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Deep Reinforcement Learning Model for Blood Bank Vehicle Routing Multi-Objective Optimization

Computers, Materials & Continua 2022年第2期70卷 3955-3967页

作者： Meteb M.Altaf a.med samir roshdy Hatoon S.AlSagri Director of Advanced Manufacturing and Industry 4.0 Center King Abdul-Aziz City for Science and Technology RiyadhSaudi Arabia Data Science and AI Senior Manager Vodafone CairoEgypt Information System Department Al Imam Mohammad Ibn Saud Islamic UniversityRiyadhSaudi Arabia

The overall healthcare system has been prioritized within development top lists *** many national populations are aging,combined with the availability of sophisticated medical treatments,healthcare expenditures are rapidly *** banks are a major component of any healthcare system,which store and provide the blood products needed for organ transplants,emergency medical treatments,and routine *** delivery of blood products is vital,especially in emergency ***,blood delivery process parameters such as safety and speed have received attention in the literature,as well as other parameters such as delivery *** this paper,delivery time and cost are modeled mathematically and marked as objective functions requiring simultaneous optimization.A solution is proposed based on Deep Reinforcement Learning(DRL)to address the formulated delivery functions as Multi-objective Optimization Problems(MOPs).The basic concept of the solution is to decompose the MOP into a scalar optimization sub-problems set,where each one of these sub-problems is modeled as a separate Neural Network(NN).The overall model parameters for each sub-problem are optimized based on a neighborhood parameter transfer and DRL training *** optimization step for the subproblems is undertaken collaboratively to optimize the overall *** solutions can be directly obtained using the trained ***,the multi-objective blood bank delivery problem is addressed in this *** technical advantage of this approach is that once the trainedmodel is available,it can be scaled without the need formodel *** scoring can be obtained directly using a straightforward computation of the NN layers in a limited *** proposed technique provides a set of technical strength points such as the ability to generalize and solve rapidly compared to othermulti-objective *** model was trained and tested on 5 major hospi

关键词： Optimization blood bank deep neural network reinforcement learning blood centers multi-objective optimization

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Antibody to El peptide of hepatitis C virus genotype 4 inhibits virus binding and entry to HepG2 cells in vitro

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World Journal of Gastroenterology 2006年第16期12卷 2530-2535页

作者： Mostafa K EL-Awady Ashraf A Tabll Khaled Atef Samar S Yousef Moataza H Omran Yasmin EI-Abd Noha G Bader-Eldin Ahmad M Salem samir F Zohny Wael T EI-Garf Department of Biomedical Technology National Research CenterCairoEgypt Department of Biochemistry Faculty of ScienceAin Shams UniversityCairoEgypt

AIM： To analyze the neutralizing activity of antibodies against E1 region of hepatitis C virus （HCV）. Specific polydonal antibody was raised via immunization of New Zealand rabbits with a synthetic peptide that had been derived from the E1 region of HCV and was shown to be highly conserved among HCV published genotypes. METHODS： Hyper-immune HCV E1 antibodies were incubated over night at 4 ℃ with serum samples positive for HCV RNA, with viral loads ranging from 615 to 3.2 million IU/mL. Treated sera were incubated with HepG2 cells for 90 min. Blocking of viral binding and entry into cells by anti E1 antibody were tested by means of RTPCR and flow cytometry. RESULTS： Direct immunostaining using FITC conjugated E1 antibody followed by Flow cytometric analysis showed reduced mean fluorescence intensity in samples pre-incubated with E1 antibody compared with untreated samples. Furthermore, 13 out of 18 positive sera （72%） showed complete inhibition of infectivity as detected by RT-PCR. CONCLUSION： In house produced E1 antibody, blocks binding and entry of HCV virion infection to target cells suggesting the involvement of this epitope in virus binding and entry. Isolation of these antibodies that block virus attachment to human cells are useful as therapeutic reagents.

关键词： Flow cytometry Hepatitis C virus E1 envelope Therapeutic antibodies Direct immuno-fluorescence HepG2 cells

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