Ahmed Sedik

Kafelsheikh University, Computer and information, Department Member

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Assistant professor @ Faculty of Artificial intelligence Kafrelsheikh University.
Phone: 201149801000

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Papers by Ahmed Sedik

Immunoregulatory role of hesperidin against ovalbumin (OVA)-induced bronchial asthma and depression in rats

Naunyn-Schmiedeberg's Archives of Pharmacology

Links between bronchial asthma and depression have recently become a great subject of interest. T... more Links between bronchial asthma and depression have recently become a great subject of interest. The present study was carried out to assess the protective role of hesperidin against ovalbumin (OVA)-induced bronchial asthma that is associated with depression in rats, for this purpose, four groups. Rats were sensitized with intraperitoneal administration of 200 μg OVA/10 mg aluminum hydroxide (Al (OH) 3 for 3 consecutive days then at day 11 followed by intranasal challenge with OVA (1.5 mg/kg) at days 19, 20, and 21. Rats were pretreated with hesperidin (100 & 200 mg/kg) 1h before OVA challenge. At the end of the study, behavioral tests, biochemical indices, and histopathological architectures of lung and brain tissues were evaluated. Our findings showed that hesperidin significantly ameliorated the reduction in motor activity, motor coordination, forced swimming, CD4, CD25 and foxp3, interleukin-10 (IL-10), dopamine, serotonin, and neurotrophin-3 (NT3) as well as alleviated the eleva...

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Synergistic effect of arginine and Lactobacillus plantarum against potassium dichromate induced-acute liver and kidney injury in rats: Role of iNOS and TLR4/NF-κB signaling pathways

PubMed, 2023

Objectives: Our study was conducted to evaluate the synergistic effect of arginine (ARG) and Lact... more Objectives: Our study was conducted to evaluate the synergistic effect of arginine (ARG) and Lactobacillus plantarum against potassium dichromate (K2Cr2O7) induced-acute hepatic and kidney injury. Materials and methods: Fifty male Wistar rats were divided into five groups. The control group received distilled water. The potassium dichromate group (PDC) received a single dose of PDC (20 mg/kg; SC). The arginine group (ARG) and Lactobacillus plantarum group received either daily doses of ARG (100 mg/kg, PO) or L. plantarum (109 CFU/ml, PO) for 14 days. The combination group (ARG+L. plantarum) received daily doses of ARG (100 mg/kg) with L. plantarum (109 CFU/ml), orally for 14 days, before induction of acute liver and kidney injury. Forty eight hours after the last dose of PDC, serum biochemical indices, oxidative stress biomarkers, pro-inflammatory cytokines, histopathological and immunohistochemical analysis were evaluated. Results: Combining ARG with L. plantarum restored the levels of serum hepatic & kidney enzymes, hepatic & renal oxidative stress biomarkers, and TLR 4/ NF-κB signaling pathway. Furthermore, they succeeded in decreasing the expression of iNOS and ameliorate the hepatic and renal markers of apoptosis: Caspase-3, Bax, and Bcl2. Conclusion: This study depicts that combining ARG with L. plantarum exerted a new bacteriotherapy against hepatic and renal injury caused by PDC.

Neuromodulatory role of L-arginine: nitric oxide precursor against thioacetamide-induced-hepatic encephalopathy in rats via downregulation of NF-κB-mediated apoptosis

Environmental Science and Pollution Research, Jun 28, 2023

The aim of the present study was to investigate the impact of arginine (ARG), a nitric oxide (NO)... more The aim of the present study was to investigate the impact of arginine (ARG), a nitric oxide (NO) precursor, on thioacetamide (TAA)-induced hepatic encephalopathy (HE) in rats by injection of TAA (100 mg/kg, i.p) three times per week for six consecutive weeks. TAA-injected rats were administered ARG (100 mg/kg; p.o.) concurrently with TAA for the six consecutive weeks. Blood samples were withdrawn, and rats were sacrificed; liver and brain tissues were isolated. Results of the present study demonstrated that ARG administration to TAA-injected rats revealed a restoration in the serum and brain ammonia levels as well as serum aspartate transaminase, alanine transaminase, and alkaline phosphatase and total bilirubin levels as well as behavioral alterations evidenced by restoration in locomotor activity, motor skill performance, and memory impairment. ARG showed also improvement in the hepatic and neuro-biochemical values, pro-inflammatory cytokines, and oxidative stress biomarkers. All these results were confirmed by histopathological evaluation as well as ultrastructural imaging of the cerebellum using a transmission electron microscope. Furthermore, treatment with ARG could ameliorate the immunological reactivity of nuclear factor erythroid-2-related factor 2 (Nrf2) and cleaved caspase-3 proteins in the cerebellum and hepatic tissues. From all the previous results, it can be fulfilled that ARG showed a beneficial role in modulating the adverse complications associated with TAA-induced HE in rats via reducing hyperammonemia and downregulating nuclear factor kappa B (NF-κB)-mediated apoptosis.

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Neuromodulatory Role of L-arginine; Nitric Oxide Precursor Against Thioacetamide Induced-Hepatic Encephalopathy in Rats via Downregulation of Nrf-2/NF- κB -mediated Apoptosis

Research Square (Research Square), Jan 25, 2022

The goal of the current study was to investigate the impact of arginine (ARG); a NO precursor, on... more The goal of the current study was to investigate the impact of arginine (ARG); a NO precursor, on thioacetamide (TAA)-induced HE. To achieve this goal, rats received TAA (100 mg/kg, i.p) thrice weekly for six consecutive weeks to induce HE. TAA-injected rats were administered ARG (100 mg/kg/day; p.o.) concurrently with TAA for six consecutive weeks. Blood was then withdrawn and rats were sacri ced, liver and brain tissues were isolated. Results of the present study demonstrated that ARG administration to TAAinduced HE in rats revealed a restoration in the serum and brain ammonia levels as well as serum AST, ALT, ALP and T. bilirubin levels as well as behavioral alterations evidenced by restoration in locomotor activity, motor skill performance, and memory impairment. ARG showed also attenuation in hepatic and brain biochemical biomarkers, pro-in ammatory cytokines and oxidative stress biomarkers. All these results were con rmed by histopathological evaluation as well as ultra-structural image of the cerebellum using a transmission electron microscope. Furthermore, treatment with ARG exhibited ameliorated immunological reactivity of Nrf2 and cleaved caspase-3 proteins in cerebellum and hepatic tissue. From all the previous ndings, it can be concluded that ARG showed a bene cial role in modulating the adverse complications associated with TAA-induced hepatic encephalopathy in rats via reducing hyperammonemia and downregulating ROS and Nrf-2/NF-κB-mediated apoptosis.

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Cold plasma approach fortifies the topical application of thymoquinone intended for wound healing via up-regulating the levels of TGF-ß, VEGF, and α-SMA in rats

International Immunopharmacology

Trigonelline attenuates hepatic complications and molecular alterations in high-fat high-fructose diet-induced insulin resistance in rats

Canadian Journal of Physiology and Pharmacology, 2017

The present study aimed to evaluate the effect of trigonelline (TRG) on the hepatic complications... more The present study aimed to evaluate the effect of trigonelline (TRG) on the hepatic complications associated with high-fat high-fructose (HFHF) diet-induced insulin resistance (IR) in rats. IR was induced by giving a saturated fat diet and 10% fructose in drinking water to rats for 8 weeks. Insulin-resistant rats were orally treated with TRG (50 and 100 mg/kg), sitagliptin (SIT; 5 mg/kg), or a combination of TRG (50 mg/kg) and SIT (5 mg/kg) for 14 days. Liver homogenates were used for assessment of hepatic lipids, oxidative stress biomarkers, and inflammatory cytokines. Histopathological and DNA cytometry examinations were carried out for hepatic and pancreatic tissues. Hepatic tissues were examined using Fourier-transform infrared spectroscopy for assessment of any molecular changes. Results of the present study revealed that oral treatment of insulin-resistant rats with TRG or TRG in combination with SIT significantly decreased homeostatic model assessment of IR, hepatic lipids, o...

Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications

Computers & Electrical Engineering, May 1, 2022

Timing of surgery following SARS‐CoV‐2 infection: an international prospective cohort study

Anaesthesia, 2021

SummaryPeri‐operative SARS‐CoV‐2 infection increases postoperative mortality. The aim of this stu... more SummaryPeri‐operative SARS‐CoV‐2 infection increases postoperative mortality. The aim of this study was to determine the optimal duration of planned delay before surgery in patients who have had SARS‐CoV‐2 infection. This international, multicentre, prospective cohort study included patients undergoing elective or emergency surgery during October 2020. Surgical patients with pre‐operative SARS‐CoV‐2 infection were compared with those without previous SARS‐CoV‐2 infection. The primary outcome measure was 30‐day postoperative mortality. Logistic regression models were used to calculate adjusted 30‐day mortality rates stratified by time from diagnosis of SARS‐CoV‐2 infection to surgery. Among 140,231 patients (116 countries), 3127 patients (2.2%) had a pre‐operative SARS‐CoV‐2 diagnosis. Adjusted 30‐day mortality in patients without SARS‐CoV‐2 infection was 1.5% (95%CI 1.4–1.5). In patients with a pre‐operative SARS‐CoV‐2 diagnosis, mortality was increased in patients having surgery wi...

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SARS‐CoV‐2 infection and venous thromboembolism after surgery: an international prospective cohort study

Anaesthesia, 2021

SummarySARS‐CoV‐2 has been associated with an increased rate of venous thromboembolism in critica... more SummarySARS‐CoV‐2 has been associated with an increased rate of venous thromboembolism in critically ill patients. Since surgical patients are already at higher risk of venous thromboembolism than general populations, this study aimed to determine if patients with peri‐operative or prior SARS‐CoV‐2 were at further increased risk of venous thromboembolism. We conducted a planned sub‐study and analysis from an international, multicentre, prospective cohort study of elective and emergency patients undergoing surgery during October 2020. Patients from all surgical specialties were included. The primary outcome measure was venous thromboembolism (pulmonary embolism or deep vein thrombosis) within 30 days of surgery. SARS‐CoV‐2 diagnosis was defined as peri‐operative (7 days before to 30 days after surgery); recent (1–6 weeks before surgery); previous (≥7 weeks before surgery); or none. Information on prophylaxis regimens or pre‐operative anti‐coagulation for baseline comorbidities was no...

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Fatigue State Detection for Tired Persons in Presence of Driving Periods

IEEE Access

Due to the increasing of traffic accidents, there is an urgent need to control and reduce driving... more Due to the increasing of traffic accidents, there is an urgent need to control and reduce driving mistakes. Driver fatigue or drowsiness is one of these major mistakes. Many algorithms have been developed to address this issue by detecting fatigue and alerting the driver to this dangerous condition. The major problem of the developed algorithms is their detection accuracy, as well as the time required to detect fatigue status and alert the driver. The accuracy and the time represent a critical condition that affects the reduction of traffic accidents. Several datasets have been used in the development of fatigue or drowsy detection techniques. These data are gathered from the deriver's brain Electroencephalogram (EEG) signals or from video streaming recordings of the driver behavior. This paper develops two distinct approaches, the first based on the use of machine learning classifiers and the second depends on the use of deep learning models to produce a high-performance fatigue detection system. The machine learning approach is used to process EEG signals, whereas the deep learning approach is used to process video streams. In machine learning classifiers, Support Vector Machine (SVM) provides up to 98% of detection accuracy, which is the highest accuracy among the other five deployed classifiers. In deep learning models, Convolutional Neural Network (CNN) provides up to 99% detection accuracy, which is the highest accuracy among the other two deployed models. The experimental results demonstrate that the two proposed algorithms provide the highest detection accuracy with the shortest Testing Time (TT) when compared to all other recent and efficient fatigue detection algorithms. INDEX TERMS EEG signals, fatigue detection systems, video streaming, support vector machine, convolutional neural network, testing time.

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An Adaptive Fatigue Detection System Based on 3D CNNs and Ensemble Models

Symmetry

Due to the widespread issue of road accidents, researchers have been drawn to investigate strateg... more Due to the widespread issue of road accidents, researchers have been drawn to investigate strategies to prevent them. One major contributing factor to these accidents is driver fatigue resulting from exhaustion. Various approaches have been explored to address this issue, with machine and deep learning proving to be effective in processing images and videos to detect asymmetric signs of fatigue, such as yawning, facial characteristics, and eye closure. This study proposes a multistage system utilizing machine and deep learning techniques. The first stage is designed to detect asymmetric states, including tiredness and non-vigilance as well as yawning. The second stage is focused on detecting eye closure. The machine learning approach employs several algorithms, including Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Multi-layer Perceptron (MLP), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF). Meanwhile, the deep learning approach utilizes 2D and 3D Co...

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SFBC Recognition over Orthogonal Frequency Division Multiplexing Schemes in the Presence of Inphase and Quadrature Phase Discrepancies for Cognitive Radio Applications

Sensors

A radio is adaptive if it can autonomously analyze the communications environment and instantly m... more A radio is adaptive if it can autonomously analyze the communications environment and instantly modify its settings to achieve the best possible efficiency. In orthogonal frequency division multiplexing (OFDM) transmissions, identifying the space frequency block coding (SFBC) category utilized is one of the most important tasks of an adaptive receiver. Previous approaches to this problem did not take into consideration the fact that real systems typically suffer from transmission defects. This study offers a novel maximum likelihood recognizer capable of distinguishing between SFBC OFDM waveforms in the context of inphase and quadrature phase differences (IQDs). The theoretical findings show that IQDs arising from the transmitter and recipient can be combined with channel paths to generate so-called effective channel paths. The conceptual examination demonstrates that the outlined maximum likelihood strategy of the SFBC recognition and effective channel estimation processes is imple...

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WFT-Fati-Dec: Enhanced Fatigue Detection AI System Based on Wavelet Denoising and Fourier Transform

Applied Sciences

As the number of road accidents increases, it is critical to avoid making driving mistakes. Drive... more As the number of road accidents increases, it is critical to avoid making driving mistakes. Driver fatigue detection is a concern that has prompted researchers to develop numerous algorithms to address this issue. The challenge is to identify the sleepy drivers with accurate and speedy alerts. Several datasets were used to develop fatigue detection algorithms such as electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG), and electromyogram (EMG) recordings of the driver’s activities e.g., DROZY dataset. This study proposes a fatigue detection system based on Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) with machine learning and deep learning classifiers. The FFT and DWT are used for feature extraction and noise removal tasks. In addition, the classification task is carried out on the combined EEG, EOG, ECG, and EMG signals using machine learning and deep learning algorithms including 1D Convolutional Neural Networks (1D CNNs), Concatenated ...

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An efficient edge/cloud medical system for rapid detection of level of consciousness in emergency medicine based on explainable machine learning models

Neural Computing and Applications

Emergency medicine (EM) is one of the attractive research fields in which researchers investigate... more Emergency medicine (EM) is one of the attractive research fields in which researchers investigate their efforts to diagnose and treat unforeseen illnesses or injuries. There are many tests and observations are involved in EM. Detection of the level of consciousness is one of these observations, which can be detected using several methods. Among these methods, the automatic estimation of the Glasgow coma scale (GCS) is studied in this paper. The GCS is a medical score used to describe a patient’s level of consciousness. This type of scoring system requires medical examination that may not be available with the shortage of the medical expert. Therefore, the automatic medical calculation for a patient’s level of consciousness is highly needed. Artificial intelligence has been deployed in several applications and appears to have a high performance regarding providing automatic solutions. The main objective of this work is to introduce the edge/cloud system to improve the efficiency of t...

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Target Detection and Recognition for Traffic Congestion in Smart Cities Using Deep Learning-Enabled UAVs: A Review and Analysis

Applied Sciences

In smart cities, target detection is one of the major issues in order to avoid traffic congestion... more In smart cities, target detection is one of the major issues in order to avoid traffic congestion. It is also one of the key topics for military, traffic, civilian, sports, and numerous other applications. In daily life, target detection is one of the challenging and serious tasks in traffic congestion due to various factors such as background motion, small recipient size, unclear object characteristics, and drastic occlusion. For target examination, unmanned aerial vehicles (UAVs) are becoming an engaging solution due to their mobility, low cost, wide field of view, accessibility of trained manipulators, a low threat to people’s lives, and ease to use. Because of these benefits along with good tracking effectiveness and resolution, UAVs have received much attention in transportation technology for tracking and analyzing targets. However, objects in UAV images are usually small, so after a neural estimation, a large quantity of detailed knowledge about the objects may be missed, whi...

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Deep fake news detection system based on concatenated and recurrent modalities

Expert Systems with Applications

Analysis of Brain MRI: AI-Assisted Healthcare Framework for the Smart Cities

Intelligent Automation & Soft Computing

Synergistic effect of aminoguanidine and l-carnosine against thioacetamide-induced hepatic encephalopathy in rats: behavioral, biochemical, and ultrastructural evidence

Canadian Journal of Physiology and Pharmacology, 2021

Hepatic encephalopathy depicts the cluster of neurological alterations that occur during acute or... more Hepatic encephalopathy depicts the cluster of neurological alterations that occur during acute or chronic hepatic injury. Hyperammonemia, inflammatory injury, and oxidative stress are the main predisposing factors for the direct and indirect changes in cerebral metabolism causing encephalopathy. The aim of this study was to evaluate the possible synergistic effect between aminoguanidine (AG; 100 mg/kg, p.o.) and l-carnosine (CAR; 200 mg/kg, p.o.) on hepatic encephalopathy that was induced by thioacetamide (TAAÍ¾ 100 mg/kg, i.p.) administered three times weekly for six weeks. Behavioral changes, biochemical parameters, histopathological analysis, and immunohistochemical and ultrastructural studies were conducted 24 h after the last treatment. Combining AG with CAR improved TAA-induced locomotor impairment and motor incoordination evidenced by reduced locomotor activity and decline in motor skill performance, as well as ameliorated cognitive deficits. Moreover, both drugs restored the...

An efficient deep learning model for classification of thermal face images

Journal of Enterprise Information Management, 2020

Purpose The objective of this paper is to perform infrared (IR) face recognition efficiently with... more Purpose The objective of this paper is to perform infrared (IR) face recognition efficiently with convolutional neural networks (CNNs). The proposed model in this paper has several advantages such as the automatic feature extraction using convolutional and pooling layers and the ability to distinguish between faces without visual details. Design/methodology/approach A model which comprises five convolutional layers in addition to five max-pooling layers is introduced for the recognition of IR faces. Findings The experimental results and analysis reveal high recognition rates of IR faces with the proposed model. Originality/value A designed CNN model is presented for IR face recognition. Both the feature extraction and classification tasks are incorporated into this model. The problems of low contrast and absence of details in IR images are overcome with the proposed model. The recognition accuracy reaches 100% in experiments on the Terravic Facial IR Database (TFIRDB).

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Smart and Automated Diagnosis of COVID-19 Using Artificial Intelligence Techniques

Intelligent Automation & Soft Computing, 2022

Machine Learning (ML) techniques have been combined with modern technologies across medical field... more Machine Learning (ML) techniques have been combined with modern technologies across medical fields to detect and diagnose many diseases. Meanwhile, given the limited and unclear statistics on the Coronavirus Disease 2019 (COVID-19), the greatest challenge for all clinicians is to find effective and accurate methods for early diagnosis of the virus at a low cost. Medical imaging has found a role in this critical task utilizing a smart technology through different image modalities for COVID-19 cases, including X-ray imaging, Computed Tomography (CT) and magnetic resonance image (MRI) that can be used for diagnosis by radiologists. This paper combines ML with imaging analysis in an artificial deep learning approach for COVID-19 detection. The proposed methodology is based on convolutional long short term memory (ConvLSTM) to diagnose COVID-19 automatically from X-ray images. The main features are extracted from regions of interest in the medical images, and an intelligent classifier is used for the classification task. The proposed model has been tested on a dataset of X-ray images for COVID-19 and normal cases to evaluate the detection performance. The ConvLSTM model has achieved the desired results with high accuracy of 91.8%, 95.7%, 97.4%, 97.7% and 97.3% at 10, 20, 30, 40 and 50 epochs that will detect COVID-19 patients and reduce the medical diagnosis workload.

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