Overview & Learning Objectives:
The BioGears engine models human physiology using mathematical models that represent the different organ systems in the body and their feedback mechanisms. The fluid dynamics (pressure, flow, and volumes) and thermal properties (temperature and heat transfer) are calculated using lumped-parameter models (electric circuit analog). A variety of feedback mechanisms then affect the circuits in a single system or multiple systems via system interactions.
This presentation was given at MMVR 2016, audience members learned how to use the capabilities of BioGears to understand how it could influence undergraduate, graduate, and professional education, power training simulations, and interface with hardware (sensors).
The BioGears Project is an open source, multi-purpose physiology engine. The Engine uses lump parameter models and electric analog circuits to model and simulate realistic human response to trauma and treatment. This presentation provides a brief overview of our circuit design and implementation.
The demo provided the audience and opportunity to learn how to operate the BioGears physiology engine through the user interface, including creating scenarios, running scenarios, and viewing outputs.
BioGears is a physiological modeling software developed by Applied Research Associates that simulates various human systems and their interactions. It models the endocrine, nervous, cardiovascular, respiratory, renal, gastrointestinal, and other body systems, as well as substances including blood components, hormones, nutrients, and drugs. The software computes processes such as homeostasis, hemodynamics, energy balance, pulmonary functions, and substance diffusion throughout the body. BioGears is funded by the U.S. Army and allows users to simulate various medical conditions, insults, and interventions.
Common Practice Guidelines: A Significant Gap in Computational Modeling and S...BioGearsEngine
BioGears® is an open source, general-purpose human physiology simulation engine. The goal of BioGears is to provide consistent physiology for use by the medical modeling and simulation community. The accuracy of this whole-body model is assessed through a validation process which compares the simulation results to a range of observed values within the reference population. BioGears has been customized for integration with real-time training simulations to produce virtual patient responses in both software and hardware scenario-based applications. A software architecture that leverages a common data model was created to provide a well-defined interaction paradigm for models at different spatial and temporal scales. The framework can be applied for integration towards the development of predictive multiscale models for in silico clinical trials.
This talk describes:
· The motivation and purpose of the BioGears program
· The utilization of extensive user community engagement to inform design and implementation decisions
· The use of validation to provide assurance in results
· The process of translating physiology modeling and simulation research into clinical use
· The decision to allow physiology model extensions and improvements to be integrated by the user base over time
It will also include recommended best practices and lessons learned in getting BioGears adopted. Finally, perceived gaps and limitations in guidelines for establishing standardized simulation-based medicine will be presented, along with recommendations for future improvements.
This is an initiative started at the Interagency Modeling and Analysis Group and Multiscale Modeling Consortium
BioGears Overview for SSIH Healthcare Systems Modeling & Simulation Affinity ...BioGearsEngine
Our Principal Investigator, Austin Baird, provides an overview of the BioGears Human Physiology Engine and explains several use cases for physiology modeling and simulation.
Scientific & systematic collection of data for clinical study is called as Clinical Data Management-
Clinical Data Management-Web Based Data Capture EDC & RDC , Oracle
SAS
Office software
UW Catalyst data collection (University of Washington)
REDCAP (Research electronic data capture)
OPENCLINICA
STUDY TRAX
The BioGears team presented at IMSH on how we developed a comprehensive model of human physiology. BioGears is open source, extensible, and can be integrated with a variety of training and immersive learning tools.
The BioGears Project is an open source, multi-purpose physiology engine. The Engine uses lump parameter models and electric analog circuits to model and simulate realistic human response to trauma and treatment. This presentation provides a brief overview of our circuit design and implementation.
The demo provided the audience and opportunity to learn how to operate the BioGears physiology engine through the user interface, including creating scenarios, running scenarios, and viewing outputs.
BioGears is a physiological modeling software developed by Applied Research Associates that simulates various human systems and their interactions. It models the endocrine, nervous, cardiovascular, respiratory, renal, gastrointestinal, and other body systems, as well as substances including blood components, hormones, nutrients, and drugs. The software computes processes such as homeostasis, hemodynamics, energy balance, pulmonary functions, and substance diffusion throughout the body. BioGears is funded by the U.S. Army and allows users to simulate various medical conditions, insults, and interventions.
Common Practice Guidelines: A Significant Gap in Computational Modeling and S...BioGearsEngine
BioGears® is an open source, general-purpose human physiology simulation engine. The goal of BioGears is to provide consistent physiology for use by the medical modeling and simulation community. The accuracy of this whole-body model is assessed through a validation process which compares the simulation results to a range of observed values within the reference population. BioGears has been customized for integration with real-time training simulations to produce virtual patient responses in both software and hardware scenario-based applications. A software architecture that leverages a common data model was created to provide a well-defined interaction paradigm for models at different spatial and temporal scales. The framework can be applied for integration towards the development of predictive multiscale models for in silico clinical trials.
This talk describes:
· The motivation and purpose of the BioGears program
· The utilization of extensive user community engagement to inform design and implementation decisions
· The use of validation to provide assurance in results
· The process of translating physiology modeling and simulation research into clinical use
· The decision to allow physiology model extensions and improvements to be integrated by the user base over time
It will also include recommended best practices and lessons learned in getting BioGears adopted. Finally, perceived gaps and limitations in guidelines for establishing standardized simulation-based medicine will be presented, along with recommendations for future improvements.
This is an initiative started at the Interagency Modeling and Analysis Group and Multiscale Modeling Consortium
BioGears Overview for SSIH Healthcare Systems Modeling & Simulation Affinity ...BioGearsEngine
Our Principal Investigator, Austin Baird, provides an overview of the BioGears Human Physiology Engine and explains several use cases for physiology modeling and simulation.
Scientific & systematic collection of data for clinical study is called as Clinical Data Management-
Clinical Data Management-Web Based Data Capture EDC & RDC , Oracle
SAS
Office software
UW Catalyst data collection (University of Washington)
REDCAP (Research electronic data capture)
OPENCLINICA
STUDY TRAX
The BioGears team presented at IMSH on how we developed a comprehensive model of human physiology. BioGears is open source, extensible, and can be integrated with a variety of training and immersive learning tools.
This document summarizes a presentation about ensuring data quality in the PHIS+ consortium, which integrates clinical and administrative data across multiple children's hospitals for comparative effectiveness research. It describes the process of developing common data models, semantically mapping local data elements to standards, collecting data using a toolkit with validation, processing the data through a platform to standardize terminology and storage, and conducting various automated and manual checks for data quality issues. These included checks for missing or invalid data, relationships between test results and specimens/cultures, and study-specific assessments through chart review. The final database contained over 4.5 million records across various domains with standardized coding to support health services research.
This document proposes an agent-based information management architecture using mobile agents to monitor long-duration space crews. Mobile agents would collect sensor data related to bone loss, integrate and analyze the data, and alert crew if issues are detected. The architecture is intended to promote efficient bandwidth use, onboard analysis, and reduced crew burden. As a test case, the document examines bone loss monitoring and identifies key parameters like activity, diet, and acid-base balance that affect urinary calcium levels and need to be measured. Existing sensor technologies for parameters are also surveyed.
Enabling Clinical Data Reuse with openEHR Data Warehouse EnvironmentsLuis Marco Ruiz
Databases for Clinical Information Systems are difficult to
design and implement, especially when the design should be
compliant with a formal specification or standard. The
openEHR specifications offer a very expressive and generic
model for clinical data structures, allowing semantic
interoperability and compatibility with other standards like
HL7 CDA, FHIR, and ASTM CCR. But openEHR is not only
for data modeling, it specifies an EHR Computational
Platform designed to create highly modifiable future-proof
EHR systems, and to support long term economically viable
projects, with a knowledge-oriented approach that is
independent from specific technologies. Software Developers
find a great complexity in designing openEHR compliant
databases since the specifications do not include any
guidelines in that area. The authors of this tutorial are
developers that had to overcome these challenges. This
tutorial will expose different requirements, design principles,
technologies, techniques and main challenges of implementing
an openEHR-based Clinical Database, with examples and
lessons learned to help designers and developers to overcome the challenges more easily
Enabling Clinical Data Reuse with openEHR Data Warehouse EnvironmentsLuis Marco Ruiz
Modern medicine needs methods to enable access to data,
captured during health care, for research, surveillance,
decision support and other reuse purposes. Initiatives like the
National Patient Centered Clinical Research Network in the
US and the Electronic Health Records for Clinical Research
in the EU are facilitating the reuse of Electronic Health
Record (EHR) data for clinical research. One of the barriers
for data reuse is the integration and interoperability of
different Healthcare Information Systems (HIS). The reason is
the differences among the HIS information and terminology
models. The use of EHR standards like openEHR can alleviate
these barriers providing a standard, unambiguous,
semantically enriched representation of clinical data to
enable semantic interoperability and data integration. Few
works have been published describing how to drive
proprietary data stored in EHRs into standard openEHR
repositories. This tutorial provides an overview of the key
concepts, tools and techniques necessary to implement an
openEHR-based Data Warehouse (DW) environment to reuse
clinical data. We aim to provide insights into data extraction
from proprietary sources, transformation into openEHR
compliant instances to populate a standard repository and
enable access to it using standard query languages and
services
This document provides an introduction and overview of a Biomedical Instrumentation course. It outlines the course instructor, information like credit hours and dates, recommended resources including a course website and textbooks, and the overall course outline. The course outline introduces key topics that will be covered like transducers, biopotentials, electrocardiograms, biopotential amplifiers, and measurements of various body systems. It also discusses the components of a medical instrumentation system and different operating modes like direct, indirect, sampling and continuous modes.
This document proposes a non-invasive method to detect hypoglycemic events in patients with type 1 diabetes using wearable sensors. The method involves collecting physiological and activity data from sensors like ECG, accelerometers and breathing monitors. Machine learning models analyze the data to detect glycemic events, which are then represented semantically and used to generate alerts. Preliminary tests show the physiological model can accurately detect hypoglycemic events based on continuous glucose monitor data. The system aims to help patients and practitioners monitor insulin levels without invasive blood glucose testing.
Researchers at EPA’s National Center for Computational Toxicology integrate advances in biology, chemistry, and computer science to examine the toxicity of chemicals and help prioritize chemicals for further research based on potential human health risks. The goal of this research program is to quickly evaluate thousands of chemicals, but at a much reduced cost and shorter time frame relative to traditional approaches. The data generated by the Center includes characterization of thousands of chemicals across hundreds of high-throughput screening assays, consumer use and production information, pharmacokinetic properties, literature data, physical-chemical properties as well as the predictive computational modeling of toxicity and exposure. We have developed a number of databases and applications to deliver the data to the public, academic community, industry stakeholders, and regulators. This presentation will provide an overview of our work to develop an architecture that integrates diverse large-scale data from the chemical and biological domains, our approaches to disseminate these data, and the delivery of models supporting predictive computational toxicology. In particular, this presentation will review our new publicly-accessible CompTox Dashboard as the first application built on our newly developed architecture. This abstract does not reflect U.S. EPA policy.
This document provides an overview of data integration in biology, including why it is needed, common problems, and popular approaches. It discusses the many different biological data sources and standards that have been developed for integration. Different architectures for data integration are described, including data warehousing, federation, and view integration. Key variables that affect integration like scope, domain, and interfaces are outlined. Important standards, ontologies, guidelines and tools that support integration are also reviewed.
Over the past five years the Royal Society of Chemistry has become world renowned for its public domain compound database that integrates chemical structures with online resources and available data. ChemSpider regularly serves over 50,000 users per day who are seeking chemistry related data. In parallel we have used ChemSpider and available software services to underpin a number of grant-based projects that we have been involved with: Open PHACTS – a semantic web project integrating chemistry and biology data, PharmaSea – seeking out new natural products from the ocean and the National Chemical Database Service for the United Kingdom. We are presently developing a new architecture that will offer broader scope in terms of the types of chemistry data that can be hosted. This presentation will provide an overview of our Cheminformatics activities at RSC, the development of a new architecture for a data repository that will underpin a global chemistry network, and the challenges ahead, as well as our activities in releasing software and data to the chemistry community.
Can a combination of constrained-based and kinetic modeling bridge time scale...Natal van Riel
This document summarizes Natal van Riel's presentation on using parameter transition analysis (PTA) to model progressive metabolic adaptations associated with diseases. PTA involves nesting simulations with time-dependent parameters within parameter estimation to identify parameter trajectories that connect phenotype snapshots over time. This allows linking changes in the metabolome to the proteome and transcriptome. As an example, a model of liver lipoprotein metabolism was used to analyze the effects of activating the liver X receptor and predict reductions in SR-B1 expression based on metabolic adaptations. The approach provides insights into disease mechanisms and ways to prevent side effects of therapies.
Untether Your Data with EndoGear: Wireless Volumetric Blood Flow and Pressure...InsideScientific
Join Cole McLarty and Dr. Danielle Senador for an introduction to the newest biotelemetry system in the life science field, EndoGear.”
Tethered Transonic flow probes have been instrumental in life science protocols for the past 40 years. They have delivered absolute blood volume with a high level of accuracy, durability, and biocompatibility. However, tethered monitoring is complex and does not allow for the synchronized assessment of blood pressure. EndoGear allows for continuous assessment of high-fidelity, solid-state pressure, and volumetric blood flow. Using EndoGear to collect cardiac output and systemic pressure recordings, Cole and Danielle review the new platform and the research opportunities that it brings.
Key Topics Include:
What sets this equipment apart from other telemetry systems on the market
How EndoGear fits into various fields of study including physiology, safety pharmacology, and behavioral science
How EndoGear can fit into your protocols today, including data acquisition, flow probe customization, and power supply options
What strategies can be employed for analyzing months’ worth of continuous blood flow, pressure, temperature, and activity
Registry Participation 101: A Step-by-Step Guide to What You Really Need to K...Wellbe
This document provides an overview of registry participation and collecting patient-reported outcome measures through a registry. It discusses the University of Wisconsin's process for collecting PROs in their orthopedic clinics in two phases: a pilot phase and a full implementation phase. The pilot involved collecting PROs in 6 clinics using Epic and tablet computers. Lessons learned included that an integrated tablet/portal solution and coordinated project management were important. The full implementation will expand PRO collection to all orthopedic locations and improve reporting automation.
Application of Flow Cytometry in Haematology.pptxmosesemmanuel11
This document provides an overview of flow cytometric analysis in hematology laboratories. It describes flow cytometry as a technique that allows for the rapid, multiparametric analysis of blood cells as they pass through a laser beam. The key components and working principles of flow cytometry are explained, including fluidics, optical, and electronic systems. Applications of flow cytometry in hematology are discussed, such as immunophenotyping of leukemia and lymphoma. Both advantages like speed and automation, and limitations such as cost and complexity are reviewed.
Metabolic Profiling_techniques and approaches.pptSachin Teotia
This document discusses metabolomics profiling and the challenges faced by analytical chemists. It outlines the group's work at Aristotle University on developing new analytical methods, standardizing data extraction and quality control protocols, identifying metabolites, and collaborating across disciplines. The group aims to address bottlenecks in areas like instrumentation variability, data treatment, identification, and lack of standardization. Their work seeks to advance the field and provide insights into biochemistry, biomarkers, disease, and treatment responses through holistic analysis of small molecules.
This document discusses metabolomics profiling and the challenges faced by analytical chemists. It outlines the group's work at Aristotle University on developing new analytical methods, standardizing data extraction and quality control protocols, identifying metabolites, and collaborating across disciplines. The group aims to address bottlenecks in analytical procedures, data treatment, and lack of standardization. Their work seeks to advance metabolomics as an expanding field that provides insights into biochemistry and discovers biomarkers for health and disease.
Learn how large-scale normalized data empowers the critical early phases of drug discovery.
To address the core concerns about data quality, comprehensiveness and comparability, the Reaxys product team has developed a completely new repository for bioactivity information. Reaxys Medicinal Chemistry stands as a unique source for normalized data in vitro efficacy, in vivo animal models, compound metabolism, pharmacokinetics and toxicity. This presentation takes a look at how this approach to data supports critical early discovery methods such as in silico screening and target profiling.
Development Of A Nutritional Assessment System For Ventilated Pediatric PatientsD_Walding
The document discusses the development of a nutritional assessment system for ventilated pediatric patients at Texas Children's Hospital. Clinical engineers worked with clinicians to design a non-invasive system using mass spectrometry and pneumotac measurements to accurately measure metabolic rates and energy expenditure in critically ill pediatric patients, accounting for complications from ventilation. The system was validated through testing against established methods and shown to be useful for clinical nutritional assessment in the changing ICU environment.
Flow cytometry allows for rapid analysis of physical and chemical characteristics of single cells. It measures properties like cell size, granularity, and surface antigens by passing single cells through a laser beam and detecting light scattering. This provides quantitative results on multiple cell parameters simultaneously. Cells are stained with fluorescent antibodies targeting specific antigens. When excited by lasers, the antibodies emit light of distinct wavelengths, allowing identification of cell types. Flow cytometry is useful for applications like phenotyping, cell cycle analysis, and measuring intracellular proteins. It requires cells in single suspension, fluorescent reagents, and a flow cytometer instrument.
ChemSpider – disseminating data and enabling an abundance of chemistry platformsKen Karapetyan
ChemSpider is one of the chemistry community’s primary public compound databases. Containing tens of millions of chemical compounds and its associated data ChemSpider serves data to many tens of websites and software applications at this point. This presentation will provide an overview of the expanding reach of the ChemSpider platform and the nature of solutions that it helps to enable. We will also discuss some of the future directions for the project that are envisaged and how we intend to continue expanding the impact for the platform.
Low Complexity System Designs for Medical Cyber Physical Human SystemsMDPnP_UIUC
Prepare and inject drugs, assist with medical procedures
Head nurse: Record diagnosis, treatments and patient conditions
Physician in charge: Diagnose patient condition and order treatments
Medical devices: Monitor and display patient conditions
Code sheet: Record diagnosis, treatments and patient conditions
This model helps understand the workflow and information flow.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
This document summarizes a presentation about ensuring data quality in the PHIS+ consortium, which integrates clinical and administrative data across multiple children's hospitals for comparative effectiveness research. It describes the process of developing common data models, semantically mapping local data elements to standards, collecting data using a toolkit with validation, processing the data through a platform to standardize terminology and storage, and conducting various automated and manual checks for data quality issues. These included checks for missing or invalid data, relationships between test results and specimens/cultures, and study-specific assessments through chart review. The final database contained over 4.5 million records across various domains with standardized coding to support health services research.
This document proposes an agent-based information management architecture using mobile agents to monitor long-duration space crews. Mobile agents would collect sensor data related to bone loss, integrate and analyze the data, and alert crew if issues are detected. The architecture is intended to promote efficient bandwidth use, onboard analysis, and reduced crew burden. As a test case, the document examines bone loss monitoring and identifies key parameters like activity, diet, and acid-base balance that affect urinary calcium levels and need to be measured. Existing sensor technologies for parameters are also surveyed.
Enabling Clinical Data Reuse with openEHR Data Warehouse EnvironmentsLuis Marco Ruiz
Databases for Clinical Information Systems are difficult to
design and implement, especially when the design should be
compliant with a formal specification or standard. The
openEHR specifications offer a very expressive and generic
model for clinical data structures, allowing semantic
interoperability and compatibility with other standards like
HL7 CDA, FHIR, and ASTM CCR. But openEHR is not only
for data modeling, it specifies an EHR Computational
Platform designed to create highly modifiable future-proof
EHR systems, and to support long term economically viable
projects, with a knowledge-oriented approach that is
independent from specific technologies. Software Developers
find a great complexity in designing openEHR compliant
databases since the specifications do not include any
guidelines in that area. The authors of this tutorial are
developers that had to overcome these challenges. This
tutorial will expose different requirements, design principles,
technologies, techniques and main challenges of implementing
an openEHR-based Clinical Database, with examples and
lessons learned to help designers and developers to overcome the challenges more easily
Enabling Clinical Data Reuse with openEHR Data Warehouse EnvironmentsLuis Marco Ruiz
Modern medicine needs methods to enable access to data,
captured during health care, for research, surveillance,
decision support and other reuse purposes. Initiatives like the
National Patient Centered Clinical Research Network in the
US and the Electronic Health Records for Clinical Research
in the EU are facilitating the reuse of Electronic Health
Record (EHR) data for clinical research. One of the barriers
for data reuse is the integration and interoperability of
different Healthcare Information Systems (HIS). The reason is
the differences among the HIS information and terminology
models. The use of EHR standards like openEHR can alleviate
these barriers providing a standard, unambiguous,
semantically enriched representation of clinical data to
enable semantic interoperability and data integration. Few
works have been published describing how to drive
proprietary data stored in EHRs into standard openEHR
repositories. This tutorial provides an overview of the key
concepts, tools and techniques necessary to implement an
openEHR-based Data Warehouse (DW) environment to reuse
clinical data. We aim to provide insights into data extraction
from proprietary sources, transformation into openEHR
compliant instances to populate a standard repository and
enable access to it using standard query languages and
services
This document provides an introduction and overview of a Biomedical Instrumentation course. It outlines the course instructor, information like credit hours and dates, recommended resources including a course website and textbooks, and the overall course outline. The course outline introduces key topics that will be covered like transducers, biopotentials, electrocardiograms, biopotential amplifiers, and measurements of various body systems. It also discusses the components of a medical instrumentation system and different operating modes like direct, indirect, sampling and continuous modes.
This document proposes a non-invasive method to detect hypoglycemic events in patients with type 1 diabetes using wearable sensors. The method involves collecting physiological and activity data from sensors like ECG, accelerometers and breathing monitors. Machine learning models analyze the data to detect glycemic events, which are then represented semantically and used to generate alerts. Preliminary tests show the physiological model can accurately detect hypoglycemic events based on continuous glucose monitor data. The system aims to help patients and practitioners monitor insulin levels without invasive blood glucose testing.
Researchers at EPA’s National Center for Computational Toxicology integrate advances in biology, chemistry, and computer science to examine the toxicity of chemicals and help prioritize chemicals for further research based on potential human health risks. The goal of this research program is to quickly evaluate thousands of chemicals, but at a much reduced cost and shorter time frame relative to traditional approaches. The data generated by the Center includes characterization of thousands of chemicals across hundreds of high-throughput screening assays, consumer use and production information, pharmacokinetic properties, literature data, physical-chemical properties as well as the predictive computational modeling of toxicity and exposure. We have developed a number of databases and applications to deliver the data to the public, academic community, industry stakeholders, and regulators. This presentation will provide an overview of our work to develop an architecture that integrates diverse large-scale data from the chemical and biological domains, our approaches to disseminate these data, and the delivery of models supporting predictive computational toxicology. In particular, this presentation will review our new publicly-accessible CompTox Dashboard as the first application built on our newly developed architecture. This abstract does not reflect U.S. EPA policy.
This document provides an overview of data integration in biology, including why it is needed, common problems, and popular approaches. It discusses the many different biological data sources and standards that have been developed for integration. Different architectures for data integration are described, including data warehousing, federation, and view integration. Key variables that affect integration like scope, domain, and interfaces are outlined. Important standards, ontologies, guidelines and tools that support integration are also reviewed.
Over the past five years the Royal Society of Chemistry has become world renowned for its public domain compound database that integrates chemical structures with online resources and available data. ChemSpider regularly serves over 50,000 users per day who are seeking chemistry related data. In parallel we have used ChemSpider and available software services to underpin a number of grant-based projects that we have been involved with: Open PHACTS – a semantic web project integrating chemistry and biology data, PharmaSea – seeking out new natural products from the ocean and the National Chemical Database Service for the United Kingdom. We are presently developing a new architecture that will offer broader scope in terms of the types of chemistry data that can be hosted. This presentation will provide an overview of our Cheminformatics activities at RSC, the development of a new architecture for a data repository that will underpin a global chemistry network, and the challenges ahead, as well as our activities in releasing software and data to the chemistry community.
Can a combination of constrained-based and kinetic modeling bridge time scale...Natal van Riel
This document summarizes Natal van Riel's presentation on using parameter transition analysis (PTA) to model progressive metabolic adaptations associated with diseases. PTA involves nesting simulations with time-dependent parameters within parameter estimation to identify parameter trajectories that connect phenotype snapshots over time. This allows linking changes in the metabolome to the proteome and transcriptome. As an example, a model of liver lipoprotein metabolism was used to analyze the effects of activating the liver X receptor and predict reductions in SR-B1 expression based on metabolic adaptations. The approach provides insights into disease mechanisms and ways to prevent side effects of therapies.
Untether Your Data with EndoGear: Wireless Volumetric Blood Flow and Pressure...InsideScientific
Join Cole McLarty and Dr. Danielle Senador for an introduction to the newest biotelemetry system in the life science field, EndoGear.”
Tethered Transonic flow probes have been instrumental in life science protocols for the past 40 years. They have delivered absolute blood volume with a high level of accuracy, durability, and biocompatibility. However, tethered monitoring is complex and does not allow for the synchronized assessment of blood pressure. EndoGear allows for continuous assessment of high-fidelity, solid-state pressure, and volumetric blood flow. Using EndoGear to collect cardiac output and systemic pressure recordings, Cole and Danielle review the new platform and the research opportunities that it brings.
Key Topics Include:
What sets this equipment apart from other telemetry systems on the market
How EndoGear fits into various fields of study including physiology, safety pharmacology, and behavioral science
How EndoGear can fit into your protocols today, including data acquisition, flow probe customization, and power supply options
What strategies can be employed for analyzing months’ worth of continuous blood flow, pressure, temperature, and activity
Registry Participation 101: A Step-by-Step Guide to What You Really Need to K...Wellbe
This document provides an overview of registry participation and collecting patient-reported outcome measures through a registry. It discusses the University of Wisconsin's process for collecting PROs in their orthopedic clinics in two phases: a pilot phase and a full implementation phase. The pilot involved collecting PROs in 6 clinics using Epic and tablet computers. Lessons learned included that an integrated tablet/portal solution and coordinated project management were important. The full implementation will expand PRO collection to all orthopedic locations and improve reporting automation.
Application of Flow Cytometry in Haematology.pptxmosesemmanuel11
This document provides an overview of flow cytometric analysis in hematology laboratories. It describes flow cytometry as a technique that allows for the rapid, multiparametric analysis of blood cells as they pass through a laser beam. The key components and working principles of flow cytometry are explained, including fluidics, optical, and electronic systems. Applications of flow cytometry in hematology are discussed, such as immunophenotyping of leukemia and lymphoma. Both advantages like speed and automation, and limitations such as cost and complexity are reviewed.
Metabolic Profiling_techniques and approaches.pptSachin Teotia
This document discusses metabolomics profiling and the challenges faced by analytical chemists. It outlines the group's work at Aristotle University on developing new analytical methods, standardizing data extraction and quality control protocols, identifying metabolites, and collaborating across disciplines. The group aims to address bottlenecks in areas like instrumentation variability, data treatment, identification, and lack of standardization. Their work seeks to advance the field and provide insights into biochemistry, biomarkers, disease, and treatment responses through holistic analysis of small molecules.
This document discusses metabolomics profiling and the challenges faced by analytical chemists. It outlines the group's work at Aristotle University on developing new analytical methods, standardizing data extraction and quality control protocols, identifying metabolites, and collaborating across disciplines. The group aims to address bottlenecks in analytical procedures, data treatment, and lack of standardization. Their work seeks to advance metabolomics as an expanding field that provides insights into biochemistry and discovers biomarkers for health and disease.
Learn how large-scale normalized data empowers the critical early phases of drug discovery.
To address the core concerns about data quality, comprehensiveness and comparability, the Reaxys product team has developed a completely new repository for bioactivity information. Reaxys Medicinal Chemistry stands as a unique source for normalized data in vitro efficacy, in vivo animal models, compound metabolism, pharmacokinetics and toxicity. This presentation takes a look at how this approach to data supports critical early discovery methods such as in silico screening and target profiling.
Development Of A Nutritional Assessment System For Ventilated Pediatric PatientsD_Walding
The document discusses the development of a nutritional assessment system for ventilated pediatric patients at Texas Children's Hospital. Clinical engineers worked with clinicians to design a non-invasive system using mass spectrometry and pneumotac measurements to accurately measure metabolic rates and energy expenditure in critically ill pediatric patients, accounting for complications from ventilation. The system was validated through testing against established methods and shown to be useful for clinical nutritional assessment in the changing ICU environment.
Flow cytometry allows for rapid analysis of physical and chemical characteristics of single cells. It measures properties like cell size, granularity, and surface antigens by passing single cells through a laser beam and detecting light scattering. This provides quantitative results on multiple cell parameters simultaneously. Cells are stained with fluorescent antibodies targeting specific antigens. When excited by lasers, the antibodies emit light of distinct wavelengths, allowing identification of cell types. Flow cytometry is useful for applications like phenotyping, cell cycle analysis, and measuring intracellular proteins. It requires cells in single suspension, fluorescent reagents, and a flow cytometer instrument.
ChemSpider – disseminating data and enabling an abundance of chemistry platformsKen Karapetyan
ChemSpider is one of the chemistry community’s primary public compound databases. Containing tens of millions of chemical compounds and its associated data ChemSpider serves data to many tens of websites and software applications at this point. This presentation will provide an overview of the expanding reach of the ChemSpider platform and the nature of solutions that it helps to enable. We will also discuss some of the future directions for the project that are envisaged and how we intend to continue expanding the impact for the platform.
Low Complexity System Designs for Medical Cyber Physical Human SystemsMDPnP_UIUC
Prepare and inject drugs, assist with medical procedures
Head nurse: Record diagnosis, treatments and patient conditions
Physician in charge: Diagnose patient condition and order treatments
Medical devices: Monitor and display patient conditions
Code sheet: Record diagnosis, treatments and patient conditions
This model helps understand the workflow and information flow.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...AbdullaAlAsif1
The pygmy halfbeak Dermogenys colletei, is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
ESPP presentation to EU Waste Water Network, 4th June 2024 “EU policies driving nutrient removal and recycling
and the revised UWWTD (Urban Waste Water Treatment Directive)”
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
ESR spectroscopy in liquid food and beverages.pptx
MMVR BioGears Overview
1. 1
Agenda (1:30pm – 5:00 pm)
1. Engine Overview [~50min]
• Bio Break [10min]
2. Using the Graphical User Interface (GUI) [~50min]
• Bio Break [10min]
3. Interacting with the Application Program Interface (API)
[~50min]
4. Questions and Help [until 5pm]
Recommended Preparation
• If you would like to follow along using the GUI (item 2), please
download and unzip the Toolkit from:
https://biogearsengine.com/download
BioGears: An OpenSource Human Physiology Engine
2. 2
Presentation for MMVR
Presenters: Jeff Webb, Rachel Clipp, PhD, Aaron Bray
Applied Research Associates, Inc. (ARA)
7 April 2016
BioGears Overview
3. 3
• Program Overview
• Modeling Approach
• Verification and Validation
• Systems Review
• Architecture and Tools
• Coming Soon
BioGears Overview Agenda
5. 5
• Organization: Applied Research Associates, Inc. (ARA)
• Telemedicine & Advanced Technology Research Center (TATRC)
Award #: W81XWH-13-2-0068
• Principal Investigator: Mr. Jeff Webb
• Amount: $6,959,593
• Period of Performance: Sept 2013 – Sept 2018
• License: Apache 2.0 permissive free software
• Disclaimer: This work is supported by the US Army Medical Research and
Materiel Command. The views, opinions and/or findings contained in this report are
those of the author(s) and should not be construed as an official Department of the
Army position, policy, or decision unless so designated by other documentation.
Project Information
6. 6
High Level Objectives
• Create a publicly available physiology
research platform that enables accurate and
consistent simulated physiology across
training applications
• Lower the barrier to create medical training
content
• Engage the community to develop and
extend physiology models
• Meet the training needs of the military
• Expand the body of knowledge regarding
the use of simulated physiology for medical
education
8. 8
Milestones
2015
Link to Version History
FY13 September: project kick-off
FY14
FY15
October: Alpha Build 1.0.0 release and website launch
March: 2.0.0 release
July: 3.0.0 release
FY16
October: Beta Build 4.0.0 release and users conference
December: 5.0.0 release
March: 5.1.0 release
Summer: planned Release Candidate 6.0.0
• System updates
• Serialization, modularity, and optimization
FY17
Several releases throughout
• New systems/features
FY18
October: Final Contractual release
Maintenance only
We are here
9. 9
Current Collaborators and Integrators
• 2,700+ downloads of the engine and related files since the Alpha Build Release
(October 2014)
• 1,400+ pages of physiology modeling methodology and software documentation, and
validation data available to our user community
• Used by the government/military, academic institutions, and commercial businesses
• Let us know if/when you use BioGears and we can add you to the home page of our
website!
11. 11
Engine Overview
Computation Approach:
• Time-stepping transient analysis for linearization of differential equations
• Currently 90Hz for 2x real-time simulation on standard laptops
• Dynamically change/add/remove elements to represent physiological mechanisms
• Stabilization analysis for initialization and implementation of conditions
• Designed with low computational overhead
• Faster than real-time on typical PC, multiple instances on single or multicore processors
• Build Targets include Windows, Mac, Linux, and Raspberry Pi
Modeling Approach:
• Top-down approach to model development with bottom-up hooks for engine expansion
• Multi-scale for varying fidelity, allowing integration of models from any level
12. 12
• Discrete entities that
approximate the behavior
of a distributed system
• Electronic-
Hydraulic/Thermal
Analogy: body system fluid
dynamics and
thermodynamics modeled
using electrical circuit
math
• Generalized definitions of
Nodes, Paths, and
Elements for simple
understanding,
implementation, and
modification
Background: Lumped Parameter Modeling
Lumped Parameter Modeling of Fluids
P=Pressure, F=Flow, R=Resistance, C=Compliance, I=Inertance
13. 13
• Data model
• Generic and reusable node and path definitions
• Uses the same equations/code with native units
• Other element types not in table:
• Switch
• Valve/diode
• Polarized elements
Lumped Parameter Approach
Circuit Types and Elements
Definitions
14. 14
Connection types:
• Direct circuit connection – e.g. Anesthesia Machine and
Respiratory
• Feedback – e.g. Nervous and Endocrine
• Substance exchange – e.g. Respiratory and Cardiovascular gas
exchange
Physiology System Interaction
Environment
PK/PD
Cardiovascular
Blood
Tissue
Extravascular
Respiratory
Anesthesia Machine
Renal
Gastrointestinal
Energy
Nervous
Endocrine
Inhaler
15. 15
System Data Flow and Modeling
Approach Advantages
• Modular and extensible
• Model fidelity easily modified by adding/removing nodes and
elements to circuit
• Fully dynamic physics based mechanistic models (rather than
state based) – cascading effects
• Unlimited stacking/combining of conditions, insults,
interventions, interfaces, etc.
• Homeostasis based modeling with pathophysiology actions
• Able to integrate existing/new models
• Not necessarily lumped parameter
• Mixed fidelity
• Able to simultaneously run any number of instances/patients
Preprocess
Uses feedback mechanisms to
modify elements for the next time
step.
Process
Calculates the entire state of the
system for the next time step
Assessments
Called on demand to calculate and
set assessment specific data
Reset
Initializes the system
Initialization
Resting: Functionality specific to
resting patient stabilization
Conditions: Functionality specific
to applying conditions
PostProcess
Advances time by moving the next
time step to current
16. 16
Engine Initialization
Resting Conditions Running
Stabilization Simulation
Time = 0
• Dynamic stabilization drives towards patient homeostasis
• Each step the engine executes until all specified stabilization criteria are
satisfied
Step 1: Patient
initialization
• Systems feedback
modifies values to
achieve specified
patient parameters
(e.g. baseline mean
arterial pressure)
• Standard
environment used
Step 2: Condition
initialization
• Conditions applied
to represent new
patient homeostatic
state
• Environment
changes applied to
simulate long term
acclimation
Simulation begins
• Acute insults,
interventions, and
parameter
modification
applied
instantaneously
through actions
Step 3: Feedback
Mechanisms
• Feedback
mechanisms that
would interfere
with chronic
conditions (e.g.
baroreceptors) are
activated
Feedback
17. 17
Engine Data Library
Tidal Volume varies with
weight
Circuit is modified to
vary arterial pressure
Driver is modified to vary
heart rate
1
2 3
Patient File Parameters
Gender FunctionalResidualCapacity
Age 2: HeartRateBasline
1: Weight 3: MeanArterialPressureBaseline
Height RespirationRateBaseline
BodyFatFraction RightLungRatio
CarinaToTeethDistance TotalBloodVolumeBaseline
Contractility
Patient Modification Example
Patients
• Properties modify
system setup, circuit
values, and feedback
parameters
Substances
• Physical and transport
related properties
• E.g. MolarMass,
IonicState, Sedation
Compounds
• Concentrations of
multiple substances
• E.g. Saline, Blood
Environments
• Surrounding properties
• E.g.
AmbientTemperature,
ClothingResistance
Nutrition
• Meal composition
(masses)
• E.g. Protein, Calcium
Stabilization
• Percent difference
criteria for stabilization
– resting and
conditions
18. 18
• Anatomical Compartments defined by sub-
circuits and allow access via an anatomy tree
• Several overlapping compartment types
• Fluid
• Liquid
• Vascular
• Urine
• Chyme
• Gas
• Pulmonary
• Tissue
• Thermal
• Electrical
• Compartment properties are combined from
children (liquid example)
• Volume is a sum
• InFlow is a sum
• OutFlow is a sum
• Pressure comes from an assigned child node
(sum does not make sense)
• Substance quantities (mass, concentration,
etc.) are calculated on demand for any level in
hierarchy
• There are thousands of compartment data
values that are updated each time-step
Left Kidney
Left Renal Artery
Left Renal Vein
Left Nephron
Left Afferent Arteriole
Left Glomerular Capillaries
Left Efferent Arteriole
Left Peritubular Capillaries
Left Bowmans Capsules
Left Tubules
Left Ureter
Compartment Example: Kidney Definition
Key:
Vascular
Urine
20. 20
1. Full Scenario Suite: BioGears test suite includes scenarios to test all patient files, patient actions, substance effects, and equipment
performance. Completed for 3 circuit unit tests and 80+ scenarios.
2. Circuit Verification: Verify that the BioGears circuit solver is producing comparable results to established circuit solvers. Completed for
100+ circuit elements/combinations.
3. Timing: Suite is executed by team members throughout development. It is also automatically executed when the code base is altered in
the repository.
4. Output: Each scenario indicates the physiologic outputs for validation. Each output is tested to ensure it is within 2% of the results in the
code repository. Comparison and error plots (shown) are generated.
5. Full Results: An email is sent to all team members when verification is complete. Each scenario either Passes (green) or Fails (red).
Failures must be addressed by the team.
Verification
Source of Failure
VentilatorPressureLossScenarioResultsReport 1 1 21.258999824523926
YpieceDisconnectScenarioResultsReport 1 1 23.98900008201599
AirwayInsultObstructionResultsReport 0 1 14.930999994277954
AtropineScenarioResultsReport 0 1 13.121000051498413
BasicScenario1ResultsReport 0 1 18.195000171661377
Scenario Verification
Circuit Verification
21. 21
1. Verification: Unit tests ensure correct implementation and sound physics principles
for all tools
2. System Level Validation: All major systems (cardiovascular, respiratory, blood
chemistry, etc.) are validated for clinical output level data
3. Compartment Level Calibration: Individual organs (kidney, liver, etc.) or functional
units (trachea, alveoli, etc.) are validated wherever possible
4. Scenario Calibration & Validation: Every insult, intervention, and assessment
includes a matrix with validation data for whole body combined effects from multiple
systems
5. Combined Scenario Validation: All four showcases and several other scenarios
validated for combined effects – heavily leveraged SME consultants Bryan Bergeron
MD and Nicholas Moss PhD
Showcase Scenario Combined Effects Validation
Calibration and Validation
Scenario
Number (%) of Validation Measures in Deviation Category
Total
< 10% 10 – 30% > 30%
Combat Multitrauma 59 (64.8%) 10 (11.0%) 22 (24.2%) 91
Asthma Attack 26 (65.0%) 7 (17.5%) 7 (17.5%) 40
Heat Stroke 52 (76.5%) 10 (14.7%) 6 (8.8%) 68
Exposure 26 (86.7%) 2 (6.7%) 2 (6.7%) 30
33. 33
• Common Data Model (CDM):
Well-defined, intuitive,
interchangeable format to
standardize interfaces
• Standardized inputs, outputs,
units, and naming conventions
to aid model additions and
external model integrators
• Application Programming
Interface (API): Easy
integration and interaction in
any programming language
• Data organized logically by
Anatomy so that users are able
to easily find and pull relevant
data
• Software Development Kit
(SDK): Application examples
and stand-alone execution
• Tutorials, How-to’s, scenario
examples
Software Architecture
34. 34
• Common data structures for modeling and simulation of the human body
• Not specific to any methodology, including BioGears
• Separates the physiological data from the physiological modeling methodology
• Object Oriented Design of class structures providing a unified set of tools that
promotes fast development, compatible data sets, and well-defined interfaces
• Provides a well-defined data interchange format that disparate models can use
for standardizing inputs and outputs between each other
• Allows for specific extensions, but interfaces are defined by the CDM
Common Data Model Overview
Conceptual Data Model
XSD Schema Files
• Properties.xsd
• System.xsd
• Substance.xsd
• Scenario.xsd
• Circuit.xsd
• Anatomy.xsd
• Patient & Actions.xsd
• Equipment & Actions.xsd
• BioGears.xsd
Data forms, variables,
definitions, and relationships
Data Model Binding
Code Synthesis DLL
• Properties.cxx
• System.cxx
• Substance.cxx
• Scenario.cxx
• Circuit.cxx
• Anatomy.cxx
• Patient & Actions.cxx
• Equipment & Actions.cxx
• BioGears.cxx
Auto generated C++ Classes
-Turnkey Serialization Support
Common Data Model API
C++ DLL
• SEProperty.cpp, SEScalar.cpp,
SEScalarVolume.cpp, etc.
• SECardiovascular.cpp,
SERespiratory.cpp, etc.
• SECircuit.cpp, SECircuitNode.cpp,
SECircuitPath.cpp, etc.
• SEPatient.cpp, SESubstance.cpp,
SEDrug.cpp, etc.
• SESubstanceAdministration.cpp,
SEHemorrhage.cpp, etc.
Class library reflects the binding
and utilizes serialization support
• Unit Conversion
• Logging (log4cpp)
• Unit Test Framework
• Generic Circuit Solver
• Generic Circuit Transport
• Generic Math
• Pure Virtual Physiology Engine
Interface
• Scenario Definition and Execution
Additional general algorithms
35. 35
Setup/Modify
Next Values
Modified Nodal
Analysis
Calculate
Fluxes
Valves
Pass?
Set Node
Quantities
Advance Time
Modify Valve
States
Yes
No
Calculate
Quantities
Preprocess:
1. Systems use “Current” values to setup/modify “Next”
values via feedback mechanisms (outside of the solver)
Process:
2. Perform numerical integration by using linearization
(first order approximations) through Modified Nodal
Analysis (Ax=b)
a. Use KCL (total Flux at each node = 0) to
Calculate the Jacobian matrix (A) and right-
hand side vector (b) for each Node Potential
and Potential Source Flux (x)
b. Use the Eigen templated library linear solver
(FullPivLU) to solve for x vector
3. Calculate unknown Fluxes – using Trapezoid Rule
where applicable
4. Calculate Valves using assumed diode states (cannot
be solved directly) – iterate as necessary
5. Calculate and increment Compliance Path Quantities
– No other elements have dynamic Quantities (rigid
pipes)
6. Set Node Quantities (based on Path Quantities)
• Note: Transporter is called here
Postprocess:
7. Advance time by moving “Next” to “Current” values
Process
Preprocess
Postprocess
1
2
3
5
67
4
Circuit Solver
• Fully dynamic Modified Nodal Analysis
solver for any valid closed-loop circuit
• Solves circuit types with any units:
Electrical, Fluid, Thermal
36. 36
• Common Data Model:
• Substances move with the fluid to each node
in the circuit
• No particle deposition
• Mass is updated as at each time step based
on flow into and out of nodes
• Concentration and partial pressure are
updated after the mass
• Systems Interactions:
• Partial pressure driven diffusion moves
substances between two nodes (O2, CO2, N2)
• Perfusion limited diffusion moves substances
across the blood/tissue barrier based on flow
and partition coefficients (Drugs)
• Systemic clearance removes substances
from the vena cava to represent metabolic or
other clearance mechanisms
• Hepatic clearance removes substances from
the liver to represent the ability of the liver to
metabolize or remove substances
Transporter
Lung Vascular
Lung Tissue
Heart
Kidney Tissue
Kidney Vascular
Lung Airway
Liver Tissue
Liver Vascular
Other Tissue
Other Vascular
Bladder
GI
37. 37
Motivation
• Data driven developers’ tool to
demonstrate basic functionality
• Test Engine without command line
• Ready for testing out-of-the-box (no
compiling)
• Simple interface for creating, editing, and
executing scenario file, and creating
resulting plots of requested data
Expectations
• Only requirements are to allow editing
and execution of scenarios – not a major
focus
• Not heavily QA’ed – some known bugs
being continuously fixed
Limitations
• Clunky Java Swing for rapid prototyping
via the API
Developer GUI
39. 39
Current (hopefully deployed this summer with version 6.0.0):
• Bug fixing and system refinement
• Optimization and increased simulation speed
• State serialization – saving and loading simulations
• Modularity – more easily replace entire systems
• Renal feedback updates
• Acid-base balance – O2 & CO2 saturation modifications
• Total body substance balance and new substances
Near-Term (FY16):
• Nervous system additions
• Exocrine additions
• Endocrine additions
• Vascular fluid exchange
• Pneumothorax updates
• Gastrointestinal updates
Long-Term (FY17):
• Patient modifications (gender, body mass, etc)
• Intoxications – Ketamine proof of concept
• Airborne agents (Nerve/Pulmonary/Smoke/CO) and vaporization
• Diuretics
• Additions to blood assessments and pulmonary function test improvement
High Level Near Term Tasks
40. 40
• Use the software for any and all applications (please let us
know)
• Report problems
• Submit code
• Currently just email us
(https://www.biogearsengine.com/workwithus)
• Moving to a public repository – GitHub/BitBucket hosted
• Post and respond to Forums
(https://www.biogearsengine.com/forums)
How to Contribute
41. 41
Please Come See Our Posters!
Showcases: Rodney Metoyer Renal: Dr. Austin Baird
PK/PD: Dr. Rachel Clipp Energy: Cam Thames
44. 44
Current System Capabilities
Systems Acute Insults & Interventions Chronic Conditions Events
Cardiovascular
& Blood Chemistry
Cardiac Arrest
CPR
Hemorrhage
Pericardial Effusion
Anemia
Arrhythmia
Bradycardia
Tachycardia
Heart Failure
Pericardial Effusion
Asystole
Bradycardia & Tachycardia
Bradypnea & Tachypnea
Brain & Myocardium Oxygen Deficit
Cardiac Arrest
Hypercapnia & Hypoxia
Hyperglycemia & Hypoglycemia
Hypovolemic Shock
Pulseless Rhythm
Respiratory Airway Obstruction
Bronchoconstriction
Asthma Attack
COPD Bronchitis
Intubation
Pneumothorax
Conscious Respiration
Occlusive Dressing
Needle Decompression
COPD
Lobar Pneumonia
Energy
& Environment
Exercise
Environment Changes
Thermal Application
Dehydration
Starvation
Environment Changes
Fasciculation
Fatigue
Hyperthermia
Heat Stroke
Metabolic/Respiratory Acidosis & Alkalosis
Renal
& GI
Urinate
Consume Meal
Renal Stenosis Diuresis & Antidiuresis
Natriuresis
Dehydration
Functional Incontinence
Drugs
& Substances
& Inhaler
IV Fluid Administration
IV Drug Administration
IM Drug Administration
Inhaler Drug Administration
Anesthesia Machine Configuration
Expiratory/Inspiratory Valve
Leaks/Obstructions
Soda Lime Failure
Mask/Tube Leak
Vaporizer Failure
Ventilator Pressure Loss
Oxygen Port/Tank Pressure
Loss
Endotracheal Intubation
Esophageal Intubation
Oxygen Bottle Exhausted
Relief Valve Active
Note: More to come
45. 45
Provided Data Examples
Systems System Vital Examples
(Hundreds Total)
Compartment Examples
(Thousands Total)
Assessments
(Exhaustive)
Cardiovascular Heart Rate
Cardiac Output
Mean Arterial Pressure
Blood Volume
Pulmonary Flow
Brain Pressure
Heart Volumes
Substance Concentrations
Complete Blood Count
Comprehensive Metabolic Panel
Blood Chemistry Blood pH
Oxygen Saturation
Shunt Fraction
Hemoglobin Content
Respiratory Respiration Rate
Tidal Volume
Total Lung Volume
Pulmonary Resistance
Lung Volumes
Lung Pressures
Air Flow
Substance Volume Fractions
Pulmonary Function Test
Energy Respiratory Quotient
Total Metabolic Rate
Skin Temp
Heat Transfer Rate
Environment Ambient Temperature
Clothing Resistance
Renal Glomerular Filtration Rate
Urine Specific Gravity
Renal Blood Flow
Bladder Substance Concentrations
Urinalysis
GI Digestion Rate Stomach Contents
Drugs & Substances Partition Coefficients
Anesthesia Level
Plasma Concentration
Tissue Concentration
Anesthesia Machine Oxygen Bottle Volume
Ventilator Pressure
Vaporizer substance fractions
Tube flows
Note: These are only example outputs – there are many, many more
46. 46
Other Included BioGears Tools
Unit Converter
Unit/Feature Testing
• Validate individual tools
• Verify individual feedback
• Alerts user to introduced bugs
Verification Testing
• Full scenario suite to test all patient
files, patient actions, substance
effects, and equipment performance
• Each scenario indicates the
physiologic outputs for comparison
and generates error plots
Validation Testing
• Spreadsheet with referenced baselines
• Color coded error tables automatically
generated for all System and relevant
compartment data
Developer GUI
VentilatorPressureLossScenarioResultsReport 1 1 21.258999824523926
YpieceDisconnectScenarioResultsReport 1 1 23.98900008201599
AirwayInsultObstructionResultsReport 0 1 14.930999994277954
AtropineScenarioResultsReport 0 1 13.121000051498413
BasicScenario1ResultsReport 0 1 18.195000171661377
Verification Results Example
Developer GUI
47. 47
• Virtual Heroes developed ‘Combat Medic’, for RDECOM STTC
• Uses BioGears for live physiology and after action data
• Intended to train Combat Medics on the top three preventable
causes of death on the battlefield: Hemorrhage, Airway Obstruction
and Tension Pneumothorax
• The prototype was tested at Fort Bliss, TX by Army combat medics
Combat Medic using BioGears
Images courtesy of Combat Medic project funded by Army RDECOM-STTC
Hemorrhage Airway Obstruction Tension Pneumothorax
Link to Video
49. 49
Documentation and Tutorials
• The website includes detailed documentation for each physiology system
and software components (e.g., CDM, Toolkit, SDK, Source Code)
• This includes text and tables that explain: system background, model
limitations, equations used, and validation data sources and matrices
• https://www.biogearsengine.com
Expand
50. 50
Envisioned User Groups
Adds or replaces
systems to extend the
functionality
Ex. Physiology Modelers
BioGears Contributor
Use the engine as is via
the API
Ex. Game Developers
Engine Integrator
Uses/extends CDM
Runs BioGears engine
and/or other engine(s)
Ex. Mannequin Builder
External Model/
Engine Developer
Creates custom input to
BioGears engine for
research or instruction
Ex. Teaching Assistant
Researcher/ Educator
• The physiology engine has been designed and implemented
with 4 user groups in mind.
• Engine functionality, fidelity and extensibility critical design
decisions made to make BioGears user friendly
• Scope:
• Providing an API with open source libraries
• BioGears is not a ‘game’ – it will power immersive training content and
other M&S tools
51. 51
• Worked with subcontractor UNC Eshelman School of Pharmacy
• Pharmacodynamics also validated through scenario validation
• All drugs validated in this manor
PK/Clearance Validation Examples
Bolus
InfusionBolus
Bolus
52. 52
Using BioGears
• Canned or Dynamic Scenarios
• Training and Simulation Scenarios
• Physiology and Modeling Classroom
Education
• Data Analysis
• Physiologic Response Scenarios
Use-Case Options
• Integration of New Models, Systems,
Actions, Conditions, and Events
• New Patients, Substances, and Drugs
• New Initialization Parameters (blood
tests, lab results)
• Validation and Verification
• Data Analysis
• Injury Assessment Scoring Input
Development Future
1. Needs and Requirements Assessment
2. Validation and Calibration Data Determination
3. Model Design and Implementation
4. Model Verification – Unit Tests to Verify Functionality
5. Model Calibration – Tuning Parameters to Meet Initial Data
6. Model Validation – Use of Model/Feature in Combination To Validate
Functionality
Model/Feature Development Steps
53. 53
Implementation
Setup/Modify
Next Values
Modified Nodal
Analysis
Calculate
Fluxes
Valves
Pass?
Set Node
Quantities
Advance Time
Modify Valve
States
Yes
No
Calculate
Quantities
Preprocess:
1. Systems use “Current” values to setup/modify “Next”
values via feedback mechanisms (outside of the solver)
Process:
2. Perform numerical integration by using linearization
(first order approximations) through Modified Nodal
Analysis (Ax=b)
a. Use KCL (total Flux at each node = 0) to
Calculate the Jacobian matrix (A) and right-
hand side vector (b) for each Node Potential
and Potential Source Flux (x)
b. Use the Eigen templated library linear solver
(FullPivLU) to solve for x vector
3. Calculate unknown Fluxes – using Trapezoid Rule
where applicable
4. Calculate Valves using assumed diode states (cannot
be solved directly) – iterate as necessary
5. Calculate and increment Compliance Path Quantities
– No other elements have dynamic Quantities (rigid
pipes)
6. Set Node Quantities (based on Path Quantities)
• Note: Transporter is called here
Postprocess:
7. Advance time by moving “Next” to “Current” values
Process
Preprocess
Postprocess
1
2
3
5
67
4
54. 54
• Provides a medium for substance transport through the
human body
• Feedback related to insults/interventions propagated
through entire body
BioGears System Example: Cardiovascular Interaction
Cardiovascular
Renal
TissueGastrointestinal
Respiratory
Alveolar
Transfer
Digestion
Clearance
Tissue Diffusion
55. 55
Example Scenario: Combat Multitrauma Showcase +
Environment Change
Hemorrhage & Tension Pneumothorax
Needle Decompression
Hemorrhage Reduced (Manual Pressure)
Tourniquet (Hemorrhage Stopped) & IV (Saline)
Morphine
High Altitude Environment (End of Current Validated Scenario)
12 min 60 min
Note: Does not include
sympathetic nervous system
– we are currently designing
58. 58
Pharmacokinetics – Partition Coefficients
• An example of the PK properties from the substance files
are shown below.
• Partition coefficients can be directly input into the substance
file, bypassing the engine calculation.
60. 60
Pharmacodynamics
∆𝐸 =
𝐸 𝑚𝑎𝑥 ∗ 𝐶 𝑝
𝐸𝐶50 + 𝐶 𝑝
𝐸𝐶50 =
𝐶 𝑚𝑎𝑥
32
• Drug effects are calculated based on the
plasma concentration, the drug effect,
and the concentration at 50% effect.
• The EC50 value was not readily available for
the majority of drugs in question, so the EC50
value was calculated from the max
concentration.
• The Cmax is shown circled on the
example plot.
61. 61
• The following clinical effects were calculated:
• Heart Rate
• Diastolic and Systolic Pressure
• Respiratory Rate
• Tidal Volume
• Bronchodilation Level
• Sedation Level
• Neuromuscular Block Level
Pharmacodynamics
62. 62
Agent Threat Example Scenario
• Cortexiphan is a fictitious threat agent administered through the air.
• The substance parameters were modified in the agent file and a
scenario was created changing the ambient air to be 9.5%
cortexiphan.
Time(s)
Cortexiphan-PlasmaConcentration(ug/mL)
50 75 100 125 150 175 200 225 250 275 300
0
50
100
150
200
250
300
350
400
450
Plasma concentration increases
with increased exposure time.
Time(s)
RespirationRate(1/min)
0 30 60 90 120 150 180 210 240 270 300
14
16
18
20
22
24
26
28
Respiration rate increases as
calculated by PK/PD response
model.
64. 64
• Common Data Model
• Overview
• Class Introduction
• Physiology Engine Interface
• Static vs. Dynamic Engine Execution
• Inputs
• Conditions
• Actions
• Outputs
• Systems
• Compartments
• Assessments
• We will not go over the entire code base, just the forward
facing classes available for application developers
Application Programming Interface Overview
65. 65
• Property – Scalar, Functions, Enums, etc.
• Scalar is a double with a unit, with embedded unit converter
• System
• Physiology – Cardiovascular, Respiratory, Drugs, etc.
• Equipment
• Anesthesia Machine – Generic Machine
• ECG – Generic Waveform reader
• Inhaler – Optional spacer
• Environment – Properties external patient
• Environmental Conditions – Meteorology, Heat/Cool, ChemBio, etc.
• Patient – Body characteristics, Baselines, State
• Compartment – Specific anatomical and machine dynamics
• Flow, Volume, Pressure,
• Substance Mass/Concentration or Volume/Volume Fraction
CDM Classes (SE prefix)
66. 66
• Substance – Anything being circulated in the blood or pulmonary
systems, including drugs
• Configuration – Engine specific data that does not fit anywhere
else
• Time step, Stabilization, Coefficients, etc.
• Circuit – Generic circuit solving library
• Scenario – Engine execution instructions
• Utils
• Unit Converter – Versatile conversion engine
• General Math – Generic saturation, Kelman equation, etc.
• Logging – Logging classes built on log4cpp
• Data Tracking – Write data to file each time step as the engine runs
• Physiology Engine Interface
• Generic interface for physiology methodology based on CDM
CDM Classes (SE prefix)
67. 67
• Static execution of the engine based on a scenario file
• Specify a patient file
• Request data to be output in a column tab delimited txt file
• Conditions
• Actions
• Dynamic execution of the engine
• Initialize Engine with a patient, any conditions, and an optional results
file
• Time Controls
• GetTimeStep, GetTime, AdvanceTime
• Provide Actions
• GetCompartments()
• GetSystems
• GetAssessment()
• GetSubstances()
Static vs. Dynamic Physiology Engine Interface
68. 68
• Set up the engine to start at a specific state
bool InitializeEngine(const SEPatient& patient, const
std::vector<const SECondition*>* conditions=nullptr)
• Patient
• Anemia, Bradycardia, COPD, Pulmonary Shunt, Renal Stenosis,
Tachycardia, Heart Failure, Meal, Lobar Pneumonia, Pericardial
Effusion
• Environmental
• Initial Environment Conditions
• We have tested each condition individually, but you do have
the option to stack conditions, we have not testing all
combinations
• Test that the engine can converge on stacked conditions
Conditions (Input)
69. 69
• Instruct the engine to change its state in some way
void AdvanceModelTime()// Single Time Step
void AdvanceModelTime(double time, const
std::shared_ptr<CCompoundUnit>& unit)
bool ProcessAction(const SEAction& action)
• Action Types
• Patient – Various Insults and Interventions
• Environment – Configuration and Application
• Anesthesia Machine – Configuration and Insults
• Inhaler - Configuration
Actions (Input)
70. 70
• Physiological state data
• Respiratory Rate, Heart Rate, Metabolic Rate, etc.
• Equipment and Environment state data
• Oxygen Tank Volume, Nozzle Loss, Heater Power, etc.
• Engine implementation does not have to provide every
output, although BioGears does
const SEEnvironment* GetEnvironment()
const SEBloodChemistrySystem* GetBloodChemistrySystem()
… (much more physiology) …
const SEAnesthesiaMachine* GetAnesthesiaMachine()
const SEElectroCardioGram* GetElectroCardioGram()
Systems (Output)
71. 71
• Compartments are a generic interface for the fluid dynamics data of the body, such as
Volumes, Pressures, In Flows, and Out Flows
• A compartment can represent various fidelities of data
• Skin, Right Arm, Liver, Left Heart
• Anesthesia Machine Ventilator, Mask, etc.
• Multiple compartment types to represent different fluid dynamic types
• Gas (Pulmonary), Liquid (Blood, Chyme, Urine), Thermal (Body Heat), Tissue (Extravascular)
• Compartments have a parent/child hierarchy
• There can be multiple compartments associated with anatomy
• Substance quantities for each substance is also provided in the compartment
• A vascular compartment includes the substance masses and concentration
• A pulmonary compartment will contain the volumes and volume fractions of all substances in that
compartment
const SEAnatomyCompartments* GetAnatomyCompartments()
const SEInhalerCompartments* GetInhalerCompartments()
const SEAnesthesiaMachineCompartments* GetAnesthesiaMachineCompartments()
Compartments (Output)
72. 72
• Assessments are physiology data formed into various medical
tests and panels intended for clinicians
• Some calculation may apply
• Complete Blood Panel, Complete Metabolic Panel, Pulmonary Function
Test, and Urnialysis
• Provide an assessment object to the Engine for it to fill out
SEPulmonaryFunctionTest pft(bg->GetLogger());
bg->GetPatientAssessment(pft);
• Various states on the patient or equipment can change during
execution and state flags can be polled for these changes
• Antidiuresis, Asystole, CardiacArrest, …, RespiratoryAlkalosis,
RightMainStemIntubation, Tachycardia, Tachypnea
• OxygenBottleExhausted, RefliefValveActive, etc.
GetPatient().IsEventActive(CDM::enumPatientEvent::CardiacArrest)
Patient Assessments and Events (Output)
73. 73
• Event Callbacks
• Along with polling for events, you can provide a callback object that
the engine will call when any event is triggered
• Exceptions
• Any time an engine gets out of its designed boundary conditions, it
will throw an exception of or derived from
CommonDataModelException
• Data Track
• You can have the engine write out any system or compartment
scalar into tab delimited file at each time step.
Callbacks, Error handling and Data Tracks
74. 74
• Each engine can have its own log and will log the following types of data
• The Logger has the option to be given a LoggerForward class that an
end user provides. The logger will call methods on this class whenever it
logs giving the application a way to programmatically react to the engine
Logs
Type Description
Debug Detailed information about engine execution
Info General information of what the engine is doing
Warning The engine received something or is doing something that may or may
not invalidate results
Error The engine has performed a calculation it was not designed for but will
keep running, results are most likely invalid
Fatal The engine has entered a state it was not designed for and will stop
execution immediately. BioGears will follow this by throwing an
exception
75. 75
• Each engine can have its own log and will log the following types
of data
• Debug – Detailed information about engine execution
• Info– General information of what the engine is doing
• Warning – The engine received something or is doing something that
may or may not invalidate results
• Error– The engine has performed a calculation it was not designed for
but will keep running, results are most likely invalid
• Fatal– The engine has entered a state it was not designed for and will
stop execution immediately. BioGears will follow this by throwing an
exception.
• The Logger has the option to be given a LoggerForward class
that an end user provides. The logger will call methods on this
class whenever it logs giving the application a way to
programmatically react to the engine
Logs
76. 76
• The engine executes out of a bin directory, this is a break down of
the directories and its files required for BioGears
• config – Stabilization parameters, you may need to tweak these files if
you combine conditions
• ecg – The waveform set to use for the ecg
• environments* – a set of canned environments for the engine that the
environment can initialize to
• nutrition* – a set of canned nutrition files that can be used with the
ConsumeMeal condition and ConsumeNutrition action
• patients – a set of tested and validated patient files
• substances – the set of substance files for BioGears
• UCEDefs.txt – Unit conversion configuration file
• BioGearsConfiguration.xml – Override any BioGears configuration
property. By default this file is empty.
*Not required by the engine
OnDisk Breakdown
Editor's Notes
Common physiology system level model for modeling and simulation of the human body
Provides a well-defined data interchange format that disparate models can use for standardizing inputs and outputs between each other
Object Oriented Design of class structures providing a unified set of tools that promotes fast development, compatible data sets, and well-defined interfaces
Separates the physiological data from the physiological modeling methodology
Prevents “engineer” code mixing with data organization and design
Strongly typed design that is intended to grow via community adoption and involvement
Extension allowed for model specific extensions, but interfaces are defined by the common data model
Important to discuss that our delivery to combat medic team shows that biogears CAN be used by target audience
Integration with Unreal-based game is part of SOW requirement
Biogears engine powers all physiology, dynamically reacting to user actions
Mention UNC-CH and UT using in cirriculums – potential for student projects – ARA’s excellent intern program
We are developing these systems with 4 main user groups in mind
Design decisions and functionality are geared towards their use
Perfusion-limited is diffusion primarily driven by the flow to the space
Permeability-limited diffusion is primarily driven by the ability of the substance to be taken into the tissue. Not flow.