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Kidney transplantation remains the preferred treatment for patients with end-stage kidney disease. However, the ongoing shortage of donor organs continues to limit the availability of transplant treatments. Existing evaluation methods, such as the kidney donor profile index (KDPI) and pre-transplant donor biopsy (PTDB), have various limitations, including low discriminative power, invasiveness, and sampling errors, which reduce their effectiveness in organ quality assessment and contribute to the risk of unnecessary organ discard. In this study, we explored the dynamic optical coherence tomography (DOCT) as a label-free, non-invasive approach to monitor the viability ofex vivomouse kidneys during static cold storage over 48 hours. The dynamic metrics logarithmic intensity variance (LIV), early OCT correlation decay speed (OCDS_e), and late OCT correlation decay speed (OCDS_l) were extracted from OCT signal fluctuations to quantify temporal and spatial tissue activity and deterioration. Our results demonstrate that DOCT provides complementary information relevant to tissue viability, in addition to the morphological assessment offered by conventional OCT imaging, showing potential to improve pre-transplant organ evaluation and clinic decision-making. 

Optical coherence tomography (OCT) is an ideal imaging technique for noninvasive and longitudinal monitoring of multicellular tumor spheroids (MCTS). However, the internal structure features within MCTS from OCT images are still not fully utilized. In this study, we developed cross-statistical, cross-screening, and composite-hyperparameter feature processing methods in conjunction with 12 machine learning models to assess changes within the MCTS internal structure. Our results indicated that the effective features combined with supervised learning models successfully classify OVCAR-8 MCTS culturing with 5,000 and 50,000 cell numbers, MCTS with pancreatic tumor cells (Panc02-H7) culturing with the ratio of 0%, 33%, 50%, and 67% of fibroblasts, and OVCAR-4 MCTS treated by 2-methoxyestradiol, AZD1208, and R-ketorolac with concentrations of 1, 10, and 25 µM. This approach holds promise for obtaining multi-dimensional physiological and functional evaluations for using OCT and MCTS in anticancer studies. 

Abstract Cancer nanomedicines predominately rely on transport processes controlled by tumor‐associated endothelial cells to deliver therapeutic and diagnostic payloads into solid tumors. While the dominant role of this class of endothelial cells for nanoparticle transport and tumor delivery is established in animal models, the translational potential in human cells needs exploration. Using primary human breast cancer as a model, the differential interactions of normal and tumor‐associated endothelial cells with clinically relevant nanomedicine formulations are explored and quantified. Primary human breast cancer‐associated endothelial cells exhibit up to ≈2 times higher nanoparticle uptake than normal human mammary microvascular endothelial cells. Super‐resolution imaging studies reveal a significantly higher intracellular vesicle number for tumor‐associated endothelial cells, indicating a substantial increase in cellular transport activities. RNA sequencing and gene expression analysis indicate the upregulation of transport‐related genes, especially motor protein genes, in tumor‐associated endothelial cells. Collectively, the results demonstrate that primary human breast cancer‐associated endothelial cells exhibit enhanced interactions with nanomedicines, suggesting a potentially significant role for these cells in nanoparticle tumor delivery in human patients. Engineering nanoparticles that leverage the translational potential of tumor‐associated endothelial cell‐mediated transport into human solid tumors may lead to the development of safer and more effective clinical cancer nanomedicines.