S postupující digitalizací patologie se do popředí zájmu dostávají i aplikace metod strojového učení a umělé inteligence. Výzkum a vývoj v této oblasti je velmi rychlý, ale aplikace učících systémů v klinické praxi stále zaostávají. Cílem tohoto textu je přiblížit proces tvorby a nasazení učících systémů v digitální patologii. Začneme popisem základních vlastností dat produkovaných v rámci digitální patologie. Konkrétně pojednáme o skenerech a skenování vzorků, o ukládání a přenosu dat, o kontrole jejich kvality a přípravě pro zpracování pomocí učících systémů, zejména o anotacích. Naším cílem je prezentovat aktuální přístupy k řešení technických problémů a zároveň upozornit na úskalí, na která lze narazit při zpracování dat z digitální patologie. V první části také naznačíme, jak vypadají aktuální softwarová řešení pro prohlížení naskenovaných vzorků a implementace diagnostických postupů zahrnujících učící systémy. Ve druhé části textu popíšeme obvyklé úlohy digitální patologie a naznačíme obvyklé přístupy k jejich řešení. V této části zejména vysvětlíme, jak je nutné modifikovat standardní metody strojového učení pro zpracování velkých skenů a pojednáme o konkrétních aplikacích v diagnostice. Na závěr textu poskytneme rychlý náhled dalšího možného vývoje učících systémů v digitální patologii. Zejména ilustrujeme podstatu přechodu na velké základní modely a naznačíme problematiku virtuálního barvení vzorků. Doufáme, že tento text přispěje k lepší orientaci v rapidně se vyvíjející oblasti strojového učení v digitální patologii a tím přispěje k rychlejší adopci učících metod v této oblasti.

With the advancing digitalization of pathology, the application of machine learning and artificial intelligence methods is becoming increasingly important. Research and development in this field are progressing rapidly, but the clinical implementation of learning systems still lags behind. The aim of this text is to provide an overview of the process of developing and deploying learning systems in digital pathology. We begin by describing the fundamental characteristics of data produced in digital pathology. Specifically, we discuss scanners and sample scanning, data storage and transmission, quality control, and preparation for processing by learning systems, with a particular focus on annotations. Our goal is to present current approaches to addressing technical challenges while also highlighting potential pitfalls in processing digital pathology data. In the first part of the text, we also outline existing software solutions for viewing scanned samples and implementing diagnostic procedures that incorporate learning systems. In the second part of the text, we describe common tasks in digital pathology and outline typical approaches to solving them. Here, we explain the necessary modifications to standard machine learning methods for processing large scans and discuss specific diagnostic applications. Finally, we provide a brief overview of the potential future development of learning systems in digital pathology. We illustrate the transition to large foundational models and introduce the topic of virtual staining of samples. We hope that this text will contribute to a better understanding of the rapidly evolving field of machine learning in digital pathology and, in turn, facilitate the faster adoption of learning-based methods in this domain.

INTRODUCTION: Diabetes mellitus (DM) and associated comorbidities correspond to female infertility by many interrelated mechanisms. Yet most prior research focuses only on ovary dysfunction. Our work evaluates literature mechanisms of DM-induced uterine tube and endometrial dysfunction, corresponding impacts on female fertility, and potential evidence-based intervention targets. METHODS: We conducted a scoping review (mapping review) follows the Joanna Briggs Institute (Manual for Evidence Synthesis, 2020 version). After identifying the research questions, we conducted a comprehensive search across four electronic databases by entering the keyword "diabetes", with a combination with other keywords as the uterus, endometrium, uterine/Fallopian tube, infertility and embryo implantation. We excluded manuscripts that address the issue of gestational diabetes. Most of these studies were in animals. RESULTS: There is compelling evidence for connecting DM with uterine tube infertility via endometriosis, thyroid dysfunction, and susceptibility to infectious disease. DM damages the endometrium before pregnancy via glucose toxicity, lesions, excessive immune activity, and other mechanisms. DM also hinders endometrium receptivity and embryo-endometrium crosstalk, such as through disrupted endometrium glucose homeostasis. We also hypothesize how DM may affect the function of immune cells in uterine tube and uterus, including changes in the number and types of cells of innate and acquired immunity, disrupting immunological barrier in uterine tube, alterations in formation of neutrophil extracellular traps or polarization of macrophages. DISCUSSION: We discuss evidence for clinical practice in terms of glycaemic control, lifestyle modifications, and medical interventions. For example, there is currently substantial evidence from rodent models for using metformin for increase in endometrial thickness, number of stromal cells and blood vessels and restoration of normal endometrial architecture, and bariatric surgery for recruitment of protective immune cell types to the endometrium. We also briefly highlight the future prospects of stem cells, artificial intelligence, and other new approaches for managing DM-associated female infertility. Further studies are necessary for optimizing female reproductive outcomes.

• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

Periodontitis is a globally prevalent chronic inflammatory disease that leads to periodontal pocket formation and eventually destroys tooth-supporting structures. Hence, the drastic increase in dental implants for periodontitis has become a severe clinical issue. Injectable hydrogel based on extracellular matrix (ECM) is highly biocompatible and tissue-regenerative with tailor-made mechanical properties and high payload capacity for in situ delivery of bioactive molecules to treat periodontitis. This therapeutic tool not only enhances the drug release efficiency and treatment efficacy but also reduces operation time. Nevertheless, it remains challenging to optimize the mechanical properties and intelligent control drug release rate of injectable hydrogels to achieve the highest therapeutic outcome. Literature precedent has shown the modulation of polymer backbones (synthetic polymers, natural polysaccharides, and proteins), crosslinking strategies, other bioactive constituents, and potentially the incorporation of nanomaterials that overall improve the desirable physiochemical and biological performances as well as biodegradability. In this review, we summarize the recent advances in the development, design, and material characterizations of common injectable hydrogels. Furthermore, we highlight cutting-edge representative examples of polysaccharide-, protein- and nanocomposite-based hydrogels that mediate regenerative factors and anti-inflammatory drugs for periodontal regeneration. Finally, we express our perspectives on potential challenges and future development of multifunctional injectable hydrogels for periodontitis.

• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

Cardiovascular disease (CVD) is a leading cause of death worldwide. A key area of interest in CVD prevention is novel digital health technologies, primarily mobile health (mHealth) applications and wearable devices, that are rapidly transforming the methods of preventing and managing CVD. Studies have shown the success of smartphone applications, such as the RITMIA app (Heart SentinelTM, Parma, Italy), in successfully detecting atrial fibrillation (Afib) compared to a classic 12-lead electrocardiogram (ECG). mHealth devices should integrate these factors, based on artificial intelligence (AI) and driven by chatbots, to encourage patients to use technology through interactive, real-world, motivational, and timely feedback. Data from mHealth clinical research indicate improved medication adherence, weight control, and self-care among patients. This review highlights mHealth and wearable devices in CVD prevention, providing foresight into cardiovascular health conditions through continuous monitoring, early detection, and improved patient engagement. Additionally, it examines challenges, including ethical, regulatory, and accessibility issues, that need to be addressed before their widespread adoption. In the future, the priority must be integration with healthcare systems and equitable access. A thorough search was conducted using reputable databases such as Scopus, PubMed, and Google Scholar. Articles from 2015 to 2025, along with an article from 2002 published in reputable peer-reviewed journals, were analyzed and contextually used. We also refined our search terms and used high-quality English articles to achieve this.

• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

Current diagnostic methods for dyslexia primarily rely on traditional paper-and-pencil tasks. Advanced technological approaches, including eye-tracking and artificial intelligence (AI), offer enhanced diagnostic capabilities. In this paper, we bridge the gap between scientific and diagnostic concepts by proposing a novel dyslexia detection method, called INSIGHT, which combines a visualisation phase and a neural network-based classification phase. The first phase involves transforming eye-tracking fixation data into 2D visualisations called Fix-images, which clearly depict reading difficulties. The second phase utilises the ResNet18 convolutional neural network for classifying these images. The INSIGHT method was tested on 35 child participants (13 dyslexic and 22 control readers) using three text-reading tasks, achieving a highest accuracy of 86.65%. Additionally, we cross-tested the method on an independent dataset of Danish readers, confirming the robustness and generalizability of our approach with a notable accuracy of 86.11%. This innovative approach not only provides detailed insight into eye movement patterns when reading but also offers a robust framework for the early and accurate diagnosis of dyslexia, supporting the potential for more personalised and effective interventions.

• MeSH
• čtení MeSH
• dítě MeSH
• dyslexie * patofyziologie   diagnóza   klasifikace   MeSH
• lidé MeSH
• neuronové sítě * MeSH
• oční fixace * fyziologie   MeSH
• pohyby očí fyziologie   MeSH
• technologie sledování pohybu očí * MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: Cognitive impairment in Parkinson's disease (PD) is a key non-motor complication during the disease course. OBJECTIVES: A review of detailed cognitive instruments to detect mild cognitive impairment (PD-MCI) or dementia (PDD) is needed to establish optimal tests that facilitate diagnostic accuracy. METHODS: We performed a systematic literature review of tests that assess memory, language including premorbid intelligence, and visuospatial domains (for tests of attention and executive functions see accompanying review) to determine suitability to assess cognition in PD. Based on in-depth scrutiny of psychometric and other relevant clinimetric properties, tests were rated as "recommended," "recommended with caveats," "suggested," or "listed" by the International Parkinson and Movement Disorder Society (IPMDS) panel of experts according to the IPMDS Clinical Outcome Assessment Scientific Evaluation Committee guidelines. RESULTS: We included 39 tests encompassing 48 outcome measures. Seven tests (different versions or subtests of the test counted once) were recommended, including four for memory, one for visuospatial domains, one for language (including three measures), and one for estimated premorbid intelligence. Furthermore, 10 tests (12 measures) were "recommended with caveats," 11 were "suggested," and 11 (15 measures) were "listed." CONCLUSIONS: Recommended neuropsychological tests in memory, visuospatial functions, and language are proposed to guide the assessment of cognitive impairment and its progression in PD-MCI and PDD, and for use in clinical trials to stratify participants or as outcome measures. Novel measures being developed will need extensive validation research to be "recommended." © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

• MeSH
• jazyk (prostředek komunikace) * MeSH
• kognitivní dysfunkce * diagnóza   etiologie   MeSH
• lidé MeSH
• neuropsychologické testy * normy   MeSH
• paměť * fyziologie   MeSH
• Parkinsonova nemoc * komplikace   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• systematický přehled MeSH

Aim: Emotional intelligence (EI) is increasingly recognized as an essential competency in nursing leadership. This study explores how EI shapes the fundamental components of nursing leadership and its impact on healthcare outcomes. Design: The study is classified as qualitative research. Methods: A comprehensive literature review was performed using databases including EBSCO, Google Scholar, OVID, and Web of Science. Studies published in English between 2017 and 2022 were screened against predefined inclusion criteria. Thirty-three peer-reviewed articles were selected and subjected to contextual and thematic analysis. This qualitative approach allowed synthesis of recurring themes and insights into the influence of EI on nursing leadership and practice. Results: Emotional intelligence significantly impacts nursing leadership by improving patient outcomes, fostering teamwork, enhancing communication, and supporting quality care. Nurses with high EI nurses exhibit empathy, resilience, and positivity, contributing to stronger team dynamics, reduced turnover, and increased cohesion. Leaders with elevated EI levels earn trust, build respectful relationships, and inspire commitment. Moreover, EI reduces burnout, enhances job satisfaction, and ensures consistent quality control in nursing management. Conclusion: Emotional intelligence is fundamental to effective nursing leadership and has a positive impact on staff retention, satisfaction, and quality of care. Incorporating EI training into nursing education and recruitment is vital for sustaining nursing leadership excellence and optimizing healthcare outcomes.

• MeSH
• emoční inteligence * MeSH
• empatie MeSH
• interpersonální vztahy MeSH
• kvalitativní výzkum MeSH
• lidé MeSH
• personální management MeSH
• pracovní uspokojení MeSH
• psychický stres MeSH
• sociální dovednosti MeSH
• vůdcovství * MeSH
• Check Tag
• lidé MeSH

BACKGROUND AND OBJECTIVES: Frontotemporal lobar degeneration (FTLD) as the second most common dementia encompasses a range of syndromes and often shows overlapping symptoms with other subtypes or neurodegenerative diseases, which poses a significant clinical diagnostic challenge. Recent advancements in artificial intelligence (AI), specifically the application of machine learning (ML) algorithms to neuroimaging, have significantly progressed in addressing this challenge. This study aims to assess the diagnostic and predictive efficacy of neuroimaging feature-based AI algorithms for FTLD. METHODS: We conducted a systematic review and meta-analysis following PRISMA guidelines. We searched Pubmed, Scopus, and Web of Science for English-language, peer-reviewed studies using the following three umbrella terms: artificial intelligence, frontotemporal lobar degeneration, and neuroimaging modality. Our survey focused on computer-aided diagnosis for FTLD, employing machine/deep learning with neuroimaging radiomic features. RESULTS: The meta-analysis includes 75 articles with 20,601 subjects, including 8,051 FTLD patients. The results reveal that FTLD can be automatically classified against healthy controls (HC) with pooled sensitivity and specificity of 86% and 89%, respectively. Likewise, FTLD versus Alzheimer's disease (AD) classification exhibits pooled sensitivity and specificity of 84% and 81%, while FTLD versus Parkinson's disease (PD) demonstrates pooled sensitivity and specificity of 84% and 75%, respectively. Classification performance distinguishing FTLD from atypical Parkinsonian syndromes (APS) showed pooled sensitivity and specificity of 84% and 79%, respectively. Multiclass classification sensitivity ranges from 42% to 100%, with lower sensitivity occurring in higher class distinctions (e.g., 5-class and 11-class). DISCUSSION: Our study demonstrates the effectiveness of utilizing neuroimaging features to distinguish FTLD from HC, AD, APS, and PD in binary classification. Utilizing deep learning with multimodal neuroimaging data to differentiate FTLD subtypes and perform multiclassification among FTLD and other neurodegenerative disease holds promise for expediting diagnosis. In sum, the meta-analysis supports translation of machine learning tools in combination with imaging to clinical routine paving the way to precision medicine.

• MeSH
• frontotemporální lobární degenerace * diagnostické zobrazování   MeSH
• lidé MeSH
• neurozobrazování * metody   normy   MeSH
• strojové učení MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• metaanalýza MeSH
• systematický přehled MeSH

BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. We aimed to investigate whether neural network-derived ECG features could be used to predict future cardiovascular disease and mortality and have phenotypic and genotypic associations. METHODS: We extracted 5120 neural network-derived ECG features from an artificial intelligence-enabled ECG model trained for 6 simple diagnoses and applied unsupervised machine learning to identify 3 phenogroups. Using the identified phenogroups, we externally validated our findings in 5 diverse cohorts from the United States, Brazil, and the United Kingdom. Data were collected between 2000 and 2023. RESULTS: In total, 1 808 584 patients were included in this study. In the derivation cohort, the 3 phenogroups had significantly different mortality profiles. After adjusting for known covariates, phenogroup B had a 20% increase in long-term mortality compared with phenogroup A (hazard ratio, 1.20 [95% CI, 1.17-1.23]; P<0.0001; phenogroup A mortality, 2.2%; phenogroup B mortality, 6.1%). In univariate analyses, we found phenogroup B had a significantly greater risk of mortality in all cohorts (log-rank P<0.01 in all 5 cohorts). Phenome-wide association study showed phenogroup B had a higher rate of future atrial fibrillation (odds ratio, 2.89; P<0.00001), ventricular tachycardia (odds ratio, 2.00; P<0.00001), ischemic heart disease (odds ratio, 1.44; P<0.00001), and cardiomyopathy (odds ratio, 2.04; P<0.00001). A single-trait genome-wide association study yielded 4 loci. SCN10A, SCN5A, and CAV1 have roles in cardiac conduction and arrhythmia. ARHGAP24 does not have a clear cardiac role and may be a novel target. CONCLUSIONS: Neural network-derived ECG features can be used to predict all-cause mortality and future cardiovascular diseases. We have identified biologically plausible and novel phenotypic and genotypic associations that describe mechanisms for the increased risk identified.

• MeSH
• časové faktory MeSH
• elektrokardiografie * MeSH
• fenotyp * MeSH
• hodnocení rizik MeSH
• kardiovaskulární nemoci diagnóza   mortalita   genetika   patofyziologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• neuronové sítě * MeSH
• prediktivní hodnota testů * MeSH
• prognóza MeSH
• reprodukovatelnost výsledků MeSH
• rizikové faktory MeSH
• senioři MeSH
• srdeční frekvence MeSH
• strojové učení bez učitele MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• Geografické názvy
• Spojené státy americké MeSH

BACKGROUND: In today's digital age, demanding to interpret vast quantities of visual information with speed and accuracy, nonverbal Intelligence has become increasingly crucial for children, as it plays a key role in cognitive development and learning. While motor proficiency has been positively linked to various cognitive functions in children, its relationship with nonverbal Intelligence remains an open question. This study, therefore, explored the structural associations between motor proficiency and nonverbal Intelligence in school-aged children (6 to 11 years), focusing on potential age and sex-specific patterns. METHODS: Data were obtained from 396 children aged 6 to 11 (214 boys, 182 girls; mean age 8.9 years ±1.3) divided into younger children 6-8 years and older Children 9-11 years. Motor proficiency was assessed using the Bruininks-Oseretsky Test of Motor Proficiency, Second Edition (BOT-2), and non-verbal Intelligence was evaluated with the Raven Progressive Matrices (RPM). We conducted multigroup structural modelling with non-verbal Intelligence as a dependent latent variable. RESULTS: The BOT-2 and RPM models demonstrated an acceptable fit in Czech children. Strength-agility and Fine motor control emerged as the strongest predictors of nonverbal intelligence level assessed by five sets of RPM. Age-specific analyses revealed that the Strength-agility construct was consistently a significant predictor of nonverbal intelligence level in both age categories. However, in older children, also Fine motor control was significantly linked to nonverbal intelligence level. Sex-specific differences were also observed in the structural modelling results, indicating significant predictor non-invariance based on participants' sex. In girls, both Fine motor control and the Strength-agility constructs were significant predictors of nonverbal Intelligence level, showing stronger associations with nonverbal Intelligence than boys. For boys, only the Strength-agility construct was a significant predictor of RPM performance. CONCLUSION: This study reveals a nuanced age- and sex-specific relationship between children's motor proficiency and nonverbal Intelligence. The findings underscore the need for targeted physical interventions, particularly those emphasising fine motor and strength-agility exercises, to ensure equitable opportunities for motor skill development. Such interventions may enhance physical abilities and support cognitive development in an increasingly digital world.

• MeSH
• analýza latentních tříd MeSH
• dítě MeSH
• inteligence * fyziologie   MeSH
• lidé MeSH
• motorické dovednosti * fyziologie   MeSH
• sexuální faktory MeSH
• věkové faktory MeSH
• vývoj dítěte fyziologie   MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Česká republika MeSH