Leveraging survival analysis and machine learning for accurate prediction of breast cancer recurrence and metastasis.

Breast cancer, with its high incidence and mortality globally, necessitates early prediction of local and distant recurrence to improve treatment outcomes. This study develops and validates predictive models for breast cancer recurrence and metastasis using Recurrence-Free Survival Analysis and machine learning techniques. We merged datasets from the Molecular Taxonomy of Breast Cancer International Consortium, Memorial Sloan Kettering Cancer Center, Duke University, and the SEER program, creating a comprehensive dataset of 272, 252 rows and 23 columns.

Prognostic impact of matrix metalloproteinase 2 (MMP-2) and matrix metalloproteinase 9 (MMP-9) in Egyptian breast cancer patients.

This study aims to investigate the diagnostic and prognostic relevance of MMP-2 and MMP-9 as biomarkers for breast cancer, as well as their association with clinicopathological factors. Breast cancer is a leading contributor to cancer-related deaths among women worldwide. The discovery of biomarkers is crucial for early diagnosis, outcome prediction, and effective treatment.

Identifying vulnerabilities in essential health services: Analysing the effects of system shocks on childhood vaccination delivery in Lebanon.

Shocks effects are under-theorised in the growing literature on health system resilience. Existing work has focused on the effects of single shocks on discrete elements within the health system, typically at national level. Using qualitative system dynamics, we explored how effects of multiple shocks interacted across system levels and combined with existing vulnerabilities to produce effects on essential health services delivery, through the prism of a case study on childhood vaccination in Lebanon.

Independent concentration extraction as a novel green approach resolving overlapped UV-Vis binary spectra and HPTLC-densitometric methods for concurrent determination of levocloperastine and chlorpheniramine.

Background: The proposed research study introduces independent concentration extraction (ICE) as a novel UV-Vis spectrophotometric approach. The approach can be used for extracting the concentration of two analytes with severely overlapped spectra from their binary mixtures. ICE is based on spectral extraction platform involving simple smart successive methods that can directly extract the original zero order spectra of the analytes at their characteristic (λ).