SRD5A3-CDG: 3D structure modeling, clinical spectrum, and computer-based dysmorphic facial recognition.

Pathogenic variants in Steroid 5 alpha reductase type 3 (SRD5A3) cause rare inherited congenital disorder of glycosylation known as SRD5A3-CDG (MIM# 612379). To date, 43 affected individuals have been reported. Despite the development of various dysmorphic features in significant number of patients, facial recognition entity has not yet been established for SRD5A3-CDG. Herein, we reported a novel SRD5A3 missense pathogenic variant c.460 T > C p.(Ser154Pro). The 3D structural modeling of the SRD5A3 protein revealed additional transmembrane α-helices and predicted that the p.(Ser154Pro) variant is located in a potential active site and is capable of reducing its catalytic efficiency. Based on phenotypes of our patients and all published SRD5A3-CDG cases, we identified the most common clinical features as well as some recurrent dysmorphic features such as arched eyebrows, wide eyes, shallow nasal bridge, short nose, and large mouth. Based on facial digital 2D images, we successfully designed and validated a SRD5A3-CDG computer based dysmorphic facial analysis, which achieved 92.5% accuracy. The current work integrates genotypic, 3D structural modeling and phenotypic characteristics of CDG-SRD5A3 cases with the successful development of computer tool for accurate facial recognition of CDG-SRD5A3 complex cases to assist in the diagnosis of this particular disorder globally.

Deep Content Information Retrieval for COVID-19 Detection from Chromatic CT Scans.

In this paper, we investigate the role of the chromatic information in CT scans in COVID-19 detection and we aim to confirm the inclusion of the artificial intelligence findings in assisting COVID-19 diagnosis. This paper proposes a freezing-based convolutional neural network learning using a morphological transformation of CT images to classify COVID-19 cohorts to help in prognostication pneumonia disease monitoring. The experiments made on the collected CT images from previous works have proven to be a powerful aid to recognize the lesions in CT images which works at comprehensively greater accuracy and speed. The proposed CNN architecture has reflected the viral proliferation in infected patients and archives an accuracy of 87.56% with an improvement by 3% compared to the baseline method on the available database of CT images.

Oxidative stress markers-driven prognostic model to predict post-discharge mortality in heart failure with reduced ejection fraction.

Background: Current predictive models based on biomarkers reflective of different pathways of heart failure with reduced ejection fraction (HFrEF) pathogenesis constitute a useful tool for predicting death risk among HFrEF patients. The purpose of the study was to develop a new predictive model for post-discharge mortality risk among HFrEF patients, based on a combination of clinical patients' characteristics, N-terminal pro-B-type Natriuretic peptide (NT-proBNP) and oxidative stress markers as a potentially valuable tool for routine clinical practice. Methods: 116 patients with stable HFrEF were recruited in a prospective single-center study. Plasma levels of NT-proBNP and oxidative stress markers [superoxide dismutase (SOD), glutathione peroxidase (GPX), uric acid (UA), total bilirubin (TB), gamma-glutamyl transferase (GGT) and total antioxidant capacity (TAC)] were measured in the stable predischarge condition. Generalized linear model (GLM), random forest and extreme gradient boosting models were developed to predict post-discharge mortality risk using clinical and laboratory data. Through comprehensive evaluation, the most performant model was selected. Results: During a median follow-up of 525 days (7-930), 33 (28%) patients died. Among the three created models, the GLM presented the best performance for post-discharge death prediction in HFrEF. The predictors included in the GLM model were age, female sex, beta blockers, NT-proBNP, left ventricular ejection fraction (LVEF), TAC levels, admission systolic blood pressure (SBP), angiotensin-converting enzyme inhibitors/angiotensin receptor II blockers (ACEI/ARBs) and UA levels. Our model had a good discriminatory power for post-discharge mortality [The area under the curve (AUC) = 74.5%]. Based on the retained model, an online calculator was developed to allow the identification of patients with heightened post-discharge death risk. Conclusion: In conclusion, we created a new and simple tool that may allow the identification of patients at heightened post-discharge mortality risk and could assist the treatment decision-making.