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
Although genetic lesions responsible for some mendelian disorders can be rapidly discovered through massively parallel sequencing of whole genomes or exomes, not all diseases readily yield to such efforts. We describe the illustrative case of the simple mendelian disorder medullary cystic kidney disease type 1 (MCKD1), mapped more than a decade ago to a 2-Mb region on chromosome 1. Ultimately, only by cloning, capillary sequencing and de novo assembly did we find that each of six families with MCKD1 harbors an equivalent but apparently independently arising mutation in sequence markedly under-represented in massively parallel sequencing data: the insertion of a single cytosine in one copy (but a different copy in each family) of the repeat unit comprising the extremely long (~1.5-5 kb), GC-rich (>80%) coding variable-number tandem repeat (VNTR) sequence in the MUC1 gene encoding mucin 1. These results provide a cautionary tale about the challenges in identifying the genes responsible for mendelian, let alone more complex, disorders through massively parallel sequencing.
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
Provision of a molecularly confirmed diagnosis in a timely manner for children and adults with rare genetic diseases shortens their "diagnostic odyssey," improves disease management, and fosters genetic counseling with respect to recurrence risks while assuring reproductive choices. In a general clinical genetics setting, the current diagnostic rate is approximately 50%, but for those who do not receive a molecular diagnosis after the initial genetics evaluation, that rate is much lower. Diagnostic success for these more challenging affected individuals depends to a large extent on progress in the discovery of genes associated with, and mechanisms underlying, rare diseases. Thus, continued research is required for moving toward a more complete catalog of disease-related genes and variants. The International Rare Diseases Research Consortium (IRDiRC) was established in 2011 to bring together researchers and organizations invested in rare disease research to develop a means of achieving molecular diagnosis for all rare diseases. Here, we review the current and future bottlenecks to gene discovery and suggest strategies for enabling progress in this regard. Each successful discovery will define potential diagnostic, preventive, and therapeutic opportunities for the corresponding rare disease, enabling precision medicine for this patient population.
- MeSH
- Databases, Factual MeSH
- Exome MeSH
- Genome, Human MeSH
- Humans MeSH
- International Cooperation * MeSH
- Rare Diseases diagnosis genetics MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
A hanging mercury drop electrode and a mercury meniscus modified silver solid amalgam electrode were used as electroanalytical sensors for voltammetric determination of antineoplastic drugs carmustine, lomustine and streptozotocin containing reducible N-nitroso groups. On the example of carmustine it was shown that its one-step reduction proceeds at substantially more negative potentials at amalgam electrode as compared with mercury electrode. Both electrodes offer satisfactory repeatability of current response (relative standard deviations < 5%) using DC voltammetry and differential pulse voltammetry. The achieved limits of determination lie mostly in the 10–7 mol l–1 concentration range. The mentioned voltammetric methods were applied to determination of carmustine and lomustine in pharmaceutical formulations. Further, the mercury meniscus modified silver solid amalgam electrode was employed in a “wall-jet” amperometric detection cell in the determination of carmustine by flow injection analysis. Under optimized conditions (run electrolyte Britton– Robinson buffer of pH 7.0; flow rate 5.5 ml min–1; detection potential –1.5 V; injection volume 0.02 ml) the limit of quantitation 7.1 × 10–6 mol l–1 was achieved.
Direct current voltammetric (DCV) and differential pulse voltammetric (DPV) determination of antineoplastic agent doxorubicin (DOX) at a carbon paste electrode (CPE) was developed. Britton–Robinson buffer (pH 7.0) was used as a supporting electrolyte. The limits of detection are 8 × 10–7 mol l–1 (DCV) and 6 × 10–8 mol l–1 (DPV). The accumulation of DOX at the electrode surface was used to decrease the limits of detection down to 2.2 × 10–7 mol l–1 for adsorptive stripping DC voltammetry (DCAdSV) and 2.8 × 10–9 mol l–1 for adsorptive stripping differential pulse voltammetry (DPAdSV) at CPE. The results of the voltammetric methods were utilized for the development of a new determination of doxorubicin using HPLC with amperometric detection on CPE based on spherical microparticles of glassy carbon in a wall-jet configuration. A column with chemically bonded C18 stationary phase and a mobile phase containing 0.01 M phosphate buffer (pH 5.0)–methanol 25:75 (v/v) were used. The limit of detection is 4 × 10–7 mol l–1 (HPLC with electrochemical detection (ED)).
PURPOSE: Medulloblastoma comprises four distinct molecular subgroups: WNT, SHH, Group 3, and Group 4. Current medulloblastoma protocols stratify patients based on clinical features: patient age, metastatic stage, extent of resection, and histologic variant. Stark prognostic and genetic differences among the four subgroups suggest that subgroup-specific molecular biomarkers could improve patient prognostication. PATIENTS AND METHODS: Molecular biomarkers were identified from a discovery set of 673 medulloblastomas from 43 cities around the world. Combined risk stratification models were designed based on clinical and cytogenetic biomarkers identified by multivariable Cox proportional hazards analyses. Identified biomarkers were tested using fluorescent in situ hybridization (FISH) on a nonoverlapping medulloblastoma tissue microarray (n = 453), with subsequent validation of the risk stratification models. RESULTS: Subgroup information improves the predictive accuracy of a multivariable survival model compared with clinical biomarkers alone. Most previously published cytogenetic biomarkers are only prognostic within a single medulloblastoma subgroup. Profiling six FISH biomarkers (GLI2, MYC, chromosome 11 [chr11], chr14, 17p, and 17q) on formalin-fixed paraffin-embedded tissues, we can reliably and reproducibly identify very low-risk and very high-risk patients within SHH, Group 3, and Group 4 medulloblastomas. CONCLUSION: Combining subgroup and cytogenetic biomarkers with established clinical biomarkers substantially improves patient prognostication, even in the context of heterogeneous clinical therapies. The prognostic significance of most molecular biomarkers is restricted to a specific subgroup. We have identified a small panel of cytogenetic biomarkers that reliably identifies very high-risk and very low-risk groups of patients, making it an excellent tool for selecting patients for therapy intensification and therapy de-escalation in future clinical trials.
- MeSH
- Tissue Array Analysis MeSH
- Cytogenetics MeSH
- Child MeSH
- Risk Assessment MeSH
- In Situ Hybridization, Fluorescence MeSH
- Nuclear Proteins genetics MeSH
- Infant MeSH
- Humans MeSH
- Chromosomes, Human, Pair 11 MeSH
- Chromosomes, Human, Pair 14 MeSH
- Medulloblastoma genetics mortality pathology therapy MeSH
- Adolescent MeSH
- Young Adult MeSH
- Biomarkers, Tumor genetics MeSH
- Predictive Value of Tests MeSH
- Child, Preschool MeSH
- Prognosis MeSH
- Proportional Hazards Models MeSH
- Hedgehog Proteins * genetics MeSH
- Wnt Proteins * genetics MeSH
- Proto-Oncogene Proteins c-myc genetics MeSH
- Gene Expression Regulation, Neoplastic MeSH
- Reproducibility of Results MeSH
- Risk Factors MeSH
- Gene Expression Profiling MeSH
- Kruppel-Like Transcription Factors genetics MeSH
- Check Tag
- Child MeSH
- Infant MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Male MeSH
- Child, Preschool MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
While molecular subgrouping has revolutionized medulloblastoma classification, the extent of heterogeneity within subgroups is unknown. Similarity network fusion (SNF) applied to genome-wide DNA methylation and gene expression data across 763 primary samples identifies very homogeneous clusters of patients, supporting the presence of medulloblastoma subtypes. After integration of somatic copy-number alterations, and clinical features specific to each cluster, we identify 12 different subtypes of medulloblastoma. Integrative analysis using SNF further delineates group 3 from group 4 medulloblastoma, which is not as readily apparent through analyses of individual data types. Two clear subtypes of infants with Sonic Hedgehog medulloblastoma with disparate outcomes and biology are identified. Medulloblastoma subtypes identified through integrative clustering have important implications for stratification of future clinical trials.
In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.
- MeSH
- Autophagy * physiology MeSH
- Autophagosomes MeSH
- Biomarkers MeSH
- Biological Assay standards MeSH
- Humans MeSH
- Lysosomes MeSH
- Autophagy-Related Proteins metabolism MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
- Guideline MeSH
... Safety, and Value 39 iVACHTER c
26th edition 2 svazky : ilustrace ; 30 cm
- Conspectus
- Patologie. Klinická medicína
- NML Fields
- vnitřní lékařství
- NML Publication type
- kolektivní monografie
