A Robust Deep Model for Classification of Peptic Ulcer and Other Digestive Tract Disorders Using Endoscopic Images

Accurate patient disease classification and detection through deep-learning (DL) models are increasingly contributing to the area of biomedical imaging. The most frequent gastrointestinal (GI) tract ailments are peptic ulcers and stomach cancer. Conventional endoscopy is a painful and hectic procedure for the patient while Wireless Capsule Endoscopy (WCE) is a useful technology for diagnosing GI problems and doing painless gut imaging. However, there is still a challenge to investigate thousands of images captured during the WCE procedure accurately and efficiently because existing deep models are not scored with significant accuracy on WCE image analysis. So, to prevent emergency conditions among patients, we need an efficient and accurate DL model for real-time analysis. In this study, we propose a reliable and efficient approach for classifying GI tract abnormalities using WCE images by applying a deep Convolutional Neural Network (CNN). For this purpose, we propose a custom CNN architecture named GI Disease-Detection Network (GIDD-Net) that is designed from scratch with relatively few parameters to detect GI tract disorders more accurately and efficiently at a low computational cost. Moreover, our model successfully distinguishes GI disorders by visualizing class activation patterns in the stomach bowls as a heat map. The Kvasir-Capsule image dataset has a significant class imbalance problem, we exploited a synthetic oversampling technique BORDERLINE SMOTE (BL-SMOTE) to evenly distribute the image among the classes to prevent the problem of class imbalance. The proposed model is evaluated against various metrics and achieved the following values for evaluation metrics: 98.9%, 99.8%, 98.9%, 98.9%, 98.8%, and 0.0474 for accuracy, AUC, F1-score, precision, recall, and loss, respectively. From the simulation results, it is noted that the proposed model outperforms other state-of-the-art models in all the evaluation metrics

URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence

The tactile internet (TI) is believed to be the prospective advancement of the internet of things (IoT), comprising human-to-machine and machine-to-machine communication. TI focuses on enabling real-time interactive techniques with a portfolio of engineering, social, and commercial use cases. For this purpose, the prospective 5{th} generation (5G) technology focuses on achieving ultra-reliable low latency communication (URLLC) services. TI applications require an extraordinary degree of reliability and latency. The 3{rd} generation partnership project (3GPP) defines that URLLC is expected to provide 99.99% reliability of a single transmission of 32 bytes packet with a latency of less than one millisecond. 3GPP proposes to include an adjustable orthogonal frequency division multiplexing (OFDM) technique, called 5G new radio (5G NR), as a new radio access technology (RAT). Whereas, with the emergence of a novel physical layer RAT, the need for the design for prospective next-generation technologies arises, especially with the focus of network intelligence. In such situations, machine learning (ML) techniques are expected to be essential to assist in designing intelligent network resource allocation protocols for 5G NR URLLC requirements. Therefore, in this survey, we present a possibility to use the federated reinforcement learning (FRL) technique, which is one of the ML techniques, for 5G NR URLLC requirements and summarizes the corresponding achievements for URLLC. We provide a comprehensive discussion of MAC layer channel access mechanisms that enable URLLC in 5G NR for TI. Besides, we identify seven very critical future use cases of FRL as potential enablers for URLLC in 5G NR

Prediction Models for COVID-19 Integrating Age Groups, Gender, and Underlying Conditions

The COVID-19 pandemic has caused hundreds of thousands of deaths, millions of infections worldwide, and the loss of trillions of dollars for many large economies. It poses a grave threat to the human population with an excessive number of patients constituting an unprecedented challenge with which health systems have to cope. Researchers from many domains have devised diverse approaches for the timely diagnosis of COVID-19 to facilitate medical responses. In the same vein, a wide variety of research studies have investigated underlying medical conditions for indicators suggesting the severity and mortality of, and role of age groups and gender on, the probability of COVID-19 infection. This study aimed to review, analyze, and critically appraise published works that report on various factors to explain their relationship with COVID-19. Such studies span a wide range, including descriptive analyses, ratio analyses, cohort, prospective and retrospective studies. Various studies that describe indicators to determine the probability of infection among the general population, as well as the risk factors associated with severe illness and mortality, are critically analyzed and these findings are discussed in detail. A comprehensive analysis was conducted on research studies that investigated the perceived differences in vulnerability of different age groups and genders to severe outcomes of COVID-19. Studies incorporating important demographic, health, and socioeconomic characteristics are highlighted to emphasize their importance. Predominantly, the lack of an appropriated dataset that contains demographic, personal health, and socioeconomic information implicates the efficacy and efficiency of the discussed methods. Results are overstated on the part of both exclusion of quarantined and patients with mild symptoms and inclusion of the data from hospitals where the majority of the cases are potentially ill

A federated reinforcement learning framework for incumbent technologies in beyond 5G networks

Incumbent wireless technologies for futuristic fifth generation (5G) and beyond 5G (B5G) networks, such as IEEE 802.11 ax (WiFi), are vital to provide ubiquitous ultra-reliable and low-latency communication services with massively connected devices. Amalgamating WiFi networks with 5G/B5G networks has attracted strong researcher interest over the past two decades, because over 70 percent of mobile data traffic is generated by WiFi devices. However, WiFi channel resource scarcity for 5G/B5G is becoming ever more critical. One current problem regarding channel resource allocation is channel collision handling due to increased user densities. Reinforcement learning (RL) algorithms have recently helped develop prominent behaviorist learning techniques for resource allocation in 5G/B5G networks. An agent optimizes its behavior in an RL-based algorithm based on reward and accumulated value. However, densely deployed WiFi environments are distributed and dynamic, with frequent changes. Thus, relying on individual local estimations leads to higher error variance. Therefore, this article proposes a federated RL-based channel resource allocation framework for 5G/B5G networks, and suggests collaborating learning estimates for faster learning convergence. Experimental results verify that the proposed approach optimizes WiFi performance in terms of throughput by collaborative channel access parameter selection. This study also highlights six potential applications for the proposed framework

A secure and lightweight drones-access protocol for smart city surveillance

The rising popularity of ICT and the Internet has enabled Unmanned Aerial Vehicle (UAV) to offer advantageous assistance to Vehicular Ad-hoc Network (VANET), realizing a relay node's role among the disconnected segments in the road. In this scenario, the communication is done between Vehicles to UAVs (V2U), subsequently transforming into a UAV-assisted VANET. UAV-assisted VANET allows users to access real-time data, especially the monitoring data in smart cities using current mobile networks. Nevertheless, due to the open nature of communication infrastructure, the high mobility of vehicles along with the security and privacy constraints are the significant concerns of UAV-assisted VANET. In these scenarios, Deep Learning Algorithms (DLA) could play an effective role in the security, privacy, and routing issues of UAV-assisted VANET. Keeping this in mind, we have devised a DLA-based key-exchange protocol for UAV-assisted VANET. The proposed protocol extends the scalability and uses secure bitwise XOR operations, one-way hash functions, including user's biometric verification when users and drones are mutually authenticated. The proposed protocol can resist many well-known security attacks and provides formal and informal security under the Random Oracle Model (ROM). The security comparison shows that the proposed protocol outperforms the security performance in terms of running time cost and communication cost and has effective security features compared to other related protocols

Dose Optimization of Colistin: A Systematic Review

Colistin is considered a last treatment option for multi-drug and extensively resistant Gram-negative infections. We aimed to assess the available data on the dosing strategy of colistin. A systematic review was performed to identify all published studies on the dose optimization of colistin. Grey literature and electronic databases were searched. Data were collected in a specified form and the quality of the included articles was then assessed using the Newcastle-Ottawa scale for cohort studies, the Cochrane bias tool for randomized clinical trials (RCT), and the Joanna Briggs Institute (JBI) critical checklist for case reports. A total of 19 studies were included, of which 16 were cohort studies, one was a RCT, and two were case reports. A total of 18 studies proposed a dosing regimen for adults, while only one study proposed a dosing schedule for pediatric populations. As per the available evidence, a loading dose of 9 million international units (MIU) of colistin followed by a maintenance dose of 4.5 MIU every 12 h was considered the most appropriate dosing strategy to optimize the safety and efficacy of treatment and improve clinical outcomes. This review supports the administration of a loading dose followed by a maintenance dose of colistin in severe and life-threatening multi-drug Gram-negative bacterial infections

A Systematic Review on Clinical Safety and Efficacy of Vancomycin Loading Dose in Critically Ill Patients

Background: The clinical significance of utilizing a vancomycin loading dose in critically ill patients remains unclear. Objective: The main aim of this systematic review is to evaluate the clinical safety and efficacy of the vancomycin loading dose in critically ill patients. Methods: We performed a systematic review using PRISMA guidelines. PubMed, the Web of Science, MEDLINE, Scopus, Google Scholar, the Saudi Digital Library and other databases were searched. Studies that reported clinical outcomes among patients receiving the vancomycin LD were considered eligible. Data for this study were collected using PubMed, the Web of Science, MEDLINE, Scopus, Google Scholar and the Saudi Digital Library using the following terms: “vancomycin”, “safety”, “efficacy” and “loading dose” combined with the Boolean operator “AND” or “OR”. Results: A total of 17 articles, including 2 RCTs, 11 retrospective cohorts and 4 other studies, met the inclusion/exclusion criteria out of a total 1189 studies. Patients had different clinical characteristics representing a heterogenous group, including patients in critical condition, with renal impairment, sepsis, MRSA infection and hospitalized patients for hemodialysis or in the emergency department. Conclusions: The study shows that the target therapeutic level is achieved more easily among patients receiving a weight-based LD as compared to patients received the usual dose without an increased risk of new-onset adverse drug reactions

Dose optimization of β-lactams antibiotics in pediatrics and adults:A systematic review

Background: β-lactams remain the cornerstone of the empirical therapy to treat various bacterial infections. This systematic review aimed to analyze the data describing the dosing regimen of β-lactams. Methods: Systematic scientific and grey literature was performed in accordance with Preferred Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The studies were retrieved and screened on the basis of pre-defined exclusion and inclusion criteria. The cohort studies, randomized controlled trials (RCT) and case reports that reported the dosing schedule of β-lactams are included in this study. Results: A total of 52 studies met the inclusion criteria, of which 40 were cohort studies, 2 were case reports and 10 were RCTs. The majority of the studies (34/52) studied the pharmacokinetic (PK) parameters of a drug. A total of 20 studies proposed dosing schedule in pediatrics while 32 studies proposed dosing regimen among adults. Piperacillin (12/52) and Meropenem (11/52) were the most commonly used β-lactams used in hospitalized patients. As per available evidence, continuous infusion is considered as the most appropriate mode of administration to optimize the safety and efficacy of the treatment and improve the clinical outcomes. Conclusion: Appropriate antibiotic therapy is challenging due to pathophysiological changes among different age groups. The optimization of pharmacokinetic/pharmacodynamic parameters is useful to support alternative dosing regimens such as an increase in dosing interval, continuous infusion, and increased bolus doses

Threat of antimicrobial resistance among pilgrims with infectious diseases during Hajj : lessons learnt from COVID-19 pandemic

Hajj pilgrimage is a large mass gathering global event that may facilitate the spread and emergence of various infectious diseases as well as antimicrobial resistance (AMR) in a local and global scenario. Planning and preparing for these public health issues is a challenging and complex process for the Kingdom of Saudi Arabia (KSA) health authorities. Despite multiple efforts for the prevention and treatment of infectious diseases through longtime funding in education and medical care, the prevalence of infectious disease is still high among Hajj pilgrims. The commonly observed infectious diseases during Hajj include respiratory tract infections (influenza and pneumonia), urinary tract infections and skin infections that may necessitate the use of antimicrobials. Beta-lactams are used as a first-line treatment for hospital acquired infections as well as community acquired infections due to their broad-spectrum activity. However, most of the bacterial isolates such as Staphylococcus spp., Pseudomonas spp. and E. coli are resistant to beta-lactams. Irrational use of anti-microbials, lack of infection prevention practices and suboptimal healthcare access further exacerbate the risk of spreading AMR among Hajj pilgrims. Enhanced collaboration between countries, sharing of best practices and international cooperation are crucial in addressing AMR threats among pilgrims. Consequently, robust surveillance systems for early detection and monitoring of AMR, collaboration with national as well as international healthcare agencies, effective infection prevention and control measures, public awareness, and rational use of antimicrobials via antimicrobial stewardship programs are required to mitigate the risk of AMR and ensure the health and well-being of pilgrims during Hajj

Tackling antimicrobial resistance in primary care facilities across Pakistan : current challenges and implications for the future

Antibiotics are gradually becoming less effective against bacteria worldwide, and this issue is of particular concern in economically-developing nations like Pakistan. We undertook a scoping review in order to review the literature on antimicrobial use, prescribing, dispensing and the challenges associated with antimicrobial resistance in primary care (PC) settings in Pakistan. Furthermore, this review aims to identify potential solutions to promote appropriate use of antimicrobials in Pakistan. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) checklist, a comprehensive scoping review was conducted to review the literature of antimicrobials used, prescribed and dispensed in PC settings in Pakistan. Google Scholar and Pub-Med were searched for the period 2000–2023. Papers were analyzed on the basis of eligibility i.e., included antimicrobial use, prescribing and dispensing practices by general population at homes, by prescribers in outpatient departments of hospitals and by pharmacists/dispensers in community pharmacies, respectively. Two researchers analyzed the articles thoroughly and disagreements were resolved through discussion with a third reviewer. Both quantitative and qualitative research studies were eligible for inclusion. Additionally, the selected papers were grouped into different themes. We identified 4070 papers out of which 46 studies satisfied our eligibility criteria. The findings revealed limited understanding of antimicrobial resistance (AMR) by physicians and community pharmacists along with inappropriate practices in prescribing and dispensing antibiotics. Moreover, a notable prevalence of self-medication with antibiotics was observed among the general population, underscoring a lack of awareness and knowledge concerning proper antibiotic usage. Given the clinical and public health implications of AMR, Pakistan must prioritize its policies in PC settings. Healthcare professionals (HCPs) need to reduce inappropriate antibiotic prescribing and dispensing, improve their understanding of the AWaRe (access, watch and reserve antibiotics) classification and guidance, monitor current usage and resistance trends, as well as implement antimicrobial stewardship (ASP) activities starting in targeted locations