Pub Date : 2024-01-19DOI: 10.2174/0115743624264761231212055008
Mahrokh Abouali Gale Dari, Amir Anbiyaiee, M. Moghanibashi, R. Mohammad Jafari, F. Moramezi, Maryam Farzaneh
Breast cancer is one of the most common malignancies in women worldwide. Invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) are the most common kinds of invasive breast cancer. Several genetic, epigenetic, and environmental factors could trigger the pathogenesis of breast cancer. Breast cancer treatment generally includes surgery, radiation therapy, chemotherapy, hormonal treatment, targeted therapy, immunotherapeutic, neoadjuvant systemic therapy, and systemic therapy. Although several classical treatment methods are used in cancer therapy, molecular-based strategies can open a new perspective for breast cancer treatment. Previous studies reported that long non-coding RNAs (lncRNAs) play important roles in cancer development and progression. LncRNA TUG1 was found to target several miRNAs and regulate breast cancer cell behavior. TUG1 can induce cell proliferation and invasion of breast cancer cells via downregulation of some miRNAs. Therefore, TUG1 might be a potent biomarker for the treatment of human cancer. In this review, we summarized the functional roles of TUG1 in breast cancer.
{"title":"Exploring the Evolving Significance of lncRNA TUG1-mediated Signaling\u0000Pathways in Breast Cancer","authors":"Mahrokh Abouali Gale Dari, Amir Anbiyaiee, M. Moghanibashi, R. Mohammad Jafari, F. Moramezi, Maryam Farzaneh","doi":"10.2174/0115743624264761231212055008","DOIUrl":"https://doi.org/10.2174/0115743624264761231212055008","url":null,"abstract":"\u0000\u0000Breast cancer is one of the most common malignancies in women worldwide. Invasive\u0000ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) are the most common kinds\u0000of invasive breast cancer. Several genetic, epigenetic, and environmental factors could trigger\u0000the pathogenesis of breast cancer. Breast cancer treatment generally includes surgery, radiation\u0000therapy, chemotherapy, hormonal treatment, targeted therapy, immunotherapeutic, neoadjuvant\u0000systemic therapy, and systemic therapy. Although several classical treatment methods are used\u0000in cancer therapy, molecular-based strategies can open a new perspective for breast cancer\u0000treatment. Previous studies reported that long non-coding RNAs (lncRNAs) play important roles\u0000in cancer development and progression. LncRNA TUG1 was found to target several miRNAs\u0000and regulate breast cancer cell behavior. TUG1 can induce cell proliferation and invasion of\u0000breast cancer cells via downregulation of some miRNAs. Therefore, TUG1 might be a potent\u0000biomarker for the treatment of human cancer. In this review, we summarized the functional roles\u0000of TUG1 in breast cancer.\u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":"15 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140503311","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 : 2024-01-19DOI: 10.2174/0115743624273918240102060402
Sourav Maity, Karan Veer
Facial emotion recognition (FER) technology is enumerated as a productive interface in several operations, which has been specifically focused on as a substitute communication path among a user and an appliance for human computer interface in the previous decade. The efficiency of the facial identification model straightaway relies on the capability of classification methods. In addition, an appropriate swap between recognition efficiency and computational cost is reckoned as the most important factor for planning such models. The efficiency of facial identification model straightaway relies on the capability of classification methods. In addition, an appropriate swap between recognition efficiency and computational cost is reckoned as the most important factor for planning such models. The objective of this paper was to classify the facial emotion electromyogram (EMG) signals by means of a neural network algorithm (NN), support vector machine (SVM) algorithm, and Naive-Bayes algorithm. This research work was directed towards the correlation among the classification accuracies by applying distinct feature extraction procedures on fEMGs. At first, eight participants (six male and two female) were recruited for data recording. Four electrodes were placed on each participant's face for capturing facial gestures (happy, angry, sad, and fear) and two electrodes were placed on the wrist for grounding purposes. Data were recorded by using BIOPAC MP150. After this, the signals were filtered using a band-pass filter and segmentation techniques for enhanced processing. After that, the time-domain and frequency-domain feature extraction procedures were carried out. Time domain and frequency domain features were applied to recorded signals. In this research, we used LabVIEW and MATLAB to produce a set of characteristics from fEMG signals for four emotional conditions, such as anger, sad, fear, and happy. After the feature extraction process, the extracted features were aligned into respective emotions by applying classifiers. The extracted features were further trained and classified by applying the SVM classifier, neural network classifier, and Naive Bayes classifier in MATLAB 2020. The SVM classifier and neural network classifier generated an accuracy of 93.80% and 96.90%, respectively, whereas the Naive Bayes classifier generated an accuracy of 90.60%. Facial emotion recognition (FER) is foresighted as a progressive or futuristic model, which has attracted the attention of researchers in several areas of learning due to its higher prospects in distinct applications. Acknowledgment of the emotions through biomedical signals produced from movements of facial muscles is lately presented using an explicit and authentic route.
{"title":"Framework for the Classification of Facial Emotions Using Soft Computing\u0000Techniques","authors":"Sourav Maity, Karan Veer","doi":"10.2174/0115743624273918240102060402","DOIUrl":"https://doi.org/10.2174/0115743624273918240102060402","url":null,"abstract":"\u0000\u0000Facial emotion recognition (FER) technology is enumerated as a productive\u0000interface in several operations, which has been specifically focused on as a substitute\u0000communication path among a user and an appliance for human computer interface in the previous\u0000decade. The efficiency of the facial identification model straightaway relies on the capability\u0000of classification methods. In addition, an appropriate swap between recognition efficiency\u0000and computational cost is reckoned as the most important factor for planning such models.\u0000\u0000\u0000\u0000The efficiency of facial identification model straightaway relies on the capability of classification methods. In addition, an appropriate swap between recognition efficiency and computational cost is reckoned as the most important factor for planning such models.\u0000\u0000\u0000\u0000The objective of this paper was to classify the facial emotion electromyogram (EMG)\u0000signals by means of a neural network algorithm (NN), support vector machine (SVM) algorithm,\u0000and Naive-Bayes algorithm. This research work was directed towards the correlation among the\u0000classification accuracies by applying distinct feature extraction procedures on fEMGs. At first,\u0000eight participants (six male and two female) were recruited for data recording. Four electrodes\u0000were placed on each participant's face for capturing facial gestures (happy, angry, sad, and fear)\u0000and two electrodes were placed on the wrist for grounding purposes. Data were recorded by using\u0000BIOPAC MP150. After this, the signals were filtered using a band-pass filter and segmentation\u0000techniques for enhanced processing. After that, the time-domain and frequency-domain feature\u0000extraction procedures were carried out. Time domain and frequency domain features were\u0000applied to recorded signals. In this research, we used LabVIEW and MATLAB to produce a set\u0000of characteristics from fEMG signals for four emotional conditions, such as anger, sad, fear, and\u0000happy. After the feature extraction process, the extracted features were aligned into respective\u0000emotions by applying classifiers. The extracted features were further trained and classified by\u0000applying the SVM classifier, neural network classifier, and Naive Bayes classifier in MATLAB\u00002020.\u0000\u0000\u0000\u0000The SVM classifier and neural network classifier generated an accuracy of 93.80% and\u000096.90%, respectively, whereas the Naive Bayes classifier generated an accuracy of 90.60%.\u0000\u0000\u0000\u0000Facial emotion recognition (FER) is foresighted as a progressive or futuristic model,\u0000which has attracted the attention of researchers in several areas of learning due to its higher\u0000prospects in distinct applications. Acknowledgment of the emotions through biomedical signals\u0000produced from movements of facial muscles is lately presented using an explicit and authentic\u0000route.\u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":"84 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140502641","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}
Leishmaniasis is a phagocytic host cell invading, caused by leishmania species mostly found in developing nations. To treat leishmaniasis, a wide range of medications and potential vaccines are available, such as pentavalent antimonials, amphotericin, and miltefosined, but due to the lack of effective treatments, the high toxicity of chemotherapy and the growth of drug resistance linked to these diseases necessitate the urgent development of innovative therapeutic agents. β-carboline is a group of chemical compounds, and its derivatives have shown to be one of the potential candidates for the treatment of Leishmania. The carboline derivatives may have antileishmanial activity. Which act by interfering with the parasite’s DNA replication or metabolic processes, inhibiting the enzymes that are mainly responsible for the reproduction and metabolism of leishmania. This research undertaking's present focus is on determining the existence and virtual screening of potential β-carboline derivative, which can act as an antileishmanial agent that can prevent or stop the progression of leishmania by the integration of different computational technologies such as in silico ADMET analysis and docking. After synthesizing molecules using virtual screening, all designed compounds underwent molecular docking, and hit molecules underwent ADMET analysis. Using AutoDock Vina 1.5.6, molecular docking was carried out in the arginase receptor's active site (PDBID: 2AEB). The research and creation of compounds used virtual screening. Fifteen hits from docking experiments had high binding affinity as, in comparison to the commercially available molecule pentamidine (binding score -5.5Kcal/mol), 15 compounds (binding affinity - 7.9 to 7.0Kcal/mol). These Fifteen best hits were further examined for their ADME activity using SwissADME, and the ADME analysis identified 15 medicines as having the ideal ADME profile and improved bioavailability. This research will bring up a broad spectrum of prospects for investigation into the disciplines of computational and medical research. This will facilitate the development of new antileishmanial agents that have better stability, bioavailability, and less toxicity, side effects for use in future research studies.
{"title":"Discovery of β-carboline Based Derivatives through Computational Aid for\u0000the Treatment of Leishmania","authors":"Asifiwe Mwamafupa, Pinky Arora, Shubham Kumar, Jagtar Singh, Kriti Seksaria","doi":"10.2174/0115743624270694231213095103","DOIUrl":"https://doi.org/10.2174/0115743624270694231213095103","url":null,"abstract":"\u0000\u0000Leishmaniasis is a phagocytic host cell invading, caused by leishmania\u0000species mostly found in developing nations. To treat leishmaniasis, a wide range of medications\u0000and potential vaccines are available, such as pentavalent antimonials, amphotericin, and\u0000miltefosined, but due to the lack of effective treatments, the high toxicity of chemotherapy and\u0000the growth of drug resistance linked to these diseases necessitate the urgent development of innovative\u0000therapeutic agents. β-carboline is a group of chemical compounds, and its derivatives\u0000have shown to be one of the potential candidates for the treatment of Leishmania. The carboline\u0000derivatives may have antileishmanial activity. Which act by interfering with the parasite’s DNA\u0000replication or metabolic processes, inhibiting the enzymes that are mainly responsible for the\u0000reproduction and metabolism of leishmania.\u0000\u0000\u0000\u0000This research undertaking's present focus is on determining the existence and virtual\u0000screening of potential β-carboline derivative, which can act as an antileishmanial agent that can\u0000prevent or stop the progression of leishmania by the integration of different computational technologies\u0000such as in silico ADMET analysis and docking.\u0000\u0000\u0000\u0000After synthesizing molecules using virtual screening, all designed compounds underwent\u0000molecular docking, and hit molecules underwent ADMET analysis.\u0000\u0000\u0000\u0000Using AutoDock Vina 1.5.6, molecular docking was carried out in the arginase receptor's\u0000active site (PDBID: 2AEB). The research and creation of compounds used virtual screening.\u0000Fifteen hits from docking experiments had high binding affinity as, in comparison to the\u0000commercially available molecule pentamidine (binding score -5.5Kcal/mol), 15 compounds\u0000(binding affinity - 7.9 to 7.0Kcal/mol). These Fifteen best hits were further examined for their\u0000ADME activity using SwissADME, and the ADME analysis identified 15 medicines as having\u0000the ideal ADME profile and improved bioavailability.\u0000\u0000\u0000\u0000This research will bring up a broad spectrum of prospects for investigation into the\u0000disciplines of computational and medical research. This will facilitate the development of new\u0000antileishmanial agents that have better stability, bioavailability, and less toxicity, side effects for\u0000use in future research studies.\u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":"74 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140503285","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 : 2023-12-21DOI: 10.2174/0115743624264947231217161150
Hanieh Gholamia, Hassan Akrami, Hosseinali Sassan, N. Erfani, Mohammad Reza Fattahi, Mojdeh Heidari
Hepatocellular carcinoma (HCC) is the most common type of cancer. Although HCC treatment has greatly improved over the past few decades, patient survival rates are still very low. Therefore, it is essential to find new treatments for HCC. Apoptosis has been shown to be the most effective in disrupting cancer growth. Improper functioning of proteins in apoptosis can lead to cancer growth. MicroRNAs (miRNAs) are key regulators in the development and progression of HCC. Irregular expression of miRNAs involved in apoptosis signaling can lead to tumorigenesis. Therefore, we investigated the effect of the hsa-miR-4270 inhibitor on cell proliferation and apoptosis. HepG2 cells were cultured at 37°C and 95% air. Transfection of HepG2 cells was performed by miR-4270 inhibitor and lipofectamine 2000. Cell proliferation of HepG2 cells was determined with MTT assay and different concentrations of miR-4270 specific inhibitors. DNA laddering assay was performed to evaluate the induction of apoptosis. Finally, the transcription level of genes involved in apoptosis, including BAX, BCL2, Caspase3, and p53, was measured by real-time RT-PCR. The results of MTT and DNA laddering assays showed that the miR-4270 inhibitor declined cell proliferation and induced apoptosis in HepG2 cells. Also, the results of quantitative real-time RT-PCR indicated an upregulation of transcription of BAX, p53 and Caspase3 genes and a decline in expression of BCL2 gene. Taken together, we found hsa-miR-4270 inhibitor decreased cell proliferation and induced apoptosis in the HepG2 cell line, which can be used as a new therapeutic strategy for HCC patients.
{"title":"The Effect of Silencing MiR-4270 on Apoptosis in HCC Cell Line","authors":"Hanieh Gholamia, Hassan Akrami, Hosseinali Sassan, N. Erfani, Mohammad Reza Fattahi, Mojdeh Heidari","doi":"10.2174/0115743624264947231217161150","DOIUrl":"https://doi.org/10.2174/0115743624264947231217161150","url":null,"abstract":"Hepatocellular carcinoma (HCC) is the most common type of cancer. Although HCC treatment has greatly improved over the past few decades, patient survival rates are still very low. Therefore, it is essential to find new treatments for HCC. Apoptosis has been shown to be the most effective in disrupting cancer growth. Improper functioning of proteins in apoptosis can lead to cancer growth. MicroRNAs (miRNAs) are key regulators in the development and progression of HCC. Irregular expression of miRNAs involved in apoptosis signaling can lead to tumorigenesis. Therefore, we investigated the effect of the hsa-miR-4270 inhibitor on cell proliferation and apoptosis. HepG2 cells were cultured at 37°C and 95% air. Transfection of HepG2 cells was performed by miR-4270 inhibitor and lipofectamine 2000. Cell proliferation of HepG2 cells was determined with MTT assay and different concentrations of miR-4270 specific inhibitors. DNA laddering assay was performed to evaluate the induction of apoptosis. Finally, the transcription level of genes involved in apoptosis, including BAX, BCL2, Caspase3, and p53, was measured by real-time RT-PCR. The results of MTT and DNA laddering assays showed that the miR-4270 inhibitor declined cell proliferation and induced apoptosis in HepG2 cells. Also, the results of quantitative real-time RT-PCR indicated an upregulation of transcription of BAX, p53 and Caspase3 genes and a decline in expression of BCL2 gene. Taken together, we found hsa-miR-4270 inhibitor decreased cell proliferation and induced apoptosis in the HepG2 cell line, which can be used as a new therapeutic strategy for HCC patients.","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":"3 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139167203","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 : 2023-12-01DOI: 10.2174/0115743624250965231116060824
Milad Iravani, Abbas Ebrahimi Kalan, Maryam Moghadam Salimi, Ali Jahan
Transcranial photobiomodulation (PBM) therapy has emerged as a promising alternative therapeutic option for the management of neurological and psychiatric disorders. However, the underlying mechanisms of PBM therapy and its effects on motor performance in brain disorders are not yet fully understood. The aim of this literature review is to provide a more detailed and evidence-based explanation of the rationale and intent behind the correlation between PBM therapy and its effects on motor performance in brain disorders. A literature search was performed in the databases "PubMed/Medline", "Scopus," and "Google Scholar" for all relevant English language papers. A combination of different keywords was used for the database search. Video articles, patents, review articles, book chapters, articles using other transcranial methods, non-transcranial PBM, and case reports were excluded. Out of the 2174 papers, 18 addressed the effect of PBM on motor performance. Among these, four studies were on ischemic stroke models and individuals with stroke, six studies on models associated with traumatic brain injury (TBI), five studies on models associated with neurodegenerative diseases and Parkinson's disease, and four studies related to models and patients with central nervous system inflammation. All studies have shown that motor parameters improve with PBM. In two studies on healthy individuals, 65 showed improvement in motor function and 16 showed improvement in motor evoked potential. In most studies (n=10), the wavelength used was between 800 and 900 nm. Near-infrared or LED continuous light was used in most studies. However, two studies compared the effects of pulsed and continuous waves and found the superiority of pulsed over continuous waves. PBM therapy appears to be useful in brain injury, inducing changes at the behavioral, motor, cellular, and chemical levels. Recent studies suggest that PBM therapy may have potential benefits in improving motor performance in brain disorders, including stroke, traumatic brain injury, Parkinson's disease, and demyelination. However, further research is needed to determine the optimal parameters for PBM therapy and to investigate its effects on motor function in different brain disorders. Overall, PBM therapy appears to be a promising therapeutic option for brain injury and warrants further investigation.
{"title":"The Effect of Transcranial Photobiomodulation for Motor Performance\u0000Improvement in Patients with Brain Disorders","authors":"Milad Iravani, Abbas Ebrahimi Kalan, Maryam Moghadam Salimi, Ali Jahan","doi":"10.2174/0115743624250965231116060824","DOIUrl":"https://doi.org/10.2174/0115743624250965231116060824","url":null,"abstract":"\u0000\u0000Transcranial photobiomodulation (PBM) therapy has emerged as a\u0000promising alternative therapeutic option for the management of neurological and psychiatric\u0000disorders. However, the underlying mechanisms of PBM therapy and its effects on motor performance in brain disorders are not yet fully understood. The aim of this literature review is to\u0000provide a more detailed and evidence-based explanation of the rationale and intent behind the\u0000correlation between PBM therapy and its effects on motor performance in brain disorders.\u0000\u0000\u0000\u0000A literature search was performed in the databases \"PubMed/Medline\", \"Scopus,\" and\u0000\"Google Scholar\" for all relevant English language papers. A combination of different keywords\u0000was used for the database search. Video articles, patents, review articles, book chapters, articles\u0000using other transcranial methods, non-transcranial PBM, and case reports were excluded.\u0000\u0000\u0000\u0000Out of the 2174 papers, 18 addressed the effect of PBM on motor performance. Among\u0000these, four studies were on ischemic stroke models and individuals with stroke, six studies on\u0000models associated with traumatic brain injury (TBI), five studies on models associated with neurodegenerative diseases and Parkinson's disease, and four studies related to models and patients\u0000with central nervous system inflammation. All studies have shown that motor parameters improve with PBM. In two studies on healthy individuals, 65 showed improvement in motor function and 16 showed improvement in motor evoked potential. In most studies (n=10), the wavelength used was between 800 and 900 nm. Near-infrared or LED continuous light was used in\u0000most studies. However, two studies compared the effects of pulsed and continuous waves and\u0000found the superiority of pulsed over continuous waves.\u0000\u0000\u0000\u0000PBM therapy appears to be useful in brain injury, inducing changes at the behavioral, motor, cellular, and chemical levels. Recent studies suggest that PBM therapy may have\u0000potential benefits in improving motor performance in brain disorders, including stroke, traumatic brain injury, Parkinson's disease, and demyelination. However, further research is needed to\u0000determine the optimal parameters for PBM therapy and to investigate its effects on motor function in different brain disorders. Overall, PBM therapy appears to be a promising therapeutic\u0000option for brain injury and warrants further investigation.\u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":"28 35","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138624074","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 : 2023-11-24DOI: 10.2174/0115743624273536231105142321
Sajad Najafi, Farhoodeh Ghaedrahmati, Mahrokh Abouali Gale Dari, Maryam Farzaneh, R. Mohammad Jafari
Cervical cancer is ranked as the fourth most frequently diagnosed cancer and the fourth leading cause of cancer-related deaths among females. Cervical cancer is a complex disease influenced by various genetic, epigenetic, and environmental factors. While treatment options such as radiotherapy, chemotherapy, and hormonal therapy exist, the prognosis remains poor due to high rates of distant and lymphatic metastasis. Recent research has shed light on the role of non-coding RNAs (ncRNAs) in cervical cancer development, with circular RNAs (circRNAs) emerging as a potentially significant regulator of cellular processes. Through targeting miRNAs/mRNAs, circRNAs can impact cell growth and invasion in cervical cancer cells, making them a promising biomarker for diagnosis and treatment. This review provides an overview of the functional roles of circRNAs in the context of cervical cancer.
{"title":"The Regulatory Role of Circular RNAs as miRNA Sponges in Cervical Cancer","authors":"Sajad Najafi, Farhoodeh Ghaedrahmati, Mahrokh Abouali Gale Dari, Maryam Farzaneh, R. Mohammad Jafari","doi":"10.2174/0115743624273536231105142321","DOIUrl":"https://doi.org/10.2174/0115743624273536231105142321","url":null,"abstract":"Cervical cancer is ranked as the fourth most frequently diagnosed cancer and the fourth leading cause of cancer-related deaths among females. Cervical cancer is a complex disease influenced by various genetic, epigenetic, and environmental factors. While treatment options such as radiotherapy, chemotherapy, and hormonal therapy exist, the prognosis remains poor due to high rates of distant and lymphatic metastasis. Recent research has shed light on the role of non-coding RNAs (ncRNAs) in cervical cancer development, with circular RNAs (circRNAs) emerging as a potentially significant regulator of cellular processes. Through targeting miRNAs/mRNAs, circRNAs can impact cell growth and invasion in cervical cancer cells, making them a promising biomarker for diagnosis and treatment. This review provides an overview of the functional roles of circRNAs in the context of cervical cancer.","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":"11 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139242346","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 : 2023-10-10DOI: 10.2174/0115743624247062230926110428
Shivani Sharma, Amit Mittal, Navneet Khurana
Background: High or abnormal blood sugar levels are the hallmark of diabetes mellitus (DM), a metabolic disorder that will be one of the major causes of mortality in 2021. SGLT2 inhibitors have recently shown beneficial effects in the treatment of diabetes by reducing hyperglycemia and glucosuria. Objective: Molecular docking and ADMET studies of Schiff base- based 1, 3, 4-thiadiazole scaffold as SGLT2 inhibitors. objective: Molecular docking and ADMET studies of Schiff base based 1, 3, 4-thiadiazole scaffold as SGLT2 inhibitors. Methods: Chem draw Ultra 16.0 software was used to draw the structures of newly designed molecules of Schiff base-based 1, 3, 4-thiadiazole, which were then translated into 3D structures. For the molecular docking study, AutoDock Vina 1.5.6 software was employed. Lazar in silico and Swiss ADME predictors were used to calculate in silico ADMET characteristics. Results: We have designed 111 novel Schiff base-based 1, 3, 4-thiadiazole derivatives as SGLT2 inhibitors. A total of 10 compounds from the thiadiazole series were found to have higher binding affinity to the SGLT2 protein than dapagliflozin. SSS 56 had the best docking scores and binding affinities, with -10.4 Kcal/mol, respectively. In silico ADMET parameters demonstrated that the best binding compounds were found to be non-carcinogenic with LogP = 2.53-4.02. result: We have designed 111 novel Schiff base based 1, 3, 4-thiadiazole derivatives as SGLT2 inhibitors. A total of 10 compounds from the thiadiazole series were found to have higher binding affinity to the SGLT2 protein than dapagliflozin. SSS 56 had the best docking scores and binding affinities, with -10.4 Kcal/mol, respectively. In silico ADMET parameters demonstrated that best binding compounds found to be non-carcinogenic with LogP = 2.53-4.02. Conclusion: Novel Schiff base-based 1, 3, 4-thiadiazole were designed and binding affinity was assessed against SGLT2 protein, which resulted in a new lead molecule with a maximal binding affinity and estimated to be noncarcinogenic with an optimal partition coefficient (iLogP = 2.53- 4.02). conclusion: Novel Schiff base based 1, 3, 4-thiadiazole were designed and binding affinity were assessed against SGLT2 protein which resulted in a new lead molecule with a maximal binding affinity and estimated to be noncarcinogenic with an optimal partition coefficient (iLogP = 2.53-4.02).
{"title":"Utilization of Computational Tools for the Discovery of Schiff Base-based 1, 3, 4-thiadiazole Scaffold as SGLT2 Inhibitors","authors":"Shivani Sharma, Amit Mittal, Navneet Khurana","doi":"10.2174/0115743624247062230926110428","DOIUrl":"https://doi.org/10.2174/0115743624247062230926110428","url":null,"abstract":"Background: High or abnormal blood sugar levels are the hallmark of diabetes mellitus (DM), a metabolic disorder that will be one of the major causes of mortality in 2021. SGLT2 inhibitors have recently shown beneficial effects in the treatment of diabetes by reducing hyperglycemia and glucosuria. Objective: Molecular docking and ADMET studies of Schiff base- based 1, 3, 4-thiadiazole scaffold as SGLT2 inhibitors. objective: Molecular docking and ADMET studies of Schiff base based 1, 3, 4-thiadiazole scaffold as SGLT2 inhibitors. Methods: Chem draw Ultra 16.0 software was used to draw the structures of newly designed molecules of Schiff base-based 1, 3, 4-thiadiazole, which were then translated into 3D structures. For the molecular docking study, AutoDock Vina 1.5.6 software was employed. Lazar in silico and Swiss ADME predictors were used to calculate in silico ADMET characteristics. Results: We have designed 111 novel Schiff base-based 1, 3, 4-thiadiazole derivatives as SGLT2 inhibitors. A total of 10 compounds from the thiadiazole series were found to have higher binding affinity to the SGLT2 protein than dapagliflozin. SSS 56 had the best docking scores and binding affinities, with -10.4 Kcal/mol, respectively. In silico ADMET parameters demonstrated that the best binding compounds were found to be non-carcinogenic with LogP = 2.53-4.02. result: We have designed 111 novel Schiff base based 1, 3, 4-thiadiazole derivatives as SGLT2 inhibitors. A total of 10 compounds from the thiadiazole series were found to have higher binding affinity to the SGLT2 protein than dapagliflozin. SSS 56 had the best docking scores and binding affinities, with -10.4 Kcal/mol, respectively. In silico ADMET parameters demonstrated that best binding compounds found to be non-carcinogenic with LogP = 2.53-4.02. Conclusion: Novel Schiff base-based 1, 3, 4-thiadiazole were designed and binding affinity was assessed against SGLT2 protein, which resulted in a new lead molecule with a maximal binding affinity and estimated to be noncarcinogenic with an optimal partition coefficient (iLogP = 2.53- 4.02). conclusion: Novel Schiff base based 1, 3, 4-thiadiazole were designed and binding affinity were assessed against SGLT2 protein which resulted in a new lead molecule with a maximal binding affinity and estimated to be noncarcinogenic with an optimal partition coefficient (iLogP = 2.53-4.02).","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136361070","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: Epilepsy is a neurological disorder characterized by spontaneously occurring seizures known for several decades. Despite the availability of current anti-epileptic drugs, including Phenytoin, Valproate, Carbamazepine, Lamotrigine, Gabapentin, Vigabatrin, etc., a considerable 30 % of the epileptic population are drug-resistant to the available conventional medications. This suggests a need to find new drug therapy for the management of epilepsy. Moreover, prolonged use of a single drug or monotherapy can also lead to therapeutic failure owing to the inability of a single drug to exert the desired anti-epileptic effect. Hence, on the basis of the knowledge and understanding regarding the existing targets, novel agents having the ability to show therapeutic effects should be studied and investigated further. This article emphasizes the need to investigate and repurpose drug molecules for the management of epilepsy. The review elaborates on the potential targets, including Glutamate, EAAT (Excitatory nucleotide) Channel and mTOR (Mammalian Target of Rapamycin) pathway. Moreover, the discussion on the EAAT (Excitatory Amino Acid Transporters), RAS (Renin Angiotensin System), NHE (Na+/H+ exchangers), HCN (Hyperpolarization- activated cyclic nucleotide) targets and treatment approach has been supported by literature that sheds light on evidence which is validated via suitable preclinical and clinical studies.
{"title":"An Update on the Pathways and Aspects of Epilepsy Treatment Targets","authors":"Ruksar Sande, Pravin Kale, Angel Godad, Gaurav Doshi","doi":"10.2174/0115743624252836230924075249","DOIUrl":"https://doi.org/10.2174/0115743624252836230924075249","url":null,"abstract":"Abstract: Epilepsy is a neurological disorder characterized by spontaneously occurring seizures known for several decades. Despite the availability of current anti-epileptic drugs, including Phenytoin, Valproate, Carbamazepine, Lamotrigine, Gabapentin, Vigabatrin, etc., a considerable 30 % of the epileptic population are drug-resistant to the available conventional medications. This suggests a need to find new drug therapy for the management of epilepsy. Moreover, prolonged use of a single drug or monotherapy can also lead to therapeutic failure owing to the inability of a single drug to exert the desired anti-epileptic effect. Hence, on the basis of the knowledge and understanding regarding the existing targets, novel agents having the ability to show therapeutic effects should be studied and investigated further. This article emphasizes the need to investigate and repurpose drug molecules for the management of epilepsy. The review elaborates on the potential targets, including Glutamate, EAAT (Excitatory nucleotide) Channel and mTOR (Mammalian Target of Rapamycin) pathway. Moreover, the discussion on the EAAT (Excitatory Amino Acid Transporters), RAS (Renin Angiotensin System), NHE (Na+/H+ exchangers), HCN (Hyperpolarization- activated cyclic nucleotide) targets and treatment approach has been supported by literature that sheds light on evidence which is validated via suitable preclinical and clinical studies.","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136360131","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 : 2023-10-09DOI: 10.2174/0115743624245937230926100726
negar Firouzabadi, Amirhossein Sakhteman, Mahmoud Heydari, Parnaz Mohseni, Pouria Mosaddeghi, Maryam Kabiri, AmirAli Zamaninasab, Mohammad M. Zarshenas
Background: Major Depressive Disorder (MDD) is a prevalent mental health condition that affects a significant portion of the general population. Despite the availability of pharmacological, psychological, and novel neurological approaches, optimal outcomes are only achieved in roughly 50% of patients. Traditional Persian Medicine (TPM) recommends a holistic approach to cure diseases based on the etiologic cause of malfunctions. This study aimed to explore the potential of integrating TPM and systems pharmacology approaches for the management of MDD. background: Depression is considered one of the most prevalent mental illnesses worldwide. Depression and the effectiveness of antidepressants are both influenced by genetic factors. Various anti-depressant remedies have been listed in Traditional Persian medicine (TPM) manuscripts, which may be useful as adjuvant therapy in the treatment of depression. The use of network-based studies is becoming more and more crucial for comprehending how medications apply their pharmacological actions. The growing fields of systems pharmacology (SP) and bioinformatics make use of computing to comprehend how drugs function at cellular and molecular levels. A common biological route that involves protein-protein (drug-target) interactions can be mechanistically understood using SP. Methods: A network-based investigation was conducted to explore the molecular mechanisms of TPM-based herbal remedies for depression. The study utilized a network pharmacology approach to identify active compounds, targets, and pathways involved in the treatment of depression. The study also conducted a literature review to identify the effectiveness of TPMbased remedies in treating depression. objective: The objective of the current study was to investigate the molecular mechanisms underlying TPM approach in the treatment of depression. Results: The study identified several active compounds, targets, and pathways involved in the treatment of depression by TPM-based herbal remedies. The study also identified several TPMbased remedies with anti-inflammatory properties that may be effective in treating depression. The literature review supported the potential of TPM remedies for the management of MDD. Conclusion: The integration of TPM and systems pharmacology may provide a holistic insight into the management of MDD. The study's findings suggest the potential of TPM-based remedies with anti-depressant properties, and further research is needed to understand the molecular mechanisms underlying their effectiveness. Integrating TPM-based remedies with bioinformatics may provide a complementary therapeutic avenue to MDD management and improve the quality of life for MDD patients. result: According to our study, many phytochemicals found in the plants used to make TPM-based prescriptions may impact various genes linked to depressive disorders. conclusion: The findings of these investigations suggested that many of the identified herbal remedies
{"title":"Exploring Molecular Mechanisms of Traditional Persian Medicine-based Herbal Remedies for Depression: A Network-based Study","authors":"negar Firouzabadi, Amirhossein Sakhteman, Mahmoud Heydari, Parnaz Mohseni, Pouria Mosaddeghi, Maryam Kabiri, AmirAli Zamaninasab, Mohammad M. Zarshenas","doi":"10.2174/0115743624245937230926100726","DOIUrl":"https://doi.org/10.2174/0115743624245937230926100726","url":null,"abstract":"Background: Major Depressive Disorder (MDD) is a prevalent mental health condition that affects a significant portion of the general population. Despite the availability of pharmacological, psychological, and novel neurological approaches, optimal outcomes are only achieved in roughly 50% of patients. Traditional Persian Medicine (TPM) recommends a holistic approach to cure diseases based on the etiologic cause of malfunctions. This study aimed to explore the potential of integrating TPM and systems pharmacology approaches for the management of MDD. background: Depression is considered one of the most prevalent mental illnesses worldwide. Depression and the effectiveness of antidepressants are both influenced by genetic factors. Various anti-depressant remedies have been listed in Traditional Persian medicine (TPM) manuscripts, which may be useful as adjuvant therapy in the treatment of depression. The use of network-based studies is becoming more and more crucial for comprehending how medications apply their pharmacological actions. The growing fields of systems pharmacology (SP) and bioinformatics make use of computing to comprehend how drugs function at cellular and molecular levels. A common biological route that involves protein-protein (drug-target) interactions can be mechanistically understood using SP. Methods: A network-based investigation was conducted to explore the molecular mechanisms of TPM-based herbal remedies for depression. The study utilized a network pharmacology approach to identify active compounds, targets, and pathways involved in the treatment of depression. The study also conducted a literature review to identify the effectiveness of TPMbased remedies in treating depression. objective: The objective of the current study was to investigate the molecular mechanisms underlying TPM approach in the treatment of depression. Results: The study identified several active compounds, targets, and pathways involved in the treatment of depression by TPM-based herbal remedies. The study also identified several TPMbased remedies with anti-inflammatory properties that may be effective in treating depression. The literature review supported the potential of TPM remedies for the management of MDD. Conclusion: The integration of TPM and systems pharmacology may provide a holistic insight into the management of MDD. The study's findings suggest the potential of TPM-based remedies with anti-depressant properties, and further research is needed to understand the molecular mechanisms underlying their effectiveness. Integrating TPM-based remedies with bioinformatics may provide a complementary therapeutic avenue to MDD management and improve the quality of life for MDD patients. result: According to our study, many phytochemicals found in the plants used to make TPM-based prescriptions may impact various genes linked to depressive disorders. conclusion: The findings of these investigations suggested that many of the identified herbal remedies ","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135149669","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 : 2023-10-06DOI: 10.2174/0115743624257352230920091046
Shruti Jain, Ayodeji Salau
Background: When analyzing multivariate data, it can be challenging to quantify and pinpoint relationships between a collection of consistent characteristics. Reliable computational prediction of cancer patient's response to treatment based on their clinical and molecular profiles is essential in this era of precision medicine. This is essential in helping doctors choose the least contaminated and most potent restorative therapies that are now available. Better patient monitoring and selection are now possible in clinical trials. Methods: This research proposes a novel robust model to aid in the diagnosis of various cancers induced by biomarker proteins (Protein Kinase B, MAPK, and mammalian Target of Rapamycin). Later, various medications (Perifosine, Wortmannin, and Rapamycin) were proposed to cure cancer. Various studies were carried out to obtain all of the results, which aid in the identification of various types of cancer. The drugs mentioned in this essay help to ward off cancer. Scaling and normalization were carried out using parallel coordinates plots and correlation tests, respectively. The boosted tree method and kNN with multiple distance approaches were also used to generate a solid model. The medical diagnosis system was enhanced by training the boosted tree technique to identify various tumors. A robust model was validated by predicting various values that were displayed against the observed value and agreed with the advised strategy to locate biomarkers to show the value of our method. Results: The results show that the predicted and observed values agree with each other, especially for MAPK pathways. The observed correlation coefficient (r2) is 0.9847 without intercept and 0.9849 with intercept. The precise computational prediction of the reaction of cancer patients to treatment based on the patient's clinical and molecular profiles is vital in the period of exactitude medicine. Conclusion: A robust model was validated by predicting the different values that were plotted with the observed value, which agrees with the results of the proposed technique to uncover biomarkers and shows the effectiveness of our technique.
{"title":"Robust Predictive Model for Different Cancers using Biomarker Proteins","authors":"Shruti Jain, Ayodeji Salau","doi":"10.2174/0115743624257352230920091046","DOIUrl":"https://doi.org/10.2174/0115743624257352230920091046","url":null,"abstract":"Background: When analyzing multivariate data, it can be challenging to quantify and pinpoint relationships between a collection of consistent characteristics. Reliable computational prediction of cancer patient's response to treatment based on their clinical and molecular profiles is essential in this era of precision medicine. This is essential in helping doctors choose the least contaminated and most potent restorative therapies that are now available. Better patient monitoring and selection are now possible in clinical trials. Methods: This research proposes a novel robust model to aid in the diagnosis of various cancers induced by biomarker proteins (Protein Kinase B, MAPK, and mammalian Target of Rapamycin). Later, various medications (Perifosine, Wortmannin, and Rapamycin) were proposed to cure cancer. Various studies were carried out to obtain all of the results, which aid in the identification of various types of cancer. The drugs mentioned in this essay help to ward off cancer. Scaling and normalization were carried out using parallel coordinates plots and correlation tests, respectively. The boosted tree method and kNN with multiple distance approaches were also used to generate a solid model. The medical diagnosis system was enhanced by training the boosted tree technique to identify various tumors. A robust model was validated by predicting various values that were displayed against the observed value and agreed with the advised strategy to locate biomarkers to show the value of our method. Results: The results show that the predicted and observed values agree with each other, especially for MAPK pathways. The observed correlation coefficient (r2) is 0.9847 without intercept and 0.9849 with intercept. The precise computational prediction of the reaction of cancer patients to treatment based on the patient's clinical and molecular profiles is vital in the period of exactitude medicine. Conclusion: A robust model was validated by predicting the different values that were plotted with the observed value, which agrees with the results of the proposed technique to uncover biomarkers and shows the effectiveness of our technique.","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134944941","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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