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2021-01-05 00:00:00 +0000
Cardiology
Angiotensin-converting enzyme 2 (ACE2) and serine protease TMPRSS2 have been implicated in cell entry for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19). The expression of ACE2 and TMPRSS2 in the lung epithelium might have implications for the risk of SARS-CoV-2 infection and severity of COVID-19. We use human genetic variants that proxy angiotensin-converting enzyme (ACE) inhibitor drug effects and cardiovascular risk factors to investigate whether these exposures affect lung ACE2 and TMPRSS2 gene expression and circulating ACE2 levels. We observed no consistent evidence of an association of genetically predicted serum ACE levels with any of our outcomes. There was weak evidence for an association of genetically predicted serum ACE levels with ACE2 gene expression in the Lung eQTL Consortium (p = 0.014), but this finding did not replicate. There was evidence of a positive association of genetic liability to type 2 diabetes mellitus with lung ACE2 gene expression in the Gene-Tissue Expression (GTEx) study (p = 4 × 10-4) and with circulating plasma ACE2 levels in the INTERVAL study (p = 0.03), but not with lung ACE2 expression in the Lung eQTL Consortium study (p = 0.68). There were no associations of genetically proxied liability to the other cardiometabolic traits with any outcome. This study does not provide consistent evidence to support an effect of serum ACE levels (as a proxy for ACE inhibitors) or cardiometabolic risk factors on lung ACE2 and TMPRSS2 expression or plasma ACE2 levels.
2021-01-05 00:00:00 +0000
General
The use of machine learning has grown in popularity in various disciplines. Despite the popularity, the apparent 'black box' nature of such tools continues to be an area of concern. In this article, we attempt to unravel the complexity of this black box by exploring the use of artificial neural networks (ANNs), coupled with graph theory, to model and interpret the spatial distribution of building damage from extreme wind events at a community level. Structural wind damage is a topic that is mostly well understood for how wind pressure translates to extreme loading on a structure, how debris can affect that loading and how specific social characteristics contribute to the overall population vulnerability. While these themes are widely accepted, they have proven difficult to model in a cohesive manner, which has led primarily to physical damage models considering wind loading only as it relates to structural capacity. We take advantage of this modelling difficulty to reflect on two different ANN models for predicting the spatial distribution of structural damage due to wind loading. Through graph theory analysis, we study the internal patterns of the apparent black box of artificial intelligence of the models and show that social parameters are key to predict structural damage.
2021-01-05 00:00:00 +0000
Oncology
Rationale: Platinum-based chemotherapy is one of treatment mainstay for patients with advanced lung squamous cell carcinoma (LUSC) but it is still a "one-size fits all" approach. Here, we aimed to investigate the predictive and monitoring role of circulating cell-free DNA (cfDNA) profiling for the outcome of first-line chemotherapy in patients with advanced LUSC. Methods: Peripheral blood samples of 155 patients from a phase IV trial and 42 cases from an external real-world cohort were prospectively collected. We generated a copy number variations-based classifier via machine learning algorithm to integrate molecular profiling of cfDNA, named RESPONSE SCORE (RS) to predict the treatment outcome. To monitor the treatment efficacy, cfDNA samples collected at different time points were subjected to an ultra-deep sequencing platform. Results: The results showed that patients with high RS showed substantially higher objective response rate than those with low RS in training set (P < 0.001), validation set (P < 0.001) and real-world cohort (P = 0.019). Furthermore, a significant difference was observed in both progression-free survival (training set, P < 0.001; validation set: P < 0.001; real-world cohort: P = 0.019) and overall survival (training set, P < 0.001; validation set: P = 0.037) between high and low RS group. Notably, variant allele frequency (VAF) calculated from an ultra-deep sequencing platform significantly reduced in patients experienced a complete or partial response after 2 cycles of chemotherapy (P < 0.001), while it significantly increased in these of non-responder (P < 0.001). Moreover, VAF undetectable after 2 cycles of chemotherapy was correlated with markedly better objective response rate (P < 0.001) and progression-free survival (P < 0.001) than those with detectable VAF. Conclusions: These findings indicated that the RS, a circulating cfDNA sequencing-based stratification index, could help to guide first-line chemotherapy in advanced LUSC. The change of VAF is valuable to monitor the treatment response.
2021-01-05 00:00:00 +0000
Public Health
Rationale: Long extracellular RNAs (exRNAs) in plasma can be profiled by new sequencing technologies, even with low abundance. However, cancer-related exRNAs and their variations remain understudied. Methods: We investigated different variations (i.e. differential expression, alternative splicing, alternative polyadenylation, and differential editing) in diverse long exRNA species (e.g. long noncoding RNAs and circular RNAs) using 79 plasma exosomal RNA-seq (exoRNA-seq) datasets of multiple cancer types. We then integrated 53 exoRNA-seq datasets and 65 self-profiled cell-free RNA-seq (cfRNA-seq) datasets to identify recurrent variations in liver cancer patients. We further combined TCGA tissue RNA-seq datasets and validated biomarker candidates by RT-qPCR in an individual cohort of more than 100 plasma samples. Finally, we used machine learning models to identify a signature of 3 noncoding RNAs for the detection of liver cancer. Results: We found that different types of RNA variations identified from exoRNA-seq data were enriched in pathways related to tumorigenesis and metastasis, immune, and metabolism, suggesting that cancer signals can be detected from long exRNAs. Subsequently, we identified more than 100 recurrent variations in plasma from liver cancer patients by integrating exoRNA-seq and cfRNA-seq datasets. From these datasets, 5 significantly up-regulated long exRNAs were confirmed by TCGA data and validated by RT-qPCR in an independent cohort. When using machine learning models to combine two of these validated circular and structured RNAs (SNORD3B-1, circ-0080695) with a miRNA (miR-122) as a panel to classify liver cancer patients from healthy donors, the average AUROC of the cross-validation was 89.4%. The selected 3-RNA panel successfully detected 79.2% AFP-negative samples and 77.1% early-stage liver cancer samples in the testing and validation sets. Conclusions: Our study revealed that different types of RNA variations related to cancer can be detected in plasma and identified a 3-RNA detection panel for liver cancer, especially for AFP-negative and early-stage patients.
2021-01-05 00:00:00 +0000
Oncology
This paper reviews the factors related to hypothyroidism after radiotherapy in patients with head and neck cancer to facilitate the prevention of radiation-induced hypothyroidism and reduce its incidence. Hypothyroidism is a common complication after radiotherapy in patients with head and neck cancer, wherein the higher the radiation dose to the thyroid and pituitary gland, the higher the incidence of hypothyroidism. With prolonged follow-up time, the incidence of hypothyroidism gradually increases. Intensity modulated radiotherapy should limit the dose to the thyroid, which would reduce the incidence of hypothyroidism. In addition, the risk factors for hypothyroidism include small thyroid volume size, female sex, and previous neck surgery. The incidence of radiation-induced hypothyroidism in head and neck cancer is related to the radiation dose, radiotherapy technique, thyroid volume, sex, and age. A prospective, large sample and long-term follow-up study should be carried out to establish a model of normal tissue complications that are likely to be related to radiation-induced hypothyroidism.
2021-01-05 00:00:00 +0000
General
Accurate prediction of heading date under various environmental conditions is expected to facilitate the decision-making process in cultivation management and the breeding process of new cultivars adaptable to the environment. Days to heading (DTH) is a complex trait known to be controlled by multiple genes and genotype-by-environment interactions. Crop growth models (CGMs) have been widely used to predict the phenological development of a plant in an environment; however, they usually require substantial experimental data to calibrate the parameters of the model. The parameters are mostly genotype-specific and are thus usually estimated separately for each cultivar. We propose an integrated approach that links genotype marker data with the developmental genotype-specific parameters of CGMs with a machine learning model, and allows heading date prediction of a new genotype in a new environment. To estimate the parameters, we implemented a Bayesian approach with the advanced Markov chain Monte-Carlo algorithm called the differential evolution adaptive metropolis and conducted the estimation using a large amount of data on heading date and environmental variables. The data comprised sowing and heading dates of 112 cultivars/lines tested at 7 locations for 14 years and the corresponding environmental variables (day length and daily temperature). We compared the predictive accuracy of DTH between the proposed approach, a CGM, and a single machine learning model. The results showed that the extreme learning machine (one of the implemented machine learning models) was superior to the CGM for the prediction of a tested genotype in a tested location. The proposed approach outperformed the machine learning method in the prediction of an untested genotype in an untested location. We also evaluated the potential of the proposed approach in the prediction of the distribution of DTH in 103 F2 segregation populations derived from crosses between a common parent, Koshihikari, and 103 cultivars/lines. The results showed a high correlation coefficient (ca. 0.8) of the 10, 50, and 90th percentiles of the observed and predicted distribution of DTH. In this study, the integration of a machine learning model and a CGM was better able to predict the heading date of a new rice cultivar in an untested potential environment.
Cardiology
ACE inhibition and cardiometabolic risk factors, lung ACE2 and TMPRSS2 gene expression, and plasma ACE2 levels: a Mendelian randomization study.
In Royal Society open science
Gill Dipender, Arvanitis Marios, Carter Paul, Hernández Cordero Ana I, Jo Brian, Karhunen Ville, Larsson Susanna C, Li Xuan, Lockhart Sam M, Mason Amy, Pashos Evanthia, Saha Ashis, Tan Vanessa Y, Zuber Verena, Bossé Yohan, Fahle Sarah, Hao Ke, Jiang Tao, Joubert Philippe, Lunt Alan C, Ouwehand Willem Hendrik, Roberts David J, Timens Wim, van den Berge Maarten, Watkins Nicholas A, Battle Alexis, Butterworth Adam S, Danesh John, Di Angelantonio Emanuele, Engelhardt Barbara E, Peters James E, Sin Don D, Burgess Stephen
2020-Nov
COVID-19, Mendelian randomization, angiotensin-converting enzyme inhibitors, genetic epidemiology
General
Interpreting the socio-technical interactions within a wind damage-artificial neural network model for community resilience.
In Royal Society open science
Pilkington Stephanie F, Mahmoud Hussam N
2020-Nov
debris, machine learning, structural damage, tornado
Oncology
Utilization of circulating cell-free DNA profiling to guide first-line chemotherapy in advanced lung squamous cell carcinoma.
In Theranostics
Jiang Tao, Jiang Liyan, Dong Xiaorong, Gu Kangsheng, Pan Yueyin, Shi Qin, Zhang Guojun, Wang Huijuan, Zhang Xiaochun, Yang Nong, Li Yuping, Xiong Jianping, Yi Tienan, Peng Min, Song Yong, Fan Yun, Cui Jiuwei, Chen Gongyan, Tan Wei, Zang Aimin, Guo Qisen, Zhao Guangqiang, Wang Ziping, He Jianxing, Yao Wenxiu, Wu Xiaohong, Chen Kai, Hu Xiaohua, Hu Chunhong, Yue Lu, Jiang Da, Wang Guangfa, Liu Junfeng, Yu Guohua, Li Junling, Zhang Henghui, Wu Lihong, Fang Lu, Liang Dandan, Zhao Yi, Zhao Weihong, Xie Wenmin, Ren Shengxiang, Zhou Caicun
2021
Non-small-cell lung cancer, cell-free DNA, chemotherapy, machine learning
Public Health
Integrative analysis of long extracellular RNAs reveals a detection panel of noncoding RNAs for liver cancer.
In Theranostics
Zhu Yumin, Wang Siqi, Xi Xiaochen, Zhang Minfeng, Liu Xiaofan, Tang Weina, Cai Peng, Xing Shaozhen, Bao Pengfei, Jin Yunfan, Zhao Weihao, Chen Yinghui, Zhao Huanan, Jia Xiaodong, Lu Shanshan, Lu Yinying, Chen Lei, Yin Jianhua, Lu Zhi John
2021
RNA biomarker, cancer, circular RNA, extracellular RNA, liquid biopsy, noncoding RNA
Oncology
Research progress of radiation-induced hypothyroidism in head and neck cancer.
In Journal of Cancer
Zhou Ling, Chen Jia, Tao Chang-Juan, Chen Ming, Yu Zhong-Hua, Chen Yuan-Yuan
2021
Head and Neck Cancer, Hypothyroidism, Radiotherapy
General
Predicting Rice Heading Date Using an Integrated Approach Combining a Machine Learning Method and a Crop Growth Model.
In Frontiers in genetics ; h5-index 62.0
Chen Tai-Shen, Aoike Toru, Yamasaki Masanori, Kajiya-Kanegae Hiromi, Iwata Hiroyoshi
2020
Markov chain Monte-Carlo, bayesian inference, crop growth model, differential evolution adaptive metropolis, machine learning
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