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| 1 | Molecular signaling mechanisms of apoptosis in hereditary non-polyposis colorectal cancer显示文摘Colorectal cancer is the second most leading cause of cancer related deaths in the western countries. One of the forms of colorectal cancer is hereditary non-polyposis colorectal cancer (HNPCC), also known as 'Lynch syndrome'. It is the most common hereditary form of cancer accounting for 5%-10% of all colon cancers. HNPCC is a dominant autosomal genetic disorder caused by germ line mutations in mismatch repair genes. Human mismatch repair genes play a crucial role in genetic stability of DNA, the inactivation of which results in an increased rate of mutation and often a loss of mismatch repair function. Recent studies have shown that certain mismatch repair genes are involved in the regulation of key cellular processes including apoptosis. Thus, differential expression of mismatch repair genes particularly the contributions of MLH1 and MSH2 play important roles in therapeutic resistance to certain cytotoxic drugs such as cisplatin that is used normally as chemoprevention. An understanding of the role of mismatch repair genes in molecular signaling mechanism of apoptosis and its involvement in HNPCC needs attention for further work into this important area of cancer research, and this review article is intended to accomplish that goal of linkage of apoptosis with HNPCC. The current review was not intended to provide a comprehensive enumeration of the entire body of literature in the area of HNPCC or mismatch repair system or apoptosis; it is rather intended to focus primarily on the current state of knowledge of the role of mismatch repair proteins in molecular signaling mechanism of apoptosis as it relates to understanding of HNPCC. | Samar Hassen Nawab Ali Parimal Chowdhury | 2012 | World Journal of Gastrointestinal Pathophysiology2012，3，3： | 7 |
| 2 | Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method显示文摘In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial pattern.Thereby,electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability.Due to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG domain.In this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as input.To minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant features.To this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the classification.Further,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test accuracy.Performance evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark schemes.Our proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively. | Muhammad Irfan Ali Raza Faisal Althobiani Nasir Ayub Muhammad Idrees Zain Ali Kashif Rizwan Abdullah Saeed Alwadie Saleh Mohammed Ghonaim Hesham Abdushkour Saifur Rahman Omar Alshorman Samar Alqhtani | 2022 | Computers, Materials & Continua2022，，9： | 2 |
| 3 | Cardiac and renal effects of omapatrilat,a vasopeptidase inhibitor,in rats with experimental congestive heart failure显示文摘 | Abassi Zaid A Yahia Ali Zeid Samar | 2005 | Am J Physiol Heart Circ Physiol2005，288，： | 1 |
| 4 | Energy Theft Identification Using Adaboost Ensembler in the Smart Grids显示文摘One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously possible.For this purpose,we have taken historical data of energy thieves and normal users.To avoid imbalance observation,biased estimates,we applied the interpolation method.Furthermore,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.By proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time complexity.To minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark scheme.Our proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,respectively.Furthermore,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques. | Muhammad Irfan Nasir Ayub Faisal Althobiani Zain Ali Muhammad Idrees Saeed Ullah Saifur Rahman Abdullah Saeed Alwadie Saleh Mohammed Ghonaim Hesham Abdushkour Fahad Salem Alkahtani Samar Alqhtani Piotr Gas | 2022 | Computers, Materials & Continua2022，，7： | 1 |
| 5 | High prevalence of human herpesvirus 8 in schizophrenic patients显示文摘 | Neila Hannachi Yousri El Kissi Samar Samoud Jaafar Nakhli Leila Letaief Samia Gaabout Bechir Ben Hadj Ali Jalel Boukadida | 2013 | Psychiatry Research2013，，： | 1 |
| 6 | Decentration and Cataract Formation 10 Years Following Posterior Chamber Silicone Phakic Intraocular Lens Implantation 显示文摘 | Samar A Al-Swailem Ali A Al-Rajhi | 2006 | Journal of Refractive Surgery2006，22，5： | 1 |
| 7 | A U-Net-Based CNN Model for Detection and Segmentation of Brain Tumor显示文摘Human brain consists of millions of cells to control the overall structure of the human body.When these cells start behaving abnormally,then brain tumors occurred.Precise and initial stage brain tumor detection has always been an issue in the field of medicines for medical experts.To handle this issue,various deep learning techniques for brain tumor detection and segmentation techniques have been developed,which worked on different datasets to obtain fruitful results,but the problem still exists for the initial stage of detection of brain tumors to save human lives.For this purpose,we proposed a novel U-Net-based Convolutional Neural Network(CNN)technique to detect and segmentizes the brain tumor for Magnetic Resonance Imaging(MRI).Moreover,a 2-dimensional publicly available Multimodal Brain Tumor Image Segmentation(BRATS2020)dataset with 1840 MRI images of brain tumors has been used having an image size of 240×240 pixels.After initial dataset preprocessing the proposed model is trained by dividing the dataset into three parts i.e.,testing,training,and validation process.Our model attained an accuracy value of 0.98%on the BRATS2020 dataset,which is the highest one as compared to the already existing techniques. | Rehana Ghulam Sammar Fatima Tariq Ali Nazir Ahmad Zafar Abdullah A.Asiri Hassan A.Alshamrani Samar M.Alqhtani Khlood M.Mehdar | 2023 | Computers, Materials & Continua2023，，1： | 0 |
| 8 | Association of Allergic Symptoms with Dengue Infection and Severity:A Systematic Review and Meta-analysis显示文摘The relationship between the severity of dengue infection and allergy is still obscure. We conducted an electronic search across 12 databases for relevant articles reporting allergic symptoms, dengue infection, and dengue classification. These studies were categorized according to dengue severity and allergy symptoms, and a meta-analysis was performed by pooling the studies in each category. Among the included 57 articles, pruritus was the most common allergic sign followed by non-specified allergy and asthma(28.6%, 13%, and 6.5%, respectively). Despite the reported significant association of dengue with pruritus and total Ig E level(P \ 0.05), in comparison with non-dengue cases and healthy controls, there was no association between the different severe dengue group with pruritus, skin allergy, food allergy or asthma. However,removing the largest study revealed a significant association between asthma with dengue hemorrhagic fever(DHF) rather than dengue fever(DF). In comparison with DF, DHF was associated with Ig E positivity. Furthermore, specific-Ig E level was higher in secondary DF rather than primary DF. There was a possible association between allergy symptoms and dengue severity progression. Further studies are needed to clarify this association. | Nguyen Dang Kien Amr Ehab El-Qushayri Ali Mahmoud Ahmed Adnan Safi Sarah Abdel Mageed Samar Muhammed Mehyar Mohammad Rashidul Hashan Sedighe Karimzadeh Kenji Hirayama Nguyen Tien Huy | 2020 | Virologica Sinica2020，35，1： | 0 |
| 9 | Correction to: Association of Allergic Symptoms with Dengue Infection and Severity:A Systematic Review and Meta-analysis显示文摘The spelling of the fifth author’s name was misspelled.The byline should appear as shown above.The original article has been corrected. | Nguyen Dang Kien Amr Ehab El-Qushayri Ali Mahmoud Ahmed Adnan Safi Sarah Abdel Mageed Samar Muhammed Mehyar Mohammad Rashidul Hashan Sedighe Karimzadeh Kenji Hirayama Nguyen Tien Huy | 2020 | Virologica Sinica2020，35，1： | 0 |
| 10 | A Transfer Learning Based Approach for COVID-19 Detection Using Inception-v4 Model显示文摘Coronavirus(COVID-19 or SARS-CoV-2)is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries.The rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its transmission.There is a signifi-cant increase in the number of patients infected,resulting in a lack of test resources and kits in most countries.To overcome this panicked state of affairs,researchers are looking forward to some effective solutions to overcome this situa-tion:one of the most common and effective methods is to examine the X-radiation(X-rays)and computed tomography(CT)images for detection of Covid-19.How-ever,this method burdens the radiologist to examine each report.Therefore,to reduce the burden on the radiologist,an effective,robust and reliable detection system has been developed,which may assist the radiologist and medical specia-list in effective detecting of COVID.We proposed a deep learning approach that uses readily available chest radio-graphs(chest X-rays)to diagnose COVID-19 cases.The proposed approach applied transfer learning to the Deep Convolutional Neural Network(DCNN)model,Inception-v4,for the automatic detection of COVID-19 infection from chest X-rays images.The dataset used in this study contains 1504 chest X-ray images,504 images of COVID-19 infection,and 1000 normal images obtained from publicly available medical repositories.The results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%. | Ali Alqahtani Shumaila Akram Muhammad Ramzan Fouzia Nawaz Hikmat Ullah Khan Essa Alhashlan Samar MAlqhtani Areeba Waris Zain Ali | 2023 | Intelligent Automation & Soft Computing2023，，2： | 0 |
| 11 | Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples显示文摘Assistive devices for disabled people with the help of Brain-Computer Interaction(BCI)technology are becoming vital bio-medical engineering.People with physical disabilities need some assistive devices to perform their daily tasks.In these devices,higher latency factors need to be addressed appropriately.Therefore,the main goal of this research is to implement a real-time BCI architecture with minimum latency for command actuation.The proposed architecture is capable to communicate between different modules of the system by adopting an automotive,intelligent data processing and classification approach.Neuro-sky mind wave device has been used to transfer the data to our implemented server for command propulsion.Think-Net Convolutional Neural Network(TN-CNN)architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data classification.Data collection and processing are the responsibility of the central integrated server for system load minimization.Testing of implemented architecture and deep learning model shows excellent results.The proposed system integrity level was the minimum data loss and the accurate commands processing mechanism.The training and testing results are 99%and 93%for custom model implementation based on TN-CNN.The proposed real-time architecture is capable of intelligent data processing unit with fewer errors,and it will benefit assistive devices working on the local server and cloud server. | Ali Usman Javed Ferzund Ahmad Shaf Muhammad Aamir Samar Alqhtani Khlood M.Mehdar Hanan Talal Halawani Hassan A.Alshamrani Abdullah A.Asiri Muhammad Irfan | 2023 | Computer Systems Science & Engineering2023，46，8： | 0 |
| 12 | Flow Injection Chemiluminescence Method for Nalbuphine Hydrochloride in Pharmaceutical Formulations Using Tris(2,2′-bipyridyl)ruthenium(II)Chloride-diperiodatocuprate(III)Reaction显示文摘A sensitive and selective method employing chemiluminescence(CL)coupled with flow injection(FI)is reported for nalbuphine hydrochloride(NAL)assay in pharmaceutical formulations.The enhancement effect of NAL on the CL reaction between tris(2,2′-bipyridyl)ruthenium(II)chloride-diperiodatocuprate(III){Ru[(bpy)_(3)]^(2+)-Cu(III)complex}in acidic medium is used as analytical measurement.The optimal conditions of the CL reaction were sulfuric acid 1.0×10^(−3) mol/L,Ru[(bpy)s]2+7.5×10^(−5) mol/L,Cu(III)/Ag(III)complexes 4.0×10^(−4)/5.0×10^(−4) mol/L,sample loop volume of 120µL and flow rate of 2.5 mL/min.The sensitivities of the method in terms of detection(S/N=3)and quantification(S/N=10)limits are 5×10^(−4) and 0.001 ppm(1 ppm=1 mg/L),respectively.The linear response of the instrument in the form of CL intensity with respect to NAL concentration is over the range 0.001-15.0 ppm(R^(2)=0.9999)with relative standard deviation from 0.8%to 3.2%and injection throughput of 120 injection/h.The applications of the method include the quantitative analysis of NAL in pharmaceutical injection samples.Variations and the average results of the proposed method are not significantly different from the results of a reported method by applying F-and paired student t-test.The most likely CL reaction mechanism is written in accordance with spectrophotometric and CL studies. | AHMED Khan MUHAMMAD Asghar MOHAMMED Yaqoob MASOOD Ahmed Siddiqui SAMAR Ali | 2021 | Chemical Research in Chinese Universities2021，37，3： | 0 |
| 13 | Chemiluminescence Determination of Cefixime Trihydrate Based on Acidic Diperiodatoargentate(Ⅲ)-Rhodamine 6-G System显示文摘A novel chemiluminescence(CL) method is proposed for cefixime trihydrate(CFX) determination based on its eiiliancement effect on diperiodatoargentate(III)(DPA)-rhodamine 6-G(Rh6-G) reaction in conjunction with flow injection analysis(FIA). A linear calibration curve was achieved over the range from 0.01 mg/L to 2.5 mg/L CFX(R^2=0.999,n=8) with relative standard deviation(RSD) of 1.4%-3.8%(n=4)), limit of detection(LOD) of 3.0×10^-3 mg/L(S/N=3),injection tliroughput of 180/h and regression equation of y=1113.2x-14.596[y=CL intensity (mV) and x=concentration of CFX(mg/L)]. The method was successfully applied to CFX determination in pharmaceutical formulations and the recoveries(%) for proposed FI-CL and a reported spectrophotometric method by applying the Student t-test [calculated t-test value: t=1.079215, and tabulated t-distributed(95%)=2.200985] were not significantly diflerent. The CFX was efficiently extracted and no significant effect of commonly found excipients in the pharmaceutical formulations was observed. The mechanism of CL reaction is discussed briefly. | MUHAMMAD Yousaf MOHAMMED Yaqoob MUHAMMAD Asghar SAMAR Ali | 2019 | Chemical Research in Chinese Universities2019，35，5： | 0 |
| 14 | Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation显示文摘A brain tumor is a mass or growth of abnormal cells in the brain.In children and adults,brain tumor is considered one of the leading causes of death.There are several types of brain tumors,including benign(non-cancerous)and malignant(cancerous)tumors.Diagnosing brain tumors as early as possible is essential,as this can improve the chances of successful treatment and survival.Considering this problem,we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models(Resnet50,Vgg16,Vgg19,U-Net)and their integration for computer-aided detection and localization systems in brain tumors.These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas.The dataset consists of 120 patients.The pre-trained models have been used to classify tumor or no tumor images,while integrated models are applied to segment the tumor region correctly.We have evaluated their performance in terms of loss,accuracy,intersection over union,Jaccard distance,dice coefficient,and dice coefficient loss.From pre-trained models,the U-Net model achieves higher performance than other models by obtaining 95%accuracy.In contrast,U-Net with ResNet-50 out-performs all other models from integrated pre-trained models and correctly classified and segmented the tumor region. | Muhammad Irfan Ahmad Shaf Tariq Ali Umar Farooq Saifur Rahman Salim Nasar Faraj Mursal Mohammed Jalalah Samar M.Alqhtani Omar AlShorman | 2023 | Computers, Materials & Continua2023，，7： | 0 |
| 15 | Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images显示文摘The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients worldwide.Even in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original ones.The artificially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented data.To over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artificially realistic data for MRI images.The GAN network contains mainly two parts known as generator and discriminator.Commonly,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural network.In this research,the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging(CE-MRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been used.Our proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods. | Abdullah A.Asiri Ahmad Shaf Tariq Ali Muhammad Aamir Ali Usman Muhammad Irfan Hassan A.Alshamrani Khlood M.Mehdar Osama M.Alshehri Samar M.Alqhtani | 2023 | Intelligent Automation & Soft Computing2023，，4： | 0 |
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