Shuo Li

Chemotherapy (CT) suffers from poor selectivity and systemic toxicity, while photodynamic therapy (PDT) and photothermal therapy (PTT) are inherently limited by tumor hypoxia and thermoresistance. Herein, we develop a trimodal synergistic nanoplatform ( PDM‐BDP NPs) for ovarian cancer treatment by integrating PDT, mild PTT (mPTT), and CT into a single azo‐bond‐linked prodrug system. Upon 808 nm laser irradiation , PDM‐BDP NPs generate reactive oxygen species and mild hyperthermia, while simultaneously triggering azo‐bond cleavage to release the chemotherapeutic agent phenylenediamine mustard ( PDM ) and transform into NH ₂ ‐BDP , which retains photothermal activity for sustained thermal output. PDM‐BDP NPs exhibit efficient cellular uptake, predominant mitochondrial co‐localization, and potent photocytotoxicity against A2780 cells with low dark toxicity. Transcriptomics reveals multi‐target reprogramming involving proteotoxic stress, DNA damage, and TNF/MAPK activation. Laser‐activated PDM‐BDP NPs achieve subtotal tumor ablation with excellent biocompatibility. This trimodal synergistic strategy provides a promising approach for precise ovarian cancer therapy.

Everyday speech perception often occurs under concurrent cognitive demands, but it remains unclear how speech-extrinsic load influences the neural processing of continuous speech. Here, electroencephalography (EEG) recordings from 29 healthy adults are analyzed with temporal response functions (TRFs), artificial neural network (ANN) decoding, and phase-lag index (PLI)-based connectivity measures during speech listening with or without concurrent mental arithmetic. Higher cognitive load reduces the amount of speech-envelope information recoverable from EEG, as reflected by lower delta-band TRF prediction accuracy and lower ANN-based decoding performance, whereas theta-band encoding accuracy did not differ significantly between conditions. In the delta-band TRF, cognitive load is associated with reduced response amplitude without an obvious shift in dominant peak latency. Higher load is also accompanied by selective changes in delta-band sensor-level phase coupling and a more clustered, more small-world-like topology in the thresholded delta-band PLI subnetwork. These findings suggest that speech-extrinsic cognitive load weakens neural representations of continuous speech and alters frontotemporal coupling patterns, even in the absence of speech degradation or competing talkers.

This study herein presents the rational design and synthesis of a series of meso ‐CF ₃ ‐substituted BODIPY dyes to achieve controllable molecular aggregation and enhanced near‐infrared (NIR) phototherapeutic performance. By constructing a D‐A‐D architecture and introducing furan/thiophene groups, the resulting BODIPYs ( OBB , OHB , SBB and SHB ) form distinct intermolecular aggregates. Single‐crystal X‐ray diffraction analyses reveal that specific hydrogen‐bonding interactions (C‐H⋯F), with precise distances ranging from 2.336 Å to 2.542 Å, induce self‐assembly to form hydrogen‐bonded‐mediated J aggregates. This controlled aggregation induces significant bathochromic shifts, extending absorption into the NIR‐I and NIR‐II regions (up to 937 nm in film), and generates broad absorption bands beneficial for laser matching. Among the self‐assembled nanoparticles (NPs), OBB NPs demonstrate superior properties, including a high photothermal conversion efficiency of 49.7%, a large Stokes shift (87 nm) in water, and type‐I reactive oxygen species generation. In vitro studies against ovarian cancer cells confirm that OBB NPs exhibit low dark toxicity but potent photo‐triggered cytotoxicity, effectively inducing cell apoptosis and necrosis through an ROS‐mediated mechanism. This work underscores the critical role of intermolecular hydrogen bonding in manipulating aggregation‐induced optical properties and provides a fundamental strategy for developing highly effective organic phototherapeutic agents.

Lipid metabolism reprogramming has long been noticed as the hallmark of ovarian cancer, in order to maintain proliferative features including rapid cell division, metastasis capability, and chemotherapy resistance, as well as to survive under environmental stress, alteration of lipid metabolic pathways takes place, especially over-expression of rate-limiting enzymes, enhances lipid uptake, fatty acid synthesis, β-oxidation, lipid storage, and cellular membrane construction. In lipid-rich ascites and omental tumor microenvironments, the biological functions of stromal and immune cells change, forming a premetastatic niche and immunosuppressive tumor microenvironment via modifying extracellular matrix components and secreting cytokines. The crosstalk between stromal, immune, and ovarian cancer cells results in tumor proliferation, metastasis, and escape of immune surveillance. Given the importance of lipid metabolism for ovarian cancer survival, targeting lipid metabolism key enzymes in ovarian cancer or stromal tumor microenvironment may bring novel insights for ovarian cancer treatment.

The charge-reversal nano-drug delivery system (CRNDDS) is a promising system for delivering chemotherapy drugs and has gained widespread application in cancer treatment. In this review, we summarize the recent advancements in CRNDDSs in terms of cancer treatment. We also delve into the charge-reversal mechanism of the CRNDDSs, focusing on the acid-responsive, redox-responsive, and enzyme-responsive mechanisms. This study elucidates how these systems undergo charge transitions in response to specific microenvironmental stimuli commonly found in tumor tissues. Furthermore, this review explores the pivotal role of CRNDDSs in tumor diagnosis and treatment, and their potential limitations. By leveraging the unique physiological characteristics of tumors, such as the acidic pH, specific redox potential, and specific enzyme activity, these systems demonstrate enhanced accumulation and penetration at tumor sites, resulting in improved therapeutic efficacy and diagnostic accuracy. The implications of this review highlight the potential of charge-reversal drug delivery systems as a novel and targeted strategy for cancer therapy and diagnosis.

Biodegradable polymers have been extensively researched in the field of biomedicine. Polylactic-co-glycolic acid (PLGA), a biodegradable polymer material, has been widely used in drug delivery systems and has shown great potential in various medical fields, including vaccines, tissue engineering such as bone regeneration and wound healing, and 3D printing. Cancer, a group of diseases with high mortality rates worldwide, has recently garnered significant attention in the field of immune therapy research. In recent years, there has been growing interest in the delivery function of PLGA in tumor immunotherapy. In tumor immunotherapy, PLGA can serve as a carrier to load antigens on its surface, thereby enhancing the immune system’s ability to attack tumor cells. Additionally, PLGA can be used to formulate tumor vaccines and immunoadjuvants, thereby enhancing the efficacy of tumor immunotherapy. PLGA nanoparticles (NPs) can also enhance the effectiveness of tumor immunotherapy by regulating the activity and differentiation of immune cells, and by improving the expression and presentation of tumor antigens. Furthermore, due to the diverse physical properties and surface modifications of PLGA, it has a wider range of potential applications in tumor immunotherapy through the loading of various types of drugs or other innovative substances. We aim to highlight the recent advances and challenges of plga in the field of oncology therapy to stimulate further research and development of innovative PLGA-based approaches, and more effective and personalized cancer therapies.

Background: Emotion recognition using EEG signals enables clinicians to assess patients’ emotional states with precision and immediacy. However, the complexity of EEG signal data poses challenges for traditional recognition methods. Deep learning techniques effectively capture the nuanced emotional cues within these signals by leveraging extensive data. Nonetheless, most deep learning techniques lack interpretability while maintaining accuracy.Methods: We developed an interpretable end-to-end EEG emotion recognition framework rooted in the hybrid CNN and transformer architecture. Specifically, temporal convolution isolates salient information from EEG signals while filtering out potential high-frequency noise. Spatial convolution discerns the topological connections between channels. Subsequently, the transformer module processes the feature maps to integrate high-level spatiotemporal features, enabling the identification of the prevailing emotional state.Results: Experiments’ results demonstrated that our model excels in diverse emotion classification, achieving an accuracy of 74.23% ± 2.59% on the dimensional model (DEAP) and 67.17% ± 1.70% on the discrete model (SEED-V). These results surpass the performances of both CNN and LSTM-based counterparts. Through interpretive analysis, we ascertained that the beta and gamma bands in the EEG signals exert the most significant impact on emotion recognition performance. Notably, our model can independently tailor a Gaussian-like convolution kernel, effectively filtering high-frequency noise from the input EEG data.Discussion: Given its robust performance and interpretative capabilities, our proposed framework is a promising tool for EEG-driven emotion brain-computer interface.

Decoding speech envelopes from electroencephalogram (EEG) signals holds potential as a research tool for objectively assessing auditory processing, which could contribute to future developments in hearing loss diagnosis. However, current methods struggle to meet both high accuracy and interpretability. We propose a deep learning model called the auditory decoding transformer (ADT) network for speech envelope reconstruction from EEG signals to address these issues. The ADT network uses spatio-temporal convolution for feature extraction, followed by a transformer decoder to decode the speech envelopes. Through anticausal masking, the ADT considers only the current and future EEG features to match the natural relationship of speech and EEG. Performance evaluation shows that the ADT network achieves average reconstruction scores of 0.168 and 0.167 on the SparrKULee and DTU datasets, respectively, rivaling those of other nonlinear models. Furthermore, by visualizing the weights of the spatio-temporal convolution layer as time-domain filters and brain topographies, combined with an ablation study of the temporal convolution kernels, we analyze the behavioral patterns of the ADT network in decoding speech envelopes. The results indicate that low- (0.5–8 Hz) and high-frequency (14–32 Hz) EEG signals are more critical for envelope reconstruction and that the active brain regions are primarily distributed bilaterally in the auditory cortex, consistent with previous research. Visualization of attention scores further validated previous research. In summary, the ADT network balances high performance and interpretability, making it a promising tool for studying neural speech envelope tracking.

Epithelial ovarian cancer (EOC) is a lethal gynecological cancer, of which paclitaxel resistance is the major factor limiting treatment outcomes, and identification of paclitaxel resistance-related genes is arduous. We obtained transcriptomic data from seven paclitaxel-resistant ovarian cancer cell lines and corresponding sensitive cell lines. Define genes significantly up-regulated in at least three resistant cell lines, meanwhile they did not down-regulate in the other resistant cell lines as candidate genes. Candidate genes were then ranked according to the frequencies of significant up-regulation in resistant cell lines, defining genes with the highest rankings as paclitaxel resistance-related genes (PRGs). Patients were grouped based on the median expression of PRGs. The lipid metabolism-related gene set and the oncological gene set were established and took intersections with genes co-upregulated with PRGs, obtaining 229 co-upregulated genes associated with lipid metabolism and tumorigenesis. The PPI network obtained 19 highly confidential synergistic targets (interaction score > 0.7) that directly associated with CPT1A. Finally, FASN and SCD were up-stream substrate provider and competitor of CPT1A, respectively. Western blot and qRT-PCR results confirmed the over-expression of CPT1A, SCD and FASN in the A2780/PTX cell line. The inhibition of CPT1A, SCD and FASN down-regulated cell viability and migration, pharmacological blockade of CPT1A and SCD increased apoptosis rate and paclitaxel sensitivity of A2780/PTX. In summary, our novel bioinformatic methods can overcome difficulties in drug resistance evaluation, providing promising therapeutical strategies for paclitaxel-resistant EOC via taregting lipid metabolism-related enzymes.