Wildfires affect soils in multiple ways, leading to numerous challenges for colonizing microorganisms. Although it is thought that fire-adapted microorganisms lie at the forefront of postfire ecosystem recovery, the specific strategies that these organisms use to thrive in burned soils remain largely unknown. Through bioactivity screening of bacterial isolates from burned soils, we discovered that several Paraburkholderia spp. isolates produced a set of unusual rhamnolipid surfactants with a natural methyl ester modification. These rhamnolipid methyl esters (RLMEs) exhibited enhanced antimicrobial activity against other postfire microbial isolates, including pyrophilous Pyronema fungi and Amycolatopsis bacteria, compared to the typical rhamnolipids made by organisms such as Pseudomonas spp. RLMEs also showed enhanced surfactant properties and facilitated bacterial motility on agar surfaces. In vitro assays further demonstrated that RLMEs improved aqueous solubilization of polycyclic aromatic hydrocarbons, which are potential carbon sources found in char. Identification of the rhamnolipid biosynthesis genes in the postfire isolate, Paraburkholderia kirstenboschensis str. F3, led to the discovery of rhlM, whose gene product is responsible for the unique methylation of rhamnolipid substrates. RhlM is the first characterized bacterial representative of a large class of integral membrane methyltransferases that are widespread in bacteria. These results indicate multiple roles for RLMEs in the postfire lifestyle of Paraburkholderia isolates, including enhanced dispersal, solubilization of potential nutrients, and inhibition of competitors. Our findings shed new light on the chemical adaptations that bacteria employ to navigate, grow, and outcompete other soil community members in postfire environments.

BACKGROUND: There are few studies to date of interventions to increase viral hepatitis screening among Asian Americans, who have high rates of chronic hepatitis B (HBV) infection. OBJECTIVE: To develop, implement, and test the efficacy of a mobile application (Hepatitis App) delivered in four languages to increase HBV screening among Asian Americans. DESIGN: Cluster-randomized clinical trial. PARTICIPANTS: Four hundred fifty-two Asian American patients ≥ 18 years of age, who had no prior HBV testing, and received primary care within two healthcare systems in San Francisco, CA. INTERVENTIONS: The intervention group received the Hepatitis App, delivering interactive video education on viral hepatitis in English, Cantonese, Mandarin, or Vietnamese and a provider printout (Provider Alert) and Provider Panel Notification. The comparison group received a mobile application delivering nutrition and physical activity education and Provider Panel Notification. MAIN MEASURES: Primary outcomes were patient-provider discussion about HBV and documentation of a HBV screening test within 3 months post-intervention. Secondary outcome was documentation of an order for a HBV screening test. KEY RESULTS: Participants had a mean age of 57 years and were 64% female, 80% foreign-born, and 44% with limited English fluency. At post-visit, over 80% of intervention participants reported they liked using the Hepatitis App. At 3-month follow-up, the intervention group was more likely than the comparison group (all P < 0.001) to have discussed HBV with their provider (70% vs.16%), have a HBV test ordered (44% vs.10%), and receive a HBV test (38% vs.8%). In multivariable analyses, the intervention odds ratio for HBV test ordering was 7.6 (95% CI: 3.9, 14.8) and test receipt was 7.5 (95% CI: 3.6, 15.5). CONCLUSIONS: A multi-lingual educational intervention using a mobile application in primary care clinics was well received by Asian American patients, enhanced patient-provider communication about HBV, and increased HBV screening. Technology can improve healthcare quality among Asian Americans. TRIAL REGISTRATION: ClinicalTrials.gov NCT02139722 ( https://clinicaltrials.gov/ct2/show/NCT02139722 ).

Background

Hepatitis C virus (HCV) screening remains suboptimal. We assessed the efficacy of a mobile application and provider alert in enhancing HCV screening among Asian Americans.

Methods

A secondary analysis of a cluster-randomized clinical trial was performed during the birth cohort screening era to assess the efficacy of a Hepatitis App (intervention), a multilingual mobile application delivering interactive video education on viral hepatitis and creating a Provider Alert printout, at primary care clinics within 2 healthcare systems in San Francisco from 2015 to 2017. A comparison group received usual care and a similar intervention on nutrition and physical activity. The outcome was electronic health record (EHR) documentation of HCV screening along with patient-provider communication about testing and test ordering.

Results

Four hundred fifty-two participants (mean age 57 years, 36% male, 80% foreign-born) were randomized by provider clusters to the intervention (n = 270) or comparison groups (n = 182). At 3-month follow up, the intervention group was more likely than the comparison group to be aware of HCV (75% vs 59%, P = .006), to discuss HCV testing with their providers (63% vs 13%, P < .001), to have HCV testing ordered (39% vs 10%, P < .001), and to have EHR-verified HCV testing (30% vs 6%, P < .001). Within the intervention group, being born between 1945 and 1965 (odds ratio, 3.15; 95% confidence interval, 1.35-7.32) was associated with increased HCV testing.

Conclusions

The Hepatitis App delivered in primary care settings was effective in increasing HCV screening in a socioeconomically diverse Asian American cohort. This highlights the importance of mobile technology as a patient-centered strategy to address gaps in HCV care.

Objectives

To determine how self-reported level of exposure to patients with novel coronavirus 2019 (COVID-19) affected the perceived safety, training and well-being of residents and fellows.

Methods

We administered an anonymous, voluntary, web-based survey to a convenience sample of trainees worldwide. The survey was distributed by email and social media posts from April 20th to May 11th, 2020. Respondents were asked to estimate the number of patients with COVID-19 they cared for in March and April 2020 (0, 1-30, 31-60, >60). Survey questions addressed (1) safety and access to personal protective equipment (PPE), (2) training and professional development and (3) well-being and burnout.

Results

Surveys were completed by 1420 trainees (73% residents, 27% fellows), most commonly from the USA (n=670), China (n=150), Saudi Arabia (n=76) and Taiwan (n=75). Trainees who cared for a greater number of patients with COVID-19 were more likely to report limited access to PPE and COVID-19 testing and more likely to test positive for COVID-19. Compared with trainees who did not take care of patients with COVID-19 , those who took care of 1-30 patients (adjusted OR [AOR] 1.80, 95% CI 1.29 to 2.51), 31-60 patients (AOR 3.30, 95% CI 1.86 to 5.88) and >60 patients (AOR 4.03, 95% CI 2.12 to 7.63) were increasingly more likely to report burnout. Trainees were very concerned about the negative effects on training opportunities and professional development irrespective of the number of patients with COVID-19 they cared for.

Conclusion

Exposure to patients with COVID-19 is significantly associated with higher burnout rates in physician trainees.

The use of computational modeling algorithms to guide the design of novel enzyme catalysts is a rapidly growing field. Force-field based methods have now been used to engineer both enzyme specificity and activity. However, the proportion of designed mutants with the intended function is often less than ten percent. One potential reason for this is that current force-field based approaches are trained on indirect measures of function rather than direct correlation to experimentally-determined functional effects of mutations. We hypothesize that this is partially due to the lack of data sets for which a large panel of enzyme variants has been produced, purified, and kinetically characterized. Here we report the kcat and KM values of 100 purified mutants of a glycoside hydrolase enzyme. We demonstrate the utility of this data set by using machine learning to train a new algorithm that enables prediction of each kinetic parameter based on readily-modeled structural features. The generated dataset and analyses carried out in this study not only provide insight into how this enzyme functions, they also provide a clear path forward for the improvement of computational enzyme redesign algorithms.

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations.

BACKGROUND & AIMS: Hepatocellular carcinoma (HCC) surveillance rates are suboptimal in clinical practice. We aimed to elicit providers opinions on the following aspects of HCC surveillance: preferred strategies, barriers and facilitators, and the impact of a patients HCC risk on the choice of surveillance modality. METHODS: We conducted a web-based survey among gastroenterology and hepatology providers (40% faculty physicians, 21% advanced practice providers, 39% fellow-trainees) from 26 US medical centers in 17 states. RESULTS: Of 654 eligible providers, 305 (47%) completed the survey. Nearly all (98.4%) of the providers endorsed semi-annual HCC surveillance in patients with cirrhosis, with 84.2% recommending ultrasound ± alpha fetoprotein (AFP) and 15.4% recommending computed tomography (CT) or magnetic resonance imaging (MRI). Barriers to surveillance included limited HCC treatment options, screening test effectiveness to reduce mortality, access to transportation, and high out-of-pocket costs. Facilitators of surveillance included professional society guidelines. Most providers (72.1%) would perform surveillance even if HCC risk was low (≤0.5% per year), while 98.7% would perform surveillance if HCC risk was ≥1% per year. As a patients HCC risk increased from 1% to 3% to 5% per year, providers reported they would be less likely to order ultrasound ± AFP (83.6% to 68.9% to 57.4%; P < .001) and more likely to order CT or MRI ± AFP (3.9% to 26.2% to 36.1%; P < .001). CONCLUSIONS: Providers recommend HCC surveillance even when HCC risk is much lower than the threshold suggested by professional societies. Many appear receptive to risk-based HCC surveillance strategies that depend on patients estimated HCC risk, instead of our current one-size-fits all strategy.