Glycemic control in children with type 1 diabetes treated with the advanced hybrid closed loop system 2-year prospective, observational, two-center study

Background and aims

MiniMed 780G is the first Advanced Hybrid Closed Loop (AHCL) system in Poland, approved in the EU in 2020. To date, observations of glycemic control up to 12 months have been published. This study aimed to analyze glycemic control and anthropometric parameters in children and adolescents with type 1 diabetes (T1D) after two years of using the AHCL system.

Materials and methods

We prospectively collected anthropometric data, pump, and continuous glucose records of fifty T1D children (9.9 ± 2.4 years, 24 (48%) boys, T1D for 3.9 ± 2.56 years) using an AHCL system. We compared the two-week AHCL records obtained after AHCL enrollment with data 6, 12, and 24 months after starting AHCL.

Results

Time in range (70-180 mg/dl) and BMI z-score did not change during the 2 years of observation (p>0.05). The percentage of autocorrection in total daily insulin increased significantly (p<0.005).

Conclusion

Glycemic control in the investigated group of children with T1D treated with the AHCL system for 2 years remained stable. Children in this group maintained weight and optimal metabolic control, most likely due to autocorrection boluses.

Transcriptional Inflammatory Signature in Healthy Donors and Different Radiotherapy Cancer Patients

Cancer and ionizing radiation exposure are associated with inflammation. To identify a set of radiation-specific signatures of inflammation-associated genes in the blood of partially exposed radiotherapy patients, differential expression of 249 inflammatory genes was analyzed in blood samples from cancer patients and healthy individuals. The gene expression analysis on a cohort of 63 cancer patients (endometrial, head and neck, and prostate cancer) before and during radiotherapy (24 h, 48 h, ~1 week, ~4-8 weeks, and 1 month after the last fraction) identified 31 genes and 15 up- and 16 down-regulated genes. Transcription variability under normal conditions was determined using blood drawn on three separate occasions from four healthy donors. No difference in inflammatory expression between healthy donors and cancer patients could be detected prior to radiotherapy. Remarkably, repeated sampling of healthy donors revealed an individual endogenous inflammatory signature. Next, the potential confounding effect of concomitant inflammation was studied in the blood of seven healthy donors taken before and 24 h after a flu vaccine or ex vivo LPS (lipopolysaccharide) treatment; flu vaccination was not detected at the transcriptional level and LPS did not have any effect on the radiation-induced signature identified. Finally, we identified a radiation-specific signature of 31 genes in the blood of radiotherapy patients that were common for all cancers, regardless of the immune status of patients. Confirmation via MQRT-PCR was obtained for BCL6, MYD88, MYC, IL7, CCR4 and CCR7. This study offers the foundation for future research on biomarkers of radiation exposure, radiation sensitivity, and radiation toxicity for personalized radiotherapy treatment.

Epigenetic signature of ionizing radiation in therapy-related AML patients

Therapy-related acute myeloid leukaemia (t-AML) is a late side effect of previous chemotherapy (ct-AML) and/or radiotherapy (rt-AML) or immunosuppressive treatment. t-AMLs, which account for ∼10-20 % of all AML cases, are extremely aggressive and have a poor prognosis compared to de novo AML. Our hypothesis is that exposure to radiation causes genome-wide epigenetic changes in rt-AML. An epigenome-wide association study was undertaken, measuring over 850K methylation sites across the genome from fifteen donors (five healthy, five de novo, and five t-AMLs). The study predominantly focussed on 94K sites that lie in CpG-rich gene promoter regions. Genome-wide hypomethylation was discovered in AML, primarily in intergenic regions. Additionally, genes specific to AML were identified with promoter hypermethylation. A two-step validation was conducted, both internally, using pyrosequencing to measure methylation levels in specific regions across fifteen primary samples, and externally, with an additional eight AML samples. We demonstrated that the MEST and GATA5 gene promoters, which were previously identified as tumour suppressors, were noticeably hypermethylated in rt-AML, as opposed to other subtypes of AML and control samples. These may indicate the epigenetic involvement in the development of rt-AML at the molecular level and could serve as potential targets for drug therapy in rt-AML.

miR-122 and miR-21 are Stable Components of miRNA Signatures of Early Lung Cancer after Validation in Three Independent Cohorts

Several panels of circulating miRNAs have been reported as potential biomarkers of early lung cancer, yet the overlap of components between different panels is limited, and the universality of proposed biomarkers has been minimal across proposed panels. To assess the stability of the diagnostic potential of plasma miRNA signature of early lung cancer among different cohorts, a panel of 24 miRNAs tested in the frame of one lung cancer screening study (MOLTEST-2013, Poland) was validated with material collected in the frame of two other screening studies (MOLTEST-BIS, Poland; and SMAC, Italy) using the same standardized analytical platform (the miRCURY LNA miRNA PCR assay). On analysis of selected miRNAs, two associated with lung cancer development, miR-122 and miR-21, repetitively differentiated healthy participants from individuals with lung cancer. Additionally, miR-144 differentiated controls from cases specifically in subcohorts with adenocarcinoma. Other tested miRNAs did not overlap in the three cohorts. Classification models based on neither a single miRNA nor multicomponent miRNA panels (24-mer and 7-mer) showed classification performance sufficient for a standalone diagnostic biomarker (AUC, 75%, 71%, and 53% in MOLTEST-2013, SMAC, and MOLTEST-BIS, respectively, in the 7-mer model). The performance of classification in the MOLTEST-BIS cohort with the lowest contribution of adenocarcinomas was increased when only this cancer type was considered (AUC, 60% in 7-mer model).

Combining Low-Dose Computer-Tomography-Based Radiomics and Serum Metabolomics for Diagnosis of Malignant Nodules in Participants of Lung Cancer Screening Studies

Radiomics is an emerging approach to support the diagnosis of pulmonary nodules detected via low-dose computed tomography lung cancer screening. Serum metabolome is a promising source of auxiliary biomarkers that could help enhance the precision of lung cancer diagnosis in CT-based screening. Thus, we aimed to verify whether the combination of these two techniques, which provides local/morphological and systemic/molecular features of disease at the same time, increases the performance of lung cancer classification models. The collected cohort consists of 1086 patients with radiomic and 246 patients with serum metabolomic evaluations. Different machine learning techniques, i.e., random forest and logistic regression were applied for each omics. Next, model predictions were combined with various integration methods to create a final model. The best single omics models were characterized by an AUC of 83% in radiomics and 60% in serum metabolomics. The model integration only slightly increased the performance of the combined model (AUC equal to 85%), which was not statistically significant. We concluded that radiomics itself has a good ability to discriminate lung cancer from benign lesions. However, additional research is needed to test whether its combination with other molecular assessments would further improve the diagnosis of screening-detected lung nodules.

Sex- and age-specific aspects of human peripheral T-cell dynamics

Background

The diversity of the antigenic T cell receptor (TCR) repertoire clonally expressed on T lymphocytes is a key element of the adaptive immune system protective functions. A decline in diversity in the older adults is associated with health deterioration. This diversity is generated by the rearrangement of TRB genes coding for TCR chains during lymphocyte differentiation in the thymus, but is essentially maintained by peripheral T lymphocytes proliferation for most of life. Deep sequencing of rearranged TRB genes from blood cells allows the monitoring of peripheral T cell repertoire dynamics. We analysed two aspects of rearranged TRB diversity, related to T lymphocyte proliferation and to the distribution of the T cell clone size, in a collection of repertoires obtained from 1 to 74 years-old donors.

Results

Our results show that peripheral T lymphocytes expansion differs according to the recombination status of their TRB loci. Their proliferation rate changes with age, with different patterns in men and women. T cell clone size becomes more heterogeneous with time, and, in adults, is always more even in women. Importantly, a longitudinal analysis of TRB repertoires obtained at ten years intervals from individual men and women confirms the findings of this cross-sectional study.

Conclusions

Peripheral T lymphocyte proliferation partially depends on their thymic developmental history. The rate of proliferation of T cells differing in their TRB rearrangement status is different in men and women before the age of 18 years old, but similar thereafter.

Multivariate piecewise linear regression model to predict radiosensitivity using the association with the genome-wide copy number variation

Introduction

The search for biomarkers to predict radiosensitivity is important not only to individualize radiotherapy of cancer patients but also to forecast radiation exposure risks. The aim of this study was to devise a machine-learning method to stratify radiosensitivity and to investigate its association with genome-wide copy number variations (CNVs) as markers of sensitivity to ionizing radiation.

Methods

We used the Affymetrix CytoScan HD microarrays to survey common CNVs in 129 fibroblast cell strains. Radiosensitivity was measured by the surviving fraction at 2 Gy (SF2). We applied a dynamic programming (DP) algorithm to create a piecewise (segmented) multivariate linear regression model predicting SF2 and to identify SF2 segment-related distinctive CNVs.

Results

SF2 ranged between 0.1384 and 0.4860 (mean=0.3273 The DP algorithm provided optimal segmentation by defining batches of radio-sensitive (RS), normally-sensitive (NS), and radio-resistant (RR) responders. The weighted mean relative errors (MRE) decreased with increasing the segments' number. The borders of the utmost segments have stabilized after partitioning SF2 into 5 subranges.

Discussion

The 5-segment model associated C-3SFBP marker with the most-RS and C-7IUVU marker with the most-RR cell strains. Both markers were mapped to gene regions (MCC and SLC1A6, respectively). In addition, C-3SFBP marker is also located in enhancer and multiple binding motifs. Moreover, for most CNVs significantly correlated with SF2, the radiosensitivity increased with the copy-number decrease.In conclusion, the DP-based piecewise multivariate linear regression method helps narrow the set of CNV markers from the whole radiosensitivity range to the smaller intervals of interest. Notably, SF2 partitioning not only improves the SF2 estimation but also provides distinctive markers. Ultimately, segment-related markers can be used, potentially with tissues' specific factors or other clinical data, to identify radiotherapy patients who are most RS and require reduced doses to avoid complications and the most RR eligible for dose escalation to improve outcomes.

Pathological changes or technical artefacts? The problem of the heterogenous databases in COVID-19 CXR image analysis

BACKGROUND

When the COVID-19 pandemic commenced in 2020, scientists assisted medical specialists with diagnostic algorithm development. One scientific research area related to COVID-19 diagnosis was medical imaging and its potential to support molecular tests. Unfortunately, several systems reported high accuracy in development but did not fare well in clinical application. The reason was poor generalization, a long-standing issue in AI development. Researchers found many causes of this issue and decided to refer to them as confounders, meaning a set of artefacts and methodological errors associated with the method. We aim to contribute to this steed by highlighting an undiscussed confounder related to image resolution.

METHODS

20 216 chest X-ray images (CXR) from worldwide centres were analyzed. The CXRs were bijectively projected into the 2D domain by performing Uniform Manifold Approximation and Projection (UMAP) embedding on the radiomic features (rUMAP) or CNN-based neural features (nUMAP) from the pre-last layer of the pre-trained classification neural network. Additional 44 339 thorax CXRs were used for validation. The comprehensive analysis of the multimodality of the density distribution in rUMAP/nUMAP domains and its relation to the original image properties was used to identify the main confounders.

RESULTS

nUMAP revealed a hidden bias of neural networks towards the image resolution, which the regular up-sampling procedure cannot compensate for. The issue appears regardless of the network architecture and is not observed in a high-resolution dataset. The impact of the resolution heterogeneity can be partially diminished by applying advanced deep-learning-based super-resolution networks.

CONCLUSIONS

rUMAP and nUMAP are great tools for image homogeneity analysis and bias discovery, as demonstrated by applying them to COVID-19 image data. Nonetheless, nUMAP could be applied to any type of data for which a deep neural network could be constructed. Advanced image super-resolution solutions are needed to reduce the impact of the resolution diversity on the classification network decision.

Evaluation of zero counts to better understand the discrepancies between bulk and single-cell RNA-Seq platforms

Recent advances in sample preparation and sequencing technology have made it possible to profile the transcriptomes of individual cells using single-cell RNA sequencing (scRNA-Seq). Compared to bulk RNA-Seq data, single-cell data often contain a higher percentage of zero reads, mainly due to lower sequencing depth per cell, which affects mostly measurements of low-expression genes. However, discrepancies between platforms are observed regardless of expression level. Using four paired datasets with multiple samples each, we investigated technical and biological factors that can contribute to this expression shift. Using two separate machine learning models we found that, in addition to expression level, RNA integrity, gene or UTR3 length, and the number of transcripts potentially also influence the occurrence of zeros. These findings could enable the development of novel analytical methods for cross-platform expression shift correction. We also identified genes and biological pathways in our diverse datasets that consistently showed differences when assessed at the single cell versus bulk level to assist in interpreting analysis across transcriptomic platforms. At the gene level, 25 genes (0.12%) were found in all datasets as discordant, but at the pathway level, 7 pathways (2.02%) showed shared enrichment in discordant genes.

MiniMed 780GTM in children with type 1 diabetes under seven years of age: Prospective open-label, single-arm, double-center, follow-up study

BACKGROUND

Given the steadily rising incidence of type 1 diabetes (T1D), particularly among the youngest preschool children, coupled with well-documented challenges of achieving and maintaining optimal metabolic control in this age group, there is a growing need for advanced technological devices.

OBJECTIVE

To evaluate glycaemic control in children below the age of seven with type 1 diabetes (T1D) and assess the safety of the advanced hybrid closed loop (AHCL) system in comparison to the previous treatment method, a sensor-augmented pump with predictive low-glucose suspend (SAP-PLGS).

METHOD

Data from 10 children (aged 2.60-6.98 years) with T1D who transitioned to the AHCL system from SAP-PLGS were analysed. SAP-PLGS records from two weeks prior to the initiation of AHCL were compared with records from the initial four weeks post-switch (excluding the training period). These data were examined at two 2-week intervals and compared with records from two weeks post six-month usage of the AHCL.

RESULTS

A significant decrease in the average nighttime glucose concentration was observed compared to pre-AHCL values (p= 0.001, concordance W = 0.53). The Glucose Management Indicator (GMI) value significantly decreased from 6.88 ± 0.37% to 6.52 ± 0.32% (p= 0.018, rbc = 0.93) immediately following the device switch and stabilized at 6.50 ± 0.28% (p= 0.001, W = 0.53) and 6.55 ± 0.41% (p= 0.001, W = 0.53) at subsequent stages of the study. An improvement was also observed in mean glucose values for time spent < 54 mg/dl, while the proportion of time within this range was maintained, both during the day (p< 0.001, W = 0.58) and at night (p= 0.002, W = 0.83).

CONCLUSION

The AHCL MiniMed 780GTM system improved glycaemic control in the studied group of children under seven years of age with T1D compared to previous SAP-PLGS therapy. It proved to be safe for delivering insulin in this age group.

Body mass index and partial remission in 119 children with type 1 diabetes-a 6-year observational study

Background/objective

This long-term study aimed to analyze the associations between BMI Z-score, HbA1c, and daily insulin requirement (DIR) and the prevalence and duration of partial remission (PR) in children and adolescents with type 1 diabetes (T1D).

Methods

After retrieving retrospective data for 195 patients from their health records at 24, 48, and 72 months after T1D diagnosis, the study group was comprised of 119 (57 girls) children with a complete dataset for all 6 years. PR was defined according to the ISPAD guidelines. Analyses were carried out in the whole group and subgroups according to PR duration: no PR at all (NPR), PR lasting less than 2 years (PR < 2), and PR at least 2 years (PR ≥ 2).

Results

PR was observed in 63% of the patients (78.9% of overweight and 100% of obese patients). NPR patients showed the lowest mean initial BMI Z-score [-0.65 ± 1.29 vs. 0.02 ± 1.42, (PR < 2), p = 0.01 and vs. 0.64 ± 1.43 (PR ≥ 2), p = 0.17]. The dissimilarity in BMI across patients declined over time. Within the NPR group, the initial mean BMI Z-score significantly increased within the first 2 years (unadjusted p < 0.001) and remained constant afterward. In the PR <2 group, the highest increase in BMI Z-score occurred after 4 years (p < 0.001) and then decreased (p = 0.04). In the PR ≥2, the BMI Z-score slightly decreased within the first 2 years (p = 0.02), then increased (p = 0.03) and remained unchanged for the last 2 years. Six years after T1D started, the mean DIRs do not differ among the patient groups (ANOVA p = 0.272).

Conclusion

During 6 years of follow-up, PR occurred in almost two-thirds of the studied children including almost all overweight and obese children. We observed a gradual normalization of the BMI Z-score at the end of the follow-up. BMI Z-score increased slightly in children with no remission initially but remained later constant until the end of observation. In both remitter groups, the increase in BMI Z-score appeared later when the protective honeymoon period ended. Regardless of BMI Z-score, the β-cell destruction process progresses, and after 6 years, the DIR is similar for all patients.

GMM-Based Expanded Feature Space as a Way to Extract Useful Information for Rare Cell Subtypes Identification in Single-Cell Mass Cytometry

Cell subtype identification from mass cytometry data presents a persisting challenge, particularly when dealing with millions of cells. Current solutions are consistently under development, however, their accuracy and sensitivity remain limited, particularly in rare cell-type detection due to frequent downsampling. Additionally, they often lack the capability to analyze large data sets. To overcome these limitations, a new method was suggested to define an extended feature space. When combined with the robust clustering algorithm for big data, it results in more efficient cell clustering. Each marker's intensity distribution is presented as a mixture of normal distributions (Gaussian Mixture Model, GMM), and the expanded space is created by spanning over all obtained GMM components. The projection of the initial flow cytometry marker domain into the expanded space employs GMM-based membership functions. An evaluation conducted on three established cellular identification algorithms (FlowSOM, ClusterX, and PARC) utilizing the most substantial publicly available annotated dataset by Samusik et al. demonstrated the superior performance of the suggested approach in comparison to the standard. Although our approach identified 20 cell clusters instead of the expected 24, their intra-cluster homogeneity and inter-cluster differences were superior to the 24-cluster FlowSOM-based solution.

Proteomic Profile Distinguishes New Subpopulations of Breast Cancer Patients with Different Survival Outcomes

As a highly heterogeneous disease, breast cancer (BRCA) demonstrates a diverse molecular portrait. The well-established molecular classification (PAM50) relies on gene expression profiling. It insufficiently explains the observed clinical and histopathological diversity of BRCAs. This study aims to demographically and clinically characterize the six BRCA subpopulations (basal, HER2-enriched, and four luminal ones) revealed by their proteomic portraits. GMM-based high variate protein selection combined with PCA/UMAP was used for dimensionality reduction, while the k-means algorithm allowed patient clustering. The statistical analysis (log-rank and Gehan-Wilcoxon tests, hazard ratio HR as the effect size ES) showed significant differences across identified subpopulations in Disease-Specific Survival (p = 0.0160) and Progression-Free Interval (p = 0.0264). Luminal subpopulations vary in prognosis (Disease-Free Interval, p = 0.0277). The A2 subpopulation is of the poorest, comparable to the HER2-enriched subpopulation, prognoses (HR = 1.748, referenced to Luminal B, small ES), while A3 is of the best (HR = 0.250, large ES). Similar to PAM50 subtypes, no substantial dependency on demographic and clinical factors was detected across Luminal subpopulations, as measured by χ2 test and Cramér's V for ES, and ANOVA with appropriate post hocs combined with η2 or Cohen's d-type ES, respectively. Progesterone receptors can serve as the potential A2 biomarker within Luminal patients. Further investigation of molecular differences is required to examine the potential prognostic or clinical applications.

POLCOVID: a multicenter multiclass chest X-ray database (Poland, 2020-2021)

The outbreak of the SARS-CoV-2 pandemic has put healthcare systems worldwide to their limits, resulting in increased waiting time for diagnosis and required medical assistance. With chest radiographs (CXR) being one of the most common COVID-19 diagnosis methods, many artificial intelligence tools for image-based COVID-19 detection have been developed, often trained on a small number of images from COVID-19-positive patients. Thus, the need for high-quality and well-annotated CXR image databases increased. This paper introduces POLCOVID dataset, containing chest X-ray (CXR) images of patients with COVID-19 or other-type pneumonia, and healthy individuals gathered from 15 Polish hospitals. The original radiographs are accompanied by the preprocessed images limited to the lung area and the corresponding lung masks obtained with the segmentation model. Moreover, the manually created lung masks are provided for a part of POLCOVID dataset and the other four publicly available CXR image collections. POLCOVID dataset can help in pneumonia or COVID-19 diagnosis, while the set of matched images and lung masks may serve for the development of lung segmentation solutions.

Can T1-Weighted Magnetic Resonance Imaging Significantly Improve Mini-Mental State Examination-Based Distinguishing Between Mild Cognitive Impairment and Early-Stage Alzheimer's Disease?

BACKGROUND

Detecting early-stage Alzheimer's disease (AD) is still problematic in clinical practice. This work aimed to find T1-weighted MRI-based markers for AD and mild cognitive impairment (MCI) to improve the screening process.

OBJECTIVE

Our assumption was to build a screening model that would be accessible and easy to use for physicians in their daily clinical routine.

METHODS

The multinomial logistic regression was used to detect status: AD, MCI, and normal control (NC) combined with the Bayesian information criterion for model selection. Several T1-weighted MRI-based radiomic features were considered explanatory variables in the prediction model.

RESULTS

The best radiomic predictor was the relative brain volume. The proposed method confirmed its quality by achieving a balanced accuracy of 95.18%, AUC of 93.25%, NPV of 97.93%, and PPV of 90.48% for classifying AD versus NC for the European DTI Study on Dementia (EDSD). The comparison of the two models: with the MMSE score only as an independent variable and corrected for the relative brain value and age, shows that the addition of the T1-weighted MRI-based biomarker improves the quality of MCI detection (AUC: 67.04% versus 71.08%) while maintaining quality for AD (AUC: 93.35% versus 93.25%). Additionally, among MCI patients predicted as AD inconsistently with the original diagnosis, 60% from ADNI and 76.47% from EDSD were re-diagnosed as AD within a 48-month follow-up. It shows that our model can detect AD patients a few years earlier than a standard medical diagnosis.

CONCLUSION

The created method is non-invasive, inexpensive, clinically accessible, and efficiently supports AD/MCI screening.

DiviK: divisive intelligent K-means for hands-free unsupervised clustering in big biological data

BACKGROUND

Investigating molecular heterogeneity provides insights into tumour origin and metabolomics. The increasing amount of data gathered makes manual analyses infeasible-therefore, automated unsupervised learning approaches are utilised for discovering tissue heterogeneity. However, automated analyses require experience setting the algorithms' hyperparameters and expert knowledge about the analysed biological processes. Moreover, feature engineering is needed to obtain valuable results because of the numerous features measured.

RESULTS

We propose DiviK: a scalable stepwise algorithm with local data-driven feature space adaptation for segmenting high-dimensional datasets. The algorithm is compared to the optional solutions (regular k-means, spatial and spectral approaches) combined with different feature engineering techniques (None, PCA, EXIMS, UMAP, Neural Ions). Three quality indices: Dice Index, Rand Index and EXIMS score, focusing on the overall composition of the clustering, coverage of the tumour region and spatial cluster consistency, are used to assess the quality of unsupervised analyses. Algorithms were validated on mass spectrometry imaging (MSI) datasets-2D human cancer tissue samples and 3D mouse kidney images. DiviK algorithm performed the best among the four clustering algorithms compared (overall quality score 1.24, 0.58 and 162 for d(0, 0, 0), d(1, 1, 1) and the sum of ranks, respectively), with spectral clustering being mostly second. Feature engineering techniques impact the overall clustering results less than the algorithms themselves (partial [Formula: see text] effect size: 0.141 versus 0.345, Kendall's concordance index: 0.424 versus 0.138 for d(0, 0, 0)).

CONCLUSIONS

DiviK could be the default choice in the exploration of MSI data. Thanks to its unique, GMM-based local optimisation of the feature space and deglomerative schema, DiviK results do not strongly depend on the feature engineering technique applied and can reveal the hidden structure in a tissue sample. Additionally, DiviK shows high scalability, and it can process at once the big omics data with more than 1.5 mln instances and a few thousand features. Finally, due to its simplicity, DiviK is easily generalisable to an even more flexible framework. Therefore, it is helpful for other -omics data (as single cell spatial transcriptomic) or tabular data in general (including medical images after appropriate embedding). A generic implementation is freely available under Apache 2.0 license at https://github.com/gmrukwa/divik .

CMB-HUNT: Automatic detection of cerebral microbleeds using a deep neural network

Cerebral microbleeds (CMBs) are gaining increasing interest due to their importance in diagnosing cerebral small vessel diseases. However, manual inspection of CMBs is time-consuming and prone to human error. Existing automated or semi-automated solutions still have insufficient detection sensitivity and specificity. Furthermore, they frequently use more than one magnetic resonance imaging modality, but these are not always available. The majority of AI-based solutions use either numeric or image data, which may not provide sufficient information about the true nature of CMBs. This paper proposes a deep neural network with multi-type input data for automated CMB detection (CMB-HUNT) using only susceptibility-weighted imaging data (SWI). Combination of SWIs and radiomic-type numerical features allowed us to identify CMBs with high accuracy without the need for additional imaging modalities or complex predictive models. Two independent datasets were used: one with 304 patients (39 with CMBs) for training and internal system validation and one with 61 patients (21 with CMBs) for external validation. For the hold-out testing dataset, CMB-HUNT reached a sensitivity of 90.0%. As results of testing showed, CMB-HUNT outperforms existing methods in terms of the number of FPs per case, which is the lowest reported thus far (0.54 FPs/patient). The proposed system was successfully applied to the independent validation set, reaching a sensitivity of 91.5% with 1.9 false positives per patient and proving its generalization potential. The results were comparable to previous studies. Our research confirms the usefulness of deep learning solutions for CMB detection based only on one MRI modality.

Influence of single-cell RNA sequencing data integration on the performance of differential gene expression analysis

Large-scale comprehensive single-cell experiments are often resource-intensive and require the involvement of many laboratories and/or taking measurements at various times. This inevitably leads to batch effects, and systematic variations in the data that might occur due to different technology platforms, reagent lots, or handling personnel. Such technical differences confound biological variations of interest and need to be corrected during the data integration process. Data integration is a challenging task due to the overlapping of biological and technical factors, which makes it difficult to distinguish their individual contribution to the overall observed effect. Moreover, the choice of integration method may impact the downstream analyses, including searching for differentially expressed genes. From the existing data integration methods, we selected only those that return the full expression matrix. We evaluated six methods in terms of their influence on the performance of differential gene expression analysis in two single-cell datasets with the same biological study design that differ only in the way the measurement was done: one dataset manifests strong batch effects due to the measurements of each sample at a different time. Integrated data were visualized using the UMAP method. The evaluation was done both on individual gene level using parametric and non-parametric approaches for finding differentially expressed genes and on gene set level using gene set enrichment analysis. As an evaluation metric, we used two correlation coefficients, Pearson and Spearman, of the obtained test statistics between reference, test, and corrected studies. Visual comparison of UMAP plots highlighted ComBat-seq, limma, and MNN, which reduced batch effects and preserved differences between biological conditions. Most of the tested methods changed the data distribution after integration, which negatively impacts the use of parametric methods for the analysis. Two algorithms, MNN and Scanorama, gave very poor results in terms of differential analysis on gene and gene set levels. Finally, we highlight ComBat-seq as it led to the highest correlation of test statistics between reference and corrected dataset among others. Moreover, it does not distort the original distribution of gene expression data, so it can be used in all types of downstream analyses.

Prospective Open-Label, Single-Arm, Single-Center Follow-Up Study of the Application of the Advanced Hybrid Closed Loop System in Well-Controlled Children and Adolescents with Type 1 Diabetes

Background: The aim of this prospective open-label single-arm single-center follow-up study was to analyze glycemic control in children and adolescents with type 1 diabetes treated with the advanced hybrid closed loop (AHCL) system in relation to a sensor-augmented pump with low-glucose suspend (SAP-LGS) or predictive low-glucose suspend (SAP-PLGS). Materials and Methods: The data for 50 children and adolescents (age 5.5-19.6 years) with type 1 diabetes, receiving insulin through an AHCL system after being switched from SAP-LGS/PLGS systems, were included in the analysis. The SAP-LGS/PLGS records from 2 weeks preceding the AHCL connection were compared with the records from the first 4 weeks of AHCL use, represented as two separate 2-week intervals. Results: Significant improvements in most of the parameters, namely time spent in the range of 70-140 mg/dL (from 53.80% ± 12.35% to 61.70% ± 8.42%, P < 0.001) and 70-180 mg/dL (from 76.17% ± 10.28% to 81.32% ± 7.71%, P < 0.001), average sensor glucose (from 138.61 ± 16.66 to 130.02 ± 10.91 mg/dL, P < 0.001), and glucose management indicator (from 6.54% ± 0.45% to 6.27% ± 0.29%, P = 0.001), were observed within 2 weeks of switching to the AHCL. More evident improvements were observed for the parameters monitored at night than during the day. The potential limitations of this study were the short observation time, lack of glycated hemoglobin measurements, and no control arm. Conclusion: The AHCL system can significantly improve glycemic control even in well-controlled children and adolescents with type 1 diabetes by increasing the proportion of time spent in the narrower range of 70-140 mg/dL and decreasing the mean glucose concentration, especially during the night.

Early and Midterm Results of Orthotopic Heart Transplantation in Poland (2015-2019)

Orthotopic heart transplantation (OHT) has become one of the most expensive and resource-consuming treatment options for patients with end-stage heart failure. It is therefore useful to review clinical data, such as treatment duration after surgery and midterm follow-up in this group of patients. Contemporary epidemiologic data on early and midterm OHT follow-ups including patient demographics, hospitalization rates and related post-OHT morbidity, and mortality are scarce in Poland. The aim of the study was to determine early survival, hospitalization rates related to OHT and related morbidity, and mortality in Poland in the recent decade.

Body mass index, basal insulin and glycemic control in children with type 1 diabetes treated with the advanced hybrid closed loop system remain stable - 1-year prospective, observational, two-center study

BACKGROUND

Information on the influence of insulin treatment using advanced hybrid closed loop systems (AHCL) on body weight of young patients with type 1 diabetes (T1D) is scarce. The aim of this study was to observe whether there were any changes in body mass index (BMI) of children and adolescents with T1D treated using the Medtronic Minimed 780G AHCL after 1 year of follow up and to analyze potential associations between these changes and the insulin doses.

MATERIALS AND METHODS

For 50 children and adolescents (age 5.4-16.8 years, 24 (48%) boys, T1D for 3.9 ± 2.56 years) using an AHCL system anthropometric and AHCL data were collected prospectively. BMI Z-scores and two-week AHCL records obtained after AHCL enrollment were compared with data after 6 months and also 1 year after starting AHCL.

RESULTS

The BMI Z-score of the patients at 1 year follow-up did not change from time of AHCL initiation (0.51 ± 2.79 vs 0.57 ± 2.85, p>0.05). There was a slight increase in total daily insulin per kg of body weight (0.67 ± 0.21 U/kg vs 0.80 ± 0.21 U/kg, p <0.001), but the percent of basal insulin was unchanged (34.88 ± 6.91% vs 35.08 ± 6.30%, p>0.05). We observed also no change (AHCL start vs after 1 year) in glycemic control parameters: average sensor glucose (131.36± 11.04 mg/dL vs 132.45 ± 13.42 mg/dL, p>0.05), coefficient of variation (34.99± 5.17% vs 34.06 ± 5.38%, p>0.05), glucose management indicator (6.45 ± 0.26% vs 6.48 ± 0.32%, p>0.05), and time spent in the range of 70-180 mg/dL (79.28 ± 8.12% vs 80.40 ± 8.25%, p>0.05).

CONCLUSION

During the 1 year of follow-up the BMI of children and adolescents with T1D treated with an AHCL system remained stable. Although there was a slight increase in the total daily insulin dose, the percent of basal insulin was unchanged. The patients maintained recommended glycemic control.

Importance of SNP Dependency Correction and Association Integration for Gene Set Analysis in Genome-Wide Association Studies

A typical genome-wide association study (GWAS) analyzes millions of single-nucleotide polymorphisms (SNPs), several of which are in a region of the same gene. To conduct gene set analysis (GSA), information from SNPs needs to be unified at the gene level. A widely used practice is to use only the most relevant SNP per gene; however, there are other methods of integration that could be applied here. Also, the problem of nonrandom association of alleles at two or more loci is often neglected. Here, we tested the impact of incorporation of different integrations and linkage disequilibrium (LD) correction on the performance of several GSA methods. Matched normal and breast cancer samples from The Cancer Genome Atlas database were used to evaluate the performance of six GSA algorithms: Coincident Extreme Ranks in Numerical Observations (CERNO), Gene Set Enrichment Analysis (GSEA), GSEA-SNP, improved GSEA for GWAS (i-GSEA4GWAS), Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA), and Over-Representation Analysis (ORA). Association of SNPs to phenotype was calculated using modified McNemar's test. Results for SNPs mapped to the same gene were integrated using Fisher and Stouffer methods and compared with the minimum p-value method. Four common measures were used to quantify the performance of all combinations of methods. Results of GSA analysis on GWAS were compared to the one performed on gene expression data. Comparing all evaluation metrics across different GSA algorithms, integrations, and LD correction, we highlighted CERNO, and MAGENTA with Stouffer as the most efficient. Applying LD correction increased prioritization and specificity of enrichment outcomes for all tested algorithms. When Fisher or Stouffer were used with LD, sensitivity and reproducibility were also better. Using any integration method was beneficial in comparison with a minimum p-value method in specific combinations. The correlation between GSA results from genomic and transcriptomic level was the highest when Stouffer integration was combined with LD correction. We thoroughly evaluated different approaches to GSA in GWAS in terms of performance to guide others to select the most effective combinations. We showed that LD correction and Stouffer integration could increase the performance of enrichment analysis and encourage the usage of these techniques.

Making Use of Comparable Health Data to Improve Quality of Care and Outcomes in Diabetes: The EUBIROD Review of Diabetes Registries and Data Sources in Europe

Background

Registries and data sources contain information that can be used on an ongoing basis to improve quality of care and outcomes of people with diabetes. As a specific task of the EU Bridge Health project, we carried out a survey of diabetes-related data sources in Europe.

Objectives

We aimed to report on the organization of different sources of diabetes information, including their governance, information infrastructure and dissemination strategies for quality control, service planning, public health, policy and research.

Methods

Survey using a structured questionnaire to collect targeted data from a network of collaborating institutions managing registries and data sources in 17 countries in the year 2017.

Results

The 18 data sources participating in the study were most frequently academic centres (44.4%), national (72.2%), targeting all types of diabetes (61.1%) covering no more than 10% of the target population (44.4%). Although population-based in over a quarter of cases (27.8%), sources relied predominantly on provider-based datasets (38.5%), fewer using administrative data (16.6%). Data collection was continuous in the majority of cases (61.1%), but 50% could not perform data linkage. Public reports were more frequent (72.2%) as well as quality reports (77.8%), but one third did not provide feedback to policy and only half published ten or more peer reviewed papers during the last 5 years.

Conclusions

The heterogeneous implementation of diabetes registries and data sources hampers the comparability of quality and outcomes across Europe. Best practices exist but need to be shared more effectively to accelerate progress and deliver equitable results for people with diabetes.

Are we confident that final-year medical students know at least basics about diabetes?: A preliminary report from the multicenter, survey-based Diabetes Know-Me study

BACKGROUND

We present the results of the pilot study of a multinational "Diabetes Know-Me" project investigating knowledge regarding diabetes of medical students. This is the first collaborative project of the ISPAD JENIOUS group.

METHODS

Students of the final year of medical studies from six countries answered a 25-question survey regarding basic knowledge concerning diabetes (1091 surveys handed out, response rate 86%).

RESULTS

Among the responders (58% female) 90% confirmed attending diabetology classes; 11% planned to specialize in diabetology. There were significant differences between countries in the median score of correct answers ranging from 10/25 to 22/25. Attending diabetes classes (20 vs. 13/25, p < 0.0001) was the strongest factor associated with improved knowledge about diabetes (other factors analyzed were: gender, familiar/personal experience of diabetes, interest to specialize in diabetology).

CONCLUSIONS

Basic knowledge about diabetes remains a challenge. Participating in classes concerning diabetes contributed the most to the diabetes-related knowledge among students of the final year of medical faculties.

Symptom-based early-stage differentiation between SARS-CoV-2 versus other respiratory tract infections-Upper Silesia pilot study

In the DECODE project, data were collected from 3,114 surveys filled by symptomatic patients RT-qPCR tested for SARS-CoV-2 in a single university centre in March-September 2020. The population demonstrated balanced sex and age with 759 SARS-CoV-2( +) patients. The most discriminative symptoms in SARS-CoV-2( +) patients at early infection stage were loss of taste/smell (OR = 3.33, p < 0.0001), body temperature above 38℃ (OR = 1.67, p < 0.0001), muscle aches (OR = 1.30, p = 0.0242), headache (OR = 1.27, p = 0.0405), cough (OR = 1.26, p = 0.0477). Dyspnea was more often reported among SARS-CoV-2(-) (OR = 0.55, p < 0.0001). Cough and dyspnea were 3.5 times more frequent among SARS-CoV-2(-) (OR = 0.28, p < 0.0001). Co-occurrence of cough, muscle aches, headache, loss of taste/smell (OR = 4.72, p = 0.0015) appeared significant, although co-occurrence of two symptoms only, cough and loss of smell or taste, means OR = 2.49 (p < 0.0001). Temperature > 38℃ with cough was most frequent in men (20%), while loss of taste/smell with cough in women (17%). For younger people, taste/smell impairment is sufficient to characterise infection, whereas in older patients co-occurrence of fever and cough is necessary. The presented study objectifies the single symptoms and interactions significance in COVID-19 diagnoses and demonstrates diverse symptomatology in patient groups.

Analysing Intercellular Communication in Astrocytic Networks Using "Astral"

Astrocytic networks are critically involved in regulating the activity of neuronal networks. However, a comprehensive and ready-to-use data analysis tool for investigating functional interactions between the astrocytes is missing. We developed the novel software package named "Astral" to analyse intercellular communication in astrocytic networks based on live-cell calcium imaging. Our method for analysing calcium imaging data does not require the assignment of regions of interest. The package contains two applications: the core processing pipeline for detecting and quantifying Ca⁺⁺ events, and the auxiliary visualization tool for controlling data quality. Our method allows for the network-wide quantification of Ca⁺⁺ events and the analysis of their intercellular propagation. In a set of proof-of-concept experiments, we examined Ca⁺⁺ events in flat monolayers of primary astrocytes and confirmed that inter-astrocytic interactions depend on the permeability of gap junctions and connexin hemichannels. The Astral tool is particularly useful for studying astrocyte-neuronal interactions on the network level. We demonstrate that compared with purely astrocytic cultures, spontaneous generation of Ca⁺⁺ events in astrocytes that were co-cultivated with neurons was significantly increased. Interestingly, the increased astrocytic Ca⁺⁺ activity after long-term co-cultivation with neurons was driven by the enhanced formation of gap junctions and connexin hemichannels but was not affected by silencing neuronal activity. Our data indicate the necessity for systematic investigation of astrocyte-neuronal interactions at the network level. For this purpose, the Astral software offers a powerful tool for processing and quantifying calcium imaging data.

Kras mutations and PU.1 promoter methylation are new pathways in murine radiation-induced AML

Therapy-related and more specifically radiotherapy-associated acute myeloid leukaemia (AML) is a well-recognized potential complication of cytotoxic therapy for the treatment of a primary cancer. The CBA mouse model is used to study radiation leukaemogenesis mechanisms with Sfpi1/PU.1 deletion and point mutation already identified as driving events during AML development. To identify new pathways, we analysed 123 mouse radiation-induced AML (rAML) samples for the presence of mutations identified previously in human AML and found three genes to be mutated; Sfpi1 R235 (68%), Flt3-ITD (4%) and Kras G12 (3%), of which G12R was previously unreported. Importantly, a significant decrease in Sfpi1 gene expression is found almost exclusively in rAML samples without an Sfpi1 R235 mutation and is specifically associated with up-regulation of mir-1983 and mir-582-5p. Moreover, this down-regulation of Sfpi1 mRNA is negatively correlated with DNA methylation levels at specific CpG sites upstream of the Sfpi1 transcriptional start site. The down regulation of Sfpi1/PU.1 has also been reported in human AML cases revealing one common pathway of myeloid disruption between mouse and human AML where dysregulation of Sfpi1/PU.1 is a necessary step in AML development.

Radiomics and artificial intelligence in lung cancer screening

Lung cancer is responsible for more fatalities than any other cancer worldwide, with 1.76 million associated deaths reported in 2018. The key issue in the fight against this disease is the detection and diagnosis of all pulmonary nodules at an early stage. Artificial intelligence (AI) algorithms play a vital role in the automated detection, segmentation, and computer-aided diagnosis of malignant lesions. Among the existing algorithms, radiomics and deep-learning-based types appear to show the most promise. Radiomics is a growing field related to the extraction of a set of features from an image, which allows for automated classification of medical images into a predefined group. The process comprises a series of consecutive steps including image acquisition and pre-processing, segmentation of the desired region of interest, calculation of defined features, feature engineering, and construction of the classification model. The features calculated in this process are mainly shape features, as well as first- and higher-order texture features. To date, more than 100 features have been defined, although this number varies depending on the application. The greatest challenge in radiomics is building a cross-validated model based on a selected set of calculated features known as the radiomic signature. Numerous radiomic signatures have successfully been developed; however, reproducibility and clinical validity of the results obtained constitutes a considerable challenge of modern radiomics. Deep learning algorithms are another rapidly evolving technique and are recognized as a valuable tool in the field of medical image analysis for the detection, characterization, and assessment of lesions. Such an approach involves the design of artificial neural network architecture while upholding the goal of high classification accuracy. This paper illuminates the evolution and current state of artificial intelligence methods in lung imaging and the detection and diagnosis of pulmonary nodules, with a particular emphasis on radiomics and deep learning methods.

Bone status in adolescents and young adults with type 1 diabetes: a 10-year longitudinal study

INTRODUCTION

This study presents a 10-year longitudinal assessment of bone status in adolescents and young adults with type 1 diabetes (T1D).

MATERIAL AND METHODS

Thirty-two patients (12 female, aged 20.5 ± 3.93 years, T1D duration 13.9 ± 1.97 years) were studied using quantitative ultrasound (QUS) and dual-energy X-ray absorptiometry (DXA). Standard deviation scores (SDS) for these results were calculated. The following clinical parameters were analysed: sex, age, T1D duration, anthropometric parameters, daily insulin requirement (DIR), mean glycated haemoglobin (HbA1c) in the year preceding the examination, medication other than insulin, history of bone fractures, and comorbidities.

RESULTS

The current and past (measured 10 years earlier) QUS results did not differ and showed a significant correlation (r = 0.55, p = 0.001). We found no relation of QUS results and anthropometric parameters or gender. DXA parameters did not correlate with the present QUS measurement. DXA and QUS results were independent of HbA1c, co-morbidities, or intake of additional medicaments.

CONCLUSIONS

Bone status parameters of the examined patients with currently suboptimal glycaemic control were found to be lowered in comparison to a normative reference population, both at baseline and follow-up, although no further deterioration was observed during the 10-year follow-up period.

Identification of two novel mutations in human acute myeloid leukemia cases

Acute myeloid leukemia (AML) is an aggressive cancer that progresses rapidly with a poor prognosis. Cytogenetic analysis provides the most accurate determination of diagnosis and prognosis however, about 42-48% of AML patients have a cytogenetically normal karyotype. Genetic analysis can provide further information and the identification of new mutations could result in improved risk stratification, prognosis and better understanding of the mechanisms of AML leukaemogenesis. In this study, we analyzed genetic alterations in 16 human AML cases by Haloplex sequencing with confirmation of two previously unreported mutations in the genes DNMT3A and RUNX1 by Sanger sequencing or pyrosequencing. The two novel mutations consist of two frameshift mutations identified in two different AML patients and reported as deleterious by bioinformatic analysis. These mutations confirm the exclusion and co-occurrence of specific gene mutation patterns in AML and may provide further information for patient diagnosis and prognosis.

Systemic modulation of stress and immune parameters in patients treated for prostate adenocarcinoma by intensity-modulated radiation therapy or stereotactic ablative body radiotherapy

Background

In this exploratory study, the impact of local irradiation on systemic changes in stress and immune parameters was investigated in eight patients treated with intensity-modulated radiation therapy (IMRT) or stereotactic ablative body radiotherapy (SABR) for prostate adenocarcinoma to gain deeper insights into how radiotherapy (RT) modulates the immune system.

Patients and methods

RT-qPCR, flow cytometry, metabolomics, and antibody arrays were used to monitor a panel of stress- and immune-related parameters before RT, after the first fraction (SABR) or the first week of treatment (IMRT), after the last fraction, and 3 weeks later in the blood of IMRT (N = 4) or SABR (N = 4) patients. Effect size analysis was used for comparison of results at different timepoints.

Results

Several parameters were found to be differentially modulated in IMRT and SABR patients: the expression of TGFB1, IL1B, and CCL3 genes; the expression of HLA-DR on circulating monocytes; the abundance and ratio of phosphatidylcholine and lysophosphatidylcholine metabolites in plasma. More immune modulators in plasma were modulated during IMRT than SABR, with only two common proteins, namely GDF-15 and Tim‑3.

Conclusion

Locally delivered RT induces systemic modulation of the immune system in prostate adenocarcinoma patients. IMRT and SABR appear to specifically affect distinct immune components.

Electronic supplementary material

The online version of this article (10.1007/s00066-020-01637-5) contains supplementary material, which is available to authorized users.

BatchI: Batch effect Identification in high-throughput screening data using a dynamic programming algorithm

Motivation

In contemporary biological experiments, bias, which interferes with the measurements, requires attentive processing. Important sources of bias in high-throughput biological experiments are batch effects and diverse methods towards removal of batch effects have been established. These include various normalization techniques, yet many require knowledge on the number of batches and assignment of samples to batches. Only few can deal with the problem of identification of batch effect of unknown structure. For this reason, an original batch identification algorithm through dynamical programming is introduced for omics data that may be sorted on a timescale.

Results

BatchI algorithm is based on partitioning a series of high-throughput experiment samples into sub-series corresponding to estimated batches. The dynamic programming method is used for splitting data with maximal dispersion between batches, while maintaining minimal within batch dispersion. The procedure has been tested on a number of available datasets with and without prior information about batch partitioning. Datasets with a priori identified batches have been split accordingly, measured with weighted average Dice Index. Batch effect correction is justified by higher intra-group correlation. In the blank datasets, identified batch divisions lead to improvement of parameters and quality of biological information, shown by literature study and Information Content. The outcome of the algorithm serves as a starting point for correction methods. It has been demonstrated that omitting the essential step of batch effect control may lead to waste of valuable potential discoveries.

Availability and implementation

The implementation is available within the BatchI R package at http://zaed.aei.polsl.pl/index.php/pl/111-software.

Supplementary information

Supplementary data are available at Bioinformatics online.

Molecular Profiling for Predictors of Radiosensitivity in Patients with Breast or Head-and-Neck Cancer

Nearly half of all cancers are treated with radiotherapy alone or in combination with other treatments, where damage to normal tissues is a limiting factor for the treatment. Radiotherapy-induced adverse health effects, mostly of importance for cancer patients with long-term survival, may appear during or long time after finishing radiotherapy and depend on the patient’s radiosensitivity. Currently, there is no assay available that can reliably predict the individual’s response to radiotherapy. We profiled two study sets from breast (n = 29) and head-and-neck cancer patients (n = 74) that included radiosensitive patients and matched radioresistant controls.. We studied 55 single nucleotide polymorphisms (SNPs) in 33 genes by DNA genotyping and 130 circulating proteins by affinity-based plasma proteomics. In both study sets, we discovered several plasma proteins with the predictive power to find radiosensitive patients (adjusted p < 0.05) and validated the two most predictive proteins (THPO and STIM1) by sandwich immunoassays. By integrating genotypic and proteomic data into an analysis model, it was found that the proteins CHIT1, PDGFB, PNKD, RP2, SERPINC1, SLC4A, STIM1, and THPO, as well as the VEGFA gene variant rs69947, predicted radiosensitivity of our breast cancer (AUC = 0.76) and head-and-neck cancer (AUC = 0.89) patients. In conclusion, circulating proteins and a SNP variant of VEGFA suggest that processes such as vascular growth capacity, immune response, DNA repair and oxidative stress/hypoxia may be involved in an individual’s risk of experiencing radiation-induced toxicity.

GaMRed-Adaptive Filtering of High-Throughput Biological Data

Data filtering based on removing non-informative features, with unchanged signal between compared experimental conditions, can significantly increase sensitivity of methods used to detect differentially expressed genes or other molecular components measured in high-throughput biological experiments. Criteria for data filtering can be stated on the basis of averages or variances of signal levels across samples. The crucial parts of feature filtering are selection of filter type and cut-off threshold, which are specific to the particular dataset. In this paper, we present an algorithm and a stand-alone application, GaMRed, for adaptive filtering insignificant features in high-throughput data, based on Gaussian mixture decomposition. We have tested the performance of our algorithm using datasets from three different high-throughput biological experiments. We estimated the number of differentially expressed features after applying multiple testing correction and performed functional analysis of obtained features using Gene Ontology terms. Also, we checked if the control of false discovery rate and family-wise error rate after applying feature filtering remains at appropriate level. GaMRed is fast, automatic, and does not require expert knowledge in parameter tuning. The algorithm increases sensitivity of methods used to find differentially expressed features and biological validity of the findings. The program can be downloaded from: http://zaed.aei.polsl.pl/index.php/pl/oprogramowanie-zaed.

Gene set enrichment for reproducible science: comparison of CERNO and eight other algorithms

MOTIVATION

Analysis of gene set (GS) enrichment is an essential part of functional omics studies. Here, we complement the established evaluation metrics of GS enrichment algorithms with a novel approach to assess the practical reproducibility of scientific results obtained from GS enrichment tests when applied to related data from different studies.

RESULTS

We evaluated eight established and one novel algorithm for reproducibility, sensitivity, prioritization, false positive rate and computational time. In addition to eight established algorithms, we also included Coincident Extreme Ranks in Numerical Observations (CERNO), a flexible and fast algorithm based on modified Fisher P-value integration. Using real-world datasets, we demonstrate that CERNO is robust to ranking metrics, as well as sample and GS size. CERNO had the highest reproducibility while remaining sensitive, specific and fast. In the overall ranking Pathway Analysis with Down-weighting of Overlapping Genes, CERNO and over-representation analysis performed best, while CERNO and GeneSetTest scored high in terms of reproducibility.

AVAILABILITY AND IMPLEMENTATION

tmod package implementing the CERNO algorithm is available from CRAN (cran.r-project.org/web/packages/tmod/index.html) and an online implementation can be found at http://tmod.online/. The datasets analyzed in this study are widely available in the KEGGdzPathwaysGEO, KEGGandMetacoreDzPathwaysGEO R package and GEO repository.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Germline DNA Retention in Murine and Human Rearranged T Cell Receptor Gene Coding Joints: Alternative Recombination Signal Sequences and V(D)J Recombinase Errors

The genes coding for the antigenic T cell receptor (TR) subunits are assembled in thymocytes from discrete V, D, and J genes by a site-specific recombination process. A tight control of this activity is required to prevent potentially detrimental recombination events. V, D, and J genes are flanked by semi-conserved nucleotide motives called recombination signal sequences (RSSs). V(D)J recombination is initiated by the precise introduction of a DNA double-strand break exactly at the border of the genes and their RSSs by the RAG recombinase. RSSs are therefore physically separated from the coding region of the genes before assembly of a rearranged TR gene. During a high throughput profiling of TRB genes in mice, we identified rearranged TRB genes in which part or all of a flanking RSS was retained in V-D or D-J coding joints. In some instances, this retention of germline DNA resulted from the use of an upstream alternative RSS. However, we also identified TRB sequences where retention of germline DNA occurred in the absence of alternative RSS, suggesting that RAG activity was mis-targeted during recombination. Similar events were also identified in human rearranged TRB and TRG genes. The use of alternative RSSs during V(D)J recombination illustrates the complexity of RAG-RSSs interactions during V(D)J recombination. While the frequency of errors resulting from mis-targeted RAG activity is very low, we believe that these RAG errors may be at the origin of oncogenic translocations and are a threat for genetic stability in developing lymphocytes.

Prevalence of diabetes in Poland: a combined analysis of national databases

AIMS

To assess the number of people with diabetes in Poland using combined national sources and to evaluate the usefulness of data from an insurance system for epidemiological purposes.

METHODS

The data were collected from four sources: 1) 2013 all-billing records of the national insurance system comprising people of all age groups undergoing procedures or receiving services in primary healthcare, specialist practices and hospitals and also those receiving drugs; 2) an epidemiological study, NATPOL, that involved the assessment of people with undiagnosed diabetes; 3) the RECEPTOmetr Sequence study on prescriptions; and 4) regional child diabetes registries.

RESULTS

In 2013, 1.76 million people (0.98 million women and 0.79 million men) had medical consultations (coded E10-E14) and 2.13 million people (1.19 million women and 0.94 million men) purchased drugs or strip tests for diabetes. A total of 0.04 million people who used medical services did not buy drugs. In total, the number of people with diabetes in the insurance system was 2.16 million (1.21 million women and 0.95 million men), which corresponds to 6.1% (95% CI 6.11-6.14) of women and 5.1% (95% CI 5.12-5.14) of men. Including undiagnosed cases, the total number of people with diabetes in Poland was 2.68 million in 2013.

CONCLUSION

The estimated prevalence of diabetes (diagnosed and undiagnosed cases) in Poland is 6.97%. Data from the national insurance system with full coverage of the population can be treated as a reliable source of information on diseases with well-defined diagnosis and treatment methods, combined with an assessment of the number of undiagnosed individuals.

Combining CDKN1A gene expression and genome-wide SNPs in a twin cohort to gain insight into the heritability of individual radiosensitivity

Individual variability in response to radiation exposure is recognised and has often been reported as important in treatment planning. Despite many efforts to identify biomarkers allowing the identification of radiation sensitive patients, it is not yet possible to distinguish them with certainty before the beginning of the radiotherapy treatment. A comprehensive analysis of genome-wide single-nucleotide polymorphisms (SNPs) and a transcriptional response to ionising radiation exposure in twins have the potential to identify such an individual. In the present work, we investigated SNP profile and CDKN1A gene expression in blood T lymphocytes from 130 healthy Caucasians with a complex level of individual kinship (unrelated, mono- or dizygotic twins). It was found that genetic variation accounts for 66% (95% CI 37-82%) of CDKN1A transcriptional response to radiation exposure. We developed a novel integrative multi-kinship strategy allowing investigating the role of genome-wide polymorphisms in transcriptomic radiation response, and it revealed that rs205543 (ETV6 gene), rs2287505 and rs1263612 (KLF7 gene) are significantly associated with CDKN1A expression level. The functional analysis revealed that rs6974232 (RPA3 gene), involved in mismatch repair (p value = 9.68e-04) as well as in RNA repair (p value = 1.4e-03) might have an important role in that process. Two missense polymorphisms with possible deleterious effect in humans were identified: rs1133833 (AKIP1 gene) and rs17362588 (CCDC141 gene). In summary, the data presented here support the validity of this novel integrative data analysis strategy to provide insights into the identification of SNPs potentially influencing radiation sensitivity. Further investigations in radiation response research at the genomic level should be therefore continued to confirm these findings.

Favourable outcomes in patients with early-stage non-small-cell lung cancer operated on by video-assisted thoracoscopic surgery: a propensity score-matched analysis

OBJECTIVES

The video-assisted thoracoscopic surgery (VATS) approach has become a standard for the treatment of early-stage non-small-cell lung cancer (NSCLC). Recently published meta-analyses proved the benefit of VATS versus thoracotomy for overall survival (OS) and reduction of postoperative complications. The aim of this study was to compare early outcomes, long-term survival and rate of postoperative complications of the VATS approach versus thoracotomy.

METHODS

In this retrospective cohort study, we analysed 982 individuals who underwent surgical resection for Stage I-IIA NSCLC between 2007 and 2015. Thirty- and 90-day mortality rates, length of hospital stay, rate of complications and OS were assessed. Propensity score matching was performed to compare 2 groups of patients. Two hundred and twenty-five individuals from the thoracotomy group and 225 patients from the VATS group were matched regarding pTNM, sex, the Charlson comorbidity index, type of resection and histological diagnosis.

RESULTS

In the propensity score-matched patient group, the VATS approach was associated with a significant benefit regarding OS (P = 0.042). Although no significant difference was observed (P = 0.14) in the 3-year survival rate of patients who had a thoracotomy versus VATS, the 5-year survival rate among patients with VATS increased significantly (61% vs 78%, P = 0.0081). The adjusted VATS-related hazard ratio for pTNM, sex and age was 0.63 (95% confidence interval 0.40-0.98). The VATS surgical approach also reduced both the rate of postoperative atelectasis (4% for VATS vs 10% for open thoracotomy; P = 0.0052) and the need for blood transfusions (4% vs 12% respectively, P = 0.0054) and significantly shortened the postoperative length of stay (mean 7.25 vs 9.34 days, P < 0.0001). No significant differences in the 30-day mortality (1% vs 1%, P = 0.66) and 90-day mortality (1% vs 1%, P = 0.48) rates were observed.

CONCLUSIONS

Patients with early-stage NSCLC operated on with VATS had fewer complications, shorter postoperative length of stay and better OS compared to those who were operated on by thoracotomy.

Ionizing radiation affects the composition of the proteome of extracellular vesicles released by head-and-neck cancer cells in vitro

Exosomes and other extracellular vesicles are key players in cell-to-cell communication, and it has been proposed that they are involved in different aspects of the response to ionizing radiation, including transmitting the radiation-induced bystander effect and mediating radioresistance. The functional role of exosomes depends on their molecular cargo, including proteome content. Here we aimed to establish the proteome profile of exosomes released in vitro by irradiated UM-SCC6 cells derived from human head-and-neck cancer and to identify processes associated with radiation-affected proteins. Exosomes and other small extracellular vesicles were purified by size-exclusion chromatography from cell culture media collected 24 h after irradiation of cells with a single 2, 4 or 8 Gy dose, and then proteins were identified using a shotgun LC-MS/MS approach. Exosome-specific proteins encoded by 1217 unique genes were identified. There were 472 proteins whose abundance in exosomes was significantly affected by radiation (at any dose), including 425 upregulated and 47 downregulated species. The largest group of proteins affected by radiation (369 species) included those with increased abundance at all radiation doses (≥2 Gy). Several gene ontology terms were associated with radiation-affected exosome proteins. Among overrepresented processes were those involved in the response to radiation, the metabolism of radical oxygen species, DNA repair, chromatin packaging, and protein folding. Hence, the protein content of exosomes released by irradiated cells indicates their actual role in mediating the response to ionizing radiation.

Increased plasma concentration of 4-pyridone-3-carboxamide-1-ß-D-ribonucleoside (4PYR) in lung cancer. Preliminary studies

4-pyridone-3-carboxamide-1-β-D-ribonucleoside (4PYR) is a new nicotinamide derivative, which is potentially toxic to the endothelium. Dysfunction of the endothelium promotes cancer cell proliferation, invasiveness, and inflammatory signaling. The aim of this study was to analyze 4PYR concentration in the plasma of lung cancer patients and its relationship to other known biochemical parameters associated with the endothelium function. The concentration of 4PYR, nicotinamide, 1-methylnicotinamide (MNA), amino acids, and their derivatives were measured in samples obtained from patients with primary squamous cell carcinoma (n = 48) and control group (n = 100). The concentration of 4PYR and 4PYR/MNA ratio were significantly higher in lung cancer patients as compared to controls (0.099 ± 0.009 vs. 0.066 ± 0.006 µmol/L and 1.10 ± 0.08 vs. 1.97 ± 0.15, respectively). The plasma arginine/asymmetric dimethylarginine (Arg/ADMA) ratio was considerably lower in lung cancer patients (253 ± 17 vs. 369 ± 19) as well as plasma MNA (0.057 ± 0.004 vs. 0.069 ± 0.003 µmol/L). There was no difference in the plasma concentrations of nicotinamide and nicotinamide riboside in both groups (0.116 ± 0.019 vs. 0.131 ± 0.014 and 0.102 ± 0.006 vs. 0.113 ± 0.011, respectively). In this study, a higher 4PYR concentration was observed for the first time in patients with squamous cell carcinoma. This change may be related to the endothelial dysfunction that promote cancer progression since 4PYR and its derivatives are known to disrupt glycolytic pathway.

Integrative multiomics study for validation of mechanisms in radiation-induced ischemic heart disease in Mayak workers

Previous studies have suggested that exposure to ionizing radiation increases the risk of ischemic heart disease (IHD). The data from the Mayak nuclear worker cohort have indicated enhanced risk for IHD incidence. The goal of this study was to elucidate molecular mechanisms of radiation-induced IHD by integrating proteomics data with a transcriptomics study on post mortem cardiac left ventricle samples from Mayak workers categorized in four radiation dose groups (0 Gy, < 100 mGy, 100-500 mGy, > 500 mGy). The proteomics data that were newly analysed here, originated from a label-free analysis of cardiac samples. The transcriptomics analysis was performed on a subset of these samples. Stepwise linear regression analyses were used to correct the age-dependent changes in protein expression, enabling the separation of proteins, the expression of which was dependent only on the radiation dose, age or both of these factors. Importantly, the majority of the proteins showed only dose-dependent expression changes. Hierarchical clustering of the proteome and transcriptome profiles confirmed the separation of control and high-dose samples. Restrictive (separate p-values) and integrative (combined p-value) approaches were used to investigate the enrichment of biological pathways. The integrative method proved superior in the validation of the key biological pathways found in the proteomics analysis, namely PPAR signalling, TCA cycle and glycolysis/gluconeogenesis. This study presents a novel, improved, and comprehensive statistical approach of analysing biological effects on a limited number of samples.

Discrimination of normal oral mucosa from oral cancer by mass spectrometry imaging of proteins and lipids

Identification of biomarkers for molecular classification of cancer and for differentiation between cancerous and normal epithelium remains a vital issue in the field of head and neck cancer. Here we aimed to compare the ability of proteome and lipidome components to discriminate oral cancer from normal mucosa. Tissue specimens including squamous cell cancer and normal epithelium were analyzed by MALDI mass spectrometry imaging. Two molecular domains of tissue components were imaged in serial sections-peptides (resulting from trypsin-processed proteins) and lipids (primarily zwitterionic phospholipids), then regions of interest corresponding to cancer and normal epithelium were compared. Heterogeneity of cancer regions was higher than the heterogeneity of normal epithelium, and the distribution of peptide components was more heterogeneous than the distribution of lipid components. Moreover, there were more peptide components than lipid components that showed significantly different abundance between cancer and normal epithelium (median of the Cohen's effect was 0.49 and 0.31 in case of peptide and lipid components, respectively). Multicomponent cancer classifier was tested (vs. normal epithelium) using tissue specimens from three patients and then validated with a tissue specimen from the fourth patient. Peptide-based signature and lipid-based signature allowed cancer classification with a weighted accuracy of 0.85 and 0.69, respectively. Nevertheless, both classifiers had very high precision (0.98 and 0.94, respectively). We concluded that though molecular differences between cancerous and normal mucosa were higher in the proteome domain than in the analyzed lipidome subdomain, imaging of lipidome components also enabled discrimination of oral cancer and normal epithelium. Therefore, both cancer proteome and lipidome are promising sources of biomarkers of oral malignancies.

Stage I non-small-cell lung cancer: long-term results of lobectomy versus sublobar resection from the Polish National Lung Cancer Registry

OBJECTIVES

Anatomical lobar resection and mediastinal lymphadenectomy remain the standard for the treatment of early stage non-small-cell lung cancer (NSCLC) and are preferred over procedures such as segmentectomy or wedge resection. However, there is an ongoing debate concerning the influence of the extent of the resection on overall survival. The aim of this article was to assess the overall survival for different types of resection for Stage I NSCLC.

METHODS

We performed a retrospective analysis of the results of the surgical treatment of Stage I NSCLC. Between 1 January 2007 and 31 December 2013, the data from 6905 patients who underwent Stage I NSCLC operations were collected in the Polish National Lung Cancer Registry (PNLCR) and overall survival was assessed. A propensity score-matched analysis was used to compare 3 groups of patients, each consisting of 231 patients who underwent lobectomy, segmentectomy, or wedge resection.

RESULTS

In the unmatched and matched patient groups, lobectomy and segmentectomy were associated with a significant benefit compared to wedge resection regarding overall survival (log-rank P  < 0.001 and P  = 0.001). The Cox proportional hazard ratio comparing segmentectomy and lobectomy to wedge resection was 0.54 [95% confidence interval (CI): 0.37-0.77) and 0.44 (95% CI: 0.38-0.50), respectively, indicating a significant improvement in survival. There was no difference in the 5-year survival of patients after lobectomy (79.1%; 95% CI: 77.7-80.4%) or segmentectomy (78.3%; 95% CI: 70.6-86.0%). The 30-day mortality rate was 1.6, 2.6 and 1.4% for lobectomy, segmentectomy and wedge resection, respectively. Wedge resection was associated with a significantly lower 5-year survival rate (58.1%; 95% CI: 53.6-62.5%) compared to segmentectomy (78.3%; 95% CI: 70.6-86.0%) and lobectomy (79.1%; 95% CI: 77.7-80.5%). The propensity score matched analysis confirmed most of the results of the comparisons of unmatched study groups.

CONCLUSIONS

Wedge resection was associated with significantly lower 3-year and 5-year survival rates compared to the other methods of resection. There was no significant difference in 3-year or 5-year survival rates between lobectomy and segmentectomy. Segmentectomy, but not wedge resection, could be considered an alternative to lobectomy in the treatment of patients with Stage I NSCLC.

Region-Specific Methylation Profiling in Acute Myeloid Leukemia

Alteration of DNA methylation level in cancer diseases leads to deregulation of gene expression-silencing of tumor suppressor genes and enhancing of protooncogenes. There are several tools devoted to the problem of identification of CpG sites' demethylation but majority of them focuses on single site level and does not allow for quantification of region methylation changes. The aim was to create an adaptive algorithm supporting detection of differentially methylated CpG sites and genomic regions specific for acute myeloid leukemia. Knowledge on AML methylation fingerprint helps in better understanding the epigenetics of leukemogenesis. Proposed algorithm is data driven and does not use predefined quantification thresholds. Gaussian mixture modeling supports classification of CpG sites to several levels of demethylation. p value integration allows for translation from single site demethylation to the demethylation of gene promoter and body regions. Methylation profiles of healthy controls and AML patients were examined (GEO:GSE63409). The differences in whole genome methylation profiles were observed. The methylation profile differs significantly among genomic regions. The lowest methylation level was observed for promoter regions, while sites from intergenic regions were by average higher methylated. The observed number of AML related down methylated sites has not substantially exceeded the expected number by chance. Intergenic regions were characterized by the highest percentage of AML up methylated sites. Methylation enhancement/diminution is the most frequent for intergenic region while methylation compensation (positive or negative) is specific for promoter regions. Functional analysis performed for AML down methylated or extreme high up methylated genes showed strong connection to the leukemic processes.

Transcriptomic and proteomic analysis of mouse radiation-induced acute myeloid leukaemia (AML)

A combined transcriptome and proteome analysis of mouse radiation-induced AMLs using two primary AMLs, cell lines from these primaries, another cell line and its in vivo passage is reported. Compared to haematopoietic progenitor and stem cells (HPSC), over 5000 transcriptome alterations were identified, 2600 present in all materials. 55 and 3 alterations were detected in the proteomes of the cell lines and primary/in vivo passage material respectively, with one common to all materials. In cell lines, approximately 50% of the transcriptome changes are related to adaptation to cell culture, and in the proteome this proportion was higher. An AML 'signature' of 17 genes/proteins commonly deregulated in primary AMLs and cell lines compared to HPSCs was identified and validated using human AML transcriptome data. This also distinguishes primary AMLs from cell lines and includes proteins such as Coronin 1, pontin/RUVBL1 and Myeloperoxidase commonly implicated in human AML. C-Myc was identified as having a key role in radiation leukaemogenesis. These data identify novel candidates relevant to mouse radiation AML pathogenesis, and confirm that pathways of leukaemogenesis in the mouse and human share substantial commonality.

Markers of Anemia in Children with Type 1 Diabetes

AIM

The aim of the study was to assess markers of anemia in type 1 diabetes (T1D) children, compare them to results obtained in the control group, and estimate their relation to BMI SDS.

METHODS

94 (59% ♀) T1D children without other autoimmune disorders, aged 12.5 ± 4.1 years, T1D duration: 4.2 ± 3.6 years, HbA1c 7.3 ± 1.5% (57 ± 12.6 mmol/mol). Sex- and age-matched controls (43 children). In all children, anthropometric measurements, the blood count, iron turnover parameters, and vitamin B12 concentration were taken.

RESULTS

T1DM children had significantly higher red cell distribution width (RDW) (13.6 versus 12.6%; p < 0.001), hepcidin (0.25 versus 0.12 ng/ml; p < 0.001), and vitamin B12 concentrations (459 versus 397 pg/ml; p < 0.01) and lower TIBC (59.09 versus 68.15 μmol/l; p < 0.001) than in the control group. Logistic regression revealed that RDW, TIBC (both p < 0.001), and hepcidin (p < 0.05) significantly differentiated both groups. In T1DM children, BMI SDS negatively correlated with vitamin B12 (p < 0.01) concentration and mean corpuscular hemoglobin concentration (p < 0.05) and positively with TIBC (p < 0.01) and HbA1c (p < 0.001).

CONCLUSIONS

Patients and controls differed especially in terms of RDW and TIBC. In studied T1DM children, BMI SDS was associated to iron metabolism parameters and vitamin B12 concentration.

Updated 24-year trend of Type 1 diabetes incidence in children in Poland reveals a sinusoidal pattern and sustained increase

AIMS

To present the incidence trend for Type 1 diabetes in Polish children aged 0-14 years, updated using data collected during 2005-2012, and assess the reliability of the predictive model constructed previously using the 1989-2004 database.

METHODS

Children aged < 15 years with newly diagnosed Type 1 diabetes are recorded prospectively (EURODIAB criteria) in several regional registers in Poland. Age- and gender-standardized incidence rates for Type 1 diabetes were calculated per 100 000 persons/year. Incidence rates were analysed in terms of the dependency on age, gender, geographical region and population density. Incidence rate trends over time were modelled using generalized linear models.

RESULTS

The mean standardized incidence for 1989-2012 was 12.72 per 100 000 persons/year [95% confidence interval (CI), 11.35 to 14.21]. Over the 24-year observation period, the incidence increased from 5.36 to 22.74 per 100 000 persons/year. The lowest incidence rate was in children aged 0-4 years (8.35, 95% CI 7.27 to 9.57 per 100 000 persons/year). There was no difference between genders, or urban and rural regions. Incidence rates were higher in northern compared with southern Poland [14.04 (95% CI 12.59 to 15.63) vs. 11.94 (95% CI 10.62 to 13.39) per 100 000 persons/year]. The new data corrected the earlier predictive model by changing the estimates of some factors related to patient age, gender and their interactions with the remaining factors. The incidence rate shows periodic 5.33-year fluctuations. The periodicity component allows for a more accurate prediction of the incidence rate over time.

CONCLUSIONS

This cohort study reveals a sustained increase in Type 1 diabetes incidence in Polish children aged 0-14 years with regular, sinusoidal fluctuations and a slight levelling off in past few years. It is of concern that are the highest increases in incidence are found in children aged 0-4 years.

Panel of serum metabolites discriminates cancer patients and healthy participants of lung cancer screening - a pilot study

INTRODUCTION

Blood biomarkers may support early diagnosis of lung cancer by enabling pre-selection of candidates for computed tomography screening or discrimination between benign and malignant screening-detected nodules. We aimed to identify features of serum metabolome distinguishing individuals with early-detected lung cancer from healthy participants of the lung cancer screening program.

METHODS

Blood samples were collected in the course of a low-dose computed tomography screening program performed in the Gdansk district (Northern Poland). The analysis included 31 patients with screening-detected lung cancer and the pair-matched group of 92 healthy controls. The gas chromatography coupled to mass spectrometry (GC/MS) approach was used to identify and quantify small metabolites present in serum.

RESULTS

There were several metabolites detected in the sera whose abundances discriminated patients with lung cancer from controls. Majority of the differentiating components were downregulated in cancer samples, including amino acids, carboxylic acids and tocopherols, whereas benzaldehyde was the only compound significantly upregulated. A classifier including nine serum metabolites allowed separation of cancer and control samples with 100% sensitivity and 95% specificity.

CONCLUSIONS

Signature of serum metabolites discriminating between cancer patients and healthy participants of the early lung cancer screening program was identified using a GC/MS metabolomics approach. This signature, though not validated in an independent dataset, deserves further investigation in a larger cohort study.