{"title":"Correlations among core species corresponding to the clinical staging of periodontitis","authors":"Qian Zhang, Minger Zhen, Peng Li, X. Chi, Yifei Zhang","doi":"10.32604/biocell.2023.024948","DOIUrl":"https://doi.org/10.32604/biocell.2023.024948","url":null,"abstract":"","PeriodicalId":55384,"journal":{"name":"Biocell","volume":"10 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84697445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/biocell.2023.028975
A. Jang
Exposure to air pollutants such as PM10, PM2.5, PM0.1, O3, CO, NO2, and SO2, and biological pollutants are important factors causing the evolution and furtherance of obstructive lung diseases (OLD), including asthma and chronic obstructive pulmonary disease (COPD). Asthma is the most frequent chronic inflammatory airway disease, characterized by breathlessness, wheezing, chest tightness, and cough, together with the presence of exaggerated expiratory airflow fluctuation that varies over time. COPD is a heterogeneous lung condition characterized by chronic respiratory symptoms such as dyspnea, cough, expectoration, and/or exacerbations due to abnormalities of the airways and/or alveoli that cause persistent, often progressive, airflow obstruction. Understanding the molecular mechanisms and cellular processes based on the development of OLD on exposure to air pollutants will provide insights into the solution of pathogenesis, prevention, and treatment of these conditions. The molecular mechanisms and cellular process involved in signal transduction pathway plays a role in the binding of extracellular signaling molecules and ligands to receptors placed on the cell surface or on the inner side cell that trigger inflammation that occurs, especially when something important enters the cell to bring into a cascade response. This binding then alters the cell metabolism, shape, and gene expression in the airway. This review aimed to reveal the effect of air pollutants on the molecular mechanisms and cellular processes involved in the signal transduction pathways in OLD.
{"title":"Molecular mechanisms and cellular process in signal transduction pathway related to air pollutants in obstructive lung diseases: A mini-review","authors":"A. Jang","doi":"10.32604/biocell.2023.028975","DOIUrl":"https://doi.org/10.32604/biocell.2023.028975","url":null,"abstract":"Exposure to air pollutants such as PM10, PM2.5, PM0.1, O3, CO, NO2, and SO2, and biological pollutants are important factors causing the evolution and furtherance of obstructive lung diseases (OLD), including asthma and chronic obstructive pulmonary disease (COPD). Asthma is the most frequent chronic inflammatory airway disease, characterized by breathlessness, wheezing, chest tightness, and cough, together with the presence of exaggerated expiratory airflow fluctuation that varies over time. COPD is a heterogeneous lung condition characterized by chronic respiratory symptoms such as dyspnea, cough, expectoration, and/or exacerbations due to abnormalities of the airways and/or alveoli that cause persistent, often progressive, airflow obstruction. Understanding the molecular mechanisms and cellular processes based on the development of OLD on exposure to air pollutants will provide insights into the solution of pathogenesis, prevention, and treatment of these conditions. The molecular mechanisms and cellular process involved in signal transduction pathway plays a role in the binding of extracellular signaling molecules and ligands to receptors placed on the cell surface or on the inner side cell that trigger inflammation that occurs, especially when something important enters the cell to bring into a cascade response. This binding then alters the cell metabolism, shape, and gene expression in the airway. This review aimed to reveal the effect of air pollutants on the molecular mechanisms and cellular processes involved in the signal transduction pathways in OLD.","PeriodicalId":55384,"journal":{"name":"Biocell","volume":"24 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85678412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/biocell.2023.028186
{"title":"Effects of docosahexaenoic acid or arachidonic acid supplementation on the behavior of cardiomyocytes derived from human pluripotent stem cells","authors":"","doi":"10.32604/biocell.2023.028186","DOIUrl":"https://doi.org/10.32604/biocell.2023.028186","url":null,"abstract":"","PeriodicalId":55384,"journal":{"name":"Biocell","volume":"29 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85983591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/biocell.2023.026182
Qiaojing Jia, Yu V. Fu
{"title":"The microRNA pathway activation in insect cell model upon Autographa californica multiple nucleopolyhedrovirus infection","authors":"Qiaojing Jia, Yu V. Fu","doi":"10.32604/biocell.2023.026182","DOIUrl":"https://doi.org/10.32604/biocell.2023.026182","url":null,"abstract":"","PeriodicalId":55384,"journal":{"name":"Biocell","volume":"47 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79266087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01Epub Date: 2022-11-18DOI: 10.32604/biocell.2021.0xxx
Wei Wang, Yanrong Pei, Shui-Hua Wang, Juan Manuel Gorrz, Yu-Dong Zhang
Since 2019, the coronavirus disease-19 (COVID-19) has been spreading rapidly worldwide, posing an unignorable threat to the global economy and human health. It is a disease caused by severe acute respiratory syndrome coronavirus 2, a single-stranded RNA virus of the genus Betacoronavirus. This virus is highly infectious and relies on its angiotensin-converting enzyme 2-receptor to enter cells. With the increase in the number of confirmed COVID-19 diagnoses, the difficulty of diagnosis due to the lack of global healthcare resources becomes increasingly apparent. Deep learning-based computer-aided diagnosis models with high generalisability can effectively alleviate this pressure. Hyperparameter tuning is essential in training such models and significantly impacts their final performance and training speed. However, traditional hyperparameter tuning methods are usually time-consuming and unstable. To solve this issue, we introduce Particle Swarm Optimisation to build a PSO-guided Self-Tuning Convolution Neural Network (PSTCNN), allowing the model to tune hyperparameters automatically. Therefore, the proposed approach can reduce human involvement. Also, the optimisation algorithm can select the combination of hyperparameters in a targeted manner, thus stably achieving a solution closer to the global optimum. Experimentally, the PSTCNN can obtain quite excellent results, with a sensitivity of 93.65%±1.86%, a specificity of 94.32%±2.07%, a precision of 94.30%±2.04%, an accuracy of 93.99%±1.78%, an F1-score of 93.97%±1.78%, Matthews Correlation Coefficient of 87.99%±3.56%, and Fowlkes-Mallows Index of 93.97%±1.78%. Our experiments demonstrate that compared to traditional methods, hyperparameter tuning of the model using an optimisation algorithm is faster and more effective.
{"title":"PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN.","authors":"Wei Wang, Yanrong Pei, Shui-Hua Wang, Juan Manuel Gorrz, Yu-Dong Zhang","doi":"10.32604/biocell.2021.0xxx","DOIUrl":"10.32604/biocell.2021.0xxx","url":null,"abstract":"<p><p>Since 2019, the coronavirus disease-19 (COVID-19) has been spreading rapidly worldwide, posing an unignorable threat to the global economy and human health. It is a disease caused by severe acute respiratory syndrome coronavirus 2, a single-stranded RNA virus of the genus Betacoronavirus. This virus is highly infectious and relies on its angiotensin-converting enzyme 2-receptor to enter cells. With the increase in the number of confirmed COVID-19 diagnoses, the difficulty of diagnosis due to the lack of global healthcare resources becomes increasingly apparent. Deep learning-based computer-aided diagnosis models with high generalisability can effectively alleviate this pressure. Hyperparameter tuning is essential in training such models and significantly impacts their final performance and training speed. However, traditional hyperparameter tuning methods are usually time-consuming and unstable. To solve this issue, we introduce Particle Swarm Optimisation to build a PSO-guided Self-Tuning Convolution Neural Network (PSTCNN), allowing the model to tune hyperparameters automatically. Therefore, the proposed approach can reduce human involvement. Also, the optimisation algorithm can select the combination of hyperparameters in a targeted manner, thus stably achieving a solution closer to the global optimum. Experimentally, the PSTCNN can obtain quite excellent results, with a sensitivity of 93.65%±1.86%, a specificity of 94.32%±2.07%, a precision of 94.30%±2.04%, an accuracy of 93.99%±1.78%, an F1-score of 93.97%±1.78%, Matthews Correlation Coefficient of 87.99%±3.56%, and Fowlkes-Mallows Index of 93.97%±1.78%. Our experiments demonstrate that compared to traditional methods, hyperparameter tuning of the model using an optimisation algorithm is faster and more effective.</p>","PeriodicalId":55384,"journal":{"name":"Biocell","volume":"47 2","pages":"373-384"},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7613982/pdf/EMS158588.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10517202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/biocell.2023.022065
Vijay Kumar, Praveen C. Ramamurthy, Simranjeet Singh, Daljeet Singh Dhanjal, Parul Parihar, D. Bhatia, R. Prasad, Joginder Singh
{"title":"Phytochemistry and ethnomedicinal qualities of metabolites from Phyllanthus emblica L.: A review","authors":"Vijay Kumar, Praveen C. Ramamurthy, Simranjeet Singh, Daljeet Singh Dhanjal, Parul Parihar, D. Bhatia, R. Prasad, Joginder Singh","doi":"10.32604/biocell.2023.022065","DOIUrl":"https://doi.org/10.32604/biocell.2023.022065","url":null,"abstract":"","PeriodicalId":55384,"journal":{"name":"Biocell","volume":"132 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74860181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/biocell.2023.025865
Yu-Han Ji, Yong Liang, Ziyi Yang, Ning Ai
{"title":"SW-Net: A novel few-shot learning approach for disease subtype prediction","authors":"Yu-Han Ji, Yong Liang, Ziyi Yang, Ning Ai","doi":"10.32604/biocell.2023.025865","DOIUrl":"https://doi.org/10.32604/biocell.2023.025865","url":null,"abstract":"","PeriodicalId":55384,"journal":{"name":"Biocell","volume":"132 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77418398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/biocell.2023.025957
Wenli Zeng, Feng Ling, Kainuo Dang, Qing-xia Chi
{"title":"SPP1 and the risk score model to improve the survival prediction of patients with hepatocellular carcinoma based on multiple algorithms and back propagation neural networks","authors":"Wenli Zeng, Feng Ling, Kainuo Dang, Qing-xia Chi","doi":"10.32604/biocell.2023.025957","DOIUrl":"https://doi.org/10.32604/biocell.2023.025957","url":null,"abstract":"","PeriodicalId":55384,"journal":{"name":"Biocell","volume":"11 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80986612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"KIF15, a key regulator of nasopharyngeal carcinoma development mediated by the P53 pathway","authors":"Yongliwang, S. Qu, Yong Yang, Ying Qin, Fei Liu, Guangwu Huang","doi":"10.32604/biocell.2023.025280","DOIUrl":"https://doi.org/10.32604/biocell.2023.025280","url":null,"abstract":"","PeriodicalId":55384,"journal":{"name":"Biocell","volume":"86 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82424938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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