A mapping-free natural language processing-based technique for sequence search in nanopore long-reads.

Background: In unforeseen situations, such as nuclear power plant's or civilian radiation accidents, there is a need for effective and computationally inexpensive methods to determine the expression level of a selected gene panel, allowing for rough dose estimates in thousands of donors. The new generation in-situ mapper, fast and of low energy consumption, working at the level of single nanopore output, is in demand. We aim to create a sequence identification tool that utilizes natural language processing techniques and ensures a high level of negative predictive value (NPV) compared to the classical approach.

Transcriptomic Profile of Mouse Brain Ageing in Early Developmental Stages.

Ageing is a continuous process that can cause neurodevelopmental changes in the body. Several studies have examined its effects, but few have focused on how time affects biological processes in the early stages of brain development. As studying the changes that occur in the early stages of life is important to prevent age-related neurological and psychiatric disorders, we aim to focus on these changes.

Relative effect size-based profiles as an alternative to differentiation analysis in multi-species single-cell transcriptional studies.

Combining data from experiments on multispecies studies provides invaluable contributions to the understanding of basic disease mechanisms and pathophysiology of pathogens crossing species boundaries. The task of multispecies gene expression analysis, however, is often challenging given annotation inconsistencies and in cases of small sample sizes due to bias caused by batch effects. In this work we aim to demonstrate that an alternative approach to standard differential expression analysis in single cell RNA-sequencing (scRNA-seq) based on effect size profiles is suitable for the fusion of data from small samples and multiple organisms.

Venom Immunotherapy Does Not Affect Survival of Patients with Malignant Tumor in Poland.

Allergen immunotherapy (AIT) is a well-established and efficient method of causative treatment for allergic rhinitis, asthma and insect venom allergy. Traditionally, a recent history of malignant neoplasm is regarded as a contraindication to AIT due to concerns that AIT might stimulate tumor growth. However, there are no data confirming that the silencing of the Th2 response affects prognosis in cancer.

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.

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.

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 AML. Our hypothesis is that exposure to radiation causes genome-wide epigenetic changes in rt-AML.

Short- and long-term effects of radiation exposure at low dose and low dose rate in normal human VH10 fibroblasts.

Introduction: Experimental studies complement epidemiological data on the biological effects of low doses and dose rates of ionizing radiation and help in determining the dose and dose rate effectiveness factor.

Methods: Human VH10 skin fibroblasts exposed to 25, 50, and 100 mGy of Cs gamma radiation at 1.6, 8, 12 mGy/h, and at a high dose rate of 23.

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.

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.

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.

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.

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.

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 -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.

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.

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.

The International Association for the Study of Lung Cancer Early Lung Imaging Confederation Open-Source Deep Learning and Quantitative Measurement Initiative.

Introduction: With global adoption of computed tomography (CT) lung cancer screening, there is increasing interest to use artificial intelligence (AI) deep learning methods to improve the clinical management process. To enable AI research using an open-source, cloud-based, globally distributed, screening CT imaging data set and computational environment that are compliant with the most stringent international privacy regulations that also protect the intellectual properties of researchers, the International Association for the Study of Lung Cancer sponsored development of the Early Lung Imaging Confederation (ELIC) resource in 2018. The objective of this report is to describe the updated capabilities of ELIC and illustrate how this resource can be used for clinically relevant AI research.

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.

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.

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.

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.

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.

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.

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.