Effective forecasting of demand for healthcare services requires nine steps: 1. Assemble historical data. 2. Analyze historical trends. 3. Identify key demand drivers. 4. Identify relevant benchmarks. 5. Model existing conditions. 6. Develop core assumptions for population-based demand. 7. Develop core assumptions for provider-level demand. 8. Create a baseline forecast of future demand. 9. Test sensitivity of projections to changes in core assumptions.
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New Phytol
January 2025
Section for Plant Biochemistry and Copenhagen Plant Science Centre, Department of Plant and Environmental Sciences, University of Copenhagen, 1871, Frederiksberg, Denmark.
Lupins are promising protein crops that accumulate toxic quinolizidine alkaloids (QAs) in the seeds, complicating their end-use. QAs are synthesized in green organs (leaves, stems, and pods) and a subset of them is transported to the seeds during fruit development. The exact sites of biosynthesis and accumulation remain unknown; however, mesophyll cells have been proposed as sources, and epidermal cells as sinks.
View Article and Find Full Text PDFRen Fail
December 2025
Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA.
Identifying risk factors for disease onset and progression has been a core focus in nephrology research. Mendelian Randomization (MR) has emerged as a powerful genetic epidemiological approach, utilizing genome-wide association studies (GWAS) to establish causal relationships between modifiable risk factors and kidney disease outcomes. MR uses genetic variants as instrumental variables to infer causal relationships between exposures and disease outcomes.
View Article and Find Full Text PDFJ Cheminform
January 2025
Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, UK.
Current strategies centred on either merging or linking initial hits from fragment-based drug design (FBDD) crystallographic screens generally do not fully leaverage 3D structural information. We show that an algorithmic approach (Fragmenstein) that 'stitches' the ligand atoms from this structural information together can provide more accurate and reliable predictions for protein-ligand complex conformation than general methods such as pharmacophore-constrained docking. This approach works under the assumption of conserved binding: when a larger molecule is designed containing the initial fragment hit, the common substructure between the two will adopt the same binding mode.
View Article and Find Full Text PDFPharmacoepidemiol Drug Saf
January 2025
Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
One of the key challenges in pharmacoepidemiological studies is that of uncontrolled confounding, which occurs when confounders are poorly measured, unmeasured or unknown. Self-controlled designs can help address this issue, as their key comparison is not between people, but periods of time within the same person. This controls for all time-stable confounders (genetics) and in the absence of time-varying confounding negates the need for an external control group.
View Article and Find Full Text PDFImmunol Rev
January 2025
Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, Indiana, USA.
αβ T cell receptor (TCR) recognition of peptide-MHC complexes lies at the core of adaptive immunity, balancing specificity and cross-reactivity to facilitate effective antigen discrimination. Early structural studies established basic frameworks helpful for understanding and contextualizing TCR recognition and features such as peptide specificity and MHC restriction. However, the growing TCR structural database and studies launched from structural work continue to reveal exceptions to common assumptions and simplifications derived from earlier work.
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