Multiple costing tools have been developed to understand the resources required to build and sustain implementation of the International Health Regulations (IHR), including a detailed costing tool developed by WHO ("WHO Costing Tool") and 2 action-based costing tools, Georgetown University's IHR Costing Tool and CDC's Priority Actions Costing Tool (PACT). The relative performance of these tools is unknown. Nigeria costed its National Action Plan for Health Security (NAPHS) using the WHO Costing Tool. We conducted a desktop review, using the other tools to compare the cost estimates generated using different costing approaches. Technical working groups developed activity plans and estimated component costs using the WHO Costing Tool during a weeklong workshop with approximately 60 participants from various ministries, departments, and federal agencies. We retrospectively applied the IHR Costing Tool and PACT to generate rapid cost estimates required to achieve a Joint External Evaluation (JEE) score of "demonstrated capacity" (level 4). The tools generated similar activities for implementation. Cost estimates varied based on the anticipated procurement and human resources requirements and by the level of implementation (eg, health facility-level versus local government area-level procurement). The desktop IHR Costing Tool and PACT tools required approximately 2 and 8 person-hours to complete, respectively. A strategic costing approach, wherein governments select from a menu of recommended and costed actions following the JEE to develop a NAPHS, could accelerate implementation of plans. Major cost drivers, including procurement and human resources, should be prioritized based on anticipated resource availability and countries' priorities.
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http://dx.doi.org/10.1089/hs.2019.0063 | DOI Listing |
J Chem Theory Comput
January 2025
Exscientia, Schrödinger Building, Oxford Science Park, Oxford OX4 4GE, U.K.
The development of machine-learning (ML) potentials offers significant accuracy improvements compared to molecular mechanics (MM) because of the inclusion of quantum-mechanical effects in molecular interactions. However, ML simulations are several times more computationally demanding than MM simulations, so there is a trade-off between speed and accuracy. One possible compromise are hybrid machine learning/molecular mechanics (ML/MM) approaches with mechanical embedding that treat the intramolecular interactions of the ligand at the ML level and the protein-ligand interactions at the MM level.
View Article and Find Full Text PDFComput Biol Chem
December 2024
School of Software, Henan Polytechnic University, Jiaozuo 454003, China. Electronic address:
Background: Compound-protein interaction (CPI) is essential to drug discovery and design, where traditional methods are often costly and have low success rates. Recently, the integration of machine learning and deep learning in CPI research has shown potential to reduce costs and enhance discovery efficiency by improving protein target identification accuracy. Additionally, with an urgent need for novel therapies against complex diseases, CPI investigation could lead to the identification of effective new drugs.
View Article and Find Full Text PDFShock
October 2024
Massachusetts General Hospital, Department of Pediatrics.
Background: Early, accurate determination of disease severity in an emergency setting is paramount for improving patient outcomes and healthcare costs. Monocyte anisocytosis, quantified as monocyte distribution width (MDW), has been shown to correspond with immune dysregulation. We hypothesize that MDW is broadly associated with illness severity related to sepsis and serious infection in children.
View Article and Find Full Text PDFJ Appl Lab Med
January 2025
Service of Biochemistry, Clínica Universidad de Navarra, Pamplona, Spain.
Background: In prolactinoma diagnosis, current guidelines recommend prolactin (PRL) assessment, considering values exceeding 200 ng/mL highly suggestive of prolactinoma. However, subtler hyperprolactinemia is more common, and to rule out potential prolactinomas, pituitary resonance magnetic imaging (MRI) studies are necessary. These present limitations in terms of availability, costs, and delays in diagnosis.
View Article and Find Full Text PDFMol Ecol Resour
January 2025
Section for Molecular Ecology and Evolution, Globe Institute, University of Copenhagen, Copenhagen, Denmark.
Reduced representation sequencing (RRS) has proven to be a cost-effective solution for sequencing subsets of the genome in non-model species for large-scale studies. However, the targeted nature of RRS approaches commonly introduces large amounts of missing data, leading to reduced statistical power and biased estimates in downstream analyses. Genotype imputation, the statistical inference of missing sites across the genome, is a powerful alternative to overcome the caveats associated with missing sites.
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