An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups

A Bibliometric Analysis of Distributed Incremental Clustering on Images

Unstructured information is continuously irregular and streaming information from such a sequence is tedious because it lacks labels and accumulates with time. This is possible using Incremental Clustering algorithms that use previously learned information to accommodate new data and avoid retraining. This paper therefore seeks to understand the status of Distributed Incremental Clustering on images with text and numerical values, its limitations, scope, and other details to devise a better algorithm in future. To further enhance the analysis, we have also included methodology, which can be used to perform clustering on images or documents based on its content

ROBUST DETECTION OF TEXT IN NATURAL SCENE IMAGES

Detection of text and identification in natural scene images has applications in computer vision systems such as license plate detection, automatic street sign translation, image retrieval and help for visually challenged people. The text images has complex background, blur, occluded text, different font-styles, and noises in image and variation in illumination. Hence, scene text recognition puts forth challenges in computer vision. Hence, a potent method based on Maximally Stable External Regions (MSER) has been used as described in this paper. Here, the text characters are clustered, separating them from high probable non-text characters with the help of text categorizer. The algorithm is then verified by testing it on images based on the predefined rules

Robust Detection of Text in Natural Scene Images

Detection of text and identification in natural scene images has applications in computer vision systems such as license plate detection, automatic street sign translation, image retrieval and help for visually challenged people. The text images has complex background, blur, occluded text, different font-styles, and noises in image and variation in illumination. Hence, scene text recognition puts forth challenges in computer vision. Hence, a potent method based on Maximally Stable External Regions (MSER) has been used as described in this paper. Here, the text characters are clustered, separating them from high probable non-text characters with the help of text categorizer. The algorithm is then verified by testing it on images based on the predefined rules

COVID-19 vaccine-associated myocarditis: Analysis of the suspected cases reported to the EudraVigilance and a systematic review of the published literature.

Myocarditis secondary to Coronavirus Disease 2019 (COVID-19) vaccination has been reported in the literature. This study aimed to characterize the reported cases of myocarditis after COVID-19 vaccination based on age, gender, doses, and vaccine type from published literature and the EudraVigilance database. We performed an analysis in the EudraVigilance database (until December 18, 2021) and a systematic review of published literature for reported cases of suspected myocarditis and pericarditis (until 30th June 2022) after the COVID-19 vaccination. EudraVigilance database analysis revealed 16,514 reported cases of myocarditis or pericarditis due to the vaccination with COVID-19 vaccines. The cases of myo- or pericarditis were reported predominantly in the age group of 18-64 (n = 12,214), and in males with a male-to-female (M: F) ratio of 1.7:1. The mortality among myocarditis patients was low, with 128 deaths (2 cases per 10.000.000 administered doses) being reported. For the systematic review, 72 studies with 1026 cases of myocarditis due to the vaccination with COVID-19 vaccines were included. The analysis of published cases has revealed that the male gender was primarily affected with myocarditis post-COVID-vaccination. The median (IQR) age of the myocarditis cases was 24.6 [19.5-34.6] years, according to the systematic review of the literature. Myocarditis cases were most frequently published after the vaccination with m-RNA vaccines and after the second vaccination dose. The overall mortality of published cases was low (n = 5). Myocarditis is a rare serious adverse event associated with a COVID-19 vaccination. With early recognition and management, the prognosis of COVID-19 vaccine-induced myocarditis is favorable. [Abstract copyright: © 2023 The Author(s).