Hyaluronic acid (HA), a main component of the extracellular matrix, is widely utilized to deliver anticancer drugs due to its biocompatibility, biodegradability, non-toxicity, non-immunogenicity and numerous modification sites, such as carboxyl and hydroxyl groups. Moreover, HA serves as a natural ligand for tumor-targeted drug delivery systems, as it contains the endocytic HA receptor, CD44, which is overexpressed in many cancer cells. Therefore, HA-based nanocarriers have been developed to improve drug delivery efficiency and distinguish between healthy and cancerous tissues, resulting in reduced residual toxicity and off-target accumulation. This article comprehensively reviews the fabrication of anticancer drug nanocarriers based on HA in the context of prodrugs, organic carrier materials (micelles, liposomes, nanoparticles, microbubbles and hydrogels) and inorganic composite nanocarriers (gold nanoparticles, quantum dots, carbon nanotubes and silicon dioxide). Additionally, the progress achieved in the design and optimization of these nanocarriers and their effects on cancer therapy are discussed. Finally, the review provides a summary of the perspectives, the lessons learned so far and the outlook towards further developments in this field.
Background:The bimodal balance-recovery model predicts that corticospinal tract (CST) integrity in the affected hemisphere influences the partterns of brain recovery after stroke. Repetitive transcranial magnetic stimulation (rTMS) has been used to promote functional recovery of stroke patients by modulating motor cortical excitability and inducing reorganization of neural networks. This study aimed to explore how to optimize the efficiency of repetitive transcranial magnetic stimulation to promote upper limb functional recovery after stroke according to bimodal balance-recovery model. Methods: 60 patients who met the inclusion criteria were enrolled to high CST integrity group (n = 30) or low CST integrity group (n = 30), and further assigned randomly to receive high-frequency rTMS (HF-rTMS), low-frequency rTMS (LF-rTMS) or sham rTMS in addition to routine rehabilitation, with 10 patients in each group. Outcome measures included Fugl-Meyer scale for upper extremity (FMA-UE), Wolf Motor Function (WMFT) scale and Modified Barthel Index (MBI) scale which were evaluated at baseline and after 21 days of treatment. Results: For patients with high CST integrity, the LF group achieved higher FMA-UE, WMFT and MBI scores improvements after treatment when compared to the HF group and sham group. For patients with low CST integrity, after 21 days treatment, only the HF group showed significant improvements in FMA-UE and WMFT scores. For MBI assessment, the HF group revealed significantly better improvements than the LF group and sham group. Conclusions: For stroke patients with high CST integrity, low-frequency rTMS is superior to high-frequency rTMS in promoting upper limb motor function recovery. However, only high-frequency rTMS can improve upper limb motor function of stroke patients with low CST integrity.
Hepatocellular carcinoma (HCC) is a common malignant tumor that affecting many people's lives globally. The common risk factors for HCC include being overweight and obese. The liver is the center of lipid metabolism, synthesizing most cholesterol and fatty acids. Abnormal lipid metabolism is a significant feature of metabolic reprogramming in HCC and affects the prognosis of HCC patients by regulating inflammatory responses and changing the immune microenvironment. Targeted therapy and immunotherapy are being explored as the primary treatment strategies for HCC patients with unresectable tumors. Here, we detail the specific changes of lipid metabolism in HCC and its impact on both these therapies for HCC. HCC treatment strategies aimed at targeting lipid metabolism and how to integrate them with targeted therapy or immunotherapy rationally are also presented.
This paper considers storm surge prediction using a neural network and considering multiple physical characteristics. Based on the factors that influence storm surges and historical observation data, we divide the input to the neural network into time features extracted from the prediction target and the auxiliary features that affect storm surges, and construct a feature gate within multiple recurrent neural network (RNN) cells. Historical hurricane data are used to assess the effectiveness and accuracy of the proposed model. Comparative analysis against a long short-term memory (LSTM) storm surge prediction model is conducted to verify the prediction performance of the proposed method. The comparison results show that the multi-RNN model is superior to the LSTM model in terms of four evaluation metrics and for all lead times. In particular, the multi-RNN model accurately predicts the maximum storm surge water level, and the prediction results are more consistent with the rise and fall of the water. A comparison of the storm surge forecasts using inputs from different time intervals under different evaluation indices confirms the generalization and stability of our proposed model. The experiments of storm surge prediction at six stations further confirm the wide applicability of the model.
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