MIU

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The Main Container of Intellectual Outputs of Misr International University.

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A TALE OF TWO CITIES
(2024) Rabiee,Youssef Hesham.
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Accuracy of artificial intelligence technology in detecting number of root canal of human maxillary first molar : Diagnostic Accuracy Experimental Study /
Nofal, Sara Ragab,; Supervisor : Nihal Ezzat Sabet, Ahmed Hussein Abu El-Ezz. Includes Arabic Summary.
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Accuracy of artificial intelligence technology in detecting number of root canals in human mandibular first molars obturated and Indicated for retreatment : (Diagnostic accuracy experimental study) /
Alkady, Albaraa Samir,; Supervisor : Ahmed Abdelrahman Hashem, Ahmed Ghobashy, Ahmed Hussein. Includes Arabic Summary.
Statement of problem: Failure to disinfect and to locate all canals is a major reason for root canal primary and secondary failures, which may lead to periapical periodontitis. Cone beam computed tomography (CBCT) is considered the gold standard in canal detection although its use must be justified beforehand due to its high radiation dose. Using artificial intelligence (AI) software may enhance canal detection and avoid human error in interpreting CBCT images. Aim of the study: To evaluate the accuracy of new AI technology for detecting root canals in mandibular first molars retreatment cases in comparison to dentist clinical access cavity and CBCT imaging. Materials and methods: Thirty-five patients with obturated lower first molar(s) referred for retreatment was participated in this study. After a pre-treatment periapical x-ray to aid practitioner in access cavity formation a CBCT was performed for all cases. Stage 1: CBCT scans performed to all participants were randomly distributed and observed by the principle investigator and supervisors and the number of canals found was recorded. Stage 2: patients were randomly distributed on 6 post graduate students* students enrolled in the endodontic master’s program at MIU* students were then performed access cavity, the number of canals found will be recorded. Stage 3: CBCT images were uploaded to AI software, and the number of canals detected was be recorded. Data collected will be compared using 3 groups: Group 1: CBCT with co supervisor interpretation the control group Group 2: clinically after performing access cavity. Group 3: CBCT with the AI technology. All cases that failed to be detected by the AI software were then evaluated for more morphological features that may influence the accuracy of AI software.
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Accuracy of artificial intelligence technology in detecting number of root canals of obturated human maxillary second molars indicated for retreatment : diagnostic accuracy experimental study /
Hegab, Ayman Alaa Hussein,; Supervisor : Nihal Sabet, Ahmed Khalaf. Includes Arabic Summary.
Statement of the Problem: Endodontic treatment has always been a challenging procedure and failure to detect all canals and completely disinfecting the root canal system could lead to an endodontic failure. In retreatment cases, the endodontic procedure becomes more challenging and tricky requiring high knowledge and skills. Artificial intelligence software makes it easier in analysis of CBCT scans reducing dentist errors and improving treatment success rates. Aim of the study: The aim of the study is to evaluate the accuracy and reliability of an artificially intelligent software that would analyze, aid, support, and help the endodontist to detect the correct number of root canals of 35 obturated maxillary second molars indicated for retreatment in comparison to both Cone Beam Computed Tomography and Clinical observation. Materials and methods: This study included 35 Patients with Maxillary Second obturated molars indicated for re-treatment who underwent a pre-treatment CBCT, while only a pre-treatment periapical radiograph was taken to aid in access cavity preparation in the clinical stage. The study was levelled into three stages. CBCT Stage: Pre-operative CBCT scans of the patients were taken and randomly assigned to 2 co-supervisors who segmented, interpreted, and recorded the number of canals on a pre-formed information guide. Clinical Stage: A clinical stage where the enrolled patients were randomly distributed upon 6 researchers. Researchers then performed access cavities under DOM. The number of orifices found was recorded in a preformed information guide. AI stage: CBCT scans was uploaded to AI software by researcher and Number of canals found by the software were recorded. The results of the first two stages were then compared to the findings of the third stage to determine software accuracy. Cases with missed canals by the AI software underwent further evaluation of tooth morphological features, to determine the reason for the software’s detection failure. Results: There was a statistically significant difference between detected number of canals by the three methods (P-value = 0.018). AI showed higher percentage of two, three canals and lower percentage of four canals detection. The CBCT stage and the clinical stage showed complete agreement in their findings, however the CNN program inaccurately identified the proper number of canals in three cases. To evaluate the relationship between these cases and the potential impact of morphological variation on the AI software, the canal type was determined using Vertucci's classification. It was found that all three unsuccessful cases had a type II canal. There was an insignificant link between the canal morphology and the AI software's incapacity to detect canals. When the AI program was unable to accurately identify the proper number of canals, the average ratio of canal unity was around 60%. This means that around two-thirds of the canal length was considered as one canal, while only one-third of the length was considered as two separate canals.
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Accuracy of artificial intelligence technology in detecting root canal number and morphology in maxillary second molars : A diagnostic accuracy experimental study /
Sami, Hebah Mohamed Eltaher Monir Mohamed,; Supervisor : Hossam Tewfik, Alaa Diab, Ahmed Hassan Ibrahim. Includes Arabic Summary.
A comprehensive understanding of root canal space anatomy is essential for clinicians to be able to treat and prevent apical periodontitis, the main reason for endodontic therapy. Approximately 40% of failed endodontic therapy is due to missed canals, indicating lack of clinical thoroughness in treatment. Maxillary second molars present with varying internal morphologies in different individuals, which is affected by age, genetics, and gender. Inadequacy in detecting these variations during treatment is reflected by the upper molars showing the top rates of endodontic treatment failures. Although many techniques have previously been employed to facilitate canal detection, CBCT is currently considered the gold standard in clinical canal structure exploration, as it has the potential to change initial diagnoses and treatment plans in almost 50% of cases. However, due to problems such as interobserver differences in interpreting scans and the need for training clinicians to read CBCT scans, a special interest has emerged in artificial intelligence (AI) technology to help overcome these problems. In this study, the accuracy of AI software in detection of canal number was compared to the radiographic detection using CBCT and the clinical detection using a DOM in maxillary second molars. Thirty-five patients with maxillary second molars requiring primary RCT with signs and symptoms of irreversible pulpitis or necrotic teeth that are restorable were selected. The number of canals were first detected radiographically from the CBCT by experienced endodontists and recorded, then 6 postgraduate students performed an EAC under the DOM and ultrasonic troughing and recorded canal number detected. Finally, the CBCT was uploaded to the AI software to detect the canal number. AI software accuracy was determined by comparing its results to the results of the 2 other groups. Further analysis of the AI generated report was done to assess the cases that canal number was incorrectly detected to evaluate the possible morphological features that might have been the reason for the incorrect detection. Another six cases in which AI software successfully detected the number of canals were randomly selected as a comparative group. Four main features were additionally assessed thoroughly, namely, the inter orifice distance, length of the mesiobuccal root, level of canal division from the root apex, and canal anatomy based on Vertucci classification. The method of analysis of the CBCT for data extraction for this assessment was standardized for all cases and done at the level of the CEJ. Results showed that the CBCT showed the highest percentage accuracy (100%) in the interpretation of canal morphology and number, while the clinical detection followed closely (94.2%), and the AI software was the lowest percentage accuracy (82.8%) amongst the 3 modalities. Its level of confidence was generally high with 79% of the correct cases and 50% of the incorrect cases being in the highest confidence level respectively. The four additional features assessed showed that the average inter-orifice distance is lower in the incorrect group and the average percentage canal unity was much higher. This indicated that the software is less likely to detect the MB2 canal if it is unified with the MB1 for more than 50% of the entire canal length, and that the smaller the inter-orifice distance the less likely is the software in detecting the MB2 canal. The canals’ Vertucci type did not seem to influence the software’s ability to detect the canal number. In general, the results of the AI software were promising, even if it is currently less than the other 2 modalities used, since the nature of the AI software with deep learning strategies spontaneously improves with the greater amount of data input to it with time. Although many studies agreed with the results of this study regarding the percentage accuracy of AI software, however some studies showed differing results that demonstrated higher accuracy of the AI software. This could be attributed to the differences in the study design, the type of AI software used, and the sample size. However, all studies agree that the future of AI software use in endodontic canal detection is highly encouraging.
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Accuracy of artificial intelligence technology in detecting the number of canals in human maxillary first molars indicated for retreatment : Diagnostic accuracy experimental study /
Sholkamy, Mostafa Sherif,; Supervisor : Abeer Hashem Mahran, Ahmed Hussein Abu El-Ezz. Includes Arabic Summary.
Statement of Problem: Missed canals are one of the main causes of failure of primary root canal treatment. CBCT is considered the gold standard in morphology detection. Problems of using CBCT include high radiation dose and practitioner inability to interpret images. Artificial Intelligence (AI) technology may help overcome these problems. Aim: The aim of this study was to evaluate the accuracy of novel AI software in detecting the number of canals in 36 maxillary first molars indicated for retreatment, as well as, to compare it with accuracy of CBCT and clinical assessment. Materials and Methods: 36 Patients referred to MIU dental clinic for retreatment of upper first molars underwent pre-treatment CBCT, while only pretreatment periapical radiograph will be taken to aid in access cavity preparation in the clinical stage. The study included 3 stages: CBCT Stage: Pre-operative CBCT scans of the patients were taken and randomly assigned to 2 co-supervisors who upon scan segmentation recorded the number of canals on a pre-formed information guide. Clinical Stage: A clinical stage where the enrolled patients were randomly distributed upon 6 researchers. Practitioners then performed access cavities on the teeth under DOM. The number of orifices found was recorded on a preformed information guide. AI stage: CBCT images will be uploaded to AI software by primary investigator and Number of canals found by the software were recorded. Results of the first two stages were then compared to the findings of the third stage to determine software accuracy. Cases with missed canals by the AI software underwent further evaluation of tooth morphological features, to determine the reason for the software’s detection failure.
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Accuracy of artificial intelligence technology in detecting the number of root canals in human mandibular first molars : A diagnostic accuracy experimental study /
Abd-Elsamie, Salma Khaled Kamel,; Supervisor : Hossam Tewfik, Alaa Diab, Mohamed Alaa Fakhr. Includes Arabic Summary.
Artificial intelligence (AI) is revolutionizing various fields of medicine, including dentistry. In endodontics, AI has the potential to improve diagnostic accuracy, treatment planning, and overall patient care. There is minimal scientific evidence concerning the accuracy of the detection of the number of canals using Artificial Intelligence. The aim of this study is to define the accuracy of an Artificial Intelligence (AI) Software to detect the number of root canals in the mandibular first molars compared to the standard clinical method under magnification and to the gold standard using a Cone Beam Computed Tomography (CBCT). In addition, this study aims to determine the morphological variations in cases where the AI failed to detect all the canals. Thirty-Five patients presented to MIU dental clinics with mandibular first permanent molars requiring root canal treatment were selected according to the eligibility criteria. Teeth that met the criteria had pre-operative periapical x-rays taken. Adequate case and medical history were taken. Detection of the number of root canals was achieved by 3 methods: Radiographically using the CBCT as the gold standard, clinically using the DOM and using an AI software. After signing the informed consent, the patients underwent CBCT imaging using Endo mode to minimize the radiation exposure before treatment initiation. CBCT was coded based on the patients file numbers instead of names. The number of canals identified was recorded in a pre formed information guide. Using randomization software, the 35 patients were divided up amongst 6 post graduate students at random. A dental operating microscope was used to prepare the access cavity for each patient. In order to minimize the radiation dose to the patient, the CBCT was given to the postgraduate student after the number of canals was recorded. This helped to prevent inter-treatment periapical radiographs as working length X-rays. After that, the principal researcher, who was blind to the outcomes of the CBCT stage, carried out the AI stage individually. The Diagnocat AI Software was used to upload the CBCT images, and CBCT segmentation and deep learning techniques were employed. Next, the software's total number of canal identifications was recorded. The gathered data was divided into three groups based on the canal detection method into: CBCT Findings, Clinical Findings and AI Findings. Cases containing canals that were missed by the AI software had additional examination of the morphological features of each tooth from the CBCT. The six cases in which the AI software successfully detected the number of canals were randomly selected as a comparative group. For both groups the following features were included: the inter-orifice distance was measured in order to accomplish this, the length of the root, Vertucci classification type and the length of the Canal division measured from the apex and CEJ. In addition, from the axial cut of the CBCT, the inter-orifice distance was measured in the same manner between the canals that were not detected by the AI. Afterward, the mean inter-orifice distance was calculated for both the failed and successful cases. Those measurements were utilized to calculate the percentage of canal unity within the entire root length associated to the missed canals. The degree of accuracy was 100% for the DOM, 100% for the CBCT and 82.86% for the AI. It was clear that application of AI software didn’t detect the exact number of canals in all cases. The AI software detected the correct number of canals in only 29 cases. The use of DOM and CBCT detected all canals in all the 35 cases. Another analysis was done to relate the inaccuracies of the software to the morphology of the wrong cases detected. It was evident that the inter-orifice distance for the incorrectly detected cases was less than that of the cases correctly detected. Added to that, the percentage of canal unity was analyzed for the distal canals, which is the measure of the unity of both distal canals along the entire length of the root. The greater the union between both distal canals, the less likely the AI software will distinguish them as separate canals. The average percentage of canal unity of the incorrectly detected cases was greater than 50% and the percentage of canal unity for the correctly detected cases was a maximum of 20% which allowed the software to detect two distal canals.
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Accuracy of conventional and digital impressions at different span lengths of missing teeth : (In-Vitro study).
(2022) El-Sheikh, Nada Ali Mohamed,; Supervised : Rana Sherif, Mostafa Hussein .
Impression taking is a crucial step in prosthodontics, as the quality of the final prosthesis and its long term survival depends on the accuracy of this process. Conventional impressions are the most common in the clinical practice; conventional workflows and CAD/CAM technologies can be combined through indirect digitization. However, new digital techniques allow the full digitization of the workflow, by the use of IOSs. The main feature to be evaluated in an intraoral scanner is accuracy. According to ISO 12836, the accuracy of an impression technique is defined in terms of trueness and precision. Trueness is defined as the difference in measurement between the reference model and the scan model and precision is the difference in measurement between digital models created using the same impression technique. This in-vitro study was designed to evaluate the accuracy of three different impression techniques at three different span length bridges. For full arch prostheses and FPDs with more than 5-units, digital impressions do not seem as accurate as conventional impressions. Therefore, the aim of this study was to assess the accuracy in terms of trueness and precision of conventional and digital scanning (direct and indirect) techniques on different span length bridges. The bridge preparations were done on acrylic typodont models (Nissin, Kyoto, Japan) with the aid of a dental surveyor. Three different impression techniques were used, a conventional PVS impression material (Elite HD+ putty soft and light body consistencies), an intraoral scanner (CEREC Primescan) and an extraoral scanner (Medit Identica t300). The three groups (3,4 and 6-unit bridges) were divided into 3 subgroups according to the impression technique received (PVS, Primescan and Medit t300). For the trueness measurement, the three different bridge types were scanned using a desktop scanner (inEos X5) which was used as the reference scanner to obtain the reference datasets (REF STL files). The different impression techniques were used to record five different impressions (n=5) for each bridge span type and with the help of a reverse engineering 3D analysis software (Geomagic Control X), the digitized measurement models were superimposed on the reference to calculate the amount of deviation or RMS value of error. For the precision measurement, the calculations were done within each subgroup. Each scan was considered as the reference superimposing the remaining 4 scans in pairs to calculate the amount of deviation or RMS value of error. Color difference maps and reports were generated for all of the test groups. Data was recorded, tabulated and analyzed. Statistical work was done using the two-way ANOVA test. Results Regarding the different span length bridges, the best trueness and precision values were recorded for the 3-unit posterior followed in descending order by the 4-unit posterior and 6-unit anterior bridges for all three impression techniques. Regarding the impression technique, the best trueness values were recorded by Primescan followed by PVS and Medit t300. The best precision values were recorded by Medit t300 followed by Primescan and then PVS, all of which showed statistically significant difference.
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Accuracy of surgical guides fabricated using two different 3D printers for prosthetically driven implant surgery : An in-Vitro Study /
Semary, Amr Mohamed Abd Elfattah,; Supervisor : Hesham Katamish, Tarek Salah-Eldin Morsi, Mostafa Hussein.
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Accuracy of two digital scanners (intraoral, extraoral) compared to conventional impression using implant with different angulations (zero, 15°,25°) : "In vitro study" /
Rafla, Kirollos Ashraf Sobhy,; Supervisor : Rana Cherif, Lomaya Ghanem. Includes Arabic Summary.
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Accuracy of working Models and Marginal Fit of tooth Supported Provisional Dental Prosthesis Fabricated by Three Dimensional Printing Compared to CAD/CAM Milling System : (In Vitro Study) /
Sidhom, Marina Fayek Fathallah,; Supervisor : Ihab El-Sayed Mosleh, Hanaa Zaghloul.
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Advanced analytical techniques for analysis of Some therapeutic agents used in the treatment of cardiovascular diseases in dosage forms and biological fluids /
Zaki, Mariam Wasim Beniamin,; Supervisor : Ahmed Emad El-Gendy, Maha Abd Elmonem Hegazy, Lubna Ahmed Kormod. Includes Arabic Summary.
This thesis consists of three parts. • Part I: General introduction and literature review This part includes a general introduction to the pharmacological and biological effects of cardiovascular drugs of interest. This part presents the drugs of the first combination in this study, felodipine and metoprolol, and the second co administered combination, bisoprolol, rosuvastatin, and clopidogrel, this section includes all the structure-related details and physical characteristics of the compounds of interest, along with their pharmacological action. A complete and updated literature review of the different methods of analysis was summarized for the studied compounds. • Part II: Determination of Cardiovascular Drugs in Bulk and Pharmaceutical Dosage Form by Different Spectrophotometric Methods. This part is divided into four sections. Section A: Ratio Spectra Derivative for Determination of Felodipine and Metoprolol in their Pure and Pharmaceutical Dosage Form. In this section, the determination of felodipine and metoprolol was achieved by calculating first derivative ratio spectra using the numerical differentiation method despite their spectral overlap in zero-order measurements. The proposed method was adopted for the simultaneous estimation of both drugs in the range of 0.30- 15.00 µg/mL for FDP and 0.40- 22.00 µg/mL for MTP in their pure forms, laboratory prepared mixtures, and in their pharmaceutical dosage form. The statistical comparison of the results obtained with those of a reported HPLC method was made. Section B: Mean Centering of Ratio Spectra Method for Determination of Felodipine and Metoprolol in their Pure and Pharmaceutical Dosage form. In this section, the determination of felodipine and metoprolol was achieved by calculating the mean centering of ratio spectra method despite their spectral overlap in zero-order measurements. The proposed method was adopted for the simultaneous estimation of both drugs in the range of 0.30- 15.00 µg/mL for FDP and 0.40- 22.00 µg/mL for MTP in their pure forms, laboratory prepared mixtures, and in their pharmaceutical dosage form. The statistical comparison of the results obtained with those of a reported HPLC method was made. Section C: Ratio Difference Spectrophotometric Method for Determination of Felodipine and Metoprolol in their Pure and Pharmaceutical Dosage Form. In this section, the determination of felodipine and metoprolol was achieved by calculation of ratio difference after obtaining first ratio spectra despite their spectral overlap in zero-order measurements. The proposed method was adopted for the simultaneous estimation of both drugs in the range of 0.30- 15.00 µg/mL for FDP and 0.40- 22.00 µg/mL for MTP in their pure forms, laboratory prepared mixtures, and in their pharmaceutical dosage form. The statistical comparison of the results obtained with those of a reported HPLC method was made. Section D: Dual Wavelength Spectrophotometric Method for Determination of Felodipine and Metoprolol in their Pure and Pharmaceutical Dosage form. In this section, the determination of felodipine and metoprolol was achieved by the dual-wavelength method in the zero-order spectra by calculating the difference between two wavelengths for both drugs despite their spectral overlap in zero-order measurements. The proposed method was adopted for the simultaneous estimation of both drugs in the range of 0.30- 15.00 µg/mL for FDP and 0.40- 22.00 µg/mL for MTP in their pure forms, laboratory prepared mixtures, and in their pharmaceutical dosage form. The statistical comparison of the results obtained with those of a reported HPLC method was made. Part III: Simultaneous Estimation of Cardiovascular Drugs in Bulk, Pharmaceutical Dosage Forms, and spiked human plasma by Different HPLC Methods. This part is divided into three sections. Section A: Eco-friendly, Reversed Phase High Performance Liquid Chromatography (RP-HPLC) Method with UV-detection for Simultaneous Estimation of Felodipine and Metoprolol in their Pure Powder and Pharmaceutical Dosage Form. In this section, simultaneous determination of felodipine and metoprolol was performed using high-performance liquid chromatography coupled with a UV detector using a C18 column and gradient programming adjusted at (70:30, v/v) of potassium dihydrogen phosphate: ethanol then shifted to (20:80, v/v) at 4 minutes till the end of the run at pH 2.5. Olopatadine HCL was used as an internal standard. UV detection was performed at 237 nm for felodipine and 221nm for metoprolol and olopatadine. The proposed method was further applied to analyze both drugs in their pure forms, laboratory prepared mixtures as well as their pharmaceutical dosage form. The results were statistically compared to those obtained from a reported HPLC method. Section B: Eco-friendly, Bioanalytical Reversed Phase High Performance Liquid Chromatography (RP-HPLC) Method Coupled with Fluorescence Detection for Simultaneous Estimation of Felodipine and Metoprolol. In this Section, simultaneous determination of felodipine and metoprolol was performed using high-performance liquid chromatography coupled with a Fluorescence detector using C18 column and using isocratic elution with flow rate of the mobile phase at 1 mL/min adjusted at (40:60, v/v) of potassium dihydrogen phosphate: ethanol at pH of 2.5. Fluorescence detection was programmed to measure felodipine, metoprolol, and tadalafil at excitation wavelengths of 230 nm, 275 nm, and 367 nm and emission wavelengths of 300 nm, 335 nm, and 440 nm, respectively. The proposed method was further applied to analyze both drugs in their pure forms, laboratory-prepared mixtures, pharmaceutical dosage form, and spiked human plasma. Bioanalytical method validation was applied to confirm the method's applicability to human plasma. The results were statistically compared to those obtained from a reported HPLC method. Section C: Reversed Phase High Performance Liquid Chromatography (RP HPLC) Method Coupled with UV Detection for the Simultaneous Estimation of Bisoprolol, Rosuvastatin, and Clopidogrel. In this section, simultaneous determination of the co-administered drugs bisoprolol, rosuvastatin, and clopidogrel along with an internal standard olopatadine HCL using high-performance liquid chromatography coupled with UV detector using C18 column and gradient programming adjusted at (70:30, v/v) of potassium dihydrogen phosphate: acetonitrile then shifted to (20:80, v/v) at 6.5 minutes till the end of the run at pH 3. UV detection was programmed to measure bisoprolol, rosuvastatin, and clopidogrel at wavelengths 225 nm, 240 nm, and 230 nm, respectively. The proposed method was further applied to analyze co-administered drugs in their pure forms, laboratory-prepared mixtures, and pharmaceutical dosage forms. The results were statistically compared to those obtained from a reported HPLC method.
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Advanced analytical techniques for measuring some important antidiabetics in dosage forms and Bio-fluid samples /
Tantawy, Merna Abdelrasoul Mohamed,; Supervisor : Rasha Mahmoud Ahmed, Randa Abdel-Salam, Noha Ibrahim Shaaban. Includes Arabic Summary.
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ANALYTICAL STUDY FOR THE PRINCIPLES DEALING WITH HERITAGE SITES: Case Study - Downtown Cairo Cultural Heritage.
Khalil, Mirna Philip Farag Rizk.; Hany Louis Attallah, Heba Safey El-Din, Hatem Morsey Hassan.
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Analytical study of selected multicomponent pharmaceutical preparations in different matrices /
Anis, Monica Sherif Anwar,; Supervisor : Ahmed Emad Elgendy, Samah Sayed Abbas, Lubna Ahmed Kormod.
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Antibacterial Effect of a Diode Laser in the Eradication of Enterococcus feacalis, and Smear Layer Removal in Root Canal of Infected Teeth : (An in Vitro Study)
Hussein, Dina Nashaat Hassan,; Supervisor : Salma El Ashry, Ahmed Mostafa Ghobashy.
The definitive goal of root canal treatment is to obtain a root canal system free of irritants, as any remaining microorganisms can cause persistent inflammation in the periradicular tissues. Success of endodontic therapy depends on complete elimination of pathogenic micro flora from the root canal system. Enterococcus faecalis is the most common bacteria associated with persistent endodontic infections. It is a facultative gram-positive anaerobic coccus that has the ability to exist in root canals without the support of other microorganisms. It has the ability to infect the whole length of dentinal tubules within a few days, and is considered difficult to eradicate. It can also survive harsh environmental factors, and form biofilms that are difficult to detach. Biofilm disruption and root canal disinfection are the most important steps during root canal treatment. Sodium hypochlorite is one of the most commonly used irrigating solutions in endodontics. The antibacterial efficiency of sodium hypochlorite solution against E. faecalis is well known to be affected by its concentration and contact time. Adjunctive techniques of root canal disinfection have been proposed, and the introduction of lasers in endodontics is one. Lasers have dramatically improved the effectiveness and success rate of root canal treatment. According to several studies, the use of laser systems for endodontic disinfection provides an opportunity to reduce the problems concerning the difficult access of instruments and irrigants to certain areas of root canals, mainly at the apical ramifications. It has been recognized that near-infrared lasers (810 nm to 1340 nm) have greater depth of penetration when compared to the penetration power of chemical disinfectants, which allows for better bactericidal effect in deeper dentin layers. The use of photosensitizing agents has been proposed in endodontics used with methods such as ‘photodynamic therapy’ (PDT) or “photoactivated disinfection” (PAD). Photosensitizing solutions have better wetting capabilities as they have lower surface tension than sodium hypochlorite. Photosensitizer concentration, light intensity and time of application have to be precise in order to achieve optimal results. Laser thermal effect can generate damage to the dentin walls. Several studies investigated the laser-induced morphological effects on root canal walls. When they are used on dry tissue, near-infrared lasers produce characteristic thermal effects. Morphological alterations of the dentinal wall occur, and the smear layer is only partially removed. Dentinal tubules are primarily closed as a result of melting of the inorganic dentinal structures. Employing the correct parameters and treatment protocol for laser use in root canal disinfection and bacterial elimination can help replace the conventional syringe irrigation methods of disinfection, reduce the morphological alteration of root canal dentin, and improve the treatment outcome.
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Applicability of advanced analytical techniques for monitoring and detection of antibiotics in dosage forms and Bio-Fluids /
Ebrahim, Hager Mohamed Saad Mahmoud,; Supervisor : Samy El-Sayed Sayed Ahmed Emara, Ghada Mekawy Hadad Tawfeik, Walaa Zarad.
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Applicability of advanced analytical techniques for the monitoring and detection of Anti-epileptic drugs; in dosage forms and Bio-Fluids /
Sonbol, Heba Hassan Ali,; Supervisor : Samy El-Sayed Sayed Ahmed Emara, Ghada Mekawy Hadad Tawfeik, Ahmed Shawky.
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Architecture et évolution sociopolitique : Vers une lecture multiple du centre-ville du Caire (1869-1973) .
Aboamer, Mougib Elrahman Mohamed Mohamed Ahmed .; Catherine MAUMI, Galila EL KADI, Anna MADOEUF, Nicolas TIXIER, Frédéric POUSIN, Vittoria CAPRESI.
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An Artificial intelligence-based system for automatic diagnosis of siseases via EEG signals /
Hanafy, Mennato-Allah Talaat Mostafa,; Supervisor : Medhat Hussein Ahmed Awadalla, Lamiaa Sayed Abdel-Hamid. Includes Arabic Summary.
Alzheimer’s disease (AD) is known for being the main type of dementia, distinguished by developing descent in cognition and amnesia. Early diagnosis can assist in disease management and enhance patients’ overall quality of life. Electroencephalogram (EEG) has emerged as a non-invasive tool for detecting AD that has the benefit of having a high temporal resolution. AD causes several significant changes to the patients’ EEG recordings including reduced complexity, slower EEG rhythms, and changes in synchrony. This thesis explores the use of EEG signals combined with machine learning techniques to develop a computer-aided diagnosis (CAD) tool for the improvement of AD diagnosis. Although Recurrence Quantification Analysis (RQA) features have demonstrated promising results in various EEG analysis methods such as emotion detection, they have been scarcely implemented for AD detection. In this thesis, RQA features are computed to investigate their usefulness for AD detection. Specifically, four feature groups are considered for the detection of AD from EEG recordings which are (1) RQA, (2) Hjorth, (3) Statistical, and (4) Power Spectral features. Multiple classifiers are compared including Support vector machines (SVM), K-Nearest Neighbors (KNN) and Random Forest (RF). Cross-validation (CV) methods, such as 10-fold and leave one-subject-out (LOSO) CV, are used to evaluate model performance. For investigating the relevance of features extracted from original EEG vs. from decomposed EEG, results reveal that features extracted from decomposed brain frequency sub-bands significantly enhance classification accuracy when compared to those extracted from the original EEG signal. An improvement ranging from 7% to 25% is observed for 10-fold CV and from 4% to 16% for LOSO CV. Next, two feature selection methods are applied and compared. In general, both feature selection methods yielded consistent results, leading to performance improvement by 1% to 3% in all experiments. Throughout all performed investigations, RQA features results in II the best accuracies in which its accuracies outperform Hjorth, Statistical and Power Spectral features with up to 15% and 25% for 10-fold CV and LOSO CV, respectively. These results highlight the usefulness of RQA features for AD detection from EEG signals. Best results are achieved by combining best-performing features from RQA and statistical group of features extracted from the decomposed EEG signals in which achieved accuracies were 99.2% and 96.7% for 10-fold CV and LOSO, respectively, using the SVM classifier. This research contributes to the development of more reliable AD diagnostic tools and highlights the potential of EEG-based methods in clinical practice.