Search Results (14,097)

Pancreatic ductal adenocarcinoma (PDAC) remains a high-burden disease worldwide with increasing incidence, poor prognosis, and high mortality. Complete surgical resection is the only potentially curative treatment; however, due to a lack of symptoms in the early stages, most patients have advanced disease when diagnosed. Type 2 diabetes mellitus (T2DM) is a significant health concern characterized by hyperglycemia, insulin resistance, and impairment in insulin secretion. T2DM is linked with PDAC, sharing a complex bidirectional relationship. Therefore, dual causality between the two diseases represents significant challenges in practice, distinguishing existing T2DM as a PDAC risk factor from newly onset, potentially pancreatic cancer-related diabetes (PCRD). Evidence showed that new-onset diabetes (NOD) may serve as a biomarker for early diagnosis of PDAC, and several risk prediction models were developed to identify high-risk patients for further intervention. Although early PDAC detection is important, widespread screening is not currently recommended for T2DM patients due to a lack of cost-effective, efficient screening modalities. However, further risk stratification in diabetic patients is warranted to support a targeted screening strategy with economic viability. Diabetes confers ≈2-fold PDAC risk overall, with the highest relative risk in the first 2–3 years after diagnosis. Strategies using clinical signs (age ≥50–60 years, unintentional weight loss, rapid HbA1c escalation/insulin initiation) and predictive risk scores (e.g., ENDPAC) can triage NOD patients for magnetic resonance imaging/computed tomography (MRI/CT) and endoscopic ultrasound (EUS). A targeted screening approach may allow early diagnosis that could improve the prognosis of PDAC patients. This narrative review aims to synthesize current evidence linking T2DM and PDAC; delineate risk factors within diabetes populations; appraise predictive models and biomarkers for differentiating PCRD from typical T2DM; outline pragmatic, risk-adapted screening strategies, especially for NOD, and identify additional areas where further research is needed. Full article

Deep learning for semantic segmentation has made significant advances in recent years, achieving state-of-the-art performance. Medical image segmentation, as a key component of healthcare systems, plays a vital role in the diagnosis and treatment planning of diseases. Due to the fractal and scale-invariant nature of biological structures, effective medical image segmentation requires models capable of capturing hierarchical and self-similar representations across multiple spatial scales. In this paper, a Recurrent Neural Network (RNN) is explored within the Convolutional Neural Network (CNN) and Vision Transformer (ViT)-based hybrid U-shape network, named RCV-UNet. First, the ViT-based layer was developed in the bottleneck to effectively capture the global context of an image and establish long-range dependencies through the self-attention mechanism. Second, recurrent residual convolutional blocks (RRCBs) were introduced in both the encoder and decoder to enhance the ability to capture local features and preserve fine details. Third, by integrating the global feature extraction capability of ViT with the local feature enhancement strength of RRCBs, RCV-UNet achieved promising global consistency and boundary refinement, addressing key challenges in medical image segmentation. From a fractal–fractional perspective, the multi-scale encoder–decoder hierarchy and attention-driven aggregation in RCV-UNet naturally accommodate fractal-like, scale-invariant regularity, while the recurrent and residual connections approximate fractional-order dynamics in feature propagation, enabling continuous and memory-aware representation learning. The proposed RCV-UNet was evaluated on four different modalities of images, including CT, MRI, Dermoscopy, and ultrasound, using the Synapse, ACDC, ISIC 2018, and BUSI datasets. Experimental results demonstrate that RCV-UNet outperforms other popular baseline methods, achieving strong performance across different segmentation tasks. The code of the proposed method will be made publicly available. Full article

Background: Thyroid eye disease (TED), or Graves’ orbitopathy, is the most common extra-thyroidal manifestation of Graves’ disease, but it has only rarely been reported after SARS-CoV-2 vaccination. Autoimmune thyroid disease, including subacute thyroiditis and Graves’ disease, has been described following COVID-19 vaccination; we present a case series of TED occurring shortly after different COVID-19 vaccines to provide clinical data on this potential safety signal. Case presentation: We describe five women (mean age 47 years; range 27–69) who developed TED 3–20 days after COVID-19 vaccination with mRNA or adenoviral vector vaccines, three of whom had pre-existing thyroid disease. Presentations included ocular and retro-orbital pain, exophthalmos, headache, goiter, tremor, depressive symptoms, and, in one case, anterior neck pain and fever. TED severity (ETA/EUGOGO) ranged from mild to severe, with frequent findings of suppressed TSH, elevated thyroid autoantibodies, and inflammatory markers, as well as imaging evidence of exophthalmos, extraocular muscle enlargement, and diffuse or multinodular goiter. Management with intravenous corticosteroids, selenium, levothyroxine adjustment, and/or intramuscular corticosteroids led to improvement in thyroid function and inflammation by 3 months, although mild TED often persisted. Conclusions: This case series supports a temporal association between COVID-19 vaccination and new-onset or exacerbated TED in individuals with autoimmune thyroid disease. Although vaccination benefits outweigh potential risks, clinicians should remain alert to ocular and thyroid symptoms after immunization to ensure timely diagnosis and management. Full article

Mal secco disease (MSD), caused by Plenodomus tracheiphilus, poses a serious threat to Citrus limon production across the Mediterranean Basin. This study investigates the potential of high-resolution WorldView-3 imagery for detecting early-stage MSD symptoms in lemon orchards through the integration of three pansharpening algorithms(Gram–Schmidt, NNDiffuse, and Brovey) with two machine learning classifiers (Random Forest and Support Vector Machine). The Brovey-based fusion combined with Random Forest yielded the best results, achieving 80% overall accuracy, 90% precision, and 84% recall, with high spatial reliability confirmed by 10-fold cross-validation. Spectral analysis revealed that Brovey introduced the largest radiometric deviation, particularly in the NIR band, which nonetheless enhanced class separability between healthy and symptomatic crowns. These findings demonstrate that moderate spectral distortion can be tolerated, or even beneficial, for vegetation disease detection. The proposed workflow—efficient, transferable, and based solely on visible and NIR bands—offers a practical foundation for satellite-driven disease monitoring and precision management in Mediterranean citrus systems. Full article

Photoacoustic imaging (PAI) integrates the high-contrast merits of optical imaging with the high-spatial-resolution advantages of acoustic imaging, enabling the acquisition of three-dimensional images with deep tissue penetration (up to several centimeters) for in vivo disease detection and diagnosis. Among various photoacoustic nanoagents, gold nanomaterials (GNMs) have been widely explored for the PAI-based imaging analysis and photothermal therapy of diseases, owing to their strong near-infrared (NIR) absorption, which can generate distinct photoacoustic signals in deep tissues. This review focuses on recent advances and achievements in the development of functionalized gold nanoprobes, including Janus gold nanoprobes, gold nanocomposite probes (such as functionally coated GNMs and GNMs-loaded nanocarriers), and gold nanoaggregate probes (e.g., pre-assembly of GNMs and in situ aggregation of GNMs). The multifunctionalization of GNMs can enhance their PAI performance by shifting absorption to the NIR-I and NIR-II regions, while simultaneously imparting additional functionalities such as targeted delivery to disease sites and specific responsiveness to disease biomarkers. These features can render functionalized GNMs-based nanoprobes highly suitable for PAI-based analysis and the precise detection of various pathological conditions, including bacterial infections, tumors, kidney injury, and disorders affecting the ocular, gastrointestinal, cardiovascular, visceral, and lymphatic systems. Finally, this review provides a concise summary of biosafety evaluation and outlines the current challenges and future perspectives in optimizing the GNMs-based PAI methods, highlighting their potential to enhance the rapid and precise diagnosis of diseases in the future. Full article

Reproductive system diseases are common in guinea pigs, but cases presenting with the coexistence of ovarian cysts and cystic endometrial hyperplasia (CEH) are poorly documented. This case describes a two-year-old female with abdominal tenderness and an ultrasonographic diagnosis of bilateral ovarian cysts and CEH, structured accordingly to the CARE guidelines. An ovariohysterectomy was performed via a ventral midline approach under multimodal anaesthesia and analgesia. Histopathological examination confirmed rete ovarii cysts and cystic endometrial hyperplasia. The postoperative course was uneventful, with complete recovery achieved within 21 days. To the authors’ knowledge, this is the first case report, structured accordingly to the CARE guidelines, to provide complete clinical aspects, diagnostic testing (including imaging), treatment, and final confirmation of the diagnosis of both lesions in the same patient. Abdominal ultrasonography proved to be an essential tool for the simultaneous diagnosis of ovarian and uterine abnormalities in guinea pigs. Surgical management combined with multimodal anaesthesia ensured safe outcomes and satisfactory recovery. This case report also highlights the importance and value of structuring veterinary case reports according to the CARE guidelines, thereby improving the quality, transparency, and reproducibility of clinical reporting in exotic animal medicine. Full article

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most aggressive malignancies, with overall survival outcomes that have improved only modestly in recent years. Careful preoperative evaluation is essential for defining resectability and planning surgery. Modern imaging modalities, including high-resolution, contrast-enhanced CT, MRI and endoscopic ultrasound, provide a detailed assessment of vascular involvement and allow accurate staging according to various international criteria and consensus statements. In borderline and locally advanced cases, neoadjuvant therapy can aid in downsizing the tumor and increasing the likelihood of achieving negative margin resection (R0), offering long-term survival along with quality of life. When vascular invasion limits resectability, venous resection and reconstruction may permit an R0 resection in patients with borderline resectable disease that is both technically operable and physiologically tolerable for the patient. Arterial resection, however, remains controversial and is rarely justified because of its limited perioperative and survival benefits. Arterial divestment has emerged as an interesting alternative, allowing tumor clearance while avoiding full arterial reconstruction. Vascular reconstructions can be achieved through venorrhapy, end-to-end anastomosis, or segmental replacement using either autologous or synthetic grafts. With the advances in neoadjuvant treatment, the appropriate selection of candidates for vascular resection significantly increases the resectability rate, offering long-term survival along with satisfactory quality of life. In this review, a detailed literature review is performed regarding the best strategies in the diagnosis and surgical management of patients with borderline resectable and locally advanced pancreatic cancer requiring vascular resection. Full article

Urethral stricture is a disease of fibrotic narrowing that compromises the urethral mucosa and spongiosum. Oral mucosal graft urethroplasty delivers excellent outcomes in complex cases, yet its procedural demands restrict availability beyond specialized centers. Endoscopic transplantation of oral mucosa has been proposed; while feasibility is shown, clinical efficacy remains suboptimal. We asked whether extracellular vesicles from stem cells of human exfoliated deciduous teeth (SHED-EVs) promote oral mucosa fibroblast (OMF) growth under urethra-mimetic paracrine conditions and whether culture growth phase tunes EV function. SHED-EVs were collected during logarithmic (SHED-EV-L) or stationary (SHED-EV-S) phases under xeno-free conditions, isolated by a standardized workflow, and characterized by nanoparticle tracking analysis. miRNA cargo was profiled with a human miRNA microarray platform and normalized for comparative analyses. OMF proliferation was quantified in a horizontal indirect co-culture with urethral epithelial cells using incubator-based time-lapse imaging. SHED-EV-L produced a sustained pro-proliferative effect across 24–96 h, whereas SHED-EV-S showed a weaker early effect with a late catch-up; both exceeded vehicle at 96 h. Fibrosis-related miRNA heat maps showed culture growth phase-dependent patterns: SHED-EV-L displayed relatively higher signals for miR-31-3p, miR-146b-3p, several let-7 members, and selected miR-181 isoforms, whereas SHED-EV-S showed a marked relative increase of miR-486-3p; miR-21, miR-99/100, and miR-205 were broadly comparable between phases. These findings indicate that culture growth phase is a practical design lever that orients SHED-EV cargo and function, supporting phase-matched formulations for adjunctive transurethral applications and motivating in vivo validation and manufacturing-oriented quality controls. Full article

Background: Coronary artery disease (CAD) remains a significant health challenge, placing a heavy burden on people and healthcare systems worldwide. Objectives: This narrative review aims to provide a comprehensive overview of recent advancements in the diagnosis, intervention, and pharmacological management of CAD, with a focus on emerging technologies shaping its future. Methods: This is a narrative review that synthesises information from diverse sources, including clinical trials, systematic reviews, meta-analyses, and preclinical studies, to provide a comprehensive overview of the current landscape and emerging trends in CAD management. The literature included in this review was sourced from original research articles and review papers published between January 2010 and December 2025. Results: Early detection has been transformed by non-invasive imaging, such as PCAT, and the addition of invasive and non-invasive FFR technology enables quicker and more accurate diagnoses. Biomarkers, such as high-sensitivity troponin, have further improved the precision of acute coronary syndrome detection, enhancing early intervention. In interventional cardiology, new-generation drug-eluting stents (DESs) have lowered restenosis rates, whereas robotic-assisted percutaneous coronary intervention (PCI) offers precision and reduced operator radiation exposure. Furthermore, the efficacy of drug-coated balloons (DCBs) has been established in the management of in-stent restenosis, and their application in de novo coronary lesions and bifurcation anatomy remains promising. Looking ahead, nanomedicine promises targeted plaque reduction and vascular repair, while 3D-bioprinted blood vessels offer durable, biocompatible grafts for surgical applications. Pharmacological developments, including modern cholesterol-lowering drugs, have also been crucial in achieving cholesterol targets. Conclusions: Despite significant advancements in diagnosis, intervention, and pharmacotherapy, several critical challenges remain, including the need for validated biomarkers and imaging modalities to identify vulnerable atheroma before symptoms arise. Continued research is essential to improve patient outcomes and address the global burden of CAD. Full article

Background/Objectives: Colorectal cancer (CRC) is a leading cause of cancer deaths worldwide, underscoring the need for diagnostic tools that early, accurate, and clinically interpretable. Current artificial intelligence (AI) models are predominantly unimodal and lack sufficient interpretability, which restricts their clinical adoption. Methods: We propose IDF-Net, an interpretable dynamic fusion framework that integrates endoscopy, computed tomography (CT), and histopathology using modality-specific encoders, a dual-stage adaptive gating mechanism, and cross-modal attention. We conducted stratified 5-fold cross-validation and assessed interpretability using spatial heatmaps and modality attribution. We also quantified the results using the intersection-over-union metric for saliency alignment. Results: IDF-Net achieved a state-of-the-art accuracy of 0.920 (0.907–0.936) and area under the curve (AUC) of 0.991 (95% CI: 0.965–0.997), significantly outperforming unimodal and static-fusion baselines (p < 0.05). Interpretability analysis of IDF-Net demonstrated a strong alignment between Gradient-weighted Class Activation Mapping++ heatmaps and expert-annotated lesions, as well as case-specific modality contributions via SHapley Additive exPlanations values. Ablation studies confirmed the contribution of each component, with dynamic routing and cross-attention fusion improving AUC by 0.038 and 0.046, respectively. Conclusions: IDF-Net introduces a dynamically fused, multimodal diagnostic framework with integrated quantitative interpretability, demonstrating superior accuracy and strong potential for clinical translation in CRC diagnosis. The model’s adaptive design allows it to function robustly even when CT data is unavailable, aligning with common clinical pathways while leveraging additional imaging when present for comprehensive staging. Full article

Background: Tuberculosis (TB) remains a major global health problem, and the WHO central Africa region continues to bear the heaviest disease burden. Gabon is one of the high-TB-burden countries in the world; however, its national TB program performance remains weak despite financial support from international health agencies. Identifying and mapping high-TB- and multi-drug-resistant-TB (MDR-TB)-burden areas for targeted public health interventions was the objective of this study. Methods: A region-wide mixed method study was carried out, comprising ecological design and a desk review, with the use of medical records from TB diagnosis and care units in 12 health facilities located across the capital Libreville, Republic of Gabon, from 1 January through December 2022. Libreville is the region that bears the heaviest TB burden in Gabon. With the collaboration of the Agency for Space Studies and Observations (AGEOS, Gabon), collected data were transferred to and analyzed using QGIS software in order to develop satellite images. Results: In the Libreville health region, there were 4560 cases diagnosed in 2022, representing 77.9% of all cases in the country, with an annual incidence of 509 per 100,000. Spatial mapping of incident cases by county of residence showed that a large majority of the TB cases diagnosed at the CHUL care center in 2022 were from Nzeng-Ayong (range: 36–50 cases) and Owendo (26–35 cases), whereas higher TB incidence at the Nkembo care center was from Nzeng-Ayong (range: 356–455 cases) and Owendo (256–355 cases), followed by Nkembo, Akebe ville, Akebe Baraka, Akebe Plaine/plateau, Angondje, Angondje village, Charbonnages, Bikele, Pk11, Pk12, Pk9, Mindoube I, Mindoube II (66–255 cases), Sotega, and Nkok (46–65 cases). Other counties accounted for less than 45 TB cases. Considering MDR-TB cases, higher incidence was observed in Pk9 county, which accounted for six cases (14.6%), followed by Owendo, accounting for four (9.7%). Discussion: Findings suggest that Nzeng-Ayong and Owendo are high-TB-burden counties in Libreville, whereas Pk9 and Owendo counties are counties categorized as high-MDR-TB-incidence areas. They should be subject to targeted to public health interventions to enhance TB control in Libreville. Full article

Background: To identify the radiomics features of both granulomas and tumorlets (TL) and to assess the potential role of radiomics in differentiating these two diseases. Methods: From 2013 to 2021, ninety patients who had undergone lung surgery and pre-operative chest CT evaluation, with pathologically proven granulomas or TL, were retrospectively enrolled. Two radiologists, in consensus, manually segmented the lesions on CT images. Radiomic features were then automatically extracted from these segmentations using dedicated software. The performance of CT radiomics features in differentiating TL from granulomas was tested by receiver operating characteristic curves and the areas under the curve (AUCs), calculating sensitivity and specificity. Results: The final population consisted of 55 patients (38 female; mean age 64 ± 14 years), 32 with TL and 23 with granulomas. Significant differences were found in 16/107 radiomic features: 3 Shape, 1 First Order, 2 Grey Level Co-occurrence Matrix (GLCM), 2 Gray Level Dependence Matrix (GLDM), 4 Grey Level Run Length Matrix (GLRLM), and 4 Gray Level Size Zone Matrix (GLSZM). Flatness and Long Run High Gray Level Emphasis showed the best performances in discriminating TL from granulomas (AUC: 0.903; sensitivity: 100%; specificity: 80%; and AUC: 0.896; sensitivity: 92.3%; specificity: 76.5%; respectively; both p < 0.001). Conclusions: Radiomics may be a non-invasive imaging tool for characterization of small lung nodules, differentiating granulomas from TL, and may play a role in preventing TL growth and its possible malignant evolution, avoiding delayed diagnosis. Full article

Background and Objective: Thymic hyperplasias constitute a heterogeneous group of primarily non-neoplastic conditions characterized by either enlargement of the thymic parenchyma or the proliferation of lymphoid follicles. Differentiating these entities is crucial, as they exhibit significant differences in their epidemiology, clinical associations, and implications for patient management. This review aims to synthesize the current knowledge on true thymic hyperplasia, rebound thymic hyperplasia, thymic follicular hyperplasia, and thymic hyperplasia with lymphoepithelial sialadenitis-like features, and to delineate their diagnostic boundaries in histology. Methods: A systematic literature review of the Medline database was conducted, encompassing all publications available up to November 2025. The search terms included thymic hyperplasia, true thymic hyperplasia, rebound thymic hyperplasia, thymic follicular hyperplasia, and lymphoepithelial-sialadenitis-like thymic hyperplasia. Studies concerning the histopathological, radiological, and clinical presentations as well as reference works concerning age-related thymus size and weight were included. Limitations stem from the scarcity of systematic investigations and heterogeneity of the available studies. Results: Thymic hyperplasia encompasses biologically diverse entities unified solely by their benign nature, yet distinguished by distinct histological patterns and clinical scenarios. True thymic hyperplasia and rebound hyperplasia preserve the organ’s fundamental architecture and lack association with autoimmunity; however, they differ in age distribution and clinical context. Thymic follicular hyperplasia reflects an immune-mediated process, strongly linked to early-onset myasthenia gravis, and is characterized by germinal-center formation within thymic lobes. Lymphoepithelial-sialadenitis-like thymic hyperplasia, by contrast, represents an epithelial–lymphoid proliferation with pronounced architectural distortion, frequent cystic changes, and notable associations with systemic non-myasthenic autoimmune diseases as well as thymic mucosa-associated-lymphatic-tissue lymphoma. Conclusions: Overall, clinical history, serological data, imaging, and detailed histopathology remain indispensable for differentiating thymic hyperplasias, some of which necessitate further systemic evaluation due to their association with autoimmune diseases or lymphomas. Full article

Background/Objectives: To present, discuss, and illustrate the role of multiparametric magnetic resonance imaging (MPMRI) in the evaluation of biliary tree (BT) complications after liver transplantation (LT) as an integrated part into the multidisciplinary team approach for personalized patients’ treatment. Methods: We retrospectively analyzed the MPMRI findings of 317 patients out of 1080 cases with LT, admitted to the Fundeni Clinical Institute from January 2005 to June 2025, who developed biliary complications. Results: Biliary complications after LT evaluated by MPMRI included anastomotic strictures in 235 cases (74%), intra- or extrahepatic bile leaks/biloma in 56 patients (18%), secondary cholangitis due to pyogenic cholangitis in 91 cases (29%), liver abscesses in 23 patients (7%), BT lithiasis in 27 patients (8.5%), disease recurrence in 26 cases (8%), and extrinsic BT compression in 1 case (0.3%). Conclusions: MPMRI plays a crucial role for the evaluation of BT complications, with the protocol being optimized in correlation with the clinical question or suspicion and with the clinical status of the patient. Full article

Lensless microscopy is a well-established imaging approach that replaces traditional lenses with phase modulators, enabling compact, low-cost, and computationally driven analysis of biological samples. In this work, we show how ray tracing simulations can be used to optimize lensless imaging systems for automated classification, particularly for detecting red blood cell (RBC) disease. Rather than improving the machine learning classification algorithm, our focus is on refining optical parameters such as element spacing and modulator type to maximize classification performance. We modeled a lensless microscope in Zemax OpticStudio (ray tracing) and compared the results against Fourier optics simulations. Despite not explicitly modeling diffraction, ray tracing produced classification results largely consistent with wave optics simulations, confirming its effectiveness for parameter optimization in lensless imaging setups used for classification tasks. Furthermore, to show the flexibility of the ray tracing model, we introduced a microlens array (MLA) as the phase modulator and performed the classification task on the generated patterns. These results establish ray tracing as an efficient tool for the optical design of lensless microscopy systems intended for machine learning based biomedical applications. The developed lensless microscopy model enables the generation of datasets for training neural networks. Full article