Hollow Cu2MoS4 nanoparticles loaded with immune checkpoint inhibitors reshape the tumor microenvironment to enhance immunotherapy for pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a fatal disease that responds poorly to single-drug immunotherapy with PD-L1 (CD274) inhibitors. Here, we prepared mesoporous nanomaterials Cu2MoS4 (CMS)/PEG loaded with PD-L1 inhibitor BMS-1 and CXCR4 inhibitor Plerixafor to form the nanodrug CMS/PEG-B-P. In vitro experiments, CMS/PEG-B-P have a more substantial inhibitory effect on the expression of PD-L1 and CXCR4 as well as to promote the apoptosis of pancreatic cancer cells KPC and suppressed KPC cell proliferation were detected by flow cytometry, qPCR and Western blotting (WB). Promotes the release of the cytotoxic substance reactive oxygen species (ROS) and the production of the immunogenic cell death (ICD) marker calreticulin (CRT) in KPC cells. CMS/PEG-B-P was also detected to have a certain activating effect on mouse immune cells, dendritic cells (mDC) and macrophage RAW264.7. Subcutaneous tumorigenicity experiments in C57BL/6 mice verified that CMS/PEG-B-P had an inhibitory effect on the growth of tumors and remodeling of the tumor immune microenvironment, including infiltration of CD4+ and CD8+ T cells and polarization of macrophages, as well as reduction of immunosuppressive cells. Meanwhile, CMS/PEG-B-P was found to have different effects on the release of cytokines in the tumor immune microenvironment, including The levels of immunostimulatory cytokines INF-γ and IL-12 are increased and the levels of immunosuppressive cytokines IL-6, IL-10 and IFN-α are decreased. In conclusion, nanomaterial-loaded immune checkpoint inhibitor therapies can enhance the immune response and reduce side effects, a combination that shows great potential as a new immunotherapeutic approach. STATEMENT OF SIGNIFICANCE: Pancreatic ductal adenocarcinoma (PDAC) is a fatal disease that has a low response to single-drug immunotherapy with PD-L1 (CD274) inhibitors. We preared PEG-modified mesoporous nanomaterials Cu2MoS4 (CMS) loaded with PD-L1 inhibitor BMS-1 and CXCR4 inhibitor Plerixafor to form the nanodrug CMS/PEG-B-P. Our study demonstrated that Nanomaterial-loaded immune checkpoint inhibitor therapies can enhance the immune response and reduce side effects, a combination that shows great potential as a new immunotherapeutic approach.

Football players' strength training method using image processing based on machine learning.

This work addresses the declining physical fitness levels observed in both football players and the general population. The objective is to investigate the impact of functional strength training on the physical capabilities of football players and to develop a machine learning-based approach for posture recognition. A total of 116 adolescents aged 8 to 13 participating in football training are randomly assigned to either an experimental group (n = 60) or a control group (n = 56). Both groups underwent 24 training sessions, with the experimental group engaging in 15-20 minutes of functional strength training after each session. Machine learning techniques, specifically the backpropagation neural network (BPNN) in deep learning, are utilized to analyze the kicking actions of football players. Movement speed, sensitivity, and strength are employed as input vectors for the BPNN to compare the images of players' movements, while the similarity between the kicking actions and standard movements served as the output result to enhance training efficiency. The experimental group's kicking scores are compared to their pre-experiment scores, demonstrating a statistically significant improvement. Moreover, statistically significant differences are observed in the 5*25m shuttle running, throwing, and set kicking between the control and experimental groups. These findings highlight the significant enhancement in strength and sensitivity achieved through functional strength training in football players. The results contribute to the development of training programs for football players and the overall improvement of training efficiency.

Efficiently targeted therapy of glioblastoma xenograft via multifunctional biomimetic nanodrugs.

BACKGROUND: Glioblastoma multiforme (GBM) is a fatal malignant primary brain tumor in adults. The therapeutic efficacy of chemotherapeutic drugs is limited due to the blood-brain barrier (BBB), poor drug targeting, and short biological half-lives. Multifunctional biomimetic nanodrugs have great potential to overcome these limitations of chemotherapeutic drugs. METHODS: We synthesized and characterized a biomimetic nanodrug CMS/PEG-DOX-M. The CMS/PEG-DOX-M effectively and rapidly released DOX in U87 MG cells. Cell proliferation and apoptosis assays were examined by the MTT and TUNEL assays. The penetration of nanodrugs through the BBB and anti-tumor efficacy were investigated in the orthotopic glioblastoma xenograft models. RESULTS: We showed that CMS/PEG-DOX-M inhibited cell proliferation of U87 MG cells and effectively induced cell apoptosis of U87 MG cells. Intracranial antitumor experiments showed that free DOX hardly penetrated the BBB, but CMS/PEG-DOX-M effectively reached the orthotopic intracranial tumor through the BBB and significantly inhibited tumor growth. Immunofluorescence staining of orthotopic tumor tissue sections confirmed that nanodrugs promoted apoptosis of tumor cells. This study developed a multimodal nanodrug treatment system with the enhanced abilities of tumor-targeting, BBB penetration, and cancer-specific accumulation of chemotherapeutic drugs by combining chemotherapy and photothermal therapy. It can be used as a flexible and effective GBM treatment system and it may also be used for the treatment of other central nervous systems (CNS) tumors and extracranial tumors.

SPiForest: An Anomaly Detecting Algorithm Using Space Partition Constructed by Probability Density-Based Inverse Sampling.

The SPiForest, a new isolation-based approach to outlier detection, constructs iTrees on the space containing all attributes by probability density-based inverse sampling. Most existing iForest (iF)-based approaches can precisely and quickly detect outliers scattering around one or more normal clusters. However, the performance of these methods seriously decreases when facing outliers whose nature "few and different" disappears in subspace (e.g., anomalies surrounded by normal samples). To solve this problem, SPiForest is proposed, which is different from existing approaches. First, SPiForest uses the principal component analysis (PCA) to find principal components and estimate each component's probability density function (pdf). Second, SPiForest utilizes the inv-pdf, which is inversely proportional to the pdf estimated from the given dataset, to generate support points in the space containing all attributes. Third, the hyperplane decided by these support points is used to isolate the outliers in the space. Next, these steps are repeated to build an iTree. Finally, many iTrees construct a forest for outlier detection. SPiForest provides two benefits: 1) it isolates outliers with fewer hyperplanes, which significantly improves the accuracy and 2) it effectively detects the outliers whose nature "few and different" disappears in subspace. Comparative analyses and experiments show that the SPiForest achieves a significant improvement in terms of area under the curve (AUC) when compared with the state-of-the-art methods. Specifically, our method improves by at most 17.7% on AUC when compared to iF-based algorithms.

Altered Structural and Functional Patterns Within Executive Control Network Distinguish Frontal Glioma-Related Epilepsy.

Background: The tumor invasion of the frontal lobe induces changes in the executive control network (ECN). It remains unclear whether epileptic seizures in frontal glioma patients exacerbate the structural and functional alterations within the ECN, and whether these changes can be used to identify glioma-related seizures at an early stage. This study aimed to investigate the altered structural and functional patterns of ECN in frontal gliomas without epilepsy (non-FGep) and frontal gliomas with epilepsy (FGep) and to evaluate whether the patterns can accurately distinguish glioma-related epilepsy. Methods: We measured gray matter (GM) volume, regional homogeneity (ReHo), and functional connectivity (FC) within the ECN to identify the structural and functional changes in 50 patients with frontal gliomas (29 non-FGep and 21 FGep) and 39 healthy controls (CN). We assessed the relationships between the structural and functional changes and cognitive function using partial correlation analysis. Finally, we applied a pattern classification approach to test whether structural and functional abnormalities within the ECN can distinguish non-FGep and FGep from CN subjects. Results: Within the ECN, non-FGep and FGep showed increased local structure (GM) and function (ReHo), and decreased FC between brain regions compared to CN. Also, non-FGep and FGep showed differential patterns of structural and functional abnormalities within the ECN, and these abnormalities are more severe in FGep than in non-FGep. Lastly, FC between the right superior frontal gyrus and right dorsolateral prefrontal cortex was positively correlated with episodic memory scores in non-FGep and FGep. In particular, the support vector machine (SVM) classifier based on structural and functional abnormalities within ECN could accurately distinguish non-FGep and FGep from CN, and FGep from non-FGep on an individual basis with very high accuracy, area under the curve (AUC), sensitivity, and specificity. Conclusion: Tumor invasion of the frontal lobe induces local structural and functional reorganization within the ECN, exacerbated by the accompanying epileptic seizures. The ECN abnormalities can accurately distinguish the presence or absence of epileptic seizures in frontal glioma patients. These findings suggest that differential ECN patterns can assist in the early identification and intervention of epileptic seizures in frontal glioma patients.