ABSTRACT
Natural plant-derived antimicrobial nanocoatings have been synthesized by mixing brewed tea with cinnamaldehyde oil. Concurrent addition of copper or silver salts produces hybrid tea-cinnamaldehyde-copper or tea-cinnamaldehyde-silver nanocoatings, respectively. Tea-cinnamaldehyde, tea-cinnamaldehyde-copper, and tea-cinnamaldehyde-silver coatings are all found to display strong antibacterial efficacy against both Gram-negative Escherichia coli and Gram-positive Staphylococcus aureus (Log10 Reduction = 8.44 and 7.90, respectively). Tea-cinnamaldehyde-copper and tea-cinnamaldehyde-silver hybrid nanocoatings deposited onto nonwoven polypropylene provide 98.6 and 99.8% deactivation, respectively, toward murine coronavirus MHV-A59 (a potential surrogate for COVID-19 global pandemic coronavirus SARS-CoV-2). Key advantages of this approach are that the coating fabrication involves just a single step, utilizes cheap reagents (which are widely available over the counter to the general public), does not require any equipment apart from a container, and the coatings spontaneously adhere to a variety of substrate materials (including silicon, glass, polyester, nonwoven polypropylene, poly(tetrafluoroethylene), and cotton). Tea is one of the most ubiquitous beverages in the world, meaning that these antimicrobial coatings could be produced locally almost anywhere and by anyone without the need for any specialized technical training or expertize (for example, at remote field hospitals during humanitarian crises and in low-income countries). © 2021 The Authors. Published by American Chemical Society.
ABSTRACT
Background: Patient safety concerns that arose during COVID-19, related to blood shortages at a large oncological transfusion center, foregrounded the need for predictive modeling tools to optimize blood product inventory control. A maximum surgical blood ordering schedule (MSBOS), is a tool used to assist clinicians in predicting intraoperative blood usage based on retrospective historical data within an institution. Although MSBOS proves to be valuable, it is rudimentary in nature. Not only is data collection cumbersome but the data generated may not reflect current surgical practices and inter-patient variability may skew procedural averages. Predictive blood modeling is contingent generation of a digital health dashboard (DHB). DHB are electronically embedded in the electronic health record (EHR) to collect perioperative data. Coupling the generated informatics (patient demographics, diagnosis, laboratory results, procedural type, medications/supplements, surgeon) with machine learning allows for creation of patient centered predictive blood modeling algorithms and better inventory control. Methods: To characterize blood use across various procedures at our institution, we engaged information technology specialists to create a Web Intelligence report by integrating data from both an EHR and a lab information system (LIS) into a single repository. Information obtained illuminated a master procedure list, blood product usage patterns, and characterized patient demographics during January-March, 2020. Data is continuously extracted to create a perpetually updated MSBOS while secondarily functioning to cultivate data for future predictive machine learning algorithms. Results: Data analysis demonstrated 5598 procedures were performed during the first quarter of 2020. Procedures not transfused with packed red blood cells (pRBCs) totaled to 4,156 and 1,442 had a greater than or equal to 10% probability of requiring pRBCs. Our current practices reflected our overall cross-match to transfusion ratio ( C:T) was 5.4 to 1. Concerted collaboration, resulting in preparation of pre-surgical blood product orders according laboratory generated MSBOS schedule could decrease the C:T to 1.7 to 1. Additionally, high intraprocedural pRBCs variability was identified in current procedural subtypes. Conclusions: Traditionally generated MSBOS are functionally limited and may not be reflective of current surgical practices. Additionally, inter-patient variability may distort some procedural type guidance. Creating an integrated data report, eliminates some of the inherent limitations of traditional MSBOS. Moving forward, the cultivated data if coupled with machine learning has the potential to create transferable proprietary algorithms that proactively predict individual patient transfusion needs.
ABSTRACT
Background: In response to the COVID-19 pandemic, a dedicated intensive care unit for patients infected with SARS-CoV-2 was created at our institution. We noticed a marked increase in the number of blood cultures positive for coagulase-negative Staphylococcus species (CoNS) that highlights unique challenges that arise with the creation of new units and workflows. Methods: We reviewed all blood culture results from the COVID-19 intensive care unit (CoVICU) from April 15 to May 29. We reviewed all blood cultures taken from the oncology ward, medical intensive care unit (MICU), and emergency department (ED) for the same time frame as a comparison. We calculated contamination rates, using the clinical microbiology laboratory criteria for possible contaminants based on species and number of positive blood cultures. Results: There were 324 total blood cultures collected from the CoVICU with 27/324 (8.3%) positive for organisms deemed contaminant, 10/324 (3.1%) were positive considered bloodstream infections (BSI);the ratio of BSI:contaminant was 1:2.7. For the MICU, ED, and oncology units contamination rates were 2/197 (1%), 33/747 (4.4%), and 2/334 (0.6%), respectively;and the ratio of BSI:contaminant was 5:1, 2.2:1, and 17.5:1, respectively. There was a significant relationship between contamination rates and unit, X2(3, N = 1602) = 30.85, p < 0.001. Conclusion: Upon investigation, peripheral blood draw kits were not stocked in the CoVICU. Additionally, certain components of standard work for blood culture collection (e.g. glove exchange) could not be performed per usual practice due to isolation precautions. Peripheral blood draws were routinely performed by nurses in CoVICU and MICU while phlebotomy performed these in other comparison units. We suspect that lack of availability of blood draw kits and disruption of typical workflow in isolation rooms contributed to an unusually high number of contaminated blood cultures among patients admitted to the CoVICU. Notably, the CoVICU and MICU providers were the same pool of caregivers, further supporting a process issue related to isolation precautions. Institutions should be aware of the need for extra attention to supply chain management and examination of disruption to standard work that arise in the management of COVID-19 patients.
ABSTRACT
The COVID-19 pandemic and phased nationwide lockdown have impacted negatively on individuals with tuberculosis (TB) and routine TB services. Through a literature review and the perspective of members of a national TB Think Tank task team, we describe the impact of the pandemic and lockdown on TB patients and services as well as the potential long-term setback to TB control in South Africa (SA). Strategies to mitigate risk and impact are explored, together with opportunities to leverage synergies from both diseases to the benefit of the National TB Programme (NTP). With the emergence of COVID-19, activities to address this new pandemic have been prioritised across all sectors. Within the health system, the health workforce and resources have been redirected away from routine services towards the new disease priority. The social determinants of health have deteriorated during the lockdown, potentially increasing progression to TB disease and impacting negatively on people with TB and their households, resulting in additional barriers to accessing TB care, with early reports of a decline in TB testing rates. Fewer TB diagnoses, less attention to adherence and support during TB treatment, poorer treatment outcomes and consequent increased transmission will increase the TB burden and TB-related mortality. People with TB or a history of TB are likely to be vulnerable to COVID-19. Modifications to current treatment practices are suggested to reduce visits to health facilities and minimise the risks of COVID-19 exposure. The COVID-19 pandemic has the potential to negatively impact on TB control in TB-endemic settings such as SA. However, there are COVID-19-related health systems-strengthening developments that may help the NTP mitigate the impact of the pandemic on TB control. By integrating TB case finding into the advanced screening, testing, tracing and monitoring systems established for COVID-19, TB case finding and linkage to care could increase, with many more TB patients starting treatment. Similarly, integrating knowledge and awareness of TB into the increased healthcare worker and community education on infectious respiratory diseases, behavioural practices around infection prevention and control, and cough etiquette, including destigmatisation of mask use, may contribute to reducing TB transmission. However, these potential gains could be overwhelmed by the impact of increasing poverty and other social determinants of health on the burden of TB.