Telepathology enables remote diagnostic consultation through digital image transmission and is particularly valuable in settings where subspecialty expertise is not immediately available. This study evaluated the diagnostic accuracy of PathoZoom®, a web-based platform that allows real-time remote review of microscope images via mobile devices during intraoperative frozen section examination in a high-complexity academic hospital. Seventy-five consecutive frozen section cases, processed between January and April 2024 at the University Hospital Trust of Modena/UNIMORE, were included. All cases were first diagnosed using conventional light microscopy (LM) and then, after a washout period, the same slides were reviewed remotely using the PathoZoom® system and mobile smartphones. Diagnostic concordance between LM and telepathology was assessed, classifying discrepancies as major or minor, depending on their clinical impact in accordance with the current guidelines of the College of American Pathologists guidelines. Overall concordance was 96% (72/75 cases). One major discordance occurred in a basal cell carcinoma margin assessment, interpreted as negative by telepathology and positive by LM. Two minor discrepancies involved polymorphonuclear cell counts in orthopedic specimens. No differences in performance were observed between mobile devices. Thanks to the technological setting and results, PathoZoom® and Smartphones are proposed as a diagnostic option for intraoperative consultation. Results support safety, feasibility and cost effectiveness of mobile-based telepathology in complex surgical settings and geographically extended healthcare networks.

Dermatomyositis (DM) is a rare autoimmune disease leading to inflammatory and degenerative changes affecting skin and muscles. Interstitial lung disease is a major risk factor for morbidity and mortality in DM. The precise immune mechanism underlying DM, particularly in the context of lung involvement, remains poorly understood. This research focuses on the significance of dendritic cell subsets and Langerhans cells in the immune irregularities observed in DM.The study analyzed 12 cases of DM with lung involvement, and compared the findings to 12 cases with skin involvement. All subjects were untreated at the time of diagnosis, with ages ranging from the 20 to 80 years. Immunohistochemistry was utilized to characterize dendritic cell subsets and to assess cells expressing MDA5. Six cases of rheumatoid arthritis (RA) with fibrosing pneumonia were investigated for comparison.We identified dendritic cells and lymphocytes expressing the melanoma differentiation gene 5 (MDA5) protein. Monocytoid dendritic cells predominated in DM, and were absent in RA, therefore might serve as a diagnostic marker, separating DM from other autoimmune diseases. Our findings suggest that DCs and lymphocytes are constantly attacked by autoantibodies for MDA5, as these cells express the antigen. Furthermore, mature DC in skin and lung and Langerhans in the skin are replaced by immature DC. This might explain prolonged activation of the immune system resulting in fibrosis in both skin and lung tissues. The loss of Langerhans cells might be responsible for an increased penetrance of antigens through the epidermis.

Collagenous colitis (CC) is diagnosed histologically and is characterised by a thickened subepithelial collagen band together with inflammatory and epithelial changes. Although routine haematoxylin and eosin (H&E) staining is sufficient for diagnosis in most cases, visual assessment of the collagen band can be challenging in borderline or heterogeneous specimens. Additional stains may be required in diagnostically difficult situations. To develop a machine-learning–based algorithm for detecting subepithelial collagen band-associated regions in routine H&E-stained colonic biopsy images as a decision-support tool for histopathological assessment. H&E-stained colonic biopsy specimens from 36 patients with histologically confirmed CC were imaged at 20 × magnification (1392 × 1040 pixels). Images were segmented into 1,000 superpixels using the Simple Linear Iterative Clustering (SLIC) algorithm. Superpixels overlapping with expert-provided rough annotations of the collagen band were labelled and characterised using normalised RGB histograms. A feed-forward neural network classifier (three hidden layers, 10 neurons per layer) was trained to distinguish collagen band–associated from non-collagen regions. Class imbalance was addressed by data augmentation of minority-class superpixels. Post-processing with connected-component size filtering was applied to enforce spatial continuity. Superpixel-level performance was evaluated quantitatively, and image-level outputs were assessed using expert acceptability scoring. The classifier achieved a superpixel-wise accuracy of 0.928 (sensitivity 0.898, specificity 0.953). Size-based post-processing substantially reduced isolated false-positive detections. At the image level, the final algorithm achieved an acceptability accuracy of 0.846 according to expert evaluation. The model successfully highlighted subepithelial collagen band–associated regions consistent with expert annotations but did not model additional diagnostic features required for complete CC diagnosis. Our superpixel-based neural network highlights collagen-rich regions in H&E-stained colonic biopsies, offering decision support for pathologists. As diagnosis of collagenous colitis requires broader histopathological and clinical context, this method is intended as a decision-support tool rather than a stand-alone diagnostic solution.

Cervical adenocarcinoma admixed with neuroendocrine carcinoma (A-NEC) is a rare and aggressive tumor with limited molecular characterization and no standardized treatment. This study aims to delineate its clinicopathological and molecular features to improve diagnostic accuracy and identify potential therapeutic targets. Fourteen rigorously diagnosed cervical A-NEC cases (2016–2024) were retrospectively analyzed according to the WHO 2020 criteria. Comprehensive evaluation included cytology, histomorphology, HPV genotyping, and immunohistochemical profiling of neuroendocrine markers, Ki-67, p53, PTEN, PI3K, and PD-L1. All patients presented with abnormal bleeding or discharge. While cytology detected abnormalities in all evaluable cases (10/10), it did not identify neuroendocrine components. Histology revealed interdigitated HPV-associated adenocarcinoma and neuroendocrine carcinoma (predominantly small cell type). Molecular analysis showed component-specific heterogeneity: although TP53 and PD-L1 expression were concordant, the neuroendocrine component exhibited PTEN loss in all 8 evaluable cases (8/8) and PI3K overexpression in 5/8 cases-a pattern not observed in adenocarcinoma components. NEC demonstrated high proliferative activity (median Ki-67 80%). Survival events were concentrated within the first 24 months. This clinicopathological analysis of 14 cervical A‑NEC cases highlights the diagnostic limitations of cytology and the need for histopathological confirmation in surgical specimens. The neuroendocrine component exhibits distinct molecular features (PTEN loss, PI3K overexpression), indicating dysregulation of the PI3K‑AKT‑mTOR pathway as a potential research target. Most events occur within 24 months, underscoring the importance of early monitoring. These findings improve the understanding of this rare malignancy and support future multicenter studies.