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

Investigation of molecular heterogeneity provides insights about tumor origin and metabolomics. Increasing amount of data gathered makes manual analyses infeasible. Automated unsupervised learning approaches are exercised for this purpose. However, this kind of analysis requires a lot of experience with setting its hyperparameters and usually an upfront knowledge about the number of expected substructures. Moreover, numerous measured molecules require additional step of feature engineering to provide valuable results. In this work we propose DiviK: a scalable auto-tuning algorithm for segmentation of high-dimensional datasets, and a method to assess the quality of the unsupervised analysis. DiviK is validated on two separate high-throughput datasets acquired by Mass Spectrometry Imaging in 2D and 3D. Proposed algorithm could be one of the default choices to consider during initial exploration of Mass Spectrometry Imaging data. With comparable clustering quality, it brings the possibility of focusing on different levels of dataset nuance, while requires no number of expected structures specified upfront. Finally, due to its simplicity, DiviK is easily generalizable to even more flexible framework, with other clustering algorithm used instead of k-means. Generic implementation is freely available under Apache 2.0 license at https://github.com/gmrukwa/divik.Comment: 8 pages, 7 figure

Effects of depression and anxiety on asthma-related quality of life

INTRODUCTION: Asthma is the most prevalent chronic disease in adults. It affects their quality of life. Studies confirm that depression and anxiety occurs in asthma patients. MATERIAL AND METHODS: The study involved 96 patients with asthma divided into two groups: patients with controlled (n = 33) and uncontrolled asthma (n = 63). The analysis of asthma control was performed on the basis of the ACT (Asthma Control Test) results. The study used SF-36 (Short Form 36) questionnaire and HADS (Hospital and Depression Scale) Scale. RESULTS: An analysis of the correlations between QoL (Quality of Life) and the level of depression revealed a decrease in QoL scores in MCS (Mental Component Score) domain in the group with controlled asthma (71.8 — patients without depression, 53.4 — patients with probable depression, and 51.4 — patients with depression; p = 0.032). A similar analysis of the correlations between QoL and the level of anxiety in this group of patients proved no correlations in PCS (Physical Component Score) and MCS domains. In the group of patients with uncontrolled asthma, anxiety and depression correlated negatively with the QoL in PCS and MCS domains. Anxiety and depression are found in asthma patients, with higher severity observed in patients with uncontrolled asthma. Female gender, the level of asthma control, asthma severity, smoking, as well as diagnoses of anxiety and depression are predictors of a significantly lower QoL in asthma. CONCLUSIONS: Anxiety and depression are found in asthma patients, with higher severity observed in patients with uncontrolled asthma. Female gender, the level of asthma control, asthma severity, smoking, as well as diagnoses of anxiety and depression are predictors of a significantly lower quality of life in asthma.INTRODUCTION: Asthma is the most prevalent chronic disease in adults. It affects their quality of life. Studies confirm that depression and anxiety occurs in asthma patients. MATERIAL AND METHODS: The study involved 96 patients with asthma divided into two groups: patients with controlled (n = 33) and uncontrolled asthma (n = 63). The analysis of asthma control was performed on the basis of the ACT (Asthma Control Test) results. The study used SF-36 (Short Form 36) questionnaire and HADS (Hospital and Depression Scale) Scale. RESULTS: An analysis of the correlations between QoL (Quality of Life) and the level of depression revealed a decrease in QoL scores in MCS (Mental Component Score) domain in the group with controlled asthma (71.8 — patients without depression, 53.4 — patients with probable depression, and 51.4 — patients with depression; p = 0.032). A similar analysis of the correlations between QoL and the level of anxiety in this group of patients proved no correlations in PCS (Physical Component Score) and MCS domains. In the group of patients with uncontrolled asthma, anxiety and depression correlated negatively with the QoL in PCS and MCS domains. Anxiety and depression are found in asthma patients, with higher severity observed in patients with uncontrolled asthma. Female gender, the level of asthma control, asthma severity, smoking, as well as diagnoses of anxiety and depression are predictors of a significantly lower QoL in asthma. CONCLUSIONS: Anxiety and depression are found in asthma patients, with higher severity observed in patients with uncontrolled asthma. Female gender, the level of asthma control, asthma severity, smoking, as well as diagnoses of anxiety and depression are predictors of a significantly lower quality of life in asthma

Classification supporting COVID-19 diagnostics based on patient survey data

Distinguishing COVID-19 from other flu-like illnesses can be difficult due to ambiguous symptoms and still an initial experience of doctors. Whereas, it is crucial to filter out those sick patients who do not need to be tested for SARS-CoV-2 infection, especially in the event of the overwhelming increase in disease. As a part of the presented research, logistic regression and XGBoost classifiers, that allow for effective screening of patients for COVID-19, were generated. Each of the methods was tuned to achieve an assumed acceptable threshold of negative predictive values during classification. Additionally, an explanation of the obtained classification models was presented. The explanation enables the users to understand what was the basis of the decision made by the model. The obtained classification models provided the basis for the DECODE service (decode.polsl.pl), which can serve as support in screening patients with COVID-19 disease. 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 3,000 examples is based on questionnaires collected at a hospital in Poland.Comment: 39 pages, 5 figure

Seeking for Genetic Signature of Radiosensitivity- Methods for Data Analysis

Abstract. The aim of the study was to develop a data analysis strategy capable of discovering the genetic background of radiosensitivity. Radiosensitivity is the relative predisposition of cells, tissues, organs or organisms to the harmful effect of radiation. Effects of radiation include the mutation of DNA . Identification of polymorphisms and genes responsible for an organism's radiosensitivity increases the knowledge about the cell cycle and the mechanism of radiosensitivity, possibly providing the researchers with a better understanding of the process of carcinogenesis. To obtain this information, mathematical modelling and data mining methods were used

Mass spectrometry-based analysis of therapy-related changes in serum proteome patterns of patients with early-stage breast cancer

<p>Abstract</p> <p>Background</p> <p>The proteomics approach termed proteome pattern analysis has been shown previously to have potential in the detection and classification of breast cancer. Here we aimed to identify changes in serum proteome patterns related to therapy of breast cancer patients.</p> <p>Methods</p> <p>Blood samples were collected before the start of therapy, after the surgical resection of tumors and one year after the end of therapy in a group of 70 patients diagnosed at early stages of the disease. Patients were treated with surgery either independently (26) or in combination with neoadjuvant chemotherapy (5) or adjuvant radio/chemotherapy (39). The low-molecular-weight fraction of serum proteome was examined using MALDI-ToF mass spectrometry, and then changes in intensities of peptide ions registered in a mass range between 2,000 and 14,000 Da were identified and correlated with clinical data.</p> <p>Results</p> <p>We found that surgical resection of tumors did not have an immediate effect on the mass profiles of the serum proteome. On the other hand, significant long-term effects were observed in serum proteome patterns one year after the end of basic treatment (we found that about 20 peptides exhibited significant changes in their abundances). Moreover, the significant differences were found primarily in the subgroup of patients treated with adjuvant therapy, but not in the subgroup subjected only to surgery. This suggests that the observed changes reflect overall responses of the patients to the toxic effects of adjuvant radio/chemotherapy. In line with this hypothesis we detected two serum peptides (registered m/z values 2,184 and 5,403 Da) whose changes correlated significantly with the type of treatment employed (their abundances decreased after adjuvant therapy, but increased in patients treated only with surgery). On the other hand, no significant correlation was found between changes in the abundance of any spectral component or clinical features of patients, including staging and grading of tumors.</p> <p>Conclusions</p> <p>The study establishes a high potential of MALDI-ToF-based analyses for the detection of dynamic changes in the serum proteome related to therapy of breast cancer patients, which revealed the potential applicability of serum proteome patterns analyses in monitoring the toxicity of therapy.</p

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.

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

Background/objectiveThis 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).MethodsAfter 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 &lt; 2), and PR at least 2 years (PR ≥ 2).ResultsPR 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 &lt; 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 &lt; 0.001) and remained constant afterward. In the PR &lt;2 group, the highest increase in BMI Z-score occurred after 4 years (p &lt; 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).ConclusionDuring 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

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.Peer reviewe

Establishing propositional truth-value in counterfactual and real-world contexts during sentence comprehension: Differential sensitivity of the left and right inferior frontal gyri

What makes a proposition true or false has traditionally played an essential role in philosophical and linguistic theories of meaning. A comprehensive neurobiological theory of language must ultimately be able to explain the combined contributions of real-world truth-value and discourse context to sentence meaning. This fMRI study investigated the neural circuits that are sensitive to the propositional truth-value of sentences about counterfactual worlds, aiming to reveal differential hemispheric sensitivity of the inferior prefrontal gyri to counterfactual truth-value and real-world truth-value. Participants read true or false counterfactual conditional sentences (“If N.A.S.A. had not developed its Apollo Project, the first country to land on the moon would be Russia/America”) and real-world sentences (“Because N.A.S.A. developed its Apollo Project, the first country to land on the moon has been America/Russia”) that were matched on contextual constraint and truth-value. ROI analyses showed that whereas the left BA 47 showed similar activity increases to counterfactual false sentences and to real-world false sentences (compared to true sentences), the right BA 47 showed a larger increase for counterfactual false sentences. Moreover, whole-brain analyses revealed a distributed neural circuit for dealing with propositional truth-value. These results constitute the first evidence for hemispheric differences in processing counterfactual truth-value and real-world truth-value, and point toward additional right hemisphere involvement in counterfactual comprehension