Relationship between chlorhexidine gluconate concentration and microbial colonization of patients' skin.

OBJECTIVE: To characterize the relationship between chlorhexidine gluconate (CHG) skin concentration and skin microbial colonization. DESIGN: Serial cross-sectional study. SETTING/PARTICIPANTS: Adult patients in medical intensive care units (ICUs) from 7 hospitals; from 1 hospital, additional patients colonized with carbapenemase-producing Enterobacterales (CPE) from both ICU and non-ICU settings. All hospitals performed routine CHG bathing in the ICU. METHODS: Skin swab samples were collected from adjacent areas of the neck, axilla, and inguinal region for microbial culture and CHG skin concentration measurement using a semiquantitative colorimetric assay. We used linear mixed effects multilevel models to analyze the relationship between CHG concentration and microbial detection. We explored threshold effects using additional models. RESULTS: We collected samples from 736 of 759 (97%) eligible ICU patients and 68 patients colonized with CPE. On skin, gram-positive bacteria were cultured most frequently (93% of patients), followed by Candida species (26%) and gram-negative bacteria (20%). The adjusted odds of microbial recovery for every twofold increase in CHG skin concentration were 0.84 (95% CI, 0.80-0.87; P < .001) for gram-positive bacteria, 0.93 (95% CI, 0.89-0.98; P = .008) for Candida species, 0.96 (95% CI, 0.91-1.02; P = .17) for gram-negative bacteria, and 0.94 (95% CI, 0.84-1.06; P = .33) for CPE. A threshold CHG skin concentration for reduced microbial detection was not observed. CONCLUSIONS: On a cross-sectional basis, higher CHG skin concentrations were associated with less detection of gram-positive bacteria and Candida species on the skin, but not gram-negative bacteria, including CPE. For infection prevention, targeting higher CHG skin concentrations may improve control of certain pathogens.

A patient safety knowledge graph supporting vaccine product development.

BACKGROUND: Knowledge graphs are well-suited for modeling complex, unstructured, and multi-source data and facilitating their analysis. During the COVID-19 pandemic, adverse event data were integrated into a knowledge graph to support vaccine safety surveillance and nimbly respond to urgent health authority questions. Here, we provide details of this post-marketing safety system using public data sources. In addition to challenges with varied data representations, adverse event reporting on the COVID-19 vaccines generated an unprecedented volume of data; an order of magnitude larger than adverse events for all previous vaccines. The Patient Safety Knowledge Graph (PSKG) is a robust data store to accommodate the volume of adverse event data and harmonize primary surveillance data sources. METHODS: We designed a semantic model to represent key safety concepts. We built an extract-transform-load (ETL) data pipeline to parse and import primary public data sources; align key elements such as vaccine names; integrated the Medical Dictionary for Regulatory Activities (MedDRA); and applied quality metrics. PSKG is deployed in a Neo4J graph database, and made available via a web interface and Application Programming Interfaces (APIs). RESULTS: We import and align adverse event data and vaccine exposure data from 250 countries on a weekly basis, producing a graph with 4,340,980 nodes and 30,544,475 edges as of July 1, 2022. PSKG is used for ad-hoc analyses and periodic reporting for several widely available COVID-19 vaccines. Analysis code using the knowledge graph is 80% shorter than an equivalent implementation written entirely in Python, and runs over 200 times faster. CONCLUSIONS: Organizing safety data into a concise model of nodes, properties, and edge relationships has greatly simplified analysis code by removing complex parsing and transformation algorithms from individual analyses and instead managing these centrally. The adoption of the knowledge graph transformed how the team answers key scientific and medical questions. Whereas previously an analysis would involve aggregating and transforming primary datasets from scratch to answer a specific question, the team can now iterate easily and respond as quickly as requests evolve (e.g., "Produce vaccine-X safety profile for adverse event-Y by country instead of age-range").

Impact of measurement and feedback on chlorhexidine gluconate bathing among intensive care unit patients: A multicenter study.

OBJECTIVE: To assess whether measurement and feedback of chlorhexidine gluconate (CHG) skin concentrations can improve CHG bathing practice across multiple intensive care units (ICUs). DESIGN: A before-and-after quality improvement study measuring patient CHG skin concentrations during 6 point-prevalence surveys (3 surveys each during baseline and intervention periods). SETTING: The study was conducted across 7 geographically diverse ICUs with routine CHG bathing. PARTICIPANTS: Adult patients in the medical ICU. METHODS: CHG skin concentrations were measured at the neck, axilla, and inguinal region using a semiquantitative colorimetric assay. Aggregate unit-level CHG skin concentration measurements from the baseline period and each intervention period survey were reported back to ICU leadership, which then used routine education and quality improvement activities to improve CHG bathing practice. We used multilevel linear models to assess the impact of intervention on CHG skin concentrations. RESULTS: We enrolled 681 (93%) of 736 eligible patients; 92% received a CHG bath prior to survey. At baseline, CHG skin concentrations were lowest on the neck, compared to axillary or inguinal regions (P < .001). CHG was not detected on 33% of necks, 19% of axillae, and 18% of inguinal regions (P < .001 for differences in body sites). During the intervention period, ICUs that used CHG-impregnated cloths had a 3-fold increase in patient CHG skin concentrations as compared to baseline (P < .001). CONCLUSIONS: Routine CHG bathing performance in the ICU varied across multiple hospitals. Measurement and feedback of CHG skin concentrations can be an important tool to improve CHG bathing practice.

Splenic abscess caused by Cutibacterium acnes in a patient with multiple tooth extractions.

A woman in her 40s with a history of dental abscess presenting with a 3-month history of nightly fevers, malaise, fatigue and acutely worsening left flank pain was found to have a splenic abscess replacing almost the entire splenic parenchyma on abdominal CT. Abscess aspirate showed Gram-positive rods, and both aerobic and anaerobic cultures grew Cutibacterium acnes (previously Propionibacterium acnes), a common member of the skin microbiome. Prior case reports of C. acnes splenic abscess all involved parental inoculation via needle use. However, in the context of no percutaneous needle exposure and multiple tooth extractions immediately preceding her symptoms, the most likely source of her infection is oral flora with haematogenous or lymphatic spread to the spleen.

Prosthetic hip infection due to Campylobacter jejuni.

A woman in her 60s with a left hip prosthesis was presented with left hip pain and fever. She had an elevated white blood cell count and inflammatory markers. Synovial fluid Gram stain demonstrated curved Gram-negative rods identified as Campylobacter jejuni The patient initially refused surgery and after 3 months underwent one-stage exchange after which she was treated with 12 weeks of levofloxacin. Her inflammatory markers normalised and she was clinically doing well at her 6-month follow-up. C. jejuni is a rare cause of prosthetic joint infection and should be included in the differential diagnosis when a patient has risk factors even without significant preceding gastrointestinal symptoms. Per most recent Infectious Diseases Society of America guidelines, treatment after one-stage revision includes 4-6 weeks of intravenous antimicrobials followed by possible oral suppression therapy, while the European guidelines recommend 12 weeks of orally bioavailable antibiotics.

Disseminated histoplasmosis presenting as bilateral lower extremity paresis.

Histoplasmosis is the most common endemic mycosis in the United States and is caused by the organism Histoplasma capsulatum. Infection is often asymptomatic or self-limited, but when symptomatic it usually presents in the form of pulmonary histoplasmosis. In its most severe form, H. capsulatum can spread to extrapulmonary sites causing disseminated infection. Here we present a peculiar case of central nervous system (CNS) histoplasmosis wherein multiple focal spinal cord lesions were the only manifestation of CNS infection, causing bilateral lower extremity paresis and loss of sensation. Although uncommon, CNS histoplasmosis should be included in the differential diagnosis when a patient presents with meningitis, encephalitis, or isolated brain or spinal cord lesions in endemic areas.

Carbon footprint assessment of Western Australian Groundwater Recycling Scheme.

This research has determined the carbon footprint or the carbon dioxide equivalent (CO2 eq) of potable water production from a groundwater recycling scheme, consisting of the Beenyup wastewater treatment plant, the Beenyup groundwater replenishment trial plant and the Wanneroo groundwater treatment plant in Western Australia, using a life cycle assessment approach. It was found that the scheme produces 1300 tonnes of CO2 eq per gigalitre (GL) of water produced, which is 933 tonnes of CO2 eq higher than the desalination plant at Binningup in Western Australia powered by 100% renewable energy generated electricity. A Monte Carlo Simulation uncertainty analysis calculated a Coefficient of Variation value of 5.4%, thus confirming the accuracy of the simulation. Electricity input accounts for 83% of the carbon dioxide equivalent produced during the production of potable water. The chosen mitigation strategy was to consider the use of renewable energy to generate electricity for carbon intensive groundwater replenishment trial plant. Depending on the local situation, a maximum of 93% and a minimum of 21% greenhouse gas saving from electricity use can be attained at groundwater replenishment trial plant by replacing grid electricity with renewable electricity. In addition, the consideration of vibrational separation (V-Sep) that helps reduce wastes generation and chemical use resulted in a 4.03 tonne of CO2 eq saving per GL of water produced by the plant.

Protein Simulation Data in the Relational Model.

High performance computing is leading to unprecedented volumes of data. Relational databases offer a robust and scalable model for storing and analyzing scientific data. However, these features do not come without a cost-significant design effort is required to build a functional and efficient repository. Modeling protein simulation data in a relational database presents several challenges: the data captured from individual simulations are large, multi-dimensional, and must integrate with both simulation software and external data sites. Here we present the dimensional design and relational implementation of a comprehensive data warehouse for storing and analyzing molecular dynamics simulations using SQL Server.

Implementation of 3D spatial indexing and compression in a large-scale molecular dynamics simulation database for rapid atomic contact detection.

BACKGROUND: Molecular dynamics (MD) simulations offer the ability to observe the dynamics and interactions of both whole macromolecules and individual atoms as a function of time. Taken in context with experimental data, atomic interactions from simulation provide insight into the mechanics of protein folding, dynamics, and function. The calculation of atomic interactions or contacts from an MD trajectory is computationally demanding and the work required grows exponentially with the size of the simulation system. We describe the implementation of a spatial indexing algorithm in our multi-terabyte MD simulation database that significantly reduces the run-time required for discovery of contacts. The approach is applied to the Dynameomics project data. Spatial indexing, also known as spatial hashing, is a method that divides the simulation space into regular sized bins and attributes an index to each bin. Since, the calculation of contacts is widely employed in the simulation field, we also use this as the basis for testing compression of data tables. We investigate the effects of compression of the trajectory coordinate tables with different options of data and index compression within MS SQL SERVER 2008. RESULTS: Our implementation of spatial indexing speeds up the calculation of contacts over a 1 nanosecond (ns) simulation window by between 14% and 90% (i.e., 1.2 and 10.3 times faster). For a 'full' simulation trajectory (51 ns) spatial indexing reduces the calculation run-time between 31 and 81% (between 1.4 and 5.3 times faster). Compression resulted in reduced table sizes but resulted in no significant difference in the total execution time for neighbour discovery. The greatest compression (~36%) was achieved using page level compression on both the data and indexes. CONCLUSIONS: The spatial indexing scheme significantly decreases the time taken to calculate atomic contacts and could be applied to other multidimensional neighbor discovery problems. The speed up enables on-the-fly calculation and visualization of contacts and rapid cross simulation analysis for knowledge discovery. Using page compression for the atomic coordinate tables and indexes saves ~36% of disk space without any significant decrease in calculation time and should be considered for other non-transactional databases in MS SQL SERVER 2008.

Generation of a consensus protein domain dictionary.

MOTIVATION: The discovery of new protein folds is a relatively rare occurrence even as the rate of protein structure determination increases. This rarity reinforces the concept of folds as reusable units of structure and function shared by diverse proteins. If the folding mechanism of proteins is largely determined by their topology, then the folding pathways of members of existing folds could encompass the full set used by globular protein domains. RESULTS: We have used recent versions of three common protein domain dictionaries (SCOP, CATH and Dali) to generate a consensus domain dictionary (CDD). Surprisingly, 40% of the metafolds in the CDD are not composed of autonomous structural domains, i.e. they are not plausible independent folding units. This finding has serious ramifications for bioinformatics studies mining these domain dictionaries for globular protein properties. However, our main purpose in deriving this CDD was to generate an updated CDD to choose targets for MD simulation as part of our dynameomics effort, which aims to simulate the native and unfolding pathways of representatives of all globular protein consensus folds (metafolds). Consequently, we also compiled a list of representative protein targets of each metafold in the CDD. AVAILABILITY AND IMPLEMENTATION: This domain dictionary is available at www.dynameomics.org.

Dynameomics: a comprehensive database of protein dynamics.

The dynamic behavior of proteins is important for an understanding of their function and folding. We have performed molecular dynamics simulations of the native state and unfolding pathways of over 2000 protein/peptide systems (approximately 11,000 independent simulations) representing the majority of folds in globular proteins. These data are stored and organized using an innovative database approach, which can be mined to obtain both general and specific information about the dynamics and folding/unfolding of proteins, relevant subsets thereof, and individual proteins. Here we describe the project in general terms and the type of information contained in the database. Then we provide examples of mining the database for information relevant to protein folding, structure building, the effect of single-nucleotide polymorphisms, and drug design. The native state simulation data and corresponding analyses for the 100 most populated metafolds, together with related resources, are publicly accessible through http://www.dynameomics.org.

Dynameomics: a multi-dimensional analysis-optimized database for dynamic protein data.

The Dynameomics project is our effort to characterize the native-state dynamics and folding/unfolding pathways of representatives of all known protein folds by way of molecular dynamics simulations, as described by Beck et al. (in Protein Eng. Des. Select., the first paper in this series). The data produced by these simulations are highly multidimensional in structure and multi-terabytes in size. Both of these features present significant challenges for storage, retrieval and analysis. For optimal data modeling and flexibility, we needed a platform that supported both multidimensional indices and hierarchical relationships between related types of data and that could be integrated within our data warehouse, as described in the accompanying paper directly preceding this one. For these reasons, we have chosen On-line Analytical Processing (OLAP), a multi-dimensional analysis optimized database, as an analytical platform for these data. OLAP is a mature technology in the financial sector, but it has not been used extensively for scientific analysis. Our project is further more unusual for its focus on the multidimensional and analytical capabilities of OLAP rather than its aggregation capacities. The dimensional data model and hierarchies are very flexible. The query language is concise for complex analysis and rapid data retrieval. OLAP shows great promise for the dynamic protein analysis for bioengineering and biomedical applications. In addition, OLAP may have similar potential for other scientific and engineering applications involving large and complex datasets.

Dynameomics: design of a computational lab workflow and scientific data repository for protein simulations.

Dynameomics is a project to investigate and catalog the native-state dynamics and thermal unfolding pathways of representatives of all protein folds using solvated molecular dynamics simulations, as described in the preceding paper. Here we introduce the design of the molecular dynamics data warehouse, a scalable, reliable repository that houses simulation data that vastly simplifies management and access. In the succeeding paper, we describe the development of a complementary multidimensional database. A single protein unfolding or native-state simulation can take weeks to months to complete, and produces gigabytes of coordinate and analysis data. Mining information from over 3000 completed simulations is complicated and time-consuming. Even the simplest queries involve writing intricate programs that must be built from low-level file system access primitives and include significant logic to correctly locate and parse data of interest. As a result, programs to answer questions that require data from hundreds of simulations are very difficult to write. Thus, organization and access to simulation data have been major obstacles to the discovery of new knowledge in the Dynameomics project. This repository is used internally and is the foundation of the Dynameomics portal site http://www.dynameomics.org. By organizing simulation data into a scalable, manageable and accessible form, we can begin to address substantial questions that move us closer to solving biomedical and bioengineering problems.