Klebsiella pneumoniae capsular polysaccharide (CPS) antigen was evaluated for their capability to increase immune responses. And, CPS neutralizing antibodies were approved as the main response to vaccination in many disease. Therefore, killed Stapthylococcus aureus bacteria was employed to evaluate K. pneumoniae CPS adjuvanticity. The mice groups were immunized (orally, intra-peritoneally and by swab skin)with a dose of (25μl of formalin killed S. aureus (1.5 x 108) with a CPS at dose 175μl/kg at a conc.50 μg/ml) vaccination occurred in first day then recurrent vaccination as booster dose beyond seven days. After first 7 days, the results revealed elevation of IL2,4,10,12 and IgG levels occurred mainly in oral and swab skin groups, an

Linear discriminant analysis and logistic regression are the most widely used in multivariate statistical methods for analysis of data with categorical outcome variables .Both of them are appropriate for the development of linear  classification models .linear discriminant analysis has been that the data of explanatory variables must be distributed multivariate normal distribution. While logistic regression no assumptions on the distribution of the explanatory data. Hence ,It is assumed that logistic regression is the more flexible and more robust method in case of violations of these assumptions.

In this paper we have been focus for the comparison between three forms for classification data belongs

Human cutaneous leishmaniasis (CL) is caused by Leishmania sp. parasite and endemic in Iraq. The current study was including analysis of available database from Iraqi CDC to determine the distribution of CL cases for the period (2008-2015 years) in Iraq. Total reported cases for this period were 17001 (range 2.9-10.5 per 100,000 individuals). Highest reported cases were recorded in the year 2015 (4000 cases). Male infections cases of CL (50.8%) were more than female infections (49.2%). Highest infections of CL were observed in the age group (5-14yr.) as (34.6%), While the lowest infection of CL were observed in the age group (>1yr.) as (4.3%). Highest infection case of CL was observed in the middle and west of Iraq (53%). In contrast,

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

This abstract focuses on the significance of wireless body area networks (WBANs) as a cutting-edge and self-governing technology, which has garnered substantial attention from researchers. The central challenge faced by WBANs revolves around upholding quality of service (QoS) within rapidly evolving sectors like healthcare. The intricate task of managing diverse traffic types with limited resources further compounds this challenge. Particularly in medical WBANs, the prioritization of vital data is crucial to ensure prompt delivery of critical information. Given the stringent requirements of these systems, any data loss or delays are untenable, necessitating the implementation of intelligent algorithms. These algorithms play a pivota

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

This study aimed to measure the alterations in serum zinc (Zn) and acute phase
proteins ( C-reactive protein and Ceruloplasmin) concentrations in patients with
visceral leishmaniasis (VL). A total of 62 individuals were enrolled in this study :
52 individuals were infected with visceral leishmaniasis and 10 individuals as
healthy control. Serum zinc levels were significantly (p<0.05) decreased in patient
group(76.25 ± 4.59 μg/dl ) when compared with healthy control (103.75 ± 3.77
μg/dl ) . C-reactive protein , as a mediator of innate immunity, removed damaged
cells by activating the classical complement pathway revealed elevated levels in
patients (4.36± 0.23mg/l ) when compared with the healthy control (2

The aim of this study is to assess the prevalence of lung infections among a group of hospitalized cancer patients who received chemotherapy as well as to describe a population of these patients. The clinical data and demographic information were collected from the archived files of  in-patients  referred to  hematology center  / Baghdad Teaching Hospital / Medical City , ministry of health, Iraq  during the period  of  2018.

    This study was carried out on 250 patients with different types of cancer ,they were mostly of age group (40 - 49)  59 / 250 (23.6)% , (14-19) 49 /250 (19.6%) and (60-69) 41/ 250(16.4%) . The patients had two major types of hematological malignancies

Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor