Efficient and Rapid Microfluidics Production of Bio-Inspired Nanoparticles Derived from Silkworm for Enhanced Breast Cancer Treatment.

: In the quest for sustainable and biocompatible materials, silk fibroin (SF), derived from natural silk, has emerged as a promising candidate for nanoparticle production. This study aimed to fabricate silk fibroin particles (SFPs) using a novel swirl mixer previously presented by our group, evaluating their characteristics and suitability for drug delivery applications, including magnetic nanoparticles and dual-drug encapsulation with curcumin (CUR) and 5-fluorouracil (5-FU). : SFPs were fabricated via microfluidics-assisted desolvation using a swirl mixer, ensuring precise mixing kinetics.

Evaluation of cyclin-dependent kinase 4/6 inhibitor-induced serum creatinine elevations in patients with hormone receptor positive breast cancer.

Background: Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6i) have improved the efficacy of endocrine therapy in hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer (BC) and are now used in both early-stage and metastatic disease. Recent case reports suggest that pseudo-serum creatinine (Scr) elevations are likely a class effect of CDK4/6i.

Methods: This single-center retrospective analysis included patients aged ≥18 years who received at least one dose of palbociclib, ribociclib, or abemaciclib for the treatment of HR+/HER2- BC in the early or advanced setting.

Generative adversarial networks accurately reconstruct pan-cancer histology from pathologic, genomic, and radiographic latent features.

Artificial intelligence models have been increasingly used in the analysis of tumor histology to perform tasks ranging from routine classification to identification of molecular features. These approaches distill cancer histologic images into high-level features, which are used in predictions, but understanding the biologic meaning of such features remains challenging. We present and validate a custom generative adversarial network-HistoXGAN-capable of reconstructing representative histology using feature vectors produced by common feature extractors.

Predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer using a machine learning approach.

Background: For patients with breast cancer undergoing neoadjuvant chemotherapy (NACT), most of the existing prediction models of pathologic complete response (pCR) using clinicopathological features were based on standard statistical models like logistic regression, while models based on machine learning mostly utilized imaging data and/or gene expression data. This study aims to develop a robust and accessible machine learning model to predict pCR using clinicopathological features alone, which can be used to facilitate clinical decision-making in diverse settings.

Methods: The model was developed and validated within the National Cancer Data Base (NCDB, 2018-2020) and an external cohort at the University of Chicago (2010-2020).