Pub Date : 2022-05-13DOI: 10.2174/1574362417666220513151926
S. F. Joharah, S. Mohideen
Ultrasound imaging is a standard examination during pregnancy that can measure specific biometric parameters towards prenatal diagnosis and estimating gestational age. Fetal head circumference (HC) is a significant factor in determining fetus growth and health. This paper proposes a multi-task deep convolutional neural network for automatic segmentation and estimation of HC (Fetal head circumference) ellipse by minimizing a compound cost function composed of segmentation dice score and MSE of ellipse parameters. Ultrasound-based fetal biometric measurements, such as head circumference (HC) and biparietal diameter (BPD (BIPARIETAL DIAMETER)), are commonly used to evaluate the gestational age and diagnose fetal central nervous system (CNS) pathology. Since manual measurements are operator-dependent and time-consuming, there have been numerous researches on automated methods. However, existing computerized methods still are not satisfactory in terms of accuracy and reliability, owing to difficulties in dealing with various artefacts in ultrasound images. This paper focuses on fetal head biometry and develops a deep-learning-based method for estimating HC (Fetal head circumference) and BPD (BIPARIETAL DIAMETER) with a high degree of accuracy and reliability. The proposed method effectively identifies the head boundary by differentiating tissue image patterns concerning the ultrasound propagation direction. The proposed method was trained with 102 labelled data set and tested to 70 ultrasound images. We achieved a success rate of 92.31% for HC (Fetal head circumference) and BPD (BIPARIETAL DIAMETER) estimations and an accuracy of 87.14% for the plane acceptance check.
{"title":"Evaluation of fetal head circumference (hc) and biparietal diameter (bpd (biparietal diameter)) in ultrasound images using multi-task deep convolutional neural network","authors":"S. F. Joharah, S. Mohideen","doi":"10.2174/1574362417666220513151926","DOIUrl":"https://doi.org/10.2174/1574362417666220513151926","url":null,"abstract":"Ultrasound imaging is a standard examination during pregnancy that can measure specific biometric parameters towards prenatal diagnosis and estimating gestational age. Fetal head circumference (HC) is a significant factor in determining fetus growth and health.\u0000\u0000\u0000\u0000This paper proposes a multi-task deep convolutional neural network for automatic segmentation and estimation of HC (Fetal head circumference) ellipse by minimizing a compound cost function composed of segmentation dice score and MSE of ellipse parameters. Ultrasound-based fetal biometric measurements, such as head circumference (HC) and biparietal diameter (BPD (BIPARIETAL DIAMETER)), are commonly used to evaluate the gestational age and diagnose fetal central nervous system (CNS) pathology. Since manual measurements are operator-dependent and time-consuming, there have been numerous researches on automated methods. However, existing computerized methods still are not satisfactory in terms of accuracy and reliability, owing to difficulties in dealing with various artefacts in ultrasound images.\u0000\u0000\u0000\u0000This paper focuses on fetal head biometry and develops a deep-learning-based method for estimating HC (Fetal head circumference) and BPD (BIPARIETAL DIAMETER) with a high degree of accuracy and reliability.\u0000\u0000\u0000\u0000The proposed method effectively identifies the head boundary by differentiating tissue image patterns concerning the ultrasound propagation direction. The proposed method was trained with 102 labelled data set and tested to 70 ultrasound images. We achieved a success rate of 92.31% for HC (Fetal head circumference) and BPD (BIPARIETAL DIAMETER) estimations and an accuracy of 87.14% for the plane acceptance check.","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42993550","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}
Pub Date : 2022-04-12DOI: 10.2174/1574362417666220412132348
Sahithi Ginjupalli, V. Radhesyam, Manne Suneetha, Gunti Sahithi, Satagopam Sai Keerthana
Prior Authorization is the widely used process by Health Insurance companies in United States before they agree to cover prescribed medication under Medical Insurance. However traditional approach includes long length paper works, leading patients getting delayed in getting their claim processed. This delay may deteriorate patient’s medical condition. Also due to man made errors there is a chance of incorrect decision making process on the claims. On the other hand, physicians are losing their time in getting their prescribed medication approved. It is essential to reduce the wait time of patients and tedious work of physicians for healthcare to be effective. This demands advanced technology which can aid in boosting the decision making process of prior authorization methodology. The aim of this work is to digitize the prior authorization process by implementing classification algorithms which can classify the prior authorization applications into Accepted/Rejected/Partially Accepted classes. Proposed a web application which inputs prior authorization claim details and outputs the predicted class of the claim. Analyzed and collected significant features by implementing Feature selection. Developed classification models using Artificial Neural Networks, Random Forest. Implemented model validation techniques to evaluate classifiers performance. From the research findings Generic medication cost, type of Health insurance plan, Addictive nature and side effects of the prescribed drug, patient physical qualities like Age/Gender/Current Medical condition are the significant attributes that impact the decision making process in prior authorization process. Then implemented classifiers exhibited accurate performance on the Train and Test data. Amongst Artificial Neural Networks portrayed the more accuracy. Further analyzed confusion matrix for developed models. In addition to that performed k-fold cross validation and availed performance evaluation metrics to validate the model performance. Ameliorated Healthcare by removing time, location barriers in Prior Authorization process while ensuring patients get quality and economical medication. The proposed web application with machine learning predictive model as backend, automates the prior authorization process by classifying the applications in few seconds.
{"title":"Digitization of Prior Authorization in Healthcare Management using Machine Learning","authors":"Sahithi Ginjupalli, V. Radhesyam, Manne Suneetha, Gunti Sahithi, Satagopam Sai Keerthana","doi":"10.2174/1574362417666220412132348","DOIUrl":"https://doi.org/10.2174/1574362417666220412132348","url":null,"abstract":"\u0000\u0000Prior Authorization is the widely used process by Health Insurance companies in United States before they agree to cover prescribed medication under Medical Insurance. However traditional approach includes long length paper works, leading patients getting delayed in getting their claim processed. This delay may deteriorate patient’s medical condition. Also due to man made errors there is a chance of incorrect decision making process on the claims. On the other hand, physicians are losing their time in getting their prescribed medication approved. It is essential to reduce the wait time of patients and tedious work of physicians for healthcare to be effective. This demands advanced technology which can aid in boosting the decision making process of prior authorization methodology.\u0000\u0000\u0000\u0000The aim of this work is to digitize the prior authorization process by implementing classification algorithms which can classify the prior authorization applications into Accepted/Rejected/Partially Accepted classes. Proposed a web application which inputs prior authorization claim details and outputs the predicted class of the claim.\u0000\u0000\u0000\u0000Analyzed and collected significant features by implementing Feature selection. Developed classification models using Artificial Neural Networks, Random Forest. Implemented model validation techniques to evaluate classifiers performance.\u0000\u0000\u0000\u0000From the research findings Generic medication cost, type of Health insurance plan, Addictive nature and side effects of the prescribed drug, patient physical qualities like Age/Gender/Current Medical condition are the significant attributes that impact the decision making process in prior authorization process. Then implemented classifiers exhibited accurate performance on the Train and Test data. Amongst Artificial Neural Networks portrayed the more accuracy. Further analyzed confusion matrix for developed models. In addition to that performed k-fold cross validation and availed performance evaluation metrics to validate the model performance.\u0000\u0000\u0000\u0000Ameliorated Healthcare by removing time, location barriers in Prior Authorization process while ensuring patients get quality and economical medication. The proposed web application with machine learning predictive model as backend, automates the prior authorization process by classifying the applications in few seconds.\u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43976479","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}
Pub Date : 2022-04-05DOI: 10.2174/1574362417666220405151738
K. Vanitha, D. Satyanarayana, M. Prasad
In the extraction of information from multimodality images, anatomical and functional image fusion became an effective tool in the applications of clinical imaging. Objective: A new approach to fuse anatomical and functional images that use the combination of activity measure and intuitionistic fuzzy sets in NSST domain is presented. First, the high and low-frequency sub-images of source images are obtained by utilizing NSST decomposition, which represents them in multi-scale and multi-directions. Next, the high-frequency sub-images are applied to intuitionistic fuzzy sets, in which the fused coefficients are selected using an activity measure called fuzzy entropy. The multiplication of weighted local energy and weighted sum modified Laplacian is used as an activity measure to fuse the low-frequency sub-images. At last, the reconstruction of the final fused image is done by applying the inverse NSST on the above-fused coefficients. The efficacy of the proposed fuzzy-based method is verifiable by five different modalities of anatomical and functional images. Both subjective and objective calculations showed better results than existing methods.
{"title":"Multimodal medical image fusion based on intuitionistic fuzzy sets and weighted local energy in nsst domain","authors":"K. Vanitha, D. Satyanarayana, M. Prasad","doi":"10.2174/1574362417666220405151738","DOIUrl":"https://doi.org/10.2174/1574362417666220405151738","url":null,"abstract":"\u0000\u0000In the extraction of information from multimodality images, anatomical and functional image fusion became an effective tool in the applications of clinical imaging. Objective: A new approach to fuse anatomical and functional images that use the combination of activity measure and intuitionistic fuzzy sets in NSST domain is presented.\u0000\u0000\u0000\u0000First, the high and low-frequency sub-images of source images are obtained by utilizing NSST decomposition, which represents them in multi-scale and multi-directions. Next, the high-frequency sub-images are applied to intuitionistic fuzzy sets, in which the fused coefficients are selected using an activity measure called fuzzy entropy.\u0000\u0000\u0000\u0000The multiplication of weighted local energy and weighted sum modified Laplacian is used as an activity measure to fuse the low-frequency sub-images. At last, the reconstruction of the final fused image is done by applying the inverse NSST on the above-fused coefficients.\u0000\u0000\u0000\u0000The efficacy of the proposed fuzzy-based method is verifiable by five different modalities of anatomical and functional images. Both subjective and objective calculations showed better results than existing methods.\u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46952716","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}
Pub Date : 2022-04-04DOI: 10.2174/1574362417666220404115140
P. S. Kumar, K. Veer
{"title":"Even after vaccination, is the second wave of covid-19 in india more dangerous?","authors":"P. S. Kumar, K. Veer","doi":"10.2174/1574362417666220404115140","DOIUrl":"https://doi.org/10.2174/1574362417666220404115140","url":null,"abstract":"<jats:sec>\u0000<jats:title />\u0000<jats:p />\u0000</jats:sec>","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42722154","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}
Pub Date : 2022-03-29DOI: 10.2174/1574362417666220329152528
Israa A. Al-ataby, Wamidh H. Talib
Wild thyme (Thymus serpyllum) belongs to the Lamiaceae family. They were used traditionally to treat different sorts of diseases, including cancer. The current study aims to evaluate both anticancer and immunomodulatory activities of wild thyme water extract. The antiproliferative activities of the extract were tested against different cancer cell lines using MTT assay, while the degree of apoptosis induction and VEGF expression were detected using ELISA. The lymphocyte proliferation assay was used to evaluate the acquired immunity, whereas both: nitro blue tetrazolium assay and the neutral red method were used to assess the innate activity; phagocytosis and pinocytosis, respectively. Balb/C mice were inoculated with the EMT6/P breast cancer cells and received the extract orally for 14 days. GC-MS and LC-MS were used to determine the composition of the wild thyme water extract. Results showed that wild thyme had significant apoptosis induction and angiogenesis suppression effects. The extract stimulated lymphocyte proliferation, phagocytosis and pinocytosis strongly. Seventy percent (70%) of the mice taking this extract did not develop tumors, with a percentage of tumor reduction (49.4%). Rosmarinic acid was the highest in the wild thyme water extract in GC-MS and LC-MS. Wild thyme herbal infusion is rich in phytochemicals that have the potential to activate the immune system and inhibit tumor progression. Further testing is required to understand the exact molecular mechanisms of this extract. Further studies are also needed to test the wild thyme infusion against tumors of different types established in mice.
{"title":"Wild thyme herbal infusion consumption suppresses tumor growth in a murine model of breast cancer","authors":"Israa A. Al-ataby, Wamidh H. Talib","doi":"10.2174/1574362417666220329152528","DOIUrl":"https://doi.org/10.2174/1574362417666220329152528","url":null,"abstract":"\u0000\u0000Wild thyme (Thymus serpyllum) belongs to the Lamiaceae family. They were used traditionally to treat different sorts of diseases, including cancer.\u0000\u0000\u0000\u0000The current study aims to evaluate both anticancer and immunomodulatory activities of wild thyme water extract.\u0000\u0000\u0000\u0000The antiproliferative activities of the extract were tested against different cancer cell lines using MTT assay, while the degree of apoptosis induction and VEGF expression were detected using ELISA. The lymphocyte proliferation assay was used to evaluate the acquired immunity, whereas both: nitro blue tetrazolium assay and the neutral red method were used to assess the innate activity; phagocytosis and pinocytosis, respectively. Balb/C mice were inoculated with the EMT6/P breast cancer cells and received the extract orally for 14 days. GC-MS and LC-MS were used to determine the composition of the wild thyme water extract.\u0000\u0000\u0000\u0000Results showed that wild thyme had significant apoptosis induction and angiogenesis suppression effects. The extract stimulated lymphocyte proliferation, phagocytosis and pinocytosis strongly. Seventy percent (70%) of the mice taking this extract did not develop tumors, with a percentage of tumor reduction (49.4%). Rosmarinic acid was the highest in the wild thyme water extract in GC-MS and LC-MS.\u0000\u0000\u0000\u0000Wild thyme herbal infusion is rich in phytochemicals that have the potential to activate the immune system and inhibit tumor progression. Further testing is required to understand the exact molecular mechanisms of this extract. Further studies are also needed to test the wild thyme infusion against tumors of different types established in mice.\u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48373895","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}
Pub Date : 2022-03-28DOI: 10.2174/1574362417666220328144453
P. Mathur, Tanu Sharma, K. Veer
Heart disease is considered as one of the complex diseases that has affected a large number of people around world. Therefore, it is important to detect and identify cardiac diseases at early stages A large number of methods are already present that detect various heart diseases, however, there are some limitations in these methods that degraded their overall performance. In this paper, an effective and efficient method based on convolutional neural network (CNN) and feed forward artificial neural network (FFANN) is proposed that can effectively detect cardiac diseases after analysing the Electrocardiogram (ECG) signals. In this ongoing study, the transformed signals are used to extract the information from the processed data. The extracted features are then passed to the proposed CNN-FFANN classifiers for training and testing purpose. The performance of the proposed CNN-FFANN model is evaluated in the MATLAB software in terms of performance matrices. The simulated outcomes proved that the proposed CNN-FFANN model is more accurate and efficient in detecting heart diseases from ECG signals and can be adopted for future biomedical applications.
{"title":"Analysis of CNN and feed forward ANN model for the evaluation of ECG signal","authors":"P. Mathur, Tanu Sharma, K. Veer","doi":"10.2174/1574362417666220328144453","DOIUrl":"https://doi.org/10.2174/1574362417666220328144453","url":null,"abstract":"\u0000\u0000Heart disease is considered as one of the complex diseases that has affected a large number of people around world.\u0000\u0000\u0000\u0000Therefore, it is important to detect and identify cardiac diseases at early stages\u0000\u0000\u0000\u0000A large number of methods are already present that detect various heart diseases, however, there are some limitations in these methods that degraded their overall performance.\u0000\u0000\u0000\u0000In this paper, an effective and efficient method based on convolutional neural network (CNN) and feed forward artificial neural network (FFANN) is proposed that can effectively detect cardiac diseases after analysing the Electrocardiogram (ECG) signals. In this ongoing study, the transformed signals are used to extract the information from the processed data. The extracted features are then passed to the proposed CNN-FFANN classifiers for training and testing purpose.\u0000\u0000\u0000\u0000The performance of the proposed CNN-FFANN model is evaluated in the MATLAB software in terms of performance matrices.\u0000\u0000\u0000\u0000The simulated outcomes proved that the proposed CNN-FFANN model is more accurate and efficient in detecting heart diseases from ECG signals and can be adopted for future biomedical applications.\u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43225984","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}
Pub Date : 2022-03-07DOI: 10.2174/1574362417666220307100017
Sachin Sharma, K. Veer
The second wave of coronavirus has appeared to be an extensive uphill of the number of daily new confirmed cases, recovered cases, and deaths than the first wave in India and the whole world. In India, the second wave of COVID-19 is much dangerous than the first wave that hit on 14th April 2020. The maximum number of new cases was 406901 recorded on May 7, 5.3 times more than the first wave peak. Many researchers worldwide are using machine learning prediction models to forecast the upcoming trends of this pandemic. This study used an Autoregressive Integrated Moving Average (ARIMA) model to predict the daily new confirmed cases, daily new deaths, and daily new recoveries between and after the second wave of COVID-19 in India. The dataset was collected from March 14, 2020- July 7, 2021, using the ARIMA model to predict corona cases for the next 60 days. In the context of the current scenario in India, the second wave will score low new cases in mid-August 2021, and the third wave will hit the country in the middle of September 2021. The ARIMA model was chosen based on AIC (Akaike Information Criterion) values and acquired the maximum accuracy of 95%.
与印度和全世界的第一波疫情相比，第二波冠状病毒疫情似乎是每日新增确诊病例、康复病例和死亡人数的一大上坡路。在印度，第二波新冠肺炎比2020年4月14日爆发的第一波疫情危险得多。5月7日记录的最大新增病例数为406901例，是第一波高峰的5.3倍。世界各地的许多研究人员正在使用机器学习预测模型来预测这场疫情即将到来的趋势。这项研究使用自回归综合移动平均（ARIMA）模型来预测印度新冠肺炎第二波疫情期间和之后的每日新增确诊病例、每日新增死亡人数和每日新增康复人数。该数据集收集于2020年3月14日至2021年7月7日，使用ARIMA模型预测未来60天的电晕病例。在印度目前的情况下，第二波将在2021年8月中旬出现较低的新增病例，第三波将于2021年9月中旬袭击该国。ARIMA模型是根据AIC（Akaike Information Criterion）值选择的，其最大准确率为95%。
{"title":"EVALUATION AND PREDICTION OF END OF SECOND WAVE AND STARTING OF THIRD WAVE COVID-19 CASES IN INDIA","authors":"Sachin Sharma, K. Veer","doi":"10.2174/1574362417666220307100017","DOIUrl":"https://doi.org/10.2174/1574362417666220307100017","url":null,"abstract":"The second wave of coronavirus has appeared to be an extensive uphill of the number of daily new confirmed cases, recovered cases, and deaths than the first wave in India and the whole world. In India, the second wave of COVID-19 is much dangerous than the first wave that hit on 14th April 2020. The maximum number of new cases was 406901 recorded on May 7, 5.3 times more than the first wave peak. Many researchers worldwide are using machine learning prediction models to forecast the upcoming trends of this pandemic.\u0000\u0000\u0000\u0000This study used an Autoregressive Integrated Moving Average (ARIMA) model to predict the daily new confirmed cases, daily new deaths, and daily new recoveries between and after the second wave of COVID-19 in India. The dataset was collected from March 14, 2020- July 7, 2021, using the ARIMA model to predict corona cases for the next 60 days.\u0000\u0000\u0000\u0000In the context of the current scenario in India, the second wave will score low new cases in mid-August 2021, and the third wave will hit the country in the middle of September 2021.\u0000\u0000\u0000\u0000The ARIMA model was chosen based on AIC (Akaike Information Criterion) values and acquired the maximum accuracy of 95%.","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48859900","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}
Pub Date : 2021-12-01DOI: 10.2174/157436241603211103150134
Ankita Sharma, S. Nandi
Cellular proliferation is the process of mitotic cell division by which a cell grows, replicates its DNA and divides to produce two daughter cells. Mitotic cycle is regulated by cyclin-dependent kinases (CDKs) and tyrosine proteine kinases (TPKs). CDKs are binary proline-directed serine-threonine-specific protein kinases consisting of a positive regulatory subunit known as cyclin. The role of the CDKs is to control cell cycle progression through phosphorylation of proteins that function at specific cell cycle stages [1]. Tyrosine kinase catalyzes phosphorylation of tyrosine residues in proteins. The phosphorylation of kinase residue results in many signal transduction cascades wherein extracellular signal is being transmitted by cell membrane receptors such as EGFr/FGFr/PDGFr/C-src. Extra cellular signal enters into the nucleus to cause genetic mutation which further leads to the progression of cancer [2]. CDKs and TPKs can regulate the checkpoints. Their control mechanism ensures that everything is ready for DNA synthesis and mitosis phase division; thereafter complete cell division. Dispute of the checkpoints may lead to abnormal signal transmission that may produce oncogenic cell signaling and cancer. Abnormal signal transduction may be produced by the different mutagens such as virus, bacteria and chemical [1].
{"title":"High Throughput Screening of Focused Virtual Library and Synthetic Protocols of Marine Sponge Derived Hymenialdisine Analogs as Potential Abnormal Signal Transduction Inhibitors","authors":"Ankita Sharma, S. Nandi","doi":"10.2174/157436241603211103150134","DOIUrl":"https://doi.org/10.2174/157436241603211103150134","url":null,"abstract":"Cellular proliferation is the process of mitotic cell division by which a cell grows, replicates its DNA and divides to produce two daughter cells. Mitotic cycle is regulated by cyclin-dependent kinases (CDKs) and tyrosine proteine kinases (TPKs). CDKs are binary proline-directed serine-threonine-specific protein kinases consisting of a positive regulatory subunit known as cyclin. The role of the CDKs is to control cell cycle progression through phosphorylation of proteins that function at specific cell cycle stages [1]. Tyrosine kinase catalyzes phosphorylation of tyrosine residues in proteins. The phosphorylation of kinase residue results in many signal transduction cascades wherein extracellular signal is being transmitted by cell membrane receptors such as EGFr/FGFr/PDGFr/C-src. Extra cellular signal enters into the nucleus to cause genetic mutation which further leads to the progression of cancer [2]. CDKs and TPKs can regulate the checkpoints. Their control mechanism ensures that everything is ready for DNA synthesis and mitosis phase division; thereafter complete cell division. Dispute of the checkpoints may lead to abnormal signal transmission that may produce oncogenic cell signaling and cancer. Abnormal signal transduction may be produced by the different mutagens such as virus, bacteria and chemical [1].","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48355976","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}
Pub Date : 2021-10-15DOI: 10.2174/1574362416666211015120514
Arjun Anant, K. Kaur, Vivek Asati
Thiosemicarbazones belongs to the group of semicarbazides which contains sulfur atom instead of the oxygen atom. Several studies have shown that they are effective against extracellular protozoans like Trichomonas vaginalis, Plasmodium falciparum, Trypanosoma cruzi and other parasites. The current research involves pharmacophore model design, 3-D-QSAR, virtual screening, and docking studies, all of which are evaluated using various parameters. The present study was performed by Schrodinger software. A total of 40 ligands were selected for the development of 3D QSAR models. To predict the pIC50 values in 3D-QSAR analysis, the entire dataset was divided into two sets, training and test sets, in a 7:3 ratio. The selected pharmacophore hypothesis has been selected for the virtual screening study. DHHRR_1 emerged as the best pharmacophore model with a survival score of 5.80. The 3D QSAR study showed a significant model with R2 =0.91 and. Q2 = 0.73. The series top-scoring compound 7e had a docking score of -10.44 and showed interactions with the amino acids ARG-265, PHE-227, and LEU-531 required for activity. The developed pharmacophore model has been used for screening of ZINC compounds where ZINC26244107, ZINC13469100, ZINC01290725and ZINC01350173 showed thebest XP docking scores (-11.60, -11.27, -11.35, -10.52, consecutively) with binding important amino acids ARG265, HIE185 and LEU 531 against plasmodium falciparum, PDB ID: 5TBO. These results wereevaluated with thestandard antimalarial drug chloroquine. ADME analysis showed the drug-likeness properties of the compounds. The results of the present study may be helpful for the future development of antimalarial compounds against Plasmodium falciparum.
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