Background: The sterile insect technique and transgenic equivalents are considered promising tools for controlling vector-borne disease in an age of increasing insecticide and drug-resistance. Combining vector interventions with artemisinin-based therapies may achieve the twin goals of suppressing malaria endemicity while managing artemisinin resistance. While the cost-effectiveness of these controls has been investigated independently, their combined usage has not been dynamically optimized in response to ecological and epidemiological processes.
Results: An optimal control framework based on coupled models of mosquito population dynamics and malaria epidemiology is used to investigate the cost-effectiveness of combining vector control with drug therapies in homogeneous environments with and without vector migration. The costs of endemic malaria are weighed against the costs of administering artemisinin therapies and releasing modified mosquitoes using various cost structures. Larval density dependence is shown to reduce the cost-effectiveness of conventional sterile insect releases compared with transgenic mosquitoes with a late-acting lethal gene. Using drug treatments can reduce the critical vector control release ratio necessary to cause disease fadeout.
Conclusions: Combining vector control and drug therapies is the most effective and efficient use of resources, and using optimized implementation strategies can substantially reduce costs.
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http://dx.doi.org/10.1186/s12936-018-2321-6 | DOI Listing |
Sci Rep
January 2025
College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.
View Article and Find Full Text PDFSci Rep
January 2025
Ministry of Higher Education, Mataria Technical College, Cairo, 11718, Egypt.
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.
View Article and Find Full Text PDFObjective: The objective of this research was to devise and authenticate a predictive model that employs CT radiomics and deep learning methodologies for the accurate prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC).
Methods: A total of 143 ccRCC patients were included in the training cohort, and 62 ccRCC patients were included in the validation cohort. The CT images from all patients were normalized, and the tumor regions were manually segmented via ITK-SNAP software.
Updates Surg
January 2025
Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy.
Clinical risk prediction models are ubiquitous in many surgical domains. The traditional approach to develop these models involves the use of regression analysis. Machine learning algorithms are gaining in popularity as an alternative approach for prediction and classification problems.
View Article and Find Full Text PDFJ Inorg Biochem
January 2025
College of Chemistry and Chemical Engineering, China University of Petroleum (East China), Qingdao 266580, China. Electronic address:
Developing multifunctional nanomedicines represents a frontier. We have engineered a high-capacity DNA vector basing rolling circle amplification for the delivery of copper sulfide nanoparticles (CuS NPs) and doxorubicin (DOX), coupled with multivalent aptamers (MA) that precisely target tumors, culminating in a multifunctional nanoplatform (RMALCu@DOX), which combines the chemotherapy (CT)/photothermal therapy (PTT)/chemodynamic therapy (CDT). The vector (RMAL) boasts exceptional biocompatibility and incorporates multiple copy units, enabling the precise loading of numerous CuS NPs, forming RMALCu which possesses a robust photothermal effect and superior Fenton-like catalytic activity, heralding a project of minimally invasive dual-mode (PTT/CDT) therapy.
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