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Radiology Radiology

Longitudinal trajectories of pneumonia lesions and lymphocyte counts associated with disease severity among convalescent COVID-19 patients: a group-based multi-trajectory analysis.

In BMC pulmonary medicine ; h5-index 38.0

BACKGROUND : To explore the long-term trajectories considering pneumonia volumes and lymphocyte counts with individual data in COVID-19.

METHODS : A cohort of 257 convalescent COVID-19 patients (131 male and 126 females) were included. Group-based multi-trajectory modelling was applied to identify different trajectories in terms of pneumonia lesion percentage and lymphocyte counts covering the time from onset to post-discharge follow-ups. We studied the basic characteristics and disease severity associated with the trajectories.

RESULTS : We characterised four distinct trajectory subgroups. (1) Group 1 (13.9%), pneumonia increased until a peak lesion percentage of 1.9% (IQR 0.7-4.4) before absorption. The slightly decreased lymphocyte rapidly recovered to the top half of the normal range. (2) Group 2 (44.7%), the peak lesion percentage was 7.2% (IQR 3.2-12.7). The abnormal lymphocyte count restored to normal soon. (3) Group 3 (26.0%), the peak lesion percentage reached 14.2% (IQR 8.5-19.8). The lymphocytes continuously dropped to 0.75 × 109/L after one day post-onset before slowly recovering. (4) Group 4 (15.4%), the peak lesion percentage reached 41.4% (IQR 34.8-47.9), much higher than other groups. Lymphopenia was aggravated until the lymphocytes declined to 0.80 × 109/L on the fourth day and slowly recovered later. Patients in the higher order groups were older and more likely to have hypertension and diabetes (all P values < 0.05), and have more severe disease.

CONCLUSIONS : Our findings provide new insights to understand the heterogeneous natural courses of COVID-19 patients and the associations of distinct trajectories with disease severity, which is essential to improve the early risk assessment, patient monitoring, and follow-up schedule.

Shi Nannan, Huang Chao, Zhang Qi, Shi Chunzi, Liu Fengjun, Song Fengxiang, Hou Qinguo, Shen Jie, Shan Fei, Su Xiaoming, Liu Cheng, Zhang Zhiyong, Shi Lei, Shi Yuxin


COVID-19, Clinical course, Group-based multi-trajectory modelling, Lymphocyte, Pneumonia

General General

A graph convolutional neural network for gene expression data analysis with multiple gene networks.

In Statistics in medicine

Spectral graph convolutional neural networks (GCN) are proposed to incorporate important information contained in graphs such as gene networks. In a standard spectral GCN, there is only one gene network to describe the relationships among genes. However, for genomic applications, due to condition- or tissue-specific gene function and regulation, multiple gene networks may be available; it is unclear how to apply GCNs to disease classification with multiple networks. Besides, which gene networks may provide more effective prior information for a given learning task is unknown a priori and is not straightforward to discover in many cases. A deep multiple graph convolutional neural network is therefore developed here to meet the challenge. The new approach not only computes a feature of a gene as the weighted average of those of itself and its neighbors through spectral GCNs, but also extracts features from gene-specific expression (or other feature) profiles via a feed-forward neural networks (FNN). We also provide two measures, the importance of a given gene and the relative importance score of each gene network, for the genes' and gene networks' contributions, respectively, to the learning task. To evaluate the new method, we conduct real data analyses using several breast cancer and diffuse large B-cell lymphoma datasets and incorporating multiple gene networks obtained from "GIANT 2.0" Compared with the standard FNN, GCN, and random forest, the new method not only yields high classification accuracy but also prioritizes the most important genes confirmed to be highly associated with cancer, strongly suggesting the usefulness of the new method in incorporating multiple gene networks.

Yang Hu, Zhuang Zhong, Pan Wei


Laplacian, deep learning, feed-forward neural network, gene expression data, spectral graph theory

General General

Computational strategies for the discovery of biological functions of health foods, nutraceuticals and cosmeceuticals: a review.

In Molecular diversity

Scientific and consumer interest in healthy foods (also known as functional foods), nutraceuticals and cosmeceuticals has increased in the recent years, leading to an increased presence of these products in the market. However, the regulations across different countries that define the type of claims that may be made, and the degree of evidence required to support these claims, are rather inconsistent. Moreover, there is also controversy on the effectiveness and biological mode of action of many of these products, which should undergo an exhaustive approval process to guarantee the consumer rights. Computational approaches constitute invaluable tools to facilitate the discovery of bioactive molecules and provide biological plausibility on the mode of action of these products. Indeed, methodologies like QSAR, docking or molecular dynamics have been used in drug discovery protocols for decades and can now aid in the discovery of bioactive food components. Thanks to these approaches, it is possible to search for new functions in food constituents, which may be part of our daily diet, and help to prevent disorders like diabetes, hypercholesterolemia or obesity. In the present manuscript, computational studies applied to this field are reviewed to illustrate the potential of these approaches to guide the first screening steps and the mechanistic studies of nutraceutical, cosmeceutical and functional foods.

Carpio Laureano E, Sanz Yolanda, Gozalbes Rafael, Barigye Stephen J


Cosmeceuticals, Docking, Health foods, Machine learning, Molecular dynamics, Nutraceuticals, QSAR

General General

Protocol for improving diffraction quality of leucyl-tRNA synthetase 1 with methylation and post-crystallization soaking and cooling in cryoprotectants.

In STAR protocols

Leucyl-tRNA synthetase 1 (LARS1) synthesizes Leu-tRNALeu for protein synthesis and plays an important role in mTORC1 activation by sensing intracellular leucine concentrations. Here, we describe a protocol for the purification, reductive methylation, binding affinity measurement by microscale thermophoresis, T i value measurement by Tycho, and post-crystallization soaking and cooling in cryoprotectants to improve crystallization of LARS1. Collectively, this allowed us to build the RagD binding domain, which was shown to be a dynamic region of LARS1 refractory to crystallization. For complete details on the use and execution of this protocol, please refer to Kim et al. (2021).

Kim Sulhee, Yoon Ina, Kim Sunghoon, Hwang Kwang Yeon


Protein Biochemistry, Structural Biology, X-ray Crystallography

Internal Medicine Internal Medicine

Timesias: A machine learning pipeline for predicting outcomes from time-series clinical records.

In STAR protocols

The prediction of outcomes is a critical part of the clinical surveillance for hospitalized patients. Here, we present Timesias, a machine learning pipeline which predicts outcomes from real-time sequential clinical data. The strategy implemented in Timesias is the first-place solution in the crowd-sourcing DII (discover, innovate, impact) National Data Science Challenge involving more than 100,000 patients, achieving 0.85 as evaluated by AUROC (area under receiver operator characteristic curve) in predicting the early onset of sepsis status. Timesias is freely available via PyPI and GitHub. For complete details on the use and execution of this protocol, please refer to Guan et al. (2021).

Zhang Hanrui, Yi Daiyao, Guan Yuanfang


Bioinformatics, Clinical Protocol, Health Sciences

General General

Pedestrian attribute recognition using trainable Gabor wavelets.

In Heliyon

Surveillance cameras are everywhere keeping an eye on pedestrians or people as they navigate through the scene. Within this context, our paper addresses the problem of pedestrian attribute recognition (PAR). This problem entails the extraction of different attributes such as age-group, clothing style, accessories, footwear style etc. This is a multi-label problem with a host of challenges even for human observers. As such, the topic has rightly attracted attention recently. In this work, we integrate trainable Gabor wavelet (TGW) layers inside a convolution neural network (CNN). Whereas other researchers have used fixed Gabor filters with the CNN, the proposed layers are learnable and adapt to the dataset for a better recognition. We test our method on publicly available challenging datasets and demonstrate considerable improvements over state of the art approaches.

Junejo Imran N, Ahmed Naveed, Lataifeh Mohammad


Attribute recognition, Computer vision, Deep learning