Cíl: Molekulární klasifikace endometriálních karcinomů (EK) dělí tyto tumory do čtyř distinktních skupin definovaných genetickým pozadím. Vzhledem k prokázanému klinickému významu se genetické vyšetření EK stává nedílnou součástí dia gnostického postupu. Doporučený dia gnostický algoritmus zahrnuje molekulárně genetický průkaz mutace genu POLE, přičemž všechny další potřebné parametry se vyšetřují pouze imunohistochemicky. Cílem této studie je sdílet naše zkušenosti s molekulární klasifikací EK, která je na našem pracovišti prováděna pomocí imunohistochemie a následně sekvenování nové generace (NGS). Metodika: Do studie byly zařazeny všechny EK dia gnostikované na Šiklově ústavu patologie ve FN Plzeň a v Bioptické laboratoři, s. r. o., od roku 2020 do současnosti. Všechny EK byly prospektivně vyšetřeny nejprve imunohistochemicky (MMR proteiny, p53) a následně molekulárně geneticky pomocí NGS za použití „customizovaného Gyncore panelu“ (zahrnujícího geny POLE, POLD1, MSH2, MSH6, MLH1, PMS2, TP53, PTEN, ARID1A, PIK3CA, PIK3R1, CTNNB1, KRAS, NRAS, BRCA1, BRCA2, BCOR, ERBB2), na jehož základě byly rozčleněny do čtyř molekulárně distinktních skupin [POLE mutované EK (typ 1), hypermutované (MMR deficientní, typ 2), EK bez specifického molekulárního profilu (NSMP, typ 3) a TP53 mutované („copy number high“, typ 4) ]. Výsledky: Soubor zahrnuje celkem 270 molekulárně klasifikovaných EK. Osmnáct případů (6,6 %) bylo klasifikováno jako POLE mutované, 85 případů (31,5 %) jako hypermutované (MMR deficientní), 137 případů (50,7 %) jako EK bez specifického molekulárního profilu, 30 případů (11,1 %) jako TP53 mutované. Dvanáct případů (4,4 %) bylo zařazeno jako „multiple classifier“. Skupina NSMP se často vyznačovala mnohočetnými genetickými alteracemi, přičemž nejčastější byla mutace genu PTEN (44 % v rámci NSMP), následovaly PIK3CA (30 %), ARID1A (21 %) a KRAS (9 %). Závěr: Molekulární klasifikace EK pomocí metody NGS umožňuje v porovnání s doporučeným dia gnostickým algoritmem spolehlivější klasifikaci EK do jednotlivých molekulárních skupin. Kromě toho dovoluje NGS vyšetření odkrýt komplexní genetické pozadí jednotlivých EK, což má význam zvláště v rámci skupiny „bez specifického molekulárního profilu“, kde jsou tato data podkladem pro výzkum léčebných schémat s příslibem cílené terapie tohoto typu nádorů.

Objective: Molecular classification of endometrial carcinomas (EC) divides these neoplasms into four distinct subgroups defined by a molecular background. Given its proven clinical significance, genetic examination is becoming an integral component of the diagnostic procedure. Recommended diagnostic algorithms comprise molecular genetic testing of the POLE gene, whereas the remaining parameters are examined solely by immunohistochemistry. The aim of this study is to share our experiences with the molecular classification of EC, which has been conducted using immunohistochemistry and next-generation sequencing (NGS) at our department. Methods: This study includes all cases of EC diagnosed at Šikl's Department of Pathology and Biopticka Laboratory Ltd. from 2020 to the present. All ECs were prospectively examined by immunohistochemistry (MMR, p53), fol lowed by NGS examination using a customized Gyncore panel (including genes POLE, POLD1, MSH2, MSH6, MLH1, PMS2, TP53, PTEN, ARID1A, PIK3CA, PIK3R1, CTNNB1, KRAS, NRAS, BRCA1, BRCA2, BCOR, ERBB2), based on which the ECs were classified into four molecularly distinct groups [POLE mutated EC (type 1), hypermutated (MMR deficient, type 2), EC with no specific molecular profile (type 3), and TP53 mutated (“copy number high”, type 4)]. Results: The cohort comprised a total of 270 molecularly classified ECs. Eighteen cases (6.6%) were classified as POLE mutated EC, 85 cases (31.5%) as hypermutated EC (MMR deficient), 137 cases (50.7%) as EC of no specific molecular profile, and 30 cases (11.1%) as TP53 mutated EC. Twelve cases (4.4%) were classified as “multiple classifier” endometrial carcinoma. ECs of no specific molecular profile showed multiple genetic alterations, with the most common mutations being PTEN (44% within the group of NSMP), fol lowed by PIK3CA (30%), ARID1A (21%), and KRAS (9%). Conclusion: In comparison with recommended diagnostic algorithms, NGS provides a more reliable classification of EC into particular molecular subgroups. Furthermore, NGS reveals the complex molecular genetic background in individual ECs, which is especially significant within ECs with no specific molecular profile. These data can serve as a springboard for the research of therapeutic programs committed to targeted therapy in this type of tumor.

• MeSH
• imunohistochemie klasifikace   metody   MeSH
• klasifikace metody   MeSH
• lidé MeSH
• molekulární patologie metody   MeSH
• mutace genetika   MeSH
• nádory endometria * diagnóza   genetika   klasifikace   patologie   MeSH
• vysoce účinné nukleotidové sekvenování * klasifikace   metody   MeSH
• Check Tag
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• klinická studie MeSH
• práce podpořená grantem MeSH

Improving patient care and advancing scientific discovery requires responsible sharing of research data, healthcare records, biosamples, and biomedical resources that must also respect applicable use conditions. Defining a standard to structure and manage these use conditions is a complex and challenging task. This is exemplified by a near unlimited range of asset types, a high variability of applicable conditions, and differing applications at the individual or collective level. Furthermore, the specifics and granularity required are likely to vary depending on the ultimate contexts of use. All these factors confound alignment of institutional missions, funding objectives, regulatory and technical requirements to facilitate effective sharing. The presented work highlights the complexity and diversity of the problem, reviews the current state of the art, and emphasises the need for a flexible and adaptable approach. We propose Digital Use Conditions (DUC) as a framework that addresses these needs by leveraging existing standards, striking a balance between expressiveness versus ambiguity, and considering the breadth of applicable information with their context of use.

• MeSH
• lidé MeSH
• šíření informací * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH

• Publikační typ
• abstrakt z konference MeSH

• Publikační typ
• abstrakt z konference MeSH

The diagnosis of solid tumors of epithelial origin (carcinomas) represents a major part of the workload in clinical histopathology. Carcinomas consist of malignant epithelial cells arranged in more or less cohesive clusters of variable size and shape, together with stromal cells, extracellular matrix, and blood vessels. Distinguishing stroma from epithelium is a critical component of artificial intelligence (AI) methods developed to detect and analyze carcinomas. In this paper, we propose a novel automated workflow that enables large-scale guidance of AI methods to identify the epithelial component. The workflow is based on re-staining existing hematoxylin and eosin (H&E) formalin-fixed paraffin-embedded sections by immunohistochemistry for cytokeratins, cytoskeletal components specific to epithelial cells. Compared to existing methods, clinically available H&E sections are reused and no additional material, such as consecutive slides, is needed. We developed a simple and reliable method for automatic alignment to generate masks denoting cytokeratin-rich regions, using cell nuclei positions that are visible in both the original and the re-stained slide. The registration method has been compared to state-of-the-art methods for alignment of consecutive slides and shows that, despite being simpler, it provides similar accuracy and is more robust. We also demonstrate how the automatically generated masks can be used to train modern AI image segmentation based on U-Net, resulting in reliable detection of epithelial regions in previously unseen H&E slides. Through training on real-world material available in clinical laboratories, this approach therefore has widespread applications toward achieving AI-assisted tumor assessment directly from scanned H&E sections. In addition, the re-staining method will facilitate additional automated quantitative studies of tumor cell and stromal cell phenotypes.

• MeSH
• barvení a značení MeSH
• deep learning * MeSH
• eosin MeSH
• epitelové buňky MeSH
• hematoxylin MeSH
• keratiny * MeSH
• lidé MeSH
• umělá inteligence MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Provenance is information describing the lineage of an object, such as a dataset or biological material. Since these objects can be passed between organizations, each organization can document only parts of the objects life cycle. As a result, interconnection of distributed provenance parts forms distributed provenance chains. Dependant on the actual provenance content, complete provenance chains can provide traceability and contribute to reproducibility and FAIRness of research objects. In this paper, we define a lightweight provenance model based on W3C PROV that enables generation of distributed provenance chains in complex, multi-organizational environments. The application of the model is demonstrated with a use case spanning several steps of a real-world research pipeline - starting with the acquisition of a specimen, its processing and storage, histological examination, and the generation/collection of associated data (images, annotations, clinical data), ending with training an AI model for the detection of tumor in the images. The proposed model has become an open conceptual foundation of the currently developed ISO 23494 standard on provenance for biotechnology domain.

• Publikační typ
• časopisecké články MeSH

The distributed nature of modern research emphasizes the importance of collecting and sharing the history of digital and physical material, to improve the reproducibility of experiments and the quality and reusability of results. Yet, the application of the current methodologies to record provenance information is largely scattered, leading to silos of provenance information at different granularities. To tackle this fragmentation, we developed the Common Provenance Model, a set of guidelines for the generation of interoperable provenance information, and to allow the reconstruction and the navigation of a continuous provenance chain. This work presents the first version of the model, available online, based on the W3C PROV Data Model and the Provenance Composition pattern.

• MeSH
• biologické vědy * MeSH
• reprodukovatelnost výsledků MeSH
• Publikační typ
• časopisecké články MeSH

Various biological resources, such as biobanks and disease-specific registries, have become indispensable resources to better understand the epidemiology and biological mechanisms of disease and are fundamental for advancing medical research. Nevertheless, biobanks and similar resources still face significant challenges to become more findable and accessible by users on both national and global scales. One of the main challenges for users is to find relevant resources using cataloging and search services such as the BBMRI-ERIC Directory, operated by European Research Infrastructure on Biobanking and Biomolecular Resources (BBMRI-ERIC), as these often do not contain the information needed by the researchers to decide if the resource has relevant material/data; these resources are only weakly characterized. Hence, the researcher is typically left with too many resources to explore and investigate. In addition, resources often have complex procedures for accessing holdings, particularly for depletable biological materials. This article focuses on designing a system for effective negotiation of access to holdings, in which a researcher can approach many resources simultaneously, while giving each resource team the ability to implement their own mechanisms to check if the material/data are available and to decide if access should be provided. The BBMRI-ERIC has developed and implemented an access and negotiation tool called the BBMRI-ERIC Negotiator. The Negotiator enables access negotiation to more than 600 biobanks from the BBMRI-ERIC Directory and other discovery services such as GBA/BBMRI-ERIC Locator or RD-Connect Finder. This article summarizes the principles that guided the design of the tool, the terminology used and underlying data model, request workflows, authentication and authorization mechanism(s), and the mechanisms and monitoring processes to stimulate the desired behavior of the resources: to effectively deliver access to biological material and data.

• MeSH
• banky biologického materiálu * MeSH
• biomedicínský výzkum * MeSH
• šíření informací MeSH
• Publikační typ
• časopisecké články MeSH

Publikace se zaměřuje na postup koronavirové pandemie, zejména na situaci v Česku. Obsahuje rozhovory se známými osobnostmi. Určeno široké veřejnosti.

• MeSH
• Betacoronavirus MeSH
• COVID-19 MeSH
• dějiny 21. století MeSH
• koronavirové infekce MeSH
• pandemie MeSH
• socioekonomické faktory MeSH
• významné osobnosti MeSH
• zdravotní politika MeSH
• Check Tag
• dějiny 21. století MeSH
• Publikační typ
• populární práce MeSH
• rozhovory MeSH
• sborníky MeSH
• Geografické názvy
• Česká republika MeSH

• Konspekt
• Veřejné zdraví a hygiena
• NLK Obory
• veřejné zdravotnictví
• virologie

• MeSH
• biodiverzita MeSH
• ekosystém * MeSH
• klimatické změny * MeSH
• Publikační typ
• dopisy MeSH
• komentáře MeSH