Bi2S3/Ti3C2-TPP nano-heterostructures induced by near-infrared for photodynamic therapy combined with photothermal therapy on hypoxic tumors.

BACKGROUND: Photodynamic therapy (PDT) efficacy of bismuth sulfide (Bi2S3) semiconductor has been severely restricted by its electron-hole pairs (e--h+) separation inefficiency and oxygen (O2) deficiency in tumors, which greatly hinders reactive oxygen species (ROS) generation and further clinical application of Bi2S3 nanoparticles (NPs) in biomedicine. RESULTS: Herein, novel Bi2S3/titanium carbide (Ti3C2) two-dimensional nano-heterostructures (NHs) are designed to realize multimode PDT of synchronous O2 self-supply and ROS generation combined with highly efficient photothermal tumor elimination for hypoxic tumor therapy. Bi2S3/Ti3C2 NHs were synthesized via the in situ synthesis method starting from Ti3C2 nanosheets (NSs), a classical type of MXene nanostructure. Compared to simple Bi2S3 NPs, Bi2S3/Ti3C2 NHs significantly extend the absorption to the near-infrared (NIR) region and enhance the photocatalytic activity owing to the improved photogenerated carrier separation, where the hole on the valence band (VB) of Bi2S3 can react with water to supply O2 for the electron on the Ti3C2 NSs to generate ·O2- and ·OH through electron transfer. Furthermore, they also achieve 1O2 generation through energy transfer due to O2 self-supply. After the modification of triphenylphosphium bromide (TPP) on Bi2S3/Ti3C2 NHs, systematic in vitro and in vivo evaluations were conducted, revealing that the synergistic-therapeutic outcome of this nanoplatform enables complete eradication of the U251 tumors without recurrence by NIR laser irradiation, and it can be used for computed tomography (CT) imaging because of the strong X-ray attenuation ability. CONCLUSION: This work expands the phototherapeutic effect of Bi2S3-based nanoplatforms, providing a new strategy for hypoxic tumor theranostics.

An Immune-Related Long Noncoding RNA Pair as a New Biomarker to Predict the Prognosis of Patients in Breast Cancer.

Background: Immune-related long non-coding RNAs (irlncRNAs) might remodel the tumor immune microenvironment by changing the inherent properties of tumor cells and the expression of immune genes, which have been used to predict the efficacy of immunotherapy and the prognosis of various tumors. However, the value of irlncRNAs in breast cancer (BRCA) remains unclear. Materials and Methods: Initially, transcriptome data and immune-related gene sets were downloaded from The Cancer Genome Atlas (TCGA) database. The irlncRNAs were extracted from the Immunology Database and Analysis Portal (ImmPort) database. Differently expressed irlncRNAs (DEirlncRNAs) were further identified by utilizing the limma R package. Then, univariate and multivariate Cox regression analyses were conducted to select the DEirlncRNAs associated with the prognosis of BRCA patients. In addition, the univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were performed to determine the DEirlncRNA pairs with the independent prediction capability of prognosis in BRCA patients. Finally, the chosen DEirlncRNA pair would be evaluated in terms of survival time, clinicopathological characteristics, tumor-infiltrating immune cells, immune checkpoints (ICs), signaling pathways, and potential small-molecule drugs. Results: A total of 21 DEirlncRNA pairs were extracted, and among them, lncRNA MIR4435-2HG and lncRNA U62317.1 were chosen to establish a risk signature that served as an independent prognostic biomarker in BRCA patients. Patients in the high-risk group had a worse prognosis than those in the low-risk group, and they also had an abundance of infiltration of CD4+ T and CD8+ T cells to enhance the immune response to tumor cells. Furthermore, the risk signature showed a strong correlation with ICs, signaling pathways, and potential small-molecule drugs. Conclusion: Our research revealed that the risk signature independent of specific DEirlncRNA pair expression was closely associated with the prognosis and tumor immune microenvironment in BRCA patients and had the potential to function as an independent prognostic biomarker and a predictor of immunotherapy for BRCA patients, which would provide new insights for BRCA accurate treatment.

An Intelligent Multi-View Active Learning Method Based on a Double-Branch Network.

Artificial intelligence is one of the most popular topics in computer science. Convolutional neural network (CNN), which is an important artificial intelligence deep learning model, has been widely used in many fields. However, training a CNN requires a large amount of labeled data to achieve a good performance but labeling data is a time-consuming and laborious work. Since active learning can effectively reduce the labeling effort, we propose a new intelligent active learning method for deep learning, which is called multi-view active learning based on double-branch network (MALDB). Different from most existing active learning methods, our proposed MALDB first integrates two Bayesian convolutional neural networks (BCNNs) with different structures as two branches of a classifier to learn the effective features for each sample. Then, MALDB performs data analysis on unlabeled dataset and queries the useful unlabeled samples based on different characteristics of two branches to iteratively expand the training dataset and improve the performance of classifier. Finally, MALDB combines multiple level information from multiple hidden layers of BCNNs to further improve the stability of sample selection. The experiments are conducted on five extensively used datasets, Fashion-MNIST, Cifar-10, SVHN, Scene-15 and UIUC-Sports, the experimental results demonstrate the validity of our proposed MALDB.