BACKGROUND: Advances in paediatric type 1 diabetes management and increased use of diabetes technology have led to improvements in glycaemia, reduced risk of severe hypoglycaemia, and improved quality of life. Since 1993, progressively lower HbA1c targets have been set. The aim of this study was to perform a longitudinal analysis of HbA1c, treatment regimens, and acute complications between 2013 and 2022 using data from eight national and one international paediatric diabetes registries. METHODS: In this longitudinal analysis, we obtained data from the Australasian Diabetes Data Network, Czech National Childhood Diabetes Register, Danish Registry of Childhood and Adolescent Diabetes, Diabetes Prospective Follow-up Registry, Norwegian Childhood Diabetes Registry, England and Wales' National Paediatric Diabetes Audit, Swedish Childhood Diabetes Registry, T1D Exchange Quality Improvement Collaborative, and the SWEET initiative. All children (aged ≤18 years) with type 1 diabetes with a duration of longer than 3 months were included. Investigators compared data from 2013 to 2022; analyses performed on data were pre-defined and conducted separately by each respective registry. Data on demographics, HbA1c, treatment regimen, and event rates of diabetic ketoacidosis and severe hypoglycaemia were collected. ANOVA was performed to compare means between registries and years. Joinpoint regression analysis was used to study significant breakpoints in temporal trends. FINDINGS: In 2022, data were available for 109 494 children from the national registries and 35 590 from SWEET. Between 2013 and 2022, the aggregated mean HbA1c decreased from 8·2% (95% CI 8·1-8·3%; 66·5 mmol/mol [65·2-67·7]) to 7·6% (7·5-7·7; 59·4mmol/mol [58·2-60·5]), and the proportion of participants who had achieved HbA1c targets of less than 7% (<53 mmol/mol) increased from 19·0% to 38·8% (p<0·0001). In 2013, the aggregate event rate of severe hypoglycaemia rate was 3·0 events per 100 person-years (95% CI 2·0-4·9) compared with 1·7 events per 100 person-years (1·0-2·7) in 2022. In 2013, the aggregate event rate of diabetic ketoacidosis was 3·1 events per 100 person-years (95% CI 2·0-4·8) compared with 2·2 events per 100 person-years (1·4-3·4) in 2022. The proportion of participants with insulin pump use increased from 42·9% (95% CI 40·4-45·5) in 2013 to 60·2% (95% CI 57·9-62·6) in 2022 (mean difference 17·3% [13·8-20·7]; p<0·0001), and the proportion of participants using continuous glucose monitoring (CGM) increased from 18·7% (95% CI 9·5-28·0) in 2016 to 81·7% (73·0-90·4) in 2022 (mean difference 63·0% [50·3-75·7]; p<0·0001). INTERPRETATION: Between 2013 and 2022, glycaemic outcomes have improved, parallel to increased use of diabetes technology. Many children had HbA1c higher than the International Society for Pediatric and Adolescent Diabetes (ISPAD) 2022 target. Reassuringly, despite targeting lower HbA1c, severe hypoglycaemia event rates are decreasing. Even for children with type 1 diabetes who have access to specialised diabetes care and diabetes technology, further advances in diabetes management are required to assist with achieving ISPAD glycaemic targets. FUNDING: None. TRANSLATIONS: For the Norwegian, German, Czech, Danish and Swedish translations of the abstract see Supplementary Materials section.

• MeSH
• Diabetes Mellitus, Type 1 * epidemiology   blood   drug therapy   MeSH
• Child MeSH
• Glycated Hemoglobin * analysis   MeSH
• Hypoglycemia epidemiology   MeSH
• Hypoglycemic Agents * therapeutic use   MeSH
• Infant MeSH
• Blood Glucose * analysis   MeSH
• Humans MeSH
• Longitudinal Studies MeSH
• Adolescent MeSH
• Child, Preschool MeSH
• Registries * statistics & numerical data   MeSH
• Glycemic Control statistics & numerical data   methods   MeSH
• Treatment Outcome MeSH
• Check Tag
• Child MeSH
• Infant MeSH
• Humans MeSH
• Adolescent MeSH
• Male MeSH
• Child, Preschool MeSH
• Female MeSH
• 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

BACKGROUND: As the healthcare sector evolves, Artificial Intelligence's (AI's) potential to enhance laboratory medicine is increasingly recognized. However, the adoption rates and attitudes towards AI across European laboratories have not been comprehensively analyzed. This study aims to fill this gap by surveying European laboratory professionals to assess their current use of AI, the digital infrastructure available, and their attitudes towards future implementations. METHODS: We conducted a methodical survey during October 2023, distributed via EFLM mailing lists. The survey explored six key areas: general characteristics, digital equipment, access to health data, data management, AI advancements, and personal perspectives. We analyzed responses to quantify AI integration and identify barriers to its adoption. RESULTS: From 426 initial responses, 195 were considered after excluding incomplete and non-European entries. The findings revealed limited AI engagement, with significant gaps in necessary digital infrastructure and training. Only 25.6 % of laboratories reported ongoing AI projects. Major barriers included inadequate digital tools, restricted access to comprehensive data, and a lack of AI-related skills among personnel. Notably, a substantial interest in AI training was expressed, indicating a demand for educational initiatives. CONCLUSIONS: Despite the recognized potential of AI to revolutionize laboratory medicine by enhancing diagnostic accuracy and efficiency, European laboratories face substantial challenges. This survey highlights a critical need for strategic investments in educational programs and infrastructure improvements to support AI integration in laboratory medicine across Europe. Future efforts should focus on enhancing data accessibility, upgrading technological tools, and expanding AI training and literacy among professionals. In response, our working group plans to develop and make available online training materials to meet this growing educational demand.

• MeSH
• Laboratories, Clinical MeSH
• Humans MeSH
• Surveys and Questionnaires MeSH
• Artificial Intelligence * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Geographicals
• Europe MeSH

Cílem sdílení genomických dat je umožnit bezpečný přístup k těmto údajům především za účelem výzkumu, personalizované zdravotní péče a tvorby zdravotních politik. Sdílení dat má potenciál urychlit výzkum a přinést významný pokrok v chápání zdraví a nemocí, avšak naráží na právní a etické problémy spojené s ochranou soukromí a důvěrnosti informací. Kromě mnohdy neintuitivní evropské legislativy vedoucí k různým právním interpretacím, existují v jednotlivých zemích Evropské unie další národní pravidla, která mohou nakládání s genomickými daty dále specifikovat. Tato různorodost komplikuje mezinárodní spolupráci a sdílení dat, a to nejenom v genetice, ale i v jiných oblastech biomedicínského výzkumu. Tato práce analyzuje základní právní rámec a jeho aplikaci umožňující sdílení genomických dat a objasňuje pojmy dalšího zpracování, sekundárního využití a účelu zpracování dat. Dále zdůrazňuje význam souhlasu subjektů údajů a specifických výjimek z obecného zákazu zpracování citlivých dat. Pro efektivní sdílení genomických dat je nezbytné dodržovat evropské a národní právní předpisy, včetně jasného stanovení účelu a právního základu zpracování. Mezinárodní spolupráce vyžaduje harmonizaci právních předpisů a důkladnou správu dat. Tento článek analyzuje základní dynamiku a zákonnost sdílení dat v oblasti genomického výzkumu.

The aim of genomic data sharing is to enable secure access to this data, primarily for research, personalized healthcare and health policy-making. Data sharing has the potential to accelerate research and bring about significant advances in the understanding of health and disease, but it faces legal and ethical issues related to the protection of privacy and confidentiality of information. In addition to the often counterintuitive European legislation leading to different legal interpretations, there are other national rules in individual European Union countries that can further specify the handling of genomic data. This diversity complicates international cooperation and data sharing, not only in genetics but also in other areas of biomedical research. This thesis analyzes the basic legal framework and its application enabling the sharing of genomic data and clarifies the concepts of further processing, secondary use and purpose of data processing. Furthermore, it stresses the importance of data subjects' consent and specific exceptions to the general ban on processing sensitive data. For effective sharing of genomic data, it is essential to comply with European and national legislation, including a clear definition of the purpose and legal basis of processing. International cooperation requires regulatory harmonization and robust data management. This paper analyzes the fundamental dynamics and legality of data sharing in the field of genomic research.

• MeSH
• Electronic Data Processing ethics   legislation & jurisprudence   MeSH
• Genomics * legislation & jurisprudence   MeSH
• Humans MeSH
• Liability, Legal MeSH
• Information Dissemination legislation & jurisprudence   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH

OBJECTIVES: The aim of the study was to identify potential areas for improvement in the prevention of oral diseases in pregnant women by assessing their oral care habits and awareness regarding oral health. METHODS: An original, anonymous, web-based survey was conducted among women at any stage of pregnancy. The survey consisted of 23 questions regarding oral care habits, knowledge about oral health of mother and child, general and oral health changes, and attendance of oral healthcare services during pregnancy. The data analysis was performed using IBM SPSS 27.0 version software. Descriptive statistics, Chi-square and Wilcoxon signed-rank tests were used to analyse the data. The level of statistical significance was set at p < 0.05. RESULTS: A total of 714 pregnant women participated in the study, with a mean (SD) age of 30.2 (4.4) years. Majority of the respondents demonstrated acceptable oral health-related knowledge and habits. A lack of interdental care among pregnant women was discovered. Nearly a third (27.6%) of the respondents reported a decline in their oral health during pregnancy. The most commonly reported general and oral health issues during pregnancy were increased stomach acid levels (71.3%) and gum bleeding (43.3%). Pregnant women were most frequently informed about the importance of oral care by an obstetrician-gynaecologist (25.4%). CONCLUSIONS: The study revealed the need for targeted interventions to enhance oral health awareness and practices among pregnant women in Lithuania. While overall oral hygiene habits were acceptable, deficiencies in interdental care and knowledge regarding oral health during pregnancy were evident. Higher level of education and urban residency were associated with superior oral care practices of pregnant women. In order to improve oral health of mother and child, interdisciplinary collaboration and dissemination of accessible, evidence-based information are essential.

• MeSH
• Adult MeSH
• Humans MeSH
• Oral Hygiene * statistics & numerical data   MeSH
• Oral Health * statistics & numerical data   MeSH
• Surveys and Questionnaires MeSH
• Pregnancy MeSH
• Pregnant People * psychology   MeSH
• Health Knowledge, Attitudes, Practice * MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Pregnancy MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Geographicals
• Lithuania MeSH

Komunikační zdravotní gramotnost je klíčová pro efektivní interakci mezi pacienty a zdravotníky. Její omezení může vést k nerovnostem v oblasti zdraví. Ukrajinští váleční uprchlíci čelí překážkám při přístupu ke zdravotní péči, zejména kvůli jazykovým a kulturním bariérám. Studie analyzuje jejich úroveň komunikační zdravotní gramotnosti a srovnává ji s českou populací. Kvantitativní výzkum využil standardizovaný dotazník HLS19-COM-P. V listopadu 2023 bylo metodou sběru dat prostřednictvím on-line dotazníku (CAWI) dotázáno 1182 ukrajinských uprchlíků. Analýza probíhala v softwaru IBM SPSS Statistics verze 28. Komunikační zdravotní gramotnost uprchlíků byla výrazně nižší než u české populace. Největší bariéry zahrnovaly obtíže s vyjadřováním zdravotních problémů (40 %), pokládáním otázek lékaři (50 %) a sdíleným rozhodováním (55 %). Nízká úroveň komunikační zdravotní gramotnosti byla spojena s horším využíváním zdravotní péče. Pro snížení nerovností ve zdraví je nutné posílit jazykovou podporu uprchlíků, zvýšit dostupnost interkulturních pracovníků a zavést edukační programy na zlepšení komunikační zdravotní gramotnosti.

Communicative health literacy is essential for effective interaction between patients and healthcare professionals. Its limitations may contribute to health inequalities. Ukrainian war refugees face significant barriers in accessing healthcare, primarily due to language and cultural differences. This study analyses their level of communicative health literacy and compares it with the Czech population. A quantitative study was conducted using the standardized HLS19-COM-P questionnaire. In November 2023, 1,182 Ukrainian refugees were surveyed using the CAWI method. Data analysis was performed using IBM SPSS Statistics software version 28. The communicative health literacy of refugees was significantly lower than that of the Czech population. The most significant barriers included difficulties in expressing health problems (40%), asking questions to physicians (50%), and participating in shared decision-making (55%). A low level of communicative health literacy was associated with poorer utilization of healthcare services. To reduce health inequalities, it is necessary to strengthen language support for refugees, increase the availability of intercultural mediators, and implement educational programs aimed at improving communicative health literacy.

• MeSH
• Communication Barriers MeSH
• Humans MeSH
• Surveys and Questionnaires MeSH
• Refugees * MeSH
• Warfare and Armed Conflicts MeSH
• Health Literacy * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Chart MeSH
• Research Support, Non-U.S. Gov't MeSH
• Geographicals
• Czech Republic MeSH
• Ukraine MeSH

AIM: Exposure to light at night and meal time misaligned with the light/dark (LD) cycle-typical features of daily life in modern 24/7 society-are associated with negative effects on health. To understand the mechanism, we developed a novel protocol of complex chronodisruption (CD) in which we exposed female rats to four weekly cycles consisting of 5-day intervals of constant light and 2-day intervals of food access restricted to the light phase of the 12:12 LD cycle. METHODS: We examined the effects of CD on behavior, estrous cycle, sleep patterns, glucose homeostasis and profiles of clock- and metabolism-related gene expression (using RT qPCR) and liver metabolome and lipidome (using untargeted metabolomic and lipidomic profiling). RESULTS: CD attenuated the rhythmic output of the central clock in the suprachiasmatic nucleus via Prok2 signaling, thereby disrupting locomotor activity, the estrous cycle, sleep patterns, and mutual phase relationship between the central and peripheral clocks. In the periphery, CD abolished Per1,2 expression rhythms in peripheral tissues (liver, pancreas, colon) and worsened glucose homeostasis. In the liver, it impaired the expression of NAD+, lipid, and cholesterol metabolism genes and abolished most of the high-amplitude rhythms of lipids and polar metabolites. Interestingly, CD abolished the circadian rhythm of Cpt1a expression and increased the levels of long-chain acylcarnitines (ACar 18:2, ACar 16:0), indicating enhanced fatty acid oxidation in mitochondria. CONCLUSION: Our data show the widespread effects of CD on metabolism and point to ACars as biomarkers for CD due to misaligned sleep and feeding patterns.

• MeSH
• Circadian Clocks physiology   MeSH
• Circadian Rhythm * physiology   MeSH
• Photoperiod MeSH
• Liver * metabolism   MeSH
• Carnitine * analogs & derivatives   metabolism   MeSH
• Rats MeSH
• Metabolome * MeSH
• Suprachiasmatic Nucleus metabolism   MeSH
• Animals MeSH
• Check Tag
• Rats MeSH
• Female MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH

Digitalizace postupně proniká do velké části medicínských oblastí včetně patologie. Společně s digitálním zpracováním dat přichází aplikace metod umělé inteligence za účelem zjednodušení rutinních procesů, zvýšení bezpečnosti apod. Ačkoliv se obecné povědomí o metodách umělé inteligence zvyšuje, stále není pravidlem, že by odborníci z netechnických oborů měli detailní představu o tom, jak takové systémy fungují a jak se učí. Cílem tohoto textu je přístupnou formou vysvětlit základy strojového učení s využitím příkladů a ilustrací z oblasti digitální patologie. Nejedná se samozřejmě o ucelený přehled ani o představení nejmodernějších metod. Držíme se spíše úplných základů a představujeme fundamentální myšlenky, které stojí za většinou učících systémů, s použitím nejjednodušších modelů. V textu se věnujeme zejména rozhodovacím stromům, jejichž funkce je snadno vysvětlitelná, a elementárním neuronovým sítím, které jsou hlavním modelem používaným v dnešní umělé inteligenci. Pokusíme se také popsat postup spolupráce mezi lékaři, kteří dodávají data, a informatiky, kteří s jejich pomocí vytvářejí učící systémy. Věříme, že tento text pomůže překlenout rozdíly mezi znalostmi lékařů a informatiků a tím přispěje k efektivnější mezioborové spolupráci.

Digitalization has gradually made its way into many areas of medicine, including pathology. Along with digital data processing comes the application of artificial intelligence methods to simplify routine processes, enhance safety, etc. Although general awareness of artificial intelligence methods is increasing, it is still not common for professionals from non-technical fields to have a detailed understanding of how such systems work and learn. This text aims to explain the basics of machine learning in an accessible way using examples and illustrations from digital pathology. This is not intended to be a comprehensive overview or an introduction to cutting-edge methods. Instead, we use the simplest models to focus on fundamental concepts behind most learning systems. The text concentrates on decision trees, whose functionality is easy to explain, and basic neural networks, the primary models used in today’s artificial intelligence. We also attempt to describe the collaborative process between medical specialists, who provide the data, and computer scientists, who use this data to develop learning systems. This text will help bridge the knowledge gap between medical professionals and computer scientists, contributing to more effective interdisciplinary collaboration.

• MeSH
• Humans MeSH
• Pathology * trends   MeSH
• Machine Learning * trends   MeSH
• Artificial Intelligence trends   MeSH
• Check Tag
• Humans MeSH

BACKGROUND: Myasthenia gravis (MG) is a rare autoimmune disorder. Several new treatment concepts have emerged in recent years, but access to these treatments varies due to differing national reimbursement regulations, leading to disparities across Europe. This highlights the need for high-quality data collection by stakeholders to establish MG registries. A European MG registry could help bridge the treatment access gap across different countries, offering critical data to support regulatory decisions, foster international collaborations, and enhance clinical and epidemiological research. Several national MG registries already exist or are in development. To avoid duplication and ensure harmonization in data collection, a modified Delphi procedure was implemented to identify essential data elements for inclusion in national registries. RESULTS: Following a literature review, consultations with patient associations and pharmaceutical companies, and input from multiple European MG experts, 100 data elements were identified. Of these, 62 reached consensus for inclusion and classification, while only 1 item was agreed for exclusion. 30 items failed to reach the ≥ 80% agreement threshold and were excluded. Among the 62 accepted items, 21 were classified as mandatory data elements, 32 optional, and 9 items pertained to the informed consent form. CONCLUSIONS: Through a modified Delphi procedure, consensus was successfully achieved. This consensus-based approach represents a crucial step toward harmonizing MG registries across Europe. The resulting dataset will facilitate the sharing of knowledge and enhance European collaborations. Furthermore, the harmonized data may assist in regulatory or reimbursement decisions regarding novel therapies, as well as address treatment access disparities between European countries.

• MeSH
• Delphi Technique * MeSH
• Consensus MeSH
• Humans MeSH
• Myasthenia Gravis * therapy   diagnosis   MeSH
• Registries * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Geographicals
• Europe MeSH