Introduction:Modifiable Areal Unit Problems are a major source of spatial uncertainty, but their impact on infectious diseases and epidemic detection is unknown. Methods:CMS claims (2016-2019) which included infectious disease codes learned through Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) were extracted and analysed at two different units of geography; states and 'home to work commute extent' mega regions. Analysis was per member per month. Rolling average above the series median within geography and agent of infection was used to assess peak detection. Spatial random forest was used to assess region segmentation by agent of infection. Results:Mega-regions produced better peak discovery for most, but not all agents of infection. Variable importance and Gini measures from spatial random forest show agent-location discrimination between states and regions. Conclusion:Researchers should defend their geographic unit of report used in peer review studies on an agent by-agent basis.
This study was conducted to determine the secondary metabolites and antibacterial potential of the leaf extracts Newbouldia laevis on some uropathogen strains and also to carry out phytochemical screening of the sample. The antibacterial activity of N. laevis leaves extracted with methanol, hot and cold water were evaluated against medically important bacteria strains of Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus saprophyticus and Enterococcus faecalis. The results showed a remarkable inhibition of the bacteria growth against the tested organisms with Staphylococcus saprophyticus had highest activity (20 mm). In general, all the tested organisms showed a significant activity with the extracts. The conventional antibiotics used against these organisms showed that all the isolates were resistant to augmentin, ceftazidime, cefuroxime, and cloxacillin. The phytochemical analysis of the plants was carried out. The microbial activity of the Newbouldia laevis was due to the presence of various secondary metabolites. The GC-MS analysis showed that the most abundant phytochemical in the extract is linoleoyl chloride (76.3%). Hence, these plants can be used to discover bioactive natural products that may serve as leads in the development of new pharmaceuticals research activities.
In fourteen months the number of infected people with SARS-COV-2 has reached more than 159 millions and from those more than 3 million have resulted in death. There is now a consensus that the airborne saliva droplets, that are produced while speaking, coughingor sneezing by infected people is one of the most likely routes of transmission of the corona virus disease (COVID-19). The expelled droplets can measure between 0.4 and 450 μm in diameter. Once the droplets are in the air, they are subject to the gravitational, and air frictional forces that dictate their motion. Through exhaustive aerodynamic studies it has been shown that the aerosol droplets (less than 5 μm) can remain in the environment for very long periods of time and be transported by air currents. Larger droplets take shorter times and land within a circle of 1.5 to 2 m radius. Of key importance is the droplet size distribution and many efforts have been done to characterize this. By modeling the production of the number of saliva droplets with log-log Gaussian distributions, the virial load of the expelled droplets is estimated as a function of droplet size. Assuming a constant virus density, we estimate the amount of virus delivered into the environment. The use of face masks reduce drastically the amount of droplets emitted to the air by an infected person and to be inhaled by a healthy one. We emphasize the great importance of using adequate face protection to minimize COVID-19, transmission and to reduce the death toll due to this disease.
Background: Children’s blood stream infections mainly due to resistant pathogens found to be a higher risk of hospital mortality in low and middle income countries. This study intended to assess the bacterial profile and drug resistance of isolates of blood stream infection among under- 5 children in Southern Ethiopia. Methods: Retrospectively lab recorded data’s were retrieved to assess the bacterial isolates and rates antibiotic resistance at Hawassa University Comprehensive Specialized Hospital. Results: Of 323 bloods culture performed 116 (35.9%) were positive for potential bacterial BSI. Positivity of blood culture decrease with age, hence neonates blood stream infection stream infection were higher than other age groups (OR, 3; 95% CI 1.5–5.1; p=0.001). The major gram positive isolate CoNS and S. aureus have shown highest level of resistance to penicillin 61.3% and tetracycline 78.8% whereas less level of resistance was reported to Meropenem 6.4%, Ceftriaxone 13% and doxycycline 13%. Although most of gram negatives isolates were resistant for tested antibiotics, K. pneumoniae has shown leaser resistance to cefotaxime and chloramphenicol. Conclusion: A high level of blood stream infection accompanied by high drug resistant isolates was reported. The great burden in neonate than other children was reported. As a result to reduce the burden the hospital administrators need to facilities effective infection prevention and control programmes, effective hygiene practices and well-functioning environment along with educating patient about proper hygiene practices.
After a year of hard battling with the novel coronavirus SARS Cov-2, the COVID-19 pandemic continues had a catastrophic effect on society and health worldwide. This pandemic has changed labor and economic relations in almost every country in the world, and the investment that has been made in the development of new treatment protocols and the creation of vaccines has been enormous. Important laboratories, hospitals and research centers around the world have been fighting against SARS-Cov-2, and within these researches computer vision has played a prominent role. The main aim of this work is to carry out a reflection on the enhancement of the microscopic images of the novel coronavirus SARS-Cov-2 from the results obtained and published. We will analyze the effectiveness of the algorithms proposed to highlight the S-spikes, and we will detail why deep learning, despite the popularity achieved, in this case was not beneficial.
