Evaluation of protein extraction methods to improve meta-proteomics analysis of treated wastewater biofilms

Metaproteomics can be used to study functionally active biofilm-based bacterial populations in reclaimed water distribution systems, which in turn result in bacterial regrowth that impacts the water quality. However, existing protein extraction methods have differences in their protein recovery and have not been evaluated for their efficacies in reclaimed water biofilm samples. In this study, we first evaluated six different protein extraction methods with diverse chemical and physical properties on a mixture of bacterial cell culture. Based on a weighting scores-based evaluation, the extraction protocols in order of decreasing performance are listed as B-PER > RIPA > PreOmics > SDS > AllPrep > Urea. The highest four optimal methods on cell culture were further tested against treated wastewater non-chlorinated and chlorinated effluent biofilms. In terms of protein yield, our findings showed that RIPA performed the best; however, the highest number of proteins were extracted from SDS and PreOmics. Furthermore, SDS and PreOmics worked best to rupture gram-positive and gram-negative bacterial cell walls. Considering the five evaluation factors, PreOmics obtained highest weighted score, indicating its potential effectiveness in extracting proteins from biofilms. This study provides the first insight into evaluating protein extraction methods to facilitate metaproteomics for complex reclaimed water matrices.

Telehealth Impact in Frontier Critical Access Hospitals: Mixed Methods Evaluation

BACKGROUND

Frontier areas are sparsely populated counties in states where 65% of the counties have 6 or fewer residents per square mile. Residents access primary care at critical access hospitals (CAHs) located in these rural communities but must travel great distances for specialty care. Telehealth could address access challenges; however, there are barriers to broader use, including reimbursement and the need for practical implementation support. The Centers for Medicare & Medicaid Services implemented the Frontier Community Health Integration Project (FCHIP) Demonstration to assess the impact of telehealth payment change and technical assistance to adopt and sustainably use telehealth for CAHs treating Medicare fee-for-service patients in frontier regions.

OBJECTIVE

We evaluated the impact of the FCHIP Demonstration telehealth payment change and technical assistance on telehealth adoption and ongoing use using a mixed methods approach.

METHODS

We conducted a mixed methods evaluation of the 8 CAHs in Montana, Nevada, and North Dakota that participated in the FCHIP program. Key informant interviews and FCHIP program document review were conducted and analyzed using thematic analysis to understand how CAHs implemented their telehealth programs and the facilitators of program adoption and maintenance. Medicare fee-for-service claims were analyzed from August 2013 to July 2019 relative to a group of CAHs that did not participate in the demonstration project to understand the frequency of telehealth use for Medicare fee-for-service beneficiaries receiving care at the participating CAHs before and during the Demonstration program.

RESULTS

CAH staff noted several key factors for establishing and sustaining a telehealth program: clinical and administrative staff champions, infrastructure changes, training on telehealth processes, and establishing strong relationships with specialists at distant facilities to deliver telehealth services to patients of CAH. There was a modest increase in telehealth services billed to Medicare during the FCHIP Demonstration that were limited to a handful of CAHs.

CONCLUSIONS

The frontier setting is characterized by a low population; and thus, the volumes of telehealth services provided in both the CAHs and comparison sites are low. Overall, CAHs reported that patient satisfaction was high and expressed the desire for more virtual services. Telehealth service selection was informed by perceived community needs and specialist availability. CAHs made infrastructure changes to support telehealth and expressed the desire for more virtual services. Implementation support services helped CAHs integrate telehealth into clinical and operational workflows. There was some increase in telehealth services billed to Medicare, but the volume billed was low and not enough to substantially improve hospital revenue. Future work to inform policy and practice could include standardized, formal community need assessments and assistance finding distant providers to meet those needs and further technical assistance around billing, service selection, and ongoing use to support sustainability.

Current Approaches to Vaccine Safety Using Observational Data: A Rationale for the EUMAEUS (Evaluating Use of Methods for Adverse Events Under Surveillance-for Vaccines) Study Design

Post-marketing vaccine safety surveillance aims to detect adverse events following immunization in a population. Whether certain methods of surveillance are more precise and unbiased in generating safety signals is unclear. Here, we synthesized information from existing literature to provide an overview of the strengths, weaknesses, and clinical applications of epidemiologic and analytical methods used in vaccine monitoring, focusing on cohort, case-control and self-controlled designs. These designs are proposed to be evaluated in the EUMAEUS (Evaluating Use of Methods for Adverse Event Under Surveillance-for vaccines) study because of their widespread use and potential utility. Over the past decades, there have been an increasing number of epidemiological study designs used for vaccine safety surveillance. While traditional cohort and case-control study designs remain widely used, newer, novel designs such as the self-controlled case series and self-controlled risk intervals have been developed. Each study design comes with its strengths and limitations, and the most appropriate study design will depend on availability of resources, access to records, number and distribution of cases, and availability of population coverage data. Several assumptions have to be made while using the various study designs, and while the goal is to mitigate any biases, violations of these assumptions are often still present to varying degrees. In our review, we discussed some of the potential biases (i.e., selection bias, misclassification bias and confounding bias), and ways to mitigate them. While the types of epidemiological study designs are well established, a comprehensive comparison of the analytical aspects (including method evaluation and performance metrics) of these study designs are relatively less well studied. We summarized the literature, reporting on two simulation studies, which compared the detection time, empirical power, error rate and risk estimate bias across the above-mentioned study designs. While these simulation studies provided insights on the analytic performance of each of the study designs, its applicability to real-world data remains unclear. To bridge that gap, we provided the rationale of the EUMAEUS study, with a brief description of the study design; and how the use of real-world multi-database networks can provide insights into better methods evaluation and vaccine safety surveillance.

The Impact of Systematic Review Automation Tools on Methodological Quality and Time Taken to Complete Systematic Review Tasks: Case Study

BACKGROUND

Systematic reviews (SRs) are considered the highest level of evidence to answer research questions; however, they are time and resource intensive.

OBJECTIVE

When comparing SR tasks done manually, using standard methods, versus those same SR tasks done using automated tools, (1) what is the difference in time to complete the SR task and (2) what is the impact on the error rate of the SR task?

METHODS

A case study compared specific tasks done during the conduct of an SR on prebiotic, probiotic, and synbiotic supplementation in chronic kidney disease. Two participants (manual team) conducted the SR using current methods, comprising a total of 16 tasks. Another two participants (automation team) conducted the tasks where a systematic review automation (SRA) tool was available, comprising of a total of six tasks. The time taken and error rate of the six tasks that were completed by both teams were compared.

RESULTS

The approximate time for the manual team to produce a draft of the background, methods, and results sections of the SR was 126 hours. For the six tasks in which times were compared, the manual team spent 2493 minutes (42 hours) on the tasks, compared to 708 minutes (12 hours) spent by the automation team. The manual team had a higher error rate in two of the six tasks-regarding Task 5: Run the systematic search, the manual team made eight errors versus three errors made by the automation team; regarding Task 12: Assess the risk of bias, 25 assessments differed from a reference standard for the manual team compared to 20 differences for the automation team. The manual team had a lower error rate in one of the six tasks-regarding Task 6: Deduplicate search results, the manual team removed one unique study and missed zero duplicates versus the automation team who removed two unique studies and missed seven duplicates. Error rates were similar for the two remaining compared tasks-regarding Task 7: Screen the titles and abstracts and Task 9: Screen the full text, zero relevant studies were excluded by both teams. One task could not be compared between groups-Task 8: Find the full text.

CONCLUSIONS

For the majority of SR tasks where an SRA tool was used, the time required to complete that task was reduced for novice researchers while methodological quality was maintained.

Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles

The exploration of three-dimensional chromatin interaction and organization provides insight into mechanisms underlying gene regulation, cell differentiation and disease development. Advances in chromosome conformation capture technologies, such as high-throughput chromosome conformation capture (Hi-C) and chromatin interaction analysis by paired-end tag (ChIA-PET), have enabled the exploration of chromatin interaction and organization. However, high-resolution Hi-C and ChIA-PET data are only available for a limited number of cell lines, and their acquisition is costly, time consuming, laborious and affected by theoretical limitations. Increasing evidence shows that DNA sequence and epigenomic features are informative predictors of regulatory interaction and chromatin architecture. Based on these features, numerous computational methods have been developed for the prediction of chromatin interaction and organization, whereas they are not extensively applied in biomedical study. A systematical study to summarize and evaluate such methods is still needed to facilitate their application. Here, we summarize 48 computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles, categorize them and compare their performance. Besides, we provide a comprehensive guideline for the selection of suitable methods to predict chromatin interaction and organization based on available data and biological question of interest.

Assessing the utility of public health surveillance using specificity, sensitivity, and lives saved

In modern surveillance of public health, data may be reported in a timely fashion and include spatial data on cases in addition to the time of their occurrence. This has lead to many recent developments in statistical methods to detect events of public health importance. However, there has been relatively little work about how to compare such methods. One powerful rationale for performing surveillance is earlier detection of events of public health significance; previous evaluation tools have focused on metrics that include the timeliness of detection in addition to sensitivity and specificity. However, such metrics have not accounted for the number of persons affected by the events. We re-examine the rationale for this surveillance and conclude that earlier detection is preferred because it can prevent additional morbidity and mortality. On the basis this observation, we propose evaluating the number of cases prevented by each detection method, and include this information in assessing the value of different detection methods. Using this approach incorporates more information about the events and the detection and provides a sound basis for making decisions about which detection methods to employ.