Epigallocatechin-3-gallate at the nanoscale: a new strategy for cancer treatment.

CONTEXT: Epigallocatechin-3-gallate (EGCG), the predominant catechin in green tea, has shown the potential to combat various types of cancer cells through its ability to modulate multiple signaling pathways. However, its low bioavailability and rapid degradation hinder its clinical application. OBJECTIVE: This review explores the potential of nanoencapsulation to enhance the stability, bioavailability, and therapeutic efficacy of EGCG in cancer treatment. METHODS: We searched the PubMed database from 2019 to the present, using 'epigallocatechin gallate', 'EGCG', and 'nanoparticles' as search terms to identify pertinent literature. This review examines recent nano-engineering technology advancements that encapsulate EGCG within various nanocarriers. The focus was on evaluating the types of nanoparticles used, their synthesis methods, and the technologies applied to optimize drug delivery, diagnostic capabilities, and therapeutic outcomes. RESULTS: Nanoparticles improve the physicochemical stability and pharmacokinetics of EGCG, leading to enhanced therapeutic outcomes in cancer treatment. Nanoencapsulation allows for targeted drug delivery, controlled release, enhanced cellular uptake, and reduced premature degradation of EGCG. The studies highlighted include those where EGCG-loaded nanoparticles significantly inhibited tumor growth in various models, demonstrating enhanced penetration and efficacy through active targeting mechanisms. CONCLUSIONS: Nanoencapsulation of EGCG represents a promising approach in oncology, offering multiple therapeutic benefits over its unencapsulated form. Although the results so far are promising, further research is necessary to fully optimize the design of these nanosystems to ensure their safety, efficacy, and clinical viability.

A Fusion Network with Stacked Denoise Autoencoder and Meta Learning for Lateral Walking Gait Phase Recognition and Multi-Step-Ahead Prediction.

Lateral walking gait phase recognition and prediction are the premise of hip exoskeleton application in lateral resistance walk exercise. In this work, we presented a fusion network with stacked denoise autoencoder and meta learning (SDA-NN-ML) to recognize gait phase and predict gait percentage from IMU signals. Experiments were conducted to detect the four lateral walking gait phases and predict their percentage in 10 healthy subjects across different speeds. The performance of SDA-NN-ML and other widely used algorithms including Support Vector Machine (SVM), Adaptive Boosting (AdaBoost) and Long Short Term Memory (LSTM) were evaluated. The cross-subject recognition accuracy of SDA-NN-ML (89.94%) decreased by 4.62% compared to the training accuracy, which outperformed SVM (8.60%), AdaBoost (5.61%), and LSTM (7.12%). For real-time and cross-subject prediction of gait phase percentage, the RMSE of SDA-NN-ML (0.2043) outperformed that of a single regression network (0.2426). With a signal noise ratio of 100:30, the cross-subject recognition accuracy decreased by a mere 5.70%, while the prediction result (RMSE) of SDA-NN-ML increased by 0.0167 when compared to the noise-free results. SDA-NN-ML demonstrates a stable multi-step-ahead prediction ability with an accuracy higher than 82.50% and an RMSE of less than 0.23 when the ahead time is less than 200 ms. The results demonstrated that the proposed method has high accuracy and robust performance in lateral walking gait recognition and prediction.