Traditional manual blood smear diagnosis methods are time-consuming and prone to errors, often relying heavily on the experience of clinical laboratory analysts for accuracy. As breakthroughs in key technologies such as neural networks and deep learning continue to drive digital transformation in the medical field, image recognition technology is increasingly being leveraged to enhance existing medical processes. In recent years, advancements in computer technology have led to improved efficiency in the identification of blood cells in blood smears through the use of image recognition technology. This paper provides a comprehensive summary of the methods and steps involved in utilizing image recognition algorithms for diagnosing diseases in blood smears, with a focus on malaria and leukemia. Furthermore, it offers a forward-looking research direction for the development of a comprehensive blood cell pathological detection system.

Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles1-3. Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing the important slide-level context4. Here we present Prov-GigaPath, a whole-slide pathology foundation model pretrained on 1.3 billion 256 × 256 pathology image tiles in 171,189 whole slides from Providence, a large US health network comprising 28 cancer centres. The slides originated from more than 30,000 patients covering 31 major tissue types. To pretrain Prov-GigaPath, we propose GigaPath, a novel vision transformer architecture for pretraining gigapixel pathology slides. To scale GigaPath for slide-level learning with tens of thousands of image tiles, GigaPath adapts the newly developed LongNet5 method to digital pathology. To evaluate Prov-GigaPath, we construct a digital pathology benchmark comprising 9 cancer subtyping tasks and 17 pathomics tasks, using both Providence and TCGA data6. With large-scale pretraining and ultra-large-context modelling, Prov-GigaPath attains state-of-the-art performance on 25 out of 26 tasks, with significant improvement over the second-best method on 18 tasks. We further demonstrate the potential of Prov-GigaPath on vision-language pretraining for pathology7,8 by incorporating the pathology reports. In sum, Prov-GigaPath is an open-weight foundation model that achieves state-of-the-art performance on various digital pathology tasks, demonstrating the importance of real-world data and whole-slide modelling.

Interpretability in machine learning has become increasingly important as machine learning is being used in more and more applications, including those with high-stakes consequences such as healthcare where Interpretability has been regarded as a key to the successful adoption of machine learning models. However, using confounding/irrelevant information in making predictions by deep learning models, even the interpretable ones, poses critical challenges to their clinical acceptance. That has recently drawn researchers\' attention to issues beyond the mere interpretation of deep learning models. In this paper, we first investigate application of an inherently interpretable prototype-based architecture, known as ProtoPNet, for breast cancer classification in digital pathology and highlight its shortcomings in this application. Then, we propose a new method that uses more medically relevant information and makes more accurate and interpretable predictions. Our method leverages the clustering concept and implicitly increases the number of classes in the training dataset. The proposed method learns more relevant prototypes without any pixel-level annotated data. To have a more holistic assessment, in addition to classification accuracy, we define a new metric for assessing the degree of interpretability based on the comments of a group of skilled pathologists. Experimental results on the BreakHis dataset show that the proposed method effectively improves the classification accuracy and interpretability by respectively 8 % and 18 % . Therefore, the proposed method can be seen as a step toward implementing interpretable deep learning models for the detection of breast cancer using histopathology images.

Epstein-Barr virus (EBV) is a pathogen known to cause a number of malignancies, often taking years for them to develop after primary infection. EBV-associated gastric cancer (EBVaGC) is one such malignancy, and is an immunologically, molecularly and pathologically distinct entity from EBV-negative gastric cancer (EBVnGC). In comparison with EBVnGCs, EBVaGCs overexpress a number of immune regulatory genes to help form an immunosuppressive tumor microenvironment (TME), have improved prognosis, and overall have an \"immune-hot\" phenotype. This review provides an overview of the histopathology, clinical features and clinical outcomes of EBVaGCs. We also summarize the differences between the TMEs of EBVaGCs and EBVnGCs, which includes significant differences in cell composition and immune infiltration. A list of available EBVaGC and EBVnGC gene expression datasets and computational tools are also provided within this review. Finally, an overview is provided of the various chemo- and immuno-therapeutics available in treating gastric cancers (GCs), with a focus on EBVaGCs.

Chronic kidney disease (CKD) represents one of today\'s main public health problems. Serum creatinine measurement and estimation of the glomerular filtration rate (GFR) are the main tools for evaluating renal function. There are several equations to estimate GFR, and CKD-EPI equation (Chronic Kidney Disease - Epidemiology) is the most recommended one. There are still some controversies regarding serum creatinine measurement and GFR estimation, since several factors can interfere in this process. An important recent change was the removal of the correction for race from the equations for estimating GFR, which overestimated kidney function, and consequently delayed the implementation of treatments such as dialysis and kidney transplantation. In this consensus document from the Brazilian Societies of Nephrology and Clinical Pathology and Laboratory Medicine, the main concepts related to the assessment of renal function are reviewed, as well as possible existing controversies and recommendations for estimating GFR in clinical practice.

Digital pathology (the technology whereby glass histology slides are scanned at high resolution, digitised, stored and shared with pathologists, who can view them using microscopy software on a screen) is transforming the delivery of clinical diagnostic pathology services around the world. In addition to adding value to clinical histopathology practice, digital histology slides provide a versatile medium to achieve the educational needs of a variety of learners including undergraduate students, postgraduate doctors in training and those pursuing continuing professional development portfolios. In this guide, we will review the principal use cases for digital slides in training and education and I will share tips for successful use of digital pathology to support a range of learners based on experience gathered at Leeds Teaching Hospitals National Health Service Trust and the National Pathology Imaging Co-Operative during the last 5 years of digital slide usage.

BACKGROUND: Bullous pilomatricoma is a rare variant of pilomatricoma. As it has been published in sporadic case reports, a limited understanding of its clinicopathological characteristics restricts its effective diagnosis and treatment.
OBJECTIVE: This study aimed to analyze the clinicopathological and immunohistochemical characteristics of bullous pilomatricoma to better understand the bullous transformation of pilomatricoma.
METHODS: The authors conducted a retrospective study of 12 patients with bullous pilomatricoma and compared their clinical, histopathological, and immunohistochemical data with those of patients with ordinary pilomatricoma.
RESULTS: Bullous pilomatricoma showed no sex preference, with a mean onset age of 31.2 years. The common sites were the upper extremities and trunk. Bullous pilomatricoma had a shorter disease duration, a larger diameter, and a greater tendency to increase in size than those of ordinary pilomatricoma. Histopathologically, bullous pilomatricoma had a shorter duration, lesser calcification, more mitotic figures, and distinct dermal features from those of ordinary pilomatricoma. Immunohistochemically, the expression of Matrix Metalloprotease (MMP)-2, MMP-9, vascular endothelial growth factor receptor-3 (VEGFR-3), and VEGF-C was elevated.
CONCLUSIONS: The study was retrospective, and the sample size was small.
CONCLUSIONS: The distinctive features of bullous pilomatricoma potentially result from dermal changes associated with the release of angiogenic factors and proteolytic enzymes. This comprehensive analysis provides novel insights into the clinical features and pathogenesis of bullous pilomatricoma.

This case study session of the hepatobiliary system was held during the 42nd Annual Society of Toxicologic Pathology Symposium in Summerlin, Nevada. The case studies highlighed potential hepatic and biliary toxicity liabilities. This article comprises several of the case studies that were presented during the session which included copper-associated hepatitis in a dog, sinusoidal obstruction syndrome in non-human primates, hepatic cytoplasmic alteration in mice and rats, and Kupffer cell hyperplasia/granulomatous inflammation in rats. Presenters, when applicable, provided case signalment, anatomic/clinical pathology data, and diagnoses and discussed potential pathogeneses.

Integration of digital pathology (DP) into clinical diagnostic workflows is increasingly receiving attention as new hardware and software become available. To facilitate the adoption of DP, the Swiss Digital Pathology Consortium (SDiPath) organized a Delphi process to produce a series of recommendations for DP integration within Swiss clinical environments. This process saw the creation of 4 working groups, focusing on the various components of a DP system (1) scanners, quality assurance and validation of scans, (2) integration of Whole Slide Image (WSI)-scanners and DP systems into the Pathology Laboratory Information System, (3) digital workflow-compliance with general quality guidelines, and (4) image analysis (IA)/artificial intelligence (AI), with topic experts for each recruited for discussion and statement generation. The work product of the Delphi process is 83 consensus statements presented here, forming the basis for \"SDiPath Recommendations for Digital Pathology\". They represent an up-to-date resource for national and international hospitals, researchers, device manufacturers, algorithm developers, and all supporting fields, with the intent of providing expectations and best practices to help ensure safe and efficient DP usage.

Digital pathology (DP) is increasingly entering routine clinical pathology diagnostics. As digitization of the routine caseload advances, implementation of digital image analysis algorithms and artificial intelligence tools becomes not only attainable, but also desirable in daily sign out. The Swiss Digital Pathology Consortium (SDiPath) has initiated a Delphi process to generate best-practice recommendations for various phases of the process of digitization in pathology for the local Swiss environment, encompassing the following four topics: i) scanners, quality assurance, and validation of scans; ii) integration of scanners and systems into the pathology laboratory information system; iii) the digital workflow; and iv) digital image analysis (DIA)/artificial intelligence (AI). The current article focuses on the DIA-/AI-related recommendations generated and agreed upon by the working group and further verified by the Delphi process among the members of SDiPath. Importantly, they include the view and the currently perceived needs of practicing pathologists from multiple academic and cantonal hospitals as well as private practices.
UNASSIGNED: Digitale Pathologe (DP) wird zunehmend in der Routinediagnostik der klinischen Pathologie eingesetzt. Mit fortschreitender Digitalisierung der Routinefälle wird die Implementierung digitaler Bildanalysealgorithmen und künstlicher Intelligenz nicht nur machbar, sondern auch für die tägliche Praxis wünschenswert. Das Schweizer Konsortium für Digitale Pathologie (Swiss Digital Pathology Consortium, SDiPath) hat einen Delphi-Prozess initiiert, um Empfehlungen für das beste Vorgehen in Bezug auf verschiedene Phasen des Digitalisierungsprozesses in der Pathologie für die lokalen Verhältnisse in der Schweiz zu erstellen, dazu gehören die folgenden 4 Themen: i) Scanner, Qualitätssicherung und Validierung der eingescannten Schnittpräparate; ii) Integration von Scannern und Systemen in das Laborinformationssystem; iii) der digitale Arbeitsablauf; und iv) digitale Bildanalyse (DIA)/künstliche Intelligenz (KI). Im vorliegenden Beitrag liegt der Schwerpunkt auf den Empfehlungen hinsichtlich DIA und KI, die von einer Arbeitsgruppe erarbeitet und mittels Delphi-Prozess durch die Mitglieder des SDiPath-Konsortiums bestätigt wurden. Wichtig ist, dass sie die Ansichten und die aktuellen Bedürfnisse praktisch tätiger Pathologen aus verschiedenen Lehr- und Kantonsspitälern sowie aus privaten Praxen einbeziehen.