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Isolation and Identification of food Probiotic Saccharomyces boulardii by using traditional methods, Vitek 2 system and molecular identification methods.: Isolation and Identification of food Probiotic Saccharomyces boulardii by using traditional methods, Vitek 2 system and molecular identification methods.
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This study was aimed to isolate and identify Saccharomyces boulardii from Mangosteen fruits (Garcinia mangostana L.) by traditional and molecular identification methods To get safe and healthy foods probiotics for use, The isolates and two commercial strains were subjected to cultural, morphological and biochemical tests, The colonies of the isolates were spherical, smooth, mucoidal, dull and white to cream colour on SD agar media .The shape of cells was globose to ovoid and sometimes with budding, in a single form or clustered like a beehive. The isolates and two commercial strains were unable to metabolized galactose and lactose , Results shows that all isolates were unable to utilize potassium nitrate and not grow in the presence of (0.01%) cyclohexamide. Also the isolates and two commercial strains were identified by the Vitek 2 identification system, as S. cerevisiae with probability 90-94%, Since there are no data in this device includes S. boulardii. Because the cultural, morphological and biochemical tests didn’t provide sufficient evidence to distinguish between S. boulardii and S. cerevisiae, the probiotics strains must be identified up to genus and strain levels by internationally accepted methods, So the SbR7 isolate which shown high probiotic advantages and two commercial strains were diagnosed molecularly by using specific primers targeting sequence for the region (ITS) of the 5.8S rRNA gene Genomic DNA was isolated from SbR7 Isolate and ITS region of the 5.8S rRNA gene was amplified using PCR. PCR products was sequenced and compared with the sequence of this region in the DNA of S. boulardii available in GenBank (NCBI) using the program BLASTn. Results revealed, this isolate was almost genetically identical (99%) with S. boulardii standard strains.

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Publication Date
Sat Oct 28 2023
Journal Name
Baghdad Science Journal
Qualitative and Quantitative Determination of Dapagliflozin Propanediol Monohydrate and Its Related Substances and Degradation Products Using LC-MS and Preparative Chromatography Methods
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Dapagliflozin is a novel sodium-glucose cotransporter type 2 inhibitor. This work aims to develop a new
validated sensitive RP-HPLC coupled with a mass detector method for the determination of dapagliflozin, its
alpha isomer, and starting material in the presence of dapagliflozin major degradation products and an internal
standard (empagliflozin). The separation was achieved on BDS Hypersil column (length of 250mm, internal
diameter of 4.6 mm and 5-μm particle size) at a temperature of 35℃. Water and acetonitrile were used as
mobile phase A and B by gradient mode at a flow rate of 1 mL/min. A wavelength of 224nm was selected to
perform detection using a photo diode array detector. The method met the

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Publication Date
Wed Feb 22 2023
Journal Name
Iraqi Journal Of Science
Synthesis, Identification and evaluation of antibacterial activity of some new substituted N-benzyl-5-Bromo Isatin
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This research includes synthesis of new heterocyclic derivatives of N-benzyl-5-bromoisatin. New 1, 2, 4-triazole, oxazoline and thiazoline derivatives of [N-benzyl-5-bromo-3-(Ethyliminoacetate)-indole-2-one] (2) have been synthesized. The preparation process started by the reaction of 5-bromoisatin with sodium hydride in dimethylformamide (DMF) at 0°C, gave suspension of sodium salt of 5-bromoisatin and subsequent reaction with benzylchloride to give N-benzyl-5-bromoisatin (1). Compound (1) reacted with ethylglycinate (Schiff base) obtained the intermediate compound (2) which reacted with different reagents in two ways. The first way, compound (2) reacted with (hydrazine hydrate, semicarbazide, phenylsemicarbazide and thiosemicarbazide)

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Publication Date
Mon Apr 07 2025
Journal Name
Al-nahrain Journal For Engineering Sciences
Navigating the Challenges and Opportunities of Tiny Deep Learning and Tiny Machine Learning in Lung Cancer Identification
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Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detectio

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Publication Date
Sat Jul 01 2017
Journal Name
Journal Of Construction Engineering And Management
Identification, Quantification, and Classification of Potential Safety Risk for Sustainable Construction in the United States
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Publication Date
Mon Jan 01 2024
Journal Name
Aip Conference Proceedings
Comparative analysis of deep learning techniques for lung cancer identification
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One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

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Publication Date
Mon Jan 01 2024
Journal Name
Communications In Computer And Information Science
Automatic Identification of Ear Patterns Based on Convolutional Neural Network
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Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in

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Publication Date
Sat May 27 2023
Journal Name
Online Journal Of Animal And Feed Research
Phylogenetic identification of Anaplasma phagocytophilum in horses in Baghdad, Iraq
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This study aimed to detect Anaplasma phagocytophilum in horses through hematological and molecular tests. The 16S rRNA gene of the Anaplasma phagocytophilum parasite was amplified by polymerase chain reaction (PCR), then sequenced, and subjected to phylogenetic analysis to explore "Equine Granulocytic Anaplasmosis" (EGA) infection in three important gathering race horses areas in Baghdad governorate, Iraq. Blood samples were obtained from 160 horses of varying ages, three breeds, and both sexes, between January and December 2021. Prevalence and risk variables for anaplasmosis were analyzed using statistical odds ratio and chi-square tests. Results demonstrated that clinical anaplasmosis symptoms comprised jaundice, wei

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Publication Date
Sun Jan 01 2023
Journal Name
Advances In The Theory Of Nonlinear Analysis And Its Application
Numerical identification of timewise dependent coefficient in Hyperbolic inverse problem
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Publication Date
Tue Aug 01 2023
Journal Name
Baghdad Science Journal
A Developed Colorimetric Assay Using Unmodified Gold Nanoparticles for the Identification of Acinetobacter baumannii Isolates from Different Clinical Samples
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  Acinetobacter baumannii (A. baumannii ) is considered a critical healthcare problem for patients in intensive care units due to its high ability to be multidrug-resistant to most commercially available antibiotics. The aim of this study is to develop a colorimetric assay to quantitatively detect the target DNA of A. baumannii based on unmodified gold nanoparticles (AuNPs) from different clinical samples (burns, surgical wounds, sputum, blood and urine). A total of thirty-six A. baumannii clinical isolates were collected from five Iraqi hospitals in Erbil and Mosul provinces within the period from September 2020 to January 2021. Bacterial isolation and biochemical identification of isolates

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Publication Date
Mon Nov 01 2010
Journal Name
Iraqi Journal Of Physics
Night Vision monitor for IR laser spot size Identification
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The aim of this research is to design and construct a
semiconductor laser range finder operating in the near infrared range
for ranging and designation. The main part of the range finder is the
transmitter which is a semiconductor laser type GaAs of wavelength
0.904 μm with a beam expander and the receiver; a silicon pin
detector biased to approve the fast response time with it's collecting
optics. The transmitters pulse width was 200ns at a threshold current
of 10 Ampere and maximum operating current of 38 Ampere. The
repetition rate was set at 660Hz and the maximum operating output
power was around 1 watt. The divergence of the beam was 0.268o
the efficiency of the laser was 0.03% at a duty cycle of 1.32x

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