Chitosan nanoparticles (NPs) serve as useful multidrug delivery carriers in cancer chemotherapy. Chitosan has considerable potential in drug delivery systems (DDSs) for targeting tumor cells. Doxorubicin (DOX) has limited application due to its resistance and lack of specificity. Chitosan NPs have been used for DOX delivery because of their biocompatibility, biodegradability, drug encapsulation efficiency, and target specificity. In this review, various types of chitosan derivatives are discussed in DDSs to enhance the effectiveness of cancer treatments. Modified chitosan–DOX NP drug deliveries with other compounds also increase the penetration and efficiency of DOX against tumor cells. We also highlight the endogenous stimuli (pH, redox, enzyme) and exogenous stimuli (light, magnetic, ultrasound), and their positive effect on DOX drug delivery via chitosan NPs. Our study sheds light on the importance of chitosan NPs for DOX drug delivery in cancer treatment and may inspire the development of more effective approaches for cancer chemotherapy.

The use of combination therapies that employ a variety of cell death mechanisms has emerged as a promising avenue of research in the treatment of cancer. However, the optimization of therapeutic synergies when integrating different modes remains a significant challenge. To this end, we developed a multifunctional intelligent drug-carrying nanoparticle (DFMTCH NPs) based on the metal-organic framework MIL-100, loaded with doxorubicin (DOX) and disulfiram (DSF), coated with a Cu-tannic acid (Cu-TA) network and hyaluronic acid (HA), for the purpose of combined chemotherapy/chemodynamic/photothermal anti-cancer therapy. On the one hand, the DFMTCH NPs exhibited a range of therapeutic capabilities, including chemotherapy, photothermal therapy (PTT), and chemodynamic therapy (CDT), which collectively enhanced the anti-tumor efficacy of chemotherapeutic agents. In addition, DFMTCH NPs proved sensitive photoacoustic imaging (PAI) in image-guided therapy. On the other hand, DFMTCH NPs could produce reactive oxygen species (ROS) and consume glutathione (GSH) by amplifying cellular oxidative stress, while causing intracellular mitochondrial dysfunction, inducing effective cuproptosis/ferroptosis/apoptosis to inhibit tumor growth. Collectively, this work provided an innovative strategy for designing multifunctional nanoparticles for effective combination therapies to combat colorectal cancer (CRC).

Sleep staging has always been a hot topic in the field of sleep medicine, and it is the cornerstone of research on sleep problems. At present, sleep staging heavily relies on manual interpretation, which is a time-consuming and laborious task with subjective interpretation factors. In this paper, we propose an automatic sleep stage classification model based on the Bidirectional Recurrent Neural Network (BiRNN) with data bundling augmentation and label redirection for accurate sleep staging. Through extensive analysis, we discovered that the incorrect classification labels are primarily concentrated in the transition and nonrapid eye movement stage I (N1). Therefore, our model utilizes a sliding window input to enhance data bundling and an attention mechanism to improve feature enhancement after label redirection. This approach focuses on mining latent features during the N1 and transition periods, which can further improve the network model’s classification performance. We evaluated on multiple public datasets and achieved an overall accuracy rate of 87.3%, with the highest accuracy rate reaching 93.5%. Additionally, the network model’s macro F1 score reached 82.5%. Finally, we used the optimal network model to study the impact of different EEG channels on the accuracy of each sleep stage.

The release of MTO from FMMS NPs further exerted its chemotherapeutic effects and, in conjunction with ferroptosis, induced immunogenic cell death.