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Dermatology Dermatology

Association of Family History With Incidence and Outcomes of Aortic Dissection.

In Journal of the American College of Cardiology ; h5-index 167.0

BACKGROUND : Aortic dissection (AD) is a life-threatening emergency. However, the heritability and association of family history with late outcomes are unclear.

OBJECTIVES : The purpose of this study was to evaluate the effect of family history of AD on the incidence and prognosis of AD and estimate the heritability and environmental contribution in AD in Taiwan.

METHODS : Both cross-sectional and cohort studies were conducted using Taiwan National Health Insurance database. A registry parent-offspring relationship algorithm was used to reconstruct the genealogy of this population for heritability estimation. The cross-sectional study included 23,868 patients with a diagnosis of AD in 2015. The prevalence and adjusted relative risks (RRs) were evaluated, and the liability threshold model was used to examine the effects of heritability and environmental factors. Furthermore, a 1:10 propensity score-matched cohort comprising AD patients with or without a family history of AD was included to compare late outcomes in the cohort study.

RESULTS : A family history of AD in first-degree relatives was associated with an RR of 6.82 (95% confidence interval [CI]: 5.12 to 9.07). The heritability of AD was estimated to be 57.0% for genetic factors, and 3.1% and 40.0% for shared and nonshared environmental factors, respectively. After excluding individuals with Marfan syndrome or bicuspid aortic valve, a family history of AD was associated with an RR of 6.56 (95% CI: 4.92 to 8.77) for AD. Furthermore, patients with AD and a family history of AD had a higher risk of later aortic surgery than those with AD without a family history (subdistribution hazard ratio: 1.40; 95% CI: 1.12 to 1.76).

CONCLUSIONS : A family history of AD was a strong risk factor for AD. Furthermore, patients with AD with a family history of AD had a higher risk of later aortic surgery than those with no family history of AD.

Chen Shao-Wei, Kuo Chang-Fu, Huang Yu-Tung, Lin Wan-Ting, Chien-Chia Wu Victor, Chou An-Hsun, Lin Pyng-Jing, Chang Shang-Hung, Chu Pao-Hsien


aortic dissection, family aggregation, family history, long-term prognosis

General General

Machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (Canis lupus) crania.

In BMC biology

BACKGROUND : Studies of mammalian sexual dimorphism have traditionally involved the measurement of selected dimensions of particular skeletal elements and use of single data-analysis procedures. Consequently, such studies have been limited by a variety of both practical and conceptual constraints. To compare and contrast what might be gained from a more exploratory, multifactorial approach to the quantitative assessment of form-variation, images of a small sample of modern Israeli gray wolf (Canis lupus) crania were analyzed via elliptical Fourier analysis of cranial outlines, a Naïve Bayes machine-learning approach to the analysis of these same outline data, and a deep-learning analysis of whole images in which all aspects of these cranial morphologies were represented. The statistical significance and stability of each discriminant result were tested using bootstrap and jackknife procedures.

RESULTS : Our results reveal no evidence for statistically significant sexual size dimorphism, but significant sex-mediated shape dimorphism. These are consistent with the findings of prior wolf sexual dimorphism studies and extend these studies by identifying new aspects of dimorphic variation. Additionally, our results suggest that shape-based sexual dimorphism in the C. lupus cranial complex may be more widespread morphologically than had been appreciated by previous researchers.

CONCLUSION : Our results suggest that size and shape dimorphism can be detected in small samples and may be dissociated in mammalian morphologies. This result is particularly noteworthy in that it implies there may be a need to refine allometric hypothesis tests that seek to account for phenotypic sexual dimorphism. The methods we employed in this investigation are fully generalizable and can be applied to a wide range of biological materials and could facilitate the rapid evaluation of a diverse array of morphological/phenomic hypotheses.

MacLeod Norman, Kolska Horwitz Liora


Automated identification, Carnivores, Convolution neural networks, Ecomorphology, Machine learning, Morphometrics, Shape analysis

Public Health Public Health

Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization.

In BMC medical informatics and decision making ; h5-index 38.0

BACKGROUND : Gastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide.

METHODS : This paper reports an aided curation pipeline to identify potential influential genes for gastrointestinal cancer. The curation pipeline integrates biomedical literature to identify named entities by Bi-LSTM-CNN-CRF methods. The entities and their associations can be used to construct a graph, and from which we can compute the sets of co-occurring genes that are the most influential based on an influence maximization algorithm.

RESULTS : The sets of co-occurring genes that are the most influential that we discover include RARA - CRBP1, CASP3 - BCL2, BCL2 - CASP3 - CRBP1, RARA - CASP3 - CRBP1, FOXJ1 - RASSF3 - ESR1, FOXJ1 - RASSF1A - ESR1, FOXJ1 - RASSF1A - TNFAIP8 - ESR1. With TCGA and functional and pathway enrichment analysis, we prove the proposed approach works well in the context of gastrointestinal cancer.

CONCLUSIONS : Our pipeline that uses text mining to identify objects and relationships to construct a graph and uses graph-based influence maximization to discover the most influential co-occurring genes presents a viable direction to assist knowledge discovery for clinical applications.

Wang Charles C N, Jin Jennifer, Chang Jan-Gowth, Hayakawa Masahiro, Kitazawa Atsushi, Tsai Jeffrey J P, Sheu Phillip C-Y


Bi-LSTM-CNN-CRF, Co-occurrence network, Gastrointestinal cancer, Influence maximization, Text mining

General General

A model for the effective COVID-19 identification in uncertainty environment using primary symptoms and CT scans.

In Health informatics journal ; h5-index 25.0

The rapid spread of the COVID-19 virus around the world poses a real threat to public safety. Some COVID-19 symptoms are similar to other viral chest diseases, which makes it challenging to develop models for effective detection of COVID-19 infection. This article advocates a model to differentiate between COVID-19 and other four viral chest diseases under uncertainty environment using the viruses primary symptoms and CT scans. The proposed model is based on a plithogenic set, which provides higher accurate evaluation results in an uncertain environment. The proposed model employs the best-worst method (BWM) and the technique in order of preference by similarity to ideal solution (TOPSIS). Besides, this study discusses how smart Internet of Things technology can assist medical staff in monitoring the spread of COVID-19. Experimental evaluation of the proposed model was conducted on five different chest diseases. Evaluation results demonstrate that the proposed model effectiveness in detecting the COVID-19 in all five cases achieving detection accuracy of up to 98%.

Abdel-Basst Mohamed, Mohamed Rehab, Elhoseny Mohamed


Artificial Intelligence, BWM, COVID-19, CT imaging, Internet of Things, Plithogenic, TOPSIS, smart spaces, symptoms, viral chest diseases

Pathology Pathology

Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2.

In Viruses ; h5-index 58.0

Until vaccines and effective therapeutics become available, the practical solution to transit safely out of the current coronavirus disease 19 (CoVID-19) lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of results, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ~200 coronavirus disease (CoVID-19)-suspected NHS patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. Therefore, this system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories.

Rohaim Mohammed A, Clayton Emily, Sahin Irem, Vilela Julianne, Khalifa Manar E, Al-Natour Mohammad Q, Bayoumi Mahmoud, Poirier Aurore C, Branavan Manoharanehru, Tharmakulasingam Mukunthan, Chaudhry Nouman S, Sodi Ravinder, Brown Amy, Burkhart Peter, Hacking Wendy, Botham Judy, Boyce Joe, Wilkinson Hayley, Williams Craig, Whittingham-Dowd Jayde, Shaw Elisabeth, Hodges Matt, Butler Lisa, Bates Michelle D, La Ragione Roberto, Balachandran Wamadeva, Fernando Anil, Munir Muhammad


LAMP, SARS-CoV-2, artificial intelligence, diagnosis, point of care

Radiology Radiology

Classification of parotid gland tumors by using multimodal MRI and deep learning.

In NMR in biomedicine ; h5-index 41.0

Various MRI sequences have shown their potential to discriminate parotid gland tumors, including but not limited to T2 -weighted, postcontrast T1 -weighted, and diffusion-weighted images. In this study, we present a fully automatic system for the diagnosis of parotid gland tumors by using deep learning methods trained on multimodal MRI images. We used a two-dimensional convolution neural network, U-Net, to segment and classify parotid gland tumors. The U-Net model was trained with transfer learning, and a specific design of the batch distribution optimized the model accuracy. We also selected five combinations of MRI contrasts as the input data of the neural network and compared the classification accuracy of parotid gland tumors. The results indicated that the deep learning model with diffusion-related parameters performed better than those with structural MR images. The performance results (n = 85) of the diffusion-based model were as follows: accuracy of 0.81, 0.76, and 0.71, sensitivity of 0.83, 0.63, and 0.33, and specificity of 0.80, 0.84, and 0.87 for Warthin tumors, pleomorphic adenomas, and malignant tumors, respectively. Combining diffusion-weighted and contrast-enhanced T1 -weighted images did not improve the prediction accuracy. In summary, the proposed deep learning model could classify Warthin tumor and pleomorphic adenoma tumor but not malignant tumor.

Chang Yi-Ju, Huang Teng-Yi, Liu Yi-Jui, Chung Hsiao-Wen, Juan Chun-Jung


MRI, deep learning, head and neck, parotid gland tumor, transfer learning