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Cellular DNA is constantly exposed to endogenous or exogenous factors that can induce lesions. Several types of lesions have been described that can result from UV/ionizing irradiations, oxidative stress, or free radicals, among others. In order to overcome the deleterious effects of such damages, i.e., mutagenicity or cytotoxicity, cells possess a highly complex DNA repair machinery, involving repair enzymes targeting specific types of lesions through dedicated cellular pathways. In addition, DNA is highly compacted in the nucleus, the first level of compaction consisting of ~ 147 DNA base pairs wrapped around a core of histones, the so-called nucleosome core particle. In this complex environment, the DNA structure is highly constrained, and fine-tuned mechanisms involving remodeling processes are required to expose the DNA to repair enzymes and to facilitate the damage removal. However, these nucleosome-specific mechanisms remain poorly understood, and computational methods emerged only recently as powerful tools to investigate DNA damages in such complex systems as the nucleosome. In this mini-review, we summarize the latest advances brought out by computational approaches in the field, opening new exciting perspectives for the study of DNA damage and repair in the nucleosome context. [ABSTRACT FROM AUTHOR]

Automated diagnosis of complex breast ultrasound images using deep learning has important clinical implications. In this study, we propose a novel breast cancer diagnosis framework, the CAS framework, based on lesion region to effectively recognize benign and malignant tumors. Firstly, train the segmentation network CRA-ENet on a public dataset with masks and save the trained weights. Then, the weights are used to segment clinical data without masks to obtain masks. Next, cover the masks on the corresponding original images to obtain ultrasound images that only include the lesion region. Finally, the SA-Net is employed to recognize benign and malignant tumors in images. Both the CRA-ENet and SA-Net are novel models explicitly designed for this study. The CRA-ENet is a network derived from ENet through pruning, convolutional replacement, and the incorporation of a hybrid attention mechanism, which can accurately segment breast tumor boundaries, allowing precise localization of lesion regions. The SA-Net is an innovative multi-branch parallel lightweight network that can enable effective artifact suppression and extraction of lesion-relevant features, significantly improving classification accuracy. In experimental, to validate the performance and efficiency of the proposed framework on clinical data provided by Yunnan Cancer Hospital based on the public dataset BUSI. Compared with simple classification utilizing only the classification network, the proposed approach improved accuracy, precision, and F1 score by 4.01%, 3.98%, and 4.00%, respectively, demonstrating the advantages of the framework.

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Objective: The endoscopic findings associated with Helicobacter pylori –naïve status, current infection or past infection are an area of ongoing interest. Previous studies have investigated parameters with a potential diagnostic value. The aim of this study was to perform meta-analysis of the available literature to validate the diagnostic accuracy of mucosal features proposed in the Kyoto classification. Data sources: The databases of MEDLINE and Embase, clinicalTrials.gov and the Cochrane Library were systematically searched for relevant studies from October 1999 to October 2019. Methods: A bivariate random effects model was used to produce pooled diagnostic accuracy calculations for each of the studied endoscopic findings. Diagnostic odds ratios and sensitivity and specificity characteristics were calculated to identify significant predictors of H pylori status. Results: Meta-analysis included 4380 patients in 15 studies. The most significant predictor of an H pylori -naïve status was a regular arrangement of collecting venules (diagnostic odds ratio 55.0, sensitivity 78.3%, specificity 93.8%). Predictors of active H pylori infection were mucosal oedema (18.1, 63.7%, 91.1%) and diffuse redness (14.4, 66.5%, 89.0%). Map-like redness had high specificity for previous H pylori eradication (99.0%), but poor specificity (13.0%). Conclusion: The regular arrangement of collecting venules, mucosal oedema, diffuse redness and map-like redness are important endoscopic findings for determining H pylori status. This meta-analysis provides a tentative basis for developing future endoscopic classification systems. [ABSTRACT FROM AUTHOR]

Abstract Eukaryotic transcription-coupled nucleotide excision repair (TC-NER) is a pathway that removes DNA lesions capable of blocking RNA polymerase II (Pol II) transcription from the template strand. This process is initiated by lesion-arrested Pol II and the recruitment of Cockayne Syndrome B protein (CSB). In this review, we will focus on the lesion recognition steps of eukaryotic TC-NER and summarize the recent research progress toward understanding the structural basis of Pol II-mediated lesion recognition and Pol II-CSB interactions. We will discuss the roles of CSB in both TC-NER initiation and transcription elongation. Finally, we propose an updated model of tripartite lesion recognition and verification for TC-NER in which CSB ensures Pol II-mediated recognition of DNA lesions for TC-NER. [ABSTRACT FROM AUTHOR]

Automated diagnosis of complex breast ultrasound images using deep learning has important clinical implications. In this study, we propose a novel breast cancer diagnosis framework, the CAS framework, based on lesion region to effectively recognize benign and malignant tumors. Firstly, train the segmentation network CRA-ENet on a public dataset with masks and save the trained weights. Then, the weights are used to segment clinical data without masks to obtain masks. Next, cover the masks on the corresponding original images to obtain ultrasound images that only include the lesion region. Finally, the SA-Net is employed to recognize benign and malignant tumors in images. Both the CRA-ENet and SA-Net are novel models explicitly designed for this study. The CRA-ENet is a network derived from ENet through pruning, convolutional replacement, and the incorporation of a hybrid attention mechanism, which can accurately segment breast tumor boundaries, allowing precise localization of lesion regions. The SA-Net is an innovative multi-branch parallel lightweight network that can enable effective artifact suppression and extraction of lesion-relevant features, significantly improving classification accuracy. In experimental, to validate the performance and efficiency of the proposed framework on clinical data provided by Yunnan Cancer Hospital based on the public dataset BUSI. Compared with simple classification utilizing only the classification network, the proposed approach improved accuracy, precision, and F1 score by 4.01%, 3.98%, and 4.00%, respectively, demonstrating the advantages of the framework. [ABSTRACT FROM AUTHOR]

Highlights: [•] High-throughput fluorescence anisotropy assay used to measure affinity of human XPC for damaged DNA. [•] Binding affinity tested for eight DNA lesions contained in a common DNA sequence. [•] XPC binds all damaged DNA with essentially the same affinity. [•] XPC efficiently recognizes oxidative lesions repaired by BER pathway. [•] XPC may coordinate recognition of damaged DNA for multiple repair pathways. [Copyright &y& Elsevier]

There are no validated training/accreditation guidelines for capsule endoscopy. We assessed the utility of a structured training program on two experienced gastroenterologists and one endoscopy nurse. Validated studies were standardized for difficulty in blocks of three. Trainees completed a standardized data sheet for each study reported (12 studies for the physicians, 22 studies for the nurse). After each block the trainer graded performance and highlighted learning points. Statistical analysis was performed. Physician trainees accurately identified landmarks throughout, while the nurse made errors even at the end of training. Improvement in lesion detection and diagnostic accuracy improved in the nurse, but in only one of the physician trainees, highlighting the variability in learning curves. Overall performance improved in all trainees but was most marked for the nurse trainee (correlation coefficient 0.41, P = 0.06). Improvements in lesion recognition and diagnosis can be demonstrated in senior trainees and nurses following a structured training program; however, there is considerable variability. [ABSTRACT FROM AUTHOR]