AKI treated with kidney replacement therapy in critically Ill allogeneic hematopoietic stem cell transplant recipients.

Acute kidney injury (AKI) is a frequent complication following allogeneic hematopoietic stem cell transplantation (allo-HSCT), but few studies have focused on AKI treated with kidney replacement therapy (AKI-KRT), particularly among critically ill patients. We investigated the incidence, risk factors, and 90-day mortality associated with AKI-KRT in 529 critically ill adult allo-HSCT recipients admitted to the ICU within 1-year post-transplant at two academic medical centers between 2011 and 2021. AKI-KRT occurred in 111 of the 529 patients (21.0%). Lower baseline eGFR, veno-occlusive disease, thrombotic microangiopathy, admission to an ICU within 90 days post-transplant, and receipt of invasive mechanical ventilation (IMV), total bilirubin ≥5.0 mg/dl, and arterial pH <7.40 on ICU admission were each associated with a higher risk of AKI-KRT. Of the 111 patients with AKI-KRT, 97 (87.4%) died within 90 days. Ninety-day mortality was 100% in each of the following subgroups: serum albumin ≤2.0 g/dl, total bilirubin ≥7.0 mg/dl, arterial pH ≤7.20, IMV with moderate-to-severe hypoxemia, and ≥3 vasopressors/inotropes at KRT initiation. AKI-KRT was associated with a 6.59-fold higher adjusted 90-day mortality in critically ill allo-HSCT vs. non-transplanted patients. Short-term mortality remains exceptionally high among critically ill allo-HSCT patients with AKI-KRT, highlighting the importance of multidisciplinary discussions prior to KRT initiation.

Deep Learning-Based Artificial Intelligence to Investigate Targeted Nanoparticles' Uptake in TNBC Cells.

Triple negative breast cancer (TNBC) is the most aggressive subtype of breast cancer in women. It has the poorest prognosis along with limited therapeutic options. Smart nano-based carriers are emerging as promising approaches in treating TNBC due to their favourable characteristics such as specifically delivering different cargos to cancer cells. However, nanoparticles' tumour cell uptake, and subsequent drug release, are essential factors considered during the drug development process. Contemporary qualitative analyses based on imaging are cumbersome and prone to human biases. Deep learning-based algorithms have been well-established in various healthcare settings with promising scope in drug discovery and development. In this study, the performance of five different convolutional neural network models was evaluated. In this research, we investigated two sequential models from scratch and three pre-trained models, VGG16, ResNet50, and Inception V3. These models were trained using confocal images of nanoparticle-treated cells loaded with a fluorescent anticancer agent. Comparative and cross-validation analyses were further conducted across all models to obtain more meaningful results. Our models showed high accuracy in predicting either high or low drug uptake and release into TNBC cells, indicating great translational potential into practice to aid in determining cellular uptake at the early stages of drug development in any area of research.

Potential Nanotechnology-Based Therapeutics to Prevent Cancer Progression through TME Cell-Driven Populations.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer with a high risk of metastasis and therapeutic resistance. These issues are closely linked to the tumour microenvironment (TME) surrounding the tumour tissue. The association between residing TME components with tumour progression, survival, and metastasis has been well elucidated. Focusing on cancer cells alone is no longer considered a viable approach to therapy; thus, there is a high demand for TME targeting. The benefit of using nanoparticles is their preferential tumour accumulation and their ability to target TME components. Several nano-based platforms have been investigated to mitigate microenvironment-induced angiogenesis, therapeutic resistance, and tumour progression. These have been achieved by targeting mesenchymal originating cells (e.g., cancer-associated fibroblasts, adipocytes, and stem cells), haematological cells (e.g., tumour-associated macrophages, dendritic cells, and myeloid-derived suppressor cells), and the extracellular matrix within the TME that displays functional and architectural support. This review highlights the importance of nanotechnology-based therapeutics as a promising approach to target the TME and improve treatment outcomes for TNBC patients, which can lead to enhanced survival and quality of life. The role of different nanotherapeutics has been explored in the established TME cell-driven populations.