Computational Fluid Dynamics Simulations to Assess Spatial Variability and Optimal Ventilation Scenarios for Biological Laboratory Exposures.

Introduction: A significant amount of uncertainty exists regarding potential human exposure to laboratory biomaterials and organisms in Biosafety Level 2 (BSL-2) research laboratories. Computational fluid dynamics (CFD) modeling is proposed as a way to better understand potential impacts of different combinations of biomaterials, laboratory manipulations, and exposure routes on risks to laboratory workers. Methods: In this study, we use CFD models to simulate airborne concentrations of contaminants in an actual BSL-2 laboratory under different configurations. Results: Results show that ventilation configuration, sampling location, and contaminant source location can significantly impact airborne concentrations and exposures. Depending on the source location and airflow patterns, the transient and time-integrated concentrations varied by several orders of magnitude. Contaminant plumes from sources located near a return vent (or exhaust like a fume hood or ventilated biosafety cabinet) are likely to be more contained than sources that are further from the exhaust. Having a direct flow between the source and the exhaust (through-flow condition) may reduce potential exposures to individuals outside the air flow path. Conclusion: Designing a BSL-2 room with ventilation and airflow patterns that maximize through-flow conditions to the return/exhaust vents and minimize dispersion and mixing throughout the room is, therefore, recommended. CFD simulations can also be used to assist in characterizing the impacts of supply and return vent locations, room layout, and source locations on spatial and temporal contaminant concentrations. In addition, proper placement of particle sensors can also be informed by CFD simulations to provide additional characterization and monitoring of potential exposures in BSL-2 facilities.

Application of multi-criteria decision analysis techniques and decision support framework for informing select agent designation for agricultural animal pathogens.

The United States Department of Agriculture (USDA), Division of Agricultural Select Agents and Toxins (DASAT) established a list of biological agents and toxins (Select Agent List) that potentially threaten agricultural health and safety, the procedures governing the transfer of those agents, and training requirements for entities working with them. Every 2 years the USDA DASAT reviews the Select Agent List, using subject matter experts (SMEs) to perform an assessment and rank the agents. To assist the USDA DASAT biennial review process, we explored the applicability of multi-criteria decision analysis (MCDA) techniques and a Decision Support Framework (DSF) in a logic tree format to identify pathogens for consideration as select agents, applying the approach broadly to include non-select agents to evaluate its robustness and generality. We conducted a literature review of 41 pathogens against 21 criteria for assessing agricultural threat, economic impact, and bioterrorism risk and documented the findings to support this assessment. The most prominent data gaps were those for aerosol stability and animal infectious dose by inhalation and ingestion routes. Technical review of published data and associated scoring recommendations by pathogen-specific SMEs was found to be critical for accuracy, particularly for pathogens with very few known cases, or where proxy data (e.g., from animal models or similar organisms) were used to address data gaps. The MCDA analysis supported the intuitive sense that select agents should rank high on the relative risk scale when considering agricultural health consequences of a bioterrorism attack. However, comparing select agents with non-select agents indicated that there was not a clean break in scores to suggest thresholds for designating select agents, requiring subject matter expertise collectively to establish which analytical results were in good agreement to support the intended purpose in designating select agents. The DSF utilized a logic tree approach to identify pathogens that are of sufficiently low concern that they can be ruled out from consideration as a select agent. In contrast to the MCDA approach, the DSF rules out a pathogen if it fails to meet even one criteria threshold. Both the MCDA and DSF approaches arrived at similar conclusions, suggesting the value of employing the two analytical approaches to add robustness for decision making.

The development and use of decision support framework for informing selection of select agent toxins with modelling studies to inform permissible toxin amounts.

Many countries have worked diligently to establish and implement policies and processes to regulate high consequence pathogens and toxins that could have a significant public health impact if misused. In the United States, the Antiterrorism and Effective Death Penalty Act of 1996 (Public Law 104-132, 1996), as amended by the Bioterrorism Preparedness and Response Act of 2002 (Public Law 107-188, 2002) requires that the Department of Health and Human Services (HHS) [through the Centers for Disease Control and Prevention (CDC)] establish a list of bacteria, viruses, and toxins that have the potential to pose a severe threat to public health and safety. Currently, this list is reviewed and updated on a biennial basis using input from subject matter experts (SMEs). We have developed decision support framework (DSF) approaches to facilitate selection of select toxins and, where toxicity data are known, conducted modelling studies to inform selection of toxin amounts that should be excluded from select agent regulations. Exclusion limits allow laboratories to possess toxins under an established limit to support their research or teaching activities without the requirement to register with the Federal Select Agent Program. Fact sheets capturing data from a previously vetted SME workshop convened by CDC, literature review and SME input were developed to assist in evaluating toxins using the DSF approach. The output of the DSF analysis agrees with the current select toxin designations, and no other toxins evaluated in this study were recommended for inclusion on the select agent and toxin list. To inform the selection of exclusion limits, attack scenarios were developed to estimate the amount of toxin needed to impact public health. Scenarios consisted of simulated aerosol releases of a toxin in high-population-density public facilities and the introduction of a toxin into a daily consumable product supply chain. Using published inhalation and ingestion median toxic dose (TD50) and median lethal dose (LD50) values, where available, a range of toxin amounts was examined to estimate the number of people exposed to these amounts in these scenarios. Based on data generated by these models, we proposed toxin exclusion values corresponding to levels below those that would trigger a significant public health response (i.e., amounts estimated to expose up to ten people by inhalation or one hundred people by ingestion to LD50 or TD50 levels of toxin in the modeled scenarios).

Application of Multi-Criteria Decision Analysis Techniques for Informing Select Agent Designation and Decision Making.

The Centers for Disease Control and Prevention (CDC) Select Agent Program establishes a list of biological agents and toxins that potentially threaten public health and safety, the procedures governing the possession, utilization, and transfer of those agents, and training requirements for entities working with them. Every 2 years the Program reviews the select agent list, utilizing subject matter expert (SME) assessments to rank the agents. In this study, we explore the applicability of multi-criteria decision analysis (MCDA) techniques and logic tree analysis to support the CDC Select Agent Program biennial review process, applying the approach broadly to include non-select agents to evaluate its generality. We conducted a literature search for over 70 pathogens against 15 criteria for assessing public health and bioterrorism risk and documented the findings for archiving. The most prominent data gaps were found for aerosol stability and human infectious dose by inhalation and ingestion routes. Technical review of published data and associated scoring recommendations by pathogen-specific SMEs was found to be critical for accuracy, particularly for pathogens with very few known cases, or where proxy data (e.g., from animal models or similar organisms) were used to address data gaps. Analysis of results obtained from a two-dimensional plot of weighted scores for difficulty of attack (i.e., exposure and production criteria) vs. consequences of an attack (i.e., consequence and mitigation criteria) provided greater fidelity for understanding agent placement compared to a 1-to-n ranking and was used to define a region in the upper right-hand quadrant for identifying pathogens for consideration as select agents. A sensitivity analysis varied the numerical weights attributed to various properties of the pathogens to identify potential quantitative (x and y) thresholds for classifying select agents. The results indicate while there is some clustering of agent scores to suggest thresholds, there are still pathogens that score close to any threshold, suggesting that thresholding "by eye" may not be sufficient. The sensitivity analysis indicates quantitative thresholds are plausible, and there is good agreement of the analytical results with select agent designations. A second analytical approach that applied the data using a logic tree format to rule out pathogens for consideration as select agents arrived at similar conclusions.

Application of CGE to virus identification.

Protein profiling is an increasingly valuable tool for the characterization of protein populations and has been used to identify microorganisms, most often using two-dimensional gel electrophoresis followed by mass spectrometry. We present a rapid method for the identification of viruses using microfluidic chip gel electrophoresis (CGE) of high-copy number proteins to generate unique protein profiles. Viral proteins are solubilized, fluorescently labeled and then analyzed using the µChemLab™ CGE system (∼10 min overall). A Bayesian classification approach is used to classify the reproducible and visually distinct protein profiles of MS2 bacteriophage, Epstein-Barr, Respiratory Syncytial, and Vaccinia viruses as well as discriminate between closely related T2 and T4 bacteriophage.

Integrated fiber optic incoherent broadband cavity enhanced absorption spectroscopy detector for near-IR absorption measurements of nanoliter samples.

An integrated fiber-optic sensor is described that uses incoherent broadband cavity enhanced absorption spectroscopy for sensitive detection of aqueous samples in nanoliter volumes. Absorption was measured in a 100 µm gap between the ends of two short segments of multimode graded-index fiber that were integrated into a capillary using a precision machined V-grooved fixture that allowed for passive fiber alignment. The other ends of the fibers were coated with dielectric mirrors to form a 9.5 cm optical resonator. Light from a fiber-coupled superluminescent diode was directly coupled into one end of the cavity, and transmission was measured using a fiber-coupled silicon photodiode. Dilute aqueous solutions of near infrared dye were used to determine the minimum detectable absorption change of 2.4×10(-4) under experimental conditions in which pressure fluctuations limited performance. We also determined that the absolute minimum detectable absorption change would be 1.6×10(-5) for conditions of constant pressure in which absorption measurement is limited by electronic and optical noise. Tolerance requirements for alignment are also presented.

Identification of viruses using microfluidic protein profiling and Bayesian classification.

We present a rapid method for the identification of viruses using microfluidic chip gel electrophoresis (CGE) of high-copy number proteins to generate unique protein profiles. Viral proteins are solubilized by heating at 95 degrees C in borate buffer containing detergent (5 min), then labeled with fluorescamine dye (10 s), and analyzed using the microChemLab CGE system (5 min). Analyses of closely related T2 and T4 bacteriophage demonstrate sufficient assay sensitivity and peak resolution to distinguish the two phage. CGE analyses of four additional viruses--MS2 bacteriophage, Epstein-Barr, respiratory syncytial, and vaccinia viruses--demonstrate reproducible and visually distinct protein profiles. To evaluate the suitability of the method for unique identification of viruses, we employed a Bayesian classification approach. Using a subset of 126 replicate electropherograms of the six viruses and phage for training purposes, successful classification with non-training data was 66/69 or 95% with no false positives. The classification method is based on a single attribute (elution time), although other attributes such as peak width, peak amplitude, or peak shape could be incorporated and may improve performance further. The encouraging results suggest a rapid and simple way to identify viruses without requiring specialty reagents such as PCR probes and antibodies.

Microchip separations of protein biotoxins using an integrated hand-held device.

We report the development of a hand-held instrument capable of performing two simultaneous microchip separations (gel and zone electrophoresis), and demonstrate this instrument for the detection of protein biotoxins. Two orthogonal analysis methods are chosen over a single method in order to improve the probability of positive identification of the biotoxin in an unknown mixture. Separations are performed on a single fused-silica wafer containing two separation channels. The chip is housed in a microfluidic manifold that utilizes o-ring sealed fittings to enable facile and reproducible fluidic connection to the chip. Sample is introduced by syringe injection into a septum-sealed port on the device exterior that connects to a sample loop etched onto the chip. Detection of low nanomolar concentrations of fluorescamine-labeled proteins is achieved using a miniaturized laser-induced fluorescence detection module employing two diode lasers, one per separation channel. Independently controlled miniature high-voltage power supplies enable fully programmable electrokinetic sample injection and analysis. As a demonstration of the portability of this instrument, we evaluated its performance in a laboratory field test at the Defence Science and Technology Laboratory with a series of biotoxin variants. The two separation methods cleanly distinguish between members of a biotoxin test set. Analysis of naturally occurring variants of ricin and two closely related staphylococcal enterotoxins indicates the two methods can be used to readily identify ricin in its different forms and can discriminate between two enterotoxin isoforms.

Hand-held microanalytical instrument for chip-based electrophoretic separations of proteins.

The design, fabrication, and demonstration of a hand-held microchip-based analytical instrument for detection and identification of proteins and other biomolecules are reported. The overall system, referred to as muChemLab, has a modular design that provides for reliability and flexibility and that facilitates rapid assembly, fluid and microchip replacement, troubleshooting, and sample analysis. Components include two independent separation modules that incorporate interchangeable fluid cartridges, a 2-cm-square fused-silica microfluidic chip, and a miniature laser-induced fluorescence detection module. A custom O-ring sealed manifold plate connects chip access ports to a fluids cartridge and a syringe injection port and provides sample introduction and world-to-chip interface. Other novel microfluidic connectors include capillary needle fittings for fluidic connection between septum-sealed fluid reservoirs and the manifold housing the chip, enabling rapid chip priming and fluids replacement. Programmable high-voltage power supplies provide bidirectional currents up to 100 microAlpha at 5000 V, enabling real-time current and voltage monitoring and facilitating troubleshooting and methods development. Laser-induced fluorescence detection allows picomolar (10(-11) M) detection sensitivity of fluorescent dyes and nanomolar sensitivity (10(-9) M) for fluorescamine-labeled proteins. Migration time reproducibility was significantly improved when separations were performed under constant current control (0.5-1%) as compared to constant voltage control (2-8%).

Programmable modification of cell adhesion and zeta potential in silica microchips.

Spatial patterning of thin polyacrylamide films bonded to self-assembled monolayers on silica microchannels is described as a means for manipulating cell-adhesion and electroosmotic properties in microchips. Streaming potential measurements indicate that the zeta potential is reduced by at least two orders of magnitude at biological pH, and the adhesion of several kinds of cells is reduced by 80-100%. Results are shown for cover slides and in wet-etched silica microchannels. Because the polyacrylamide film is thin and transparent, this film is consistent with optical manipulation of cells and detection of cell contents. The spatial patterning technique is straightforward and has the potential to aid on-chip analysis of single adherent cells.