This study aims to determine the prevalence of Entamoeba histolytica, Entamoeba dispar and
Entamoeba moshkovskii by three methods of diagnosis (microscopic examination, cultivation and PCR) that
were compared to obtain an accurate diagnosis of Entamoeba spp. during amoebiasis. Total (n=150) stool
samples related to patients were (n = 100) and healthy controls (n= 50). Clinically diagnosed stool samples
(n=100) were collected from patients attending the consultant clinics of different hospitals in Basrah during
the period from January 2018 to January 2019. The results showed that 60% of collected samples were
positive in a direct microscopic examination. All samples were cultivated on different media; the Brain heart
infusion agar showed high efficiency and was the most suitable in cultivating the parasite. Data and results of
molecular study were indicated by DNA extraction from stool samples and used in PCR technique with
specific primers. This study identifies different infection percentage for the three species. The highest
infection in Basrah patients was Entamoeba moshkovskii 15% followed by Entamoeba dispar 10% and
Entamoeba histolytica, which was 5%.
This study proposed control system that has been presented to control the electron lens resistance in order to obtain a stabilized electron lens power. This study will layout the fundamental challenges, hypothetical plan arrangements and development condition for the Integrable Optics Test Accelerator (IOTA) in progress at Fermilab. Thus, an effective automatic gain control (AGC) unit has been introduced which prevents fluctuations in the internal resistance of the electronic lens caused by environmental influences to affect the system's current and power values and keep them in stable amounts. Utilizing this unit has obtained level balanced out system un impacted with electronic lens surrounding natural varieties.
Longitudinal data is becoming increasingly common, especially in the medical and economic fields, and various methods have been analyzed and developed to analyze this type of data.
In this research, the focus was on compiling and analyzing this data, as cluster analysis plays an important role in identifying and grouping co-expressed subfiles over time and employing them on the nonparametric smoothing cubic B-spline model, which is characterized by providing continuous first and second derivatives, resulting in a smoother curve with fewer abrupt changes in slope. It is also more flexible and can pick up on more complex patterns and fluctuations in the data.
The longitudinal balanced data profile was compiled into subgroup
... Show MoreA medical- service platform is a mobile application through which patients are provided with doctor’s diagnoses based on information gleaned from medical images. The content of these diagnostic results must not be illegitimately altered during transmission and must be returned to the correct patient. In this paper, we present a solution to these problems using blind, reversible, and fragile watermarking based on authentication of the host image. In our proposed algorithm, the binary version of the Bose_Chaudhuri_Hocquengham (BCH) code for patient medical report (PMR) and binary patient medical image (PMI) after fuzzy exclusive or (F-XoR) are used to produce the patient's unique mark using secret sharing schema (SSS). The patient’s un
... Show MoreIn this work, the fractional damped Burger's equation (FDBE) formula = 0,
In this work, the fractional damped Burger's equation (FDBE) formula = 0,
The automatic estimation of speaker characteristics, such as height, age, and gender, has various applications in forensics, surveillance, customer service, and many human-robot interaction applications. These applications are often required to produce a response promptly. This work proposes a novel approach to speaker profiling by combining filter bank initializations, such as continuous wavelets and gammatone filter banks, with one-dimensional (1D) convolutional neural networks (CNN) and residual blocks. The proposed end-to-end model goes from the raw waveform to an estimated height, age, and gender of the speaker by learning speaker representation directly from the audio signal without relying on handcrafted and pre-computed acou
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