Search Results (38)

Objective: This study proposes a novel deep learning approach for enhancing low-dose bone scintigraphy images using an Enhanced Convolutional Autoencoder (ECAE), aiming to reduce patient radiation exposure while preserving diagnostic quality, as assessed by both expert-based quantitative image metrics and qualitative evaluation. Methods: A supervised learning framework was developed using real-world paired low- and full-dose images from 105 patients. Data were acquired using standard clinical gamma cameras at the Nuclear Medicine Department of the University General Hospital of Alexandroupolis. The ECAE architecture integrates multiscale feature extraction, channel attention mechanisms, and efficient residual blocks to reconstruct high-quality images from low-dose inputs. The model was trained and validated using quantitative metrics—Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM)—alongside qualitative assessments by nuclear medicine experts. Results: The model achieved significant improvements in both PSNR and SSIM across all tested dose levels, particularly between 30% and 70% of the full dose. Expert evaluation confirmed enhanced visibility of anatomical structures, noise reduction, and preservation of diagnostic detail in denoised images. In blinded evaluations, denoised images were preferred over the original full-dose scans in 66% of all cases, and in 61% of cases within the 30–70% dose range. Conclusion: The proposed ECAE model effectively reconstructs high-quality bone scintigraphy images from substantially reduced-dose acquisitions. This approach supports dose reduction in nuclear medicine imaging while maintaining—or even enhancing—diagnostic confidence, offering practical benefits in patient safety, workflow efficiency, and environmental impact. Full article

Background/Objectives: Multiple myeloma is a malignancy of plasma cells detected in the bone marrow, inducing symptoms like anemia, hypercalcemia, renal problems, and bone fractures in multiple myeloma patients, affecting their quality of life. The bone marrow microenvironment plays a crucial role in the prognosis and progression of the disease. The purpose of this study was to examine the relationship between the percentages of the major cell populations of the bone marrow, including immune cells, and physical function/quality of life in multiple myeloma patients after first-line treatment. Methods: Twenty-one multiple myeloma patients (N = 14 men, N = 7 women) participated in the study after completing first-line treatment. Bone marrow and blood samples were taken one hundred days after transplantation, while physical function (6 min walking test, handgrip test, maximal aerobic power, and isometric strength), health-related quality of life (QLQ-C30), and body composition (DXA) were assessed 2–5 days later. Flow cytometry was used to assess the percentages of plasma cells, mast cells, B cells (total, precursors, naïve, and memory), T cells (total, CD27− and CD27+), NK/NKT cells (total, CD27− and CD27+), eosinophils, monocytes, neutrophils, myeloid progenitors, erythroblasts, and erythroid progenitors, expressed as percentages of total nucleated cells of the bone marrow. Results: The percentage of CD27+ NK/NKT cells was correlated with five parameters of the quality of life questionnaire: physical function (r = 0.78, p = 0.005), role functioning (r = 0.69, p = 0.020), fatigue (r = −0.86, p = 0.000), pain (r = 0.68, p = 0.021), and dyspnea (r = −0.80, p = 0.003). Conclusions: In conclusion, stronger immune surveillance in the bone marrow from CD27+ NK/NKT cells is correlated with better quality of life in multiple myeloma patients. Full article

Smoldering multiple myeloma (SMM) represents an intermediate stage between monoclonal gammopathy of undetermined significance and symptomatic multiple myeloma (MM), with a significant risk of progression. Bone disease is a key feature of MM, often marking the transition to symptomatic disease. Whole-body low-dose computed tomography (WBLDCT) is an easily accessible and highly sensitive imaging modality for detecting osteolytic lesions, providing an advantage over conventional skeletal surveys. In our real-world cohort, we prospectively evaluated the role of WBLDCT in the early identification of bone progression in patients with SMM based on the recommendations by the International Myeloma Working Group. A total of 113 patients were monitored with annual WBLDCT assessments; 36.3% progressed to symptomatic MM, with 9.7% progressing solely with bone lesions, highlighting the importance of early detection. Therefore, integrating annual WBLDCT assessments into clinical practice for SMM patients is essential to facilitate treatment strategies and prevent disease-related complications. This is even more important in the upcoming era of early treatment initiation for patients with SMM at high risk for progression. Full article

Central Nervous System (CNS) tumors represent a significant public health concern due to their high morbidity and mortality rates. Magnetic Resonance Imaging (MRI) has emerged as a critical non-invasive modality for the detection, diagnosis, and management of brain tumors, offering high-resolution visualization of anatomical structures. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown potential in augmenting MRI-based diagnostic accuracy for brain tumor detection. In this study, we evaluate the diagnostic performance of six fundamental MRI sequences in detecting tumor-involved brain slices using four distinct CNN architectures enhanced with transfer learning techniques. Our dataset comprises 1646 MRI slices from the examinations of 62 patients, encompassing both tumor-bearing and normal findings. With our approach, we achieved a classification accuracy of 98.6%, underscoring the high potential of CNN-based models in this context. Additionally, we assessed the performance of each MRI sequence across the different CNN models, identifying optimal combinations of MRI modalities and neural networks to meet radiologists’ screening requirements effectively. This study offers critical insights into the integration of deep learning with MRI for brain tumor detection, with implications for improving diagnostic workflows in clinical settings. Full article

Background/Objectives: Monoclonal gammopathies, such as Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM), are conditions marked by the overproduction of specific monoclonal proteins. Patients with these conditions are known to have a higher risk of fractures compared to the general population, yet there are no established guidelines for assessing or managing their skeletal health. The Trabecular Bone Score (TBS), which can be calculated from DXA images of the lumbar spine, provides additional insights into bone microarchitecture. Methods: This study aimed to determine whether TBS can serve as a supplementary tool in assessing bone loss in MGUS and SMM patients. Conducted from 2020 to 2023, the study involved 148 participants—74 diagnosed with a myeloma precursor state and 74 healthy controls—who underwent simultaneous DXA and TBS measurements. Results: The results indicated a weak positive correlation (R = 0.405) between DXA and TBS T-scores, suggesting that other factors may influence the measurements. When analyzed separately, the correlations remained weak for both MGUS (R = 0.250) and SMM (R = 0.485). Interestingly, discrepancies were noted in T-score classifications; for instance, a patient classified as normal via DXA could be deemed osteopenic or osteoporotic with TBS. Conclusions: Overall, the findings suggest that incorporating TBS alongside DXA can enhance the accuracy of bone density assessments, facilitating earlier diagnosis and treatment initiation for osteoporosis in asymptomatic patients with monoclonal gammopathies. Full article

Immunocompromised patients with hematologic diseases may experience life-threatening infections with rather uncommon manifestations. Laryngitis has been described as a potential infection in such vulnerable patients and may result in major complications, ranging from impending airway obstruction to total laryngeal necrosis. Immediate laryngoscopy is of paramount importance, as it provides quantification of laryngeal edema and evidence of necrosis. Documentation of the causative pathogen is usually feasible through tissue culture. In the literature, 14 cases of necrotizing laryngitis have already been published. Here, we present the case of a 38-year-old male with a recent diagnosis of multiple myeloma, who received the first cycle of therapy a few days before admission. The patient presented with neutropenic fever, diarrhea, and multiple organ dysfunction. His course was complicated with hemophagocytic lymphohistiocytosis and stridor. A diagnosis of necrotizing laryngitis attributed to Acinetobacter baumannii invasion of the larynx was established. This manuscript highlights that the management of patients with hematologic disease and necrotizing laryngitis should be coordinated in highly specialized centers and clinicians should have a high level of clinical suspicion and act promptly. Full article

Background: Survival rates of cancer patients have increased globally and across age groups. Challenges arising from craniofacial growth-development disturbances and dental abnormalities might warrant modifications to standard orthodontic pathways of care. Objective: The aim of this study was to systematically summarize and critically assess the available literature regarding the characteristics of orthodontic treatment in cancer survivors. Materials and Methods: A systematic search was conducted in seven databases for studies on malignant tumor survivors having undergone orthodontic intervention with fixed appliances following cancer treatment up to August 2023. The outcomes of interest included quantitative data regarding various characteristics of orthodontic treatment and the post-treatment period. The risk of bias was assessed individually with the Newcastle-Ottawa scale. Results: Out of 347 records, 4 cohort studies were eventually included in the qualitative synthesis. Leukemia was the most common malignancy type, with treatment involving mainly chemotherapy and/or radiotherapy. The duration of orthodontic treatment in cancer survivors varied. Occlusal results, quality of life, and satisfaction were comparable to healthy peers. However, in some survivors’ groups, treatment was shorter and the final results were compromised. Root resorption and oral mucositis were reported among the treated cancer survivors. Reduced occlusal outcome stability during the retention period was also reported. Conclusions: Overall, the duration of orthodontic treatment varied among cancer survivors. The occlusal results achieved were similar to those of their healthy peers, though potentially less stable. Patient-reported outcomes did not differ significantly between cancer survivors and healthy individuals treated orthodontically. Full article

An easy-to-implement and cost-effective Fiber Bragg Grating (FBG) sensor interrogation technique based on a ring Erbium-Doped Fiber Laser (EDFL) topology is proposed and experimentally assessed. The FBG sensor is part of the EDFL cavity and must have a central wavelength located within the linear region of the EDF’s amplified spontaneous emission (ASE) spectrum, which occurs at between 1530 and 1540 nm. In this manner, the wavelength-encoded response of the FBG under strain is converted to a linear variation in the laser output power, removing the need for spectrum analysis as well as any limitations from the use of external edge-filtering components. In addition, the laser linewidth is significantly reduced with respect to the FBG bandwidth, thus improving the resolution of the system, whereas its sensitivity can be controlled through pumping power. The performance of the system has been characterized by modeling and experiments for EDFs with different lengths, doping concentrations, and pumping power levels. The influence of mode-hopping in the laser cavity on the resolution and accuracy of the system has also been investigated. Full article

Around 65% of the mitigation needed for the targeted net-zero carbon aviation emissions in 2050 is expected to come from Sustainable Aviation Fuels (SAFs). In this study, an alternative gasification-driven Biomass-to-Liquid (BtL) concept for the production of SAFs is introduced and evaluated. In particular, a fuel synthesis scheme based on the double-stage fermentation of the produced syngas (syngas → acetic acid → TAGs) is assessed instead of the conventional Fischer-Tropsch (FT) or Alcohol-to-Jet (AtJ) synthesis. The objective of the present work is the techno-economic evaluation of a large-scale (200 MWth) replication of the mentioned BtL concept, whose performance has been simulated in Aspen Plus^TM (V.11) with reasonable upscaling considerations and models validated at a pilot scale. The estimated baseline Total Capital Investment (TCI) of €577 million lies in the typical range of €500–700 million that many recent techno-economic studies adopt for gasification-driven BtL plants of similar capacity, while the estimated annual operating costs of €50 million correspond to a 15–40% OpEx reduction compared to such plants. A discounted cash flow analysis was carried out, and a baseline Minimum Jet Selling Price (MJSP) equal to 1.83 €/L was calculated, while a range of 1.38–2.27 €/L emerged from the sensitivity analysis. This study sets the biological conversion of gasification-derived syngas into triglycerides (TAGs) as a promising alternative route for the production of SAFs. In general, gasification-driven BtL pathways, led by the relatively mature FT and AtJ technologies, are capable of thriving in the coming years based on their capability of advanced feedstock flexibility. Full article

It is not known whether different gait modes, or movement patterns, at the same speed elicit differences in muscle oxygen oxygenation, expressed as muscle oxygen saturation (SmO₂). Thus, the aim of this study was to compare the oxygenation of two leg muscles (vastus lateralis and gastrocnemius medialis), as well as the heart rate, respiratory gases, and blood lactate between two gait modes (walking and running) of the same speed and duration. Ten men walked and ran for 30 min each at 7 km/h in a random, counterbalanced order. SmO₂, heart rate, and respiratory gases were monitored continuously. Blood lactate was measured at rest, at the end of each exercise, and after 15 min of recovery. Data were analyzed by two-way (gait mode × time) or three-way (gait mode × muscle × time) ANOVA, as applicable. Heart rate and oxygen consumption were higher when running compared to walking. SmO₂ was lower during exercise compared to rest and recovery, in gastrocnemius medialis compared to vastus lateralis, and in running compared to walking. Blood lactate increased during exercise but did not differ between gait modes. In conclusion, running caused higher deoxygenation in leg muscles (accompanied by higher whole-body oxygen uptake and heart rate) than walking at the same speed (one that was comfortable for both gait modes), thus pointing to a higher internal load despite equal external load. Thus, preferring running over walking at the same speed causes higher local muscle deoxygenation, which may be beneficial in inducing favorable training adaptations. Full article

The use of a two-phase decanter (TwPD) for olive-oil extraction produces wastes and byproducts (a small volume of water from oil washing, olive leaves from the defoliator, and a high moisture pomace which can be destoned) that contain valuable bioactive compounds, such as phenolics and/or triterpenic acids. So far, there is no (water) or limited information (leaves and the destoned pomace fraction) on their content of bioactives, especially triterpenic acids. To contribute to the characterization of such streams from cultivars of international interest, in the present study, samples obtained from five mills from the region of Laconia (from one or two harvests) in Greece, where Koroneiki cv dominates, were screened for phenols and/or triterpenic acids. The leaves and pomace were dried at two temperatures (70 °C and/or 140 °C), and the pomace was also destoned before analysis. The liquid wastes contained low amounts of total (TPC) phenols (<140 mg gallic acid/L), hydroxytyrosol (<44 mg/L), and tyrosol (<33 mg/L). The olive leaves varied widely in TPC (12.8–57.4 mg gallic acid/g dry leaf) and oleuropein (0.4–56.8 mg/g dry leaf) but contained an appreciable amount of triterpenic acids, mainly oleanolic acid (~12.5–31 mg/g dry leaf, respectively). A higher drying temperature (140 vs. 70 °C) affected rather positively the TPC/oleuropein content, whereas triterpenic acids were unaffected. The destoned pomace TPC was 15.5–22.0 mg gallic acid/g dw, hydroxytyrosol 3.9–5.6 mg/g dw, and maslinic 5.5–19.3 mg/g dw. Drying at 140 °C preserved better its bioactive phenols, whereas triterpenic acids were not influenced. The present findings indicate that TwPD streams may have a prospect as a source of bioactives for added-value applications. Material handling, including drying conditions, may be critical but only for phenols. Full article

Raman spectroscopy (RS) techniques are attracting attention in the medical field as a promising tool for real-time biochemical analyses. The integration of artificial intelligence (AI) algorithms with RS has greatly enhanced its ability to accurately classify spectral data in vivo. This combination has opened up new possibilities for precise and efficient analysis in medical applications. In this study, healthy and cancerous specimens from 22 patients who underwent open colorectal surgery were collected. By using these spectral data, we investigate an optimal preprocessing pipeline for statistical analysis using AI techniques. This exploration entails proposing preprocessing methods and algorithms to enhance classification outcomes. The research encompasses a thorough ablation study comparing machine learning and deep learning algorithms toward the advancement of the clinical applicability of RS. The results indicate substantial accuracy improvements using techniques like baseline correction, L2 normalization, filtering, and PCA, yielding an overall accuracy enhancement of 15.8%. In comparing various algorithms, machine learning models, such as XGBoost and Random Forest, demonstrate effectiveness in classifying both normal and abnormal tissues. Similarly, deep learning models, such as 1D-Resnet and particularly the 1D-CNN model, exhibit superior performance in classifying abnormal cases. This research contributes valuable insights into the integration of AI in medical diagnostics and expands the potential of RS methods for achieving accurate malignancy classification. Full article

The objective of the present study was to evaluate the effects of an olive leaf extract obtained with an up-to-date laboratory method, when supplemented at different levels in laying hens’ diets, on egg quality, egg yolk antioxidant parameters, fatty acid content, and liver pathology characteristics. Thus, 96 laying hens of the ISA-Brown breed were allocated to 48 experimental cages with two hens in each cage, resulting in 12 replicates per treatment. Treatments were: T1 (Control: basal diet); T2 (1% olive leaf extract); T3 (2.5% olive leaf extract); T4 (Positive control: 0.1% encapsulated oregano oil). Eggshell weight and thickness were improved in all treatments compared to the control, with T2 being significantly higher till the end of the experiment (p < 0.001). Egg yolk MDA content was lower for the T2 and T4 groups, while total phenol content and Haugh units were greater in the T2. The most improved fatty acid profile was the one of T3 yolks. The α-tocopherol yolk content was higher in all groups compared to T1. No effect was observed on cholesterol content at any treatment. Based on the findings, it can be inferred that the inclusion of olive leaf extract at a concentration of 1% in the diet leads to enhancements in specific egg quality attributes, accompanied by an augmentation of the antioxidant capacity. Full article

Forest fires can result in loss of life, damage to infrastructure, and adverse environmental impacts. This study showcases an integrated approach for conducting high-detail fire risk assessment and supporting strategic planning and management of fire events in peri-urban areas that are susceptible to forest fires. The presented methodology encompasses fire hazard modeling, vulnerability and exposure assessment, and in situ observations. Numerous fire hazard scenarios were tested, simulating the spatiotemporal spread of fire events under different wind characteristics. The vulnerability of the studied areas was assessed by combining population data (density and age) and building characteristics, while the exposure parameter employed land value (EUR/m²) as an indicator for qualitatively estimating potential economic effects in the study area. Field campaigns facilitated the identification and recording of critical areas and points, including high-risk buildings and population gathering areas, which subsequently informed the mitigation and fire management planning suggestions. Moreover, field recordings acted as an iterative process for validating and updating the fire risk maps. This research work utilizes state-of-the-art techniques to achieve an analysis of fire risk at a building-block level. Overall, the study presents an applied and end-to-end methodology for effectively addressing forest fire risk in peri-urban areas. Full article

Floods are natural hazards with negative environmental and socioeconomic impacts at a local and regional level. In addition to human lives, facilities, and infrastructure, flooding is a potential threat to archaeological sites, with all the implications for the cultural heritage of each country. Technological developments of recent years, particularly concerning geospatial technologies (GIS, Remote Sensing, etc.), have brought novel advantages to hydrological modelling. This study uses geoinformatics to quantify flood hazard assessment. The study area is the ungauged torrent of Kladeos River, located in Peloponnese, Greece. Geomorphological analysis combined with hydrological modelling were performed in a GIS-based environment in order to study the hydrological behavior of the Kladeos River basin. The hydrological analysis was carried out with rainfall data and hypothetical storms using a 5 × 5 m digital terrain model. The quantitative features of the catchment were calculated in order to determine its susceptibility to flooding. The hydro-morphometric analysis revealed stream order anomalies in the drainage network which, combined with the morphology of its upper and lower parts, enhance the possibility of flood events. The primary results indicated that there is an increased possibility of extensive flooding in the archaeological site, depending on the severity of the rainfall, since the basic geomorphological characteristics favor it. The proposed methodology calculates parameters such as flow rate, flow velocity, etc., in order to measure and quantify flood hazard and risks in the area of interest. Full article