Antibiotic Resistance and its Association with Biocides Susceptibilities among Microbial Isolates in an Egyptian Hospital

Background: Recently there has been a growing concern that the indiscriminate use of antimicrobial agents in the household, food industry and in hospitals may contribute to the emergence of bacteria resistant to antibiotics.Aim of the work: To detect any possible link between the susceptibility profiles of different clinical and environmental isolates to biocides and antibiotics in an Egyptian hospital.Methods: 66 different microbial isolates were isolated from different clinical specimens and different environmental samples obtained from a University Hospital in Alexandria. These isolates were screened for their susceptibility to 22 broad spectrum antibiotics using disc agar diffusion technique. Also the susceptibility of the isolates to 6 commonly used biocides was screened through MIC determination by agar dilution technique. Correlations between the obtained data were made through Spearman’s correlation using SPSS® Statistical program.Results: 62% of the isolates were multidrug resistant (MDR); and 11% were extremely drug resistant (XDR). On the other hand, 34% of the tested isolates were multi-disinfectant reduced susceptibility (MDRS) isolates. The statistical analysis of the obtained data revealed a moderate positive correlation between antibiotic resistance and biocide tolerance (0.376≥Ï≥0.278, p<0.05). In addition, strong significant correlations (p<0.01) were also found between reduced susceptibilities to multiple biocides such as benzalkonium chloride (BK), cetrimide (CET), chlorhexidine (CHX), povidone-iodine (PVPI) and Dettol®.Conclusion: Cross-resistance between biocides and antibiotics can aggravate the existing problem of antibiotic resistance in hospitals

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Grape-Leaf Extract Attenuates Alcohol-Induced Liver Injury via Interference with NF-κB Signaling Pathway

Grape (Vitis vinifera) leaf extracts (GLEs) are known to be rich in phenolic compounds that exert potent antioxidant effects. Given the vulnerability of the liver to oxidative damage, antioxidants have been proposed as therapeutic agents and coadjuvant drugs to ameliorate liver pathologies. The current study was designed to characterize secondary metabolites and investigate the hepatoprotective effects of GLE and its underlying mechanisms. The secondary metabolites were profiled using HPLC&ndash;PDA&ndash;ESI-MS, and forty-five compounds were tentatively identified. In experimental in vivo design, liver injury was induced by oral administration of high doses of ethanol (EtOH) for 12 days to male Sprague Dawley rats that were split into five different groups. Blood samples and livers were then collected, and used for various biochemical, immunohistochemical, and histopathological analyses. Results showed that GLE-attenuated liver injury and promoted marked hepatic antioxidant effects, in addition to suppressing the increased heat-shock protein-70 expression. Moreover, GLE suppressed EtOH-induced expression of nuclear factor-&kappa;B (NF-&kappa;B) p65 subunit and proinflammatory cytokine tumor necrosis factor-&alpha;. Caspase-3 and survivin were enhanced by EtOH intake and suppressed by GLE intake. Finally, EtOH-induced histopathological changes in liver sections were markedly normalized by GLE. In conclusion, our results suggested that GLE interferes with NF-&kappa;B signaling and induces antioxidant effects, which both play a role in attenuating apoptosis and associated liver injury in a model of EtOH-induced liver damage in rats

Proteoglycans: Potential Agents in Mammographic Density and the Associated Breast Cancer Risk.

Although increased mammographic density (MD) has been well established as a marker for increased breast cancer (BC) risk, its pathobiology is far from understood. Altered proteoglycan (PG) composition may underpin the physical properties of MD, and may contribute to the associated increase in BC risk. Numerous studies have investigated PGs, which are a major stromal matrix component, in relation to MD and BC and reported results that are sometimes discordant. Our review summarises these results and highlights discrepancies between PG associations with BC and MD, thus serving as a guide for identifying PGs that warrant further research towards developing chemo-preventive or therapeutic agents targeting pre-invasive or invasive breast lesions, respectively

AI-enabled CSI fingerprinting for indoor localisation towards context-aware networking in 6G

No abstract available

Comparison of Data Mining Techniques in the Cloud for Software Engineering

Mining software engineering data has recently become an important research topic to meet the goal of improving the software engineering processes, software productivity, and quality. On the other hand, mining software engineering data poses several challenges such as high computational cost, hardware limitations, and data management issues (i.e., the availability, reliability, and security of data). To address these problems, this chapter proposes the application of data mining techniques in cloud, the environment on software engineering data, due to cloud computing benefits such as increased computing speed, scalability, flexibility, availability, and cost efficiency. It compares the performances of five classification algorithms (decision forest, neural network, support vector machine, logistic regression, and Bayes point machine) in the cloud in terms of both accuracy and runtime efficiency. It presents experimental studies conducted on five different real-world software engineering data related to the various software engineering tasks, including software defect prediction, software quality evaluation, vulnerability analysis, issue lifetime estimation, and code readability prediction. Experimental results show that the cloud is a powerful platform to build data mining applications for software engineering