A new approach to the classification of carcinogenicity.
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ABSTRACT:
SUBMITTER: Doe JE
PROVIDER: S-EPMC9325845 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundOne of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fulfill the gaps in data concerned with properties of chemicals affecting the human health. (Q)SAR models are accepted as a suitable source of information. The EU funded CAESAR project aimed to develop models for prediction of 5 endpoints for regulatory purposes. Carcinogenicity is one of the endpoints under consideration.ResultsModels for prediction of carcinogenic potency according to specific requirements of Chemical regulation were developed. The dataset of 805 non-congeneric chemicals extracted from Carcinogenic Potency Database (CPDBAS) was used. Counter Propagation Artificial Neural Network (CP ANN) algorithm was implemented. In the article two alternative models for prediction carcinogenicity are described. The first model employed eight MDL descriptors (model A) and the second one twelve Dragon descriptors (model B). CAESAR's models have been assessed according to the OECD principles for the validation of QSAR. For the model validity we used a wide series of statistical checks. Models A and B yielded accuracy of training set (644 compounds) equal to 91% and 89% correspondingly; the accuracy of the test set (161 compounds) was 73% and 69%, while the specificity was 69% and 61%, respectively. Sensitivity in both cases was equal to 75%. The accuracy of the leave 20% out cross validation for the training set of models A and B was equal to 66% and 62% respectively. To verify if the models perform correctly on new compounds the external validation was carried out. The external test set was composed of 738 compounds. We obtained accuracy of external validation equal to 61.4% and 60.0%, sensitivity 64.0% and 61.8% and specificity equal to 58.9% and 58.4% respectively for models A and B.ConclusionCarcinogenicity is a particularly important endpoint and it is expected that QSAR models will not replace the human experts opinions and conventional methods. However, we believe that combination of several methods will provide useful support to the overall evaluation of carcinogenicity. In present paper models for classification of carcinogenic compounds using MDL and Dragon descriptors were developed. Models could be used to set priorities among chemicals for further testing. The models at the CAESAR site were implemented in java and are publicly accessible.
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Project description:This paper proposes a new quantum-like method for the binary classification applied to classical datasets. Inspired by the quantum Helstrom measurement, this innovative approach has enabled us to define a new classifier, called Helstrom Quantum Centroid (HQC). This binary classifier (inspired by the concept of distinguishability between quantum states) acts on density matrices-called density patterns-that are the quantum encoding of classical patterns of a dataset. In this paper we compare the performance of HQC with respect to twelve standard (linear and non-linear) classifiers over fourteen different datasets. The experimental results show that HQC outperforms the other classifiers when compared to the Balanced Accuracy and other statistical measures. Finally, we show that the performance of our classifier is positively correlated to the increase in the number of "quantum copies" of a pattern and the resulting tensor product thereof.
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Project description:Cadmium is a toxic metal able to enter the cells through channels and transport pathways dedicated to essential ions, leading, among others, to the dysregulation of divalent ions homeostasis. Despite its recognized human carcinogenicity, the mechanisms are still under investigation. A powerful tool for mechanistic studies of carcinogenesis is the Cell Transformation Assay (CTA). We have isolated and characterized by whole genome microarray and bioinformatics analysis of differentially expressed genes (DEGs) cadmium-transformed cells from different foci (F1, F2, and F3) at the end of CTA (6 weeks). The systematic analysis of up- and down-regulated transcripts and the comparison of DEGs in transformed cells evidence different functional targets and the complex picture of cadmium-induced transformation. Only 34 in common DEGs are found in cells from all foci, and among these, only 4 genes are jointly up-regulated (Ccl2, Ccl5, IL6 and Spp1), all responsible for cytokines/chemokines coding. Most in common DEGs are down-regulated, suggesting that the switching-off of specific functions plays a major role in this process. In addition, the comparison of dysregulated pathways immediately after cadmium treatment with those in transformed cells provides a valuable means to the comprehension of the overall process.
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Project description:Evidence showing a strengthening of intense cold surge event (CSE) in East Asia, e.g. CSE of Jan 2016 and Jan-Feb 2008, is focusing attention towards the science of CSE onset prediction. Predicting the onset of such strong CSEs remains elusive as the extent of these surges varies over spatial and temporal scales. Changes in radiative cooling over Siberia in winter as potentially affected by changes in the Arctic are further expected to influence CSE occurrences in East Asia. Moreover, unprecedented and long lasting CSEs in East Asia have a very distinct Jet Stream pattern via their shifts from the climatological mean, influencing the lower troposphere. Here, using modelling framework we propose a new relationship between Jet Stream and Aleutian Low for identifying and characterizing atmospheric process that leads to CSEs in East Asia. Our results reveal new insight into the mechanisms of CSEs occurrences, the absence of which may lead to major constraints on reducing CSE onset prediction error.
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Project description:Since the turn of the century, technological advances have made it possible to obtain the molecular profile of any tissue in a cost-effective manner. Among these advances are sophisticated high-throughput assays that measure the relative abundances of microorganisms, RNA molecules, and metabolites. While these data are most often collected to gain new insights into biological systems, they can also be used as biomarkers to create clinically useful diagnostic classifiers. How best to classify high-dimensional -omics data remains an area of active research. However, few explicitly model the relative nature of these data and instead rely on cumbersome normalizations. This report (i) emphasizes the relative nature of health biomarkers, (ii) discusses the literature surrounding the classification of relative data, and (iii) benchmarks how different transformations perform for regularized logistic regression across multiple biomarker types. We show how an interpretable set of log contrasts, called balances, can prepare data for classification. We propose a simple procedure, called discriminative balance analysis, to select groups of 2 and 3 bacteria that can together discriminate between experimental conditions. Discriminative balance analysis is a fast, accurate, and interpretable alternative to data normalization.IMPORTANCE High-throughput sequencing provides an easy and cost-effective way to measure the relative abundance of bacteria in any environmental or biological sample. When these samples come from humans, the microbiome signatures can act as biomarkers for disease prediction. However, because bacterial abundance is measured as a composition, the data have unique properties that make conventional analyses inappropriate. To overcome this, analysts often use cumbersome normalizations. This article proposes an alternative method that identifies pairs and trios of bacteria whose stoichiometric presence can differentiate between diseased and nondiseased samples. By using interpretable log contrasts called balances, we developed an entirely normalization-free classification procedure that reduces the feature space and improves the interpretability, without sacrificing classifier performance.
| S-EPMC7141889 | biostudies-literature
Project description:We used a multi-round, two-party exchange game in which a healthy subject played a subject diagnosed with a DSM-IV (Diagnostic and Statistics Manual-IV) disorder, and applied a Bayesian clustering approach to the behavior exhibited by the healthy subject. The goal was to characterize quantitatively the style of play elicited in the healthy subject (the proposer) by their DSM-diagnosed partner (the responder). The approach exploits the dynamics of the behavior elicited in the healthy proposer as a biosensor for cognitive features that characterize the psychopathology group at the other side of the interaction. Using a large cohort of subjects (n = 574), we found statistically significant clustering of proposers' behavior overlapping with a range of DSM-IV disorders including autism spectrum disorder, borderline personality disorder, attention deficit hyperactivity disorder, and major depressive disorder. To further validate these results, we developed a computer agent to replace the human subject in the proposer role (the biosensor) and show that it can also detect these same four DSM-defined disorders. These results suggest that the highly developed social sensitivities that humans bring to a two-party social exchange can be exploited and automated to detect important psychopathologies, using an interpersonal behavioral probe not directly related to the defining diagnostic criteria.
| S-EPMC2958801 | biostudies-literature
Project description:Members of the Alphapapillomavirus genus are the causative agent for virtually all cases of cervical cancer. However, strains (commonly referred to as types) within this genus span the entire range of pathogenicity from highly carcinogenic (e.g., HPV16, odds ratio = 281.9, responsible for 50% of all cervical cancers), moderately carcinogenic (e.g., HPV31) to not carcinogenic (e.g., HPV71). The persistent expression of the viral oncoproteins (E6 and E7) from HPV16 has been shown to be necessary and sufficient to transform primary human keratinocytes in vitro. A plethora of functions have been described for both oncoproteins, and through functional comparisons between HPV16 and HPV6, a subset of these functions have been suggested to be oncogenic. However, extrapolating functional differences from these comparisons is unlikely to tease apart the fine details. In this review, we argue that a thorough understanding of the molecular mechanisms differentiating oncogenic from nononcogenic types should be obtained by performing functional assays in an evolutionary and epidemiological framework. We continue by interpreting some recent results using this paradigm and end by suggesting directions for future inquiries.
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Project description:Schizophrenia (SZ) and autism spectrum disorder (ASD) sharing overlapping symptoms have a long history of diagnostic confusion. It is unclear what their differences at a brain level are. Here, we propose a multimodality fusion classification approach to investigate their divergence in brain function and structure. Using brain functional network connectivity (FNC) calculated from resting-state fMRI data and gray matter volume (GMV) estimated from sMRI data, we classify the two disorders using the main data (335 SZ and 380 ASD patients) via an unbiased 10-fold cross-validation pipeline, and also validate the classification generalization ability on an independent cohort (120 SZ and 349 ASD patients). The classification accuracy reached up to 83.08% for the testing data and 72.10% for the independent data, significantly better than the results from using the single-modality features. The discriminative FNCs that were automatically selected primarily involved the sub-cortical, default mode, and visual domains. Interestingly, all discriminative FNCs relating to the default mode network showed an intermediate strength in healthy controls (HCs) between SZ and ASD patients. Their GMV differences were mainly driven by the frontal gyrus, temporal gyrus, and insula. Regarding these regions, the mean GMV of HC fell intermediate between that of SZ and ASD, and ASD showed the highest GMV. The middle frontal gyrus was associated with both functional and structural differences. In summary, our work reveals the unique neuroimaging characteristics of SZ and ASD that can achieve high and generalizable classification accuracy, supporting their potential as disorder-specific neural substrates of the two entwined disorders.
| S-EPMC9294304 | biostudies-literature
Project description:Since the introduction of glyphosate-tolerant genetically-modified plants, the global use of glyphosate has increased dramatically making it the most widely used pesticide on the planet. There is considerable controversy concerning the carcinogenicity of glyphosate with scientists and regulatory authorities involved in the review of glyphosate having markedly different opinions. One key aspect of these opinions is the degree to which glyphosate causes cancer in laboratory animals after lifetime exposure. In this review, twenty-one chronic exposure animal carcinogenicity studies of glyphosate are identified from regulatory documents and reviews; 13 studies are of sufficient quality and detail to be reanalyzed in this review using trend tests, historical control tests and pooled analyses. The analyses identify 37 significant tumor findings in these studies and demonstrate consistency across studies in the same sex/species/strain for many of these tumors. Considering analyses of the individual studies, the consistency of the data across studies, the pooled analyses, the historical control data, non-neoplastic lesions, mechanistic evidence and the associated scientific literature, the tumor increases seen in this review are categorized as to the strength of the evidence that glyphosate causes these cancers. The strongest evidence shows that glyphosate causes hemangiosarcomas, kidney tumors and malignant lymphomas in male CD-1 mice, hemangiomas and malignant lymphomas in female CD-1 mice, hemangiomas in female Swiss albino mice, kidney adenomas, liver adenomas, skin keratoacanthomas and skin basal cell tumors in male Sprague-Dawley rats, adrenal cortical carcinomas in female Sprague-Dawley rats and hepatocellular adenomas and skin keratocanthomas in male Wistar rats.
| S-EPMC7014589 | biostudies-literature
Project description:Aims: We describe a new aortic arch dissection (AcD) classification, which we have called the Fuwai classification. We then compare the clinical characteristics and long-term prognoses of different classifications. Methods: All AcD patients who underwent surgical procedures at Fuwai Hospital from 2010 to 2015 were included in the study. AcD procedures are divided into three types: Fuwai type Cp, Ct, and Cd. Type Cp is defined as the innominate artery or combined with the left carotid artery involved. Type Cd is defined as the left subclavian artery or combined with the left carotid artery involved. All other AcD surgeries are defined as type Ct. The Chi-square test was adopted for the pairwise comparison among the three types. Kaplan-Meier was used for the analysis of long-term survival and survival free of reoperation. Results: In total, 1,063 AcD patients were enrolled from 2010 to 2015: 54 patients were type Cp, 832 were type Ct, and 177 were type Cd. The highest operation proportion of Cp, Ct and Cd were partial arch replacement, total arch replacement, and TEVAR. The surgical mortality in type Ct was higher compared to type Cd (Ct vs. Cd = 9.38 vs. 1.69%, p < 0.01) and type Cp (Ct vs. Cp = 9.38 vs. 1.85%, p = 0.06). There was no difference in surgical mortality of type Cp and Cd (p = 0.93). There were no significant differences in the long-term survival rates (p = 0.38) and free of aorta-related re-operations (p = 0.19). Conclusion: The Fuwai classification is used to distinguish different AcDs. Different AcDs have different surgical mortality and use different operation methods, but they have similar long-term results.
| S-EPMC8476803 | biostudies-literature