Hasil untuk "Pathology"

T. Sheahan, A. Sims, S. Leist et al.

Middle East respiratory syndrome coronavirus (MERS-CoV) is the causative agent of a severe respiratory disease associated with more than 2468 human infections and over 851 deaths in 27 countries since 2012. There are no approved treatments for MERS-CoV infection although a combination of lopinavir, ritonavir and interferon beta (LPV/RTV-IFNb) is currently being evaluated in humans in the Kingdom of Saudi Arabia. Here, we show that remdesivir (RDV) and IFNb have superior antiviral activity to LPV and RTV in vitro. In mice, both prophylactic and therapeutic RDV improve pulmonary function and reduce lung viral loads and severe lung pathology. In contrast, prophylactic LPV/RTV-IFNb slightly reduces viral loads without impacting other disease parameters. Therapeutic LPV/RTV-IFNb improves pulmonary function but does not reduce virus replication or severe lung pathology. Thus, we provide in vivo evidence of the potential for RDV to treat MERS-CoV infections. Remdesivir (RDV) is a broad-spectrum antiviral drug with activity against MERS coronavirus, but in vivo efficacy has not been evaluated. Here, the authors show that RDV has superior anti-MERS activity in vitro and in vivo compared to combination therapy with lopinavir, ritonavir and interferon beta and reduces severe lung pathology.

S. Dominy, Casey C. Lynch, F. Ermini et al.

Gingipains from Porphyromonas gingivalis drive Alzheimer’s pathology and can be blocked with small-molecule inhibitors. Porphyromonas gingivalis, the keystone pathogen in chronic periodontitis, was identified in the brain of Alzheimer’s disease patients. Toxic proteases from the bacterium called gingipains were also identified in the brain of Alzheimer’s patients, and levels correlated with tau and ubiquitin pathology. Oral P. gingivalis infection in mice resulted in brain colonization and increased production of Aβ1–42, a component of amyloid plaques. Further, gingipains were neurotoxic in vivo and in vitro, exerting detrimental effects on tau, a protein needed for normal neuronal function. To block this neurotoxicity, we designed and synthesized small-molecule inhibitors targeting gingipains. Gingipain inhibition reduced the bacterial load of an established P. gingivalis brain infection, blocked Aβ1–42 production, reduced neuroinflammation, and rescued neurons in the hippocampus. These data suggest that gingipain inhibitors could be valuable for treating P. gingivalis brain colonization and neurodegeneration in Alzheimer’s disease.

A. Kutzelnigg, C. Lucchinetti, C. Stadelmann et al.

P. Kleihues, L. Sobin

K. P. Pritzker, K. P. Pritzker, S. Jimenez et al.

OBJECTIVE Current osteoarthritis (OA) histopathology assessment methods have difficulties in their utility for early disease, as well as their reproducibility and validity. Our objective was to devise a more useful method to assess OA histopathology that would have wide application for clinical and experimental OA assessment and would become recognized as the standard method. DESIGN An OARSI Working Group deliberated on principles, standards and features for an OA cartilage pathology assessment system. Using current knowledge of the pathophysiology of OA morphologic features, a proposed system was presented at OARSI 2000. Subsequently, this was widely circulated for comments amongst experts in OA pathology. RESULTS An OA cartilage pathology assessment system based on six grades, which reflect depth of the lesion and four stages reflecting extent of OA over the joint surface was developed. CONCLUSIONS The OARSI cartilage OA histopathology grading system appears consistent and simple to apply. Further studies are required to confirm the system's utility.

B. Vallée, K. Falchuk

P. Mcgeer, S. Itagaki, B. Boyes et al.

M. M. Benjamin

A. Martinez‐Hernandez, P. Amenta

Erik Thiringer, Fredrik K. Gustafsson, Kajsa Ledesma Eriksson et al.

Pathology foundation models (PFMs) have become central to computational pathology, aiming to offer general encoders for feature extraction from whole-slide images (WSIs). Despite strong benchmark performance, PFM robustness to real-world technical domain shifts, such as variability from whole-slide scanner devices, remains poorly understood. We systematically evaluated the robustness of 14 PFMs to scanner-induced variability, including state-of-the-art models, earlier self-supervised models, and a baseline trained on natural images. Using a multiscanner dataset of 384 breast cancer WSIs scanned on five devices, we isolated scanner effects independently from biological and laboratory confounders. Robustness is assessed via complementary unsupervised embedding analyses and a set of clinicopathological supervised prediction tasks. Our results demonstrate that current PFMs are not invariant to scanner-induced domain shifts. Most models encode pronounced scanner-specific variability in their embedding spaces. While AUC often remains stable, this masks a critical failure mode: scanner variability systematically alters the embedding space and impacts calibration of downstream model predictions, resulting in scanner-dependent bias that can impact reliability in clinical use cases. We further show that robustness is not a simple function of training data scale, model size, or model recency. None of the models provided reliable robustness against scanner-induced variability. While the models trained on the most diverse data, here represented by vision-language models, appear to have an advantage with respect to robustness, they underperformed on downstream supervised tasks. We conclude that development and evaluation of PFMs requires moving beyond accuracy-centric benchmarks toward explicit evaluation and optimisation of embedding stability and calibration under realistic acquisition variability.

Michael Okonoda, Eder Martinez, Abhilekha Dalal et al.

Convolutional Neural Networks have shown promising effectiveness in identifying different types of cancer from radiographs. However, the opaque nature of CNNs makes it difficult to fully understand the way they operate, limiting their assessment to empirical evaluation. Here we study the soundness of the standard practices by which CNNs are evaluated for the purpose of cancer pathology. Thirteen highly used cancer benchmark datasets were analyzed, using four common CNN architectures and different types of cancer, such as melanoma, carcinoma, colorectal cancer, and lung cancer. We compared the accuracy of each model with that of datasets made of cropped segments from the background of the original images that do not contain clinically relevant content. Because the rendered datasets contain no clinical information, the null hypothesis is that the CNNs should provide mere chance-based accuracy when classifying these datasets. The results show that the CNN models provided high accuracy when using the cropped segments, sometimes as high as 93\%, even though they lacked biomedical information. These results show that some CNN architectures are more sensitive to bias than others. The analysis shows that the common practices of machine learning evaluation might lead to unreliable results when applied to cancer pathology. These biases are very difficult to identify, and might mislead researchers as they use available benchmark datasets to test the efficacy of CNN methods.

G. Klöppel, M. Löhr, K. Habich et al.

Shaivy Malik, Rajat, Charanjeet Ahluwalia

Isolated primary tuberculoma of the paranasal sinuses is an exceedingly rare form of extra-pulmonary tuberculosis (EPTB), particularly in immunocompetent individuals. Its presentation is often atypical, mimicking aggressive neoplasms due to features such as local bone destruction, which complicates diagnosis and may lead to unnecessary invasive interventions. We report the case of a 32-year-old immunocompetent male presenting with chronic right nasal obstruction, rhinorrhoea, facial puffiness, hyposmia, and intermittent fever. Imaging revealed a heterogeneous mass in the right frontal, ethmoidal, and maxillary sinuses, with extensive bony erosion suggestive of a malignant etiology. Histopathological examination of biopsy tissue, however, demonstrated granulomatous inflammation with Langhans giant cells, necrosis, and acid-fast bacilli on Ziehl-Neelsen staining, confirming tuberculoma. Anti-tubercular treatment (ATT) was promptly initiated post-biopsy, leading to complete symptom resolution and no recurrence. This case sheds light on the importance of including tuberculoma in the differential diagnosis of aggressive sinonasal lesions in immunocompetent patients despite their rarity, and highlights the critical role of histopathology and ATT in effective management, potentially preventing extensive surgical resections and associated morbidity.

Madison McGraw, Amod Sharma, Dinesh Bhattarai et al.

Abstract Ischemia‐reperfusion injury (IRI) is the central contributing factor to acute kidney injury (AKI). Kidney tissue that becomes necrotic during this process releases a variety of pro‐inflammatory factors, driving activation of the complement cascade. Complement 5 (C5), in particular, has become an important therapeutic target, yet pharmacologic targeting does not achieve complete inhibition nor target all variants of this abundant protein. Here, we have generated and characterized a novel rat model of CRISPR/Cas9‐mediated global C5 deletion (C5−/−). C5−/− rats displayed no differences in growth, blood chemistry, or kidney morphology/function from wild‐type (C5+/+) counterparts at baseline. Subsequently, we compared C5−/− rats to C5+/+ littermates in a renal IRI model to assess differences in the post‐injury response. Compared to C5+/+, C5−/− rats displayed significantly improved kidney injury/function as well as the attenuation of the apoptotic pathway post‐IRI. The circulating immune cell composition was affected by C5−/− post‐injury, with significantly increased NK cells, B cells, and CD8+ T‐cells compared to C5+/+, indicating altered inflammatory signaling. Similarly, renal sections showed a reduced level of immune cell infiltration, including macrophages and neutrophils. Collectively, these results demonstrate the generation of an effective rodent model of global C5 deletion and the role of C5 as an injury‐promoting molecule during kidney IRI.

Daniël Boeke, Cedrik Blommestijn, Rebecca N. Wray et al.

Recurrence risk estimation in clear cell renal cell carcinoma (ccRCC) is essential for guiding postoperative surveillance and treatment. The Leibovich score remains widely used for stratifying distant recurrence risk but offers limited patient-level resolution and excludes imaging information. This study evaluates multimodal recurrence prediction by integrating preoperative computed tomography (CT) and postoperative histopathology whole-slide images (WSIs). A modular deep learning framework with pretrained encoders and Cox-based survival modeling was tested across unimodal, late fusion, and intermediate fusion setups. In a real-world ccRCC cohort, WSI-based models consistently outperformed CT-only models, underscoring the prognostic strength of pathology. Intermediate fusion further improved performance, with the best model (TITAN-CONCH with ResNet-18) approaching the adjusted Leibovich score. Random tie-breaking narrowed the gap between the clinical baseline and learned models, suggesting discretization may overstate individualized performance. Using simple embedding concatenation, radiology added value primarily through fusion. These findings demonstrate the feasibility of foundation model-based multimodal integration for personalized ccRCC risk prediction. Future work should explore more expressive fusion strategies, larger multimodal datasets, and general-purpose CT encoders to better match pathology modeling capacity.

Yousef Yeganeh, Maximilian Frantzen, Michael Lee et al.

While Whole Slide Imaging (WSI) scanners remain the gold standard for digitizing pathology samples, their high cost limits accessibility in many healthcare settings. Other low-cost solutions also face critical limitations: automated microscopes struggle with consistent focus across varying tissue morphology, traditional auto-focus methods require time-consuming focal stacks, and existing deep-learning approaches either need multiple input images or lack generalization capability across tissue types and staining protocols. We introduce a novel automated microscopic system powered by DeepAf, a novel auto-focus framework that uniquely combines spatial and spectral features through a hybrid architecture for single-shot focus prediction. The proposed network automatically regresses the distance to the optimal focal point using the extracted spatiospectral features and adjusts the control parameters for optimal image outcomes. Our system transforms conventional microscopes into efficient slide scanners, reducing focusing time by 80% compared to stack-based methods while achieving focus accuracy of 0.18 μm on the same-lab samples, matching the performance of dual-image methods (0.19 μm) with half the input requirements. DeepAf demonstrates robust cross-lab generalization with only 0.72% false focus predictions and 90% of predictions within the depth of field. Through an extensive clinical study of 536 brain tissue samples, our system achieves 0.90 AUC in cancer classification at 4x magnification, a significant achievement at lower magnification than typical 20x WSI scans. This results in a comprehensive hardware-software design enabling accessible, real-time digital pathology in resource-constrained settings while maintaining diagnostic accuracy.

Dominik Winter, Mai Bui, Monica Azqueta Gavaldon et al.

Scarcity of annotated data, particularly for rare or atypical morphologies, present significant challenges for cell and nuclei segmentation in computational pathology. While manual annotation is labor-intensive and costly, synthetic data offers a cost-effective alternative. We introduce a Multimodal Semantic Diffusion Model (MSDM) for generating realistic pixel-precise image-mask pairs for cell and nuclei segmentation. By conditioning the generative process with cellular/nuclear morphologies (using horizontal and vertical maps), RGB color characteristics, and BERT-encoded assay/indication metadata, MSDM generates datasests with desired morphological properties. These heterogeneous modalities are integrated via multi-head cross-attention, enabling fine-grained control over the generated images. Quantitative analysis demonstrates that synthetic images closely match real data, with low Wasserstein distances between embeddings of generated and real images under matching biological conditions. The incorporation of these synthetic samples, exemplified by columnar cells, significantly improves segmentation model accuracy on columnar cells. This strategy systematically enriches data sets, directly targeting model deficiencies. We highlight the effectiveness of multimodal diffusion-based augmentation for advancing the robustness and generalizability of cell and nuclei segmentation models. Thereby, we pave the way for broader application of generative models in computational pathology.

Annalisa Caligiuri, David Papo, Görsev Yener et al.

Spontaneous brain activity generically displays transient spatiotemporal coherent structures, which can selectively be affected in various neurological and psychiatric pathologies. Here we model the full brain's electroencephalographic activity as a high-dimensional functional network performing a trajectory in a latent graph phase space. This approach allows us to investigate the orbital stability of brain's activity and in particular its short-term predictability. We do this by constructing a non-parametric statistic quantifying the expansion of initially close functional network trajectories. We apply the method to cohorts of healthy ageing individuals, and patients previously diagnosed with Parkinson's or Alzheimer's disease. Results not only characterise brain dynamics from a new angle, but further show that functional network predictability varies in a marked scale-dependent way across healthy controls and patient groups. The path towards both pathologies is markedly different. Furthermore, healthy ageing's predictability appears to strongly differ from that of Parkinson's disease, but much less from that of patients with Alzheimer's disease.

Gianluca Carloni, Biagio Brattoli, Seongho Keum et al.

Computational pathology (CPath) has shown great potential in mining actionable insights from Whole Slide Images (WSIs). Deep Learning (DL) has been at the center of modern CPath, and while it delivers unprecedented performance, it is also known that DL may be affected by irrelevant details, such as those introduced during scanning by different commercially available scanners. This may lead to scanner bias, where the model outputs for the same tissue acquired by different scanners may vary. In turn, it hinders the trust of clinicians in CPath-based tools and their deployment in real-world clinical practices. Recent pathology Foundation Models (FMs) promise to provide better domain generalization capabilities. In this paper, we benchmark FMs using a multi-scanner dataset and show that FMs still suffer from scanner bias. Following this observation, we propose ScanGen, a contrastive loss function applied during task-specific fine-tuning that mitigates scanner bias, thereby enhancing the models' robustness to scanner variations. Our approach is applied to the Multiple Instance Learning task of Epidermal Growth Factor Receptor (EGFR) mutation prediction from H\&E-stained WSIs in lung cancer. We observe that ScanGen notably enhances the ability to generalize across scanners, while retaining or improving the performance of EGFR mutation prediction.

Birger Moell, Fredrik Sand Aronsson

This study explores voice cloning to generate synthetic speech replicating the unique patterns of individuals with dysarthria. Using the TORGO dataset, we address data scarcity and privacy challenges in speech-language pathology. Our contributions include demonstrating that voice cloning preserves dysarthric speech characteristics, analyzing differences between real and synthetic data, and discussing implications for diagnostics, rehabilitation, and communication. We cloned voices from dysarthric and control speakers using a commercial platform, ensuring gender-matched synthetic voices. A licensed speech-language pathologist (SLP) evaluated a subset for dysarthria, speaker gender, and synthetic indicators. The SLP correctly identified dysarthria in all cases and speaker gender in 95% but misclassified 30% of synthetic samples as real, indicating high realism. Our results suggest synthetic speech effectively captures disordered characteristics and that voice cloning has advanced to produce high-quality data resembling real speech, even to trained professionals. This has critical implications for healthcare, where synthetic data can mitigate data scarcity, protect privacy, and enhance AI-driven diagnostics. By enabling the creation of diverse, high-quality speech datasets, voice cloning can improve generalizable models, personalize therapy, and advance assistive technologies for dysarthria. We publicly release our synthetic dataset to foster further research and collaboration, aiming to develop robust models that improve patient outcomes in speech-language pathology.