This scoping review summarizes evidence regarding the impact of civic and community engagement of young people during the COVID-19 pandemic. Recognizing that the global pandemic not only brought challenges but also new opportunities to take a stance and to actively engage in communities and society, this review assesses the impact of the COVID-19 pandemic on young people's civic engagement across different cultural contexts and identifies key factors and processes that enable young people to engage with their community or society at large. We summarize evidence from 27 original research papers, one thought piece, and four reports conducted by global organizations such as the United Nations and OECD. Relevant research was conducted in the United States, Europe, China, Southeast Asia, South Africa, and New Zealand, addressing the development of leadership skills, civic responsibility, critical consciousness, civic and community engagement, as well as social integration. Key factors that facilitated civic engagement include national investments in online learning facilities, support for basic needs (such as education, health, and employment), and promotion and encouragement of local initiatives. The studies differed in their focus depending on the socio-cultural context encountered and future research needs to consider cultural variations and different demands on young people to inform effective practices for supporting young people's active engagement in society.

• MeSH
• COVID-19 * epidemiology   psychology   MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Pandemics MeSH
• SARS-CoV-2 * MeSH
• Social Responsibility MeSH
• Community Participation MeSH
• Leadership MeSH
• Check Tag
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Publication type
• Journal Article MeSH
• Review MeSH

INTRODUCTION: The histopathological classification for antineutrophil cytoplasmic autoantibody (ANCA)-associated glomerulonephritis (ANCA-GN) is a well-established tool to reflect the variety of patterns and severity of lesions that can occur in kidney biopsies. It was demonstrated previously that deep learning (DL) approaches can aid in identifying histopathological classes of kidney diseases; for example, of diabetic kidney disease. These models can potentially be used as decision support tools for kidney pathologists. Although they reach high prediction accuracies, their "black box" structure makes them nontransparent. Explainable (X) artificial intelligence (AI) techniques can be used to make the AI model decisions accessible for human experts. We have developed a DL-based model, which detects and classifies the glomerular lesions according to the Berden classification. METHODS: Kidney biopsy slides of 80 patients with ANCA-GN from 3 European centers, who underwent a diagnostic kidney biopsy between 1991 and 2011, were included. We also investigated the explainability of our model using Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps. These maps were analyzed by pathologists to compare the decision-making criteria of humans and the DL model and assess the impact of different training settings. RESULTS: The DL model shows a prediction accuracy of 93% for classifying lesions. The heatmaps from our trained DL models showed that the most predictive areas in the image correlated well with the areas deemed to be important by the pathologist. CONCLUSION: We present the first DL-based computational pipeline for classifying ANCA-GN kidney biopsies as per the Berden classification. XAI techniques helped us to make the decision-making criteria of the DL accessible for renal pathologists, potentially improving clinical decision-making.

• Publication type
• Journal Article MeSH

PURPOSE OF REVIEW: A critical evaluation of contemporary literature regarding the role of big data, artificial intelligence, and digital technologies in precision cardio-oncology care and survivorship, emphasizing innovative and groundbreaking endeavors. RECENT FINDINGS: Artificial intelligence (AI) algorithm models can automate the risk assessment process and augment current subjective clinical decision tools. AI, particularly machine learning (ML), can identify medically significant patterns in large data sets. Machine learning in cardio-oncology care has great potential in screening, diagnosis, monitoring, and managing cancer therapy-related cardiovascular complications. To this end, large-scale imaging data and clinical information are being leveraged in training efficient AI algorithms that may lead to effective clinical tools for caring for this vulnerable population. Telemedicine may benefit cardio-oncology patients by enhancing healthcare delivery through lowering costs, improving quality, and personalizing care. Similarly, the utilization of wearable biosensors and mobile health technology for remote monitoring holds the potential to improve cardio-oncology outcomes through early intervention and deeper clinical insight. Investigations are ongoing regarding the application of digital health tools such as telemedicine and remote monitoring devices in enhancing the functional status and recovery of cancer patients, particularly those with limited access to centralized services, by increasing physical activity levels and providing access to rehabilitation services. SUMMARY: In recent years, advances in cancer survival have increased the prevalence of patients experiencing cancer therapy-related cardiovascular complications. Traditional cardio-oncology risk categorization largely relies on basic clinical features and physician assessment, necessitating advancements in machine learning to create objective prediction models using diverse data sources. Healthcare disparities may be perpetuated through AI algorithms in digital health technologies. In turn, this may have a detrimental effect on minority populations by limiting resource allocation. Several AI-powered innovative health tools could be leveraged to bridge the digital divide and improve access to equitable care.

• Publication type
• Journal Article MeSH

BACKGROUND: Robot-assisted minimally invasive esophagectomy (RAMIE) is increasingly adopted in centers worldwide, with ongoing refinements to enhance results. This study aims to assess the current state of RAMIE worldwide and to identify potential areas for improvement. METHODS: This descriptive study analyzed prospective data from esophageal cancer patients who underwent transthoracic RAMIE in Upper GI International Robotic Association (UGIRA) centers. Main endpoints included textbook outcome rate, surgical techniques, and perioperative outcomes. Analyses were performed separately for intrathoracic (Ivor-Lewis) and cervical anastomosis (McKeown), divided into three time cohorts (2016-2018, 2019-2020, 2021-2023). A sensitivity analysis was conducted with cases after the learning curve (> 70 cases). RESULTS: Across 28 UGIRA centers, 2012 Ivor-Lewis and 1180 McKeown procedures were performed. Over the time cohorts, textbook outcome rates were 39%, 48%, and 49% for Ivor-Lewis, and 49%, 63%, and 61% for McKeown procedures, respectively. Fully robotic procedures accounted for 66%, 51%, and 60% of Ivor-Lewis procedures, and 53%, 81%, and 66% of McKeown procedures. Lymph node yield showed 27, 30, and 30 nodes in Ivor-Lewis procedures, and 26, 26, and 34 nodes in McKeown procedures. Furthermore, high mediastinal lymphadenectomy was performed in 65%, 43%, and 37%, and 70%, 48%, and 64% of Ivor-Lewis and McKeown procedures, respectively. Anastomotic leakage rates were 22%, 22%, and 16% in Ivor-Lewis cases, and 14%, 12%, and 11% in McKeown cases. Hospital stay was 13, 14, and 13 days for Ivor-Lewis procedures, and 12, 9, and 11 days for McKeown procedures. In Ivor-Lewis and McKeown, respectively, the sensitivity analysis revealed textbook outcome rates of 43%, 54%, and 51%, and 47%, 64%, and 64%; anastomotic leakage rates of 28%, 18%, and 15%, and 13%, 11%, and 10%; and hospital stay of 11, 12, and 12 days, and 10, 9, and 9 days. CONCLUSIONS: This study demonstrates favorable outcomes over time in achieving textbook outcome after RAMIE. Areas for improvement include a reduction of anastomotic leakage and shortening of hospital stay.

• MeSH
• Esophagectomy * methods   MeSH
• Middle Aged MeSH
• Humans MeSH
• Minimally Invasive Surgical Procedures methods   MeSH
• Esophageal Neoplasms * surgery   pathology   MeSH
• Follow-Up Studies MeSH
• Postoperative Complications epidemiology   MeSH
• Prognosis MeSH
• Prospective Studies MeSH
• Registries * MeSH
• Robotic Surgical Procedures * methods   MeSH
• Aged MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

The Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 and approved by ISO as a standard for sharing clinical and genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, and treatments. A phenopacket can be used as an input file for software that supports phenotype-driven genomic diagnostics and for algorithms that facilitate patient classification and stratification for identifying new diseases and treatments. There has been a great need for a collection of phenopackets to test software pipelines and algorithms. Here, we present Phenopacket Store. Phenopacket Store v.0.1.19 includes 6,668 phenopackets representing 475 Mendelian and chromosomal diseases associated with 423 genes and 3,834 unique pathogenic alleles curated from 959 different publications. This represents the first large-scale collection of case-level, standardized phenotypic information derived from case reports in the literature with detailed descriptions of the clinical data and will be useful for many purposes, including the development and testing of software for prioritizing genes and diseases in diagnostic genomics, machine learning analysis of clinical phenotype data, patient stratification, and genotype-phenotype correlations. This corpus also provides best-practice examples for curating literature-derived data using the GA4GH Phenopacket Schema.

• MeSH
• Algorithms MeSH
• Databases, Genetic MeSH
• Phenotype * MeSH
• Genomics * methods   MeSH
• Humans MeSH
• Software * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

BACKGROUND: Bipolar disorder (BD) is a complex and heterogeneous psychiatric disorder. It has been suggested that neurodevelopmental factors contribute to the etiology of BD, but a specific neurodevelopmental phenotype (NDP) of the disorder has not been identified. Our objective was to define and characterize an NDP in BD and validate its associations with clinical outcomes, polygenic risk scores, and treatment responses. METHODS: We analyzed the FondaMental Advanced Centers of Expertise for Bipolar Disorders cohort of 4468 patients with BD, a validation cohort of 101 patients with BD, and 2 independent replication datasets of 274 and 89 patients with BD. Using factor analyses, we identified a set of criteria for defining NDP. Next, we developed a scoring system for NDP load and assessed its association with prognosis, neurological soft signs, polygenic risk scores for neurodevelopmental disorders, and responses to treatment using multiple regressions, adjusted for age and gender with bootstrap replications. RESULTS: Our study established an NDP in BD consisting of 9 clinical features: advanced paternal age, advanced maternal age, childhood maltreatment, attention-deficit/hyperactivity disorder, early onset of BD, early onset of substance use disorders, early onset of anxiety disorders, early onset of eating disorders, and specific learning disorders. Patients with higher NDP load showed a worse prognosis and increased neurological soft signs. Notably, these individuals exhibited a poorer response to lithium treatment. Furthermore, a significant positive correlation was observed between NDP load and polygenic risk score for attention-deficit/hyperactivity disorder, suggesting potential overlapping genetic factors or pathophysiological mechanisms between BD and attention-deficit/hyperactivity disorder. CONCLUSIONS: The proposed NDP constitutes a promising clinical tool for patient stratification in BD.

• MeSH
• Bipolar Disorder * genetics   MeSH
• Adult MeSH
• Phenotype * MeSH
• Attention Deficit Disorder with Hyperactivity genetics   MeSH
• Cohort Studies MeSH
• Middle Aged MeSH
• Humans MeSH
• Young Adult MeSH
• Multifactorial Inheritance genetics   MeSH
• Neurodevelopmental Disorders genetics   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

The formation of memories is a complex, multi-scale phenomenon, especially when it involves integration of information from various brain systems. We have investigated the differences between a novel and consolidated association of spatial cues and amphetamine administration, using an in situ hybridisation method to track the short-term dynamics during the recall testing. We have found that remote recall group involves smaller, but more consolidated groups of neurons, which is consistent with their specialisation. By employing machine learning analysis, we have shown this pattern is especially pronounced in the VTA; furthermore, we also uncovered significant activity patterns in retrosplenial and prefrontal cortices, as well as in the DG and CA3 subfields of the hippocampus. The behavioural propensity towards the associated localisation appears to be driven by the nucleus accumbens, however, further modulated by a trio of the amygdala, VTA and hippocampus, as the trained association is confronted with test experience. Moreover, chemogenetic analysis revealed central amygdala as critical for linking appetitive emotional states with spatial contexts. These results show that memory mechanisms must be modelled considering individual differences in motivation, as well as covering dynamics of the process.

• MeSH
• Amphetamine pharmacology   MeSH
• Amygdala physiology   MeSH
• Hippocampus * physiology   MeSH
• Memory Consolidation * physiology   MeSH
• Rats MeSH
• Brain physiology   MeSH
• Neurons physiology   metabolism   MeSH
• Nucleus Accumbens * physiology   MeSH
• Reward * MeSH
• Memory physiology   MeSH
• Cues MeSH
• Prefrontal Cortex physiology   MeSH
• Mental Recall * physiology   MeSH
• Machine Learning MeSH
• Ventral Tegmental Area * physiology   MeSH
• Animals MeSH
• Check Tag
• Rats MeSH
• Male MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH

The topic of the diagnosis of phaeochromocytomas remains highly relevant because of advances in laboratory diagnostics, genetics, and therapeutic options and also the development of imaging methods. Computed tomography still represents an essential tool in clinical practice, especially in incidentally discovered adrenal masses; it allows morphological evaluation, including size, shape, necrosis, and unenhanced attenuation. More advanced post-processing tools to analyse digital images, such as texture analysis and radiomics, are currently being studied. Radiomic features utilise digital image pixels to calculate parameters and relations undetectable by the human eye. On the other hand, the amount of radiomic data requires massive computer capacity. Radiomics, together with machine learning and artificial intelligence in general, has the potential to improve not only the differential diagnosis but also the prediction of complications and therapy outcomes of phaeochromocytomas in the future. Currently, the potential of radiomics and machine learning does not match expectations and awaits its fulfilment.

• MeSH
• Pheochromocytoma * diagnostic imaging   MeSH
• Humans MeSH
• Adrenal Gland Neoplasms * diagnostic imaging   MeSH
• Paraganglioma * diagnostic imaging   MeSH
• Tomography, X-Ray Computed methods   MeSH
• Image Processing, Computer-Assisted methods   MeSH
• Radiomics MeSH
• Machine Learning MeSH
• Artificial Intelligence MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH

Our aim is to determine the number of leukocytes, T lymphocytes and B lymphocytes and the expression of activation markers CD200 and CD23 on B lymphocytes in atopic dermatitis (AD) patients (treated and not treated with dupilumab) during the pollen season. We examined 29 patients not treated with dupilumab, 24 patients treated with dupilumab and 40 healthy subjects as a control group. The count of T and B lymphocytes and their subsets were assessed by flow cytometry. The non-parametric Kruskal-Wallis one-factor analysis of variance with post hoc by Dunn's test with Bonferroni's modification was used for statistical processing. Although there was a significant improvement in skin findings in patients treated with dupilumab, the changes in immunological profile show a persistent altered immune response characterized by dysregulation and overactivation of B lymphocytes. Dupilumab therapy leads to normalization of relative T regulatory lymphocytes and total memory B lymphocytes and to decreased count of absolute CD8+ T lymphocytes. Why carry out this study?Studies investigating the immunological profile of atopic dermatitis (AD) patients during the pollen season are rare. There are no studies investigating the count of B lymphocytes (CD5+, CD22+ and CD73+ B lymphocytes) and the expression of activation markers CD23 and CD200 on B lymphocytes and on their subsets during pollen season in AD patients treated and non-treated with dupilumab therapy.What was learned from the study?In atopic dermatitis (AD) patients with and without dupilumab therapy, we confirmed the significantly higher count of absolute neutrophils, absolute monocytes, absolute eosinophils, absolute basophils, non-switched B lymphocytes, transitional B lymphocytes, CD23 memory, naive, non-switched, switched and total CD23 B lymphocytes, the relative count of CD200 memory and CD200 switched B lymphocytes.In dupilumab treated patients, we confirmed the significantly higher count of relative eosinophils, relative CD16+ eosinophils, relative CD200 non-switched B lymphocytes and lower count of absolute CD8+ T lymphocytes. Further studies should focus on investigating the effect of dupilumab on CD8+ T lymphocytes and their subpopulations.In patients without dupilumab therapy, we confirmed the significantly higher count of relative neutrophils, relative T regulatory lymphocytes and total memory B lymphocytes.The changes in the count of CD5+, CD22+ and CD73+ B lymphocytes were not observed during pollen season in both groups of AD patients.

• MeSH
• Dermatitis, Atopic * drug therapy   immunology   MeSH
• B-Lymphocytes immunology   MeSH
• Antigens, CD MeSH
• Adult MeSH
• Antibodies, Monoclonal, Humanized * therapeutic use   MeSH
• Middle Aged MeSH
• Humans MeSH
• Young Adult MeSH
• Pollen immunology   MeSH
• Receptors, IgE MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

• Keywords
• naivní model, transfuzní přípravek,
• MeSH
• Blood Donors MeSH
• Blood Banking organization & administration   MeSH
• Machine Learning supply & distribution   MeSH
• Planning Techniques MeSH
• Artificial Intelligence supply & distribution   MeSH
• Equipment and Supplies, Hospital MeSH

• Conspectus
• Patologie. Klinická medicína
• NML Publication type
• závěrečné práce