The Arteriovenous Access Stage (AVAS) classification simplifies information about suitability of vessels for vascular access (VA). It's been previously validated in a clinical study. Here, AVAS performance was tested against multiple ultrasound mapping measurements using machine learning. A prospective multicentre international study (NCT04796558) with patient recruitment from March 2021-July 2024. Demographics, risk factors, vessels parameters, types of predicted and created VA (pVA, cVA) were collected. We modelled pVA and cVA using the Random Forest algorithm. Model performance was estimated and compared using Bayesian generalized linear models. ROC AUC with 95% credible intervals was the performance metric. 1151 patients were included. ROC AUC for pVA prediction by AVAS was 0.79 (0.77;0.82) and by mapping was 0.85 (0.83;0.88). ROC AUC for cVA prediction by AVAS was 0.71 (0.69;0.74) and by mapping was 0.8 (0.78;0.83). Using AVAS with other parameters increased the ROC AUC to 0.87 for pVA (0.84;0.89) and 0.82 (0.79;0.84) for cVA. Using mapping with other parameters increased the ROC AUC to 0.88 for pVA (0.86;0.91) and 0.85 (0.83;0.88) for cVA. Multiple mapping measurements showed higher performance at VA prediction than AVAS. However, AVAS is simpler and quicker, so may be preferable for routine clinical practice.

• Klíčová slova
• Arteriovenous access, Classification system, Dialysis, Mapping, Random forest, Renal replacement therapy,
• MeSH
• arteriovenózní zkrat MeSH
• Bayesova věta MeSH
• lidé středního věku MeSH
• lidé MeSH
• prospektivní studie MeSH
• ROC křivka MeSH
• senioři MeSH
• strojové učení * MeSH
• ultrasonografie * metody   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
• srovnávací studie MeSH

OBJECTIVES: This study examined (non-)monotonic time trends in psychological and somatic complaints among adolescents, along with gender differences. METHODS: Repeated cross-sectional Health Behaviour in School-aged Children (HBSC) data from 1994 to 2022 covering 15-year-old adolescents from 41 countries (N = 470,797) were analysed. Three polynomial logistic regression models (linear, quadratic, cubic) were tested for best fit, including separate analyses by gender and health complaints dimension. RESULTS: Time trend patterns varied by gender and health complaints dimension. Increases were found in 82.3% of cases (linear 25%, quadratic U-shaped 28.7%, cubic 28.7%), while 14% showed no clear trend, and 3.7% decreased. Boys typically showed linear increases or no clear trend over time, whereas girls generally showed cubic or U-shaped trends. Psychological complaints often displayed U-shaped or cubic patterns, whereas somatic complaints mostly showed linear increases. CONCLUSION: Psychological and somatic complaints demonstrated diverse time trend patterns across countries, with non-monotonic patterns (U-shaped and cubic) frequently observed alongside linear increases. These findings highlight the complexity of changes within countries over three decades, suggesting that linear modelling may not effectively capture this heterogeneity.

• Klíčová slova
• HBSC, adolescence, cross-national, gender differences, mental health,
• MeSH
• celosvětové zdraví MeSH
• lidé MeSH
• mladiství MeSH
• pacienti bez organického nálezu MeSH
• průřezové studie MeSH
• sexuální faktory MeSH
• somatoformní poruchy epidemiologie   MeSH
• zdraví dospívajících * trendy   MeSH
• Check Tag
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: Improved prediction of prognosis among lung cancer patients could facilitate better clinical management. We aimed to study the prognostic significance of circulating proteins at the time of lung cancer diagnosis, among patients with and without smoking history. METHODS: We measured 91 proteins using the Olink Immune-Oncology panel in plasma samples that were collected at diagnosis from 244 never smoking and 742 ever smoking patients with stage I-IIIA non-small cell lung cancer (NSCLC). Patients were recruited from nine centres in Russian Federation, Poland, Serbia, Czechia, and Romania, between 2007-2016 and were prospectively followed through 2020. We used multivariable Survey-weighted Cox models to assess the relationship between overall survival and levels of proteins by adjusting for smoking, age at diagnosis, sex, education, alcohol intake, histology, and stage. RESULTS: The 5-year survival rate was higher among never than ever smoking patients (63.1% vs. 46.6%, P<0.001). In age- and sex-adjusted survival analysis, 23 proteins were nominally associated with overall survival, but after adjustment for potential confounders and correcting for multiple testing, none of the proteins showed a significant association with overall survival. In stratified analysis by smoking status, IL8 [hazard ratio (HR) per standard deviation (SD): 1.40, 95% confidence interval (CI): 1.18-1.65, P=1×10-4] and hepatocyte growth factor (HGF) (HR: 1.45, 95% CI: 1.18-1.79, P=5×10-4) were associated with survival among never smokers, but no protein was found associated with survival among ever smokers. Integrating proteins into the models with clinical risk factors did not improve the predictive performance of NSCLC prognosis [C-index of 0.63 (clinical) vs. 0.64 (clinical + proteins) for ever smokers, P=0.20; C-index of 0.68 (clinical) vs. 0.72 (clinical + proteins) for never smokers, P=0.28]. CONCLUSIONS: We found limited evidence of a potential for circulating immune- and cancer-related protein markers in lung cancer prognosis. Whereas some specific proteins appear to be uniquely associated with lung cancer survival in never smokers.

• Klíčová slova
• Lung cancer, prognosis, proteomics, smoking,
• Publikační typ
• časopisecké články MeSH

BACKGROUND AND AIMS: Turions are vegetative, dormant overwintering organs formed in aquatic plants in response to unfavourable ecological conditions. Contents of cytokinin (CK), auxin metabolites and abscisic acid (ABA) as main growth and development regulators were compared in innately dormant autumnal turions of 22 aquatic plant species of different functional ecological or taxonomic groups with those in non-dormant winter apices in three aquatic species and with those in spring turions of four species after their overwintering. METHODS: The hormones were analysed in miniature turion samples using ultraperformance liquid chromatography coupled with triple quadrupole mass spectrometry. KEY RESULTS: In innately dormant turions, the total contents of each of the four main CK types, biologically active forms and total CKs differed by two to three orders of magnitude across 22 species; the proportion of active CK forms was 0.18-67 %. Similarly, the content of four auxin forms was extremely variable and the IAA proportion as the active form was 0.014-99 %. The ABA content varied from almost zero to 54 µmol kg-1 dry weight and after overwintering it usually significantly decreased. Of all functional traits studied, hormone profiles depended most on the place of turion sprouting (surface vs bottom) and we suggest that this trait is crucial for turion ecophysiology. CONCLUSIONS: The key role of ABA in regulating turion dormancy was confirmed. However, the highly variable pattern of the ABA content in innately dormant and in overwintered turions indicates that the hormonal mechanism regulating the innate dormancy and its breaking in turions is not uniform within aquatic plants.

• Klíčová slova
• ABA, Cytokinins, auxins, functional traits, innate and imposed dormancy, mature winter buds, overwintering, phylogenetic correction, quiescence,
• MeSH
• cytokininy * metabolismus   MeSH
• kyselina abscisová metabolismus   analýza   MeSH
• kyseliny indoloctové metabolismus   MeSH
• regulátory růstu rostlin * metabolismus   MeSH
• vegetační klid fyziologie   MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• cytokininy * MeSH
• kyselina abscisová MeSH
• kyseliny indoloctové MeSH
• regulátory růstu rostlin * MeSH

There are no blood-based biomarkers distinguishing patients with relapsing-remitting (RRMS) from secondary progressive multiple sclerosis (SPMS) although evidence supports metabolomic changes according to MS disease severity. Here machine learning analysis of serum metabolomic data stratified patients with RRMS from SPMS with high accuracy and a putative score was developed that stratified MS patient subsets. The top differentially expressed metabolites between SPMS versus patients with RRMS included lipids and fatty acids, metabolites enriched in pathways related to cellular respiration, notably, elevated lactate and glutamine (gluconeogenesis-related) and acetoacetate and bOHbutyrate (ketone bodies), and reduced alanine and pyruvate (glycolysis-related). Serum metabolomic changes were recapitulated in the whole blood transcriptome, whereby differentially expressed genes were also enriched in cellular respiration pathways in patients with SPMS. The final gene-metabolite interaction network demonstrated a potential metabolic shift from glycolysis toward increased gluconeogenesis and ketogenesis in SPMS, indicating metabolic stress which may trigger stress response pathways and subsequent neurodegeneration.

• Klíčová slova
• Classification Description, Machine learning, Metabolomics, Molecular network, Transcriptomics,
• Publikační typ
• časopisecké články MeSH

BACKGROUND: The power to predict kidney allograft outcomes based on non-invasive assays is limited. Assessment of operational tolerance (OT) patients allows us to identify transcriptomic signatures of true non-responders for construction of predictive models. METHODS: In this observational retrospective study, RNA sequencing of peripheral blood was used in a derivation cohort to identify a protective set of transcripts by comparing 15 OT patients (40% females), from the TOMOGRAM Study (NCT05124444), 14 chronic active antibody-mediated rejection (CABMR) and 23 stable graft function patients ≥15 years (STA). The selected differentially expressed transcripts between OT and CABMR were used in a validation cohort (n = 396) to predict 3-year kidney allograft loss at 3 time-points using RT-qPCR. FINDINGS: Archetypal analysis and classifier performance of RNA sequencing data showed that OT is clearly distinguishable from CABMR, but similar to STA. Based on significant transcripts from the validation cohort in univariable analysis, 2 multivariable Cox models were created. A 3-transcript (ADGRG3, ATG2A, and GNLY) model from POD 7 predicted graft loss with C-statistics (C) 0.727 (95% CI, 0.638-0.820). Another 3-transcript (IGHM, CD5, GNLY) model from M3 predicted graft loss with C 0.786 (95% CI, 0.785-0.865). Combining 3-transcripts models with eGFR at POD 7 and M3 improved C-statistics to 0.860 (95% CI, 0.778-0.944) and 0.868 (95% CI, 0.790-0.944), respectively. INTERPRETATION: Identification of transcripts distinguishing OT from CABMR allowed us to construct models predicting premature graft loss. Identified transcripts reflect mechanisms of injury/repair and alloimmune response when assessed at day 7 or with a loss of protective phenotype when assessed at month 3. FUNDING: Supported by the Ministry of Health of the Czech Republic under grant NV19-06-00031.

• Klíčová slova
• Chronic antibody-mediated rejection, Kidney graft failure, Operational tolerance, Peripheral blood transcripts, RNA sequencing,
• Publikační typ
• časopisecké články MeSH

Acclimation and adaptation of metabolism to a changing environment are key processes for plant survival and reproductive success. In the present study, 241 natural accessions of Arabidopsis (Arabidopsis thaliana) were grown under two different temperature regimes, 16 °C and 6 °C, and growth parameters were recorded, together with metabolite profiles, to investigate the natural genome × environment effects on metabolome variation. The plasticity of metabolism, which was captured by metabolic distance measures, varied considerably between accessions. Both relative growth rates and metabolic distances were predictable by the underlying natural genetic variation of accessions. Applying machine learning methods, climatic variables of the original growth habitats were tested for their predictive power of natural metabolic variation among accessions. We found specifically habitat temperature during the first quarter of the year to be the best predictor of the plasticity of primary metabolism, indicating habitat temperature as the causal driver of evolutionary cold adaptation processes. Analyses of epigenome- and genome-wide associations revealed accession-specific differential DNA-methylation levels as potentially linked to the metabolome and identified FUMARASE2 as strongly associated with cold adaptation in Arabidopsis accessions. These findings were supported by calculations of the biochemical Jacobian matrix based on variance and covariance of metabolomics data, which revealed that growth under low temperatures most substantially affects the accession-specific plasticity of fumarate and sugar metabolism. Our findings indicate that the plasticity of metabolic regulation is predictable from the genome and epigenome and driven evolutionarily by Arabidopsis growth habitats.

• MeSH
• Arabidopsis * fyziologie   MeSH
• metabolom genetika   MeSH
• nízká teplota MeSH
• podnebí MeSH
• proteiny huseníčku * genetika   MeSH
• teplota MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• proteiny huseníčku * MeSH

Current classifications (World Health Organization-HAEM5/ICC) define up to 26 molecular B-cell precursor acute lymphoblastic leukemia (BCP-ALL) disease subtypes by genomic driver aberrations and corresponding gene expression signatures. Identification of driver aberrations by transcriptome sequencing (RNA-Seq) is well established, while systematic approaches for gene expression analysis are less advanced. Therefore, we developed ALLCatchR, a machine learning-based classifier using RNA-Seq gene expression data to allocate BCP-ALL samples to all 21 gene expression-defined molecular subtypes. Trained on n = 1869 transcriptome profiles with established subtype definitions (4 cohorts; 55% pediatric / 45% adult), ALLCatchR allowed subtype allocation in 3 independent hold-out cohorts (n = 1018; 75% pediatric / 25% adult) with 95.7% accuracy (averaged sensitivity across subtypes: 91.1% / specificity: 99.8%). High-confidence predictions were achieved in 83.7% of samples with 98.9% accuracy. Only 1.2% of samples remained unclassified. ALLCatchR outperformed existing tools and identified novel driver candidates in previously unassigned samples. Additional modules provided predictions of samples blast counts, patient's sex, and immunophenotype, allowing the imputation in cases where these information are missing. We established a novel RNA-Seq reference of human B-lymphopoiesis using 7 FACS-sorted progenitor stages from healthy bone marrow donors. Implementation in ALLCatchR enabled projection of BCP-ALL samples to this trajectory. This identified shared proximity patterns of BCP-ALL subtypes to normal lymphopoiesis stages, extending immunophenotypic classifications with a novel framework for developmental comparisons of BCP-ALL. ALLCatchR enables RNA-Seq routine application for BCP-ALL diagnostics with systematic gene expression analysis for accurate subtype allocation and novel insights into underlying developmental trajectories.

• Publikační typ
• časopisecké články MeSH

BACKGROUND: Genomic conditions can be associated with developmental delay, intellectual disability, autism spectrum disorder, and physical and mental health symptoms. They are individually rare and highly variable in presentation, which limits the use of standard clinical guidelines for diagnosis and treatment. A simple screening tool to identify young people with genomic conditions associated with neurodevelopmental disorders (ND-GCs) who could benefit from further support would be of considerable value. We used machine learning approaches to address this question. METHOD: A total of 493 individuals were included: 389 with a ND-GC, mean age = 9.01, 66% male) and 104 siblings without known genomic conditions (controls, mean age = 10.23, 53% male). Primary carers completed assessments of behavioural, neurodevelopmental and psychiatric symptoms and physical health and development. Machine learning techniques (penalised logistic regression, random forests, support vector machines and artificial neural networks) were used to develop classifiers of ND-GC status and identified limited sets of variables that gave the best classification performance. Exploratory graph analysis was used to understand associations within the final variable set. RESULTS: All machine learning methods identified variable sets giving high classification accuracy (AUROC between 0.883 and 0.915). We identified a subset of 30 variables best discriminating between individuals with ND-GCs and controls which formed 5 dimensions: conduct, separation anxiety, situational anxiety, communication and motor development. LIMITATIONS: This study used cross-sectional data from a cohort study which was imbalanced with respect to ND-GC status. Our model requires validation in independent datasets and with longitudinal follow-up data for validation before clinical application. CONCLUSIONS: In this study, we developed models that identified a compact set of psychiatric and physical health measures that differentiate individuals with a ND-GC from controls and highlight higher-order structure within these measures. This work is a step towards developing a screening instrument to identify young people with ND-GCs who might benefit from further specialist assessment.

• Klíčová slova
• Behavioural phenotypes, Genetic syndromes, Intellectual disability, Machine learning,
• MeSH
• dítě MeSH
• genomika MeSH
• kohortové studie MeSH
• lidé MeSH
• mentální retardace * MeSH
• mladiství MeSH
• poruchy autistického spektra * MeSH
• průřezové studie MeSH
• strojové učení MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH

Procedural aspects of compassionate care such as the terminal extubation are understudied. We used machine learning methods to determine factors associated with the decision to extubate the critically ill patient at the end of life, and whether the terminal extubation shortens the dying process. We performed a secondary data analysis of a large, prospective, multicentre, cohort study, death prediction and physiology after removal of therapy (DePPaRT), which collected baseline data as well as ECG, pulse oximeter and arterial waveforms from WLST until 30 min after death. We analysed a priori defined factors associated with the decision to perform terminal extubation in WLST using the random forest method and logistic regression. Cox regression was used to analyse the effect of terminal extubation on time from WLST to death. A total of 616 patients were included into the analysis, out of which 396 (64.3%) were terminally extubated. The study centre, low or no vasopressor support, and good respiratory function were factors significantly associated with the decision to extubate. Unadjusted time to death did not differ between patients with and without extubation (median survival time extubated vs. not extubated: 60 [95% CI: 46; 76] vs. 58 [95% CI: 45; 75] min). In contrast, after adjustment for confounders, time to death of extubated patients was significantly shorter (49 [95% CI: 40; 62] vs. 85 [95% CI: 61; 115] min). The decision to terminally extubate is associated with specific centres and less respiratory and/or vasopressor support. In this context, terminal extubation was associated with a shorter time to death.

• MeSH
• extubace metody   MeSH
• jednotky intenzivní péče MeSH
• kohortové studie MeSH
• lidé MeSH
• odpojení od ventilátoru * metody   MeSH
• péče o umírající * MeSH
• prospektivní studie MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH