This study is carried out to investigate the prevalence of Coxiella burnetii (C. burnetii) infections in cattle using an enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR) assay targeting IS1111A transposase gene. A total of 130 lactating cows were randomly selected from different areas in Wasit province, Iraq and subjected to blood and milk sampling during the period extended between November 2018 and May 2019. ELISA and PCR tests revealed that 16.15% and 10% of the animals studied were respectively positive. Significant correlations (P<0.05) were detected between the positive results and clinical data. Two positive PCR products were analyzed phylogenetically, named as C. burnetii IQ-No.5 and C. burnet

Genome sequencing has significantly improved the understanding of HIV and AIDS through accurate data on viral transmission, evolution and anti-therapeutic processes. Deep learning algorithms, like the Fined-Tuned Gradient Descent Fused Multi-Kernal Convolutional Neural Network (FGD-MCNN), can predict strain behaviour and evaluate complex patterns. Using genotypic-phenotypic data obtained from the Stanford University HIV Drug Resistance Database, the FGD-MCNN created three files covering various antiretroviral medications for HIV predictions and drug resistance. These files include PIs, NRTIs and NNRTIs. FGD-MCNNs classify genetic sequences as vulnerable or resistant to antiretroviral drugs by analyzing chromosomal information and id

Recent studies highlighted the hazard of emerging antimicrobial resistance (AMR) bacteria in domesticated animals, with multidrug resistance (MRD) patterns in poultry recorded with alarming intensity as a major reservoir. This resistance has been associated with the misuse and overuse of antimicrobial agents in poultry farms. This current study aimed to determine the multidrug resistance (MDR) patterns of Escherichia coli isolated from broiler chickens. A total of 32 human samples were taken from worker handlers (positive: 4 samples, 12.5%), and 40 samples were collected from poultry chickens (positive: 13 samples, 32.5%). The findings revealed marked resistance against six of the ten agents in the isolates (MDR being 100% in all samples an

Enterococci species (spp.) are naturally found in the gastrointestinal tract (GIT), oral cavity of both human and animals causing several infections including bacteremia, meningitis, endocarditis, intra-abdominal infections, wound infections, urinary tract infections (UTI), atherosclerosis and play a critical role in the riskiness or development of periodontitis, particularly in a suitable mouth environment (Comerlato et al., 2020; Mendes et al., 2020; Xiong et al., 2021; H Elaywe, 2007). Recurrent and continues antimicrobial resistance (AMR) of opportunistic microorganisms is one of the serious public health risk, enterococci spp are often intrinsic resistant to antibiotics class routinly used in treatment such as Cephalosporins, Macrolids

Introduction: Trichophyton indotineae has emerged globally as a highly contagious, terbinafine-resistant dermatophyte causing treatment-refractory superficial mycoses, posing a significant public health threat.This study evaluated the epidemiology of dermatophytosis in Babylon Province, Iraq, and performed molecular identification and phylogenetic analysis of local clinical isolates. Methods: Clinical specimens from 258 patients aged 1 to 71 years were analyzed. Following phenotypic characterization, genomic DNA was extracted from three representative isolates. Polymerase chain reaction amplified the internal transcribed spacer region, followed by DNA sequencing and neighbor-joining phylogenetic analysis. Results: Tinea corporis was

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD