Search Results (1,577)

Papillomaviruses (PVs) are double-stranded DNA viruses known to induce a variety of epithelial lesions in dogs, ranging from benign hyperplasia to malignancies. In regions of rich biodiversity such as the Western Amazon, data on the circulation and genetic composition of canine papillomaviruses (CPVs) remain scarce. This study investigated CPV types present in oral and cutaneous papillomatous lesions in domiciled dogs from Acre and Rondônia States, Brazil. Sixty-one dogs with macroscopically consistent lesions were clinically evaluated, and tissue samples were collected for histopathological examination and PCR targeting the L1 gene. Among these, 37% were histologically diagnosed as squamous papillomas or fibropapillomas, and 49.2% (30/61) tested positive for papillomavirus DNA. Sequencing of the L1 gene revealed that most positive samples belonged to CPV1 (Lambdapapillomavirus 2), while one case was identified as CPV8 (Chipapillomavirus 3). Complete genomes of three CPV1 strains were obtained via high-throughput sequencing and showed high identity with CPV1 strains from other Brazilian regions. Phylogenetic analysis confirmed close genetic relationships among isolates across distinct geographic areas. These findings demonstrate the circulation of genetically conserved CPVs in the Amazon and reinforce the value of molecular and histopathological approaches for the accurate diagnosis and surveillance of viral diseases in domestic dogs, especially in ecologically complex regions. Full article

Background: Pancreatic cystic lesions (PCLs), including intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs), pose a diagnostic challenge due to their variable malignant potential. Current guidelines, such as Fukuoka and American Gastroenterological Association (AGA), have moderate predictive accuracy and may lead to overtreatment or missed malignancies. Artificial intelligence (AI), incorporating machine learning (ML) and deep learning (DL), offers the potential to improve risk stratification, diagnosis, and management of PCLs by integrating clinical, radiological, and molecular data. This is the first systematic review to evaluate the application, performance, and clinical utility of AI models in the diagnosis, classification, prognosis, and management of pancreatic cysts. Methods: A systematic review was conducted in accordance with PRISMA guidelines and registered on PROSPERO (CRD420251008593). Databases searched included PubMed, EMBASE, Scopus, and Cochrane Library up to March 2025. The inclusion criteria encompassed original studies employing AI, ML, or DL in human subjects with pancreatic cysts, evaluating diagnostic, classification, or prognostic outcomes. Data were extracted on the study design, imaging modality, model type, sample size, performance metrics (accuracy, sensitivity, specificity, and area under the curve (AUC)), and validation methods. Study quality and bias were assessed using the PROBAST and adherence to TRIPOD reporting guidelines. Results: From 847 records, 31 studies met the inclusion criteria. Most were retrospective observational (n = 27, 87%) and focused on preoperative diagnostic applications (n = 30, 97%), with only one addressing prognosis. Imaging modalities included Computed Tomography (CT) (48%), endoscopic ultrasound (EUS) (26%), and Magnetic Resonance Imaging (MRI) (9.7%). Neural networks, particularly convolutional neural networks (CNNs), were the most common AI models (n = 16), followed by logistic regression (n = 4) and support vector machines (n = 3). The median reported AUC across studies was 0.912, with 55% of models achieving AUC ≥ 0.80. The models outperformed clinicians or existing guidelines in 11 studies. IPMN stratification and subtype classification were common focuses, with CNN-based EUS models achieving accuracies of up to 99.6%. Only 10 studies (32%) performed external validation. The risk of bias was high in 93.5% of studies, and TRIPOD adherence averaged 48%. Conclusions: AI demonstrates strong potential in improving the diagnosis and risk stratification of pancreatic cysts, with several models outperforming current clinical guidelines and human readers. However, widespread clinical adoption is hindered by high risk of bias, lack of external validation, and limited interpretability of complex models. Future work should prioritise multicentre prospective studies, standardised model reporting, and development of interpretable, externally validated tools to support clinical integration. Full article

Background and Objectives: Although survival after congenital cardiac malformations (CCM) has improved, little is known about Romanian children’s own perceptions of health-related quality of life (HRQoL) or their emotional burden. We compared HRQoL, depressive symptoms, and anxiety across lesion severity strata and explored clinical predictors of impaired HRQoL. Methods: In this cross-sectional study (1 May 2023–30 April 2025), 72 children (mean age 7.9 ± 3.0 years, 52.8% male) attending a tertiary cardiology clinic completed the Romanian-validated Pediatric Quality of Life Inventory (PedsQL), Children’s Depression Inventory (CDI) and the Screen for Child Anxiety-Related Emotional Disorders questionnaire (SCARED-C, child version). Lesions were classified as mild (n = 22), moderate (n = 34), or severe (n = 16). Left-ventricular ejection fraction (LVEF) and unplanned cardiac hospitalisations over the preceding 12 months were extracted from electronic records. Results: Mean PedsQL total scores declined stepwise by severity (mild 80.9 ± 7.3; moderate 71.2 ± 8.4; severe 63.1 ± 5.4; p < 0.001). CDI and SCARED-C scores rose correspondingly (CDI: 9.5 ± 3.0, 13.6 ± 4.0, 18.0 ± 2.7; anxiety: 15.2 ± 3.3, 17.2 ± 3.8, 24.0 ± 3.4; both p < 0.001). PedsQL correlated positively with LVEF (r = 0.51, p < 0.001) and negatively with hospitalisations (r = −0.39, p = 0.001), depression (r = −0.44, p < 0.001), and anxiety (r = −0.47, p < 0.001). In multivariable analysis, anatomical severity remained the sole independent predictor of lower HRQoL (β = −8.4 points per severity tier, p < 0.001; model R² = 0.45). Children with ≥ 1 hospitalisation (n = 42) reported poorer HRQoL (69.6 ± 8.0 vs. 76.1 ± 11.1; p = 0.005) and higher depressive scores (p < 0.001). Conclusions: HRQoL and internalising symptoms in Romanian children with CCM worsen with increasing anatomical complexity and recent hospital utilisation. The severity tier outweighed functional markers as the main determinant of HRQoL, suggesting that psychosocial screening and support should be scaled to lesion complexity. Integrating the routine use of the Romanian-validated PedsQL, CDI, and SCARED-C questionnaire into cardiology follow-up may help identify vulnerable patients early and guide targeted interventions. Full article

Background/Objectives: Brainstem ischemic strokes comprise 10% of ischemic strokes and are challenging to diagnose due to small lesion size and complex presentations. Diffusion-weighted imaging (DWI) is crucial for detecting ischemia, yet it can miss small lesions, especially when only axial slices are employed. This study investigated whether ischemic lesions visible in a single imaging plane correspond to smaller volumes and whether coronal DWI enhances detection compared to axial DWI alone. Methods: This retrospective single-center study examined 134 patients with brainstem ischemic strokes between December 2018 and November 2023. All patients underwent axial and coronal DWI. Clinical data, NIH Stroke Scale (NIHSS) scores, and modified Rankin Scale (mRS) scores were recorded. Diffusion-restricted lesion volumes were calculated using multiple models (planimetric, ellipsoid, and spherical), and lesion visibility per imaging plane was analyzed. Results: Brainstem ischemic strokes were detected in 85.8% of patients. Coronal DWI alone identified 6% of lesions that were undetectable on axial DWI; meanwhile, axial DWI alone identified 6.7%. Combining both improved overall sensitivity to 86.6%. Ischemic lesions visible in only one plane were significantly smaller across all volume models. Higher NIHSS scores were strongly correlated with larger diffusion-restricted lesion volumes. Coronal DWI correlated better with clinical severity than axial DWI, especially in the midbrain and medulla. Conclusions: Coronal DWI significantly improves the detection of small brainstem infarcts and should be incorporated into routine stroke imaging protocols. Infarcts visible in only one plane are typically smaller, yet still clinically relevant. Combined imaging enhances diagnostic accuracy and supports early and precise intervention in posterior circulation strokes. Full article

The rapid evolution of deep learning (DL) has fundamentally transformed the paradigm for detecting special components in agricultural products, addressing critical challenges in food safety, quality control, and precision agriculture. This comprehensive review systematically analyzes many seminal studies to evaluate cutting-edge DL applications across three core domains: contaminant surveillance (heavy metals, pesticides, and mycotoxins), nutritional component quantification (soluble solids, polyphenols, and pigments), and structural/biomarker assessment (disease symptoms, gel properties, and physiological traits). Emerging hybrid architectures—including attention-enhanced convolutional neural networks (CNNs) for lesion localization, wavelet-coupled autoencoders for spectral denoising, and multi-task learning frameworks for joint parameter prediction—demonstrate unprecedented accuracy in decoding complex agricultural matrices. Particularly noteworthy are sensor fusion strategies integrating hyperspectral imaging (HSI), Raman spectroscopy, and microwave detection with deep feature extraction, achieving industrial-grade performance ($RPD$ > 3.0) while reducing detection time by 30–100× versus conventional methods. Nevertheless, persistent barriers in the “black-box” nature of complex models, severe lack of standardized data and protocols, computational inefficiency, and poor field robustness hinder the reliable deployment and adoption of DL for detecting special components in agricultural products. This review provides an essential foundation and roadmap for future research to bridge the gap between laboratory DL models and their effective, trusted application in real-world agricultural settings. Full article

With the growing use of deep learning in medical image analysis, automated classification of fundus images is crucial for the early detection of fundus diseases. However, the complexity of fluorescein fundus angiography (FFA) images poses challenges in the accurate identification of lesions. To address these issues, we propose the Enhanced Feature Fusion ConvNeXt (EFF-ConvNeXt) model, a novel architecture combining VGG16 and an enhanced ConvNeXt for FFA image classification. VGG16 is employed to extract edge features, while an improved ConvNeXt incorporates the Context-Aware Feature Fusion (CAFF) strategy to enhance global contextual understanding. CAFF integrates an Improved Global Context (IGC) module with multi-scale feature fusion to jointly capture local and global features. Furthermore, an SKNet module is used in the final stages to adaptively recalibrate channel-wise features. The model demonstrates improved classification accuracy and robustness, achieving 92.50% accuracy and 92.30% F1 score on the APTOS2023 dataset—surpassing the baseline ConvNeXt-T by 3.12% in accuracy and 4.01% in F1 score. These results highlight the model’s ability to better recognize complex disease features, providing significant support for more accurate diagnosis of fundus diseases. Full article

Background: Despite advances in coronary artery disease (CAD) treatment, challenges persist, particularly in complex lesions. While percutaneous coronary intervention (PCI) is widely used, its outcomes can be affected by complications like restenosis. Optical coherence tomography (OCT), offering higher-resolution imaging than angiography, shows promise in guiding PCI. However, meta-analytical comparisons between OCT-guided and angiography-guided PCI remain limited. Methods: Databases, including PubMed, Scopus, Cochrane Library, and ClinicalTrials.gov, were queried through May 2025 to identify randomized controlled trials (RCTs) comparing OCT-guided PCI with angiography-guided PCI. Data were pooled using risk ratios (RRs) and mean difference (MD) with 95% confidence intervals (CIs) in a random-effects model. Results: Five RCTs involving 5737 patients (OCT: 2738 and angiography: 2999) were included. On pooled analysis, OCT-guided PCI was associated with a notable reduction in major adverse cardiovascular event (MACE) (RR: 0.71, p = 0.0001), cardiac mortality (RR: 0.43, p = 0.003), target lesion revascularization (TLR) (RR: 0.53, p = 0.007), and stroke (RR: 0.17, p = 0.02), compared to angiography-guided PCI. No significant differences were noted for all-cause mortality and myocardial infarction. Conclusions: In patients with complex coronary lesions, OCT-guided PCI reduces the risk of MACE, cardiac mortality, TLR, and stroke, compared to angiography-guided PCI only. This study supports incorporating advanced imaging techniques like OCT to improve clinical outcomes, especially in complex PCIs. Full article

Obesity and metabolic dysfunction are established risk factors for luminal breast cancer, yet current preclinical models inadequately recapitulate the complex metabolic and immune interactions driving tumorigenesis. To develop and characterize an immunocompetent rat model of luminal breast cancer induced by chronic exposure to a cafeteria diet mimicking Western obesogenic nutrition, female rats were fed a cafeteria diet or standard chow from weaning. Metabolic parameters, plasma biomarkers (including leptin, insulin, IGF-1, adiponectin, and estrone), mammary gland histology, tumor incidence, and gene expression profiles were longitudinally evaluated. Gene expression was assessed by PCR arrays and qPCR. A subgroup underwent dietary reversal to assess the reversibility of molecular alterations. Cafeteria diet induced significant obesity (mean weight 426.76 g vs. 263.09 g controls, p < 0.001) and increased leptin levels without altering insulin, IGF-1, or inflammatory markers. Histological analysis showed increased ductal ectasia and benign lesions, with earlier fibroadenoma and luminal carcinoma development in diet-fed rats. Tumors exhibited luminal phenotype, low Ki67, and elevated PAI-1 expression. Gene expression alterations were time point specific and revealed early downregulation of ID1 and COX2, followed by upregulation of MMP2, THBS1, TWIST1, and PAI-1. Short-term dietary reversal normalized several gene expression changes. Overall tumor incidence was modest (~12%), reflecting early tumor-promoting microenvironmental changes rather than aggressive carcinogenesis. This immunocompetent cafeteria diet rat model recapitulates key metabolic, histological, and molecular features of obesity-associated luminal breast cancer and offers a valuable platform for studying early tumorigenic mechanisms and prevention strategies without carcinogen-induced confounders. Full article

The human interpretation of medical images, especially for the detection of cancer in the prostate, has traditionally been a time-consuming and challenging process. Manual examination for the detection of prostate cancer is not only time-consuming but also prone to errors, carrying the risk of an excess biopsy due to the inherent limitations of human visual interpretation. With the technical advancements and rapid growth of computer resources, machine learning (ML) and deep learning (DL) models have been experimentally used for medical image analysis, particularly in lesion detection. However, several state-of-the-art models have shown promising results. There are still challenges when analysing prostate lesion images due to the distinctive and complex nature of medical images. This study offers an elaborate review of the techniques that are used to diagnose prostate cancer using medical images. The goal is to provide a comprehensive and valuable resource that helps researchers develop accurate and autonomous models for effectively detecting prostate cancer. This paper is structured as follows: First, we outline the issues with prostate lesion detection. We then review the methods for analysing prostate lesion images and classification approaches. We then examine convolutional neural network (CNN) architectures and explore their applications in deep learning (DL) for image-based prostate cancer diagnosis. Finally, we provide an overview of prostate cancer datasets and evaluation metrics in deep learning. In conclusion, this review analyses key findings, highlights the challenges in prostate lesion detection, and evaluates the effectiveness and limitations of current deep learning techniques. Full article

Background: Vascular risk factors (VRFs) are known to influence cerebral small vessel disease (cSVD) burden and progression. However, their specific impact on the presence and distribution of each cSVD imaging marker (white matter hyperintensity [WMH], perivascular spaces [PVSs], lacunes, and cerebral microbleeds [CMBs]) and their spatial distribution remains unclear. Methods: We conducted a retrospective analysis of 93 patients with lacunar stroke with a standardized investigational magnetic resonance imaging protocol using a 3T scanner. WMH and PVSs were segmented semi-automatically, and lacunes and CMBs were manually segmented. We assessed the univariable associations of four common VRFs (hypertension, hyperlipidemia, diabetes, and smoking) with the load of each cSVD marker. Then, we assessed the independent associations of these VRFs in multivariable regression models adjusted for age and sex. Spatial lesion patterns were explored with regional volumetric comparisons using Pearson’s coefficient analysis, which was adjusted for multiple comparisons, and by visually examining heatmap lesion distributions. Results: Hypertension was the VRF that exhibited stronger associations with the cSVD markers in the univariable analysis. In the multivariable analysis, only lacunes (p = 0.009) and PVSs in the basal ganglia (p = 0.014) and white matter (p = 0.016) were still associated with hypertension. In the regional analysis, hypertension showed a higher WMH load in deep structures and white matter, particularly in the posterior periventricular regions. In patients with hyperlipidemia, WMH was preferentially found in hippocampal regions. Conclusions: Hypertension was confirmed to be the VRF with the most impact on cSVD load, especially for lacunes and PVSs, while the lesion topography was variable for each VRF. These findings shed light on the complexity of cSVD expression in relation to factors detrimental to vascular health. Full article

The present paper investigates the application of convolutional neural networks (CNNs) for the classification of diabetic foot ulcers, using VGG16, VGG19 and MobileNetV2 architectures. The primary objective is to develop and compare deep learning models capable of accurately identifying ulcerated regions in clinical images of diabetic feet, thereby aiding in the prevention and effective treatment of foot ulcers. A comprehensive study was conducted using an annotated dataset of medical images, evaluating the performance of the models in terms of accuracy, precision, recall and F1-score. VGG19 achieved the highest accuracy at 97%, demonstrating superior ability to focus activations on relevant lesion areas in complex images. MobileNetV2, while slightly less accurate, excelled in computational efficiency, making it a suitable choice for mobile devices and environments with hardware constraints. The study also highlights the limitations of each architecture, such as increased risk of overfitting in deeper models and the lower capability of MobileNetV2 to capture fine clinical details. These findings suggest that CNNs hold significant potential in computer-aided clinical diagnosis, particularly in the early and precise detection of diabetic foot ulcers, where timely intervention is crucial to prevent amputations. Full article

Background and Objectives: Meniscal pathologies are common abnormalities of the knee joint and a frequent cause of knee pain. Prompt and accurate diagnosis is essential to ensure appropriate treatment. Ultrasonography is increasingly used due to its accessibility, cost- and time-efficiency, and capacity for dynamic assessment. This study aimed to evaluate the usefulness of ultrasonography in identifying specific types of meniscal tears and to assess their frequency of occurrence. Materials and Methods: A retrospective study was conducted to assess the frequency and sonographic appearance of various meniscal pathologies. The study population included all patients who underwent ultrasonographic examination of the knee in our clinic over one year for various indications (n = 430). Archived ultrasound images were retrospectively reviewed and analyzed. Results: Meniscal pathologies were identified in 134 patients. The findings included 95 cases of degenerative lesions (70.9%), 18 meniscal cyst-related pathologies (13.4%), 8 complex tears (6.0%), 5 flap tears (3.7%), 3 vertical pericapsular tears (2.2%), 3 partial thickness tears (2.2%), and 2 bucket-handle-type tears (1.5%). Each lesion type was characterized and illustrated through representative ultrasound images. Conclusions: Ultrasound imaging of meniscal pathology offers a valuable diagnostic option. By characterizing and visually documenting different meniscal lesions, this study highlights the practical potential of ultrasonography in routine clinical settings. These findings may enhance diagnostic accuracy and guide more targeted management strategies. Moreover, the results contribute to the expanding body of research on musculoskeletal ultrasonography and may encourage broader adoption of ultrasound in orthopedic diagnostics. Full article

Chronic thromboembolic pulmonary hypertension (CTEPH) is a rare, progressive form of pre-capillary pulmonary hypertension characterized by persistent, organized thromboemboli in the pulmonary vasculature, leading to vascular remodeling, elevated pulmonary artery pressures, right heart failure, and significant morbidity and mortality if untreated. Despite advances, CTEPH remains underdiagnosed due to nonspecific symptoms and overlapping features with other forms of pulmonary hypertension. Basic Methodology: This review synthesizes data from large international registries, epidemiologic studies, translational research, and multicenter clinical trials. Key methodologies include analysis of registry data to assess incidence and risk factors, histopathological examination of lung specimens, and molecular studies investigating endothelial dysfunction and inflammatory pathways. Diagnostic modalities and treatment outcomes are evaluated through observational studies and randomized controlled trials. Recent Advances and Affected Population: Research has elucidated that CTEPH arises from incomplete resolution of pulmonary emboli, with subsequent fibrotic transformation mediated by dysregulated TGF-β/TGFBI signaling, endothelial dysfunction, and chronic inflammation. Affected populations are typically older adults, often with prior venous thromboembolism, splenectomy, or prothrombotic conditions, though up to 25% have no history of acute PE. The disease burden is substantial, with delayed diagnosis contributing to worse outcomes and higher societal costs. Microvascular arteriopathy and PAH-like lesions in non-occluded vessels further complicate the clinical picture. Conclusions: CTEPH is now recognized as a treatable disease, with multimodal therapies—surgical endarterectomy, balloon pulmonary angioplasty, and targeted pharmacotherapy—significantly improving survival and quality of life. Ongoing research into molecular mechanisms and biomarker-driven diagnostics promises earlier identification and more personalized management. Multidisciplinary care and continued translational investigation are essential to further reduce mortality and optimize outcomes for this complex patient population. Full article

Introduction: Acute Myeloid Leukemia (AML) is a hematologic malignancy characterized by the clonal expansion of myeloid progenitors. While it primarily affects the bone marrow, extramedullary relapse occurs in 3–5% of cases, and it is linked to poor prognosis. Central nervous system (CNS) involvement presents diagnostic challenges due to nonspecific symptoms. CNS manifestations include leptomeningeal dissemination, nerve infiltration, parenchymal lesions, and myeloid sarcoma, occurring at any disease stage and frequently asymptomatic. Methods: A 62-year-old man with a recent history of AML in remission presented with diplopia and aching paresthesias in the left periorbital region spreading to the left frontal area. The diagnostic workup included neurological and hematological evaluation, lumbar puncture, brain CT, brain magnetic resonance imaging (MRI) with contrast, and dermatological evaluation with skin biopsy due to the appearance of nodular skin lesions on the abdomen and thorax. Results: Neurological evaluation showed hypoesthesia in the left mandibular region, consistent with left trigeminal nerve involvement, extending to the periorbital and frontal areas, and impaired adduction of the left eye with divergent strabismus in the primary position due to left oculomotor nerve palsy. Brain MRI showed an equivocal thickening of the left oculomotor nerve without enhancement. Cerebrospinal fluid (CSF) analysis initially showed elevated protein (47 mg/dL) with negative cytology; a repeat lumbar puncture one week later detected leukemic cells. Skin biopsy revealed cutaneous AML localization. A diagnosis of AML relapse with CNS and cutaneous localization was made. Salvage therapy with FLAG-IDA-VEN (fludarabine, cytarabine, idarubicin, venetoclax) and intrathecal methotrexate, cytarabine, and dexamethasone was started. Subsequent lumbar punctures were negative for leukemic cells. Due to high-risk status and extramedullary disease, the patient underwent allogeneic hematopoietic stem cell transplantation. Post-transplant aplasia was complicated by septic shock; the patient succumbed to an invasive fungal infection. Conclusions: This case illustrates the diagnostic complexity and poor prognosis of extramedullary AML relapse involving the CNS. Early recognition of neurological signs, including cranial nerve dysfunction, is crucial for timely diagnosis and management. Although initial investigations were negative, further analyses—including repeated CSF examinations and skin biopsy—led to the identification of leukemic involvement. Although neuroleukemiosis cannot be confirmed without nerve biopsy, the combination of clinical presentation, neuroimaging, and CSF data strongly supports the diagnosis of extramedullary relapse of AML. Multidisciplinary evaluation remains essential for detecting extramedullary relapse. Despite treatment achieving CSF clearance, the prognosis remains unfavorable, underscoring the need for vigilant clinical suspicion in hematologic patients presenting with neurological symptoms. Full article

Coronary computed tomography angiography (CCTA) has emerged as the leading noninvasive imaging modality for the assessment of coronary artery disease (CAD), offering high-resolution visualization of the coronary anatomy and plaque characterization. The development of fractional flow reserve derived from CCTA (FFR-CT) has further transformed the diagnostic landscape by enabling the simultaneous evaluation of both anatomical stenosis and lesion-specific ischemia. FFR-CT has demonstrated diagnostic accuracy comparable to invasive FFR. The combined use of CCTA and FFR-CT is now pivotal in a broad range of clinical scenarios, including the evaluation of stable and acute chest pain, assessment of high-risk and complex plaque features, and preoperative planning. As evidence continues to mount, CCTA and FFR-CT are positioned to become the primary gatekeepers to the cardiac catheterization laboratory, potentially reducing the number of unnecessary invasive procedures. This review highlights the growing clinical utility of FFR-CT, its integration with advanced plaque imaging, and the future potential of these technologies in redefining the management of CAD, while also acknowledging current limitations, including image quality requirements, cost, and access. Full article