Lung cancer is categorized into two main types: non-small cell lung cancer (NSCLC) and small cell lung cancer. Of these, NSCLC accounts for approximately 85% of all cases and encompasses varieties such as squamous cell carcinoma and adenocarcinoma. For patients with advanced NSCLC that do not have oncogene addiction, the preferred treatment approach is a combination of immunotherapy and chemotherapy. However, the progression-free survival (PFS) typically ranges only from about 6 to 8 months, accompanied by certain adverse events. In order to carry out individualized treatment more effectively, it is urgent to accurately screen patients with PFS for more than 12 months under this treatment regimen. Therefore, this study undertook a retrospective collection of pulmonary CT images from 60 patients diagnosed with NSCLC treated at the First Affiliated Hospital of Wenzhou Medical University. It developed a machine learning model, designated as bSGSRIME-SVM, which integrates the rime optimization algorithm with self-adaptive Gaussian kernel probability search (SGSRIME) and support vector machine (SVM) classifier. Specifically, the model initiates its process by employing the SGSRIME algorithm to identify pivotal image features. Subsequently, it utilizes an SVM classifier to assess these features, aiming to enhance the model's predictive accuracy. Initially, the superior optimization capability and robustness of SGSRIME in IEEE CEC 2017 benchmark functions were validated. Subsequently, employing color moments and gray-level co-occurrence matrix methods, image features were extracted from images of 60 NSCLC patients undergoing immunotherapy combined with chemotherapy. The developed model was then utilized for analysis. The results indicate a significant advantage of the model in predicting the efficacy of immunotherapy combined with chemotherapy for NSCLC, with an accuracy of 92.381% and a specificity of 96.667%. This lays the foundation for more accurate PFS predictions and personalized treatment plans.

N6-methyladenosine (m6A) is the most abundant chemical modification in eukaryotic cells. It is a post-transcriptional modification of mRNA, a dynamic reversible process catalyzed by methyltransferase, demethylase, and binding proteins. Ferroptosis, a unique iron-dependent cell death, is regulated by various cell metabolic events, including many disease-related signaling pathways. And different ferroptosis inducers or inhibitors have been identified that can induce or inhibit the onset of ferroptosis through various targets and mechanisms. They have potential clinical value in the treatment of diverse diseases. Until now, it has been shown that in several cancer diseases m6A can be involved in the regulation of ferroptosis, which can impact subsequent treatment. This paper focuses on the concept, function, and biological role of m6A methylation modification and the interaction between m6A and ferroptosis, to provide new therapeutic strategies for treating malignant diseases and protecting the organism by targeting m6A to regulate ferroptosis.

Radial endobronchial ultrasonography (R-EBUS) has been a surge in the development of new ultrasonography for the diagnosis of pulmonary diseases beyond the central airway. However, it faces challenges in accurately pinpointing the location of abnormal lesions. Therefore, this study proposes an improved machine learning model aimed at distinguishing between malignant lung disease (MLD) from benign lung disease (BLD) through R-EBUS features. An enhanced manta ray foraging optimization based on elite perturbation search and cyclic mutation strategy (ECMRFO) is introduced at first. Experimental validation on 29 test functions from CEC 2017 demonstrates that ECMRFO exhibits superior optimization capabilities and robustness compared to other competing algorithms. Subsequently, it was combined with fuzzy k-nearest neighbor for the classification prediction of BLD and MLD. Experimental results indicate that the proposed modal achieves a remarkable prediction accuracy of up to 99.38%. Additionally, parameters such as R-EBUS1 Circle-dense sign, R-EBUS2 Hemi-dense sign, R-EBUS5 Onionskin sign and CCT5 mediastinum lymph node are identified as having significant clinical diagnostic value.

ObjectiveThe effect of solamargine on lung adenocarcinoma and its effect on STAT1 signaling pathway mediated immune escape were studied through network pharmacology and in vitro and in vivo experiments.MethodsThe solamargine targets were screened using the TCMSP and the LUAD targets were screened using the GeneCard, OMIM, PharmGkb, TTD and DrugBank databases. PPI network analysis and target prediction were performed using GO and KEGG. Colony formation assay, EDU staining, wound healing, transwell assay, Hoechst and flow cytometry were used to detect the effects of solamargine on the proliferation, migration and apoptosis of LUAD. Western blotting (WB) and quantitative reverse transcription polymerase chain reaction (RT-qPCR) were used to detect P-STAT1 and PD-L1 expression. And immunofluorescence was used to detect P-STAT1 expression. In vivo experiments, C57BL/6 mice were divided into control group, low concentration group, high concentration group, positive control group and combination group. Every other day, following seven consecutive doses, the size of the tumor was assessed. Finally, the expressions of P-STAT1, STAT1, PD-L1 and apoptosis index proteins were detected by WB.ResultsThe anti-LUAD effect of solamargine was found by wound healing, colony formation assay, transwell assay, hoechst and EdU staining. The results of network pharmacological analysis showed that solamargine could suppress STAT1 expression level. Further enrichment assay of STAT1 showed that STAT1 was associated with immune-related pathways. In addition, molecular signal analysis by WB and RT-qPCR indicated that solamargine could reduce the expression levels of P-STAT1 and PD-L1 in a concentration-dependent manner. According to the results of in vivo assays, combination of solamargine and immune checkpoint inhibitors (ICIs) durvalumab could significantly inhibit the growth of Lewis transplanted tumors in C57BL/6 mice, and no toxic side effect was recoded.ConclusionThese results indicated that solamargine could inhibit the proliferation and promote the apoptosis of LUAD. It also could reduce the expression level of P-STAT1 protein and inhibit the expression level of PD-L1. At the same time, the combination with the ICIs can better block the expression of PD-L1 in cells, thereby inhibiting the immune escape pathway of tumor cells and achieving anti-tumor effects. This study proposed a novel combined therapeutic approach, involving the inhibition of STAT1 by solamargine in conjunction with ICIs.

Inflammatory response is a crucial factor that affects prognosis and therapeutic effect in tumor cells. Although some studies have shown that inflammation could make DNA more vulnerable to external attacks, resulting in serious DNA damage, the underlying mechanism remains unknown. Then, using tumor necrosis factor α (TNF-α) and lipopolysaccharide (LPS), this research elevated the level of inflammation in cancer cells, and hydrogen peroxide (H2O2) and ultraviolet (UV) were utilized as common reactive oxygen species (ROS)-induced DNA damage agents. We show that either H2O2 or UV achieved a more substantial antiproliferative effect in the inflammation environment compared with H2O2 or UV treatment alone. The inflammation environment enhanced H2O2- or UV-induced cell apoptosis and ROS production. Although the phenomenon that inflammation itself could trigger ROS-dependent DNA damage was well known, the underlying mechanism for the sensitization of inflammation to trigger intense DNA damage via ROS in cancer cells remains unclear. In this study, the inflammation-related genes and the corresponding expression information were obtained from the TCGA and fetched genes associated with inflammatory factors. Screening of thirteen inflammatory-related, including ATM, and prognostic genes. In addition, KEGG analysis of prognostic genes shows that biological processes such as DNA replication. ATM and ATR, which belong to the PI3/PI4-kinase family, can activate p53. Inflammation promotes the vulnerability of DNA by activating the ATM/ATR/p53 pathway, while not affecting the DNA damage repair pathway. In brief, this research suggested that inflammation made DNA vulnerable due to the amplifying H2O2- or UV-induced ROS production and the motoring ATM/ATR/p53 pathway. In addition, our findings revealed that inflammation's motoring of the ATM/ATR/p53 pathway plays a crucial role in DNA damage. Therefore, exploring the mechanism between inflammation and ROS-dependent DNA damage would be extremely valuable and innovative. This study would somewhat establish a better understanding of inflammation, DNA damage, and cancer.

Copper-dependent cell death, called cuproptosis, is connected to tumor development, prognosis, and the immune response. Nevertheless, the function of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of lung adenocarcinoma (LUAD) remains unknown. This work used R software packages to classify the raw data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases of LUAD patients. Afterward, the connections of the various subgroups, clinical pathological traits, and immune infiltration (IMIF) features with the TME mutation status were explored. Ultimately, a nomogram and calibration curve were developed, aiming at enhancing the clinical application of CRG scores and estimating the survival probability of patients. Moreover, the relationships between cuproptosis and the molecular traits, immune cell infiltration of tumor tissue, prognosis, and clinical treatment of patients were investigated in this work. Subsequently, the CRG score was established to predict overall survival (OS), and its credible predictive ability in LUAD patients was identified. Afterward, a highly credible nomogram was created to contribute to the clinical viability of the CRG score. Furthermore, as demonstrated, gene signatures could be applied in assessing tumor immune cell infiltration, clinical traits, and prognosis. In addition, high tumor mutation burden, immunological activity, and significant survival probability were characterized by low CRG scores, and high CRG scores were related to immunosuppression and stromal pathway activation. The current work also discovered a predictive CRG-related signature for LUAD patients, probably contributing to TME trait clarification and more potent immunotherapy strategy exploration.

Long noncoding RNAs (lncRNAs) have been implicated in tumorigenesis, including lung adenocarcinoma (LUAD). However, the functional and regulatory mechanisms of lncRNAs in LUAD remain poorly understood. In this study, we investigated the role of lncRNA ZBED5-AS1 in LUAD. We found that ZBED5-AS1 was upregulated in LUAD specimens and overexpressed in LUAD cell lines. ZBED5-AS1 promoted LUAD cell proliferation, migration, and invasion in vitro and promoted LUAD cell growth in vivo. ZBED5-AS1 promoted ZNF146 expression, activating the ATR/Chk1 pathway and leading to LUAD progression. We observed that exosomes from LUAD cells have a higher expression of ZBED5-AS1 compared with exosomes from the normal cell line BEAS-2B. Coculture experiments with exosomes showed that ZBED5-AS1 expression was downregulated after coculture with Si-ZBED5-AS1 exosomes, and coculture with exosomes with low ZBED5-AS1 expression inhibited proliferation and invasion of LUAD cells. Our results indicate that ZBED5-AS1 functions as an oncogenic factor in LUAD cells by targeting the ZNF146/ATR/Chk1 axis.

Lung adenocarcinoma (LUAD) is associated with poor prognosis. Identifying novel cancer targets and helpful therapeutic strategies remains a serious clinical challenge. This study detected differentially expressed genes in The Cancer Genome Atlas (TCGA) LUAD data collection. We also identified a predictive DNA biomarker, G protein-coupled receptor 37 (GPR37), which was verified as a prognostic biomarker with a critical role in tumor progression. In human LUAD specimens and microarray analyses, we determined that GPR37 was significantly upregulated and associated with a poor prognosis. GPR37 downregulation markedly inhibited the proliferation and migration of LUAD both in vitro and in vivo. Mechanistically, GPR37 could bind to CDK6, thereby facilitating tumor progression in LUAD by inducing cell cycle arrest at the G1 phase. GPR37 also facilitates tumorigenesis in xenograft tumors in vivo. High-throughput screening for GPR37-targeted drugs was performed using the Natural Products Library, which revealed the potential of Hypocrellin B to inhibit GPR37 and cell growth in LUAD. We demonstrated that Hypocrellin B suppressed LUAD cell proliferation and migration both in vitro and in vivo via GPR37 inhibition. Collectively, our findings reveal the role of GPR37 in LUAD progression and migration and the potential of GPR37 as a target for the treatment of LUAD. Thus, the specific inhibition of GPR37 by the natural product Hypocrellin B may possess the potential for the treatment of LUAD.

Introduction Although tuberculous pleural effusion (TBPE) is simply an inflammatory response of the pleura caused by tuberculosis infection, it can lead to pleural adhesions and cause sequelae of pleural thickening, which may severely affect the mobility of the chest cavity. Methods In this study, we propose bGACO-SVM, a model with good diagnostic power, for the adjunctive diagnosis of TBPE. The model is based on an enhanced continuous ant colony optimization (ACOR) with grade-based search technique (GACO) and support vector machine (SVM) for wrapped feature selection. In GACO, grade-based search greatly improves the convergence performance of the algorithm and the ability to avoid getting trapped in local optimization, which improves the classification capability of bGACO-SVM. Results To test the performance of GACO, this work conducts comparative experiments between GACO and nine basic algorithms and nine state-of-the-art variants as well. Although the proposed GACO does not offer much advantage in terms of time complexity, the experimental results strongly demonstrate the core advantages of GACO. The accuracy of bGACO-predictive SVM was evaluated using existing datasets from the UCI and TBPE datasets. Discussion In the TBPE dataset trial, 147 TBPE patients were evaluated using the created bGACO-SVM model, showing that the bGACO-SVM method is an effective technique for accurately predicting TBPE.

AIM AND OBJECTIVE:Lung cancer is the most commonly occurring cancer, which contributes to the majority of death caused by cancer, where non-small-cell lung cancer (NSCLC) accounts for approximately 85% of lung cancer. To treat NSCLC, STAT3 has been identified as a target with therapeutic potential. The neobavaisoflavone (NBIF) is one of the flavonoids of traditional Chinese medicine Psoralea corylifolial.MATERIALS AND METHODS:Human NSCLC cell lines, PC-9, H460, and A549, were applied to determine NBIF's anti-proliferative effects through cell viability and colony formation detection. The effect of NBIF on cell apoptosis was determined through flow cytometry-based assay. Western blotting was used in this study to confirm the levels of P-STAT3, Bcl-2, and Bax, which are apoptotic proteins.RESULTS:It was observed that NBIF could decrease the cell viability and its migration and induce apoptosis in human NSCLC cell lines dose-dependently. Levels of P-STAT3, as well as the downstream signals of the STAT3 pathway, were downregulated, suggesting that the tumorsuppression effects of NBIF might be related to the inhibition of STAT3 signaling. Furthermore, NBIF could contribute to the upregulation of BAX and downregulation of BCL2.CONCLUSION:NBIF might perform the anti-NSCLC efficacy as a result of the inhibition of the STAT3 pathway. Besides, our work suggests that NBIF could provide therapeutic alternatives for NSCLC.

Non-small cell lung carcinoma (NSCLC) is a major neoplastic disease with a high mortality worldwide; however, effective treatment of this disease remains a challenge. Flubendazole, a traditional anthelmintic drug, possesses potent antitumor properties; however, the detailed molecular mechanism of flubendazole activity in NSCLC needs to be further explored. In the present study, flubendazole was found to exhibit valid antitumor activity in vitro as well as in vivo . Flubendazole blocked phosphorylation of STAT3 in a dose- and time-dependent manner and regulated the transcription of STAT3 target genes encoding apoptotic proteins. Further, flubendazole inhibited STAT3 activation by inhibiting its phosphorylation and nuclear localization induced by interleukin-6 (IL-6). Notably, the autophagic flux of NSCLC cell lines was increased after flubendazole treatment. Furthermore, flubendazole downregulated the expression of BCL2, P62, and phosphorylated-mTOR, but it upregulated LC3-I/II and Beclin-1 expression, which are the main genes associated with autophagy. Collectively, these data contribute to elucidating the efficacy of flubendazole as an anticancer drug, demonstrating its potential as a therapeutic agent via its suppression of STAT3 activity and the activation of autophagy in NSCLC.

This paper has proposed an effective intelligent prediction model that can well discriminate and specify the severity of Coronavirus Disease 2019 (COVID-19) infection in clinical diagnosis and provide a criterion for clinicians to weigh scientific and rational medical decision-making. With indicators as the age and gender of the patients and 26 blood routine indexes, a severity prediction framework for COVID-19 is proposed based on machine learning techniques. The framework consists mainly of a random forest and a support vector machine (SVM) model optimized by a slime mould algorithm (SMA). When the random forest was used to identify the key factors, SMA was employed to train an optimal SVM model. Based on the COVID-19 data, comparative experiments were conducted between RF-SMA-SVM and several well-known machine learning algorithms performed. The results indicate that the proposed RF-SMA-SVM not only achieves better classification performance and higher stability on four metrics, but also screens out the main factors that distinguish severe COVID-19 patients from non-severe ones. Therefore, there is a conclusion that the RF-SMA-SVM model can provide an effective auxiliary diagnosis scheme for the clinical diagnosis of COVID-19 infection.

[This corrects the article on p. 1884 in vol. 11, PMID: 30972212.].

Lung squamous cell carcinoma (LUSC) is a leading cause of mobidity and mortality worldwide. Recently, there was a shift in the treatment pattern of immune therapy in LUSC patients; merely a small number of patients with non-small cell lung cancer (NSCLC) at advanced stages respond well to immune checkpoint blockade (ICB) therapy, and tumor mutation burden (TMB) is a valuable independent indicator of response to immune therapy. However, specific gene mutations and their relationship with TMB and tumor-infiltrating immunocytes in LUSC are still unclear. In the present paper, our team analyzed the somatically mutated genes from the ICGC (International Cancer Genome Consortium) and TCGA (The Cancer Genome Atlas) datasets and discovered that 15 frequent gene mutations occurred in both cohorts, including ZFHX4, MUC16, FLG, TP53, LRP1B, TTN, SYNE1, RYR2, CSMD3, USH2A, MUC17, DNAH5, FAM135B, COL11A1, and RYR3. Interestingly, only mutated TTN was related to higher TMB and prognostic outcomes among the 15 mutated genes. Moreover, according to the CIBERSORT algorithm, we revealed that TTN mutation enhanced the antitumor immune response. In conclusion, TTN may have important clinical implications for relevant immune therapy of lung squamous carcinoma.

Background: Carbapenem-resistant Klebsiella pneumoniae (CRKP) causing Bloodstream infection (BSI) are associated with high rates of mortality. Nevertheless, only a few studies regarding the epidemiology of CRKP BSI in south China. The purpose of this study was to describe the epidemiology, clinical characteristics, and the mortality of risk factors associated with CRKP causing bloodstream infection. Methods: A retrospective study of patients with CRKP BSI was recruited from teaching hospital in south China from January 2016 to December 2018. Clinical data were collected from medical records. Results: In total, 90 patients with CRKP BSI were enrolled in the study, while 57% (51/90) of the CRKP BSI were obtained from ICU. Most CRKP BSIs originated from hospitals (81; 85%), while the rest (9; 10%) were healthcare-associated. In univariate analysis, gastrointestinal hemorrhage (p=0.029), Pitt bacteremia score (P=0.045), Charlson comorbidity index (p=0.018) and Corticosteroids use (p=0.036) and Septic shock (p=0.001) were associated with the risk factors for mortality. In a multivariate analysis, septic shock (adjusted odds ratio [aOR] 5.591, 95% confidence interval [CI] 1.405-22.246, P=0.015) and Corticosteroids use (aOR 4.148, 95% CI 1.331-12.928, P=0.014) were independently predictors of mortality. Conclusion: Our data showed that the morbidity and mortality of CRKP BSIs patient from ICU and non-ICU was no significant difference. Standardizing operation and improving nurse quality may play an important role in CRKP BSI patient in intensive care unit. Septic shock and Corticosteroids use were the independent factors of CRKP BSI patient mortality. However, the study did not show an association between invasive procedures and the development of CRKP BSI.

In this work, an improved alternative method of the ant lion optimizer (ALO), integrating opposition-based training with two practical operators on the basis of differential evolution, named MALO, is proposed to cope with the implied weaknesses of classical ALO. Firstly, opposition-based practice is adopted into the ALO to prevent it from the searching deflation and obtain a faster convergence rate. Besides, two more operators, mutation and crossover strategies are implemented to further improve the local searching efficiency of the agents. Additionally, to verify the effectiveness of the enhanced process, comparison with existing optimizers was conducted for different benchmark functions with different qualities likewise unimodal, multimodal, and fixed-dimensional multimodaltasks were also carried out. Moreover, the extensibility test is, undertaken to assess the dimensional influence on problem consistency and optimization quality. Furthermore, the enhanced method is exploited to crack three practical, well-known constrained optimization problems, including spring plan, the concern of the welded beam case and the subject of a pressure vessel. The findings show that the introduced strategies will significantly enhance ALO's capability in optimizing different tasks. Promisingly, the proposed approach can be viewed as an efficient and effective strategy for more optimization scenarios. (C) 2020 Published by Elsevier Ltd.

Antlion optimization (ALO) is an efficient metaheuristic paradigm that imitates antlion’s foraging behavior when they search for the ants. However, the conventional variant appears to encounter difficulties in avoiding local optima stagnation and slow convergence speed in dealing with complex problems. Hence, there are problems in the performance that need to be mitigated. To alleviate these shortcomings, an improved variant called Lévy orthogonal learning ALO is developed, which enhances the efficacy of the core method with orthogonal learning strategy, Levy flight, and primary core mechanisms. To measure the effectiveness of the new method, it is compared with the basic version, variant called Levy flight ALO, and variant called orthogonal learning ALO using thirty benchmark functions from IEEE CEC 2017. Also, it is compared with 15 well-known metaheuristic algorithms. Empirical results have shown the superiority of the proposed algorithm in solving the majority of test functions in terms of solution quality and convergence speed. To further validate the efficacy of the enhanced algorithm, it is applied to common practical engineering problems with constrained and unknown search spaces. The obtained results vividly demonstrate that the proposed algorithm provides satisfactory results for solving these problems.

Grasshopper optimization algorithm (GOA) is a newly proposed meta-heuristic algorithm that simulates the biological habits of grasshopper seeking for food sources. Nonetheless, some shortcomings exist in the basic version of GOA. It may quickly drop into local optima and show slow convergence rates when facing some complex basins. In this work, an improved GOA is proposed to alleviate the core shortcomings of GOA and handle continuous optimization problems more efficiently. For this purpose, two strategies, including orthogonal learning and chaotic exploitation, are introduced into the conventional GOA to find a more stable trade-offbetween the exploration and exploitation cores. Adding orthogonal learning to GOA can enhance the diversity of agents, whereas a chaotic exploitation strategy can update the position of grasshoppers within a limited local region. To confirm the efficacy of GOA, we compared it with a variety of famous classical meta-heuristic algorithms performed on 30 IEEE CEC2017 benchmark functions. Also, it is applied to feature selection cases, and three structural design problems are employed to validate its efficacy in terms of different metrics. The experimental results illustrate that the above tactics can mitigate the deficiencies of GOA, and the improved variant can reach high-quality solutions for different problems. (C) 2020 Elsevier Ltd. All rights reserved.