Global Health Security in Vision Care: Health Systems Strengthening During Ebola Virus Disease Outbreak Responses.

During the last decade, global health security has been threatened by major Ebola virus disease outbreaks in Western Africa (2014 to 2016) and in eastern Democratic Republic of the Congo (2018 to 2020). Particularly in Western Africa, the outbreak initially overwhelmed health care capacity in already fragile health systems. Thousands of survivors were at risk of newly recognized postacute ocular complications, and their need for urgent ophthalmic care challenged national vision health systems with scarce eye care services.

The WHO R&D Blueprint: 2018 review of emerging infectious diseases requiring urgent research and development efforts.

The Research and Development (R&D) Blueprint is a World Health Organization initiative to reduce the time between the declaration of a public health emergency and the availability of effective diagnostic tests, vaccines, and treatments that can save lives and avert a public health crisis. The scope of the Blueprint extends to severe emerging diseases for which there are insufficient or no presently existing medical countermeasures or pipelines to produce them. In February 2018, WHO held an informal expert consultation to review and update the list of priority diseases, employing a prioritization methodology which uses the Delphi technique, questionnaires, multi-criteria decision analysis, and expert review to identify relevant diseases.

World Health Organization Methodology to Prioritize Emerging Infectious Diseases in Need of Research and Development.

The World Health Organization R&D Blueprint aims to accelerate the availability of medical technologies during epidemics by focusing on a list of prioritized emerging diseases for which medical countermeasures are insufficient or nonexistent. The prioritization process has 3 components: a Delphi process to narrow down a list of potential priority diseases, a multicriteria decision analysis to rank the short list of diseases, and a final Delphi round to arrive at a final list of 10 diseases. A group of international experts applied this process in January 2017, resulting in a list of 10 priority diseases.

Optimizing Multiple Analyte Injections in Surface Plasmon Resonance Biosensors with Analytes having Different Refractive Index Increments.

Surface plasmon resonance-based biosensors have been successfully applied to the study of the interactions between macromolecules and small molecular weight compounds. In an effort to increase the throughput of these SPR-based experiments, we have already proposed to inject multiple compounds simultaneously over the same surface. When specifically applied to small molecular weight compounds, such a strategy would however require prior knowledge of the refractive index increment of each compound in order to correctly interpret the recorded signal.

On-line kinetic model discrimination for optimized surface plasmon resonance experiments.

In order to improve the throughput of surface plasmon resonance-based biosensors, an on-line iterative optimization algorithm has been presented aiming at reducing experimental time and material consumption without any loss of confidence on kinetic parameters [De Crescenzo (2008) J. Mol Recognit., 21, 256-66.

Increasing throughput of surface plasmon resonance-based biosensors by multiple analyte injections.

Surface plasmon resonance-based biosensors are now acknowledged as robust and reliable instruments to determine the kinetic parameters related to the interactions between biomolecules. These kinetic parameters are used in screening campaigns: there is a considerable interest in reducing the experimental time, thus improving the throughput of the surface plasmon resonance assays. Kinetic parameters are typically obtained by analyzing data from several injections of a given analyte at different concentrations over a surface where its binding partner has been immobilized.

Estimation of analyte concentration by surface plasmon resonance-based biosensing using parameter identification techniques.

Surface plasmon resonance-based biosensors have been applied to the determination of macromolecule concentration. Up to now, the proposed experimental approaches have relied either on the generation of a calibration curve that exploits only a few data points from each sensorgram or on multiple injections of the unknown sample at various flow rates. In this article, we show that prior knowledge of the kinetic parameters related to the interaction of the species with a given partner could advantageously reduce the number of injections required by both aforementioned methods, thereby reducing experimental time while maintaining a good level of confidence on the determined concentrations.