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The development of digital pathology offers a significant opportunity to evaluate and analyze the whole slides of disease tissue effectively. In particular, the segmentation of nuclei from histopathology images plays an important role in quantitatively measuring and evaluating the acquired diseased tissue. There are many automatic methods to segment cell nuclei in histopathology images. One widely used unsupervised segmentation approach is based on standard k-means or fuzzy c-means (FCM) to process the color histopathology images to segment cell nuclei. Compared with the supervised learning method, this approach can obtain segmented nuclei without annotated nuclei labels for training, which saves a lot of labeling and training time. The color space and $k$ value among this method plays a crucial role in determining the nuclei segmentation performance. However, few works have investigated various color spaces and $k$ value selection simultaneously in unsupervised color-based nuclei segmentation with $k$-means or FCM algorithms. In this study, we will present color-based nuclei segmentation methods with standard $k$-means and FCM algorithms for histopathology images. Several color spaces of Haematoxylin and Eosin (H&E) stained histopathology data and various $k$ values among $k$-means and FCM are investigated correspondingly to explore the suitable selection for nuclei segmentation. A comprehensive nuclei dataset with 7 different organs is used to validate our proposed method. Related experimental results indicate that $L^{\ast }{a}^{\ast }{b}^{\ast }$ and the YCbCr color spaces with a $k$ of 4 are more reasonable for nuclei segmentation via $k$-means, while the $L^{\ast }{a}^{\ast }{b}^{\ast }$ color space with $k$ of 4 is useful via FCM.

Nonsmall cell lung cancer (NSCLC), encompassing lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), is a major global health challenge due to its high mortality rate. Current molecular classifications of NSCLC fail to adequately integrate subtype-specific molecular and phenotypic differences, and many are not directly applicable to clinical diagnosis, treatment, or prognosis guidance. To address this, we develop a machine learning-based tumor subtyping framework, Morphgene, that integrates morphological analysis from Hematoxylin and Eosin (H&E) stained slides with multiomics data, successfully delineating four distinct survival-related subtypes for both LUAD and LUSC. Our analysis identifies unique molecular profiles and treatment responses for these subtypes: LUAD’s Cluster C is characterized by low mutation rates and EGFR mutations, showing resistance to immunotherapy but sensitivity to targeted therapies. In contrast, LUAD’s Cluster B and LUSC’s Cluster D are likely to benefit from immunotherapy. LUSC’s Cluster A also shows enhanced survival with chemoradiotherapy. This integrated subtyping approach provides clearer insights for personalized treatment strategies in NSCLC.

The purpose of this study is to investigate the effects of normal, moderate and high doses of fluoride on rat epipyseal growth plate and surrounding bone, and to compare it with controls. In order to achieve this, 80 rats were divided into five groups (n: 16/group) and treated with 0, 1.2, 3, 50 and 100 ppm fluoridated water since birth (Groups I to V, respectively). Four rats from each group were sacrificed at 6th, 9th, 12th and 16th weeks for radiological and histopathological examinations.

There was no significant variation on programmed cell death of cartilagenous components of the growth plate and chondroid matrix histopathologically between control group and fluoride-treated groups. But, minimal irregularities on the cartilage septum of primary spongiosa were detected in one rat from Group IV and one from Group V. The main histopathologic findings of the rats which were treated with high doses of fluoride were irregular lamella, variation in calcium content, minimally enlarged Haversian canals, focal osteoblastic proliferation, rare zones of woven bone on the diaphyseal cortical bone and secondary spongiosa, increase in ossicle size and minimal focal osteoblastic proliferation on the secondary ossification center. The only consistent radiological finding was relative widening and late partial fusing of the epiphyseal growth plates of high-dose treated groups at 16th week.

As a result, high doses of fluoride did not directly affect chondrocyte morphology of growth plate on young rats. More sophisticated techniques would be beneficial for further investigations on this subject.

Hereditary motor and sensory neuropathy type V (HMSN V) is a rare disorder characterized by the clinical features of spastic paraplegia with peroneal muscular atrophy. A patient with progressive deformity of the feet due to HMSN V underwent clinical, electrophysiological, histopathological examinations, and surgical treatments. Physical examination revealed bilateral pes equinovarus and peroneal muscular atrophy with pyramidal tract features. In the lower limbs, motor nerve conduction velocity could not be determined, and sensory nerve conduction velocity was decreased. In contrast, the results of both the conduction studies were normal for the upper limbs. A sural nerve biopsy revealed a decrease in the number of myelinated fibers and in the diameter of the unmyelinated fibers, and axonal and myelin regeneration was found on light and electron microscopic examinations, suggesting an axonopathy. Since the patient had pyramidal tract features, the same pathological changes may be present in the spinal cord. In the surgical treatment to correct deformity of the feet in this patient, triple arthrodesis was necessary.

Histopathology involves the analysis of microscopic tissue images for diagnosing and studying the progress of diseases, such as cancers. Recently, Artificial Intelligence algorithms reached encouraging success in diagnosing diseases related to these medical images. However, research in this area can be hampered by several problems. Indeed, due to the sensitive nature of medical data, it is challenging to access real datasets, making it impossible to train Deep Learning models. Moreover, real datasets often contain biases or imbalances that hinder the generalization of the results on new unseen data. Variational Autoencoders are a popular class of probabilistic generative models that enable consistent training and a useful latent representation of the original input. However, there are theoretical and practical obstacles that hinder their generative potential. Here, we consider different approaches to address the challenges of synthetic data generation of histopathology images and discuss the potential impact in improving the performance of diagnosis models.

Silver nanoparticles (Ag NPs) are interesting nanotechnology materials with borderless applications in medical science, environmental science, material science and are also used in various kinds of industrial household products. Increasing usage of Ag NPs every year leads to increased risk of nanomaterial contamination in the environment, especially natural water sources with harmful effects in aquatic animals, and to ecosystem disruption. In the present study, Ag NPs toxicity was determined using brine shrimp Artemia salina as a model. A total of 10 adult brine shrimps were incubated in solutions containing various Ag NPs concentrations for 24h at room temperature. Percentages of brine shrimp dead were recorded and calculated to determine the lethal concentration. We found the LC${}_{50}$ of Ag NPs is 3521.13mg/L. Histopathology study was done using survival brine shrimps after 24h of incubation with 25% LC${}_{50}$ Ag NPs concentration. Tissue processing and H&E staining to remark pathological areas with emphasis on intestinal tract, were also done. Ag NPs obstruction was found in intestinal lumen. The intestinal epithelial cells showed hyperplasia and blebbing, increased mucous thickening, detachment from basement muscle lining, and necrosis area. These findings indicated the effect of Ag NPs and their negative impact on aquatic animals which might lead to further disruption of ecosystem.

In the past few years, a low-rate but persistent mortality was noted in the cultured abalone in the farms. This disease affected abalone reared in the summer, and resulted in an 80% cumulative mortality in the farms. The histopathology revealed various extensive fragmentation, hyaline degeneration and necrosis of the myofibers accompanying with moderate degree of hemocyte infiltration in the foot muscle. A negative-contrast electron microscopy was unable to detect viral particles from pooled tissues. No virions were observed via the electron microscopy using direct negative staining of pooled organs. Moribund abalones were performed DNA extraction, purified and DNA sequencing. These sequenced fragments had 3370/3378 (99%), 5666/5704 (99%) and 5945/5961 (99%) identities to abalone shriveling syndrome-associated virus of China isolate. Primer sets designed from the sequences of Taiwan isolate was able to amplify a 693 bp from moribund abalone. In this study, a retrospective study by using PCR revealed that 30% (12/48) of clinical chronic mortality cases had carried bacteriophage-related chimeric marine virus. Tissues from field cases of chronic mortality were applied in in situ hybridization, but without significant signal detected. Thus, etiology of mortality cases remained elusive.

In this study, we compared the accuracy of using cytological and histopathological methods to diagnose superficial masses, examining the discrepancies between these methods to clarify the clinical usefulness of cytology. Complete agreement (CA) was demonstrated in 62.62% of the masses, partial agreement (PA) in 15.10% of the masses, and disagreement (DA) in 22.28% of the masses. The accuracy in identifying cell origin was 70.05%. When diagnosing neoplasia, cytology exhibited 68.62% sensitivity and 77.22% specificity; its positive predictive value was 92.53% and negative predictive value was 37.42%. The overall agreement in diagnosing malignancy was 70.3%. We accurately diagnosed certain neoplasms based on specific cytological characteristics. Even when handling uncertain samples, cytological examination yielded high sensitivity and positive predictive values for diagnosing malignancy; this should be sufficient for veterinary clinicians to schedule further treatments.

This paper describes, for the first time, the clinical and histopathological characteristics of squamous cell carcinoma (SCC) in the hoof of a two-year-old female native Iranian sheep. Physical examination of this uncommon case revealed a large, irregularly-shaped, dark-brown-to-blackish, cauliflower-like ulcerated mass in the coronary band of the hoof which had caused lameness and bleeding. On cut-section, a dark red ulcerated stiff mass was observed. In the histopathological investigation, the moderately-differentiated SCC revealed keratin pearls accumulated moderately in cytoplasm, as well as acellular ones, and a relatively dense fibrous stroma. The large tumor cells contained a small amount of eosinophilic cytoplasm and vesicular nuclei, and in some cases, they were slightly hyperchromatic. As well, in some areas, mitotic figures as well as diverse inflammatory cells infiltration were observed. To the best of our knowledge, this is the first paper reporting an incidence of SCC in an ovine hoof.

Clustering as an exploratory technique has been a promising approach for performing data analysis. In this paper, we propose a non-parametric Bayesian inference to address clustering problem. This approach is based on infinite multivariate Beta mixture models constructed through the framework of Dirichlet process. We apply an accelerated variational method to learn the model. The motivation behind proposing this technique is that Dirichlet process mixture models are capable to fit the data where the number of components is unknown. For large-scale data, this approach is computationally expensive. We overcome this problem with the help of accelerated Dirichlet process mixture models. Moreover, the truncation is managed using kd-trees. The performance of the model is validated on real medical applications and compared to three other similar alternatives. The results show the outperformance of our proposed framework.

Background: The etiology and natural history of Kienböck’s disease remain unclear. Traditionally it has been defined as avascular necrosis of the lunate. The authors tried to demonstrate different tissue distribution, the area ratio of necrotic tissue and vessel counts inside the whole Kienböck lunate to reveal a dynamic process of the lunate collapse.

Methods: Five lunates from patients with stage III Kienböck’s disease and one cadaveric lunate not involved by Kienböck’s disease were sampled. They were sectioned, H&E stained, and evaluated. The thickness of trabecular bone and the area of necrotic tissue were measured with Image-Pro Plus. The number of vessels was counted manually.

Results: In the normal lunate, the bone trabeculae showed a uniform distribution with fatty marrow filled the interspace between the trabeculae. In the lunates with Kienböck disease, the trabeculae fracture and necrosis located in the central part with massive fibrous granular tissue proliferation. There were also some chondroid metaplasia at the palmar and dorsal ends. The trabeculae of the lunates of the Kienböck’s disease [0.188 mm (0.153 mm, 0.236 mm)] was significantly thicker than the normal lunates [0.146 mm (0.124 mm, 0.164 mm)]. The necrosis was localized around the fracture sites instead of the whole lunate. The mean necrosis area only accounts for 16.3% ± 8.9% of the whole section. Such kind of focal necrosis is quite similar to those around the traumatic fracture ends of other bones. Even in stage III Kienböck lunates, the vessels are quite abundant (221 ± 42 in one sagittal section), while the vessels inside the normal lunate were 352 ± 28.

Conclusions: There is neither massive nor obvious generalized avascular bone necrosis in our histopathology observations. The focal necrosis and vessel damage were more likely associated with the broken trabeculae inside the lunate. Based on our histopathology observations, we suggested that the progressive process of Kienböck’s disease could be described as lunate nonunion advanced collapse instead of avascular necrosis.

Angioleiomyoma is a rare benign soft tissue tumour arising from smooth muscle, representing <1% of upper limb soft tissue tumours. We report a 54-year-old male presenting with a progressively enlarging atraumatic lesion along the palmar side of the base of the ring and little finger. A biopsy was done to determine the diagnosis. Intraoperatively, the lump was found to be intimately related to the radial digital artery, it could not be excised en-bloc without transecting the radial digital artery of the little finger. Following excision, the ends of the digital artery were anastomosed. At 10-months follow-up, the hand was fully functional without any evidence of cold-intolerance or neurological deficit along the distribution of the digital nerve. We review the literature on angioleiomyoma and report careful resection of the tumour with digital artery transection and repair as a treatment option for angioleiomyoma of the digital artery.

Level of Evidence: Level V (Therapeutic)

The study and treatment of cancer is traditionally specialized to the cancer’s site of origin. However, certain phenotypes are shared across cancer types and have important implications for clinical care. To date, automating the identification of these characteristics from routine clinical data - irrespective of the type of cancer - is impaired by tissue-specific variability and limited labeled data. Whole-genome doubling is one such phenotype; whole-genome doubling events occur in nearly every type of cancer and have significant prognostic implications. Using digitized histopathology slide images of primary tumor biopsies, we train a deep neural network end-to-end to accurately generalize few-shot classification of whole-genome doubling across 17 cancer types. By taking a meta-learning approach, cancer types are treated as separate but jointly-learned tasks. This approach outperforms a traditional neural network classifier and quickly generalizes to both held-out cancer types and batch effects. These results demonstrate the unrealized potential for meta-learning to not only account for between-cancer type variability but also remedy technical variability, enabling real-time identification of cancer phenotypes that are too often costly and inefficient to obtain.

A detailed understanding of the pathophysiologic mechanisms of severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2) infection and Coronavirus disease 2019 (COVID-19) is vital for improving patient management — to facilitate prompt recognition of progression to severe disease and effective therapeutic strategies. This chapter summarizes the underlying pathophysiology in the lungs and other organs of COVID-19 patients. The roles of the cytokine storm culminating in exaggerated inflammatory responses and formation of neutrophil extracellular traps (NETs) are discussed. Pathological features of the various stages from the onset of COVID-19 are outlined — progressing from early mild infection to severe clinical illness to the critically ill phase.

The development of tools in computational pathology to assist physicians and biomedical scientists in the diagnosis of disease requires access to high-quality annotated images for algorithm learning and evaluation. Generating high-quality expert-derived annotations is time-consuming and expensive. We explore the use of crowdsourcing for rapidly obtaining annotations for two core tasks in com- putational pathology: nucleus detection and nucleus segmentation. We designed and implemented crowdsourcing experiments using the CrowdFlower platform, which provides access to a large set of labor channel partners that accesses and manages millions of contributors worldwide. We obtained annotations from four types of annotators and compared concordance across these groups. We obtained: crowdsourced annotations for nucleus detection and segmentation on a total of 810 images; annotations using automated methods on 810 images; annotations from research fellows for detection and segmentation on 477 and 455 images, respectively; and expert pathologist-derived annotations for detection and segmentation on 80 and 63 images, respectively. For the crowdsourced annotations, we evaluated performance across a range of contributor skill levels (1, 2, or 3). The crowdsourced annotations (4,860 images in total) were completed in only a fraction of the time and cost required for obtaining annotations using traditional methods. For the nucleus detection task, the research fellow-derived annotations showed the strongest concordance with the expert pathologist- derived annotations (F-M =93.68%), followed by the crowd-sourced contributor levels 1,2, and 3 and the automated method, which showed relatively similar performance (F-M = 87.84%, 88.49%, 87.26%, and 86.99%, respectively). For the nucleus segmentation task, the crowdsourced contributor level 3-derived annotations, research fellow-derived annotations, and automated method showed the strongest concordance with the expert pathologist-derived annotations (F-M = 66.41%, 65.93%, and 65.36%, respectively), followed by the contributor levels 2 and 1 (60.89% and 60.87%, respectively). When the research fellows were used as a gold-standard for the segmentation task, all three con- tributor levels of the crowdsourced annotations significantly outperformed the automated method (F-M = 62.21%, 62.47%, and 65.15% vs. 51.92%). Aggregating multiple annotations from the crowd to obtain a consensus annotation resulted in the strongest performance for the crowd-sourced segmentation. For both detection and segmentation, crowd-sourced performance is strongest with small images (400 × 400 pixels) and degrades significantly with the use of larger images (600 × 600 and 800 × 800 pixels). We conclude that crowdsourcing to non-experts can be used for large-scale labeling microtasks in computational pathology and offers a new approach for the rapid generation of labeled images for algorithm development and evaluation.