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
• Immunohistochemistry classification   methods   MeSH
• Classification methods   MeSH
• Humans MeSH
• Pathology, Molecular methods   MeSH
• Mutation genetics   MeSH
• Endometrial Neoplasms * diagnosis   genetics   classification   pathology   MeSH
• High-Throughput Nucleotide Sequencing * classification   methods   MeSH
• Check Tag
• Humans MeSH
• Female MeSH
• Publication type
• Clinical Study MeSH
• Research Support, Non-U.S. Gov't MeSH

Hodnocení a hlášení tenkojehlových aspirací uzlů štítné žlázy v Bethesda klasifikaci je široce mezinárodně i námi používaným postupem. Revidované již třetí vydání Bethesda systému hlášení tyreoidálních cytopatologií přináší změny terminologické, obsahové a také nové kapitoly. V terminologii je zřejmou změnou odstranění dvouslovných názvů tří kategorií při zachování šesti diagnostických kategorií předchozích verzí – nově: BI – ne- diagnostický, BIII – atypie blíže neurčená, BIV – folikulární neoplázie. V bližším popisu nálezů v rámci jednotlivých kategorií jsou respektovány terminologické změny přijaté pátým vydáním WHO klasifikace tyreoidálních neoplázií – zejm. doporučený název folikulární tyreoidální nodulární nemoc pro nejčastěji zastou- penou kategorii BII – benigní. V samotném hodnocení se v jednotlivých kategoriích promítají diagnostická upřesnění přijatá aktuální WHO klasifikací histopa- tologických nálezů – pokud jsou v cytologické rovině uplatnitelná. Cílenou pozornost bude třeba věnovat high grade znakům. Revidovaná verze přináší nově kapitoly věnované molekulárnímu došetřování a hodnocení pediatrické populace.

Reporting fine-needle aspiration of thyroid nodules in the Bethesda classification is a practice widely used internationally and by us. The revised third edition of the Bethesda System of Reporting Thyroid Cytopathology brings changes in terminology, content, and new chapters. In terms of terminology, an obvious change is the removal of the two-word names of three categories while maintaining the six diagnostic categories of the previous versions - new: BI – non-diag- nostic, BIII – atypia of undetermined significance, BIV – follicular neoplasia. In the detailed description of the findings within the individual categories, the ter- minological changes adopted by the fifth edition of the WHO classification of thyroid neoplasia are respected - in particular, the recommended name follicular thyroid nodular disease for the most frequently represented category BII - benign. In the evaluation itself, the diagnostic specifications accepted by the current WHO classification of histopathological findings are reflected in the individual categories - if they are applicable at the cytological level. Targeted attention will need to be paid to high grade features. The revised version brings new chapters dedicated to molecular testing and evaluation of the paediatric population.

• Keywords
• Bethesda klasifikace,
• MeSH
• Cytodiagnosis * classification   methods   MeSH
• Disease Notification MeSH
• Classification methods   MeSH
• Thyroid Neoplasms * diagnostic imaging   classification   MeSH
• Thyroid Nodule diagnostic imaging   classification   MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH

Funkční hodnocení, nikoli pouze etiologické, je zásadní pro hodnocení kvality života osob s jakýmkoli typem postižení, disabilitou. Disabilita je definována jako snížení funkčních schopností na úrovni těla, jedince nebo společnosti, která vzniká, když se zdravotní stav konfrontuje s bariérami prostředí. V souladu s doporučeními Světové zdravotnické organizace (WHO) se pro hodnocení disability používá jako objektivní nástroj Mezinárodní klasifikace funkčních schopností, disability a zdraví (MKF). V roce 2020 byla v českém jazyce vydána druhá aktualizovaná verze této klasifikace, která je v elektronické verzi volně přístupná na webových stránkách Ústavu zdravotnických informací a statistiky ČR. Níže v textu je popsaná nejen struktura MKF klasifikace, ale i metodika posuzování podle této klasifikace. V závěru článku je uvedeno několik jednoduchých vzorových příkladů, jak správně postupovat při vlastním hodnocení. Předkládaný článek má pomoci k základní orientaci v problematice funkčního hodnocení podle MKF.

A functional evaluation, not just an etiological one, is essential for the evaluation of the quality of life of persons with any type of disability. Disability is defined as a condition of the body or mind (impairment) that makes it more difficult for the person with the condition to do certain activities (activity limitation) and interact with the world around them (participation restrictions). In accordance with the recommendations of the World Health Organization (WHO), the International Classification of Functioning, Disability and Health (ICF) is used to assess disability. In 2020, a second updated version of this classification was published in the Czech language, which is freely accessible in an electronic version on the website of the Institute of Health Information and Statistics of the Czech Republic. Below in the text, not only the structure of ICF classification, but also the assessment methodology according to this classification is described. At the end of the article, there are several simple sample examples of how to proceed correctly in self-assessment. The presented article is intended to help with basic orientation in the issue of functional assessment according to ICF.

• MeSH
• Classification methods   MeSH
• Humans MeSH
• International Classification of Functioning, Disability and Health * MeSH
• Disability Evaluation MeSH
• Check Tag
• Humans MeSH

• MeSH
• Databases, Factual standards   MeSH
• Classification methods   MeSH
• Thoracica classification   MeSH
• Animals MeSH
• Check Tag
• Animals MeSH
• Publication type
• Letter MeSH

• Keywords
• Comfort scale,
• MeSH
• Pain nursing   MeSH
• Classification methods   MeSH
• Infant MeSH
• Pain Measurement MeSH
• Infant, Newborn MeSH
• Nursing Assessment MeSH
• Check Tag
• Infant MeSH
• Infant, Newborn MeSH

Rapid and reliable identification of insects is important in many contexts, from the detection of disease vectors and invasive species to the sorting of material from biodiversity inventories. Because of the shortage of adequate expertise, there has long been an interest in developing automated systems for this task. Previous attempts have been based on laborious and complex handcrafted extraction of image features, but in recent years it has been shown that sophisticated convolutional neural networks (CNNs) can learn to extract relevant features automatically, without human intervention. Unfortunately, reaching expert-level accuracy in CNN identifications requires substantial computational power and huge training data sets, which are often not available for taxonomic tasks. This can be addressed using feature transfer: a CNN that has been pretrained on a generic image classification task is exposed to the taxonomic images of interest, and information about its perception of those images is used in training a simpler, dedicated identification system. Here, we develop an effective method of CNN feature transfer, which achieves expert-level accuracy in taxonomic identification of insects with training sets of 100 images or less per category, depending on the nature of data set. Specifically, we extract rich representations of intermediate to high-level image features from the CNN architecture VGG16 pretrained on the ImageNet data set. This information is submitted to a linear support vector machine classifier, which is trained on the target problem. We tested the performance of our approach on two types of challenging taxonomic tasks: 1) identifying insects to higher groups when they are likely to belong to subgroups that have not been seen previously and 2) identifying visually similar species that are difficult to separate even for experts. For the first task, our approach reached $CDATA[$CDATA[$>$$92% accuracy on one data set (884 face images of 11 families of Diptera, all specimens representing unique species), and $CDATA[$CDATA[$>$$96% accuracy on another (2936 dorsal habitus images of 14 families of Coleoptera, over 90% of specimens belonging to unique species). For the second task, our approach outperformed a leading taxonomic expert on one data set (339 images of three species of the Coleoptera genus Oxythyrea; 97% accuracy), and both humans and traditional automated identification systems on another data set (3845 images of nine species of Plecoptera larvae; 98.6 % accuracy). Reanalyzing several biological image identification tasks studied in the recent literature, we show that our approach is broadly applicable and provides significant improvements over previous methods, whether based on dedicated CNNs, CNN feature transfer, or more traditional techniques. Thus, our method, which is easy to apply, can be highly successful in developing automated taxonomic identification systems even when training data sets are small and computational budgets limited. We conclude by briefly discussing some promising CNN-based research directions in morphological systematics opened up by the success of these techniques in providing accurate diagnostic tools.

• MeSH
• Phylogeny MeSH
• Insecta classification   MeSH
• Classification methods   MeSH
• Neural Networks, Computer * MeSH
• Reproducibility of Results MeSH
• Animals MeSH
• Check Tag
• Animals MeSH
• Publication type
• Journal Article MeSH
• Evaluation Study MeSH
• Research Support, Non-U.S. Gov't MeSH

• MeSH
• Colles' Fracture * diagnosis   surgery   MeSH
• Radius Fractures diagnosis   surgery   MeSH
• Intra-Articular Fractures diagnosis   classification   MeSH
• Classification * methods   MeSH
• Humans MeSH
• Evidence-Based Medicine MeSH
• Multicenter Studies as Topic MeSH
• Wrist Injuries diagnosis   classification   MeSH
• Radius injuries   MeSH
• Statistics as Topic MeSH
• Outcome and Process Assessment, Health Care * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH

Zlomenina hlavice femuru byla poprvé popsána Birkettem v r. 1869. K poznání této zlomeniny významně přispěla studie Christophera z r. 1924 a především Pipkinova publikace z r. 1957, jehož klasifikace se používá dodnes. V české literatuře ji poprvé popsal Morávek v r. 1912. Z historického hlediska je pro zlomeninu hlavice femuru korektní eponym Birkitt-Pipkinova zlomenina.

Femoral head fracture was initially described by Birkett in 1869. Of essential importance in this respect were the publications by Christopher in 1924 and, particularly, Pipkin's study of 1957, including his classification that is still in use today. In the Czech literature, the first description was published by Morávek in 1912. A historically correct eponym for a femoral head fracture would therefore be Birkitt-Pipkin fracture.

• MeSH
• Acetabulum pathology   injuries   MeSH
• History, 19th Century MeSH
• History, 20th Century MeSH
• History of Medicine MeSH
• Femoral Fractures * surgery   classification   complications   MeSH
• Femur Head * physiopathology   pathology   injuries   MeSH
• Classification methods   MeSH
• Humans MeSH
• Orthopedic Procedures * history   methods   utilization   MeSH
• Orthopedics history   methods   organization & administration   MeSH
• Radiography history   methods   utilization   MeSH
• Check Tag
• History, 19th Century MeSH
• History, 20th Century MeSH
• Humans MeSH
• Publication type
• Historical Article MeSH
• Case Reports MeSH

Chronická obstrukční plicní nemoc (CHOPN) je celosvětově rozšířené onemocnění se závažnými dopady jak zdravotními, tak i socioekonomickými. Onemocnění bývá doposud často poddiagnostikováno. Pacienti s CHOPN jsou často staršího věku a trpí současně zejména interními komorbiditami, onemocnění je také považováno za prekancerózu. Léčba nemocných je stupňovitá, spočívá v postupech jak nefarmakologických (zanechání kouření, dechová rehabilitace), tak farmakologických (bronchodilatancia, mukolytika, expektorancia, protizánětlivé léky) a podpůrných (vakcinace, nutriční terapie, suplementace kyslíku). Samostatnou kapitolu pak tvoří péče o pacienty v terminálním stadiu nemoci.

Chronic Obstructive Pulmonary Disease (COPD) is a widespread disease with severe health and socio‑economic impacts. The illness has so far been often under‑diagnosed. Patients with COPD are often the elderly and concurrently suffer from internal comorbidities, the disease is also considered precancerous. The treatment of patients is gradual, consists of procedures both non‑pharmacological (quit smoking, respiratory rehabilitation) and pharmacological (bronchodilators, mucolytics, expectorants, anti‑inflammatory drugs) and supportive (vaccination, nutritional therapy, oxygen supplementation). A separate chapter is care for patients in the terminal stage of the disease.

• MeSH
• Asthma diagnosis   etiology   complications   MeSH
• Bronchiectasis diagnosis   etiology   complications   MeSH
• Bronchodilator Agents administration & dosage   classification   therapeutic use   MeSH
• Influenza, Human prevention & control   MeSH
• Bronchitis, Chronic diagnosis   complications   MeSH
• Pulmonary Disease, Chronic Obstructive * diagnosis   classification   therapy   MeSH
• Diagnostic Techniques, Respiratory System * trends   utilization   MeSH
• Diagnostic Imaging methods   trends   utilization   MeSH
• Cachexia diagnosis   etiology   complications   MeSH
• Classification methods   MeSH
• Humans MeSH
• Medication Therapy Management * utilization   MeSH
• Lung Diseases, Obstructive diagnosis   etiology   therapy   MeSH
• Pulmonary Emphysema diagnosis   etiology   complications   MeSH
• Pneumococcal Vaccines therapeutic use   MeSH
• Practice Guidelines as Topic MeSH
• Spirometry methods   utilization   MeSH
• Respiration, Artificial methods   utilization   MeSH
• Check Tag
• Humans MeSH

Recently developed methods of longitudinal discriminant analysis allow for classification of subjects into prespecified prognostic groups using longitudinal history of both continuous and discrete biomarkers. The classification uses Bayesian estimates of the group membership probabilities for each prognostic group. These estimates are derived from a multivariate generalised linear mixed model of the biomarker's longitudinal evolution in each of the groups and can be updated each time new data is available for a patient, providing a dynamic (over time) allocation scheme. However, the precision of the estimated group probabilities differs for each patient and also over time. This precision can be assessed by looking at credible intervals for the group membership probabilities. In this paper, we propose a new allocation rule that incorporates credible intervals for use in context of a dynamic longitudinal discriminant analysis and show that this can decrease the number of false positives in a prognostic test, improving the positive predictive value. We also establish that by leaving some patients unclassified for a certain period, the classification accuracy of those patients who are classified can be improved, giving increased confidence to clinicians in their decision making. Finally, we show that determining a stopping rule dynamically can be more accurate than specifying a set time point at which to decide on a patient's status. We illustrate our methodology using data from patients with epilepsy and show how patients who fail to achieve adequate seizure control are more accurately identified using credible intervals compared to existing methods.

• MeSH
• Bayes Theorem * MeSH
• Discriminant Analysis MeSH
• Epilepsy diagnosis   therapy   MeSH
• Remission Induction MeSH
• Classification methods   MeSH
• Humans MeSH
• Linear Models MeSH
• Longitudinal Studies MeSH
• Multivariate Analysis MeSH
• Computer Simulation MeSH
• Probability * MeSH
• Prognosis MeSH
• Decision Making MeSH
• Sensitivity and Specificity MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH