Junaid Asghar - Academia.edu (original) (raw)
Papers by Junaid Asghar
Journal of Healthcare Engineering, Jul 10, 2023
Tis article has been retracted by Hindawi, as publisher, following an investigation undertaken by... more Tis article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of systematic manipulation of the publication and peer-review process. We cannot, therefore, vouch for the reliability or integrity of this article. Please note that this notice is intended solely to alert readers that the peer-review process of this article has been compromised. Wiley and Hindawi regret that the usual quality checks did not identify these issues before publication and have since put additional measures in place to safeguard research integrity. We wish to credit our Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation. Te corresponding author, as the representative of all authors, has been given the opportunity to register their agreement or disagreement to this retraction. We have kept a record of any response received.
Zenodo (CERN European Organization for Nuclear Research), Jan 27, 2021
PLOS ONE, Nov 10, 2022
The effective segmentation of lesion(s) from dermoscopic skin images assists the Computer-Aided D... more The effective segmentation of lesion(s) from dermoscopic skin images assists the Computer-Aided Diagnosis (CAD) systems in improving the diagnosing rate of skin cancer. The results of the existing skin lesion segmentation techniques are not up to the mark for dermoscopic images with artifacts like varying size corner borders with color similar to lesion(s) and/or hairs having low contrast with surrounding background. To improve the results of the existing skin lesion segmentation techniques for such kinds of dermoscopic images, an effective skin lesion segmentation method is proposed in this research work. The proposed method searches for the presence of corner borders in the given dermoscopc image and removes them if found otherwise it starts searching for the presence of hairs on it and eliminate them if present. Next, it enhances the resultant image using state-of-the-art image enhancement method and segments lesion from it using machine learning technique namely, GrabCut method. The proposed method was tested on PH2 and ISIC 2018 datasets containing 200 images each and its accuracy was measured with two evaluation metrics, i.e., Jaccard index, and Dice index. The evaluation results show that our proposed skin lesion segmentation method obtained Jaccard Index of 0.77, 0.80 and Dice index of 0.87, 0.82 values on PH2, and ISIC2018 datasets, respectively, which are better than state-ofthe-art skin lesion segmentation techniques.
Frontiers in Pharmacology, Sep 26, 2022
Background: Extracellular signal-regulated kinases (ERKs) are important signaling mediators in ma... more Background: Extracellular signal-regulated kinases (ERKs) are important signaling mediators in mammalian cells and, as a result, one of the major areas of research focus. The detection and quantification of ERK phosphorylation as an index of activation is normally conducted using immunoblotting, which does not allow high-throughput drug screening. Plate-based immunocytochemical assays provide a cheaper and relatively high-throughput alternative method for quantifying ERK phosphorylation. Here, we present optimization steps aimed to increase assay sensitivity and reduce variance and cost using the LI-COR In-Cell Western (I-CW) system in a recombinant CHO-K1 cell line, over-expressing the human delta-opioid receptor (hDOPr) as a model. Methods: Cells cultured in 96-well microassay plates were stimulated with three standard/selective DOPr agonists (SNC80, ADL5859, and DADLE) and a novel selective DOPr agonist (PN6047) to elicit a phospho-ERK response as an index of activation. A number of experimental conditions were investigated during the assay development. Key results: Preliminary experiments revealed a clearly visible edge-effect which significantly increased assay variance across the plate and which was reduced by pre-incubation for 30 min at room temperature. ERK phosphorylation was detectable as early as 1 min after agonist addition, with
Journal of Pharmacology and Experimental Therapeutics, Oct 8, 2019
Agonists at the d opioid receptor are known to be potent antihyperalgesics in chronic pain models... more Agonists at the d opioid receptor are known to be potent antihyperalgesics in chronic pain models and effective in models of anxiety and depression. However, some d opioid agonists have proconvulsant properties while tolerance to the therapeutic effects can develop. Previous evidence indicates that different agonists acting at the d opioid receptor differentially engage signaling and regulatory pathways with significant effects on behavioral outcomes. As such, interest is now growing in the development of biased agonists as a potential means to target specific signaling pathways and potentially improve the therapeutic profile of d opioid agonists. Here, we report on PN6047 (3-[[4-(dimethylcarbamoyl)phenyl]-[1-(thiazol-5-ylmethyl)-4piperidylidene]methyl]benzamide), a novel G protein-biased and selective d opioid agonist. In cell-based assays, PN6047 fully engages G protein signaling but is a partial agonist in both the arrestin recruitment and internalization assays. PN6047 is effective in rodent models of chronic pain but shows no detectable analgesic tolerance following prolonged treatment. In addition, PN6047 exhibited antidepressant-like activity in the forced swim test, and importantly, the drug had no effect on chemically induced seizures. PN6047 did not exhibit reward-like properties in the conditioned place preference test or induce respiratory depression. Thus, d opioid ligands with limited arrestin signaling such as PN6047 may be therapeutically beneficial in the treatment of chronic pain states. SIGNIFICANCE STATEMENT PN6047 (3-[[4-(dimethylcarbamoyl)phenyl]-[1-(thiazol-5-ylmethyl)-4piperidylidene]methyl]benzamide) is a selective, G protein-biased d opioid agonist with efficacy in preclinical models of chronic pain. No analgesic tolerance was observed after prolonged treatment, and PN6047 does not display proconvulsant activity or other opioidmediated adverse effects. Our data suggest that d opioid ligands with limited arrestin signaling will be beneficial in the treatment of chronic pain.
Frontiers in Pharmacology, Oct 4, 2022
This study aims to increase the aqueous solubility of ciprofloxacin (CPN) to improve oral bioavai... more This study aims to increase the aqueous solubility of ciprofloxacin (CPN) to improve oral bioavailability. This was carried out by formulating a stable formulation of the Self-Emulsifying Drug Delivery System (SEDDS) using various ratios of lipid/oil, surfactant, and co-surfactant. A pseudo-ternary phase diagram was designed to find an area of emulsification. Eight formulations (F1-CPN-F8-CPN) containing oleic acid oil, silicone oil, olive oil, castor oil, sunflower oil, myglol oil, polysorbate-80, polysorbate-20, PEO-200, PEO-400, PEO-600, and PG were formulated. The resultant SEDDS were subjected to thermodynamic study, size, and surface charge studies to improve preparation. Improved composition of SEDDS F5-CPN containing 40% oil, 60% polysorbate-80, and propylene glycol (Smix ratio 6: 1) were thermodynamically stable emulsions having droplet size 202.6 nm, charge surface-13.9 mV, and 0.226 polydispersity index (PDI). Fourier transform infra-red (FT-IR) studies revealed that the optimized formulation and drug showed no interactions. Scanning electron microscope tests showed the droplets have an even surface and spherical shape. It was observed that within 5 h, the concentration of released CPN from optimized formulations F5
Journal of Healthcare Engineering
Autism spectrum disorder is a severe, life-prolonged neurodevelopmental disease typified by disab... more Autism spectrum disorder is a severe, life-prolonged neurodevelopmental disease typified by disabilities that are chronic or limited in the development of socio-communication skills, thinking abilities, activities, and behavior. In children aged two to three years, the symptoms of autism are more evident and easier to recognize. The major part of the existing literature on autism spectrum disorder is covered by a prediction system based on traditional machine learning algorithms such as support vector machine, random forest, multiple layer perceptron, naive Bayes, convolution neural network, and deep neural network. The proposed models are validated by using performance measurement parameters such as accuracy, precision, and recall. In this research, autism spectrum disorder prediction has been investigated and compared using common parameters such as application type, simulation method, comparison methodology, and input data. The key purpose of this study is to give a centralized f...
Background: Viral infections such as measles virus (MV), herpes virus, and human immunodeficiency... more Background: Viral infections such as measles virus (MV), herpes virus, and human immunodeficiency virus (HIV) can lead to transient or permanent neurological or psychiatric dysfunction. However, respiratory system affecting viruses have appeared as an unbeatable challenge to the modern world. They include the human respiratory syncytial virus (hRSV), the influenza virus (IV), and the coronavirus (CoV). They cause acute respiratory infections mainly children under 5 years old and also the elderly. The most frequent clinical manifestations are febrile or afebrile seizures, status epilepticus, encephalopathies, and encephalitis. Objective: The objective of this review is to assess the effect of COVID-19 on our mood and thinking during this pandemic. Method: We reviewed the literature using different databases e.g., Google Scholar, PubMed, and Science direct etc. Results: Viral Infections badly affect the nervous system functions and ultimate can lead to the onset of neurological and ps...
Zenodo (CERN European Organization for Nuclear Research), Jan 27, 2021
Electronics
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
PLOS ONE
The effective segmentation of lesion(s) from dermoscopic skin images assists the Computer-Aided D... more The effective segmentation of lesion(s) from dermoscopic skin images assists the Computer-Aided Diagnosis (CAD) systems in improving the diagnosing rate of skin cancer. The results of the existing skin lesion segmentation techniques are not up to the mark for dermoscopic images with artifacts like varying size corner borders with color similar to lesion(s) and/or hairs having low contrast with surrounding background. To improve the results of the existing skin lesion segmentation techniques for such kinds of dermoscopic images, an effective skin lesion segmentation method is proposed in this research work. The proposed method searches for the presence of corner borders in the given dermoscopc image and removes them if found otherwise it starts searching for the presence of hairs on it and eliminate them if present. Next, it enhances the resultant image using state-of-the-art image enhancement method and segments lesion from it using machine learning technique namely, GrabCut method....
Frontiers in Pharmacology
This study aims to increase the aqueous solubility of ciprofloxacin (CPN) to improve oral bioavai... more This study aims to increase the aqueous solubility of ciprofloxacin (CPN) to improve oral bioavailability. This was carried out by formulating a stable formulation of the Self-Emulsifying Drug Delivery System (SEDDS) using various ratios of lipid/oil, surfactant, and co-surfactant. A pseudo-ternary phase diagram was designed to find an area of emulsification. Eight formulations (F1-CPN–F8-CPN) containing oleic acid oil, silicone oil, olive oil, castor oil, sunflower oil, myglol oil, polysorbate-80, polysorbate-20, PEO-200, PEO-400, PEO-600, and PG were formulated. The resultant SEDDS were subjected to thermodynamic study, size, and surface charge studies to improve preparation. Improved composition of SEDDS F5-CPN containing 40% oil, 60% polysorbate-80, and propylene glycol (Smix ratio 6: 1) were thermodynamically stable emulsions having droplet size 202.6 nm, charge surface -13.9 mV, and 0.226 polydispersity index (PDI). Fourier transform infra-red (FT-IR) studies revealed that the...
Frontiers in Pharmacology
Background: Extracellular signal-regulated kinases (ERKs) are important signaling mediators in ma... more Background: Extracellular signal-regulated kinases (ERKs) are important signaling mediators in mammalian cells and, as a result, one of the major areas of research focus. The detection and quantification of ERK phosphorylation as an index of activation is normally conducted using immunoblotting, which does not allow high-throughput drug screening. Plate-based immunocytochemical assays provide a cheaper and relatively high-throughput alternative method for quantifying ERK phosphorylation. Here, we present optimization steps aimed to increase assay sensitivity and reduce variance and cost using the LI-COR In-Cell Western (I-CW) system in a recombinant CHO-K1 cell line, over-expressing the human delta-opioid receptor (hDOPr) as a model.Methods: Cells cultured in 96-well microassay plates were stimulated with three standard/selective DOPr agonists (SNC80, ADL5859, and DADLE) and a novel selective DOPr agonist (PN6047) to elicit a phospho-ERK response as an index of activation. A number ...
Complexity
The most predominant kind of disease that is normal among ladies is breast cancer. It is one of t... more The most predominant kind of disease that is normal among ladies is breast cancer. It is one of the significant reasons among ladies, regardless of huge endeavors to stay away from it through screening developers. An automatic detection system for disease helps doctors to identify and provide accurate results, thereby minimizing the death rate. Computer-aided diagnosis (CAD) has minimum intervention of humans and produces more accurate results than humans. It will be a difficult and long task that depends on the expertise of pathologists. Deep learning methods proved to give better outcomes when correlated with ML and extricate the best highlights of the images. The main objective of this paper is to propose a deep learning technique in combination with a convolution neural network (CNN) and long short-term memory (LSTM) with a random forest algorithm to diagnose breast cancer. Here, CNN is used for feature extraction, and LSTM is used for extracted feature detection. The experiment...
Electronics
Breast cancer (BC) is a type of tumor that develops in the breast cells and is one of the most co... more Breast cancer (BC) is a type of tumor that develops in the breast cells and is one of the most common cancers in women. Women are also at risk from BC, the second most life-threatening disease after lung cancer. The early diagnosis and classification of BC are very important. Furthermore, manual detection is time-consuming, laborious work, and, possibility of pathologist errors, and incorrect classification. To address the above highlighted issues, this paper presents a hybrid deep learning (CNN-GRU) model for the automatic detection of BC-IDC (+,−) using whole slide images (WSIs) of the well-known PCam Kaggle dataset. In this research, the proposed model used different layers of architectures of CNNs and GRU to detect breast IDC (+,−) cancer. The validation tests for quantitative results were carried out using each performance measure (accuracy (Acc), precision (Prec), sensitivity (Sens), specificity (Spec), AUC and F1-Score. The proposed model shows the best performance measures (...
Computational Intelligence and Neuroscience
Diabetes mellitus (DM), commonly known as diabetes, is a collection of metabolic illnesses charac... more Diabetes mellitus (DM), commonly known as diabetes, is a collection of metabolic illnesses characterized by persistently high blood sugar levels. The signs of elevated blood sugar include increased hunger, frequent urination, and increased thirst. If DM is not treated properly, it may lead to several complications. Diabetes is caused by either insufficient insulin production by the pancreas or an insufficient insulin response by the body’s cells. Every year, 1.6 million individuals die from this disease. The objective of this research work is to use relevant features to construct a blended ensemble learning (EL)-based forecasting system and find the optimal classifier for comparing clinical outputs. The EL based on Bayesian networks and radial basis functions has been proposed in this article. The performances of five machine learning (ML) techniques, namely, logistic regression (LR), decision tree (DT) classifier, support vector machine (SVM), K-nearest neighbors (KNN), and random ...
Frontiers in Psychiatry
IntroductionDue to the complexity of symptoms in major depressive disorder (MDD), the majority of... more IntroductionDue to the complexity of symptoms in major depressive disorder (MDD), the majority of depression scales fall short of accurately assessing a patient's progress. When selecting the most appropriate antidepressant treatment in MDD, a multidimensional scale such as the Hamilton Depression Rating scale (HAM-D) may provide clinicians with more information especially when coupled with unidimensional analysis of some key factors such as depressed mood, altered sleep, psychic and somatic anxiety and suicidal ideation etc.MethodsHAM-D measurements were carried out in patients with MDD when treated with two different therapeutic interventions. The prespecified primary efficacy variables for the study were changes in score from baseline to the end of the 12 weeks on HAM-D scale (i.e., ≤ 8 or ≥50% response). The study involved three assessment points (baseline, 6 weeks and 12 weeks).ResultsEvaluation of both the absolute HAM-D scores and four factors derived from the HAM-D (depr...
Frontiers in Pharmacology
Background: Somatostatin released from the capsaicin-sensitive sensory nerves mediates analgesic ... more Background: Somatostatin released from the capsaicin-sensitive sensory nerves mediates analgesic and anti-inflammatory effects via its receptor subtype 4 (SST4) without influencing endocrine functions. Therefore, SST4 is considered to be a novel target for drug development in pain, especially chronic neuropathy which is a great unmet medical need.Purpose and Experimental Approach: Here, we examined the in silico binding, SST4-linked G protein activation and β-arrestin activation on stable SST4 expressing cells and the effects of our novel pyrrolo-pyrimidine molecules (20, 100, 500, 1,000, 2,000 µg·kg−1) on partial sciatic nerve ligation-induced traumatic mononeuropathic pain model in mice.Key Results: The novel compounds bind to the high affinity binding site of SST4 the receptor and activate the G protein. However, unlike the reference SST4 agonists NNC 26-9100 and J-2156, they do not induce β-arrestin activation responsible for receptor desensitization and internalization upon chr...
Computational Intelligence and Neuroscience
As a result of technology improvements, various features have been collected for heart disease di... more As a result of technology improvements, various features have been collected for heart disease diagnosis. Large data sets have several drawbacks, including limited storage capacity and long access and processing times. For medical therapy, early diagnosis of heart problems is crucial. Disease of heart is a devastating human disease that is quickly increasing in developed and also developing countries, resulting in death. In this type of disease, the heart normally fails to provide enough blood to different body parts in order to allow them to perform their regular functions. Early, as well as, proper diagnosis of this condition is very critical for averting further damage and also to save patients’ lives. In this work, machine learning (ML) is utilized to find out whether a person has cardiac disease or not. Both the types of ensemble classifiers, namely, homogeneous as well as heterogeneous classifiers (formed by combining two separate classifiers), have been implemented in this wo...
Frontiers in Public Health
Background and ObjectiveViral hepatitis is a major public health concern on a global scale. It pr... more Background and ObjectiveViral hepatitis is a major public health concern on a global scale. It predominantly affects the world's least developed countries. The most endemic regions are resource constrained, with a low human development index. Chronic hepatitis can lead to cirrhosis, liver failure, cancer and eventually death. Early diagnosis and treatment of hepatitis infection can help to reduce disease burden and transmission to those at risk of infection or reinfection. Screening is critical for meeting the WHO's 2030 targets. Consequently, automated systems for the reliable prediction of hepatitis illness. When applied to the prediction of hepatitis using imbalanced datasets from testing, machine learning (ML) classifiers and known methodologies for encoding categorical data have demonstrated a wide range of unexpected results. Early research also made use of an artificial neural network to identify features without first gaining a thorough understanding of the sequence ...
Journal of Healthcare Engineering, Jul 10, 2023
Tis article has been retracted by Hindawi, as publisher, following an investigation undertaken by... more Tis article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of systematic manipulation of the publication and peer-review process. We cannot, therefore, vouch for the reliability or integrity of this article. Please note that this notice is intended solely to alert readers that the peer-review process of this article has been compromised. Wiley and Hindawi regret that the usual quality checks did not identify these issues before publication and have since put additional measures in place to safeguard research integrity. We wish to credit our Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation. Te corresponding author, as the representative of all authors, has been given the opportunity to register their agreement or disagreement to this retraction. We have kept a record of any response received.
Zenodo (CERN European Organization for Nuclear Research), Jan 27, 2021
PLOS ONE, Nov 10, 2022
The effective segmentation of lesion(s) from dermoscopic skin images assists the Computer-Aided D... more The effective segmentation of lesion(s) from dermoscopic skin images assists the Computer-Aided Diagnosis (CAD) systems in improving the diagnosing rate of skin cancer. The results of the existing skin lesion segmentation techniques are not up to the mark for dermoscopic images with artifacts like varying size corner borders with color similar to lesion(s) and/or hairs having low contrast with surrounding background. To improve the results of the existing skin lesion segmentation techniques for such kinds of dermoscopic images, an effective skin lesion segmentation method is proposed in this research work. The proposed method searches for the presence of corner borders in the given dermoscopc image and removes them if found otherwise it starts searching for the presence of hairs on it and eliminate them if present. Next, it enhances the resultant image using state-of-the-art image enhancement method and segments lesion from it using machine learning technique namely, GrabCut method. The proposed method was tested on PH2 and ISIC 2018 datasets containing 200 images each and its accuracy was measured with two evaluation metrics, i.e., Jaccard index, and Dice index. The evaluation results show that our proposed skin lesion segmentation method obtained Jaccard Index of 0.77, 0.80 and Dice index of 0.87, 0.82 values on PH2, and ISIC2018 datasets, respectively, which are better than state-ofthe-art skin lesion segmentation techniques.
Frontiers in Pharmacology, Sep 26, 2022
Background: Extracellular signal-regulated kinases (ERKs) are important signaling mediators in ma... more Background: Extracellular signal-regulated kinases (ERKs) are important signaling mediators in mammalian cells and, as a result, one of the major areas of research focus. The detection and quantification of ERK phosphorylation as an index of activation is normally conducted using immunoblotting, which does not allow high-throughput drug screening. Plate-based immunocytochemical assays provide a cheaper and relatively high-throughput alternative method for quantifying ERK phosphorylation. Here, we present optimization steps aimed to increase assay sensitivity and reduce variance and cost using the LI-COR In-Cell Western (I-CW) system in a recombinant CHO-K1 cell line, over-expressing the human delta-opioid receptor (hDOPr) as a model. Methods: Cells cultured in 96-well microassay plates were stimulated with three standard/selective DOPr agonists (SNC80, ADL5859, and DADLE) and a novel selective DOPr agonist (PN6047) to elicit a phospho-ERK response as an index of activation. A number of experimental conditions were investigated during the assay development. Key results: Preliminary experiments revealed a clearly visible edge-effect which significantly increased assay variance across the plate and which was reduced by pre-incubation for 30 min at room temperature. ERK phosphorylation was detectable as early as 1 min after agonist addition, with
Journal of Pharmacology and Experimental Therapeutics, Oct 8, 2019
Agonists at the d opioid receptor are known to be potent antihyperalgesics in chronic pain models... more Agonists at the d opioid receptor are known to be potent antihyperalgesics in chronic pain models and effective in models of anxiety and depression. However, some d opioid agonists have proconvulsant properties while tolerance to the therapeutic effects can develop. Previous evidence indicates that different agonists acting at the d opioid receptor differentially engage signaling and regulatory pathways with significant effects on behavioral outcomes. As such, interest is now growing in the development of biased agonists as a potential means to target specific signaling pathways and potentially improve the therapeutic profile of d opioid agonists. Here, we report on PN6047 (3-[[4-(dimethylcarbamoyl)phenyl]-[1-(thiazol-5-ylmethyl)-4piperidylidene]methyl]benzamide), a novel G protein-biased and selective d opioid agonist. In cell-based assays, PN6047 fully engages G protein signaling but is a partial agonist in both the arrestin recruitment and internalization assays. PN6047 is effective in rodent models of chronic pain but shows no detectable analgesic tolerance following prolonged treatment. In addition, PN6047 exhibited antidepressant-like activity in the forced swim test, and importantly, the drug had no effect on chemically induced seizures. PN6047 did not exhibit reward-like properties in the conditioned place preference test or induce respiratory depression. Thus, d opioid ligands with limited arrestin signaling such as PN6047 may be therapeutically beneficial in the treatment of chronic pain states. SIGNIFICANCE STATEMENT PN6047 (3-[[4-(dimethylcarbamoyl)phenyl]-[1-(thiazol-5-ylmethyl)-4piperidylidene]methyl]benzamide) is a selective, G protein-biased d opioid agonist with efficacy in preclinical models of chronic pain. No analgesic tolerance was observed after prolonged treatment, and PN6047 does not display proconvulsant activity or other opioidmediated adverse effects. Our data suggest that d opioid ligands with limited arrestin signaling will be beneficial in the treatment of chronic pain.
Frontiers in Pharmacology, Oct 4, 2022
This study aims to increase the aqueous solubility of ciprofloxacin (CPN) to improve oral bioavai... more This study aims to increase the aqueous solubility of ciprofloxacin (CPN) to improve oral bioavailability. This was carried out by formulating a stable formulation of the Self-Emulsifying Drug Delivery System (SEDDS) using various ratios of lipid/oil, surfactant, and co-surfactant. A pseudo-ternary phase diagram was designed to find an area of emulsification. Eight formulations (F1-CPN-F8-CPN) containing oleic acid oil, silicone oil, olive oil, castor oil, sunflower oil, myglol oil, polysorbate-80, polysorbate-20, PEO-200, PEO-400, PEO-600, and PG were formulated. The resultant SEDDS were subjected to thermodynamic study, size, and surface charge studies to improve preparation. Improved composition of SEDDS F5-CPN containing 40% oil, 60% polysorbate-80, and propylene glycol (Smix ratio 6: 1) were thermodynamically stable emulsions having droplet size 202.6 nm, charge surface-13.9 mV, and 0.226 polydispersity index (PDI). Fourier transform infra-red (FT-IR) studies revealed that the optimized formulation and drug showed no interactions. Scanning electron microscope tests showed the droplets have an even surface and spherical shape. It was observed that within 5 h, the concentration of released CPN from optimized formulations F5
Journal of Healthcare Engineering
Autism spectrum disorder is a severe, life-prolonged neurodevelopmental disease typified by disab... more Autism spectrum disorder is a severe, life-prolonged neurodevelopmental disease typified by disabilities that are chronic or limited in the development of socio-communication skills, thinking abilities, activities, and behavior. In children aged two to three years, the symptoms of autism are more evident and easier to recognize. The major part of the existing literature on autism spectrum disorder is covered by a prediction system based on traditional machine learning algorithms such as support vector machine, random forest, multiple layer perceptron, naive Bayes, convolution neural network, and deep neural network. The proposed models are validated by using performance measurement parameters such as accuracy, precision, and recall. In this research, autism spectrum disorder prediction has been investigated and compared using common parameters such as application type, simulation method, comparison methodology, and input data. The key purpose of this study is to give a centralized f...
Background: Viral infections such as measles virus (MV), herpes virus, and human immunodeficiency... more Background: Viral infections such as measles virus (MV), herpes virus, and human immunodeficiency virus (HIV) can lead to transient or permanent neurological or psychiatric dysfunction. However, respiratory system affecting viruses have appeared as an unbeatable challenge to the modern world. They include the human respiratory syncytial virus (hRSV), the influenza virus (IV), and the coronavirus (CoV). They cause acute respiratory infections mainly children under 5 years old and also the elderly. The most frequent clinical manifestations are febrile or afebrile seizures, status epilepticus, encephalopathies, and encephalitis. Objective: The objective of this review is to assess the effect of COVID-19 on our mood and thinking during this pandemic. Method: We reviewed the literature using different databases e.g., Google Scholar, PubMed, and Science direct etc. Results: Viral Infections badly affect the nervous system functions and ultimate can lead to the onset of neurological and ps...
Zenodo (CERN European Organization for Nuclear Research), Jan 27, 2021
Electronics
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
PLOS ONE
The effective segmentation of lesion(s) from dermoscopic skin images assists the Computer-Aided D... more The effective segmentation of lesion(s) from dermoscopic skin images assists the Computer-Aided Diagnosis (CAD) systems in improving the diagnosing rate of skin cancer. The results of the existing skin lesion segmentation techniques are not up to the mark for dermoscopic images with artifacts like varying size corner borders with color similar to lesion(s) and/or hairs having low contrast with surrounding background. To improve the results of the existing skin lesion segmentation techniques for such kinds of dermoscopic images, an effective skin lesion segmentation method is proposed in this research work. The proposed method searches for the presence of corner borders in the given dermoscopc image and removes them if found otherwise it starts searching for the presence of hairs on it and eliminate them if present. Next, it enhances the resultant image using state-of-the-art image enhancement method and segments lesion from it using machine learning technique namely, GrabCut method....
Frontiers in Pharmacology
This study aims to increase the aqueous solubility of ciprofloxacin (CPN) to improve oral bioavai... more This study aims to increase the aqueous solubility of ciprofloxacin (CPN) to improve oral bioavailability. This was carried out by formulating a stable formulation of the Self-Emulsifying Drug Delivery System (SEDDS) using various ratios of lipid/oil, surfactant, and co-surfactant. A pseudo-ternary phase diagram was designed to find an area of emulsification. Eight formulations (F1-CPN–F8-CPN) containing oleic acid oil, silicone oil, olive oil, castor oil, sunflower oil, myglol oil, polysorbate-80, polysorbate-20, PEO-200, PEO-400, PEO-600, and PG were formulated. The resultant SEDDS were subjected to thermodynamic study, size, and surface charge studies to improve preparation. Improved composition of SEDDS F5-CPN containing 40% oil, 60% polysorbate-80, and propylene glycol (Smix ratio 6: 1) were thermodynamically stable emulsions having droplet size 202.6 nm, charge surface -13.9 mV, and 0.226 polydispersity index (PDI). Fourier transform infra-red (FT-IR) studies revealed that the...
Frontiers in Pharmacology
Background: Extracellular signal-regulated kinases (ERKs) are important signaling mediators in ma... more Background: Extracellular signal-regulated kinases (ERKs) are important signaling mediators in mammalian cells and, as a result, one of the major areas of research focus. The detection and quantification of ERK phosphorylation as an index of activation is normally conducted using immunoblotting, which does not allow high-throughput drug screening. Plate-based immunocytochemical assays provide a cheaper and relatively high-throughput alternative method for quantifying ERK phosphorylation. Here, we present optimization steps aimed to increase assay sensitivity and reduce variance and cost using the LI-COR In-Cell Western (I-CW) system in a recombinant CHO-K1 cell line, over-expressing the human delta-opioid receptor (hDOPr) as a model.Methods: Cells cultured in 96-well microassay plates were stimulated with three standard/selective DOPr agonists (SNC80, ADL5859, and DADLE) and a novel selective DOPr agonist (PN6047) to elicit a phospho-ERK response as an index of activation. A number ...
Complexity
The most predominant kind of disease that is normal among ladies is breast cancer. It is one of t... more The most predominant kind of disease that is normal among ladies is breast cancer. It is one of the significant reasons among ladies, regardless of huge endeavors to stay away from it through screening developers. An automatic detection system for disease helps doctors to identify and provide accurate results, thereby minimizing the death rate. Computer-aided diagnosis (CAD) has minimum intervention of humans and produces more accurate results than humans. It will be a difficult and long task that depends on the expertise of pathologists. Deep learning methods proved to give better outcomes when correlated with ML and extricate the best highlights of the images. The main objective of this paper is to propose a deep learning technique in combination with a convolution neural network (CNN) and long short-term memory (LSTM) with a random forest algorithm to diagnose breast cancer. Here, CNN is used for feature extraction, and LSTM is used for extracted feature detection. The experiment...
Electronics
Breast cancer (BC) is a type of tumor that develops in the breast cells and is one of the most co... more Breast cancer (BC) is a type of tumor that develops in the breast cells and is one of the most common cancers in women. Women are also at risk from BC, the second most life-threatening disease after lung cancer. The early diagnosis and classification of BC are very important. Furthermore, manual detection is time-consuming, laborious work, and, possibility of pathologist errors, and incorrect classification. To address the above highlighted issues, this paper presents a hybrid deep learning (CNN-GRU) model for the automatic detection of BC-IDC (+,−) using whole slide images (WSIs) of the well-known PCam Kaggle dataset. In this research, the proposed model used different layers of architectures of CNNs and GRU to detect breast IDC (+,−) cancer. The validation tests for quantitative results were carried out using each performance measure (accuracy (Acc), precision (Prec), sensitivity (Sens), specificity (Spec), AUC and F1-Score. The proposed model shows the best performance measures (...
Computational Intelligence and Neuroscience
Diabetes mellitus (DM), commonly known as diabetes, is a collection of metabolic illnesses charac... more Diabetes mellitus (DM), commonly known as diabetes, is a collection of metabolic illnesses characterized by persistently high blood sugar levels. The signs of elevated blood sugar include increased hunger, frequent urination, and increased thirst. If DM is not treated properly, it may lead to several complications. Diabetes is caused by either insufficient insulin production by the pancreas or an insufficient insulin response by the body’s cells. Every year, 1.6 million individuals die from this disease. The objective of this research work is to use relevant features to construct a blended ensemble learning (EL)-based forecasting system and find the optimal classifier for comparing clinical outputs. The EL based on Bayesian networks and radial basis functions has been proposed in this article. The performances of five machine learning (ML) techniques, namely, logistic regression (LR), decision tree (DT) classifier, support vector machine (SVM), K-nearest neighbors (KNN), and random ...
Frontiers in Psychiatry
IntroductionDue to the complexity of symptoms in major depressive disorder (MDD), the majority of... more IntroductionDue to the complexity of symptoms in major depressive disorder (MDD), the majority of depression scales fall short of accurately assessing a patient's progress. When selecting the most appropriate antidepressant treatment in MDD, a multidimensional scale such as the Hamilton Depression Rating scale (HAM-D) may provide clinicians with more information especially when coupled with unidimensional analysis of some key factors such as depressed mood, altered sleep, psychic and somatic anxiety and suicidal ideation etc.MethodsHAM-D measurements were carried out in patients with MDD when treated with two different therapeutic interventions. The prespecified primary efficacy variables for the study were changes in score from baseline to the end of the 12 weeks on HAM-D scale (i.e., ≤ 8 or ≥50% response). The study involved three assessment points (baseline, 6 weeks and 12 weeks).ResultsEvaluation of both the absolute HAM-D scores and four factors derived from the HAM-D (depr...
Frontiers in Pharmacology
Background: Somatostatin released from the capsaicin-sensitive sensory nerves mediates analgesic ... more Background: Somatostatin released from the capsaicin-sensitive sensory nerves mediates analgesic and anti-inflammatory effects via its receptor subtype 4 (SST4) without influencing endocrine functions. Therefore, SST4 is considered to be a novel target for drug development in pain, especially chronic neuropathy which is a great unmet medical need.Purpose and Experimental Approach: Here, we examined the in silico binding, SST4-linked G protein activation and β-arrestin activation on stable SST4 expressing cells and the effects of our novel pyrrolo-pyrimidine molecules (20, 100, 500, 1,000, 2,000 µg·kg−1) on partial sciatic nerve ligation-induced traumatic mononeuropathic pain model in mice.Key Results: The novel compounds bind to the high affinity binding site of SST4 the receptor and activate the G protein. However, unlike the reference SST4 agonists NNC 26-9100 and J-2156, they do not induce β-arrestin activation responsible for receptor desensitization and internalization upon chr...
Computational Intelligence and Neuroscience
As a result of technology improvements, various features have been collected for heart disease di... more As a result of technology improvements, various features have been collected for heart disease diagnosis. Large data sets have several drawbacks, including limited storage capacity and long access and processing times. For medical therapy, early diagnosis of heart problems is crucial. Disease of heart is a devastating human disease that is quickly increasing in developed and also developing countries, resulting in death. In this type of disease, the heart normally fails to provide enough blood to different body parts in order to allow them to perform their regular functions. Early, as well as, proper diagnosis of this condition is very critical for averting further damage and also to save patients’ lives. In this work, machine learning (ML) is utilized to find out whether a person has cardiac disease or not. Both the types of ensemble classifiers, namely, homogeneous as well as heterogeneous classifiers (formed by combining two separate classifiers), have been implemented in this wo...
Frontiers in Public Health
Background and ObjectiveViral hepatitis is a major public health concern on a global scale. It pr... more Background and ObjectiveViral hepatitis is a major public health concern on a global scale. It predominantly affects the world's least developed countries. The most endemic regions are resource constrained, with a low human development index. Chronic hepatitis can lead to cirrhosis, liver failure, cancer and eventually death. Early diagnosis and treatment of hepatitis infection can help to reduce disease burden and transmission to those at risk of infection or reinfection. Screening is critical for meeting the WHO's 2030 targets. Consequently, automated systems for the reliable prediction of hepatitis illness. When applied to the prediction of hepatitis using imbalanced datasets from testing, machine learning (ML) classifiers and known methodologies for encoding categorical data have demonstrated a wide range of unexpected results. Early research also made use of an artificial neural network to identify features without first gaining a thorough understanding of the sequence ...