Mathematical generation of data-driven hippocampal CA1 pyramidal neurons and interneurons copies via A-GLIF models for large-scale networks covering the experimental variability range.

Efficient and accurate large-scale networks are a fundamental tool in modeling brain areas, to advance our understanding of neuronal dynamics. However, their implementation faces two key issues: computational efficiency and heterogeneity. Computational efficiency is achieved using simplified neurons, whereas there are no practical solutions available to solve the problem of reproducing in a large-scale network the experimentally observed heterogeneity of the intrinsic properties of neurons. This is important, because the use of identical nodes in a network can generate artifacts which can hinder an adequate representation of the properties of a real network. To this aim, we introduce a mathematical procedure to generate an arbitrary large number of copies of simplified hippocampal CA1 pyramidal neurons and interneurons models, which exhibit the full range of firing dynamics observed in these cells - including adapting, non-adapting and bursting. For this purpose, we rely on a recently published adaptive generalized leaky integrate-and-fire (A-GLIF) modeling approach, leveraging on its ability to reproduce the rich set of electrophysiological behaviors of these types of neurons under a variety of different stimulation currents. The generation procedure is based on a perturbation of model's parameters related to the initial data, firing block, and internal dynamics, and suitably validated against experimental data to ensure that the firing dynamics of any given cell copy remains within the experimental range. A classification procedure confirmed that the firing behavior of most of the pyramidal/interneuron copies was consistent with the experimental data. This approach allows to obtain heterogeneous copies with mathematically controlled firing properties. A full set of heterogeneous neurons composing the CA1 region of a rat hippocampus (approximately 1.2 million neurons), are provided in a database freely available in the live paper section of the EBRAINS platform. By adapting the underlying A-GLIF framework, it will be possible to extend the numerical approach presented here to create, in a mathematically controlled manner, an arbitrarily large number of non-identical copies of cell populations with firing properties related to other brain areas.

A phase I/II study of GLIF combination chemotherapy for taxane/platinum-refractory/resistant endometrial cancer (GOGO-EM2).

PURPOSE: Development of new treatment strategies for endometrial cancer that has become refractory or resistant to taxane/platinum is a critical need. The present study was a phase I/II study of gemcitabine, levofolinate, irinotecan, and 5-fluorouracil (5-FU) (GLIF) combination chemotherapy to determine optimal dosages, safety, and efficacy. METHODS: Taxane/platinum-resistant or -refractory endometrial disease was defined as tumor progression within 6 months after a taxane/platinum-based regimen. Maximum tolerated dose was investigated by a 3 + 3-designed phase I study. The phase II study was conducted using the recommended doses determined in the phase I study. RESULTS: The dosages recommended for the phase II trial were determined, in the phase I trial, to be: gemcitabine 800 mg/m2, levofolinate 100 mg/m2, irinotecan 80 mg/m2, and 5-FU 1000 mg/m2. Thirty patients were enrolled, including the three patients who received GLIF therapy at the same dose as the recommended phase II dose in the phase I study. Two patients were excluded at this point due to study protocol violations, and the remaining 28 patients were included for analysis. Phase II revealed that the response and disease control rates were 7.1% (2/28) and 39.3% (11/28), respectively, and that the median PFS and OS were 3 months [95% confidence interval (CI) 3-7] and 12 months (95% CI 9-17), respectively. Febrile or grade 4 neutropenia was observed in 14% (4/28) of the cases. Grade 3 or 4 thrombocytopenia was not observed. CONCLUSION: We found that GLIF combination chemotherapy is potentially a useful treatment option for endometrial cancers refractory or resistant to taxane/platinum-based chemotherapy.

HAART Prescription Support Tool for Personalized Therapy.

The importance of decision support systems is highly acknowledged as a key strategy to improve medical safety and quality of care. A strong interoperability between the hospital Electronic Health Record (EHR) and the Clinical Decision Support System (CDSS) is the key to reach a most reliable decision support, that could aid in better diagnosis, reduce medication errors and improve practitioner performances. Interoperability is granted by the use of standards for data representation and for system intercommunication. A CDSS to support HAART (Highly active antiretroviral therapy) prescription in HIV (Human Immunodeficiency Virus) naive patients was developed within the Ligurian HIV Network. GLIF3 (GuideLine Interchange Format) standard was used to represent clinical guidelines in machine interpretable format. HSSP (Healthcare Services Specification Project) DSS (Decision Support System) standard was used to develop the CDSS web service that evaluates patient's data according to the rules emerging from the GLIF representation of the guidelines. Patient's data are extracted from hospital EHR, formatted into standard vMR (virtual Medical Record) documents and sent to the DSS web service to be evaluated. The results are displayed by a client application in an intuitive way to guide physician's decisions.

A meta-model for computer executable dynamic clinical safety checklists.

BACKGROUND: Safety checklist is a type of cognitive tool enforcing short term memory of medical workers with the purpose of reducing medical errors caused by overlook and ignorance. To facilitate the daily use of safety checklists, computerized systems embedded in the clinical workflow and adapted to patient-context are increasingly developed. However, the current hard-coded approach of implementing checklists in these systems increase the cognitive efforts of clinical experts and coding efforts for informaticists. This is due to the lack of a formal representation format that is both understandable by clinical experts and executable by computer programs. METHODS: We developed a dynamic checklist meta-model with a three-step approach. Dynamic checklist modeling requirements were extracted by performing a domain analysis. Then, existing modeling approaches and tools were investigated with the purpose of reusing these languages. Finally, the meta-model was developed by eliciting domain concepts and their hierarchies. The feasibility of using the meta-model was validated by two case studies. The meta-model was mapped to specific modeling languages according to the requirements of hospitals. RESULTS: Using the proposed meta-model, a comprehensive coronary artery bypass graft peri-operative checklist set and a percutaneous coronary intervention peri-operative checklist set have been developed in a Dutch hospital and a Chinese hospital, respectively. The result shows that it is feasible to use the meta-model to facilitate the modeling and execution of dynamic checklists. CONCLUSIONS: We proposed a novel meta-model for the dynamic checklist with the purpose of facilitating creating dynamic checklists. The meta-model is a framework of reusing existing modeling languages and tools to model dynamic checklists. The feasibility of using the meta-model is validated by implementing a use case in the system.

Status quo of knowledge expression model of clinical guidelines / 中华医学图书情报杂志

Clinical guidelines,coming into being with the development of evidence-based medicine,are of great significance in reducing medical malpractice and regulating medical behaviors.However,more effective and intelligent measures should be taken for their development and application due to their low utilization and poor compliance of doctors in their implementation.The emergence and structure of clinical guidelines were elaborated with related problems summarized.The knowledge expression models of digital clinical guidelines were analyzed and compared with the challenges faced by the current knowledge expression models of clinical guidelines pointed out.