Search Results (1,610)

Background/Objectives: This paper presents a medical image analysis application designed to facilitate advanced edge detection and fuzzy processing techniques within an intuitive, modular graphical user interface. Methods: Key functionalities include classical edge detection, Ant Colony Optimization (ACO)-based edge extraction, and fuzzy edge generation, which offer improved boundary representation in images where uncertainty and soft transitions are prevalent. Results: One of the main novelties in contrast to the initial innovative Medical Image Analyzer, iMIA, is the fact that the system includes fuzzy C-means clustering to support tissue classification and unsupervised segmentation based on pixel intensity distribution. The application also features an interactive zooming and panning module with the option to overlay edge detection results. As another novelty, fuzzy performance metrics were added, including fuzzy false negatives, fuzzy false positives, fuzzy true positives, and the fuzzy index, offering a more comprehensive and uncertainty-aware evaluation of edge detection accuracy. Conclusions: The application executable file is provided at no cost for the purposes of evaluation and testing. Full article

Sporadic colorectal cancer (CRC), the third leading cause of cancer-related death globally, arises through a continuum from normal tissue to adenomas, progressing from low-grade (LGD) to high-grade dysplasia (HGD); yet, the early epigenetic drivers of this transition remain unclear. To investigate these events, we profiled LGD and HGD adenomas using EM-seq, and identified a consensus differential methylation signature (DMS) of 626 regions through two independent bioinformatics pipelines. This signature effectively distinguished LGD from HGD in both tissue and plasma-derived cell-free DNA (cfDNA), highlighting specific methylation patterns. Functional annotation indicated enrichment for regulatory elements associated with transcription factor activity and cell signaling. Applying the DMS to the TCGA CRC dataset revealed three tumor subtypes with increasing hypermethylation and one normal cluster. The most hypermethylated subtype exhibited poor survival, high mutation burden, and disrupted transcriptional networks. While overlapping with classical CpG Island Methylator Phenotype (CIMP) categories, the DMS captured a broader spectrum of methylation alterations. These findings suggest that the DMS captures functionally relevant, antecedent epigenetic alterations in CRC progression, enabling the robust stratification of dysplasia severity and tumor subtypes. This signature holds promise for enhancing preclinical detection and molecular classification, and warrants further evaluation in larger prospective cohorts. Full article

Background/Objectives: The major question that confronts a pathologist when evaluating a lymph node biopsy is whether the process is benign or malignant, and the differential diagnosis between follicular lymphoma and reactive lymphoid tissue can be challenging. Methods: This study designed a convolutional neural network based on ResNet architecture to classify a large series of 221 cases, including 177 follicular lymphoma and 44 reactive lymphoid tissue/lymphoid hyperplasia, which were stained with hematoxylin and eosin (H&E). Explainable artificial intelligence (XAI) methods were used for interpretability. Results: The series included 1,004,509 follicular lymphoma and 490,506 reactive lymphoid tissue image-patches at 224 × 244 × 3, and was partitioned into training (70%), validation (10%), and testing (20%) sets. The performance of the training (training and validation sets) had an accuracy of 99.81%. In the testing set, the performance metrics achieved an accuracy of 99.80% at the image-patch level for follicular lymphoma. The other performance parameters were precision (99.8%), recall (99.8%), false positive rate (0.35%), specificity (99.7%), and F1 score (99.9%). Interpretability was analyzed using three methods: grad-CAM, image LIME, and occlusion sensitivity. Additionally, hybrid partitioning was performed to avoid information leakage using a patient-level independent validation set that confirmed high classification performance. Conclusions: Narrow artificial intelligence (AI) can perform differential diagnosis between follicular lymphoma and reactive lymphoma tissue, but it is task-specific and operates within limited constraints. The trained ResNet convolutional neural network (CNN) may be used as transfer learning for larger series of cases and lymphoma diagnoses in the future. Full article

This review investigates recent developments in cardiac mechano-electrical-fluid interaction (MEFI) modeling, with a focus on multiphysics simulation platforms and digital twin frameworks developed between 2015 and 2025. The purpose of the study is to assess how computational modeling methods—particularly finite element and immersed boundary techniques, monolithic and partitioned coupling schemes, and artificial intelligence (AI)-enhanced surrogate modeling—capture the integrated dynamics of cardiac electrophysiology, tissue mechanics, and hemodynamics. The goal is to evaluate the translational potential of MEFI models in clinical applications such as cardiac resynchronization therapy (CRT), arrhythmia classification, atrial fibrillation ablation, and surgical planning. Quantitative results from the literature demonstrate <5% error in pressure–volume loop predictions, >0.90 F1 scores in machine-learning-based arrhythmia detection, and <10% deviation in myocardial strain relative to MRI-based ground truth. These findings highlight both the promise and limitations of current MEFI approaches. While recent advances improve physiological fidelity and predictive accuracy, key challenges remain in achieving multiscale integration, model validation across diverse populations, and real-time clinical applicability. The review concludes by identifying future milestones for clinical translation, including regulatory model certification, standardization of validation protocols, and integration of patient-specific digital twins into electronic health record (EHR) systems. Full article

Cellulite is a widespread aesthetical dermatological condition affecting a significant proportion of postpubertal women, characterized by dimpled skin, primarily on the thighs, buttocks, and hips, which has an important psychological impact. Cellulite, also called lipodystrophy or oedematosclerotic panniculitis, causes an aesthetic change in the skin that affects the epidermis, dermis, hypodermis and subcutaneous fat in different ways. The aim of the present prospective study research was to evaluate the efficacy of electromagnetic field and negative pressure in the treatment of cellulite. Methods: A total of 35 women with an average age of 40, ranging from 18 to 50 (mean 32.2 ± 7.48), with a body mass index between 18.5 and 26.9 (mean 22 ± 3.01), were enrolled in this study. The degree of cellulite of the patients was assessed clinically using the Cellulite Severity Scale (CSS) and Nürnberger–Müller classification. All patients received one session per week for a total 12 treatment sessions with Bi-one^® LifeTouchTherapy medical device (Expo Italia Srl—Florence—Italy), which generates a combination of vacuum and electromagnetic fields (V-EMF). Total treatment time was approximately 20–30 min per patient. The GAIS score, Cellulite Severity Scale (CSS) and Nürnberger–Müller classification for cellulite was evaluated 1 month after the 12 treatments with LifeTouchTherapy. Results: A statistical difference was recorded in cellulite improvement by visual analog scale (VAS) and global aesthetic improvement scale (GAIS). Conclusions: The results of the present prospective clinical study show the efficacy and safety of Bi-one^® LifeTouchTherapy in the treatment of cellulite. Electromagnetic fields combined with negative pressure therapy promote tissue regeneration and reduce fibrosis, which results in visible cosmetic improvements of cellulite. Full article

Background: Fish escape events from aquaculture facilities are increasing and pose significant ecological, economic, and traceability concerns. Accurate methods to differentiate between wild, cultured, and escaped fish are essential for fishery management and seafood authentication. Methods: This study analyzed muscle tissue from Sparus aurata, Dicentrarchus labrax, and Argyrosomus regius using a multiomics approach. Heavy metals were quantified by ICP-MS, fatty acid profiles were assessed via GC-MS, and metabolomic and lipidomic signatures were identified using 1H NMR spectroscopy. Multivariate statistical models (MDS and PLS-LDA) were applied to classify fish origins. Results: Wild seabream showed significantly higher levels of arsenic (9.5-fold), selenium (3.5-fold), and DHA and ARA fatty acids (3.2-fold), while cultured fish exhibited increased linoleic and linolenic acids (6.5-fold). TMAO concentrations were up to 5.3-fold higher in wild fish, serving as a robust metabolic biomarker. Escaped fish displayed intermediate biochemical profiles. Multivariate models achieved a 100% classification accuracy across species and analytical techniques. Conclusions: The integration of heavy metal analysis, fatty acid profiling, and NMR-based metabolomics enables the accurate differentiation of fish origin. While muscle tissue provides reliable biomarkers relevant to human exposure, future studies should explore additional tissues such as liver and gills to improve the resolution of traceability. These methods support seafood authentication, enhance aquaculture traceability, and aid in managing the ecological impacts of escape events. Full article

Background: Endometriosis is a chronic gynecological condition marked by ectopic endometrial-like tissue, leading to inflammation, pain, and infertility. Diagnosis is often delayed by up to 10 years. Identifying non-invasive biomarkers could facilitate earlier detection. MicroRNAs, known for their stability in biological fluids and role in disease processes, have emerged as potential diagnostic tools. This pilot study investigated whether serum miRNA profiling can differentiate endometriosis from other causes of chronic pelvic pain. Methods: Serum samples from 52 patients (36 with laparoscopically confirmed endometriosis and 16 controls) treated for chronic pelvic pain at a University Endometriosis Centre were analyzed. High-throughput miRNA sequencing was performed. Feature selection reduced 4285 miRNAs to the 20 most informative MiRNAs. Machine learning models, including logistic regression, decision tree, random forest, and support vector machine, were trained and evaluated. Results: Among the tested machine learning models, support vector machine achieved the best overall performance (accuracy 0.71, precision 0.80), while logistic regression and random forest showed the highest AUC values (0.84 and 0.81, respectively), indicating strong diagnostic potential of serum miRNA profiling. Conclusions: This study demonstrates the feasibility of using serum miRNA profiling combined with machine learning for the non-invasive classification of endometriosis. The identified miRNA signature shows strong diagnostic potential and could contribute to earlier and more accurate detection of the disease. Full article

Introduction/Objectives: Osteoarthritis (OA) is a chronic systemic disease that affects the entire array of joint structures. It is one of the most common chronic, socially significant diseases, associated with a decline in the quality of life of patients and constantly increasing the cost of treatment. Clinical trial outcomes are largely inconclusive, and OA remains one of the few musculoskeletal diseases without an established disease-modifying therapy. One potential explanation is the use of ineffective tools for OA classification, patient stratification, and the assessment of disease progression. There is growing interest in musculoskeletal ultrasonography (MSK US), as it enables the dynamic visualization of the examined structures and gives information about both inflammatory and structural changes that have occurred. Determining the leading ultrasound phenotype, which depends on the most damaged tissue at a given time (bone, cartilage, synovial membrane, joint capsule, ligaments, tendons, menisci, etc.), can rationalize therapy use by selecting patients more suitable for specific treatments. This article aims to evaluate and summarize the potential of MSK US in the process of determining the clinical phenotype of OA and to emphasize the importance of this imaging modality in evaluating further therapeutic strategies. Method: A single-center prospective study conducted in the period of September 2023–June 2024 enrolled 259 consecutive patients with proven OA. The statistical program Minitab version 22.2.1 (2025) was used to analyze the data. The predominant and secondary phenotypes were tabulated for each OA localization and were presented numerically and as relative proportions (%). The rate of the most frequently occurring phenotypes was compared against that of the less frequent ones through paired z-tests. The initially acceptable type I error was set at 5%; it was further adjusted for the number of comparisons (Bonferroni). Results: The most frequent and predominant US phenotype for patients with knee OA was intra-articular effusion (n = 47, 37.90%). It was significantly higher compared to the rest of the US phenotypes: synovial proliferation (n = 22, 17.70%; p < 0.001), cartilage destruction (n = 26, 21%; p = 0.001), altered subchondral bone (n = 8, 6.50%; p < 0.001), extra-articular soft tissue changes (n = 12, 9.70%; p < 0.001), crystal deposits (n = 6, 4.8%; p < 0.001), and post-traumatic (n = 3, 2.40%; p < 0.001). The most common US phenotype for hip OA was altered subchondral bone (n = 32, 47.1%), with significant differences from intra-articular effusion (n = 12, 17.60%; p = 0.001), synovial proliferation (n = 5, 7.40; p = 0.001), cartilage destruction (n = 12, 17.60%; p = 0.001), extra-articular soft tissue changes (n = 3, 4.40%; p = 0.001), crystal deposits (n = 3, 4.40%; p = 0.001), and post-traumatic (n = 0). Altered subchondral bone was also the leading US phenotype for hand OA (n = 31, 55.40%), with significant differences compared to intra-articular effusion (n = 1, 1.80%; p < 0.001), synovial proliferation (n = 7, 12.50%; p < 0.001), cartilage destruction (n = 11, 19.60%; p < 0.001), extra-articular soft tissue changes (n = 2, 3.60%; p < 0.001), crystal deposits (n = 3, 5.40%; p < 0.001), and post-traumatic (n = 1, 1.80%, p < 0.001). For shoulder OA, extra-articular soft tissue changes were the most frequent (n = 8, 46.20%), followed by post-traumatic (n = 4, 30.70%), as the rate of both phenotypes was significantly higher compared to that of intra-articular effusion (n = 0), synovial proliferation (n = 0), cartilage destruction (n = 1, 7.70%; p = 0.003), and crystal deposits (n = 0). Conclusions: The therapeutic approach for OA is a dynamic and intricate process, for which the type of affected joint and the underlying pathogenetic mechanism at a specific stage of the disease’s evolution is essential. MSK US is one of the options for the clinical phenotyping of OA. Some of the suggested ultrasound subtypes may serve as the rationale for selecting a particular treatment. Full article

Background and Objectives: Body composition changes with aging and menopause, often leading to increased adiposity and a shift in fat distribution. While BMI is commonly used in clinical practice, it does not accurately reflect fat mass or distribution. This study aims to evaluate age-related changes in both total and regional adiposity using DXA-derived indices in Korean women aged ≥ 40 years and to assess the limitations of BMI-based obesity classification. Materials and Methods: This retrospective multicenter study analyzed the DXA scans and clinical records of 914 Korean women aged 40–80 years who attended menopause clinics across multiple institutions between 2018 and 2021. We analyzed five adiposity indices: body mass index (BMI), total body fat percentage (TB%F), fat mass index (FMI), visceral adipose tissue (VAT) area, and android-to-gynoid (A/G) fat ratio. Excess adiposity was defined as BMI ≥ 23 kg/m², TB%F ≥ 40%, FMI ≥ 9 kg/m², VAT > 100 cm², or A/G ratio > 1.0. Age group comparisons were made using ANOVA, and misclassification was assessed by comparing BMI with other indices. Results: Mean BMI increased with age, peaking in the 60s before declining in the 70s. TB%F and FMI peaked in the 50s, while VAT and A/G ratio increased continuously with age. Excess adiposity was found in 41.9% of women by TB%F, 40.5% by FMI, and 59.4% by VAT in the 70s. Notably, 22% of women with normal BMI (<23 kg/m²) had VAT > 100 cm², and 35.7% had A/G > 1.0, indicating central obesity. Conclusions: DXA-based indices provide a more accurate assessment of adiposity and associated cardiometabolic risks in aging women than BMI alone. Clinical screening strategies should consider incorporating regional fat distribution markers, particularly in midlife and postmenopausal populations, to better identify individuals at risk. Full article

BRCA1 and BRCA2 are associated with advanced prostate cancer progression and poor prognosis. Copy number variants (CNVs) of these genes play a crucial role in guiding targeted treatments, particularly for patients receiving PARP inhibitors. However, CNV detection using multiplex ligation-dependent probe amplification (MLPA) is often limited by tumor heterogeneity, leading to ambiguous results. This study therefore aimed to evaluate BRCA1/2 CNVs in advanced prostate cancer patients using droplet digital PCR (ddPCR) and compare the results with MLPA. DNA from 11 advanced prostate cancer tissues was analyzed using both methods, in parallel with four cell lines and seven healthy volunteers. Our findings revealed that ddPCR effectively classified normal CNV groups—including normal control cell lines, healthy volunteers, and samples with normal MLPA final ratios—from deletion groups, which included deletion control cell lines, samples with deletion final ratios from MLPA, and cases with previously ambiguous results. Interestingly, two cases involving BRCA1 and one case involving BRCA2 exhibited ambiguous results using MLPA; however, ddPCR enabled more precise classification by applying the Youden Index from ROC analysis and identifying optimal cutoff values of 1.35 for BRCA1 and 1.55 for BRCA2. These optimal thresholds allowed ddPCR to effectively reclassify the ambiguous MLPA cases into the deletion group. Overall, ddPCR could offer a more sensitive and reliable approach for CNV detection in heterogeneous tissue samples and demonstrates strong potential as a biomarker tool for guiding targeted therapy in advanced prostate cancer patients. However, further validation in larger cohorts is necessary to optimize cutoff precision, confirm diagnostic performance, and evaluate the full clinical utility of ddPCR. Full article

A combination of high potential productivity and ecological stability is essential for current cultivars, which is achievable by breeding. Interspecific/intergeneric hybridization remains a key approach to producing new high-yielding and resistant cultivars. Interspecific reproductive barriers (IRBs) appear in the interaction between the pollen and pistil of interspecific/intergeneric hybrids. The mechanisms underlying these hybridization barriers are to a considerable degree unknown. The pollen–pistil interaction is decisive because the pollen of distantly related plant species either is not recognized by stigma cells or is recognized as foreign, preventing pollen tube (PT) germination and/or penetration into the stigma/style/ovary. This review mainly focuses on (1) the pollen–pistil system; (2) IRB classification; (3) similarity and differences in the function of self-incompatibility (SI) barriers and IRBs; and (4) physiological and biochemical control of IRBs and their overcoming. The main goal is to illuminate the physiological, biochemical, and molecular mechanisms underlying the growth arrest of incompatible PTs and their death. In general, this review consolidates the current understanding of the interaction of the male gametophyte with the sporophyte tissues of the pistil and outlines future research directions in the area of plant reproductive biology. Full article

Dens invaginatus is a developmental dental anomaly characterized by the infolding of the enamel organ into the dental papilla during early odontogenesis. This process leads to a broad spectrum of anatomical variations, ranging from minor enamel-lined pits confined to the crown to deep invaginations extending through the root, occasionally communicating with periodontal or periapical tissues. The internal complexity of affected teeth presents diagnostic and therapeutic challenges, particularly in severe forms that mimic root canal systems or are associated with pulpal or periapical pathology. Maxillary lateral incisors are most frequently affected, likely due to their unique developmental timeline and morphological susceptibility. Although various classification systems have been proposed, Oehlers’ classification remains the most clinically relevant due to its simplicity and correlation with treatment complexity. Recent advances in diagnostic imaging, especially cone beam computed tomography (CBCT), have revolutionized the identification and classification of these anomalies. CBCT-based adaptations of Oehlers’ classification allow for the precise assessment of invagination extent and pulpal involvement, facilitating improved treatment planning. Contemporary therapeutic strategies now include calcium-silicate-based cement sealing materials, endodontic microsurgery for inaccessible anatomy, and regenerative endodontic procedures for immature teeth with necrotic pulps. Emerging developments in artificial intelligence, genetic research, and tissue engineering promise to further refine diagnostic capabilities and treatment options. Early detection remains critical to prevent complications such as pulpal necrosis or apical disease. A multidisciplinary, image-guided, and patient-centered approach is essential for optimizing clinical outcomes in cases of dens invaginatus. Full article

Introduction: The scapholunate interosseus ligament (SLIL) is critical for wrist stability, with injuries causing carpal instability and potential scapholunate advanced collapse (SLAC). This technical note presents a novel ligament-sparing surgical technique for treating SLIL tears ranging from grade 2 to 4 of the Garcia-Elias classification. Materials and Methods: A retrospective study was performed on ten patients treated with this novel technique. The technique involves a dorsal approach to the wrist through a 5–7 cm incision ulnar to Lister’s tubercle. After exposing the scapholunate joint, reduction is performed using Kirschner wires (K-wires) as joysticks, followed by stabilisation with three K-wires through the scapholunate, scapho-capitate, and radio-lunate joints. Two 2.3 mm suture anchors with double sutures are placed where the reduction K-wires were removed. One pair of sutures connects the anchors and any remaining SLIL tissue, while the second pair create a shoelace-like capsulodesis. Post-operative care includes staged K-wire removal at one and two months, with progressive rehabilitation before returning to weight-bearing activities at six months. Results: All patients improved in pain and function. The technique addresses SLIL injuries by restoring both coronal alignment through ligament repair and sagittal alignment via dorsal capsulodesis. The use of suture anchors and direct repair preserves the native tissue while reinforcing the dorsal capsule–scapholunate septum complex, avoiding the need for tendon grafts or extensive bone tunnelling. Conclusions: This ligament-sparing technique offers several advantages, including absence of donor site morbidity, minimal damage to carpal cartilage and vascularity, and preservation of surgical options should revision be necessary. The procedure effectively addresses both components of scapholunate instability while maintaining a relatively straightforward surgical approach. Full article

The Chinese mitten crab (Eriocheir sinensis) industry is currently facing the challenges of origin fraud, as well as a lack of precision and interpretability of existing traceability methods. Here, we propose a high-precision origin traceability method based on a combination of stable isotope analysis and interpretable machine learning. We sampled Chinese mitten crabs from six origins representing diverse aquatic environments and farming practices, and analyzed their δ¹³C, δ¹⁵N, δ²H, and δ¹⁸O stable isotope compositions in different sexes and tissues (hepatopancreas, muscle, and gonad). By comparing the classification performance of Random Forest, XGBoost, and Logistic Regression models, we found that the Random Forest model outperformed the others, achieving high accuracy (91.3%) in distinguishing samples from different origins. Interpretation of the optimal Random Forest model, using SHAP (SHapley Additive exPlanations) analysis, identified δ²H in male muscle, δ¹⁵N in female hepatopancreas, and δ¹³C in female hepatopancreas as the most influential features for discriminating geographic origin. This analysis highlighted the crucial role of environmental factors, such as water source, diet, and trophic level, in origin discrimination and demonstrated that isotopic characteristics of different tissues provide unique discriminatory information. This study offers a novel paradigm for stable isotope traceability based on explainable machine learning, significantly enhancing the identification capability and reliability of Chinese mitten crab origin traceability, and holds significant implications for food safety assurance. Full article