Rapid detection of multi-scale cotton pests based on lightweight GBW-YOLOv5 model.

BACKGROUND: Pest infestation is one of the primary causes of decreased cotton yield and quality. Rapid and accurate identification of cotton pest categories is essential for producers to implement effective and expeditious control measures. Existing multi-scale cotton pest detection technology still suffers from poor accuracy and rapidity of detection. This study proposed the pruned GBW-YOLOv5 (Ghost-BiFPN-WIoU You Only Look Once version 5), a novel model for the rapid detection of cotton pests. RESULTS: The detection performance of the pruned GBW-YOLOv5 model for cotton pests was evaluated based on the self-built cotton pest dataset. In comparison with the original YOLOv5 model, the pruned GBW-YOLOv5 model demonstrated significant reductions in complexity, size, and parameters by 68.4%, 66.7%, and 68.2%, respectively. Remarkably, the mean average precision (mAP) decreased by a mere 3.8%. The pruned GBW-YOLOv5 model outperformed other classic object detection models, achieving an outstanding detection speed of 114.9 FPS. CONCLUSION: The methodology proposed by our research enabled rapid and accurate identification of cotton pests, laying a solid foundation for the implementation of precise pest control measures. The pruned GBW-YOLOv5 model provided theoretical research and technical support for detecting cotton pests under field conditions. © 2024 Society of Chemical Industry.

Targeting CENP-E augments immunotherapy in non-small cell lung cancer via stabilizing PD-L1.

Centromere-associated protein E (CENP-E) plays a critical role in mitosis and chromosome misalignment, which may represent a potential therapeutic target in tumors. CENP-E is frequently overexpressed in lung cancer and act as a driver gene. However, it remains unclear whether CENP-E regulates the immune microenvironment in non-small cell lung cancer (NSCLC). Our study revealed that CENP-E is highly expressed and predicts a worse survival in NSCLC patients; inhibition of CENP-E leads to an upregulation of PD-L1 expression, consequently impacting the immune microenvironment of NSCLC by modulating the balance between CD8+ T cells and regulatory T cells (Tregs). Mechanistically, we demonstrated that downregulation of CENP-E could stabilize PD-L1 mRNA through the targeting of its 3'UTR by TTP. The genetic knockdown or pharmacological inhibition of CENP-E, in combination with PD-L1 antibody, could enhance the antitumor effect in NSCLC. Thus, our findings have revealed a role of CENP-E in immunotherapy and suggest that combination of CENP-E inhibitor with PD-L1 antibody could be an effective treatment option for NSCLC.

Breast cancer metastases to the thyroid and stomach: A case report.

The most common sites of metastasis for breast cancer are the soft tissues, bones, lungs, liver and brain; however, metastases to the gastrointestinal tract and thyroid gland from breast cancer rarely occur. The present study describes the case of a 30-year-old woman who developed gastric and thyroid metastases 5 years after her initial diagnosis of invasive ductal breast carcinoma. The initial pathological diagnosis when receiving modified radical mastectomy was invasive ductal carcinoma, and further immunohistochemical examination revealed the cancer to be estrogen receptor (-), progesterone receptor (-), human epidermal growth factor receptor 2 (HER2; ++) and Ki-67 (70%). Genetic testing indicated the HER2 amplification mutation, whereas BRCA1/2 testing was negative. A total of 21 months after surgery, during regular follow-up, the patient was revealed to have developed an enlarged lymph node in the left side of the neck and the first recurrence was confirmed. Approximately 5 years after surgery, the patient gradually developed multi-site metastasis, and developed metastases to the thyroid gland and stomach confirmed by pathology and imaging. Combined chemotherapy and targeted therapy were administered and exhibited good efficacy; however, the patient subsequently died due to heart failure. This case report describes the occurrence of gastric and thyroid metastases from breast cancer, and highlights the importance of distinguishing between metastatic and primary tumors. Distinguishing between a metastatic and primary tumor is crucial as treatment protocols vary significantly for these two types of tumors. For patients with a history of breast cancer it should first be considered whether they have metastasis of the primary disease or discomfort caused by treatment; however, the possibility of a second primary tumor cannot be ignored. If the patient has symptoms such as loss of appetite, nausea, vomiting, stomach pain and stomach discomfort, a gastroscopy should be performed in a timely manner.

Palladium Catalyzed Annulation of o-Iodo-Anilines with Propargyl Alcohols: Synthesis of Substituted Quinolines.

A palladium catalyzed annulation of o-iodo-anilines with propargyl alcohols for the synthesis of substituted quinolines has been developed. The reaction tolerates diverse functional groups under mild conditions, providing direct access to 2,4-disubstituted quinolines from easily available starting materials. A broad range of 2,4-disubstituted quinolines were efficiently prepared in good to excellent yields.

An efficient coverage method for SEMWSNs based on adaptive chaotic Gaussian variant snake optimization algorithm.

Soil element monitoring wireless sensor networks (SEMWSNs) are widely used in soil element monitoring agricultural activities. SEMWSNs monitor changes in soil elemental content during agriculture products growing through nodes. Based on the feedback from the nodes, farmers adjust irrigation and fertilization strategies on time, thus promoting the economic growth of crops. The critical issue in SEMWSNs coverage studies is to achieve maximum coverage of the entire monitoring field by adopting a smaller number of sensor nodes. In this study, a unique adaptive chaotic Gaussian variant snake optimization algorithm (ACGSOA) is proposed for solving the above problem, which also has the advantages of solid robustness, low algorithmic complexity, and fast convergence. A new chaotic operator is proposed in this paper to optimize the position parameters of individuals, enhancing the convergence speed of the algorithm. Moreover, an adaptive Gaussian variant operator is also designed in this paper to effectively avoid SEMWSNs from falling into local optima during the deployment process. Simulation experiments are designed to compare ACGSOA with other widely used metaheuristics, namely snake optimizer (SO), whale optimization algorithm (WOA), artificial bee colony algorithm (ABC), and fruit fly optimization algorithm (FOA). The simulation results show that the performance of ACGSOA has been dramatically improved. On the one hand, ACGSOA outperforms other methods in terms of convergence speed, and on the other hand, the coverage rate is improved by 7.20%, 7.32%, 7.96%, and 11.03% compared with SO, WOA, ABC, and FOA, respectively.

Coverage optimization of soil moisture wireless sensor networks based on adaptive Cauchy variant butterfly optimization algorithm.

Soil moisture wireless sensor networks (SMWSNs) are used in the field of information monitoring for precision farm irrigation, which monitors the soil moisture content and changes during crop growth and development through sensor nodes at the end. The control terminal adjusts the irrigation water volume according to the transmitted information, which is significant for increasing the crop yield. One of the main challenges of SMWSNs in practical applications is to maximize the coverage area under certain conditions of monitoring area and to minimize the number of nodes used. Therefore, a new adaptive Cauchy variant butterfly optimization algorithm (ACBOA) has been designed to effectively improve the network coverage. More importantly, new Cauchy variants and adaptive factors for improving the global and local search ability of ACBOA, respectively, are designed. In addition, a new coverage optimization model for SMWSNs that integrates node coverage and network quality of service is developed. Subsequently, the proposed algorithm is compared with other swarm intelligence algorithms, namely, butterfly optimization algorithm (BOA), artificial bee colony algorithm (ABC), fruit fly optimization algorithm (FOA), and particle swarm optimization algorithm (PSO), under the conditions of a certain initial population size and number of iterations for the fairness and objectivity of simulation experiments. The simulation results show that the coverage rate of SMWSNs after ACBOA optimization increases by 9.09%, 13.78%, 2.57%, and 11.11% over BOA, ABC, FOA, and PSO optimization, respectively.

Palladium Catalyzed Dicarbonylation of α-Iodo-Substituted Alkylidenecyclopropanes: Synthesis of Carbamoyl Substituted Indenones.

A palladium catalyzed dicarbonylation of α-iodo-substituted ACPs for the synthesis of carbamoyl substituted indenones has been developed. Two carbonyl groups were incorporated into the product with the cleavage of the proximal C-C bond of the ACPs. A broad range of carbamoyl substituted indenones were efficiently prepared in good to excellent yields.

Construction of sulfur-containing N-vinylimides: N-addition of imides to propargyl sulfonium salts.

An N-addition reaction between imides and propargyl sulfonium salts was developed to afford sulfur-containing N-vinylimides with moderate to excellent yields. Under the activation of NaOAc·3H2O, imides could undergo deprotonation and propargyl sulfonium salts could isomerize to allenic sulfonium salts. The N-nucleophilic attack initiates the reaction and gives the desired products. Various imides, including arylimides, aliphatic imides and N-(arylsulfonyl) alkyl acylamides, and even bioactive saccharin, thalidomide and pomalidomide could provide organosulfur N-vinylimides compounds. The simple, mild and metal-free reaction conditions, the broad scope of substrates, gram-scale synthesis and convenient transformation embody the synthetic superiority of this process.

Integrated Analysis of MATH-Based Subtypes Reveals a Novel Screening Strategy for Early-Stage Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is a frequently diagnosed cancer type, and many patients have already reached an advanced stage when diagnosed. Thus, it is crucial to develop a novel and efficient approach to diagnose and classify lung adenocarcinoma at an early stage. In our study, we combined in silico analysis and machine learning to develop a new five-gene-based diagnosis strategy, which was further verified in independent cohorts and in vitro experiments. Considering the heterogeneity in cancer, we used the MATH (mutant-allele tumor heterogeneity) algorithm to divide patients with early-stage LUAD into two groups (C1 and C2). Specifically, patients in C2 had lower intratumor heterogeneity and higher abundance of immune cells (including B cell, CD4 T cell, CD8 T cell, macrophage, dendritic cell, and neutrophil). In addition, patients in C2 had a higher likelihood of immunotherapy response and overall survival advantage than patients in C1. Combined drug sensitivity analysis (CTRP/PRISM/CMap/GDSC) revealed that BI-2536 might serve as a new therapeutic compound for patients in C1. In order to realize the application value of our study, we constructed the classifier (to classify early-stage LUAD patients into C1 or C2 groups) with multiple machine learning and bioinformatic analyses. The 21-gene-based classification model showed high accuracy and strong generalization ability, and it was verified in four independent validation cohorts. In summary, our research provided a new strategy for clinicians to make a quick preliminary assisting diagnosis of early-stage LUAD and make patient classification at the intratumor heterogeneity level. All data, codes, and study processes have been deposited to Github and are available online.

Machine learning and bioinformatics analysis revealed classification and potential treatment strategy in stage 3-4 NSCLC patients.

BACKGROUND: Precision medicine has increased the accuracy of cancer diagnosis and treatment, especially in the era of cancer immunotherapy. Despite recent advances in cancer immunotherapy, the overall survival rate of advanced NSCLC patients remains low. A better classification in advanced NSCLC is important for developing more effective treatments. METHOD: The calculation of abundances of tumor-infiltrating immune cells (TIICs) was conducted using Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT), xCell (xCELL), Tumor IMmune Estimation Resource (TIMER), Estimate the Proportion of Immune and Cancer cells (EPIC), and Microenvironment Cell Populations-counter (MCP-counter). K-means clustering was used to classify patients, and four machine learning methods (SVM, Randomforest, Adaboost, Xgboost) were used to build the classifiers. Multi-omics datasets (including transcriptomics, DNA methylation, copy number alterations, miRNA profile) and ICI immunotherapy treatment cohorts were obtained from various databases. The drug sensitivity data were derived from PRISM and CTRP databases. RESULTS: In this study, patients with stage 3-4 NSCLC were divided into three clusters according to the abundance of TIICs, and we established classifiers to distinguish these clusters based on different machine learning algorithms (including SVM, RF, Xgboost, and Adaboost). Patients in cluster-2 were found to have a survival advantage and might have a favorable response to immunotherapy. We then constructed an immune-related Poor Prognosis Signature which could successfully predict the advanced NSCLC patient survival, and through epigenetic analysis, we found 3 key molecules (HSPA8, CREB1, RAP1A) which might serve as potential therapeutic targets in cluster-1. In the end, after screening of drug sensitivity data derived from CTRP and PRISM databases, we identified several compounds which might serve as medication for different clusters. CONCLUSIONS: Our study has not only depicted the landscape of different clusters of stage 3-4 NSCLC but presented a treatment strategy for patients with advanced NSCLC.

Comprehensive Profiling Reveals Distinct Microenvironment and Metabolism Characterization of Lung Adenocarcinoma.

Lung adenocarcinoma has entered into an era of immunotherapy with the development of immune checkpoint inhibitors (ICIs). The identification of immune subtype is crucial to prolonging survival in patients. The tumor microenvironment (TME) and metabolism have a profound impact on prognosis and therapy. The majority of previous studies focused on only one aspect, while both of them are essential to the understanding of tumorigenesis and development. We hypothesized that lung adenocarcinoma can be stratified into immune subgroups with alterations in the TME infiltration. We aimed to explore the "TME-Metabolism-Risk" patterns in each subtypes and the mechanism behind. Glycolysis and cholesterol were selected for the analysis of metabolic states based on the first half of the study. Bioinformatic analysis was performed to investigate the transcriptomic and clinical data integrated by three lung adenocarcinoma cohorts (GSE30219, GSE31210, GSE37745, N = 415). The results were validated in an independent cohort (GSE50081, N = 127). In total, 415 lung adenocarcinoma samples were integrated and analyzed. Four major immune subtypes were indentified using bioinformatic analysis. Subtype NC1, characterized by a high level of glycolysis, with extremely low microenvironment cell infiltration. Subtype NC2, characterized by the "Silence" and "Cholesterol biosynthesis Predominant" metabolic states, with a middle degree infiltration of microenvironment cell. Subtype NC3, characterized by the lack of "Cholesterol biosynthesis Predominant" metabolic state, with abundant microenvironment cell infiltration. Subtype NC4, characterized by "Mixed" metabolic state, with a relatively low microenvironment cell infiltration. Least absolute shrinkage and selection operator (LASSO) regression and multivariate analyses were performed to calculate the risk of each sample, and we attempted to find out the potential immune escape mechanism in different subtypes. The result revealed that the lack of immune cells infiltration might contribute to the immune escape in subtypes NC1 and NC4. NC3 was characterized by the high expression of immune checkpoint molecules and fibroblasts. NC2 had defects in activation of innate immune cells. There existed an obviously survival advantage in subtype NC2. Gene set enrichment analysis (GSEA) and Gene Ontology analysis indicated that the PI3K-AKT-mTOR, TGF-ß, MYC-related pathways might be correlated with this phenomenon. In addition, some differentially expressed genes (DEGs) were indentified in subtype NC3, which might be potential targets for survival phenotype transformation.

Identification of CXCL13/CXCR5 axis's crucial and complex effect in human lung adenocarcinoma.

Immune escape and low response to immunotherapy are crucial challenges in present lung cancer treatment. In this study, we constructed a new immune-related classifier based on CXCL13/CXCR5, an important tumor microenvironment component and strongly related with the formation of tertiary lymphoid structures (TLSs) in tumor microenvironment. With the classifier, we divided patients into two main clusters and each cluster was further divided into subcluster (A1, A2, B1, B2, B3). In the later analysis, we noticed that patients in subcluster B3 had a distinct advantage over patients in A1 in survival time and immune infiltration, suggesting a more favorable response to immunotherapy. Moreover, we demonstrated the genetic and epigenetic regulation related to the subclusters and recovered four key differentially expressed genes (ERBB4, GRIN2A, IL2RA, CCND2). With several experiments, we verified the unique role of CCND2 in tumor metastasis and T cell apoptosis. Overexpressing CCND2 could significantly impair cancer cell abilities of migration and invasion and downregulate PD-1/PD-L1 signaling, which may be the cause of T cell apoptosis reduction. In the end, we constructed a regression risk model that could successfully predict ICI response. To sum up, our study established new stratification models that can successfully predict patient survival and response to ICI. And using integrative analysis of multi-omics data, four key DEGs were noticed, and CCND2, one of the four genes, was identified as a potential treatment target because of its effect in tumor metastasis and T cell apoptosis.

The Superior Ability of Human BDCA3+ (CD141+) Dendritic Cells (DCs) to Cross-Present Antigens Derived From Necrotic Lung Cancer Cells.

Dendritic cells (DCs) play a key role in initiating and regulating the immune responses to pathogens, self-antigens, and cancers. Human blood DCs comprise a family of different subsets: plasmacytoid DCs (pDCs) and CD16+, CD1c/BDCA1+, and BDCA3+ (CD141+) myeloid DCs and possess different phenotypes and functional characteristics. Lung cancer is the most common cancer, with the highest morbidity and mortality in the world. However, which DC subset plays a leading role in the lung cancer immune responses is unclear. We reanalyzed C-type lectin domain family 9 member A (CLEC9A) and CD141 (THBD) gene expression profiles from the Cancer Genome Atlas (TCGA) database and performed the Kaplan-Meier survival analysis of overall survival for several cancers according to their expression levels. Next, we investigated the capacities of five human blood DC subsets to stimulate T cell proliferation and capture, process and (cross-) present tumor antigen. Human BDCA3+ (CD141+) DCs have a superior capacity to stimulate allogeneic CD4+T cells proliferation and induce superior Th1 response compared with other DC subsets. Interestingly, toll-like receptor (TLR) agonists have little effect on DCs to induce the proliferation of naïve CD4+ T cells, but contribute to their differentiation. Importantly, BDCA3+ (CD141+) DCs possess the most potent ability to cross-present human tumor antigen after their uptake of necrotic lung cancer cells despite their lower antigen uptake. These findings suggest that human BDCA3+ (CD141+) DCs are critical mediators of cytotoxic T lymphocyte responses against EGFR-positive lung cancer. Therefore, our findings may provide theoretical basis for the development of DC-based antitumor vaccines.

Human inflammatory dendritic cells in malignant pleural effusions induce Th1 cell differentiation.

Dendritic cells are crucial for the initiation and regulation of immune responses against cancer and pathogens. DCs are heterogeneous and highly specialized antigen-presenting cells. Human DCs comprise several subsets with different phenotypes and functional properties. In the steady state, human DC subsets have been well studied. However, the components of DC subsets and their immune functions during the inflamed setting are poorly understood. We identified and characterized DC subsets in the malignant pleural effusions of NSCLC patients. We analyzed the capacity of these DC subsets to induce T-cell differentiation. We observed the presence of inflammatory DCs (infDCs) and macrophages in the malignant pleural effusions of NSCLC patients, as identified by the CD11C+HLA-DR+CD16-BDCA1+ and CD11C+HLA-DR+CD16+BDCA1- phenotypes, respectively. InfDCs represented approximately 1% of the total light-density cells in the pleural effusion and were characterized by the expression of CD206, CD14, CD11b, and CD1α, which were absent on blood DCs. InfDCs also expressed CD80, although at a low level. As infDCs did not express CD40, CD83 and CD275, they remained functionally immature. We found that TLR agonists promoted the maturation of infDCs. Compared with macrophages, infDCs had a weaker capacity to phagocytose necrotic tumor cell lysates. However, only infDCs induced autologous memory CD4+ T-cell differentiation into Th1 cells. For the first time, we found that infDCs were present in the malignant pleural effusions of NSCLC patients. We conclude that infDCs represent a distinct human DC subset and induce Th1 cell differentiation in the presence of TLR agonists.

Regioselective N-Addition/Substitution Reaction of α-Alkylidene Pyrazolinones with Propargyl Sulfonium Salts to Construct Allylthio-Containing Pyrazolones.

The regioselective N-addition/substitution reaction between α-alkylidene pyrazolinones and propargyl sulfonium salts has been developed to construct functionalized allylthio-containing pyrazolones with moderate to excellent yields. α-Alkylidene pyrazolinones act as N-nucleophilic agents which are distinguished from reported C-nucleophilic reactions. Excellent regioselectivity, readily available starting materials, the broad range of substrates, gram-scale synthesis, and simple operation illustrate the synthetic advantages of this new reaction pathway.

Clinical value of MLH1-negative circulating tumor cells in lung cancer patients.

Circulating tumor cells (CTCs) serve as valuable biomarkers. However, MutL homolog 1 (MLH1)-negative CTCs and their clinical significance in lung cancer are nearly unknown.Here, bioinformatic analysis of MLH1 expression and its clinical significance was conducted using the Oncomine, Ualcan, and Kaplan-Meier plotter websites. Size-based isolation and RNA in situ hybridization assays were used to identify CTCs and evaluate MLH1 and mesenchymal marker expression in CTCs. MLH1 was downregulated in lung cancer patients. Patients with lower MLH1 expression levels had worse prognoses. In a cohort of 32 randomly selected patients with lung cancer, the patients with poorer treatment responses had more MLH1-negative CTCs. The total CTCs, MLH1-negative CTCs and mesenchymal markers-expressing CTCs levels were negatively correlated with prognosis in the lung cancer patients.Our data showed the clinical significance of MLH1 expression in lung cancer tissues. The characterization and numeration of CTCs based on the expression of MLH1 and mesenchymal markers may be a convenient approach for predicting treatment response and prognosis in lung cancer.

FOXA1+ regulatory T cells: A novel T cell subset that suppresses antitumor immunity in lung cancer.

INTRODUCTION: Regulatory T cells (Tregs) are important in the tumor microenvironment. Many subpopulations of Tregs have participated in suppressing antitumor immunity. Recently, FOXA1+ Tregs were reported as a novel subset of Tregs that control autoimmune diseases. However, their clinical value in lung cancer is unknown. METHODS: We included 92 subjects in this study. Peripheral blood samples were collected from 15 lung cancer patients. Another 45 advanced stage lung cancer patients with malignant pleural effusion were enrolled for the analysis of FOXA1+ Tregs in pleural effusions. Lung cancer tissues were collected from 3 patients. In vitro experiments were conducted to ascertain the influence of FOXA1+ Tregs on T cells. Tumor-bearing mice model was utilized to explore the effects of Foxa1+ Treg on tumor growth and the prognoses. RESULTS: Our data demonstrated that FOXA1+ Tregs were increased in lung cancer. Moreover, patients with more FOXA1+ Tregs showed more liver metastases and poorer treatment responses. In vitro assays revealed that FOXA1+ Tregs inhibited the proliferation of T cells, the production of IFN-γ and IL-2 by T cells. FOXA1+ Tregs promoted tumor growth and indicated poor prognosis in the mice model of lung cancer. DISCUSSION: Collectively, our study is the first to investigate the suppressive function of FOXA1+ Tregs against T cells in lung cancer, and the results showed that FOXA1+ Tregs are markers of poor treatment responses in lung cancer patients. The inhibition of FOXA1+ Tregs represents a promising new strategy to enhance antitumor immunity.

Characteristics and prognostic significance of profiling the peripheral blood T-cell receptor repertoire in patients with advanced lung cancer.

Lung cancer is one of the greatest threats to human health, and is initially detected and attacked by the immune system through tumor-reactive T cells. The aim of this study was to determine the basic characteristics and clinical significance of the peripheral blood T-cell receptor (TCR) repertoire in patients with advanced lung cancer. To comprehensively profile the TCR repertoire, high-throughput sequencing was used to identify hypervariable rearrangements of complementarity determining region 3 (CDR3) of the TCR ß chain in peripheral blood samples from 64 advanced lung cancer patients and 31 healthy controls. We found that the TCR repertoire differed substantially between lung cancer patients and healthy controls in terms of CDR3 clonotype, diversity, V/J segment usage, and sequence. Specifically, baseline diversity correlated with several clinical characteristics, and high diversity reflected a better immune status. Dynamic detection of the TCR repertoire during anticancer treatment was useful for prognosis. Both increased diversity and high overlap rate between the pre- and post-treatment TCR repertoires indicated clinical benefit. Combination of the diversity and overlap rate was used to categorize patients into immune improved or immune worsened groups and demonstrated enhanced prognostic significance. In conclusion, TCR repertoire analysis served as a useful indicator of disease development and prognosis in advanced lung cancer and may be utilized to direct future immunotherapy.

Application of metabolomics by UHPLC-MS/MS in diagnostics and biomarker discovery of non-small cell lung cancer.

BACKGROUND: Targeted metabolomics was utilized in case studies of non-small cell lung cancer (NSCLC) to develop and test metabolite classifiers in serum as potential biomarkers for new lung cancer diagnostic strategies, cancer staging, and subtype determination in the Chinese population. METHODS: A total of 77 samples, including 45 NSCLC patients from stage I to IV, and 32 healthy controls were included in this study. After serum extraction, metabolic assays based on a wide range of targeted metabolome technologies and the UPLC-MS-MS detection platform were performed to detect metabolites in them. Custom database and multivariate statistical analysis were utilized to evaluate the difference of metabolome between different arms. RESULTS: A total of 296 metabolites were detected in all samples, of which 81 were found differentially expressed among lung cancer patients and controls. While the principal component analysis indicated that the metabolome analysis is clearly powerful in differentiating lung cancer patients from normal controls, no significant differences in the serum metabolites between different lung cancer stages or between adenocarcinoma and squamous cell carcinoma were observed. CONCLUSIONS: This study showed the power of the novel UPLC-MS/MS platform in serum metabolic profiling for the detection of NSCLC, which might provide new potential tumor biomarkers and can accelerate the development of new diagnostic strategies in NSCLC.

Combined inorganic base promoted N-addition/[2,3]-sigmatropic rearrangement to construct homoallyl sulfur-containing pyrazolones.

The first sequentially combined inorganic base promoted N-addition/[2,3]-sigmatropic rearrangement reaction of α-alkylidene pyrazolinones and propargyl sulfonium salts has been reported to construct homoallyl sulfur-containing pyrazolones with moderate to excellent yields. α-Alkylidene pyrazolinones function as N-nucleophilic agents distinguished from the reported C-addition reactions. Propargyl sulfonium salts were first involved in the [2,3]-sigmatropic rearrangement protocol differentiated from the well-established annulation reactions. The excellent regioselectivity, the broad scope of substrates, gram-scale synthesis and convenient transformation embody the synthetic superiority of this cascade process.