versus-host disease (GVHD) can manifest as acute or chronic complications in patients after hematopoietic cell transplantation (HCT). Oral chronic GVHD (cGVHD) occurs in approximately 70% of HCT recipients and includes lichenoid-like mucosal reactions, restricted mouth opening, and salivary gland dysfunction. However, the underlying histopathological presentation remains to be validated in large cohorts. We characterized the histopathological features of oral mucosal cGVHD and devised a scoring model in a large patient cohort (n = 112). Oral mucosal biopsy sections (n = 303) with and without oral cGVHD were identified from archived and current HCT recipients with additional healthy controls. Histological screening was performed on hematoxylin and eosinstained and periodic acid-Schiff-stained sections. A points-based grading tool (0 to 19, grade 0 to IV) was established based on intraepithelial lymphocytes and band-like inflammatory infiltrate, atrophic epithelium with basal cell liquefaction degeneration, including apoptosis, as well as separation of epithelium and pseudo-rete ridges. Validation involved 62 biopsy specimens, including post-HCT (n = 47) and healthy (n = 15) specimens. Remaining biopsy specimens (n = 199) were blindly graded by 3 observers. Histological severity was correlated with clinical diagnostic and distinctive features, demonstrating a spectrum of individual patient severity, including frequent signs of subclinical GVHD in healthy mucosa. However, oral cGVHD presented with significantly higher (P < .001) scores compared with HCT controls, with moderate to high positive likelihood ratios for inflammatory infiltrate, exocytosis, and basal membrane alterations. The grade II-IV biopsy specimens demonstrated a histopathological diagnosis of active mucosal lichenoid-like cGVHD, highlighting the importance of correlating clinical presentation with the dynamic histopathological processes for improved patient stratification. In addition, this tool could be used for assessing treatments, pathological processes, and immune cellular content to provide further insight into this debilitating disease.

Objectives/Hypothesis: This study aimed to determine whether local injection of human mesenchymal stromal cells (MSC) could modulate the early inflammatory response within injured vocal folds (VFs) to promote wound-healing processes.Study Design: Experimental xenograft model. Methods: VF injury was surgically induced by bilateral resection of the lamina propria of rabbits, and MSC were immediately injected into the injured area of both VFs. Animals were sacrificed on days 2, 4, and 24. Histological analyses were performed by hematoxylin and eosin, Masson's Trichrome, and elastin staining. Cell death was visualized by terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL), and the M2 macrophage marker, CD163, detected by immunohistochemistry. Persistence of injected MSC was evaluated by fluorescent in situ hybridization (FISH). Quantitative polymerase chain reaction was performed on the contralateral VF.Results: Histological examination at days 2 and 4 indicated that MSC were able to reduce tissue inflammation, with gene expression analysis confirming a significant reduction of proinflammatory markers, interleukin (IL)-1β, and IL-8. FISH demonstrated low-level persistence of injected MSC at both time points, and TUNEL confirmed localized cell death at the injury site. Increased levels of CD163+ anti-inflammatory macrophages indicated a change in the immune milieu, supporting wound resolution. Evidence of a more organized collagen matrix suggests that MSC may enhance the production of a functional repair tissue after injury, despite their low-level persistence within the tissue.Conclusions: This study demonstrates that MSC are able to positively modulate the early wound-healing response through resolution of the inflammatory phase and promotion of tissue repair.

Visual grading of chromogenically stained immunohistochemical (IHC) samples is subjective, time consuming, and predisposed to considerable inter- and intra-observer variations. The open-source digital analysis software, CellProfiler has been extensively used for fluorescently stained cells/tissues; however, chromogenic IHC staining is routinely used in both pathological and research diagnostics. The current investigation aimed to compare CellProfiler quantitative chromogenic IHC analyses against the gold standard manual counting. Oral mucosal biopsies from patients with chronic graft-versus-host disease were stained for CD4. Digitized images were manually counted and subjected to image analysis in CellProfiler. Inter-observer and inter-platform agreements were assessed by scatterplots with linear regression and Bland-Altman plots. Validation comparisons between the manual counters demonstrated strong intra-observer concordance (r = 0.979), particularly when cell numbers were less than 100. Scatterplots and Bland-Altman plots demonstrated strong agreement between the manual counters and CellProfiler, with the number of positively stained cells robustly correlating (r = 0.938). Furthermore, CellProfiler allowed the determination of multiple variables simultaneously, such as area stained and masking to remove any nonstained tissue and white gaps, which also demonstrated reliable agreement (r = >0.9). CellProfiler demonstrated versatility with the ability to assess large numbers of images and allowed additional parameters to be quantified. CellProfiler allowed rapid high processing capacity of chromogenically stained chronic inflammatory tissue that was reliable, accurate, and reproducible and highlights potential applications in research diagnostics.

Our knowledge of synovial tissues in patients that are scheduled for surgery as a result of temporomandibular joint (TMJ) disorders is limited. Characterising the protein profile, as well as mapping clinical preoperative variables, might increase our understanding of pathogenesis and forecast surgical outcome. A cohort of 100 patients with either disc displacement, osteoarthritis, or chronic inflammatory arthritis (CIA) was prospectively investigated for a set of preoperative clinical variables. During surgery, a synovial tissue biopsy was sampled and analysed via multi-analytic profiling. The surgical outcome was classified according to a predefined set of outcome criteria six months postoperatively. Higher concentrations of interleukin 8 (p = 0.049), matrix metalloproteinase 7 (p = 0.038), lumican (p = 0.037), and tissue inhibitor of metalloproteinase 2 (p = 0.015) were significantly related to an inferior surgical outcome. Several other proteins, which were not described earlier in the TMJ synovia, were detected but not related to surgical outcome. Bilateral masticatory muscle palpation pain had strong association to a poor outcome that was related to the diagnoses disc displacement and osteoarthritis. CIA and the patient-reported variable TMJ disability might be related to an unfavourable outcome according to the multivariate model. These findings of surgical predictors show potential in aiding clinical decision-making and they might enhance the understanding of aetiopathogenesis in TMJ disorders.

Post-transplant, patients treated with allogeneic hematopoietic cell transplantation (HCT) commonly develop acute(a) and/or chronic(c) graft-versus-host disease (GVHD) (Lee, 2017). cGVHD, an autoimmune-like disorder, can develop at multiple sites, and 45%-83% of patients develop oral manifestations (

Background Histological feature representation is advantageous for computer aided diagnosis (CAD) and disease classification when using predictive techniques based on machine learning. Explicit feature representations in computer tissue models can assist explainability of machine learning predictions. Different approaches to feature representation within digital tissue images have been proposed. Cell-graphs have been demonstrated to provide precise and general constructs that can model both low- and high-level features. The basement membrane is high-level tissue architecture, and interactions across the basement membrane are involved in multiple disease processes. Thus, the basement membrane is an important histological feature to study from a cell-graph and machine learning perspective. Results We present a two stage machine learning pipeline for generating a cell-graph from a digital H &E stained tissue image. Using a combination of convolutional neural networks for visual analysis and graph neural networks exploiting node and edge labels for topological analysis, the pipeline is shown to predict both low- and high-level histological features in oral mucosal tissue with good accuracy. Conclusions Convolutional and graph neural networks are complementary technologies for learning, representing and predicting local and global histological features employing node and edge labels. Their combination is potentially widely applicable in histopathology image analysis and can enhance explainability in CAD tools for disease prediction.

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