Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

Lung cancer is a leading cause of global mortality rates.Early detection of pulmonary tumors can significantly enhance the survival rate of patients.Recently,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high accuracy.Nevertheless,the existing method-ologies cannot obtain a high level of specificity and sensitivity.The present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet architecture.The LCSC model comprises two distinct stages.The first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung lobes.Subsequently,an improved AlexNet architecture is employed to classify lung cancer.During the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate nodules.The suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or malignant.The proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

Stage at diagnosis of colorectal cancer through diagnostic route:Who should be screened?

BACKGROUND Colorectal cancer(CRC)is a global health concern,with advanced-stage diagnoses contributing to poor prognoses.The efficacy of CRC screening has been well-established;nevertheless,a significant proportion of patients remain unscreened,with>70%of cases diagnosed outside screening.Although identifying specific subgroups for whom CRC screening should be particularly recommended is crucial owing to limited resources,the association between the diagnostic routes and identification of these subgroups has been less appreciated.In the Japanese cancer registry,the diagnostic routes for groups discovered outside of screening are primarily categorized into those with comorbidities found during hospital visits and those with CRC-related symptoms.AIM To clarify the stage at CRC diagnosis based on diagnostic routes.METHODS We conducted a retrospective observational study using a cancer registry of patients with CRC between January 2016 and December 2019 at two hospitals.The diagnostic routes were primarily classified into three groups:Cancer screening,follow-up,and symptomatic.The early-stage was defined as Stages 0 or I.Multivariate and univariate logistic regressions were exploited to determine the odds of early-stage diagnosis in the symptomatic and cancer screening groups,referencing the follow-up group.The adjusted covariates were age,sex,and tumor location.RESULTS Of the 2083 patients,715(34.4%),1064(51.1%),and 304(14.6%)belonged to the follow-up,symptomatic,and cancer screening groups,respectively.Among the 2083 patients,CRCs diagnosed at an early stage were 57.3%(410 of 715),23.9%(254 of 1064),and 59.5%(181 of 304)in the follow-up,symptomatic,and cancer screening groups,respectively.The symptomatic group exhibited a lower likelihood of early-stage diagnosis than the follow-up group[P<0.001,adjusted odds ratio(aOR),0.23;95%confidence interval(95%CI):0.19-0.29].The likelihood of diagnosis at an early stage was similar between the follow-up and cancer screening groups(P=0.493,aOR for early-stage diagnosis in the cancer screening group vs follow-up group=1.11;95%CI=0.82-1.49).CONCLUSION CRCs detected during hospital visits for comorbidities were diagnosed earlier,similar to cancer screening.CRC screening should be recommended,particularly for patients without periodical hospital visits for comorbidities.

Circular RNAs in breast cancer diagnosis,treatment and prognosis

Breast cancer has surpassed lung cancer to become the most common malignancy worldwide.The incidence rate and mortality rate of breast cancer continue to rise,which leads to a great burden on public health.Circular RNAs(circRNAs),a new class of noncoding RNAs(ncRNAs),have been recognized as important oncogenes or suppressors in regulating cancer initiation and progression.In breast cancer,circRNAs have significant roles in tumorigenesis,recurrence and multidrug resistance that are mediated by various mechanisms.Therefore,circRNAs may serve as promising targets of therapeutic strategies for breast cancer management.This study reviews the most recent studies about the biosynthesis and characteristics of circRNAs in diagnosis,treatment and prognosis evaluation,as well as the value of circRNAs in clinical applications as biomarkers or therapeutic targets in breast cancer.Understanding the mechanisms by which circRNAs function could help transform basic research into clinical applications and facilitate the development of novel circRNA-based therapeutic strategies for breast cancer treatment.

Marker Ki-67 is a potential biomarker for the diagnosis and prognosis of prostate cancer based on two cohorts

BACKGROUND Prostate cancer(PCa)is a widespread malignancy,predominantly affecting elderly males,and current methods for diagnosis and treatment of this disease continue to fall short.The marker Ki-67(MKI67)has been previously demonstrated to correlate with the proliferation and metastasis of various cancer cells,including those of PCa.Hence,verifying the association between MKI67 and the diagnosis and prognosis of PCa,using bioinformatics databases and clinical data analysis,carries significant clinical implications.AIM To explore the diagnostic and prognostic efficacy of antigens identified by MKI67 expression in PCa.METHODS For cohort 1,the efficacy of MKI67 diagnosis was evaluated using data from The Cancer Genome Atlas(TCGA)and Genotype-Tissue Expression(GTEx)databases.For cohort 2,the diagnostic and prognostic power of MKI67 expression was further validated using data from 271 patients with clinical PCa.RESULTS In cohort 1,MKI67 expression was correlated with prostate-specific antigen(PSA),Gleason Score,T stage,and N stage.The receiver operating characteristic(ROC)curve showed a strong diagnostic ability,and the Kaplan-Meier method demonstrated that MKI67 expression was negatively associated with the progression-free interval(PFI).The time-ROC curve displayed a weak prognostic capability for MKI67 expression in PCa.In cohort 2,MKI67 expression was significantly related to the Gleason Score,T stage,and N stage;however,it was negatively associated with the PFI.The time-ROC curve revealed the stronger prognostic capability of MKI67 in patients with PCa.Multivariate COX regression analysis was performed to select risk factors,including PSA level,N stage,and MKI67 expression.A nomogram was established to predict the 3-year PFI.CONCLUSION MKI67 expression was positively associated with the Gleason Score,T stage,and N stage and showed a strong diagnostic and prognostic ability in PCa.

Roles and application of exosomes in the development,diagnosis and treatment of gastric cancer

As important messengers of intercellular communication,exosomes can regulate local and distant cellular communication by transporting specific exosomal con-tents and can also promote or suppress the development and progression of gas-tric cancer(GC)by regulating the growth and proliferation of tumor cells,the tumor-related immune response and tumor angiogenesis.Exosomes transport bioactive molecules including DNA,proteins,and RNA(coding and noncoding)from donor cells to recipient cells,causing reprogramming of the target cells.In this review,we will describe how exosomes regulate the cellular immune respon-se,tumor angiogenesis,proliferation and metastasis of GC cells,and the role and mechanism of exosome-based therapy in human cancer.We will also discuss the potential application value of exosomes as biomarkers in the diagnosis and treat-ment of GC and their relationship with drug resistance.

Early diagnosis of pancreatic cancer: Shedding light on an unresolved challenge

Diagnosing early-stage pancreatic cancer(PC)remains a clinical challenge.Hence,studying novel imaging aspects that could enhance the diagnostic accuracy of malignant pancreatic precursor lesions is imperative.This article aims to un-derscore the promising role of emerging imaging aspects that may facilitate the earlier diagnosis of PC,thereby improving its management and prognosis.

Analysis of the Clinical Value of Immunohistochemical Testing in the Pathological Diagnosis of Breast Cancer

Objective:To investigate the clinical value of immunohistochemistry(IHC)detection in the pathological diagnosis of breast cancer.Methods:Eighty breast cancer patients admitted to Baoding No.1 Central Hospital from June 2022 to June 2023 were selected as study subjects.The samples were divided into a positive group(40 cases)and a negative group(40 cases)according to ER and PR test results.Immunohistochemistry was performed on all patients to compare the differences between the two groups in C-erbB-2 positive expression and axillary lymph node metastasis.Results:The positive expression rate of C-erbB-2 in the positive group(35.00%)was significantly lower than that in the negative group(80.00%),with a highly significant difference(P<0.001).The axillary lymph node metastasis rate in the positive group(40.00%)was significantly lower than that in the negative group(75.00%),with a significant difference(P<0.05).Conclusion:Immunohistochemical detection in breast cancer pathology enhances diagnostic accuracy,predicts prognosis,and supports personalized treatment by identifying ER,PR,and C-erbB-2.It is worth being widely adopted in clinical practice.

Research Progress of Ultrasound Radiomics in The Diagnosis and Treatment of Breast Cancer

With the advancement of medical research in recent years and the frequent occurrence of different types of cancer, breast cancer has gradually attracted the public’s attention. The incidence of breast cancer is rising, mainly affecting women with a high mortality rate. According to the clinical treatment effect, early diagnosis and early treatment can effectively control the mortality of breast cancer and improve patient’s quality of life. Ultrasound radiomics is an emerging field that can extract quantitative high-dimensional data from ultrasound images. Recently, ultrasound radiomics has been widely used in the clinical treatment of breast cancer. This paper analyzed the research progress of ultrasound radiomics in the diagnosis and treatment of breast cancer.

Expert Consensus on the Diagnosis and Treatment of Anticancer Drug-Induced Interstitial Lung Disease

Drug-induced interstitial lung disease(DILD)is the most common pulmonary adverse event of anticancer drugs.In recent years,the incidence of anticancer DILD has gradually increased with the rapid development of novel anticancer agents.Due to the diverse clinical manifestations and the lack of specific diagnostic criteria,DILD is difficult to diagnose and may even become fatal if not treated properly.Herein,a multidisciplinary group of experts from oncology,respiratory,imaging,pharmacology,pathology,and radiology departments in China has reached the“expert consensus on the diagnosis and treatment of anticancer DILD”after several rounds of a comprehensive investigation.This consensus aims to improve the awareness of clinicians and provide recommendations for the early screening,diagnosis,and treatment of anticancer DILD.This consensus also emphasizes the importance of multidisciplinary collaboration while managing DILD.

An Intelligent Decision Support System for Lung Cancer Diagnosis

Lung cancer is the leading cause of cancer-related death around the globe.The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis.Most diagnostic techniques can identify and classify only one type of lung cancer.It is crucial to close this gap with a system that detects all lung cancer types.This paper proposes an intelligent decision support system for this purpose.This system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save lives.Its algorithm uses a Convolutional Neural Network(CNN)tool to perform deep learning and a Random Forest Algorithm(RFA)to help classify the type of cancer present using several extracted features,including histograms and energy.Numerous simulation experiments were conducted on MATLAB,evidencing that this system achieves 98.7%accuracy and over 98%precision and recall.A comparative assessment assessing accuracy,recall,precision,specificity,and F-score between the proposed algorithm and works from the literature shows that the proposed system in this study outperforms existing methods in all considered metrics.This study found that using CNNs and RFAs is highly effective in detecting lung cancer,given the high accuracy,precision,and recall results.These results lead us to believe that bringing this kind of technology to doctors diagnosing lung cancer is critical.

EZH2 is a biomarker associated with lung cancer diagnosis and immune infiltrates without prognostic specificity: a study based on the cancer genome atlas data

Enhancer of zeste homolog 2(EZH2)is the catalytic subunit of polycomb repressive complex 2(PRC2).Dysregulation of EZH2 causes alteration of gene expression and functions,thereby promoting cancer development.Recent studies suggest that EZH2 has a potential prognostic role in patients with nonsmall cell lung cancer(NSCLC).However,the prognostic value of EZH2 expression levels in NSCLC is controversial.In this study,we evaluated the prognostic value in lung cancer(LC-LUAD/LUSC)based on data from The Cancer Genome Atlas(TCGA)database.Kruskal-Wallis test,Wilcoxon signed-rank test,and logistic regression were used to evaluate the relationship between EZH2 expression and clinicopathological features.Cox regression and the Kaplan-Meier method were adopted to evaluate prognosis-related factors.Gene set enrichment analysis(GSEA)was performed to identify the key pathways related to EZH2.The correlations between EZH2 and cancer immune infiltrates were investigated by single-sample Gene Set Enrichment Analysis(ssGSEA).EZH2 was found to be up regulated with amplification in tumor tissues in multiple LC cohorts.High EZH2 expression was associated with poorer overall survival(OS).GSEA suggested that EZH2 regulates innate immune system,ECM affiliated,matrisome,surfactant metabolism.Notably,ssGSEA indicated that EZH2 expression was positively correlated with infiltrating levels of Th2 cells and significantly negatively correlated with mast cell infiltration level.These results suggest that EZH2 is associated with LC immune infiltration and significantly over-expressed in lung cancer,and its diagnostic value is better than prognosis,which lays a foundation for further study of the immunomodulatory role of EZH2 in LC.

Auxiliary Classifier of Generative Adversarial Network for Lung Cancer Diagnosis

The classification of lung nodules is a challenging problem as the visual analysis of the nodules and non-nodules revealed homogenous textural patterns.In this work,an Auxiliary Classifier(AC)-Generative Adversarial Network(GAN)based Lung Cancer Classification(LCC)system is developed.The pro-posed AC-GAN-LCC system consists of three modules;preprocessing,Lungs Region Detection(LRD),and AC-GAN classification.A Wienerfilter is employed in the preprocessing module to remove the Gaussian noise.In the LRD module,only the lung regions(left and right lungs)are detected using itera-tive thresholding and morphological operations.In order to extract the lung region only,floodfilling and background subtraction.The detected lung regions are fed to the AC-GAN classifier to detect the nodules.It classifies the nodules into one of the two classes,i.e.,binary classification(such as nodules or non-nodules).The AC-GAN is the extended version of the conditional GAN that predicts the label of a given image.Three different optimization techniques,adaptive gradient optimi-zation,root mean square propagation optimization,and Adam optimization are employed for optimizing the AC-GAN architecture.The proposed AC-GAN-LCC system is evaluated on the Lung Image Database Consortium(LIDC)data-base Computed Tomography(CT)scan images.The proposed AC-GAN-LCC system classifies∼15000 CT slices(7310 non-nodules and 7685 nodules).It pro-vides an overall accuracy of 98.8%on the LIDC database using Adam optimiza-tion by a 10-fold cross-validation approach.

Multilevel Attention Unet Segmentation Algorithmfor Lung Cancer Based on CT Images

Lung cancer is a malady of the lungs that gravely jeopardizes human health.Therefore,early detection and treatment are paramount for the preservation of human life.Lung computed tomography(CT)image sequences can explicitly delineate the pathological condition of the lungs.To meet the imperative for accurate diagnosis by physicians,expeditious segmentation of the region harboring lung cancer is of utmost significance.We utilize computer-aided methods to emulate the diagnostic process in which physicians concentrate on lung cancer in a sequential manner,erect an interpretable model,and attain segmentation of lung cancer.The specific advancements can be encapsulated as follows:1)Concentration on the lung parenchyma region:Based on 16-bit CT image capturing and the luminance characteristics of lung cancer,we proffer an intercept histogram algorithm.2)Focus on the specific locus of lung malignancy:Utilizing the spatial interrelation of lung cancer,we propose a memory-based Unet architecture and incorporate skip connections.3)Data Imbalance:In accordance with the prevalent situation of an overabundance of negative samples and a paucity of positive samples,we scrutinize the existing loss function and suggest a mixed loss function.Experimental results with pre-existing publicly available datasets and assembled datasets demonstrate that the segmentation efficacy,measured as Area Overlap Measure(AOM)is superior to 0.81,which markedly ameliorates in comparison with conventional algorithms,thereby facilitating physicians in diagnosis.

Primary biliary cholangitis presenting with granulomatous lung disease misdiagnosed as lung cancer:A case report

BACKGROUND There are few cases of pulmonary granulomatous changes secondary to primary biliary cirrhosis(PBC).No case of granulomatous lung disease secondary to PBC misdiagnosed as lung cancer had been reported.CASE SUMMARY A middle-aged woman presented with lung nodules and was misdiagnosed with lung cancer by positron emission tomography/computed tomography.She underwent left lobectomy,and the pathology of the nodules showed granulomatous inflammation,which was then treated with antibiotics.However,a new nodule appeared.Further investigation with lung biopsy and liver serology led to the diagnosis of PBC,and chest computed tomography indicated significant reduction in the pulmonary nodule by treatment with methylprednisolone and ursodeoxycholic acid.CONCLUSION Diagnosis of pulmonary nodules requires integrating various clinical data to avoid unnecessary pulmonary lobectomy.

First-line immunotherapy for advanced non-small cell lung cancer:current progress and future prospects

Global Cancer Statistics 2022 reported the prevalence and high mortality rate of lung cancer.Notably,non-small cell lung cancer(NSCLC)accounts for the majority of the histologic types1.Precision therapy for lung cancer has progressed rapidly and immune checkpoint inhibitors(ICIs)have become a leading research topic.Indeed,ICI therapy has been shown to improve the prognosis of lung cancer patients.

Cancer-educated neutrophils promote lung cancer progression via PARP-1-ALOX5-mediated MMP-9 expression

Objective:Neutrophils are one of the most predominant infiltrating leukocytes in lung cancer tissues and are associated with lung cancer progression.How neutrophils promote lung cancer progression,however,has not been established.Methods:Kaplan–Meier plotter online analysis and tissue immunohistochemistry were used to determine the relationship between neutrophils and overall survival in lung cancer patients.The effect of neutrophils on lung cancer was determined using the Transwell migration assay,a proliferation assay,and a murine tumor model.Gene knockdown was used to determine poly ADPribose polymerase(PARP)-1 function in lung cancer-educated neutrophils.Western blot analysis and gelatin zymography were used to demonstrate the correlation between PARP-1 and matrix metallopeptidase 9(MMP-9).Immunoprecipitation coupled to mass spectrometry(IP/MS)was used to identify the proteins interacting with PARP-1.Co-immunoprecipitation(Co-IP)was used to confirm that PARP-1 interacts with arachidonate 5-lipooxygenase(ALOX5).Neutrophil PARP-1 blockage by AG14361 rescued neutrophil-promoted lung cancer progression.Results:An increased number of infiltrating neutrophils was negatively associated with overall survival in lung cancer patients(P<0.001).Neutrophil activation promoted lung cancer cell invasion,migration,and proliferation in vitro,and murine lung cancer growth in vivo.Mechanistically,PARP-1 was shown to be involved in lung cancer cell-induced neutrophil activation to increase MMP-9 expression through interacting and stabilizing ALOX5 by post-translational protein modification(PARylation).Blocking PARP-1 by gene knockdown or AG14361 significantly decreased ALOX5 expression and MMP-9 production,and eliminated neutrophil-mediated lung cancer cell invasion and in vivo tumor growth.Conclusion:We identified a novel mechanism by which PARP-1 mediates lung cancer cell-induced neutrophil activation and PARylates ALOX5 to regulate MMP-9 expression,which exacerbates lung cancer progression.

Bronchoalveolar lavage fluid assessment facilitates precision medicine for lung cancer

Lung cancer is the most common and fatal malignant disease worldwide and has the highest mortality rate among tumor-related causes of death.Early diagnosis and precision medicine can significantly improve the survival rate and prognosis of lung cancer patients.At present,the clinical diagnosis of lung cancer is challenging due to a lack of effective non-invasive detection methods and biomarkers,and treatment is primarily hindered by drug resistance and high tumor heterogeneity.Liquid biopsy is a method for detecting circulating biomarkers in the blood and other body fluids containing genetic information from primary tumor tissues.Bronchoalveolar lavage fluid(BALF)is a potential liquid biopsy medium that is rich in a variety of bioactive substances and cell components.BALF contains information on the key characteristics of tumors,including the tumor subtype,gene mutation type,and tumor environment,thus BALF may be used as a diagnostic supplement to lung biopsy.In this review,the current research on BALF in the diagnosis,treatment,and prognosis of lung cancer is summarized.The advantages and disadvantages of different components of BALF,including cells,cell-free DNA,extracellular vesicles,and micro RNA are introduced.In particular,the great potential of extracellular vesicles in precision diagnosis and detection of drug-resistant for lung cancer is highlighted.In addition,the performance of liquid biopsies with different body fluid sources in lung cancer detection are compared to facilitate more selective studies involving BALF,thereby promoting the application of BALF for precision medicine in lung cancer patients in the future.

TonEBP expression is essential in the IL-1β–induced migration and invasion of human A549 lung cancer cells

Lung cancer has the highest mortality rate among all cancers,in part because it readily metastasizes.The tumor microenvironment,comprising blood vessels,fibroblasts,immune cells,and macrophages[including tumor-associated macrophages(TAMs)],is closely related to cancer cell growth,migration,and invasion.TAMs secrete several cytokines,including interleukin(IL)-1β,which participate in cancer migration and invasion.p21-activated kinase 1(PAK1),an important signaling molecule,induces cell migration and invasion in several carcinomas.Tonicityresponsive enhancer-binding protein(TonEBP)is also known to participate in cancer cell growth,migration,and invasion.However,the mechanisms by which it increases lung cancer migration remain unclear.Therefore,in this study,we aimed to elucidate the mechanisms by which IL-1βand TonEBP affect lung cancer cell migration and invasion.We found that A549 cocultured-MΦ-secreted IL-1βinduced A549 cell migration and invasion via the PAK1 pathway.TonEBP deficiency reduced A549 cell migration and invasion and increased responsiveness to IL-1β–induced migration and invasion.PAK1 phosphorylation,which was promoted by IL-1β,was reduced when TonEBP was depleted.These results suggest that TonEBP plays an important role in IL-1βinduction and invasiveness of A549 cells via the PAK1 pathway.These findings could be valuable in identifying potential targets for lung cancer treatment.

Effect of Climate Change on Lung Cancer

This research paper aims to draw a relationship between lung cancer and climate change. With the rise of climate change in the last few decades, many organizations and people are concerned about the future of the world. Climate change has many side effects, such as air pollution, which can increase the incidence and death rates of lung and bronchus cancer. This paper aims to draw the relationship between climate change factors and lung cancer incidence and mortality rates. The main finding of this analysis was that there is a positive relationship between lung cancer incidence, death rates, and climate change indicators. The findings from this study have the potential to inform targeted public health interventions and policies, emphasizing the need for proactive strategies in mitigating the health impacts of a changing climate. Section 2 of this paper is a literature review and focuses on the findings of other scholars in this field. Section 3 of this paper is Methods and Processes and will highlight the steps used to create the program and get the results. Section 4 of this paper is Results and Analysis, and will go over the results produced by the machine learning algorithm, and will present graphs and visualizations regarding the relationship of the dependent and independent variables. The final section, Section 5, is Limitations and Conclusion, in which we will discuss possible limitations to both my dataset and my model, and will conclude the paper by presenting a big-picture view of these problems in our society.

MicroRNAs modulation in lung cancer: exploring dual mechanisms and clinical prospects

The global incidence of lung cancer is marked by a considerably elevated mortality rate.MicroRNAs(miRNAs)exert pivotal influence in the intricate orchestration of gene regulation,and their dysregulation can precipitate dire consequences,notably cancer.Within this context,miRNAs encapsulated in exosomes manifest a diversified impact on the landscape of lung cancer,wherein their actions may either foster angiogenesis,cell proliferation,and metastasis,or counteract these processes.This comprehensive review article discerns potential targets for the prospective development of therapeutic agents tailored for lung cancer.Tumor-suppressive miRNAs,such as miR-204,miR-192,miR-30a,miR-34a,miR-34b,miR-203,and miR-212,exhibit heightened expression and demonstrate the capacity to inhibit cellular proliferation and invasiveness.Conversely,the deleterious effects of tumor-promoting miRNAs like miR-21,miR-106a,miR-155,miR-205,and miR-210 can be attenuated through the application of their respective inhibitors.Distinct miRNAs selectively target various oncogenes,including NUAK Family Kinase 1(NUAK1),Snail Family Transcriptional Repressor 1(Snai1),Astrocyte elevated gene-1(AEG-1),Vimentin,Proliferation and apoptosis adaptor protein 15(PEA-15/PED),Hypoxia-inducible factor 1-alpha(HIF1),as well as tumor suppressor genes such as phosphatase and tensin homolog(PTEN),Suppressor of cytokine signaling 1(SOCS1),Tumor protein P53 binding protein 1(TP53BP1),and PH Domain and Leucine Rich Repeat Protein Phosphatase 2(PHLP22).This investigative approach proves invaluable in elucidating the specific miRNAs implicated in the deregulation of crucial genes pivotal to the pathogenesis of cancer.