Foreign bodies in the ear, nose and throat at the federal teaching hospital, Gombe-north-eastern, Nigeria

Foreign bodies (FBs) are common ENT emergencies all over the world. Children have been found to be commonly affected due to their curiosity and desire to explore their environment. Objective: To evaluate the clinical presentations, management and the outcomes of ear, nose and throat foreign bodies in a tertiary hospital setting. Method: This a three-year retrospective study of patients presented with foreign bodies to the ENT department. The demographic information, sites and sides of foreign bodies, nature of foreign bodies, management and outcomes were extracted from their records and analysed. Results: There were 34 (45.9%) males and 40 (54.1%) females with Male to Female ratio of 1: 1.8. Inanimate objects 60 (81%) were the common FBs aspirated, while 4 (5.4%) were animate FBs aspirated. In 28.4% of the patients presented to the hospital in the first three days of aspiration. The symptoms include sensation/ discomfort, accidental finding, Otalgia, ear discharge, bleeding among others. Lodgment is more common in the left nostril 9 (56.2%), throat 11 (55.0%) and right ear 19 (50.0%) among those with nasal, throat and ear foreign bodies respectively. The complications included infection, bleeding among others. Conclusion: FBs aspiration was found to be commoner among children than in adults due to the fact that children explore their environment

Reinforcement-Learning-Enabled Massive Internet of Things for 6G Wireless Communications

Recently, extensive research efforts have been devoted to developing beyond fifth generation (B5G), also referred to as sixth generation (6G) wireless networks aimed at bringing ultra-reli-able low-latency communication services. 6G is expected to extend 5G capabilities to higher communication levels where numerous connected devices and sensors can operate seamlessly. One of the major research focuses of 6G is to enable massive Internet of Things (mIoT) applications. Like Wi-Fi 6 (IEEE 802.11ax), forthcoming wireless communication networks are likely to meet massively deployed devices and extremely new smart applications such as smart cities for mIoT. However, channel scarcity is still present due to a massive number of connected devices accessing the common spectrum resources. With this expectation, next-generation Wi-Fi 6 and beyond for mIoT are anticipated to have inherent machine intelligence capabilities to access the optimum channel resources for their performance optimization. Unfortunately, current wireless communication network standards do not support the ensuing needs of machine learning (ML)-aware frameworks in terms of resource allocation optimization. Keeping such an issue in mind, we propose a reinforcement-learning-based, one of the ML techniques, a framework for a wireless channel access mechanism for IEEE 802.11 standards (i.e., Wi-Fi) in mIoT. The proposed mechanism suggests exploiting a practically measured channel collision probability as a collected dataset from the wireless environment to select optimal resource allocation in mIoT for upcoming 6G wireless communications

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

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

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

Toll-like receptor 4 is a therapeutic target for prevention and treatment of liver failure

Background and aims: Toll-like receptor 4 (TLR4) plays an essential role in mediating organ injury in acute liver failure (ALF) and acute-on-chronic liver failure (ACLF). Here we assess whether inhibiting TLR4 signaling can ameliorate liver failure and serve as a potential treatment. / Material and Methods: Circulating TLR4 ligands and hepatic TLR4 expression was measured in plasma samples and liver biopsies from patients with cirrhosis. TAK-242 (TLR4 inhibitor) was tested in vivo with 10mg/Kg, i.p. in rodent models of ACLF (bile duct ligation + lipopolysaccharide (LPS); carbontetrachloride + LPS) and ALF (Galactosamine + LPS) and in vitro on immortalized human monocytes (THP-1) and hepatocytes (HHL5).. The in vivo therapeutic effect was assessed by coma free survival, organ injury and cytokine release and in vitro by measuring IL6, IL1b or cell injury (TUNEL), respectively. / Results: In patients with cirrhosis, hepatic TLR4 expression was upregulated and circulating TLR4 ligands were increased (p<0.001). ACLF in rodents was associated with a switch from apoptotic cell death in ALF to non-apoptotic forms of cell death. TAK-242 reduced LPS induced cytokine secretion and cell death (p=0.002) in hepatocytes and monocytes in vitro. In rodent models of ACLF, TAK-242 administration improved coma free survival, reduced the degree of hepatocyte cell death in liver p<0.001) and kidneys (p=0.048) and reduced circulating cytokine levels (IL1b p<0.001). In a rodent model of ALF TAK-242 prevented organ injury (p<0.001) and systemic inflammation (IL1b p<0.001). / Conclusion: This study shows that TLR4 signaling is a key factor in the development of both ACLF and ALF and its inhibition improves severity of organ injury and outcome. TAK-242 may be of therapeutic relevance in patients with liver failure

Deep-level Transient Spectroscopy of GaAs/AlGaAs Multi-Quantum Wells Grown on (100) and (311)B GaAs Substrates

Si-doped GaAs/AlGaAs multi-quantum wells structures grown by molecular beam epitaxy on (100) and (311)B GaAs substrates have been studied by using conventional deep-level transient spectroscopy (DLTS) and high-resolution Laplace DLTS techniques. One dominant electron-emitting level is observed in the quantum wells structure grown on (100) plane whose activation energy varies from 0.47 to 1.3 eV as junction electric field varies from zero field (edge of the depletion region) to 4.7 × 106 V/m. Two defect states with activation energies of 0.24 and 0.80 eV are detected in the structures grown on (311)B plane. The Ec-0.24 eV trap shows that its capture cross-section is strongly temperature dependent, whilst the other two traps show no such dependence. The value of the capture barrier energy of the trap at Ec-0.24 eV is 0.39 eV

Association between footwear use and neglected tropical diseases: a systematic review and meta-analysis

BACKGROUND The control of neglected tropical diseases (NTDs) has primarily focused on preventive chemotherapy and case management. Less attention has been placed on the role of ensuring access to adequate water, sanitation, and hygiene and personal preventive measures in reducing exposure to infection. Our aim was to assess whether footwear use was associated with a lower risk of selected NTDs. METHODOLOGY We conducted a systematic review and meta-analysis to assess the association between footwear use and infection or disease for those NTDs for which the route of transmission or occurrence may be through the feet. We included Buruli ulcer, cutaneous larva migrans (CLM), leptospirosis, mycetoma, myiasis, podoconiosis, snakebite, tungiasis, and soil-transmitted helminth (STH) infections, particularly hookworm infection and strongyloidiasis. We searched Medline, Embase, Cochrane, Web of Science, CINAHL Plus, and Popline databases, contacted experts, and hand-searched reference lists for eligible studies. The search was conducted in English without language, publication status, or date restrictions up to January 2014. Studies were eligible for inclusion if they reported a measure of the association between footwear use and the risk of each NTD. Publication bias was assessed using funnel plots. Descriptive study characteristics and methodological quality of the included studies were summarized. For each study outcome, both outcome and exposure data were abstracted and crude and adjusted effect estimates presented. Individual and summary odds ratio (OR) estimates and corresponding 95% confidence intervals (CIs) were calculated as a measure of intervention effect, using random effects meta-analyses. PRINCIPAL FINDINGS Among the 427 studies screened, 53 met our inclusion criteria. Footwear use was significantly associated with a lower odds of infection of Buruli ulcer (OR=0.15; 95% CI: 0.08-0.29), CLM (OR=0.24; 95% CI: 0.06-0.96), tungiasis (OR=0.42; 95% CI: 0.26-0.70), hookworm infection (OR=0.48; 95% CI: 0.37-0.61), any STH infection (OR=0.57; 95% CI: 0.39-0.84), strongyloidiasis (OR=0.56; 95% CI: 0.38-0.83), and leptospirosis (OR=0.59; 95% CI: 0.37-0.94). No significant association between footwear use and podoconiosis (OR=0.63; 95% CI: 0.38-1.05) was found and no data were available for mycetoma, myiasis, and snakebite. The main limitations were evidence of heterogeneity and poor study quality inherent to the observational studies included. CONCLUSIONS/SIGNIFICANCE Our results show that footwear use was associated with a lower odds of several different NTDs. Access to footwear should be prioritized alongside existing NTD interventions to ensure a lasting reduction of multiple NTDs and to accelerate their control and elimination. PROTOCOL REGISTRATION PROSPERO International prospective register of systematic reviews CRD42012003338

Effects of betel nut on cardiovascular risk factors in a rat model

Background: Areca nut (commonly known as betel nut) chewing has been shown to be associated with metabolic syndrome and cardiovascular disease (CVD). The mechanism by which betel nut ingestion could lead to development of CVD is not precisely known; however, dyslipidemia, hyperhomocysteinemia, hypertriglyceridemia and inflammation could be some of the potential risk factors. This study was undertaken to investigate the effects of two dosages of betel nut on homocysteinemia, inflammation and some of the components of metabolic syndrome, such as hypertriglyceridemia, low HDL-cholesterol, obesity and fasting hyperglycemia in a rat model.Methods: Thirty-six adult female Sprague Dawley rats, aged 10–12 weeks were divided into three equal groups. Group-1 served as the control group (n = 12) and received water, whereas groups 2 and 3 were given water suspension of betel nut orally in two dosages, 30 mg and 60 mg, respectively for a period of 5 weeks. At the end of the fifth week, the animals were weighed and sacrificed, blood was collected and liver, kidney, spleen and stomach were removed for histological examination. Plasma/serum was analyzed for glucose, total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides, homocysteine, folate, vitamin B12 and N-acetyl-β-D-glucosaminidase (NAG) – a marker of inflammation.Results: When the mean concentration values of 3 groups were compared using one way ANOVA followed by Tukey’s HSD-test, there was a significant increase in the concentration of total cholesterol (p = 0.04) in the group receiving 30 mg/day betel nut compared to the control group. However, administration of a higher dose of betel nut (60 mg/day) had no significant effect on the serum concentrations of glucose, total cholesterol, HDL-cholesterol, LDL-cholesterol, and NAG. Histological examination of spleen revealed a dose-dependent extramedullary hematopoiesis. No other remarkable change in the tissues (liver, kidney and stomach) was observed. Mean serum/plasma levels of folate, vitamin B12 and homocysteine were not found to be significantly different in all the groups. Betel nut ingestion had no effect on the mean body weights of rats.Conclusions: Low dosage of betel nut is found to be associated with hypercholesterolemia. However, betel nut ingestion is not associated with hyperhomocysteinemia, hypertriglyceridemia, hyperglycemia, inflammation and increase in body weight in a rat model