Microscopic entities, microorganisms that drastically affect human health need to be thoroughly investigated. A biofilm is an architectural colony of microorganisms, within a matrix of extracellular polymeric substance that they produce. Biofilm contains microbial cells adherent to one-another and to a static surface (living or non-living). Bacterial biofilms are usually pathogenic in nature and can cause nosocomial infections. The National Institutes of Health (NIH) revealed that among all microbial and chronic infections, 65% and 80%, respectively, are associated with biofilm formation. The process of biofilm formation consists of many steps, starting with attachment to a living or non-living surface that will lead to formation of micro-colony, giving rise to three-dimensional structures and ending up, after maturation, with detachment. During formation of biofilm several species of bacteria communicate with one another, employing quorum sensing. In general, bacterial biofilms show resistance against human immune system, as well as against antibiotics. Health related concerns speak loud due to the biofilm potential to cause diseases, utilizing both device-related and non-device-related infections. In summary, the understanding of bacterial biofilm is important to manage and/or to eradicate biofilm-related diseases. The current review is, therefore, an effort to encompass the current concepts in biofilm formation and its implications in human health and disease.

Background: There has been much interest in environmental temperature and race as modulators of Coronavirus disease-19 (COVID-19) infection and mortality. However, in the United States race and temperature correlate with various other social determinants of health, comorbidities, and environmental influences that could be responsible for noted effects. This study investigates the independent effects of race and environmental temperature on COVID-19 incidence and mortality in United States counties. Methods: Data on COVID-19 and risk factors in all United States counties was collected. 661 counties with at least 50 COVID-19 cases and 217 with at least 10 deaths were included in analyses. Upper and lower quartiles for cases/100,000 people and halves for deaths/100,000 people were compared with t-tests. Adjusted linear and logistic regression analyses were performed to evaluate the independent effects of race and environmental temperature. Results: Multivariate regression analyses demonstrated Black race is a risk factor for increased COVID-19 cases (OR=1.22, 95% CI: 1.09−1.40, P=0.001) and deaths independent of comorbidities, poverty, access to health care, and other risk factors. Higher environmental temperature independently reduced caseload (OR=0.81, 95% CI: 0.71−0.91, P=0.0009), but not deaths. Conclusions: Higher environmental temperatures correlated with reduced COVID-19 cases, but this benefit does not yet appear in mortality models. Black race was an independent risk factor for increased COVID-19 cases and deaths. Thus, many proposed mechanisms through which Black race might increase risk for COVID-19, such as socioeconomic and healthcare-related predispositions, are inadequate in explaining the full magnitude of this health disparity.

Coxiella burnetii causes query (Q) fever, an important zoonotic disease with worldwide significance. The role of environment in the ecology of C. burnetti, and its influence on seroconversion in animals has not been elucidated in Pakistan. We carried out a cross-sectional study in Punjab province to (1) determine the prevalence and distribution of C. burnetii in soil using an ISIIII gene-based real time-polymerase chain reaction (RT-PCR) assay, (2) analyze association between the occurrence of C. burnetii in soil and its predictors i.e. soil characteristics (macro- and micro-nutrients) and several likely risk factors including the seroconversion in small ruminants at places where its genome had or had not been detected, and (3) predict homology and genetic diversity of the identified strains using sequences originated from different hosts worldwide. A total of 2425 soil samples from nine districts of Punjab province were processed. C. burnetii DNA was detected in 47 samples (1.94%, 95% CI: ±0.55) originating from 35 villages of studied districts (7.22%, 95% CI: ±2.30). The highest prevalence was found in Attock (7.11%, 95% CI: ±3.36), followed by Lahore (4.83%, 95% CI: ±3.49), Sahiwal (4.70%, 95% CI: ±2.6), Dera Ghazi Khan (2.33%, 95% CI: ±2.02), Faisalabad (1.35%, 95% CI: ±1.18) and Sheikhupura (0.68%, 95% CI: ±0.94). The odds of detecting bacterial DNA in soil was increased with a unit increase in organic matter [2.511 (95% CI: 1.453-4.340), p=0.001] and sodium [1.013 (95% CI: 1.005-1.022), p=0.001], whereas, calcium [0.984 (95% CI: 0.975-0.994), p=0.002] and potassium [0.994 (95% CI: 0.990-0.999), p=0.011] had protective effect where a unit increase in each analyte decreased odds for its occurrence by 1.0% approximately. Likewise, for categorical variables (risk factors), the odds of detecting C. burnetii were higher at locations >500m away from a main road [1.95 (95% CI: 1.06-3.78), p=0.04]. The enzyme-linked immunosorbent assay (ELISA) revealed an increased prevalence of antibodies in sheep (17.9%, 95% CI: ±5.54) compared with goats (16.4%, 95% CI: ±4.34). When determining the association between soil DNA and C. burnetii antibodies in small ruminants, the odds of detecting these antibodies were significant in sheep at the livestock barns [2.81 (95% CI: 1.20-7.37), p=0.02]. The IS1111 gene-based sequence analysis revealed a clustering of the DNA into two distinct groups with much genetic divergence (0.76-68.70%): the first group that contained sequences from Lahore district clustered with human and buffalo origin isolates, whereas the second group that contained the sequences from the remaining study districts clustered with goat-, rodent- and human-origin isolates. This study provides the first evidence of the presence of C. burnetii in the environment in Punjab province, Pakistan. Future studies are needed to ascertain the bacteria's molecular epidemiology over a wide geographical area, type the isolates, and evaluates the potential risks to human populations, particularly farmers and veterinarians.

Background: Policymakers have employed various non-pharmaceutical interventions (NPIs)such as stay-at-home orders and school closures to limit the spread of Coronavirus disease . However, these measures are not without cost, and careful analysis is critical to quantify their impact on disease spread and guide future initiatives. This study aims to measure the impact of NPIs on the effective reproductive number (Rt) and other COVID-19 outcomes in U.S. states. Methods:In order to standardize the stage of disease spread in each state, this study analyzes the weeks immediately after each state reached 500 cases. The primary outcomes were average Rt in the week following 500 cases and doubling time from 500 to 1000 cases. Linear and logistic regressions were performed in R to assess the impact of various NPIs while controlling for population density, GDP, and certain health metrics. This analysis was repeated for deaths with doubling time from 50 to 100 deaths and included several healthcare infrastructure control variables. Results:States that had a stay-at-home order in place at the time of their 500th case are associated with lower average Rt the following week compared to states without a stay-at-home order (p < 0.001) and are significantly less likely to have an Rt>1 (OR 0.07, 95% CI 0.01 to 0.37, p = 0.004). These states also experienced a significantly longer doubling time from 500 to 1000 cases (HR 0.35, 95% CI 0.17 to 0.72, p = 0.004). States in the highest quartile of average time spent at home were also slower to reach 1000 cases than those in the lowest quartile (HR 0.18, 95% CI 0.06 to 0.53, p = 0.002).Discussion: Few studies have analyzed the effect of statewide stay-at-home orders, school closures, and other social distancing measures in the U.S., which has faced the largest COVID-19 case burden. States with stay-at-home orders have a 93% decrease in the odds of having a positive Rt at a standardized point in disease burden. States that plan to scale back such measures should carefully monitor transmission metrics.

Background: Various non-pharmaceutical interventions (NPIs) such as stay-at-home orders and school closures have been employed to limit the spread of Coronavirus disease . This study measures the impact of social distancing policies on COVID-19 transmission in US states during the early outbreak phase to assess which policies were most effective. Methods:To measure transmissibility, we analyze the average effective reproductive number (R t ) in each state the week following its 500th case and doubling time from 500 to 1000 cases. Linear and logistic regressions were performed to assess the impact of various NPIs while controlling for population density, GDP, and certain health metrics. This analysis was repeated for deaths with doubling time to 100 deaths with several healthcare infrastructure control variables.Results: States with stay-at-home orders in place at the time of their 500th case were associated with lower average R t the following week compared to states without them (p<0.001) and significantly less likely to have an R t >1 (OR 0.07, 95% CI 0.01−0.37, p = 0.004). These states also experienced longer doubling time from 500 to 1000 cases (HR 0.35, 95% CI 0.17 −0.72, p = 0.004). States in the highest quartile of average time spent at home were also slower to reach 1000 cases than those in the lowest quartile (HR 0.18, 95% CI 0.06−0.53, p = 0.002).Conclusions: Stay-at-home orders had the largest effect of any policy analyzed. Multivariate analyses with cellphone tracking data suggest social distancing adherence drives these effects. States that plan to scale back such measures should carefully monitor transmission metrics.