ABSTRACT
Metallic nanomaterials have gained significant attention in cancer therapy as potential nanocarriers due to their unique properties at the nanoscale. However, nanomaterials face several drawbacks, including biocompatibility, stability, and cellular uptake. Hematite (α-Fe2O3) nanoparticles are emerging as promising nano-carriers to reduce adverse outcomes of conventional chemotherapeutics. However, the shape-mediated drug carrier mechanics of hematite nanomaterials are not raveled. In this study, we tailored hematite nanoparticles in ellipsoidal (EHNP) and spherical (SHNP) shapes with excellent biocompatibility and efficient drug encapsulation and release. We elucidate that EHNP exhibits higher cellular uptake than SHNP. With effective cellular internalization, the cisplatin-loaded EHNP showed excellent cytotoxicity with an IC50 value of 200 nM compared to the cisplatin-loaded SHNP. The flow cytometry cell sorting (FACS) analysis showed a four-fold increase in cell death by arresting the cells at the G0/G1 and G1 phases for cis-EHNP compared to cis-SHNP. The results show that ellipsoidal-shaped hematite nanoparticles can act as attractive nanocarriers with improved therapeutic efficacy in cancer therapy.
Subject(s)
Antineoplastic Agents , Breast Neoplasms , Cisplatin , Drug Carriers , Ferric Compounds , Humans , Ferric Compounds/chemistry , Ferric Compounds/pharmacology , Drug Carriers/chemistry , Breast Neoplasms/drug therapy , Breast Neoplasms/pathology , Antineoplastic Agents/chemistry , Antineoplastic Agents/pharmacology , Cisplatin/pharmacology , Cisplatin/chemistry , Female , Particle Size , Drug Screening Assays, Antitumor , Cell Survival/drug effects , Cell Proliferation/drug effects , MCF-7 CellsABSTRACT
Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine learning technique to estimate heart-rate from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery-life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects is considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.
