A simplified cluster model and a tool adapted for collaborative labeling of lung cancer CT scans.

BACKGROUND AND OBJECTIVE: Lung cancer is the most common type of cancer with a high mortality rate. Early detection using medical imaging is critically important for the long-term survival of the patients. Computer-aided diagnosis (CAD) tools can potentially reduce the number of incorrect interpretations of medical image data by radiologists. Datasets with adequate sample size, annotation, and truth are the dominant factors in developing and training effective CAD algorithms. The objective of this study was to produce a practical approach and a tool for the creation of medical image datasets. METHODS: The proposed model uses the modified maximum transverse diameter approach to mark a putative lung nodule. The modification involves the possibility to use a set of overlapping spheres of appropriate size to approximate the shape of the nodule. The algorithm embedded in the model also groups the marks made by different readers for the same lesion. We used the data of 536 randomly selected patients of Moscow outpatient clinics to create a dataset of standard-dose chest computed tomography (CT) scans utilizing the double-reading approach with arbitration. Six volunteer radiologists independently produced a report for each scan using the proposed model with the main focus on the detection of lesions with sizes ranging from 3 to 30 mm. After this, an arbitrator reviewed their marks and annotations. RESULTS: The maximum transverse diameter approach outperformed the alternative methods (3D box, ellipsoid, and complete outline construction) in a study of 10,000 computer-generated tumor models of different shapes in terms of accuracy and speed of nodule shape approximation. The markup and annotation of the CTLungCa-500 dataset revealed 72 studies containing no lung nodules. The remaining 464 CT scans contained 3151 lesions marked by at least one radiologist: 56%, 14%, and 29% of the lesions were malignant, benign, and non-nodular, respectively. 2887 lesions have the target size of 3-30 mm. Only 70 nodules were uniformly identified by all the six readers. An increase in the number of independent readers providing CT scans interpretations led to an accuracy increase associated with a decrease in agreement. The dataset markup process took three working weeks. CONCLUSIONS: The developed cluster model simplifies the collaborative and crowdsourced creation of image repositories and makes it time-efficient. Our proof-of-concept dataset provides a valuable source of annotated medical imaging data for training CAD algorithms aimed at early detection of lung nodules. The tool and the dataset are publicly available at https://github.com/Center-of-Diagnostics-and-Telemedicine/FAnTom.git and https://mosmed.ai/en/datasets/ct_lungcancer_500/, respectively.

Advances in the Application of Modified Nucleotides in SELEX Technology.

Aptamers are widely used as molecular recognition elements for detecting and blocking functional biological molecules. Since the common "alphabet" of DNA and RNA consists of only four letters, the chemical diversity of aptamers is less than the diversity of protein recognition elements built of 20 amino acids. Chemical modification of nucleotides enlarges the potential of DNA/RNA aptamers. This review describes the latest achievements in a variety of approaches to aptamers selection with an extended genetic alphabet.

Comparison of models of thrombin-binding 15-mer DNA aptamer by molecular dynamics simulation.

Two models of 15-mer thrombin-binding DNA aptamer (15TGT) were comparatively analyzed by molecular dynamics simulation using the GROMACS software package. The two original models of 15TGT were obtained by NMR and X-ray analyses. The models significantly differ in the topology of loops and the direction of oligodeoxyribonucleotide chain. The evolution of the two structures in parm99 force fields and parmbsc0 optimized for nucleic acids was analyzed in our adaptation of GROMACS architecture. It is shown that the best system for description of the 15TGT structure is the model obtained by X-ray analysis in the parmbsc0 force field.

Classification of g-quadruplex DNA on the basis of the quadruplex twist angle and planarity of g-quartets.

The present work is devoted to the analysis of the G-quadruplex DNA structure using the bioinformatics method. The interest towards quadruplex DNAs is determined by their involvement in the functioning of telomeres and onco-promoters as well as by the possibility to create on their basis aptamers and nanostructures. Here, we present an algorithm for a general analysis of the polymorphism of the G-quadruplex structure from the data bank PDB using original parameters. 74 structures were grouped according to the following parameters: the number of DNA strands, the number of G-quartets, and the location and orientation of the connecting loops. Two quantitative parameters were used to describe the quadruplex structure: the twist angle between two adjacent quartets (analogous to that for the complementary pair in the duplex DNA) and the quartet planarity (an original parameter). The distribution patterns of these values are specific for each group of quadruplex structures and are dependent upon the type of connecting loops used (diagonal, lateral or propeller). The tetramolecular loopless parallel quadruplex was used as a comparison template. The lateral loops introduce the strongest distortion into the structure of quadruplexes: the values of the twist angles are the lowest and are not typical for the other quadruplex groups. The loops of the diagonal type introduce much weaker deformation into quadruplexes; the structures with propeller loops are characterized by the optimum geometry of G-quartets. Hence, the correlation between the twist angle and the tension in the structure of quadruplex DNA is revealed.