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| 1 | Drainage vs. non-drainage after cholecystectomy for acute cholecystitis:a retrospective study显示文摘Many surgeons practice prophylactic drainage after cholecystectomy without reliable evidence,this study was conducted to answer the question whether to drain or not to drain after cholecystectomy for acute calculous cholecystitis.A retrospective review of all patients who had cholecystectomy for acute cholecystitis in Aseer Central Hospital,Abha,Saudi Arabia,was conducted from April 2010 to April 2012.Data were extracted from hospital case files.Preoperative data included clinical presentation,routine investigations and liver function tests.Operative data included excessive adhesions,bleeding,bile leak,and drain insertion.Complicated cases such as pericholecystic collections,mucocele and empyema were also reported.Patients who needed therapeutic drainage were excluded.Postoperative data included hospital stay,volume of drained fluid,time of drain removal,and drain site problems.The study included 103 patients allocated into two groups;group A(n = 38) for patients with operative drain insertion and group B(n = 65) for patients without drain insertion.The number of patients with preoperative diagnosis of acute non-complicated cholecystitis was significantly greater in group B(80%) than group A(36.8%)(P < 0.001).Operative time was significantly longer in group A.All patients who were converted from laparoscopic to open cholecystectomy were in group A.Multivariate analysis revealed that hospital stay was significantly(P < 0.001) longer in patients with preoperative complications.There was no added benefit for prophylactic drain insertion after cholecystectomy for acute calculous cholecystitis in non-complicated or in complicated cases. | Mohammed A Bawahab Walid M Abd El Maksoud Saeed A Alsareii Fahad S Al Amri Hala F Ali Abdul Rahman Nimeri Abdul Rahman M Al Amri Adel A Assiri Mohammed I Abdul Aziz | 2014 | The Journal of Biomedical Research2014，28，3： | 3 |
| 2 | Anomaly Classification Using Genetic Algorithm-Based Random Forest Modelfor Network Attack Detection显示文摘Anomaly classification based on network traffic features is an important task to monitor and detect network intrusion attacks.Network-based intrusion detection systems(NIDSs)using machine learning(ML)methods are effective tools for protecting network infrastructures and services from unpredictable and unseen attacks.Among several ML methods,random forest(RF)is a robust method that can be used in ML-based network intrusion detection solutions.However,the minimum number of instances for each split and the number of trees in the forest are two key parameters of RF that can affect classification accuracy.Therefore,optimal parameter selection is a real problem in RF-based anomaly classification of intrusion detection systems.In this paper,we propose to use the genetic algorithm(GA)for selecting the appropriate values of these two parameters,optimizing the RF classifier and improving the classification accuracy of normal and abnormal network traffics.To validate the proposed GA-based RF model,a number of experiments is conducted on two public datasets and evaluated using a set of performance evaluation measures.In these experiments,the accuracy result is compared with the accuracies of baseline ML classifiers in the recent works.Experimental results reveal that the proposed model can avert the uncertainty in selection the values of RF’s parameters,improving the accuracy of anomaly classification in NIDSs without incurring excessive time. | Adel Assiri | 2021 | Computers, Materials & Continua2021，，1： | 1 |
| 3 | Protective effect of Tamarix amplexicaulis seeds against hepatotoxicity induced by carbon tetrachloride,paracetamol,or D-galactosamine显示文摘 | Assiri Adel M A | 2006 | Journal of Saudi Chemical Society2006，10，3： | 1 |
| 4 | Advancing male age differentially alters levels and localization patterns of PLCzeta in sperm and testes from different mouse strains显示文摘Sperm-specific phospholipase C zeta(PLCζ)initiates intracellular calcium(Ca2+)transients which drive a series of concurrent events collectively termed oocyte activation.Numerous investigations have linked abrogation and absence/reduction of PLCζwith forms of male infertility in humans where oocyte activation fails.However,very few studies have examined potential relationships between PLCζand advancing male age,both of which are increasingly considered to be major effectors of male fertility.Initial efforts in humans may be hindered by inherent PLCζvariability within the human population,alongside a lack of sufficient controllable repeats.Herein,utilizing immunoblotting,immunofluorescence,and quantitative reverse transcription PCR(qRT-PCR)we examined for the first time PLCζprotein levels and localization patterns in sperm,and PLCζmRNA levels within testes,from mice at 8 weeks,12 weeks,24 weeks,and 36 weeks of age,from two separate strains of mice,C57BL/6(B6;inbred)and CD1(outbred).Collectively,advancing male age generally diminished levels and variability of PLCζprotein and mRNA in sperm and testes,respectively,when both strains were examined.Furthermore,advancing male age altered the predominant pattern of PLCζlocalization in mouse sperm,with younger mice exhibiting predominantly post-acrosomal,and older mice exhibiting both post-acrosomal and acrosomal populations of PLCζ.However,the specific pattern of such decline in levels of protein and mRNA was strain-specific.Collectively,our results demonstrate a negative relationship between advancing male age and PLCζlevels and localization patterns,indicating that aging male mice from different strains may serve as useful models to investigate PLCζin cases of male infertility and subfertility in humans. | Junaid Kashir Bhavesh V Mistry Maha Adel Gumssani Muhammad Rajab Reema Abu-Dawas Falah AlMohanna Michail Nomikos Celine Jones Raed Abu-Dawud Nadya Al-Yacoub Kevin Coward F Anthony Lai Abdullah M Assiri | 2021 | Asian Journal of Andrology2021，23，2： | 0 |
| 5 | Efficient Training of Multi-Layer Neural Networks to Achieve Faster Validation显示文摘Artificial neural networks(ANNs)are one of the hottest topics in computer science and artificial intelligence due to their potential and advantages in analyzing real-world problems in various disciplines,including but not limited to physics,biology,chemistry,and engineering.However,ANNs lack several key characteristics of biological neural networks,such as sparsity,scale-freeness,and small-worldness.The concept of sparse and scale-free neural networks has been introduced to fill this gap.Network sparsity is implemented by removing weak weights between neurons during the learning process and replacing them with random weights.When the network is initialized,the neural network is fully connected,which means the number of weights is four times the number of neurons.In this study,considering that a biological neural network has some degree of initial sparsity,we design an ANN with a prescribed level of initial sparsity.The neural network is tested on handwritten digits,Arabic characters,CIFAR-10,and Reuters newswire topics.Simulations show that it is possible to reduce the number of weights by up to 50%without losing prediction accuracy.Moreover,in both cases,the testing time is dramatically reduced compared with fully connected ANNs. | Adel Saad Assiri | 2021 | Computer Systems Science & Engineering2021，36，3： | 0 |
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