MedNexus
2021年 · 第03卷第01期
MedNexus
The coronavirus disease 2019 (COVID-19) pandemic represents an enormous challenge to all countries, regardless of their development status. The manipulation of its etiologic agent SARS-CoV-2 requires a biosafety containment level 3 laboratories (BSL-3) to understand virus biology and in vivo pathogenesis as well as the translation of new knowledge into the preclinical development of vaccines and antivirals. As such, BSL-3 facilities should be considered an integral part of any public health response to emerging infectious disease prevention, control and management. Differently from BSL-2, BSL-3 units vary considerably along the range from industrialized to the least developed countries. Innovative Developing Countries (IDCs) such as Brazil, which excelled at controlling the 2015-2017 Zika epidemic, had to face a serious flaw in its disease control and prevention structure: the scarcity and uneven geographic distribution of its BSL-3 facilities, including those for preclinical animal experimentation.
Following the emergence of COVID-19 outbreak, numbers of studies have been conducted to curtail the global spread of the virus by identifying epidemiological changes of the disease through developing statistical models, estimation of the basic reproduction number, displaying the daily reports of confirmed and deaths cases, which are closely related to the present study. Reliable and comprehensive estimation method of the epidemiological data is required to understand the actual situation of fatalities caused by the epidemic. Case fatality rate (CFR) is one of the cardinal epidemiological parameters that adequately explains epidemiology of the outbreak of a disease. In the present study, we employed two statistical regression models such as the linear and polynomial models in order to estimate the CFR, based on the early phase of COVID-19 outbreak in Nigeria (44 days since first reported COVID-19 death). The estimate of the CFR was determined based on cumulative number of confirmed cases and deaths reported from 23 March to 30 April, 2020. The results from the linear model estimated that the CFR was 3.11% (95% CI: 2.59% - 3.80%) with R2 value of 90% and p-value of < 0.0001. The findings from the polynomial model suggest that the CFR associated with the Nigerian outbreak is 3.0% and may range from 2.23% to 3.42% with R2 value of 93% and p-value of <0.0001. Therefore, the polynomial regression model with the higher R2 value fits the dataset well and provides better estimate of CFR for the reported COVID-19 cases in Nigeria.
In January 2019, the fourth rabies case caused by organ transplantation was noticed in China, with the conditions of one per year for the recent four years. Different from the previous cases, there were no definite epidemiological histories of exposure or rabies-related symptoms from this patient. This case strongly supports the call for the legislation of establishing a national-level management that will incorporate the screening programs on donors prior to the practice of organ transplantation to reduce the risks on rabies caused by organ transplantation.
As the entire world is under the grip of the coronavirus disease 2019 (COVID-19), and as many are eagerly trying to explain the origins of the virus and cause of the pandemic, it is imperative to place more attention on related potential biosafety risks. Biology and biotechnology have changed dramatically during the last ten years or so. Their reliance on digitization, automation, and their cyber-overlaps have created new vulnerabilities for unintended consequences and potentials for intended exploitation that are mostly under-appreciated. This study summarizes and elaborates on these new cyberbiosecurity challenges, (1) in terms of comprehending the evolving threat landscape and determining new risk potentials, (2) in developing adequate safeguarding measures, their validation and implementation, and (3) specific critical risks and consequences, many of them unique to the life-sciences. Drawing other's expertise and my previous work, this article reviews and critically interprets our current bio-economy situation. The goal is not to attribute causative aspects of past biosafety or biosecurity events, but to highlight the fact that the bioeconomy harbors unique features that have to be more critically assessed for their potential to unintentionally cause harm to human health or environment, or to be re-tasked with an intention to cause harm. It is concluded with recommendations that will need to be considered to help ensure converging and emerging biorisk challenges, in order to minimize vulnerabilities to the life-science enterprise, public health, and national security.
The wide use and abuse of antibiotics could make antimicrobial resistance (AMR) an increasingly serious issue that threatens global health and imposes an enormous burden on society and the economy. To avoid the crisis of AMR, we have to fundamentally change our approach. Artificial intelligence (AI) represents a new paradigm to combat AMR. Thus, various AI approaches to this problem have sprung up, some of which may be considered successful cases of domain-specific AI applications in AMR. However, to the best of our knowledge, there is no systematic review illustrating the use of these AI-based applications for AMR. Therefore, this review briefly introduces how to employ AI technology against AMR by using the predictive AMR model, the rational use of antibiotics, antimicrobial peptides (AMPs) and antibiotic combinations, as well as future research directions.
Antimicrobial resistance leads to failure of clinical antimicrobial therapy, and has raised urgent global public health concern. Humans can acquire antimicrobial resistance from drugs through the food chain or the environment (contaminated water, air, soil, or manure). While antimicrobials have been regular supplements in animal feed that maintain health and improve productivity of livestock, their over-use in feeding forage has led to a rise in antibacterial resistance. This review summarizes the current use of antimicrobials in livestock, the harmful effects of antimicrobial resistance, and the comprehensive combat measures.
South Asian (SA) countries have been fighting with the pandemic novel coronavirus disease 2019 (COVID-19) since January 2020. Earlier, the country-specific descriptive study has been done. Nevertheless, as transboundary infection, the border sharing, shared cultural and behavioral practice, effects on the temporal and spatial distribution of COVID-19 in SA is still unveiled. Therefore, this study has been revealed the spatial hotspot along with descriptive output on different parameters of COVID-19 infection. We extracted data from the WHO and the worldometer database from the onset of the outbreak up to 15 May, 2020. Europe has the highest case fatality rate (CFR, 9.22%), whereas Oceania has the highest (91.15%) recovery rate from COVID-19. Among SA countries, India has the highest number of cases (85,790), followed by Pakistan (38,799) and Bangladesh (20,065). However, the number of tests conducted was minimum in this region in comparison with other areas. The highest CFR was recorded in India (3.21%) among SA countries, whereas Nepal and Bhutan had no death record due to COVID-19 so far. The recovery rate varies from 4.75% in the Maldives to 51.02% in Sri Lanka. In Bangladesh, community transmission has been recorded, and the highest number of cases were detected in Dhaka, followed by Narayanganj and Chattogram. We detected Dhaka and its surrounding six districts, namely Gazipur, Narsingdi, Narayanganj, Munshiganj, Manikganj, and Shariatpur, as the 99% confidence-based hotspot where Faridpur and Madaripur district as the 95% confidence-based spatial hotspots of COVID-19 in Bangladesh. However, we did not find any cold spots in Bangladesh. We identified three hotspots and three cold spots at different confidence levels in India. Findings from this study suggested the "Test, Trace, and Isolation" approach for earlier detection of infection to prevent further community transmission of COVID-19.
The aim of this study was to evaluate the performance of an assay using dried plasma spot (DPS) and dried blood spot (DBS) samples for the serological detection of anti-hepatitis C virus (HCV) antibodies. Between January and July 2019, plasma, DPS and DBS specimens were collected from individuals at high-risk for HCV infection. Samples were tested for anti-HCV by ELISA, and the performance of DPS and DBS specimens was examined using results from the plasma testing, as the standard. Blood samples were collected from 329 persons, including 129 men who have sex with men and 200 intravenous drug users. Results from the plasma testing indicated that 118 samples (59.0%) were HCV positive. Data from the DPS sample testing showed sensitivity as 99.2% (95% confidence interval [CI]: 0.95-1.00) and specificity as 100% (95% CI: 0.98-1.00) for HCV detection, with Kappa of 99.3% (95% CI: 0.98-1.00) while in DBS sample testing the sensitivity as 98.3% (95% CI: 0.93-1.00) and specificity as 100% (95% CI: 0.98-1.00), with Kappa of 98.7% (95% CI: 0.97-1.00), respectively. Spearman’s correlation coefficients for the comparisons between plasma and DPS specimen, plasma and DBS specimens, DPS and DBS specimens were 0.857, 0.750, and 0.739, respectively. Compared with the results in plasma, 1 sample was not detected using the DPS specimens, and 2 samples were failed for the positive detection, using the DBS specimens. Both DPS and DBS samples were promising alternatives to plasma, for the detection of anti-HCV antibodies.
Vaporized hydrogen peroxide (VHP) is a highly active disinfectant, and VHP decontamination systems have been widely applied in hospitals, microbiological laboratories, and pharmaceutical industries. However, the decomposition of VHP into non-toxic by-products is essential. Evaluation of the disinfection efficacy of VHP is crucial to ensuring the reliability of VHP disinfection and controlling microbial contamination. In this study, a rapid and sensitive strategy is proposed to evaluate the efficacy of VHP in surface disinfection by detecting the survived and killed bacteria from VHP-exposed biological indicators (BIs). A dual-channel solid-phase cytometer is designed, and fluorescent dyes are used as indicators to automatically and accurately distinguish live cells from dead cells in the mixtures of bacteria. To verify the availability and effectiveness of the laser scanning cytometry, experiments on its application in estimating the efficacy of VHP disinfection practice have been carried out in this study, and its estimation effect compared with that of the traditional plate counting method. Results show that the proposed assay might distinctly identify live or killed cells labeled by green and red fluorescent dyes and examined the disinfection efficacy in 30 min by calculating the bactericidal rate. Compared with the plate counting method, the proposed approach is accurate and practical, with an average detection efficiency of 98.47% ± 1.55%. Moreover, an excellent correlation between the concentrations of B. subtilis var niger (ATCC 9372) measured by the proposed detection system and by the plate counting method is noticed (R2= 0.9971), indicating that this approach had advantages in the detection of trace microorganisms. To summarize, the proposed strategy appears practical and significant in many fields in which microbial counting and identification are required.
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