-omics integration links imaging phenotypes to gene and protein expression, enabling predictive modeling of tissue remodeling, risk stratification, and personalized therapy planning. Conclusions: The convergence of imaging, AI, and molecular profiling is transforming musculoskeletal diagnostics. These synergistic platforms enable early detection, multi-parametric tissue assessment, and targeted intervention. Widespread clinical integration requires robust data infrastructure, regulatory compliance, and physician education, but offers a pathway toward precision musculoskeletal care. Full article

Nowadays, recycling in pavement engineering is not a novelty. Utilization of recycled aggregates and other waste materials for the asphalt layers appeared as a well-established approach during the last decades, at least at a research level, in favor of preservation of natural resources, economical balance in road construction and reconstruction, and overall pavement sustainability. The focus on the asphalt layers does make sense based on the fact that these layers are to be more frequently replaced in the framework of periodical pavement maintenance or rehabilitation. Taking as a fact that mainly laboratory-scale studies and limited field trials have already proven the performance-based viability of using alternative materials in the asphalt layers, including waste plastic, waste glass, steel slag, waste tires in the form of rubber, reclaimed asphalt pavement (RAP), etc., this study tries to identify additional critical aspects and reasons why recycled materials are not consistently selected and uniformly applied during construction and reconstruction activities in real practice. A comprehensive discussion for interdisciplinary issues is provided with respect to (i) the challenge of comparing the performance of asphalt mixtures containing recycling materials with a reference condition status, related to mechanical testing, (ii) the aspect of recycled material availability versus peculiar conditions applied to some countries, related to socioeconomical issues, (iii) the unawareness of the actual lifecycle assessment of pavement structures with recycled mixtures, related to environmental assessment, and (iv) some legislative and health issues that could make pavement engineers reluctant to extensively use non-conventional materials. After a multi-parametric discussion, some useful remarks for fostering further research are given together with the ambition to bridge the gap between research and practice toward a greener future in pavement engineering. Full article

Early life stages are pivotal to the functioning and resilience of ecological systems, displaying heightened vulnerability to environmental changes and exposure to contaminants. Octocrylene (OC), an organic ultraviolet (UV) filter, and its breakdown product benzophenone (BP) are commonly found in aquatic environments, but their impact on keystone processes determining the success or failure of the early life stages of marine organisms remains underexplored. This study aims to assess the impacts of OC and BP at environmentally realistic concentrations (1, 10, and 100 µg.L⁻¹), over a 24 h exposure period, on larvae of the Pacific oyster (Magallana gigas). A multiparametric approach was employed, examining DNA integrity, embryo–larval development and swimming velocity. The results showed that DNA integrity and swimming velocity were not affected by OC or BP; however, both compounds increased developmental abnormalities in D-shaped larvae in all concentrations tested. Considering the robustness of morphological parameters, often assumed as irreversible, and their critical influence on larvae survival, these findings suggest that environmentally relevant concentrations of OC and BP may threaten the success of oyster larvae, potentially impacting the population’s long-term stability and, ultimately, raising ecological health issues. Full article

Background/Objectives: Kidney transplantation (kTx) is the preferred treatment for end-stage kidney disease. Limited evaluation of structural changes in transplanted kidneys hinders the timely prediction of disease progression and the implementation of treatment modifications. Protocol biopsies provide valuable insights but are invasive and carry risks of biopsy-related complications. This study investigates whether multiparametric magnetic resonance imaging (MRI), including T1 and T2 mapping and diffusion-weighted imaging (DWI), can predict kidney function and the progression of interstitial fibrosis and tubular atrophy (IF/TA) in the early post-transplant period. Methods: A prospective study was conducted at The Hospital of Lithuanian University of Health Sciences Kauno Klinikos from May 2022 to March 2024. Thirty-four patients receiving kidney transplants from deceased donors underwent baseline biopsies and post-transplant MRI scans. Follow-up assessments included kidney function evaluation, biopsies, and MRI scans at three months post-transplant. Results: Significant correlations were observed between MRI parameters and kidney function: T1 and apparent diffusion coefficient (ADC) corticomedullary differentiation (CMD) correlated with eGFR at discharge (r = −0.338, p = 0.05; r = 0.392, p = 0.022, respectively). Linear and logistic regression models demonstrated that post-transplant T1 and ADC CMD values significantly predicted kidney function at discharge. Furthermore, T1 CMD values measured 10–15 days post-transplant predicted IF/TA progression at three months post-kTx, with an area under the curve of 0.802 (95% CI: 0.616–0.987, p = 0.001) and an optimal cut-off value of −149.71 ms. The sensitivity and specificity were 0.818 and 0.273, respectively (Youden’s index = 0.545). T2 mapping was not predictive. Conclusions: This study highlights the potential immediate clinical utility of MRI-derived biomarkers, particularly ADC and T1 CMD, in centers equipped with advanced imaging capabilities as tools for assessing kidney function in the early post-transplant period. With an AUROC of 0.802, T1 CMD demonstrates strong discriminatory power for predicting IF/TA progression early in the post-transplant period. Full article

Background: Multiparametric MRI markers of liver health corrected T1 (cT1) and proton density fat fraction (PDFF) have shown utility in the management of various chronic liver diseases. We assessed the normal population reference range of both cT1 and PDFF in healthy child and adult volunteers without any known liver disease. Methods: A retrospective multi-centre pooled analysis of 102 child and young adult (9.1 years (6–18)) volunteers from three centres: Children’s Memorial Health Institute (N = 21), University Hospital Southampton (N = 28) and Hospital Infantil de Mexico (N = 53). Sex and ethnic differences were investigated for both cT1 and PDFF. Age effects were investigated with comparison to a pooled adult cohort from the UK Biobank (N = 500) and CoverScan (N = 71), covering an age range of 21 to 81 years. Results: cT1 values were normally distributed with a median of 748 ms (IQR: 725–768 ms; 2.5–97.5 percentiles: 683–820 ms). PDFF values followed a normal distribution with a median of 1.7% (IQR: 1.3–1.9%; 2.5–97.5 percentiles: 1–4.4%). There were no significant age and sex differences in cT1 and PDFF between children and young adults. No differences in cT1 and PDFF were found between ethnicities. Age comparisons showed statistically significant, but clinically negligible, cT1 (748 ms vs. 732 ms) and PDFF (2.4% vs. 1.9%) differences between paediatric and adult groups, respectively. Conclusions: Median healthy cT1 and PDFF reference ranges in children and young adults fall within the reported limits for normal of 800 ms and 5%, respectively. Full article

Genotoxic substances widely exist in the environment and the food supply, posing serious health risks due to their potential to induce DNA damage and cancer. Traditional genotoxicity assays, while valuable, are limited by insufficient sensitivity, specificity, and efficiency, particularly when applied to complex food matrices. This study introduces a multiparametric high-content analysis (HCA) for the detection of genotoxic substances in complex food matrices. The developed assay measures three genotoxic biomarkers, including γ-H2AX, p-H3, and RAD51, which enhances the sensitivity and accuracy of genotoxicity screening. Moreover, the assay effectively distinguishes genotoxic compounds with different modes of action, which not only offers a more comprehensive assessment of DNA damage and the cellular response to genotoxic stress but also provides new insights into the exploration of genotoxicity mechanisms. Notably, the five tested food matrices, including coffee, tea, pak choi, spinach, and tomato, were found not to interfere with the detection of these biomarkers under proper dilution ratios, validating the robustness and reliability of the assay for the screening of genotoxic compounds in the food industry. The integration of multiple biomarkers with HCA provides an efficient method for detecting and assessing genotoxic substances in the food supply, with potential applications in toxicology research and food safety. Full article

Advances in regenerative medicine have enabled the search for new solutions to current health problems in so far unexplored fields. Thus, we focused on cadaveric subcutaneous fat as a promising source of adipose-derived stem cells (ADSCs) that have potential to differentiate into different cell lines. With this aim, we isolated and characterized ADSCs from cadaveric samples with a postmortem interval ranging from 30 to 55 h and evaluated their ability to differentiate into chondrocytes or osteocytes. A commercial ADSC line was used as reference. Morphological and protein expression analyses were used to confirm the final stage of differentiation. Eight out of fourteen samples from patients were suitable to complete the whole protocol. Cadaveric ADSCs exhibited features of stem cells based upon several markers: CD29 (84.49 ± 14.07%), CD105 (94.38 ± 2.09%), and CD44 (99.77 ± 0.32%). The multiparametric assessment of differentiation confirmed the generation of stable lines of chondrocytes and osteocytes. In conclusion, we provide evidence supporting the feasibility of obtaining viable postmortem human subcutaneous fat ADSCs with potential application in tissue engineering and research fields. Full article

Liver biopsy remains the gold standard for the histological assessment of the liver. With clear disadvantages and the rise in the incidences of liver disease, the role of neoadjuvant chemotherapy in colorectal liver metastasis (CRLM) and an explosion of surgical management options available, non-invasive serological and imaging markers of liver histopathology have never been more pertinent in order to assess liver health and stratify patients considered for surgical intervention. Liver MRI is a leading modality in the assessment of hepatic malignancy. Recent technological advancements in multiparametric MRI software such as the LiverMultiScan^TM offers an attractive non-invasive assay of anatomy and histopathology in the pre-operative setting, especially in the context of CRLM. This narrative review examines the evidence for the LiverMultiScan^TM in the assessment of hepatic fibrosis, steatosis/steatohepatitis, and potential applications for chemotherapy-associated hepatic changes. We postulate its future role and the hurdles it must surpass in order to be implemented in the pre-operative management of patients undergoing hepatic resection for colorectal liver metastasis. Such a role likely extends to other hepatic malignancies planned for resection. Full article

This research presents a multiparameter approach to satellite component health assessment aimed at addressing the increasing demand for in-orbit satellite component health assessment. The method encompasses three key enhancements. Firstly, the utilization of the Wasserstein distance as an indicator simplifies the decision-making process for assessing the health of data distributions. This enhancement allows for a more robust handling of noisy sensor data, resulting in improved accuracy in health assessment. Secondly, the original limitation of assessing component health within the same parameter class is overcome by extending the evaluation to include multiple parameter classes. This extension leads to a more comprehensive assessment of satellite component health. Lastly, the method employs spectral clustering to determine the boundaries of different health status classes, offering an objective alternative to traditional expert-dependent approaches. By adopting this technique, the proposed method enhances the objectivity and accuracy of the health status classification. The experimental results show that the method is able to accurately describe the trends in the health status of components. Its effectiveness in real-time health assessment and monitoring of satellite components is confirmed. This research provides a valuable reference for further research on satellite component health assessment. It introduces novel and enhanced ideas and methodologies for practical applications. Full article

Prostate cancer (PCa) is a significant health concern for men worldwide, where early detection and effective diagnosis can be crucial for successful treatment. Multiparametric magnetic resonance imaging (mpMRI) has evolved into a significant imaging modality in this regard, which provides detailed images of the anatomy and tissue characteristics of the prostate gland. However, interpreting mpMRI images can be challenging for humans due to the wide range of appearances and features of PCa, which can be subtle and difficult to distinguish from normal prostate tissue. Deep learning (DL) approaches can be beneficial in this regard by automatically differentiating relevant features and providing an automated diagnosis of PCa. DL models can assist the existing clinical decision support system by saving a physician’s time in localizing regions of interest (ROIs) and help in providing better patient care. In this paper, contemporary DL models are used to create a pipeline for the segmentation and classification of mpMRI images. Our DL approach follows two steps: a U-Net architecture for segmenting ROI in the first stage and a long short-term memory (LSTM) network for classifying the ROI as either cancerous or non-cancerous. We trained our DL models on the I2CVB (Initiative for Collaborative Computer Vision Benchmarking) dataset and conducted a thorough comparison with our experimental setup. Our proposed DL approach, with simpler architectures and training strategy using a single dataset, outperforms existing techniques in the literature. Results demonstrate that the proposed approach can detect PCa disease with high precision and also has a high potential to improve clinical assessment. Full article

Photoplethysmography (PPG) is widely used to assess cardiovascular health. However, its usage and standardization are limited by the impact of variable contact force and temperature, which influence the accuracy and reliability of the measurements. Although some studies have evaluated the impact of these phenomena on signal amplitude, there is still a lack of knowledge about how these perturbations can distort the signal morphology, especially for multi-wavelength PPG (MW-PPG) measurements. This work presents a modular multi-parametric sensor system that integrates continuous and real-time acquisition of MW-PPG, contact force, and temperature signals. The implemented design solution allows for a comprehensive characterization of the effects of the variations in these phenomena on the contour of the MW-PPG signal. Furthermore, a dynamic DC cancellation circuitry was implemented to improve measurement resolution and obtain high-quality raw multi-parametric data. The accuracy of the MW-PPG signal acquisition was assessed using a synthesized reference PPG optical signal. The performance of the contact force and temperature sensors was evaluated as well. To determine the overall quality of the multi-parametric measurement, an in vivo measurement on the index finger of a volunteer was performed. The results indicate a high precision and accuracy in the measurements, wherein the capacity of the system to obtain high-resolution and low-distortion MW-PPG signals is highlighted. These findings will contribute to developing new signal-processing approaches, advancing the accuracy and robustness of PPG-based systems, and bridging existing gaps in the literature. Full article

Medicinal and herbal plants are abundant sources of phytochemicals, which are biologically active compounds with potential health benefits. The characterisation of phytochemicals has been the subject of many studies, but there is a lack of comprehensive assays to accurately assess the main phytochemical categories and their antioxidant properties. To address this, the present study has developed a multiparametric protocol comprising eight biochemical assays, which quantify the major categories of phytochemicals, including polyphenols, tannins and flavonoids, as well as their antioxidant and scavenging potential. The presented protocol offers several advantages over other methods, including higher sensitivity and significantly lower cost, making it a simpler and more affordable approach compared to commercial kits. The protocol was tested on two datasets with seventeen distinct herbal and medicinal plants, and the results demonstrated its effectiveness in accurately characterising the phytochemical composition of plant samples. The modular design of the protocol allows its adaptation to any spectrophotometric instrumentation, while all assays are simple to follow and require a minimum number of analytical steps. Full article

This research presents a novel data-based multi-parameter health assessment method to meet the growing need for the in-orbit health assessment of satellite components. This method analyzed changes in component health status by calculating distribution deviations and variation similarities in real-time operational data. Firstly, a single-parameter health state description method based on data distribution characteristics was presented. Secondly, the main health characteristic parameters were selected by mechanistic analysis and expert experience. The CRITIC method and the entropy weighting method were fused to assign reasonable weights and establish a multi-parameter component health assessment model. Then, the feasibility of a component health assessment algorithm based on data distribution characteristics was verified using real telemetry data from satellites. Finally, to verify the rationality of the presented health assessment algorithm, the results were compared with the pre-processed original data using empirical mode decomposition. The experimental results show that the method can accurately describe the change trend of the health status of the components. It proves that the method can be effectively used for the real-time health condition assessment and monitoring of satellite components. Full article

Wilson disease (WD) is a liver disorder characterized by improper copper metabolism. Although non-invasive tools are currently used to support diagnosis and management, this is still an area of unmet need, as patients present with a wide range of symptoms. Our aim was to investigate the potential utility of multiparametric magnetic resonance imaging (mpMRI) and quantitative magnetic resonance cholangiopancreatography (MRCP+) to support patient management. MRI examinations of 7 children and young adults aged 8–16 years (six at diagnosis) were performed alongside a standard of care clinical and histological examination. Images were quantitatively analyzed to derive metrics of liver (corrected T1 (cT1; fibro-inflammation), MR liver fat (proton density fat fraction; PDFF)), and biliary health (MRCP+). MRI–PDFF provided a more dynamic characterization of fat compared with that provided by ultrasound. Those with elevated histological scores of fibrosis, inflammation, and steatosis had elevated mpMRI values. MRCP+ managed to identify dilatations in the biliary tree which were not observed during the standard of care examination. mpMRI and MRCP+ metrics show early promise as markers to assess both liver and biliary health in Wilson disease. Investigations to understand and explore the utility of these markers are warranted and should be performed. Full article

In the field of electronic fetal health monitoring, computerized analysis of fetal heart rate (FHR) signals has emerged as a valid decision-support tool in the assessment of fetal wellbeing. Despite the availability of several approaches to analyze the variability of FHR signals (namely the FHRV), there are still shadows hindering a comprehensive understanding of how linear and nonlinear dynamics are involved in the control of the fetal heart rhythm. In this study, we propose a straightforward processing and modeling route for a deeper understanding of the relationships between the characteristics of the FHR signal. A multiparametric modeling and investigation of the factors influencing the FHR accelerations, chosen as major indicator of fetal wellbeing, is carried out by means of linear and nonlinear techniques, blockwise dimension reduction, and artificial neural networks. The obtained results show that linear features are more influential compared to nonlinear ones in the modeling of HRV in healthy fetuses. In addition, the results suggest that the investigation of nonlinear dynamics and the use of predictive tools in the field of FHRV should be undertaken carefully and limited to defined pregnancy periods and FHR mean values to provide interpretable and reliable information to clinicians and researchers. Full article