Integration Of Externalized Decision Models In The Definition Of Workflows For Digital Pathology

Introduction/ Background The availability of digital pathology creates opportunities for the adoption of advanced workflow solutions focused on facilitating and improving the current way of working in pathology labs. Workflow-driven applications can help achieve increased efficiency and quality, support collaboration, and provide detailed insights into the lab processes. The implementation of workflow solutions also creates effective means to monitor and measure activities, and to detect and solve issues. Our solution helps improve processes in the pathology lab (both with respect to efficiency and quality) by modeling and optimizing the existing workflows and by incorporating decision models for automatic execution of relevant tasks and path selection in these workflows. Examples of decision models relate to the implementation and automatic execution of protocols, detection of quality issues, and automatic evaluation of tests with image analysis to evaluate the need for pre-ordering additional tests. Aims This work focuses on the modeling and optimization of relevant pathology workflows to enable clinical users to efficiently and effectively leverage the deployed digital pathology solutions for faster diagnosis and better patient outcomes. Next to identifying and addressing bottlenecks in the workflow, we aim to improve performance by enriching the workflows with clinical decision support. Methods We build a workflow-driven application enabling us to support and propose optimizations for pathology processes, while leveraging the availability of a digital pathology system. We select relevant workflows and identify opportunities for automating tasks and incorporating decision support. The selected pathology processes are represented according to the BPMN standard [BP]. We used the jBPM [jb] workflow suite (compliant with BPMN 2.0) for the modeling and execution of the processes. Programmatic tasks in the workflows are linked to external services executing the logic required by the tasks. Results We proposed a workflow solution enabling the representation of decision models as externalized executable tasks in the process definition. Our approach separates the task implementations from the workflow model, ensuring scalability and allowing for the inclusion of complex decision logic in the workflow execution. In we depict a simplified model of a pathology diagnosis workflow (starting with the digitization of the slides), represented according to the BPMN modeling conventions. The example shows a workflow sequence that automatically orders a HER2 FISH when IHC is borderline according to defined customizable thresholds. The process model integrates an image analysis algorithm that scores images. Based on the score and the thresholds the decision model evaluates the condition and recommends the pre-ordering of an additional test when the score falls between the two thresholds. This leads to faster diagnosis and allows balancing the costs of an additional test versus the overhead of the pathologist by choosing the values of the thresholds.

Biofilms Formed by Pathogens in Food and Food Processing Environments

This chapter presents the ability of some pathogenic (Listeria monocytogenes, Escherichia coli, Salmonella enterica, Campylobacter jejuni, Pseudomonas aeruginosa) and toxigenic bacteria (Bacillus cereus, Staphylococcus aureus) to form biofilms and contribute to the persistence of these microorganisms in the food industry. Particularities regarding attachment and composition of biofilms formed in food and food processing environments are presented and genes involved in biofilm production are mentioned. To give a perspective on how to fight against biofilms with new means, nonconventional methods based on bacteriocins, bacteriophages, disruptive enzymes, essential oils, nanoemulsions and nanoparticles, and use of alternative technologies (cold plasma, ultrasounds, light-assisted technologies, pulsed electric field, and high pressure processing) are shortly described

Giant extracranial liposarcoma: Case report

Objective: Anaplastic liposarcoma of the head is an extremely rare entity. Seventy-seven cases of head and neck liposarcomas have been reported in the world literature since 1911. Radical surgery is the form of treatment advised.Clinical presentation: Authors report the case of a 62 years old female patient admitted in our institution for a giant extracranial tumor (122/88 mm), developed insidious over a period of 3 years and neglected. The patient agreed surgery only for the epicranial tumor. The lesion was completely removed. Postoperatory outcome was excellent concerning this tumor, although the histopathological result was not that great: high anaplastic liposarcoma.Conclusion: Liposarcoma of the scalp is rare. Diagnosis is made histologically. The histopathologic variant influences clinical behavior and prognosis. The treatment of choice is wide surgical excision.&nbsp

iManageMyHealth and iSupportMyPatients: mobile decision support and health management apps for cancer patients and their doctors

Clinical decision support systems can play a crucial role in healthcare delivery as they promise to improve health outcomes and patient safety, reduce medical errors and costs and contribute to patient satisfaction. Used in an optimal way, they increase the quality of healthcare by proposing the right information and intervention to the right person at the right time in the healthcare delivery process. This paper reports on a specific approach to integrated clinical decision support and patient guidance in the cancer domain as proposed by the H2020 iManageCancer project. This project aims at facilitating efficient self-management and management of cancer according to the latest available clinical knowledge and the local healthcare delivery model, supporting patients and their healthcare providers in making informed decisions on treatment choices and in managing the side effects of their therapy. The iManageCancer platform is a comprehensive platform of interconnected mobile tools to empower cancer patients and to support them in the management of their disease in collaboration with their doctors. The backbone of the iManageCancer platform comprises a personal health record and the central decision support unit (CDSU). The latter offers dedicated services to the end users in combination with the apps iManageMyHealth and iSupportMyPatients. The CDSU itself is composed of the so-called Care Flow Engine (CFE) and the model repository framework (MRF). The CFE executes personalised and workflow oriented formal disease management diagrams (Care Flows). In decision points of such a Care Flow, rules that operate on actual health information of the patient decide on the treatment path that the system follows. Alternatively, the system can also invoke a predictive model of the MRF to proceed with the best treatment path in the diagram. Care Flow diagrams are designed by clinical experts with a specific graphical tool that also deploys these diagrams as executable workflows in the CFE following the Business Process Model and Notation (BPMN) standard. They are exposed as services that patients or their doctors can use in their apps in order to manage certain aspects of the cancer disease like pain, fatigue or the monitoring of chemotherapies at home. The mHealth platform for cancer patients is currently being assessed in clinical pilots in Italy and Germany and in several end-user workshops

Resistance of Listeria monocytogenes to Stress Conditions Encountered in Food and Food Processing Environments

Listeria monocytogenes is a human food-borne facultative intracellular pathogen that is resistant to a wide range of stress conditions. As a consequence, L. monocytogenes is extremely difficult to control along the entire food chain from production to storage and consumption. Frequent and recent outbreaks of L. monocytogenes infections illustrate that current measures of decontamination and preservation are suboptimal to control L. monocytogenes in food. In order to develop efficient measures to prevent contamination during processing and control growth during storage of food it is crucial to understand the mechanisms utilized by L. monocytogenes to tolerate the stress conditions in food matrices and food processing environments. Food-related stress conditions encountered by L. monocytogenes along the food chain are acidity, oxidative and osmotic stress, low or high temperatures, presence of bacteriocins and other preserving additives, and stresses as a consequence of applying alternative decontamination and preservation technologies such high hydrostatic pressure, pulsed and continuous UV light, pulsed electric fields (PEF). This review is aimed at providing a summary of the current knowledge on the response of L. monocytogenes toward these stresses and the mechanisms of stress resistance employed by this important food-borne bacterium. Circumstances when L. monocytogenes cells become more sensitive or more resistant are mentioned and existence of a cross-resistance when multiple stresses are present is pointed out

Supporting patient screening to identify suitable clinical trials.

To support the efficient execution of post-genomic multi-centric clinical trials in breast cancer we propose a solution that streamlines the assessment of the eligibility of patients for available trials. The assessment of the eligibility of a patient for a trial requires evaluating whether each eligibility criterion is satisfied and is often a time consuming and manual task. The main focus in the literature has been on proposing different methods for modelling and formalizing the eligibility criteria. However the current adoption of these approaches in clinical care is limited. Less effort has been dedicated to the automatic matching of criteria to the patient data managed in clinical care. We address both aspects and propose a scalable, efficient and pragmatic patient screening solution enabling automatic evaluation of eligibility of patients for a relevant set of trials. This covers the flexible formalization of criteria and of other relevant trial metadata and the efficient management of these representations

ACGT: advancing clinico-genomic trials on cancer - four years of experience.

The challenges regarding seamless integration of distributed, heterogeneous and multilevel data arising in the context of contemporary, post-genomic clinical trials cannot be effectively addressed with current methodologies. An urgent need exists to access data in a uniform manner, to share information among different clinical and research centers, and to store data in secure repositories assuring the privacy of patients. Advancing Clinico-Genomic Trials (ACGT) was a European Commission funded Integrated Project that aimed at providing tools and methods to enhance the efficiency of clinical trials in the -omics era. The project, now completed after four years of work, involved the development of both a set of methodological approaches as well as tools and services and its testing in the context of real-world clinico-genomic scenarios. This paper describes the main experiences using the ACGT platform and its tools within one such scenario and highlights the very promising results obtained

Analysis of the suitability of existing medical ontologies for building a scalable semantic interoperability solution supporting multi-site collaboration in oncology

Semantic interoperability is essential to facilitate efficient collaboration in heterogeneous multi-site healthcare environments. The deployment of a semantic interoperability solution has the potential to enable a wide range of informatics supported applications in clinical care and research both within as ingle healthcare organization and in a network of organizations. At the same time, building and deploying a semantic interoperability solution may require significant effort to carryout data transformation and to harmonize the semantics of the information in the different systems. Our approach to semantic interoperability leverages existing healthcare standards and ontologies, focusing first on specific clinical domains and key applications, and gradually expanding the solution when needed. An important objective of this work is to create a semantic link between clinical research and care environments to enable applications such as streamlining the execution of multi-centric clinical trials, including the identification of eligible patients for the trials. This paper presents an analysis of the suitability of several widely-used medical ontologies in the clinical domain: SNOMED-CT, LOINC, MedDRA, to capture the semantics of the clinical trial eligibility criteria, of the clinical trial data (e.g., Clinical Report Forms), and of the corresponding patient record data that would enable the automatic identification of eligible patients. Next to the coverage provided by the ontologies we evaluate and compare the sizes of the sets of relevant concepts and their relative frequency to estimate the cost of data transformation, of building the necessary semantic mappings, and of extending the solution to new domains. This analysis shows that our approach is both feasible and scalable

Analysis of temporal gene regulation of Listeria monocytogenes revealed distinct regulatory response modes after exposure to high pressure processing

Background The pathogen Listeria (L.) monocytogenes is known to survive heat, cold, high pressure, and other extreme conditions. Although the response of this pathogen to pH, osmotic, temperature, and oxidative stress has been studied extensively, its reaction to the stress produced by high pressure processing HPP (which is a preservation method in the food industry), and the activated gene regulatory network (GRN) in response to this stress is still largely unknown. Results We used RNA sequencing transcriptome data of L. monocytogenes (ScottA) treated at 400 MPa and 8(circle)C, for 8 min and combined it with current information in the literature to create a transcriptional regulation database, depicting the relationship between transcription factors (TFs) and their target genes (TGs) in L. monocytogenes. We then applied network component analysis (NCA), a matrix decomposition method, to reconstruct the activities of the TFs over time. According to our findings, L. monocytogenes responded to the stress applied during HPP by three statistically different gene regulation modes: survival mode during the first 10 min post-treatment, repair mode during 1 h post-treatment, and re-growth mode beyond 6 h after HPP. We identified the TFs and their TGs that were responsible for each of the modes. We developed a plausible model that could explain the regulatory mechanism that L. monocytogenes activated through the well-studied CIRCE operon via the regulator HrcA during the survival mode. Conclusions Our findings suggest that the timely activation of TFs associated with an immediate stress response, followed by the expression of genes for repair purposes, and then re-growth and metabolism, could be a strategy of L. monocytogenes to survive and recover extreme HPP conditions. We believe that our results give a better understanding of L. monocytogenes behavior after exposure to high pressure that may lead to the design of a specific knock-out process to target the genes or mechanisms. The results can help the food industry select appropriate HPP conditions to prevent L. monocytogenes recovery during food storage.Peer reviewe