Comparable outcomes were published using a buccal mucosa graft (BMG) from the cheek and a lingual mucosa graft (LMG) from the sublingual area, for urethral augmentation or substitution. To date, no histological comparison between both grafts has been conducted. We histologically assessed BMG and LMG harvested during urethral surgeries, aiming to compare graft properties and vascular support. We conducted a prospective single cohort study, including oral mucosa urethroplasty patients. During surgery, graft dimensions and donor sites were collected, and a 0.5 × 0.5 cm sample was obtained from the prepared graft. Formalin-fixed paraffin-embedded samples were sliced at 4 micrometres (µm) and hematoxylin-eosin stained. Using a telepathology tool, all slides were digitalized and measured from 10× to 40× magnification. In each graft, global and individual layers thicknesses were assessed, including vascular density and area. Descriptive and comparative (parametrical and non-parametrical) statistical analysis occurred. We collected 57 grafts during 33 urethroplasties, with 30 BMG and 22 LMG, finally, included. The mean age was 56.6 (SD 15.2) years, and the mean graft length was 5.8 (SD 1.7) cm and the width was 1.7 (SD 0.4) cm. The median graft thickness was 1598.9 (IQR 1200–2100) µm, the mean epithelium layer was 510.2 (SD 223.7) µm, the median submucosa was 654 (IQR 378–943) µm. the median muscular was 477.6 (IQR 286–772) µm, the median vascular area was 5% (IQR 5–10), and the median adipose tissue area was 5% (IQR 0–20). LMG were significantly longer and narrower than BMG. Total graft thickness was similar between LMG and BMG, but the epithelium and submucosa layers were significantly thinner in LMG. The muscular layer was significantly thicker in LMG. Vascular density and vascular areas were not significantly different between both types of grafts. LMG showed significantly less adipose tissue compared with BMG. Our findings show LMG and BMG for urethroplasty surgeries share the same thickness and blood supply, despite having significantly different graft sizes as well as mucosal and submucosal layers thickness.

In glioblastoma (GBM) patients, maximal safe resection remains a challenge today due to its invasiveness and diffuse parenchymal infiltration. In this context, plasmonic biosensors could potentially help to discriminate tumor tissue from peritumoral parenchyma based on differences in their optical properties. A nanostructured gold biosensor was used ex vivo to identify tumor tissue in a prospective series of 35 GBM patients who underwent surgical treatment. For each patient, two paired samples, tumor and peritumoral tissue, were extracted. Then, the imprint left by each sample on the surface of the biosensor was individually analyzed, obtaining the difference between their refractive indices. The tumor and non-tumor origins of each tissue were assessed by histopathological analysis. The refractive index (RI) values obtained by analyzing the imprint of the tissue were significantly lower (p = 0.0047) in the peritumoral samples (1.341, Interquartile Range (IQR) 1.339–1.349) compared with the tumor samples (1.350, IQR 1.344–1.363). The ROC (receiver operating characteristic) curve showed the capacity of the biosensor to discriminate between both tissues (area under the curve, 0.8779, p < 0.0001). The Youden index provided an optimal RI cut-off point of 0.003. The sensitivity and specificity of the biosensor were 81% and 80%, respectively. Overall, the plasmonic-based nanostructured biosensor is a label-free system with the potential to be used for real-time intraoperative discrimination between tumor and peritumoral tissue in patients with GBM.

(1) Background: Focal and segmental glomerulosclerosis (FSGS) is a pattern of injury that results from podocyte loss in the setting of a wide variety of injurious mechanisms. These include both acquired and genetic as well as primary and secondary causes, or a combination thereof, without optimal therapy, and a high rate of patients develop end-stage renal disease (ESRD). Genetic studies have helped improve the global understanding of FSGS syndrome; thus, we hypothesize that patients with primary FSGS may have underlying alterations in adhesion molecules or extracellular matrix glycoproteins related to previously unreported mutations that may be studied through next-generation sequencing (NGS). (2) Methods: We developed an NGS panel with 29 genes related to adhesion and extracellular matrix glycoproteins. DNA was extracted from twenty-three FSGS patients diagnosed by renal biopsy; (3) Results: The average number of accumulated variants in FSGS patients was high. We describe the missense variant ITGB3c.1199G>A, which is considered pathogenic; in addition, we discovered the nonsense variant CDH1c.499G>T, which lacks a Reference SNP (rs) Report and is considered likely pathogenic. (4) Conclusions: To the best of our knowledge, this is the first account of a high rate of change in extracellular matrix glycoproteins and adhesion molecules in individuals with adult-onset FSGS. The combined effect of all these variations may result in a genotype that is vulnerable to the pathogenesis of glomerulopathy.

We present PRECISE (PRostate Expert-annotated Contiguous IHC–H&E Serial sEctions), a hybrid histopathology dataset of paired hematoxylin and eosin (H&E) and immunohistochemistry (IHC) whole-slide images (WSIs), comprising 37 prostate core needle biopsies from 25 patients, each with matched H&E and CKAPM+racemase staining. To the best of our knowledge, this is the first publicly available dataset offering spatially harmonized, pixel-level expert annotations across both staining modalities in prostate biopsy WSIs — directly mirroring the two-stage (H&E-then-IHC) clinical diagnostic workflow used to resolve morphological uncertainty, restricted to cases in which that workflow reached diagnostic consensus. The dataset contains 24,387 annotations spanning seven diagnostically critical classes: malignant glands, benign glands, stromal tissue, intraductal carcinoma (IDC-P), high-grade prostatic intraepithelial neoplasia (HGPIN), atypical intraductal proliferation (AIP), and tissue artifacts. Unlike existing resources, which focus on binary tumor classification or lack IHC pairing, this dataset captures the full morphological spectrum encountered in routine prostate pathology, including rare precursor lesions and confounding entities underrepresented in current benchmarks. Annotations were validated through a structured three-stage consensus by two expert uropathologists, with IHC serving as biological ground truth for boundary definition. PRECISE is designed as a robust benchmark for multimodal semantic segmentation and self-supervised learning, and is openly released to promote reproducible research and accelerate AI-assisted diagnosis in prostate cancer. Our dataset is available at 10.5281/zenodo.20721779.

We present an expert-labelled image dataset designed to support automated analysis of image-guided prostate core needle biopsies (CNBs) in the context of prostate cancer diagnosis. The dataset includes 37 whole slide images (WSIs), corresponding to 60 CNBs stained with hematoxylin and eosin (H&E), from 32 patients. The dataset contains 15480 manual expert annotations, covering benign glands, tumor, stromal tissue, adipose tissue, nerves, nerve ganglia, blood vessels, muscle, and inflammatory cells. Additional recorded features include stromal retractions, normal and abnormal glandular secretions, and tissue or digital image artifacts. These annotations represent the normal findings that pathologists encounter in routine diagnosis. The annotations were performed by two experienced uropathologists with final labels determined by consensus; immunohistochemical techniques were used as guidance. This dataset provides a foundation for training and benchmarking machine learning models for prostate biopsy assessment and is openly available to facilitate reproducibility and further research.