In this paper we propose a debate on the role of mathematical models in evaluating control strategies for vector-borne infections. Mathematical models must have their complexity adjusted to their goals, and we have basically two classes of models. At one extreme we have models that are intended to check if our intuition about why a certain phenomenon occurs is correct. At the other extreme, we have models whose goals are to predict future outcomes. These models are necessarily very complex. There are models in between these classes. Here we examine two models, one of each class and study the possible pitfalls that may be incurred. We begin by showing how to simplify the description of a complicated model for a vector-borne infection. Next, we examine one example found in a recent paper that illustrates the dangers of basing control strategies on models without considering their limitations. The model in this paper is of the second class. Following this, we review an interesting paper (a model of the first class) that contains some biological assumptions that are inappropriate for dengue but may apply to other vector-borne infections. In conclusion, we list some misgivings about modelling presented in this paper for debate.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9507249 | PMC |
http://dx.doi.org/10.1017/S0950268814002660 | DOI Listing |
Physiol Plant
January 2025
Laboratory of Biochemistry, Institut Químic de Sarrià, Universitat Ramon Llull, Barcelona, Spain.
Photosynthetic microalgae are promising green cell factories for the sustainable production of high-value chemicals and biopharmaceuticals. The chloroplast organelle is being developed as a chassis for synthetic biology as it contains its own genome (the plastome) and some interesting advantages, such as high recombinant protein titers and a diverse and dynamic metabolism. However, chloroplast engineering is currently hampered by the lack of standardized cloning tools and Design-Build-Test-Learn workflows to ease genomic and metabolic engineering.
View Article and Find Full Text PDFMed Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
Med Phys
January 2025
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Background: Diffusing alpha-emitters Radiation Therapy ("Alpha DaRT") is a promising new radiation therapy modality for treating bulky tumors. Ra-carrying sources are inserted intratumorally, producing a therapeutic alpha-dose region with a total size of a few millimeter via the diffusive motion of Ra's alpha-emitting daughters. Clinical studies of Alpha DaRT have reported 100% positive response (30%-100% shrinkage within several weeks), with post-insertion swelling in close to half of the cases.
View Article and Find Full Text PDFMed Phys
January 2025
Department of Engineering Physics, Tsinghua University, Beijing, China.
Background: X-ray grating-based dark-field imaging can sense the small angle scattering caused by object's micro-structures. This technique is sensitive to the porous microstructure of lung alveoli and has the potential to detect lung diseases at an early stage. Up to now, a human-scale dark-field CT (DF-CT) prototype has been built for lung imaging.
View Article and Find Full Text PDFGeroscience
January 2025
Department of Neurology, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea.
Background: Superagers, older adults with exceptional cognitive abilities, show preserved brain structure compared to typical older adults. We investigated whether superagers have biologically younger brains based on their structural integrity.
Methods: A cohort of 153 older adults (aged 61-93) was recruited, with 63 classified as superagers based on superior episodic memory and 90 as typical older adults, of whom 64 were followed up after two years.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!