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Biomedical Application of Identified Biomarkers Gene Expression Based Early Diagnosis and Detection in Cervical Cancer with Modified Probabilistic Neural Network. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:4946154. [PMID: 36134120 PMCID: PMC9482500 DOI: 10.1155/2022/4946154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 04/15/2022] [Accepted: 05/13/2022] [Indexed: 11/17/2022]
Abstract
Cervical squamous cell carcinoma (CSC) is expected to rise to become the fourth most prevalent cancer in women globally and to replace breast cancer as the top cause of death in women in the future years, according to the World Health Organization. According to the World Health Organization, developing countries are responsible for 86 percent of all cervical cancer cases globally in women aged 15 to 44 (WHO). Cancer mortality is associated with the largest amount of monotonous antecedent in low- and middle-income nations, while cancer mortality is associated with the least amount of monotonous antecedent in high-income countries. Cervical cancer is thought to be caused by aberrant proliferation of cells in the cervix that is capable of stealing or invading other human organs, according to current thinking. Cancer of the cerebral cell is the most prevalent kind of cancer in women. It is expected that cervical squamous cell carcinoma (CSC) will be the fourth most frequent cancer in the world and the main cause of death in women by the year 2050. Despite the fact that technology has improved tremendously since then, this is still the case. When compared to high-income countries, low- and middle-income countries have the highest consistent antecedent for cancer mortality, according to the World Cancer Research Fund. Cancerous growths of cells in the cervix, such as cervical cancer, are caused by cells that have the ability to steal from or invade auxiliary organs of the body, as is the case with cervical cancer. Although technological advances have been made in recent years, gene expression profiling continues to be a prominent approach in the investigation of cervical cancer. Since then, researchers have had the opportunity to examine a gene coexpression network, which has evolved into an exceptionally comprehensive technique for microarray research. This has helped them to get a better understanding of the human genome. When a specific biological issue is addressed, gene coexpression networks retain a considerable percentage of their once vast component of physiognomy, which was previously immense. When comparing the properties of genes in a population, it is well known that feature selection may be used to choose genes that outperform the rest of the genes in the population. There are several benefits to feature selection, and this is only one of them. Typically used gene selection approaches have been shown to be insufficient in acquiring the best potential sequence of genes for training purposes, and as a result, the accuracy of the classifier has likely suffered as a result of this. Recently, a considerable number of scientists have advocated for the use of optimization approaches in the process of gene selection, and this trend is expected to continue. A metaheuristic algorithm may be used to choose a suitable subset of genes, according to the preceding assertion, which is also consistent with the metaheuristic approach. A Modified Probabilistic Neural Network differs from other networks in that the underlying gene expression associated with DEGs and standard data in a Modified Probabilistic Neural Network is not uniformly distributed as it is in other networks (MPN). As previously said, selecting the most relevant genes or repeating genes is a vital step in the prediction process. It was this technique that was used in the research of cervical cancer. Since then, researchers have had the opportunity to examine a gene coexpression network, which has evolved into an exceptionally comprehensive technique for microarray research. This has helped them to get a better understanding of the human genome. When a specific biological issue is addressed, gene coexpression networks are able to preserve a previously major section of the face that had been lost. When comparing the properties of genes in a population, it is well known that feature selection may be used to choose genes that outperform the rest of the genes in the population. There are several benefits to feature selection, and this is only one of them. Typically used gene selection approaches have been shown to be insufficient in acquiring the best potential sequence of genes for training purposes, and as a result, the accuracy of the classifier has likely suffered as a result of this. In the field of gene selection, several scholars have argued in favor of the employment of optimization approaches. A metaheuristic algorithm may be used to choose a suitable subset of genes, according to the preceding assertion, which is also consistent with the metaheuristic approach. It was discovered that Modified Probabilistic Neural Networks (MPNs) had a different distribution of gene expression linked with DEGs and normal data than other networks, which had not been previously seen. This was previously unknown. Following what has been said before, selecting the most appropriate or repeated genes is a critical task throughout the prediction process.
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Mochida K, Nishii R, Hirayama T. Decoding Plant-Environment Interactions That Influence Crop Agronomic Traits. PLANT & CELL PHYSIOLOGY 2020; 61:1408-1418. [PMID: 32392328 PMCID: PMC7434589 DOI: 10.1093/pcp/pcaa064] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 04/26/2020] [Indexed: 05/16/2023]
Abstract
To ensure food security in the face of increasing global demand due to population growth and progressive urbanization, it will be crucial to integrate emerging technologies in multiple disciplines to accelerate overall throughput of gene discovery and crop breeding. Plant agronomic traits often appear during the plants' later growth stages due to the cumulative effects of their lifetime interactions with the environment. Therefore, decoding plant-environment interactions by elucidating plants' temporal physiological responses to environmental changes throughout their lifespans will facilitate the identification of genetic and environmental factors, timing and pathways that influence complex end-point agronomic traits, such as yield. Here, we discuss the expected role of the life-course approach to monitoring plant and crop health status in improving crop productivity by enhancing the understanding of plant-environment interactions. We review recent advances in analytical technologies for monitoring health status in plants based on multi-omics analyses and strategies for integrating heterogeneous datasets from multiple omics areas to identify informative factors associated with traits of interest. In addition, we showcase emerging phenomics techniques that enable the noninvasive and continuous monitoring of plant growth by various means, including three-dimensional phenotyping, plant root phenotyping, implantable/injectable sensors and affordable phenotyping devices. Finally, we present an integrated review of analytical technologies and applications for monitoring plant growth, developed across disciplines, such as plant science, data science and sensors and Internet-of-things technologies, to improve plant productivity.
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Affiliation(s)
- Keiichi Mochida
- RIKEN Center for Sustainable Resource Science, Tsurumi-ku, Yokohama, Japan
- Kihara Institute for Biological Research, Yokohama City University, Totsuka-ku, Yokohama, Japan
- Graduate School of Nanobioscience, Yokohama City University, Kanazawa-ku, Yokohama, Japan
- Institute of Plant Science and Resources, Okayama University, Kurashiki, Japan
- Corresponding author: E-mail, ; Fax, +81-45-503-9609
| | - Ryuei Nishii
- School of Information and Data Sciences, Nagasaki University, Nagasaki, Japan
| | - Takashi Hirayama
- Institute of Plant Science and Resources, Okayama University, Kurashiki, Japan
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Lorenzon R, Mariotti-Ferrandiz E, Aheng C, Ribet C, Toumi F, Pitoiset F, Chaara W, Derian N, Johanet C, Drakos I, Harris S, Amselem S, Berenbaum F, Benveniste O, Bodaghi B, Cacoub P, Grateau G, Amouyal C, Hartemann A, Saadoun D, Sellam J, Seksik P, Sokol H, Salem JE, Vicaut E, Six A, Rosenzwajg M, Bernard C, Klatzmann D. Clinical and multi-omics cross-phenotyping of patients with autoimmune and autoinflammatory diseases: the observational TRANSIMMUNOM protocol. BMJ Open 2018; 8:e021037. [PMID: 30166293 PMCID: PMC6119447 DOI: 10.1136/bmjopen-2017-021037] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Revised: 07/02/2018] [Accepted: 07/17/2018] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION Autoimmune and autoinflammatory diseases (AIDs) represent a socioeconomic burden as the second cause of chronic illness in Western countries. In this context, the TRANSIMMUNOM clinical protocol is designed to revisit the nosology of AIDs by combining basic, clinical and information sciences. Based on classical and systems biology analyses, it aims to uncover important phenotypes that cut across diagnostic groups so as to discover biomarkers and identify novel therapeutic targets. METHODS AND ANALYSIS TRANSIMMUNOM is an observational clinical protocol that aims to cross-phenotype a set of 19 AIDs, six related control diseases and healthy volunteers . We assembled a multidisciplinary cohort management team tasked with (1) selecting informative biological (routine and omics type) and clinical parameters to be captured, (2) standardising the sample collection and shipment circuit, (3) selecting omics technologies and benchmarking omics data providers, (4) designing and implementing a multidisease electronic case report form and an omics database and (5) implementing supervised and unsupervised data analyses. ETHICS AND DISSEMINATION The study was approved by the institutional review board of Pitié-Salpêtrière Hospital (ethics committee Ile-De-France 48-15) and done in accordance with the Declaration of Helsinki and good clinical practice. Written informed consent is obtained from all participants before enrolment in the study. TRANSIMMUNOM's project website provides information about the protocol (https://www.transimmunom.fr/en/) including experimental set-up and tool developments. Results will be disseminated during annual scientific committees appraising the project progresses and at national and international scientific conferences. DISCUSSION Systems biology approaches are increasingly implemented in human pathophysiology research. The TRANSIMMUNOM study applies such approach to the pathophysiology of AIDs. We believe that this translational systems immunology approach has the potential to provide breakthrough discoveries for better understanding and treatment of AIDs. TRIAL REGISTRATION NUMBER NCT02466217; Pre-results.
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Affiliation(s)
- Roberta Lorenzon
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | | | - Caroline Aheng
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Claire Ribet
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Ferial Toumi
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
| | - Fabien Pitoiset
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Wahiba Chaara
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Nicolas Derian
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Catherine Johanet
- Immunology Department, Saint-Antoine Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- UFR 967, Sorbonne Université, Paris, France
| | - Iannis Drakos
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
| | - Sophie Harris
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
| | - Serge Amselem
- Laboratoire de génétique, UMR S933, Sorbonne Université, INSERM, Paris, France
| | - Francis Berenbaum
- Rheumatology Department, Saint-Antoine Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- UMR S938, Sorbonne Université, INSERM, Paris, France
| | - Olivier Benveniste
- Internal Medicine and Clinical Immunology Department, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- UMR 974, Sorbonne Université, Paris, France
| | - Bahram Bodaghi
- Département Hospitalo-Universitaire Vision and Handicaps ‘ViewMaintain’, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, Île-de-France, France
| | - Patrice Cacoub
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Internal Medicine and Clinical Immunology Department, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Gilles Grateau
- Internal Medicine Department, Tenon Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- UMR S933, Sorbonne Université, INSERM, Paris, France
| | - Chloe Amouyal
- Diabetology Department, Institute of Cardiometabolism and Nutrition (ICAN), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, Île-de-France, France
| | - Agnes Hartemann
- Diabetology Department, Institute of Cardiometabolism and Nutrition (ICAN), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, Île-de-France, France
| | - David Saadoun
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Internal Medicine and Clinical Immunology Department, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Jeremie Sellam
- Rheumatology Department, Saint-Antoine Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- UMR S938, Sorbonne Université, INSERM, Paris, France
| | - Philippe Seksik
- Gastroenterology Department, Saint-Antoine Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- GRC-UPMC 03, Sorbonne Université, Paris, France
| | - Harry Sokol
- Gastroenterology Department, Saint-Antoine Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- GRC-UPMC 03, Sorbonne Université, Paris, France
| | - Joe-Elie Salem
- CIC-1421, Pharmacology Department, INSERM, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Eric Vicaut
- Unité de recherche clinique, UMR 942, Saint-Louis Lariboisière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Adrien Six
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Michelle Rosenzwajg
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Claude Bernard
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - David Klatzmann
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
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