The Effects of Budesonide Inhalation Treatment on the Expression Levels of Serum IL-6, TGF-ß1, and IgE and Pulmonary Function in Patients with Cough Variant Asthma and an Evaluation of Treatment Efficacy.

Objective: To retrospectively study the effects of budesonide inhalation combined with conventional symptomatic treatment on serum inflammatory factor expression levels and pulmonary function in patients with cough variant asthma (CVA) and to evaluate treatment efficacy. Methods: This retrospective cohort study included 200 patients diagnosed with CVA at the Second Hospital of Jiaxing between January 2022 and June 2023 and given conventional symptomatic treatment plus budesonide inhalation were included in this study. Patients were divided into a no remission group, a partial remission group and a complete remission group based on treatment effect. The expression levels of serum inflammatory factors, cough symptom scores, and small airway function indicators in the three groups of patients at different time points were compared. Results: In the three groups of CVA patients, after receiving budesonide inhalation combined with conventional symptomatic treatment, the expression levels of serum IL-5, IL-6, IL-8, TNF-α, TGF-ß1, and IgE and number of eosinophils significantly decreased (P <0.05). There were statistically significant differences in the IL-6 and TGF-ß1 levels among the three groups of CVA patients at T1, T2 and T3. There were statistically significant differences in IgE levels, number of eosinophils, cough symptom scores, and small airway function indicators between T2 and T3 (P<0.05). The receiver operating characteristic (ROC) curve prediction analysis revealed significant differences in the expression of IL-6 and TGF-ß1 at T1, T2, and T3. Conclusion: Budesonide inhalation combined with conventional symptomatic treatment can significantly reduce the levels of serum inflammatory factors in patients with CVA to reduce inflammation and the allergic response, thereby reducing the cough symptom score, improving pulmonary function, and improving therapeutic efficacy. In addition, IL-6 and TGF-ß1 can be used as early predictors of budesonide inhalation efficacy.

Cinchonine exerts anti-tumor and immunotherapy sensitizing effects in lung cancer by impairing autophagic-lysosomal degradation.

Currently, there are several treatments approaches available for lung cancer; however, patients who develop drug resistance or have poor survival rates urgently require new therapeutic strategies for lung cancer. In autophagy, damaged proteins or organelles are enclosed within autophagic vesicles with a bilayer membrane structure and transported to the lysosomes for degradation and recirculation. Autophagy is a crucial pathway involved in the clearance of reactive oxygen species (ROS) and damaged mitochondria. Meanwhile, inhibiting autophagy is a promising strategy for cancer treatment. In this study, we found for the first time that Cinchonine (Cin) can act as an autophagy suppressor and exert anti-tumor effects. Cin significantly inhibited the proliferation, migration, and invasion of cancer cells in vitro and the tumor growth and metastasis in vivo, without obvious toxic effects. We found that Cin suppressed the autophagic process by blocking autophagosome degradation through the inhibition of the maturation of lysosomal hydrolases. Cin-mediated autophagy inhibition resulted in the elevated ROS level and the accumulation of damaged mitochondria, which in turn promoted apoptosis. N-acetylcysteine, a potential ROS scavenger, significantly suppressed Cin-induced apoptosis. Additionally, Cin upregulated programmed death-ligand 1 (PD-L1) expression in lung cancer cells by inhibiting autophagy. Compared with monotherapy and control group, the combined administration of anti-PD-L1 antibody and Cin significantly reduced tumor growth. These results suggest that Cin exerts anti-tumor effects by inhibiting autophagy, and that the combination of Cin and PD-L1 blockade has synergistic anti-tumor effects. The data demonstrates the significant clinical potential of Cin in lung cancer treatment.

The Prognostic Value and Potential Mechanism of Tumor-Nutrition-Inflammation Index and Genes in Patients with Advanced Lung Cancer.

Background: Lung cancer (LC) has the highest mortality rate all over the world. It is necessary to search for novel potential biomarkers that are easily accessible and inexpensive in identifying patients with LC at early stage. Methods: A total of 195 patients with advanced LC who have received first-line chemotherapy were involved in this study. The optimized cut-off values of AGR and SIRI (AGR = albumin/globulin; SIRI = neutrophil ∗ monocyte/lymphocyte) were determined by survival function analysis based on R software. COX regression analysis was performed to obtain the independent factors for establishing the nomogram model. A nomogram model comprising these independent prognostic parameters was built for the TNI (tumor-nutrition-inflammation index) score calculation. The predictive accuracy was demonstrated through ROC curve and calibration curves after index concordance. Results: The optimized cut-off values of AGR and SIRI were 1.22 and 1.60, respectively. It was revealed that liver metastasis, SCC, AGR, and SIRI were independent prognostic factors in advanced lung cancer by Cox analysis. Afterwards, the nomogram model comprised of these independent prognostic parameters was built for TNI scores calculation. Based on the TNI quartile values, patients were divided into four groups. And it was indicated that higher TNI had worse OS (P < 0.05) via Kaplan-Meier analysis and log-rank test. Moreover, the C-index and 1-year AUC area were 0.756 (0.723-0.788) and 75.62, respectively. There was high consistency shown in the calibration curves between predicted and actual survival proportions in the TNI model. In addition, tumor-nutrition-inflammation index and genes play an important role in LC development that might affect some pathways related to tumor development including cell cycle, homologous recombination, and P53 signaling pathway from a molecular level. Conclusion: TNI might be an analytical tool which was practical and precise for survival prediction of patients with advanced LC. Tumor-nutrition-inflammation index and genes play an important role in LC development. A preprint has previously been published [1].

Recent advances of nanomaterial-based anti-angiogenic therapy in tumor vascular normalization and immunotherapy.

Anti-angiogenesis therapy and immunotherapy are the first-line therapeutic strategies for various tumor treatments in the clinic, bringing significant advantages for tumor patients. Recent studies have shown that anti-angiogenic therapy can potentiate immunotherapy, with many clinical trials conducted based on the combination of anti-angiogenic agents and immune checkpoint inhibitors (ICIs). However, currently available clinical dosing strategies and tools are limited, emphasizing the need for more improvements. Although significant progress has been achieved, several big questions remained, such as how to achieve cell-specific targeting in the tumor microenvironment? How to improve drug delivery efficiency in tumors? Can nanotechnology be used to potentiate existing clinical drugs and achieve synergistic sensitization effects? Over the recent few years, nanomedicines have shown unique advantages in antitumor research, including cell-specific targeting, improved delivery potentiation, and photothermal effects. Given that the applications of nanomaterials in tumor immunotherapy have been widely reported, this review provides a comprehensive overview of research advances on nanomaterials in anti-angiogenesis therapy, mainly focusing on the immunosuppressive effects of abnormal tumor vessels in the tumor immune microenvironment, the targets and strategies of anti-angiogenesis nanomedicines, and the potential synergistic effects and molecular mechanisms of anti-angiogenic nanomedicines in combination with immunotherapy, ultimately providing new perspectives on the nanomedicine-based synergy between anti-angiogenic and immunotherapy.

Construction of a predictive model for immunotherapy efficacy in lung squamous cell carcinoma based on the degree of tumor-infiltrating immune cells and molecular typing.

BACKGROUND: To construct a predictive model of immunotherapy efficacy for patients with lung squamous cell carcinoma (LUSC) based on the degree of tumor-infiltrating immune cells (TIIC) in the tumor microenvironment (TME). METHODS: The data of 501 patients with LUSC in the TCGA database were used as a training set, and grouped using non-negative matrix factorization (NMF) based on the degree of TIIC assessed by single-sample gene set enrichment analysis (GSEA). Two data sets (GSE126044 and GSE135222) were used as validation sets. Genes screened for modeling by least absolute shrinkage and selection operator (LASSO) regression and used to construct a model based on immunophenotyping score (IPTS). RNA extraction and qPCR were performed to validate the prognostic value of IPTS in our independent LUSC cohort. The receiver operating characteristic (ROC) curve was constructed to determine the predictive value of the immune efficacy. Kaplan-Meier survival curve analysis was performed to evaluate the prognostic predictive ability. Correlation analysis and enrichment analysis were used to explore the potential mechanism of IPTS molecular typing involved in predicting the immunotherapy efficacy for patients with LUSC. RESULTS: The training set was divided into a low immune cell infiltration type (C1) and a high immune cell infiltration type (C2) by NMF typing, and the IPTS molecular typing based on the 17-gene model could replace the results of the NMF typing. The area under the ROC curve (AUC) was 0.82. In both validation sets, the IPTS of patients who responded to immunotherapy were significantly higher than those who did not respond to immunotherapy (P = 0.0032 and P = 0.0451), whereas the AUC was 0.95 (95% CI = 1.00-0.84) and 0.77 (95% CI = 0.58-0.96), respectively. In our independent cohort, we validated its ability to predict the response to cancer immunotherapy, for the AUC was 0.88 (95% CI = 1.00-0.66). GSEA suggested that the high IPTS group was mainly involved in immune-related signaling pathways. CONCLUSIONS: IPTS molecular typing based on the degree of TIIC in the TME could well predict the efficacy of immunotherapy in patients with LUSC with a certain prognostic value.

A Dual Neural Architecture Combined SqueezeNet with OctConv for LiDAR Data Classification.

Light detection and ranging (LiDAR) is a frequently used technique of data acquisition and it is widely used in diverse practical applications. In recent years, deep convolutional neural networks (CNNs) have shown their effectiveness for LiDAR-derived rasterized digital surface models (LiDAR-DSM) data classification. However, many excellent CNNs have too many parameters due to depth and complexity. Meanwhile, traditional CNNs have spatial redundancy because different convolution kernels scan and store information independently. SqueezeNet replaces a part of 3 × 3 convolution kernels in CNNs with 1 × 1 convolution kernels, decomposes the original one convolution layer into two layers, and encapsulates them into a Fire module. This structure can reduce the parameters of network. Octave Convolution (OctConv) pools some feature maps firstly and stores them separately from the feature maps with the original size. It can reduce spatial redundancy by sharing information between the two groups. In this article, in order to improve the accuracy and efficiency of the network simultaneously, Fire modules of SqueezeNet are used to replace the traditional convolution layers in OctConv to form a new dual neural architecture: OctSqueezeNet. Our experiments, conducted using two well-known LiDAR datasets and several classical state-of-the-art classification methods, revealed that our proposed classification approach based on OctSqueezeNet is able to provide competitive advantages in terms of both classification accuracy and computational amount.