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General General

Analytical technologies to revolutionize the environmental mutagenesis-and genome- research -from the basics to the cutting-edge research-: the Open Symposium of the Japanese Environmental Mutagen and Genome Society (JEMS), 2022.

In Genes and environment : the official journal of the Japanese Environmental Mutagen Society

The Open Symposium of the Japanese Environmental Mutagen and Genome Society (JEMS) entitled "Analytical technologies to revolutionize the environmental mutagenesis and genome research -From the basics to the cutting-edge research-" was held online, on June 11th, 2022. The purpose of this symposium was to provide an opportunity to highlight the cutting-edge research for measurement technologies, and informational and computational (in silico) sciences for the purpose of applying them to deepen scientific knowledge and better understanding the relationship between genes and environmental mutagens. These advanced technologies and sciences are indispensable for the prediction of pharmacokineticses, mutagenicities of chemical substances, and structures of biomolecules including chromosomes. In this symposium, we invited six scientists who are continuing to expand the frontiers in the fields of health data science. Herein, the organizers present a summary of the symposium.

Koyama Naoki, Sassa Akira

2023-Mar-09

Carcinogenesis, DNA damage, Directed evolution, Environmental mutagen, In silico mutagenicity, Machine learning model, Mutagenesis, Single-cell DNA replication sequencing, xenoBiotic

General General

Analytical technologies to revolutionize the environmental mutagenesis-and genome- research -from the basics to the cutting-edge research-: the Open Symposium of the Japanese Environmental Mutagen and Genome Society (JEMS), 2022.

In Genes and environment : the official journal of the Japanese Environmental Mutagen Society

The Open Symposium of the Japanese Environmental Mutagen and Genome Society (JEMS) entitled "Analytical technologies to revolutionize the environmental mutagenesis and genome research -From the basics to the cutting-edge research-" was held online, on June 11th, 2022. The purpose of this symposium was to provide an opportunity to highlight the cutting-edge research for measurement technologies, and informational and computational (in silico) sciences for the purpose of applying them to deepen scientific knowledge and better understanding the relationship between genes and environmental mutagens. These advanced technologies and sciences are indispensable for the prediction of pharmacokineticses, mutagenicities of chemical substances, and structures of biomolecules including chromosomes. In this symposium, we invited six scientists who are continuing to expand the frontiers in the fields of health data science. Herein, the organizers present a summary of the symposium.

Koyama Naoki, Sassa Akira

2023-Mar-09

Carcinogenesis, DNA damage, Directed evolution, Environmental mutagen, In silico mutagenicity, Machine learning model, Mutagenesis, Single-cell DNA replication sequencing, xenoBiotic

Public Health Public Health

Assessment of Alzheimer-related pathologies of dementia using machine learning feature selection.

In Alzheimer's research & therapy ; h5-index 49.0

Although a variety of brain lesions may contribute to the pathological assessment of dementia, the relationship of these lesions to dementia, how they interact and how to quantify them remains uncertain. Systematically assessing neuropathological measures by their degree of association with dementia may lead to better diagnostic systems and treatment targets. This study aims to apply machine learning approaches to feature selection in order to identify critical features of Alzheimer-related pathologies associated with dementia. We applied machine learning techniques for feature ranking and classification to objectively compare neuropathological features and their relationship to dementia status during life using a cohort (n=186) from the Cognitive Function and Ageing Study (CFAS). We first tested Alzheimer's Disease and tau markers and then other neuropathologies associated with dementia. Seven feature ranking methods using different information criteria consistently ranked 22 out of the 34 neuropathology features for importance to dementia classification. Although highly correlated, Braak neurofibrillary tangle stage, beta-amyloid and cerebral amyloid angiopathy features were ranked the highest. The best-performing dementia classifier using the top eight neuropathological features achieved 79% sensitivity, 69% specificity and 75% precision. However, when assessing all seven classifiers and the 22 ranked features, a substantial proportion (40.4%) of dementia cases was consistently misclassified. These results highlight the benefits of using machine learning to identify critical indices of plaque, tangle and cerebral amyloid angiopathy burdens that may be useful for classifying dementia.

Rajab Mohammed D, Jammeh Emmanuel, Taketa Teruka, Brayne Carol, Matthews Fiona E, Su Li, Ince Paul G, Wharton Stephen B, Wang Dennis

2023-Mar-10

Alzheimer’s, Beta-amyloid, Dementia, Feature selection, Machine learning, Neuropathology

General General

Predicting the toxicity of nanoparticles using artificial intelligence tools: a systematic review.

In Nanotoxicology ; h5-index 51.0

Nanoparticles have been used extensively in different scientific fields. Due to the possible destructive effects of nanoparticles on the environment or the biological systems, their toxicity evaluation is a crucial phase for studying nanomaterial safety. In the meantime, experimental approaches for toxicity assessment of various nanoparticles are expensive and time-consuming. Thus, an alternative technique, such as artificial intelligence (AI), could be valuable for predicting nanoparticle toxicity. Therefore, in this review, the AI tools were investigated for the toxicity assessment of nanomaterials. To this end, a systematic search was performed on PubMed, Web of Science, and Scopus databases. Articles were included or excluded based on pre-defined inclusion and exclusion criteria, and duplicate studies were excluded. Finally, twenty-six studies were included. The majority of the studies were conducted on metal oxide and metallic nanoparticles. In addition, Random Forest (RF) and Support Vector Machine (SVM) had the most frequency in the included studies. Most of the models demonstrated acceptable performance. Overall, AI could provide a robust, fast, and low-cost tool for the evaluation of nanoparticle toxicity.

Banaye Yazdipour Alireza, Masoorian Hoorie, Ahmadi Mahnaz, Mohammadzadeh Niloofar, Ayyoubzadeh Seyed Mohammad

2023-Mar-08

** nanomaterials, Nanoparticles, artificial intelligence, safety, toxicity**

General General

Evaluation of endometrial receptivity by ultrasound elastography to predict pregnancy outcome is a non-invasive and worthwhile method.

In Biotechnology & genetic engineering reviews

Up to today, there is no effective, specific and non-invasive evaluation method to assess the endometrial receptivity. This study aimed to establish a non-invasive and effective model with the clinical indicators to evaluate endometrial receptivity. Ultrasound elastography can reflect the overall state of the endometrium. Ultrasonic elastography images from 78 hormonally prepared frozen embryo transfer (FET) patients were assessed in this study. Meanwhile, the clinical indicators reflecting endometrium in the transplantation cycle were collected. The patients were received to transfer only one high-quality blastocyst. A novel code rule that can generate a large number of 0-1 symbols was designed to collect data on different factors. At the same time, a logistic regression model of the machine learning process with an automatic combination of factors was designed for analysis. The logistic regression model was based on age, body mass index, waist-hip ratio, endometrial thickness, perfusion index (PI), resistance index (RI), elastic grade, elastic ratio cutoff value, serum estradiol level and 9 other indicators. The accuracy rate of predicting pregnancy outcome of the logistic regression model was 76.92%. Elastic ultrasound can reflect the endometrial receptivity of patients in FET cycles. We established a prediction model including ultrasound elastography and the model precisely predicted the pregnancy outcome. The predictive accuracy of endometrial receptivity by the predictive model is significantly higher than that of the single clinical indicator. The prediction model by integrating the clinical indicators to evaluate endometrial receptivity may be a non-invasive and worthwhile method for evaluating endometrial receptivity.

Li Meiling, Zhu Xianjun, Wang Liping, Fu Haiyan, Zhao Wei, Zhou Chen, Chen Li, Yao Bing

2023-Mar-08

Elastography, Endometrial Receptivity, Frozen Embryo Transplantation, Pregnancy Outcomes

Radiology Radiology

Systematic radiomics analysis based on multiparameter MRI to preoperatively predict the expression of Ki67 and histological grade in patients with bladder cancer.

In The British journal of radiology

OBJECTIVES : Bladder cancer is among the most prevalent urothelial malignancies. Radiomics-based preoperative prediction of Ki67 and histological grade will facilitate clinical decision-making.

METHODS : This retrospective study recruited 283 bladder cancer patients between 2012 and 2021. Multiparameter MRI sequences included: T1WI, T2WI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The radiomics features of intratumoral and peritumoral regions were extracted simultaneously. Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms were employed to select the features. Six machine learning-based classifiers were adopted to construct the radiomics models, and the best was chosen for the model construction.

RESULTS : The mRMR and LASSO algorithms were more suitable for Ki67 and histological grade, respectively. Additionally, Ki67 had a higher proportion of intratumoral features, while peritumoral features accounted for a greater proportion of the histological grade. Random forests performed the best in predicting both pathological outcomes. Consequently, the multiparameter MRI (MP-MRI) models achieved area under the curve (AUC) values of 0.977 and 0.852 for Ki67 in training and test sets, respectively, and 0.972 and 0.710 for the histological grade.

CONCLUSION : Radiomics holds the potential to predict multiple pathological outcomes of bladder cancer preoperatively and are expected to provide clinical decision-making guidance. Furthermore, our work inspired the process of radiomics research.

ADVANCES IN KNOWLEDGE : This study demonstrated that different feature selection techniques, segmentation regions, classifiers, and MRI sequences will affect the performance of the model. We systematically demonstrated that radiomics can predict histological grade and Ki67.

Fan Xuhui, Yu Hongwei, Ni Xie, Chen Guihua, Li Tiewen, Chen Jingwen, He Meijuan, Liu Hao, Wang Han, Yin Xiaorui

2023-Mar-08