Detection of Bacteroides fragilis LuxR gene, involved in quorum sensing, among colitis patients in Mosul, Iraq

Bacteroides fragilis is the most anaerobic bacteria that infect humans, particularly in the abdominal cavity. Its pathogenesis is linked to numerous virulence factors. Understanding these factors and exploring alternative options for the use of antibiotics in the treatment of this bacterium, molecular techniques offer several advantages over traditional culture techniques because they are easier and more specific. The present study aimed to  use specific primers for the 16sRNA and LuxR genes to identify B. fragilis. Genetic identification of the B. fragilis isolates was performed using the 16SrRNA gene, and the obtained sequences were submitted to National Centre for Biotechnology Information (NCBI) with accession numbers (OQ448827, OQ448828). Each strain was assigned a unique strain name, AS. AWB94 and AS. AWB79. From the total of all samples, it was found that the growth of various types of bacteria constituted ( 76%), and the samples that did not have growth formed (24%). It was noted that Bacteroidetes constituted only two isolates (2.7%), and these two isolates possessed the gene for quorum sensing (luxR gene), while the results confirmed that they do not possess the sialidase (nanH) enzyme gene. Both isolates possessed the quorum sensing gene (LuxR) out of one hundred samples. This suggests that the isolates have a quorum-sensing mechanism responsible for cell-to-cell communication, multidrug resistance, and biofilm formation.

Vehicular Ad Hoc Networks: Growth and Survey for Three Layers

A vehicular ad hoc network (VANET) is a mobile ad hoc network that allows wireless communication between vehicles, as well as between vehicles and roadside equipment. Communication between vehicles promotes safety and reliability, and can be a source of entertainment. We investigated the historical development, characteristics, and application fields of VANET and briefly introduced them in this study. Advantages and disadvantages were discussed based on our analysis and comparison of various classes of MAC and routing protocols applied to VANET. Ideas and breakthrough directions for inter-vehicle communication designs were proposed based on the characteristics of VANET. This article also illustrates physical, MAC, and network layer in details which represent the three layers of VANET. The main works of the active research institute on VANET were introduced to help researchers track related advanced research achievements on the subject

Exploring the Potential of A-ResNet in Person-Independent Face Recognition and Classification

This study offers a novel face recognition and classification method based on classifiers that use statistical local features. The use of ResNet has generated growing interest in a variety of areas of image processing and computer vision in recent years and demonstrated its usefulness in several applications, especially for facial image analysis, which includes tasks as varied as face detection, face recognition, facial expression analysis, demographic classification, etc. This paper is divided into two steps i.e. face recognition and classification. The first step in face recognition is automatic data cleansing which is done with the help of Multi-Task Cascaded Convolutional Neural Networks (MTCNNs) and face.evoLVe, followed by parameter changes in MTCNN to prevent dirty data. The authors next trained two models: Inception-ResNetV1, which had pre-trained weights, and Altered-ResNet (A-ResNet), which used Conv2d layers in ResNet for feature extraction and pooling and softmax layers for classifications. The authors use the best optimizer after comparing a number of them during the training phase, along with various combinations of batch and epoch. A-ResNet, the top model overall, detects 86/104 Labelled Faces in the Wild (LFW) dataset images in 0.50 seconds. The proposed approach was evaluated and received an accuracy of 91.7%. Along with this, the system achieved a training accuracy of 98.53% and a testing accuracy of 99.15% for masked face recognition. The proposed method exhibits competitive outcomes when measured against other cutting-edge algorithms and models. Finally, when it comes to why the suggested model is superior to ResNet, it may be because the A-ResNet is simpler thus it can perform at its best with little data, whereas deeper networks require higher data size

Requirements for applying the beyond budget method in preparing the budget of the University of Mosul: A field study

The current research aims to clarify the concept of the Beyond Budget method and the most important principles of its preparation, in addition to determining the requirements for the use of the Beyond Budget method at the University of Mosul, where the importance of the research stems from the importance of the budget in improving the performance of the governmental unit, as the application of more flexible methods in budget preparation would It limits the pressures of the environmental variables that the unit may be exposed to, and the researchers relied on a qualitative analysis of the respondents answers to the interview questions, and the researchers were able to know the requirements and acceptability of the Beyond Budget method at the University of  Mosul. The researchers reached a number of conclusions, the most important of which was the inability of the current method adopted in preparing the budget of the University of Mosul to be an effective tool for evaluation and to provide information that helps decision-making which requires moving to modern methods of preparing budgets, and the use of the method Beyond Budget at the University of Mosul

Reliable Fuzzy-Based Multi-Path Routing Protocol Based on Whale Optimization Algorithm to Improve QOS in 5G Networks for IOMT Applications

The Internet of Medical Things (IoMT) faces stiff competition from the 5th Generation (5G) communication standard, which includes attributes like short and long transmission ranges, Device to Device (D2D) connectivity, low latency, and high node density. To function in the linked ecosystem, IoMT based on 5G is anticipated to have a diversity of energy and mobility. It is currently difficult to create an IoMT routing system based on 5G that maximizes energy efficiency, lowers transmission latency, and increases network lifespan. The "Quality of Services (QoS)" in 5G-based IoMT is improved by the Reliable Fuzzy-based Multi-path routing system shown in this study. The Whale Optimization Algorithm (WOA) enhances the routing protocol performance. The residual energy-based Cluster Head (CH) selection strategy rotates the CH location among nodes with greater energy levels than the others. The method chooses the following set of CHs for the network that is suitable for IoMT applications by considering initial energy, residual energy, and an ideal value of CHs. According to the simulation results, our suggested routing technique enhances QoS in comparison to current approaches

Examining Saudi Physicians’ Approaches to Communicate Bad News and Bridging Generational Gaps

Breaking bad news is an intrinsic aspect of physicians’ clinical practices. This study aims to investigate how Saudi physicians manage the process of communicating bad news and explore potential differences in breaking bad news practices between young physicians (interns) and their older colleagues. From 1 March to 15 April 2023, ok an anonymous online cross-sectional survey was conducted to explore the communication practices of Saudi physicians concerning breaking bad news using the Communicating Bad News Questionnaire. The physicians were recruited through convenience and snowball sampling methods, and the survey questionnaire was distributed on various social media platforms, including Facebook, Twitter, LinkedIn, and WhatsApp. Data were analyzed using R version 4.2.1. A total of 782 physicians were included in this study. Male physicians represented 50.9% of the participants. Three-quarters (74.7%) were aged 25–30 years. The largest proportion of physicians (45.3%) were interns, followed by junior residents (22.9%), senior residents (11.0%), and specialists (6.5%). The median years of experience was 1.0, ranging from 0 to 45 years. Regarding the place of work, most physicians (86.6%) worked in hospitals, while 13.4% worked in primary healthcare centers. A total of 14.8% said they were not comfortable with discussing patients’/relatives’ issues (20.60 among interns vs. 10.50% among non-interns, χ2 = 27.50, p = 0.0001), 66.6% reported being trained to break bad news (59.60% among interns vs. 72.40% among non-interns, χ2 = 14.34, p = 0.001), 59.1% reported breaking bad news to the patient, 37.9% reported to the family, and 3.1% reported to both, with no significant difference between interns and non-interns. A substantial proportion of physicians reported feeling uncomfortable discussing sensitive issues with patients and their relatives despite having received training to deliver bad news and being willing to communicate bad news directly to patients. Notably, our analysis identified a significant disparity between intern and non-intern physicians, particularly in terms of their comfort level in addressing patient-related concerns and access to breaking bad news training