Correct and timely detection of Mycobacterium tuberculosis (MTB) resistance against existing tuberculosis (TB) drugs is essential for the limit of TB amplification. The objectives of the projects are (1) to develop classification models that help isoniazid-resistant TB diagnosis, (2) to find the best performed classification algorithm, and (3) to rank the gene mutations according to feature importance. The python sklearn and matplotlib packages were frequently utilized throughout the research for data curation, classification model development, and feature importance ranking. Additionally, area under the curve (AUC), precision, sensitivity, specificity, F1 score, and correct classification rate measured for model performances, and Gini importance calculated feature importance. Gradient boosting found to overperform other classification models with the highest accuracy mean 0f 0.852, and its overfitting error exposed the need for dimensionality reduction prior to model training. Gene 625 and 331 were the most significant features in this project, and this suggested the potential of machine learning (ML) to find new resistance makers. The results confirmed the application of ML in clinical settings for quicker and better prediction of drug resistance based on large genome sequencing data. With future studies focusing on less studied and second-line TB drugs, classification models could decrease mortality and prevent the amplification of existing antibiotic resistance by allowing early diagnosis and treatment. CCS CONCEPTS • Computing methodologies∼Machine learning∼Learning paradigms∼Supervised learning∼Supervised learning by classification
{"title":"Application of machine learning to Isoniazid resistance analysis","authors":"Zhou Yang","doi":"10.1145/3571532.3571541","DOIUrl":"https://doi.org/10.1145/3571532.3571541","url":null,"abstract":"Correct and timely detection of Mycobacterium tuberculosis (MTB) resistance against existing tuberculosis (TB) drugs is essential for the limit of TB amplification. The objectives of the projects are (1) to develop classification models that help isoniazid-resistant TB diagnosis, (2) to find the best performed classification algorithm, and (3) to rank the gene mutations according to feature importance. The python sklearn and matplotlib packages were frequently utilized throughout the research for data curation, classification model development, and feature importance ranking. Additionally, area under the curve (AUC), precision, sensitivity, specificity, F1 score, and correct classification rate measured for model performances, and Gini importance calculated feature importance. Gradient boosting found to overperform other classification models with the highest accuracy mean 0f 0.852, and its overfitting error exposed the need for dimensionality reduction prior to model training. Gene 625 and 331 were the most significant features in this project, and this suggested the potential of machine learning (ML) to find new resistance makers. The results confirmed the application of ML in clinical settings for quicker and better prediction of drug resistance based on large genome sequencing data. With future studies focusing on less studied and second-line TB drugs, classification models could decrease mortality and prevent the amplification of existing antibiotic resistance by allowing early diagnosis and treatment. CCS CONCEPTS • Computing methodologies∼Machine learning∼Learning paradigms∼Supervised learning∼Supervised learning by classification","PeriodicalId":355088,"journal":{"name":"Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128435326","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}
Flushing response after alcohol consumption is common in East Asia, caused by alcohol enzyme deficiency. Alcohol dehydrogenase (ADH), the major enzyme in alcohol metabolism pathways, are deficient to dissolve alcohol. People with decreased numbers and activity of enzymes are at higher risks of esophageal cancers. However, there is a lack of alcohol degrading products and relevant researches to address the issue in the current market. Although there are commercially available products to relieve symptoms and protect patients’ liver, none of them are dedicated to primarily enhancing enzyme activity for more efficient clearance of alcohol. Therefore, our research aims to offer a synthetic-biology solution for people with ADH deficiency to metabolize alcohol efficiently and reduce negative health effects. To do this, we took a two-step approach. First, we did computational analysis on designing and engineering three genetic circuits that can produce the most ADH in an alcohol-rich environment. This provides a great opportunity for ADH to degrade alcohol. Furthermore, we investigated different circuit dynamics under different drinking patterns (e.g., fast drinker, slow drinker, etc.) and were able to recommend customized circuit based on different situations. Second, we conducted basic laboratory experiments to select probiotic strain for future engineering purposes. Namely, we cultured Escherichia. Coli, Bacillus subtilis, Lactobacillus delbrueckii subsp. Bulgaricus, and Streptococcus thermophilus under different concentrations of alcohol to select the most alcohol-tolerant probiotic. Overall, our research develops an engineered probiotic system in which the gene circuit can efficiently decay alcohol based on various drinking patterns.
{"title":"Gene Circuit Construction and Simulation in Probiotics to Metabolize Alcohol","authors":"Jing Liang, Zhe Wu","doi":"10.1145/3571532.3571544","DOIUrl":"https://doi.org/10.1145/3571532.3571544","url":null,"abstract":"Flushing response after alcohol consumption is common in East Asia, caused by alcohol enzyme deficiency. Alcohol dehydrogenase (ADH), the major enzyme in alcohol metabolism pathways, are deficient to dissolve alcohol. People with decreased numbers and activity of enzymes are at higher risks of esophageal cancers. However, there is a lack of alcohol degrading products and relevant researches to address the issue in the current market. Although there are commercially available products to relieve symptoms and protect patients’ liver, none of them are dedicated to primarily enhancing enzyme activity for more efficient clearance of alcohol. Therefore, our research aims to offer a synthetic-biology solution for people with ADH deficiency to metabolize alcohol efficiently and reduce negative health effects. To do this, we took a two-step approach. First, we did computational analysis on designing and engineering three genetic circuits that can produce the most ADH in an alcohol-rich environment. This provides a great opportunity for ADH to degrade alcohol. Furthermore, we investigated different circuit dynamics under different drinking patterns (e.g., fast drinker, slow drinker, etc.) and were able to recommend customized circuit based on different situations. Second, we conducted basic laboratory experiments to select probiotic strain for future engineering purposes. Namely, we cultured Escherichia. Coli, Bacillus subtilis, Lactobacillus delbrueckii subsp. Bulgaricus, and Streptococcus thermophilus under different concentrations of alcohol to select the most alcohol-tolerant probiotic. Overall, our research develops an engineered probiotic system in which the gene circuit can efficiently decay alcohol based on various drinking patterns.","PeriodicalId":355088,"journal":{"name":"Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122544371","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}
Yufei Jia, Nan Gao, Xiujun Yao, Jiaqi Nie, Tiantian Chen
Abstract. Diabetes is a common disease that affects human health, and its incidence is on the rise in recent years. With the increase in the number of diabetic patients, the prolongation of the course of the disease and the aggravation of the disease, insulin pen injection therapy, as an important treatment method for lowering blood sugar, is more and more widely used in the blood sugar control of diabetic patients. In my country, 61.53% of diabetic patients maintain stable blood sugar levels through insulin injection, but in reality, less than 36.2% of patients can achieve ideal blood sugar control through insulin. With the development of computer technology, many hospitals have begun to realize the shortcomings of the original insulin pen management methods. Because the computer management system is flexible, open, safe, and easy to maintain, if the computer is used in the hospital to manage the insulin pen injection technology and further standardize the design of the insulin pen injection program, the dosage form, use method and injection device of insulin will be affected. There will be great progress. In this paper, the quality management of endocrinology insulin injection is tracked and monitored through computer program teaching, and an intelligent insulin pen is designed and developed. After applying this system for endocrinology insulin injection management, the incidence of insulin injection defects is significantly reduced, and the number of annual safety hazards is significantly reduced. Down from 24 to 3. At the same time, it is concluded that the program teaching mode can promote the patient's understanding and mastery of the insulin pen, and help the patient to use it more safely.
{"title":"Application of Computer Management System Function in Standard Design of Insulin Pen Injection Program","authors":"Yufei Jia, Nan Gao, Xiujun Yao, Jiaqi Nie, Tiantian Chen","doi":"10.1145/3571532.3571545","DOIUrl":"https://doi.org/10.1145/3571532.3571545","url":null,"abstract":"Abstract. Diabetes is a common disease that affects human health, and its incidence is on the rise in recent years. With the increase in the number of diabetic patients, the prolongation of the course of the disease and the aggravation of the disease, insulin pen injection therapy, as an important treatment method for lowering blood sugar, is more and more widely used in the blood sugar control of diabetic patients. In my country, 61.53% of diabetic patients maintain stable blood sugar levels through insulin injection, but in reality, less than 36.2% of patients can achieve ideal blood sugar control through insulin. With the development of computer technology, many hospitals have begun to realize the shortcomings of the original insulin pen management methods. Because the computer management system is flexible, open, safe, and easy to maintain, if the computer is used in the hospital to manage the insulin pen injection technology and further standardize the design of the insulin pen injection program, the dosage form, use method and injection device of insulin will be affected. There will be great progress. In this paper, the quality management of endocrinology insulin injection is tracked and monitored through computer program teaching, and an intelligent insulin pen is designed and developed. After applying this system for endocrinology insulin injection management, the incidence of insulin injection defects is significantly reduced, and the number of annual safety hazards is significantly reduced. Down from 24 to 3. At the same time, it is concluded that the program teaching mode can promote the patient's understanding and mastery of the insulin pen, and help the patient to use it more safely.","PeriodicalId":355088,"journal":{"name":"Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science","volume":"204 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115159215","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}
Marta Rosaria Marino, Massimo Majolo, Marco Grasso, Giuseppe Russo, G. Longo, M. Triassi, Teresa Angela Trunfio
The analysis of the hospital length of stay could provide a new perspective in the design of strategies to optimize the patients’ management, the clinical operations, and, not least, improve the overall healthcare service quality perceived by both patients, healthcare staff, and healthcare administrators and decision-makers. In the case of kidney injuries, the possibility to predict the length of stay is crucial for ensuring proper management of the patients undergoing surgical interventions. It is therefore significant for clinicians to have the option to anticipate the length of stay of patients and to deal with the main features influencing it to decrease the hospital length of stay. In this work we center around patients undergoing kidney surgery. Information was gathered from more than 3000 cases of kidney surgeries at the national hospital "A.O.R.N. Antonio Cardarelli" of Naples. A machine learning approach has been proposed to classify and predict the LOS of patients undergoing kidney surgery, and the tested algorithms have been assessed and compared in terms of performance metrics. Best predictive models have been identified and described in view of their potential impact in the improvement of the kidney surgery management procedures.
对住院时间的分析可以为优化患者管理、临床操作的策略设计提供一个新的视角，尤其是可以提高患者、医护人员、医疗保健管理人员和决策者对整体医疗保健服务质量的感知。在肾损伤的情况下，预测住院时间的可能性对于确保接受手术干预的患者的适当管理至关重要。因此，对于临床医生来说，有机会预测患者的住院时间，并处理影响住院时间的主要特征，以减少住院时间是很重要的。在这项工作中，我们以接受肾脏手术的患者为中心。资料收集自那不勒斯国立医院“A.O.R.N. Antonio Cardarelli”的3000多例肾脏手术。提出了一种机器学习方法来分类和预测接受肾脏手术的患者的LOS，并根据性能指标对测试的算法进行了评估和比较。鉴于其在改善肾脏手术管理程序方面的潜在影响，已确定并描述了最佳预测模型。
{"title":"A machine learning approach to predict the hospital length of stay after kidney surgery","authors":"Marta Rosaria Marino, Massimo Majolo, Marco Grasso, Giuseppe Russo, G. Longo, M. Triassi, Teresa Angela Trunfio","doi":"10.1145/3571532.3571539","DOIUrl":"https://doi.org/10.1145/3571532.3571539","url":null,"abstract":"The analysis of the hospital length of stay could provide a new perspective in the design of strategies to optimize the patients’ management, the clinical operations, and, not least, improve the overall healthcare service quality perceived by both patients, healthcare staff, and healthcare administrators and decision-makers. In the case of kidney injuries, the possibility to predict the length of stay is crucial for ensuring proper management of the patients undergoing surgical interventions. It is therefore significant for clinicians to have the option to anticipate the length of stay of patients and to deal with the main features influencing it to decrease the hospital length of stay. In this work we center around patients undergoing kidney surgery. Information was gathered from more than 3000 cases of kidney surgeries at the national hospital \"A.O.R.N. Antonio Cardarelli\" of Naples. A machine learning approach has been proposed to classify and predict the LOS of patients undergoing kidney surgery, and the tested algorithms have been assessed and compared in terms of performance metrics. Best predictive models have been identified and described in view of their potential impact in the improvement of the kidney surgery management procedures.","PeriodicalId":355088,"journal":{"name":"Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130912009","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}
N. Lin, Masaki Kitamura, K. Hirayama, Ryohei Hamano, K. Umemura
We synthesized double-stranded deoxyribonucleic acid (dsDNA)-wrapped single walled carbon nanotube complexes (dsDNA-SWNT complexes). These dsDNA-SWNT complexes were applied in detecting antioxidant potencies of two biomaterials: coconut water and banana powder solutions. UV-vis spectroscopy was employed to examine the optical response changes of dsDNA-SWNT complexes when these two biomaterials were injected. The wavelength range was from 700 to 1100 nm. After injecting coconut water or banana powder solutions, absorbance values of (6,5) and (7,5) chirality peaks were increased, and this means that these biomaterials have antioxidant potencies. If the initial absorbance of dsDNA-SWNT complexes is defined as 100%, the absorbance values of (6,5) and (7,5) peaks were increased by 105.01 ± 0.94 % and 104.37 ± 0.93 % respectively when coconut water solution was injected. Additionally, a red-shift was seen in this case. When banana powder solution was injected, the absorbances were increased by 101.73 ± 1.20 % and 101.29 ± 0.89 % respectively. No peak shift was resulted in this case. Atomic force microscopy (AFM) images revealed that structures of SWNTs were clearly seen even after adding coconut water solution.
{"title":"Antioxidant Properties of Asian Originated Biomaterials Detected with DNA-wrapped Single Walled Carbon Nanotubes","authors":"N. Lin, Masaki Kitamura, K. Hirayama, Ryohei Hamano, K. Umemura","doi":"10.1145/3571532.3571550","DOIUrl":"https://doi.org/10.1145/3571532.3571550","url":null,"abstract":"We synthesized double-stranded deoxyribonucleic acid (dsDNA)-wrapped single walled carbon nanotube complexes (dsDNA-SWNT complexes). These dsDNA-SWNT complexes were applied in detecting antioxidant potencies of two biomaterials: coconut water and banana powder solutions. UV-vis spectroscopy was employed to examine the optical response changes of dsDNA-SWNT complexes when these two biomaterials were injected. The wavelength range was from 700 to 1100 nm. After injecting coconut water or banana powder solutions, absorbance values of (6,5) and (7,5) chirality peaks were increased, and this means that these biomaterials have antioxidant potencies. If the initial absorbance of dsDNA-SWNT complexes is defined as 100%, the absorbance values of (6,5) and (7,5) peaks were increased by 105.01 ± 0.94 % and 104.37 ± 0.93 % respectively when coconut water solution was injected. Additionally, a red-shift was seen in this case. When banana powder solution was injected, the absorbances were increased by 101.73 ± 1.20 % and 101.29 ± 0.89 % respectively. No peak shift was resulted in this case. Atomic force microscopy (AFM) images revealed that structures of SWNTs were clearly seen even after adding coconut water solution.","PeriodicalId":355088,"journal":{"name":"Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129136169","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}
Resting-state functional magnetic resonance imaging (rs-fMRI) based dynamic functional connectivity (dynamic FC) networks have been used to better comprehend the functioning of the brain, and have been used to early stage (i.e., mild cognitive impairment, MCI). Deep learning (e.g., convolutional neural network, CNN) approaches have recently been used to analyze dynamic FC networks, and they outperform classic machine learning methods. The sequence information of temporal properties from dynamic FC networks is largely ignored in previous investigations. To that aim, we propose a neural network based on CNN and TCN model for extracting spatial and temporal features from dynamic FC networks using rs-fMRI data for brain disease categorization in this research. The efficiency of our suggested technique in binary classification tasks is demonstrated by experimental findings on 134 ADNI individuals.
{"title":"Extracting spatial-temporal characteristics from Dynamic Connectivity Network with rs-fMRI Data for AD Classification","authors":"R. Chen, Guixia Kang","doi":"10.1145/3571532.3571543","DOIUrl":"https://doi.org/10.1145/3571532.3571543","url":null,"abstract":"Resting-state functional magnetic resonance imaging (rs-fMRI) based dynamic functional connectivity (dynamic FC) networks have been used to better comprehend the functioning of the brain, and have been used to early stage (i.e., mild cognitive impairment, MCI). Deep learning (e.g., convolutional neural network, CNN) approaches have recently been used to analyze dynamic FC networks, and they outperform classic machine learning methods. The sequence information of temporal properties from dynamic FC networks is largely ignored in previous investigations. To that aim, we propose a neural network based on CNN and TCN model for extracting spatial and temporal features from dynamic FC networks using rs-fMRI data for brain disease categorization in this research. The efficiency of our suggested technique in binary classification tasks is demonstrated by experimental findings on 134 ADNI individuals.","PeriodicalId":355088,"journal":{"name":"Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114330401","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}
Safi an Ayurvedic medicine being used from times immoral have been analysed for the presence of various phytochemicals, minerals by various analytical procedures. The phytochemicals like, tannins, flavonoids, carbohydrates, and steroids have been found present and phenols, proteins and tannins have been found in a good amount quantitatively. The antioxidant power of Safi by DPPH and H2O2 scavenging activity showed that the contents of the Safi like, Triterpenoids, phenols, tannins act as good free radical scavengers. The HPLC analysis showed the presence of tannic acid, Azadirachtin, Chalcones and Caffic acid. The UV-Visible analysis showed the presence of tetra triterpenoids. In addition the Safi extracts showed the presence of Lead, Cadmium and Arsenic very much higher than the permittable limit of consumption.
{"title":"The Chemical and Spectroscopic Analysis of Unani Medicine Safi","authors":"M. Mir, S. Hasnain","doi":"10.1145/3571532.3571552","DOIUrl":"https://doi.org/10.1145/3571532.3571552","url":null,"abstract":"Safi an Ayurvedic medicine being used from times immoral have been analysed for the presence of various phytochemicals, minerals by various analytical procedures. The phytochemicals like, tannins, flavonoids, carbohydrates, and steroids have been found present and phenols, proteins and tannins have been found in a good amount quantitatively. The antioxidant power of Safi by DPPH and H2O2 scavenging activity showed that the contents of the Safi like, Triterpenoids, phenols, tannins act as good free radical scavengers. The HPLC analysis showed the presence of tannic acid, Azadirachtin, Chalcones and Caffic acid. The UV-Visible analysis showed the presence of tetra triterpenoids. In addition the Safi extracts showed the presence of Lead, Cadmium and Arsenic very much higher than the permittable limit of consumption.","PeriodicalId":355088,"journal":{"name":"Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122628686","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}
Lung cancer is one of primal and ubiquitous cause of cancer related fatalities in the world. Leading cause of these fatalities is non-small cell lung cancer (NSCLC) with a percentage of 85. The major subtypes of NSCLC are Lung Adenocarcinoma (LUAD), Lung Squamous cell carcinoma (LUSC), and Large cell carcinoma. Early-stage surgical detection and removal of tumor offers a favorable prognosis and better survival rates. However, more than 75% patients have stage III/IV at the time of diagnosis and despite advanced major developments in oncology survival rates remain poor. Carcinogens produce widespread DNA methylation changes within cells. These changes are characterized by globally hyper or hypo methylated regions around CpG islands. Many of these changes occur early in tumorigenesis and are highly prevalent across a tumor type. In this study, DNA methylation profiles were extracted from TCGA for 418 LUAD and 370 LUSC tissue samples from patients compared with 32 and 42 non-malignant ones respectively. A standard pipeline was performed to consider significant differentially methylated sites as primary biomarkers, while secondary biomarkers were obtained from associated comorbidities and associated disease genes from meta-analysis study. Concordant candidates were utilized for NSCLC relevant biomarker candidates. Gene ontology annotations were used to calculate gene-pair distance matrix for all candidate biomarkers. Clustering algorithms were utilized to categorize candidate genes into different functional groups using gene distance matrix. There were 35 CpG loci identified as key biomarkers by comparing TCGA training cohort with GEO testing cohort from these functional groups.
{"title":"Detection of Novel Gene Biomarkers in non-small cell lung cancer using integrated approaches in DNA methylation expression","authors":"Tun-Wen Pai","doi":"10.1145/3571532.3571534","DOIUrl":"https://doi.org/10.1145/3571532.3571534","url":null,"abstract":"Lung cancer is one of primal and ubiquitous cause of cancer related fatalities in the world. Leading cause of these fatalities is non-small cell lung cancer (NSCLC) with a percentage of 85. The major subtypes of NSCLC are Lung Adenocarcinoma (LUAD), Lung Squamous cell carcinoma (LUSC), and Large cell carcinoma. Early-stage surgical detection and removal of tumor offers a favorable prognosis and better survival rates. However, more than 75% patients have stage III/IV at the time of diagnosis and despite advanced major developments in oncology survival rates remain poor. Carcinogens produce widespread DNA methylation changes within cells. These changes are characterized by globally hyper or hypo methylated regions around CpG islands. Many of these changes occur early in tumorigenesis and are highly prevalent across a tumor type. In this study, DNA methylation profiles were extracted from TCGA for 418 LUAD and 370 LUSC tissue samples from patients compared with 32 and 42 non-malignant ones respectively. A standard pipeline was performed to consider significant differentially methylated sites as primary biomarkers, while secondary biomarkers were obtained from associated comorbidities and associated disease genes from meta-analysis study. Concordant candidates were utilized for NSCLC relevant biomarker candidates. Gene ontology annotations were used to calculate gene-pair distance matrix for all candidate biomarkers. Clustering algorithms were utilized to categorize candidate genes into different functional groups using gene distance matrix. There were 35 CpG loci identified as key biomarkers by comparing TCGA training cohort with GEO testing cohort from these functional groups.","PeriodicalId":355088,"journal":{"name":"Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122628715","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}
Marta Rosaria Marino, E. Raiola, Ruggero Rispo, Giuseppe Russo, G. Longo, M. Triassi, Teresa Angela Trunfio
The Coronavirus 2 (SARS-CoV-2), causing severe acute respiratory syndrome, is the source of the global pandemic known as Coronavirus Disease-2019 (COVID-19). The comorbidities, such as diabetes mellitus, cardiovascular disorders such hypertension, kidney disease, lung disease, and age, affect COVID-19 effects severity (1-3). This new disease has changed surgery practice in most countries around the world. (4) In order to maintain social distance, surgery departments may take a variety of steps, such as canceling face-to-face outpatient and nonurgent appointments, screening scheduled clinic visits, conducting telephone consultations with patients who have nonurgent conditions, and rescheduling appointments for a few months. (5) The focus of this work is to analyze the activity of the Department of liver transplant surgery in “A.O.R.N. Antonio Cardarelli" of Naples (Italy) was analyzed. In particular, the data-set obtained in the year 2019 (pre-pandemic) was compared with that in the following year 2020 (pandemic). The data refers to patients undergoing liver transplantation.
{"title":"Analysis of the impact of COVID-19 on the organization of liver transplantation","authors":"Marta Rosaria Marino, E. Raiola, Ruggero Rispo, Giuseppe Russo, G. Longo, M. Triassi, Teresa Angela Trunfio","doi":"10.1145/3571532.3571553","DOIUrl":"https://doi.org/10.1145/3571532.3571553","url":null,"abstract":"The Coronavirus 2 (SARS-CoV-2), causing severe acute respiratory syndrome, is the source of the global pandemic known as Coronavirus Disease-2019 (COVID-19). The comorbidities, such as diabetes mellitus, cardiovascular disorders such hypertension, kidney disease, lung disease, and age, affect COVID-19 effects severity (1-3). This new disease has changed surgery practice in most countries around the world. (4) In order to maintain social distance, surgery departments may take a variety of steps, such as canceling face-to-face outpatient and nonurgent appointments, screening scheduled clinic visits, conducting telephone consultations with patients who have nonurgent conditions, and rescheduling appointments for a few months. (5) The focus of this work is to analyze the activity of the Department of liver transplant surgery in “A.O.R.N. Antonio Cardarelli\" of Naples (Italy) was analyzed. In particular, the data-set obtained in the year 2019 (pre-pandemic) was compared with that in the following year 2020 (pandemic). The data refers to patients undergoing liver transplantation.","PeriodicalId":355088,"journal":{"name":"Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129914334","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}
Registration of CT-X-rays is crucial in high-precision orthopedic surgery. In this study, a deep learning network integrating convolution and transformer modules is proposed as a model for measuring image similarity for the registration of CT-X-rays. By training the network model to approximate the geodesic distance of Riemann space, the model has the property of convex function, to avoid falling into a local optimum. To further reduce the translation error of registration, this study introduces a spine detection network based on Yolov5, detects the spine of the target image and the image to be registered, obtains the spine position information and readjusts the translation component of the pose. The method used in this study has been tested, and the translation error and rotation error are lower than 3.05 mm and 1.96°, respectively.
{"title":"CT-X-Ray Registration Via Spatial-Projective Dual Transformer Network Fused With Target Detection","authors":"Zheng Zhang, Danni Ai, Haixiao Geng, Jian Yang","doi":"10.1145/3571532.3571546","DOIUrl":"https://doi.org/10.1145/3571532.3571546","url":null,"abstract":"Registration of CT-X-rays is crucial in high-precision orthopedic surgery. In this study, a deep learning network integrating convolution and transformer modules is proposed as a model for measuring image similarity for the registration of CT-X-rays. By training the network model to approximate the geodesic distance of Riemann space, the model has the property of convex function, to avoid falling into a local optimum. To further reduce the translation error of registration, this study introduces a spine detection network based on Yolov5, detects the spine of the target image and the image to be registered, obtains the spine position information and readjusts the translation component of the pose. The method used in this study has been tested, and the translation error and rotation error are lower than 3.05 mm and 1.96°, respectively.","PeriodicalId":355088,"journal":{"name":"Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science","volume":"9 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133205147","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}
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