Artificial intelligence and database for NGS-based diagnosis in rare disease

Rare diseases (RDs) are rare complex genetic diseases affecting a conservative estimate of 300 million people worldwide. Recent Next-Generation Sequencing (NGS) studies are unraveling the underlying genetic heterogeneity of this group of diseases. NGS-based methods used in RDs studies have improved the diagnosis and management of RDs. Concomitantly, a suite of bioinformatics tools has been developed to sort through big data generated by NGS to understand RDs better. However, there are concerns regarding the lack of consistency among different methods, primarily linked to factors such as the lack of uniformity in input and output formats, the absence of a standardized measure for predictive accuracy, and the regularity of updates to the annotation database. Today, artificial intelligence (AI), particularly deep learning, is widely used in a variety of biological contexts, changing the healthcare system. AI has demonstrated promising capabilities in boosting variant calling precision, refining variant prediction, and enhancing the user-friendliness of electronic health record (EHR) systems in NGS-based diagnostics. This paper reviews the state of the art of AI in NGS-based genetics, and its future directions and challenges. It also compare several rare disease databases

Detection and characterization of defects in directed energy deposited multi-material components using full waveform inversion and reverse time migration

Virtual and Physical Prototyping1741047-105

Detection and characterisation of defects in directed energy deposited multi-material components using full waveform inversion and reverse time migration

Directed energy deposition (DED) is capable in producing complex or high-value components with good mechanical properties. Despite these potential advantages, the quality and integrity of multi-material DED parts, remains a challenging issue that limits its wide applications. Material porosity in multi-material components is detrimental since it may lead to premature structural failure. This paper proposes a two-stage ultrasonic method to characterise the internal structure to enhance the understanding of the process parameters on material porosity. In this method, the low-frequency model building aims at reconstructing background structure and the high-frequency imaging targets at small defects. The first stage is based on the gradient sampling full-waveform inversion for the estimation of the velocity model, which is then used as the initial model for the reverse time migration for reflectivity. The experimental results show that accurate reconstructions of the interface between two materials and defects in multi-material DED components can be achieved

Malaysia Stroke Council guide on acute stroke care service during COVID-19 pandemic

On the 18th of March 2020, the Malaysia government declared a movement control order (MCO) due to the unprecedented COVID-19 pandemic. Although the majority of patients presented with respiratory-related symptoms, COVID-19 patients may present atypically with neurological manifestations and may even have an increased risk of stroke. The Malaysia Stroke Council is concerned regarding the level of care given to stroke patients during this pandemic. During the recent National Stroke Workflow Steering Committee meeting, a guide was made based on the currently available evidences to assist Malaysian physicians providing acute stroke care in the hospital setting in order to provide the best stroke care while maintaining their own safety. The guide comprises of prehospital stroke awareness, hyperacute stroke care, stroke care unit and intensive care unit admission, post-stroke rehabilitation and secondary prevention practice. We urge continuous initiative to provide the best stroke care possible and ensure adequate safety for both patients and the stroke care team