Resistance to FOXM1 inhibitors in breast cancer is accompanied by impeding ferroptosis and apoptotic cell death.

PURPOSE: Cancer treatments often become ineffective because of acquired drug resistance. To characterize changes in breast cancer cells accompanying development of resistance to inhibitors of the oncogenic transcription factor, FOXM1, we investigated the suppression of cell death pathways, especially ferroptosis, in FOXM1 inhibitor-resistant cells. We also explored whether ferroptosis activators can synergize with FOXM1 inhibitors and can overcome FOXM1 inhibitor resistance. METHODS: In estrogen receptor-positive and triple-negative breast cancer cells treated with FOXM1 inhibitor NB73 and ferroptosis activators dihydroartemisinin and JKE1674, alone and in combination, we measured suppression of cell viability, motility, and colony formation, and monitored changes in gene and protein pathway expressions and mitochondrial integrity. RESULTS: Growth suppression of breast cancer cells by FOXM1 inhibitors is accompanied by increased cell death and alterations in mitochondrial morphology and metabolic activity. Low doses of FOXM1 inhibitor strongly synergize with ferroptosis inducers to reduce cell viability, migration, colony formation, and expression of proliferation-related genes, and increase intracellular Fe+2 and lipid peroxidation, markers of ferroptosis. Acquired resistance to FOXM1 inhibition is associated with increased expression of cancer stem-cell markers and proteins that repress ferroptosis, enabling cell survival and drug resistance. Notably, resistant cells are still sensitive to growth suppression by low doses of ferroptosis activators, effectively overcoming the acquired resistance. CONCLUSION: Delineating changes in viability and cell death pathways that can overcome drug resistance should be helpful in determining approaches that might best prevent or reverse resistance to therapeutic targeting of FOXM1 and ultimately improve patient clinical outcomes.

Imbalanced spectral data analysis using data augmentation based on the generative adversarial network.

Spectroscopic techniques generate one-dimensional spectra with distinct peaks and specific widths in the frequency domain. These features act as unique identities for material characteristics. Deep neural networks (DNNs) has recently been considered a powerful tool for automatically categorizing experimental spectra data by supervised classification to evaluate material characteristics. However, most existing work assumes balanced spectral data among various classes in the training data, contrary to actual experiments, where the spectral data is usually imbalanced. The imbalanced training data deteriorates the supervised classification performance, hindering understanding of the phase behavior, specifically, sol-gel transition (gelation) of soft materials and glycomaterials. To address this issue, this paper applies a novel data augmentation method based on a generative adversarial network (GAN) proposed by the authors in their prior work. To demonstrate the effectiveness of the proposed method, the actual imbalanced spectral data from Pluronic F-127 hydrogel and Alpha-Cyclodextrin hydrogel are used to classify the phases of data. Specifically, our approach improves 8.8%, 6.4%, and 6.2% of the performance of the existing data augmentation methods regarding the classifier's F-score, Precision, and Recall on average, respectively. Specifically, our method consists of three DNNs: the generator, discriminator, and classifier. The method generates samples that are not only authentic but emphasize the differentiation between material characteristics to provide balanced training data, improving the classification results. Based on these validated results, we expect the method's broader applications in addressing imbalanced measurement data across diverse domains in materials science and chemical engineering.

Synthesis and real-time characterization of self-healing, injectable, fast-gelling hydrogels based on alginate multi-reducing end polysaccharides (MREPs).

Polysaccharide-based hydrogels are promising for many biomedical applications including drug delivery, wound healing, and tissue engineering. We illustrate herein self-healing, injectable, fast-gelling hydrogels prepared from multi-reducing end polysaccharides, recently introduced by the Edgar group. Simple condensation of reducing ends from multi-reducing end alginate (M-Alg) with amines from polyethylene imine (PEI) in water affords a dynamic, hydrophilic polysaccharide network. Trace amounts of acetic acid can accelerate the gelation time from hours to seconds. The fast-gelation behavior is driven by rapid Schiff base formation and strong ionic interactions induced by acetic acid. A cantilever rheometer enables real-time monitoring of changes in viscoelastic properties during hydrogel formation. The reversible nature of these crosslinks (imine bonds, ionic interactions) provides a hydrogel with low toxicity in cell studies as well as self-healing and injectable properties. Therefore, the self-healing, injectable, and fast-gelling M-Alg/PEI hydrogel holds substantial promise for biomedical, agricultural, controlled release, and other applications.

Targeting Health-Related Social Risks in the Clinical Setting: New Policy Momentum and Practice Considerations.

The federal government is funding a sea change in health care by investing in interventions targeting social determinants of health, which are significant contributors to illness and health inequity. This funding power has encouraged states, professional and accreditation organizations, health care entities, and providers to focus heavily on social determinants. We examine how this shift in focus affects clinical practice in the fields of oncology and emergency medicine, and highlight potential areas of reform.