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  • 1
    Online Resource
    Online Resource
    Screening Support System Based on Patient Survey Data—Case Study on Classification of Initial, Locally Collected COVID-19 Data
    MDPI AG ; 2021
    In:  Applied Sciences Vol. 11, No. 22 ( 2021-11-15), p. 10790-
    Details
    In: Applied Sciences, MDPI AG, Vol. 11, No. 22 ( 2021-11-15), p. 10790-
    Abstract: New diseases constantly endanger the lives of populations, and, nowadays, they can spread easily and constitute a global threat. The COVID-19 pandemic has shown that the fight against a new disease may be difficult, especially at the initial stage of the epidemic, when medical knowledge is not complete and the symptoms are ambiguous. The use of machine learning tools can help to filter out those sick patients who do not need to be tested for spreading the pathogen, especially in the event of an overwhelming increase in disease transmission. This work presents a screening support system that can precisely identify patients who do not carry the disease. The decision of the system is made on the basis of patient survey data that are easy to collect. A case study on a data set of symptomatic COVID-19 patients shows that the system can be effective in the initial phase of the epidemic. The case study presents an analysis of two classifiers that were tuned to achieve an assumed acceptable threshold of negative predictive values during classification. Additionally, an explanation of the obtained classification models is presented. The explanation enables the users to understand the basis of the decision made by the model. The obtained classification models provide the basis for the DECODE service, which could serve as support in screening patients with COVID-19 disease at the initial stage of the pandemic. Moreover, the data set constituting the basis for the analyses performed is made available to the research community. This data set, consisting of more than 3000 examples, is based on questionnaires collected at a hospital in Poland.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app112210790
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2704225-X
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  • 2
    Online Resource
    Online Resource
    Symptom-based early-stage differentiation between SARS-CoV-2 versus other respiratory tract infections—Upper Silesia pilot study
    Springer Science and Business Media LLC ; 2021
    In:  Scientific Reports Vol. 11, No. 1 ( 2021-06-30)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2021-06-30)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-021-93046-6
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2615211-3
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  • 3
    Online Resource
    Online Resource
    Influence of Polymorphisms in DNA Repair Genes XPD, XRCC1 and MGMT on DNA Damage Induced by Gamma Radiation and its Repair in Lymphocytes In Vitro
    Radiation Research Society ; 2005
    In:  Radiation Research Vol. 164, No. 2 ( 2005-08), p. 132-140
    Details
    In: Radiation Research, Radiation Research Society, Vol. 164, No. 2 ( 2005-08), p. 132-140
    Type of Medium: Online Resource
    ISSN: 0033-7587 , 1938-5404
    URL: Article
    DOI: 10.1667/RR3400
    RVK:
    WA 15000
    Language: English
    Publisher: Radiation Research Society
    Publication Date: 2005
    detail.hit.zdb_id: 2135113-2
    detail.hit.zdb_id: 80322-4
    SSG: 11
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    Online Resource
  • 4
    Online Resource
    Online Resource
    DataSheet1.PDF
    Frontiers Media SA ; 2021
    In:  Frontiers in Genetics Vol. 12 ( 2021-12-9)
    Details
    In: Frontiers in Genetics, Frontiers Media SA, Vol. 12 ( 2021-12-9)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1664-8021
    URL: Article
    URL: Article
    DOI: 10.3389/fgene.2021.767358
    DOI: 10.3389/fgene.2021.767358.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2606823-0
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  • 5
    Online Resource
    Online Resource
    Impact of heat shock transcription factor 1 on global gene expression profiles in cells which induce either cytoprotective or pro-apoptotic response following hyperthermia
    Springer Science and Business Media LLC ; 2013
    In:  BMC Genomics Vol. 14, No. 1 ( 2013-12)
    Details
    In: BMC Genomics, Springer Science and Business Media LLC, Vol. 14, No. 1 ( 2013-12)
    Abstract: Elevated temperatures induce activation of the heat shock transcription factor 1 (HSF1) which in somatic cells leads to heat shock proteins synthesis and cytoprotection. However, in the male germ cells (spermatocytes) caspase-3 dependent apoptosis is induced upon HSF1 activation and spermatogenic cells are actively eliminated. Results To elucidate a mechanism of such diverse HSF1 activity we carried out genome-wide transcriptional analysis in control and heat-shocked cells, either spermatocytes or hepatocytes. Additionally, to identify direct molecular targets of active HSF1 we used chromatin immunoprecipitation assay (ChIP) combined with promoter microarrays (ChIP on chip). Genes that are differently regulated after HSF1 binding during hyperthermia in both types of cells have been identified. Despite HSF1 binding to promoter sequences in both types of cells, strong up-regulation of Hsps and other genes typically activated by the heat shock was observed only in hepatocytes. In spermatocytes HSF1 binding correlates with transcriptional repression on a large scale. HSF1-bound and negatively regulated genes encode mainly for proteins required for cell division, involved in RNA processing and piRNA biogenesis. Conclusions Observed suppression of the transcription could lead to genomic instability caused by meiotic recombination disturbances, which in turn might induce apoptosis of spermatogenic cells. We propose that HSF1-dependent induction of cell death is caused by the simultaneous repression of many genes required for spermatogenesis, which guarantees the elimination of cells damaged during heat shock. Such activity of HSF1 prevents transmission of damaged genetic material to the next generation.
    Type of Medium: Online Resource
    ISSN: 1471-2164
    URL: Article
    DOI: 10.1186/1471-2164-14-456
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2013
    detail.hit.zdb_id: 2041499-7
    SSG: 12
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  • 6
    Online Resource
    Online Resource
    Table_1.xlsx
    Frontiers Media SA ; 2023
    In:  Frontiers in Oncology Vol. 13 ( 2023-10-2)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 13 ( 2023-10-2)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    URL: Article
    DOI: 10.3389/fonc.2023.1154222
    DOI: 10.3389/fonc.2023.1154222.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2649216-7
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  • 7
    Online Resource
    Online Resource
    Pathological changes or technical artefacts? The problem of the heterogenous databases in COVID-19 CXR image analysis
    Elsevier BV ; 2023
    In:  Computer Methods and Programs in Biomedicine Vol. 240 ( 2023-10), p. 107684-
    Details
    In: Computer Methods and Programs in Biomedicine, Elsevier BV, Vol. 240 ( 2023-10), p. 107684-
    Type of Medium: Online Resource
    ISSN: 0169-2607
    URL: Article
    DOI: 10.1016/j.cmpb.2023.107684
    RVK:
    XA 37224
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 1466281-4
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  • 8
    Online Resource
    Online Resource
    POLCOVID: a multicenter multiclass chest X-ray database (Poland, 2020–2021)
    Springer Science and Business Media LLC ; 2023
    In:  Scientific Data Vol. 10, No. 1 ( 2023-06-02)
    Details
    In: Scientific Data, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2023-06-02)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2052-4463
    URL: Article
    DOI: 10.1038/s41597-023-02229-5
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2775191-0
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  • 9
    Online Resource
    Online Resource
    Ranking metrics in gene set enrichment analysis: do they matter?
    Springer Science and Business Media LLC ; 2017
    In:  BMC Bioinformatics Vol. 18, No. 1 ( 2017-12)
    Details
    In: BMC Bioinformatics, Springer Science and Business Media LLC, Vol. 18, No. 1 ( 2017-12)
    Type of Medium: Online Resource
    ISSN: 1471-2105
    URL: Article
    DOI: 10.1186/s12859-017-1674-0
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
    detail.hit.zdb_id: 2041484-5
    SSG: 12
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  • 10
    Online Resource
    Online Resource
    Activation maps of convolutional neural networks as a tool for brain degeneration tracking in early diagnosis of dementia in Parkinson’s disease based on magnetic resonance imaging
    Springer Science and Business Media LLC ; 2023
    In:  Signal, Image and Video Processing Vol. 17, No. 8 ( 2023-11), p. 4115-4121
    Details
    In: Signal, Image and Video Processing, Springer Science and Business Media LLC, Vol. 17, No. 8 ( 2023-11), p. 4115-4121
    Abstract: Identification of Parkinson’s disease (PD) patients at risk for development of dementia is crucial for early intervention. However, diagnosing dementia in PD patients requires the use of a time-consuming and complex battery of psychological tests performed by an experienced psychologist. The study aims to prove the usefulness of convolutional neural networks for the identification of brain areas related to the progress of cognitive impairment by using standard magnetic resonance imaging (MRI) sequences. T1 & T2 sequences of 18 patients were used in the pilot study. Activation maps were generated, and the brain regions most involved in the classification process were identified, showing areas potentially significant in the diagnosis of cognitive impairment severity. The cerebellum was proven significant for distinguishing the above-mentioned classes in relative cerebellum volume (ANOVA p value = 0.0038 with large effect size $$\eta ^{2}$$ η 2 = 0.5254) and folding ( p value = 0.0031, $$\eta ^{2}$$ η 2 = 0.5357), which is consistent with reports by other authors. Our analysis demonstrates that convolutional neural networks combined with a proper image preprocessing pipeline could be used for feature extraction in MRI sequences and can successfully support the identification of disease-specific abnormalities of the brain image.
    Type of Medium: Online Resource
    ISSN: 1863-1703 , 1863-1711
    URL: Article
    DOI: 10.1007/s11760-023-02643-7
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2391619-9
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