Massimo Majolo, A. M. Ponsiglione, G. Longo, G. Russo, M. Triassi, E. Raiola, G. Improta
The Emergency Department (ED) is one of the main points of hospital access. Within the ED, where it is not possible to predict accurately the demand, the timeliness and appropriateness of the response are fundamental elements. The clinicians who work within the ED have seen to complicate the already difficult work activity by particular conditions such as overcrowding. This phenomenon, which characterizes ED in almost all developing countries, can cause patient abandonment, delays and dissatisfaction. To be able to study it and seek improvement solutions, one way is to investigate the length of stay (LOS). In this study, a maximum stay threshold will be identified and the ED-LOS of patients who arrived at ED of A.O.R.N. “Antonio Cardarelli” of Naples (Italy) in the last quarter of 2019 will be studied with statistical analysis first and logistic regression then. Age, triage code, time and mode of arrival proved to be the main risk factors for prolonged ED-LOS.
{"title":"Studying length of stay in the Emergency Department of A.O.R.N. “Antonio Cardarelli” of Naples","authors":"Massimo Majolo, A. M. Ponsiglione, G. Longo, G. Russo, M. Triassi, E. Raiola, G. Improta","doi":"10.1145/3498731.3498753","DOIUrl":"https://doi.org/10.1145/3498731.3498753","url":null,"abstract":"The Emergency Department (ED) is one of the main points of hospital access. Within the ED, where it is not possible to predict accurately the demand, the timeliness and appropriateness of the response are fundamental elements. The clinicians who work within the ED have seen to complicate the already difficult work activity by particular conditions such as overcrowding. This phenomenon, which characterizes ED in almost all developing countries, can cause patient abandonment, delays and dissatisfaction. To be able to study it and seek improvement solutions, one way is to investigate the length of stay (LOS). In this study, a maximum stay threshold will be identified and the ED-LOS of patients who arrived at ED of A.O.R.N. “Antonio Cardarelli” of Naples (Italy) in the last quarter of 2019 will be studied with statistical analysis first and logistic regression then. Age, triage code, time and mode of arrival proved to be the main risk factors for prolonged ED-LOS.","PeriodicalId":166893,"journal":{"name":"Proceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127293645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose: The national drug negotiation work continues to deepen. Although the negotiation mechanism is becoming mature, there are still deficiencies. Through combing the current negotiation policy content, deficiencies are found, and suggestions for improvement are put forward on this basis. Method: Use the policy comparison method to sort out the contents of the five rounds of national drug negotiations, find out the points to be improved in the policy, and then use the literature search method to put forward suggestions for improvement. Result: The current national drug negotiation work is constantly improving, but there are still shortcomings, such as the issue of reciprocity in the negotiation and the lack of rare disease drugs in the catalog. Conclusion: Dynamic drug price adjustments, third-party regulatory agencies, and special protection for rare diseases have important reference significance for solving the current deficiencies in the country's drug negotiation work
{"title":"A Systematic Review of National Drug Negotiations","authors":"Jiayu Gu","doi":"10.1145/3498731.3498749","DOIUrl":"https://doi.org/10.1145/3498731.3498749","url":null,"abstract":"Purpose: The national drug negotiation work continues to deepen. Although the negotiation mechanism is becoming mature, there are still deficiencies. Through combing the current negotiation policy content, deficiencies are found, and suggestions for improvement are put forward on this basis. Method: Use the policy comparison method to sort out the contents of the five rounds of national drug negotiations, find out the points to be improved in the policy, and then use the literature search method to put forward suggestions for improvement. Result: The current national drug negotiation work is constantly improving, but there are still shortcomings, such as the issue of reciprocity in the negotiation and the lack of rare disease drugs in the catalog. Conclusion: Dynamic drug price adjustments, third-party regulatory agencies, and special protection for rare diseases have important reference significance for solving the current deficiencies in the country's drug negotiation work","PeriodicalId":166893,"journal":{"name":"Proceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115542712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alzheimer's Disease (AD), a progressive neurodegenerative disease caused by abundant abnormal extracellular amyloid-β plaques and intracellular assemblage of tau inclusions, can severely affect the living of patients as it causes dementia. As aging is the most important risk factor of AD, an increasing aging global population will lead to more serious problems in the future. The progression of AD includes three stages: the asymptomatic stage, Mild Cognitive Impairment and dementia. At present, most diagnoses methods are focused on MCI and dementia stages of the disease as disease during the asymptomatic stage is challenging. At present, effective diagnostic methods include neuropathological diagnosis that focus on the macroscopic features and microscopic features of AD, biomarkers such as cerebrospinal fluid biomarkers and other biomarkers in the human plasma, imaging techniques including structural magnetic resonance imaging and F-fluorodeoxyglucose-position emission tomography, psychological and behavioral tests, etc., but most of them have obvious deficiencies. Novel diagnostic methods, such as saliva biomarkers, which cost less and are more accurate in diagnosing AD can have promises for early diagnosis of AD, leading better patient outcomes. With the advancement of emergence of technologies, such as artificial intelligence, more effective diagnosis methods will be available, and the efficiency of diagnosing AD will increase, benefiting patients and their families. This paper provides an overview of AD and investigates conventional and novel methods for its diagnosis.
{"title":"An overview of Alzheimer's disease and its diagnosis using conventional and novel methods","authors":"Xuanning Zhao","doi":"10.1145/3498731.3498761","DOIUrl":"https://doi.org/10.1145/3498731.3498761","url":null,"abstract":"Alzheimer's Disease (AD), a progressive neurodegenerative disease caused by abundant abnormal extracellular amyloid-β plaques and intracellular assemblage of tau inclusions, can severely affect the living of patients as it causes dementia. As aging is the most important risk factor of AD, an increasing aging global population will lead to more serious problems in the future. The progression of AD includes three stages: the asymptomatic stage, Mild Cognitive Impairment and dementia. At present, most diagnoses methods are focused on MCI and dementia stages of the disease as disease during the asymptomatic stage is challenging. At present, effective diagnostic methods include neuropathological diagnosis that focus on the macroscopic features and microscopic features of AD, biomarkers such as cerebrospinal fluid biomarkers and other biomarkers in the human plasma, imaging techniques including structural magnetic resonance imaging and F-fluorodeoxyglucose-position emission tomography, psychological and behavioral tests, etc., but most of them have obvious deficiencies. Novel diagnostic methods, such as saliva biomarkers, which cost less and are more accurate in diagnosing AD can have promises for early diagnosis of AD, leading better patient outcomes. With the advancement of emergence of technologies, such as artificial intelligence, more effective diagnosis methods will be available, and the efficiency of diagnosing AD will increase, benefiting patients and their families. This paper provides an overview of AD and investigates conventional and novel methods for its diagnosis.","PeriodicalId":166893,"journal":{"name":"Proceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129608731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An estimated 19 million new cancer cases and almost 10 million global death due to cancer in 2020 have highlighted the cardinal importance of accurate diagnostic assays for the early detection of cancer. Detecting circulating tumor cells (CTCs) offers a new approach for cancer diagnosis and treatment. Multi-disciplinary research teams have tried to develop CTC-detection assays, with the establishment of an FDA-approved test for clinical CTC enumeration and with others under development. CTC detection techniques mainly rely on physical separation and microfluidic-based methods, particularly microfiltration devices. Molecular diagnostic assays are a major group of tests used to diagnose CTCs. Among molecular diagnostic assays to detect CTCs, biochemical techniques such as immunoaffinity is a basis for enrichment involving positive and negative selection. Besides enumeration, there are multiple other techniques for CTC analysis, such as genomic, transcriptomic, epigenetic, proteomic, and multimodal analysis. However, the current CTC enumeration technology is unable to address many biological challenges associated with CTC characteristics such as extreme rarity, heterogeneous nature and varied phenotype. Future research is required for the development and application of new detection and therapeutic methods. In this review, the technical and clinical applications of the different CTCs molecular diagnostic assays are summarized, and suggestions are made to overcome caveats for CTC detection techniques in the future.
{"title":"The Role of Circulating Tumor Cells in Diagnosis of Cancer: Cancer and Circulating Tumor Cells","authors":"Siqi Wu","doi":"10.1145/3498731.3498758","DOIUrl":"https://doi.org/10.1145/3498731.3498758","url":null,"abstract":"An estimated 19 million new cancer cases and almost 10 million global death due to cancer in 2020 have highlighted the cardinal importance of accurate diagnostic assays for the early detection of cancer. Detecting circulating tumor cells (CTCs) offers a new approach for cancer diagnosis and treatment. Multi-disciplinary research teams have tried to develop CTC-detection assays, with the establishment of an FDA-approved test for clinical CTC enumeration and with others under development. CTC detection techniques mainly rely on physical separation and microfluidic-based methods, particularly microfiltration devices. Molecular diagnostic assays are a major group of tests used to diagnose CTCs. Among molecular diagnostic assays to detect CTCs, biochemical techniques such as immunoaffinity is a basis for enrichment involving positive and negative selection. Besides enumeration, there are multiple other techniques for CTC analysis, such as genomic, transcriptomic, epigenetic, proteomic, and multimodal analysis. However, the current CTC enumeration technology is unable to address many biological challenges associated with CTC characteristics such as extreme rarity, heterogeneous nature and varied phenotype. Future research is required for the development and application of new detection and therapeutic methods. In this review, the technical and clinical applications of the different CTCs molecular diagnostic assays are summarized, and suggestions are made to overcome caveats for CTC detection techniques in the future.","PeriodicalId":166893,"journal":{"name":"Proceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116274484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhenyu He, Yong Liang, Ling Huang, Wenzhong Wang, Jinfeng Wang
Cancer is one of the great medical problems that mankind is facing today. With the help of DNA microarray, we can analyze thousands of genes simultaneously. The analysis of cancer samples with microarray technique is a hot topic in the field of bioinformatics. There are usually quite a lot genes in the microarray datasets, so it is time-consuming for us to classify samples with all these genes. For this reason, it is necessary for us to conduct feature gene selection. Regularization can serve as a method for feature selection. In this paper, we proposed a method called L1/2+2 and Fuzzy Measure Gene Selection (LFMGS). The method can be divided into two parts. Firstly, the L1/2+2 regularization is adopted to remove most of genes. Then L1/2+2 regularization and fuzzy measure are combined to obtain the sparse solution of fuzzy measures, and then a small number of genes are eliminated based on the final gene rank. Experimental results on seven datasets show the superiority of our method over the other four methods comprehensively considering accuracy, sensitivity and specificity, and the number of selected genes.
{"title":"Feature Gene Selection based on L1/2+2 Regularization","authors":"Zhenyu He, Yong Liang, Ling Huang, Wenzhong Wang, Jinfeng Wang","doi":"10.1145/3498731.3498737","DOIUrl":"https://doi.org/10.1145/3498731.3498737","url":null,"abstract":"Cancer is one of the great medical problems that mankind is facing today. With the help of DNA microarray, we can analyze thousands of genes simultaneously. The analysis of cancer samples with microarray technique is a hot topic in the field of bioinformatics. There are usually quite a lot genes in the microarray datasets, so it is time-consuming for us to classify samples with all these genes. For this reason, it is necessary for us to conduct feature gene selection. Regularization can serve as a method for feature selection. In this paper, we proposed a method called L1/2+2 and Fuzzy Measure Gene Selection (LFMGS). The method can be divided into two parts. Firstly, the L1/2+2 regularization is adopted to remove most of genes. Then L1/2+2 regularization and fuzzy measure are combined to obtain the sparse solution of fuzzy measures, and then a small number of genes are eliminated based on the final gene rank. Experimental results on seven datasets show the superiority of our method over the other four methods comprehensively considering accuracy, sensitivity and specificity, and the number of selected genes.","PeriodicalId":166893,"journal":{"name":"Proceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133916658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the next-generation sequencing (NGS) technologies developing, numerous genetic data are available for researchers and clinical doctors. Nonsynonymous single nucleotide variant (nonsynonymous SNV) is a common type of genetic mutation which possibly leads to diseases. However, classifying observed SNVs to benign or pathogenic variants with high confidence remains challenging. Inspired by ensemble learning and Gradient Boosting Decision Tree (GBDT), a machine learning algorithm, we proposed a novel prediction model named PPSNV to identify the pathogenicity of nonsynonymous SNVs. We integrated 14 features to train our model and tested it in two independent datasets. The results showed outstanding performance was achieved by the proposed predictors compared with four commonly used prediction tools.
随着下一代测序(NGS)技术的发展，研究人员和临床医生可以获得大量基因数据。非同义单核苷酸变异(Nonsynonymous single nucleotide variant, SNV)是一种常见的可能导致疾病的基因突变。然而，将观察到的snv高可信度地分类为良性或致病变异仍然具有挑战性。受集成学习和梯度增强决策树(GBDT)机器学习算法的启发，我们提出了一种新的预测模型PPSNV来识别非同音snv的致病性。我们整合了14个特征来训练我们的模型，并在两个独立的数据集中进行了测试。结果表明，与四种常用的预测工具相比，所提出的预测器取得了显著的效果。
{"title":"PPSNV: A Novel Predictor for Pathogenicity of Nonsynonymous SNV based on Ensemble Learning","authors":"Xu Zhen, G. Lin","doi":"10.1145/3498731.3498741","DOIUrl":"https://doi.org/10.1145/3498731.3498741","url":null,"abstract":"With the next-generation sequencing (NGS) technologies developing, numerous genetic data are available for researchers and clinical doctors. Nonsynonymous single nucleotide variant (nonsynonymous SNV) is a common type of genetic mutation which possibly leads to diseases. However, classifying observed SNVs to benign or pathogenic variants with high confidence remains challenging. Inspired by ensemble learning and Gradient Boosting Decision Tree (GBDT), a machine learning algorithm, we proposed a novel prediction model named PPSNV to identify the pathogenicity of nonsynonymous SNVs. We integrated 14 features to train our model and tested it in two independent datasets. The results showed outstanding performance was achieved by the proposed predictors compared with four commonly used prediction tools.","PeriodicalId":166893,"journal":{"name":"Proceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122633886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent studies have revealed that immune responses and related pathways are actively involved into the progression of Alzheimer's disease (AD) / AD-like pathology in both patients and disease models. Single-cell studies further discovered the accumulation of disease associated microglia (DAM) along with the progression of AD pathology and the pro-inflammatory activity was regulated by the Trem2-dependent pathways. Given the significance of microglia population in mediating the clearance of plaques, it is important to understand the mechanism of microglia activation and their response changes to the plaque accumulation in AD-affected brains. By observing the gene expression level across brain regions in 5xFAD mice, we showed that hippocampus was most affected during AD-like pathology progression and most up-regulated genes were pro-inflammatory. Then, by looking at the microglia gene expression data, we revealed the distinguished activation of disease-associated immune responses at the late stage in 5xFAD mice and the discrepancy between 5xFAD and wild type mice was increased with aging. In addition, we discussed the importance of Trem2 in microglia at the late stage of 5xFAD and showed that Trem2 knock-out not only decreased microglia immune responses but also changed the micro-environment that microglia resided in. Based on this, we continued argued that the interaction between microglia and other cell types, including neurons, astrocytes and oligodendrocytes, had significant impacts on the microglia-mediated plaque clearance in AD/AD-like pathology affected brain tissues.
{"title":"Activation of Microglial Genes and Crosstalk with Micro-environment in Modulating Immunological Pathology in Alzheimer's Disease","authors":"Chuanbin Wu","doi":"10.1145/3498731.3498739","DOIUrl":"https://doi.org/10.1145/3498731.3498739","url":null,"abstract":"Recent studies have revealed that immune responses and related pathways are actively involved into the progression of Alzheimer's disease (AD) / AD-like pathology in both patients and disease models. Single-cell studies further discovered the accumulation of disease associated microglia (DAM) along with the progression of AD pathology and the pro-inflammatory activity was regulated by the Trem2-dependent pathways. Given the significance of microglia population in mediating the clearance of plaques, it is important to understand the mechanism of microglia activation and their response changes to the plaque accumulation in AD-affected brains. By observing the gene expression level across brain regions in 5xFAD mice, we showed that hippocampus was most affected during AD-like pathology progression and most up-regulated genes were pro-inflammatory. Then, by looking at the microglia gene expression data, we revealed the distinguished activation of disease-associated immune responses at the late stage in 5xFAD mice and the discrepancy between 5xFAD and wild type mice was increased with aging. In addition, we discussed the importance of Trem2 in microglia at the late stage of 5xFAD and showed that Trem2 knock-out not only decreased microglia immune responses but also changed the micro-environment that microglia resided in. Based on this, we continued argued that the interaction between microglia and other cell types, including neurons, astrocytes and oligodendrocytes, had significant impacts on the microglia-mediated plaque clearance in AD/AD-like pathology affected brain tissues.","PeriodicalId":166893,"journal":{"name":"Proceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122856753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Delta variant is considered as Variant of Concern (VOC) due to its high transmissibility compared to the original strain and other variants. This paper assessed the transmissibility of the delta variant in the UK from March to July by estimating effective reproduction numbers. First, effective reproduction number R at initial exponential phase of outbreak was calculated by using exponential growth method, then real time reproduction number Rt was estimated by using the time-dependent method. Depending on the observed generation interval distribution, R is 1.39 and Rt is merging to 1 along the time. The result is significantly lower than the basic reproduction number R0 reported by several institutions. From the first emergence of delta variant in UK to July, even though the daily infection cases were increasing due to step-by-step lockdown relaxation, the transmissibility of delta is slightly diminishing. Control measures especially the vaccination program are considered effective from this perspective. The analysis was performed based on daily cumulative delta infection number from March to July provided by Public Health England (PHE). R studio and Microsoft Excel were used in data processing and visualization.
{"title":"Dynamics of Covid-19 Delta Variant in the UK: the estimate of Reproduction Number","authors":"Yujie Ni","doi":"10.1145/3498731.3498754","DOIUrl":"https://doi.org/10.1145/3498731.3498754","url":null,"abstract":"Delta variant is considered as Variant of Concern (VOC) due to its high transmissibility compared to the original strain and other variants. This paper assessed the transmissibility of the delta variant in the UK from March to July by estimating effective reproduction numbers. First, effective reproduction number R at initial exponential phase of outbreak was calculated by using exponential growth method, then real time reproduction number Rt was estimated by using the time-dependent method. Depending on the observed generation interval distribution, R is 1.39 and Rt is merging to 1 along the time. The result is significantly lower than the basic reproduction number R0 reported by several institutions. From the first emergence of delta variant in UK to July, even though the daily infection cases were increasing due to step-by-step lockdown relaxation, the transmissibility of delta is slightly diminishing. Control measures especially the vaccination program are considered effective from this perspective. The analysis was performed based on daily cumulative delta infection number from March to July provided by Public Health England (PHE). R studio and Microsoft Excel were used in data processing and visualization.","PeriodicalId":166893,"journal":{"name":"Proceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124806108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Revealing the molecular regulation mechanism of cell fate decision is of great significance for understanding stem cell differentiation and tissue homeostasis. In this paper, a coarse-grained endogenous network for endodermal liver differentiation is constructed, which is composed of five transcription factors and their interaction was collected from the accumulated biological knowledge. The stable states and transition states with biological significance are obtained from the dynamics of the network, by which previously unobserved cell states during the differentiation of liver cells were predicted. In addition, the landscape of the liver cell differentiation is also predicted from the computing results. This landscape not only contains the classical endoderm liver cell differentiation roadmap; but also predicts more complex differentiation paths. This study shows that the construction of the dynamic model of the endogenous network is an effective tool for more in-depth research in the mechanism of liver cell differentiation and explain the genesis of liver diseases
{"title":"Endogenous Network Reveals the Landscape of Liver Lineage Differentiation","authors":"Xiao Liu, Mengyao Wang, Qi Chang","doi":"10.1145/3498731.3498736","DOIUrl":"https://doi.org/10.1145/3498731.3498736","url":null,"abstract":"Revealing the molecular regulation mechanism of cell fate decision is of great significance for understanding stem cell differentiation and tissue homeostasis. In this paper, a coarse-grained endogenous network for endodermal liver differentiation is constructed, which is composed of five transcription factors and their interaction was collected from the accumulated biological knowledge. The stable states and transition states with biological significance are obtained from the dynamics of the network, by which previously unobserved cell states during the differentiation of liver cells were predicted. In addition, the landscape of the liver cell differentiation is also predicted from the computing results. This landscape not only contains the classical endoderm liver cell differentiation roadmap; but also predicts more complex differentiation paths. This study shows that the construction of the dynamic model of the endogenous network is an effective tool for more in-depth research in the mechanism of liver cell differentiation and explain the genesis of liver diseases","PeriodicalId":166893,"journal":{"name":"Proceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127436932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract: Single-cell sequencing is an emerging technique that allows high-throughput data analysis at an individual cell resolution and is applied in diverse fields of biology. Due to the large amount of data, downstream analysis is very complicated, and cell type annotation is a critical step; however, it is currently difficult to obtain good results. Single-cell sequencing has resulted in new breakthroughs in multi-omics, such as CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by sequencing), which allows the measurement of surface marker proteins simultaneously with the sequencing of mRNA at the single-cell level. In this study, a CITE-seq dataset of human PBMCs (peripheral blood mononuclear cells) was annotated using the most popular reference-based annotation methods, including SingleR, Seurat with the RNA-seq dataset, and Seurat with both the RNA and protein datasets; the results were then compared with RNA and protein expression levels to determine the role of proteins in cell annotation. The results indicate that protein expression can supplement datasets with some mRNAs with low expression to improve accuracy. With the verification of single-cell biomarkers, the multi-omics annotation method Seurat with both the RNA and protein databases showed the best performance, especially in the differentiation of NK cells and T cells and of dendritic cells and monocytes. This study shows the significance of multi-omics information for improving cell annotation and has great potential for perfecting these annotations with more data support.
摘要:单细胞测序是一项新兴的技术，可以在单个细胞分辨率下进行高通量数据分析，并应用于生物学的各个领域。由于数据量大，下游分析非常复杂，细胞类型标注是关键步骤;然而，目前很难取得良好的效果。单细胞测序带来了多组学的新突破，例如CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by sequencing)，它允许在单细胞水平上测量mRNA的同时测量表面标记蛋白。在本研究中，使用最流行的基于参考的注释方法对人外周血单个核细胞的CITE-seq数据集进行注释，包括SingleR、Seurat与RNA-seq数据集以及Seurat与RNA和蛋白质数据集的注释;然后将结果与RNA和蛋白质表达水平进行比较，以确定蛋白质在细胞注释中的作用。结果表明，蛋白质表达可以用一些低表达的mrna补充数据集，以提高准确性。通过对单细胞生物标志物的验证，结合RNA和蛋白质数据库的多组学注释方法Seurat在NK细胞和T细胞、树突状细胞和单核细胞的分化中表现出最好的性能。本研究显示了多组学信息对改进细胞注释的重要意义，并且在有更多数据支持的情况下具有完善细胞注释的巨大潜力。
{"title":"A comparative study of cell type annotation methods for immune cells using single-cell sequencing technology","authors":"Tian-Yu Zhang","doi":"10.1145/3498731.3498735","DOIUrl":"https://doi.org/10.1145/3498731.3498735","url":null,"abstract":"Abstract: Single-cell sequencing is an emerging technique that allows high-throughput data analysis at an individual cell resolution and is applied in diverse fields of biology. Due to the large amount of data, downstream analysis is very complicated, and cell type annotation is a critical step; however, it is currently difficult to obtain good results. Single-cell sequencing has resulted in new breakthroughs in multi-omics, such as CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by sequencing), which allows the measurement of surface marker proteins simultaneously with the sequencing of mRNA at the single-cell level. In this study, a CITE-seq dataset of human PBMCs (peripheral blood mononuclear cells) was annotated using the most popular reference-based annotation methods, including SingleR, Seurat with the RNA-seq dataset, and Seurat with both the RNA and protein datasets; the results were then compared with RNA and protein expression levels to determine the role of proteins in cell annotation. The results indicate that protein expression can supplement datasets with some mRNAs with low expression to improve accuracy. With the verification of single-cell biomarkers, the multi-omics annotation method Seurat with both the RNA and protein databases showed the best performance, especially in the differentiation of NK cells and T cells and of dendritic cells and monocytes. This study shows the significance of multi-omics information for improving cell annotation and has great potential for perfecting these annotations with more data support.","PeriodicalId":166893,"journal":{"name":"Proceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130692901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：