Relevant bibliographies by topics / Multimarker model

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Academic literature on the topic 'Multimarker model'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Multimarker model"

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Mohseni, AL Wagenaar SMJ van Kuijk, MEA Spaanderman, and C. Ghossein-Doha. "268. A multimarker model for aberrant cardiac geometry after PE." Pregnancy Hypertension 13 (October 2018): S115. http://dx.doi.org/10.1016/j.preghy.2018.08.339.

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Tsai, M. Y., C. K. Hsiao, and S. H. Wen. "A Bayesian Spatial Multimarker Genetic Random-Effect Model for Fine-Scale Mapping." Annals of Human Genetics 72, no. 5 (September 2008): 658–69. http://dx.doi.org/10.1111/j.1469-1809.2008.00459.x.

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Linke, Steven P., Troy M. Bremer, Christopher D. Herold, Guido Sauter, and Cornelius Diamond. "A multimarker model to predict outcome in tamoxifen-treated breast cancer patients." Clinical Cancer Research 12, no. 4 (February 15, 2006): 1175–83. http://dx.doi.org/10.1158/1078-0432.ccr-05-1562.

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Gould Rothberg, Bonnie E., Aaron J. Berger, Annette M. Molinaro, Antonio Subtil, Michael O. Krauthammer, Robert L. Camp, William R. Bradley, Stephan Ariyan, Harriet M. Kluger, and David L. Rimm. "Melanoma Prognostic Model Using Tissue Microarrays and Genetic Algorithms." Journal of Clinical Oncology 27, no. 34 (December 1, 2009): 5772–80. http://dx.doi.org/10.1200/jco.2009.22.8239.

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PurposeAs a result of the questionable risk-to-benefit ratio of adjuvant therapies, stage II melanoma is currently managed by observation because available clinicopathologic parameters cannot identify the 20% to 60% of such patients likely to develop metastatic disease. Here, we propose a multimarker molecular prognostic assay that can help triage patients at increased risk of recurrence.MethodsProtein expression for 38 candidates relevant to melanoma oncogenesis was evaluated using the automated quantitative analysis (AQUA) method for immunofluorescence-based immunohistochemistry in formalin-fixed, paraffin-embedded specimens from a cohort of 192 primary melanomas collected during 1959 to 1994. The prognostic assay was built using a genetic algorithm and validated on an independent cohort of 246 serial primary melanomas collected from 1997 to 2004.ResultsMultiple iterations of the genetic algorithm yielded a consistent five-marker solution. A favorable prognosis was predicted by ATF2 ln(non-nuclear/nuclear AQUA score ratio) of more than –0.052, p21WAF1nuclear compartment AQUA score of more than 12.98, p16INK4Aln(non-nuclear/nuclear AQUA score ratio) of ≤ −0.083, β-catenin total AQUA score of more than 38.68, and fibronectin total AQUA score of ≤ 57.93. Primary tumors that met at least four of these five conditions were considered a low-risk group, and those that met three or fewer conditions formed a high-risk group (log-rank P < .0001). Multivariable proportional hazards analysis adjusting for clinicopathologic parameters shows that the high-risk group has significantly reduced survival on both the discovery (hazard ratio = 2.84; 95% CI, 1.46 to 5.49; P = .002) and validation (hazard ratio = 2.72; 95% CI, 1.12 to 6.58; P = .027) cohorts.ConclusionThis multimarker prognostic assay, an independent determinant of melanoma survival, might be beneficial in improving the selection of stage II patients for adjuvant therapy.
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Spelman, Richard J., Wouter Coppieters, Latifa Karim, Johan A. M. van Arendonk, and Henk Bovenhuis. "Quantitative Trait Loci Analysis for Five Milk Production Traits on Chromosome Six in the Dutch Holstein-Friesian Population." Genetics 144, no. 4 (December 1, 1996): 1799–807. http://dx.doi.org/10.1093/genetics/144.4.1799.

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Twenty Dutch Holstein-Friesian families, with a total of 715 sires, were evaluated in a granddaughter experiment design for marker-QTL associations. Five traits—milk, fat and protein yield and fat and protein percent—were analyzed. Across-family analysis was undertaken using multimarker regression principles. One and two QTL models were fitted. Critical values for the test statistic were calculated empirically by permuting the data. Individual trait distributions of permuted test statistics differed and, thus distributions, had to be calculated for each trait. Experimentwise critical values, which account for evaluating marker-QTL associations on all 29 autosomal bovine chromosomes and for five traits, were calculated. A QTL for protein percent was identified in one and two QTL models and was significant at the 1 and 2% level, respectively. Extending the multimarker regression approach to an analysis including two QTL was limited by families not being informative at all markers, which resulted in singularity. Below average heterozygosity for the first and last marker lowered information content for the first and last marker bracket. Highly informative markers at the ends of the mapped chromosome would overcome the decrease in information content in the first and last marker bracket and singularity for the two QTL model.
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Lee, Shin-Wha, Ha-Young Lee, Hyo Joo Bang, Hye-Jeong Song, Sek Won Kong, and Yong-Man Kim. "An Improved Prediction Model for Ovarian Cancer Using Urinary Biomarkers and a Novel Validation Strategy." International Journal of Molecular Sciences 20, no. 19 (October 5, 2019): 4938. http://dx.doi.org/10.3390/ijms20194938.

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This study was designed to analyze urinary proteins associated with ovarian cancer (OC) and investigate the potential urinary biomarker panel to predict malignancy in women with pelvic masses. We analyzed 23 biomarkers in urine samples obtained from 295 patients with pelvic masses scheduled for surgery. The concentration of urinary biomarkers was quantitatively assessed by the xMAP bead-based multiplexed immunoassay. To identify the performance of each biomarker in predicting cancer over benign tumors, we used a repeated leave-group-out cross-validation strategy. The prediction models using multimarkers were evaluated to develop a urinary ovarian cancer panel. After the exclusion of 12 borderline tumors, the urinary concentration of 17 biomarkers exhibited significant differences between 158 OCs and 125 benign tumors. Human epididymis protein 4 (HE4), vascular cell adhesion molecule (VCAM), and transthyretin (TTR) were the top three biomarkers representing a higher concentration in OC. HE4 demonstrated the highest performance in all samples withOC(mean area under the receiver operating characteristic curve (AUC) 0.822, 95% CI: 0.772–0.869), whereas TTR showed the highest efficacy in early-stage OC (AUC 0.789, 95% CI: 0.714–0.856). Overall, HE4 was the most informative biomarker, followed by creatinine, carcinoembryonic antigen (CEA), neural cell adhesion molecule (NCAM), and TTR using the least absolute shrinkage and selection operator (LASSO) regression models. A multimarker panel consisting of HE4, creatinine, CEA, and TTR presented the best performance with 93.7% sensitivity (SN) at 70.6% specificity (SP) to predict OC over the benign tumor. This panel performed well regardless of disease status and demonstrated an improved performance by including menopausal status. In conclusion, the urinary biomarker panel with HE4, creatinine, CEA, and TTR provided promising efficacy in predicting OC over benign tumors in women with pelvic masses. It was also a non-invasive and easily available diagnostic tool.
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Kuznetsov, V. A., T. N. Enina, A. M. Soldatova, T. I. Petelina, S. M. Dyachkov, and L. A. Salamova. "Multimarker approach for assessing efficiency of cardiac resynchronization therapy in patients with sinus rhythm." Jounal of arrhythmology 27, no. 1 (June 4, 2020): 21–29. http://dx.doi.org/10.35336/va-2020-1-21-29.

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Purpose: to design mathematical model, that can predict positive response to cardiac resynchronization therapy (CRT) in patients with congestive heart failure (CHF) and sinus rhythm, according to complex analysis of neurohumoral and immune activation biomarkers, fibrosis, renal dysfunction, echocardiography.Material and methods: parameters of echocardiography, plasma levels of NT-proBNP, interleukins-1β, 6, 10, tumor necrosis factor α, С-reactive protein (СRP), matrix metalloproteinase-9 (ММР-9), tissue inhibitors of metalloproteinase 1 and 4, cystatin С (CYSTATIN) were studied in 40 CHF patients with sinus rhythm (65% coronary artery disease patients, 75% males, mean age 54.8±10.6 years old) during the period of maximum decrease of left ventricular end-systolic volume (LVESV) (mean duration 27.5 [11.1; 46.3] months). Responders (decrease in LVESV ≥15%) and non-responders (decrease in LVESV ˂15%) were identified.Results: the number of responders was 26 (65%). Initial set of variables included: age, left ventricular ejection fraction (EF), systolic pressure in the pulmonary artery, right ventricle size and NT-proBNP, СRP, ММР-9, CYSTATIN. According to logistic regression analysis, a model was created: F=3.231 + 0.344 х EF - 3.479 x CYSTATIN - 0.039 х ММР-9 - 0.638 х CRР. Prediction of response to CRT (P) was carried out using the equation: Р=1/(1+е(-F)); a less than 0.696 p-value was associated with membership of non-responders group; p-value greater than or equaled to 0.696 was associated with group of responders. The specificity of the model was 92.9%, sensitivity - 83.3%, AUC=0.952 (р˂0.001).Conclusions: the proposed model, based on assessment of left ventricular EF and laboratory data, that reflect key mechanisms of development and progression of CHF - immune inflammation, fibrosis, renal dysfunction, suggests a possible response to CRT.
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Yurkovetsky, Zoya, Steven Skates, Aleksey Lomakin, Brian Nolen, Trenton Pulsipher, Francesmary Modugno, Jeffrey Marks, et al. "Development of a Multimarker Assay for Early Detection of Ovarian Cancer." Journal of Clinical Oncology 28, no. 13 (May 1, 2010): 2159–66. http://dx.doi.org/10.1200/jco.2008.19.2484.

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PurposeEarly detection of ovarian cancer has great promise to improve clinical outcome.Patients and MethodsNinety-six serum biomarkers were analyzed in sera from healthy women and from patients with ovarian cancer, benign pelvic tumors, and breast, colorectal, and lung cancers, using multiplex xMAP bead-based immunoassays. A Metropolis algorithm with Monte Carlo simulation (MMC) was used for analysis of the data.ResultsA training set, including sera from 139 patients with early-stage ovarian cancer, 149 patients with late-stage ovarian cancer, and 1,102 healthy women, was analyzed with MMC algorithm and cross validation to identify an optimal biomarker panel discriminating early-stage cancer from healthy controls. The four-biomarker panel providing the highest diagnostic power of 86% sensitivity (SN) for early-stage and 93% SN for late-stage ovarian cancer at 98% specificity (SP) was comprised of CA-125, HE4, CEA, and VCAM-1. This model was applied to an independent blinded validation set consisting of sera from 44 patients with early-stage ovarian cancer, 124 patients with late-stage ovarian cancer, and 929 healthy women, providing unbiased estimates of 86% SN for stage I and II and 95% SN for stage III and IV disease at 98% SP. This panel was selective for ovarian cancer showing SN of 33% for benign pelvic disease, SN of 6% for breast cancer, SN of 0% for colorectal cancer, and SN of 36% for lung cancer.ConclusionA panel of CA-125, HE4, CEA, and VCAM-1, after additional validation, could serve as an initial stage in a screening strategy for epithelial ovarian cancer.
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Giurgea, Georgiana-Aura, Katrin Zlabinger, Alfred Gugerell, Dominika Lukovic, Bonni Syeda, Ljubica Mandic, Noemi Pavo, et al. "Multimarker Approach to Identify Patients with Coronary Artery Disease at High Risk for Subsequent Cardiac Adverse Events: The Multi-Biomarker Study." Biomolecules 10, no. 6 (June 15, 2020): 909. http://dx.doi.org/10.3390/biom10060909.

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In our prospective non-randomized, single-center cohort study (n = 161), we have evaluated a multimarker approach including S100 calcium binding protein A12 (S100A1), interleukin 1 like-receptor-4 (IL1R4), adrenomedullin, copeptin, neutrophil gelatinase-associated lipocalin (NGAL), soluble urokinase plasminogen activator receptor (suPAR), and ischemia modified albumin (IMA) in prediction of subsequent cardiac adverse events (AE) during 1-year follow-up in patients with coronary artery disease. The primary endpoint was to assess the combined discriminatory predictive value of the selected 7 biomarkers in prediction of AE (myocardial infarction, coronary revascularization, death, stroke, and hospitalization) by canonical discriminant function analysis. The main secondary endpoints were the levels of the 7 biomarkers in the groups with/without AE; comparison of the calculated discriminant score of the biomarkers with traditional logistic regression and C-statistics. The canonical correlation coefficient was 0.642, with a Wilk’s lambda value of 0.78 and p < 0.001. By using the calculated discriminant equation with the weighted mean discriminant score (centroid), the sensitivity and specificity of our model were 79.4% and 74.3% in prediction of AE. These values were higher than that of the calculated C-statistics if traditional risk factors with/without biomarkers were used for AE prediction. In conclusion, canonical discriminant analysis of the multimarker approach is able to define the risk threshold at the individual patient level for personalized medicine.
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Zagidullin, Naufal, Lukas J. Motloch, Diana Gareeva, Aysilu Hamitova, Irina Lakman, Ilja Krioni, Denis Popov, et al. "Combining Novel Biomarkers for Risk Stratification of Two-Year Cardiovascular Mortality in Patients with ST-Elevation Myocardial Infarction." Journal of Clinical Medicine 9, no. 2 (February 18, 2020): 550. http://dx.doi.org/10.3390/jcm9020550.

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ST-elevation myocardial infarction (STEMI) is one of the main reasons for morbidity and mortality worldwide. In addition to the classic biomarker NT-proBNP, new biomarkers like ST2 and Pentraxin-3 (Ptx-3) have emerged as potential tools in stratifying risk in cardiac patients. Indeed, multimarker approaches to estimate prognosis of STEMI patients have been proposed and their potential clinical impact requires investigation. In our study, in 147 patients with STEMI, NT-proBNP as well as serum levels of ST2 and Ptx-3 were evaluated. During two-year follow-up (FU; 734.2 ± 61.2 d) results were correlated with risk for cardiovascular mortality (CV-mortality). NT-proBNP (HR = 1.64, 95% CI = 1.21–2.21, p = 0.001) but also ST2 (HR = 1.000022, 95% CI = 1.00–1.001, p < 0.001) were shown to be reliable predictors of CV-mortality, while the highest predictive power was observed with Ptx-3 (HR = 3.1, 95% CI = 1.63–5.39, p < 0.001). When two biomarkers were combined in a multivariate Cox regression model, relevant improvement of risk assessment was only observed with NT-proBNP+Ptx-3 (AIC = 209, BIC = 214, p = 0.001, MER = 0.75, MEV = 0.64). However, the highest accuracy was seen using a three-marker approach (NT-proBNP + ST2 + Ptx-3: AIC = 208, BIC = 214, p < 0.001, MER = 0.77, MEV = 0.66). In conclusion, after STEMI, ST2 and Ptx-3 in addition to NT-proBNP were associated with the incidence of CV-mortality, with multimarker approaches enhancing the accuracy of prediction of CV-mortality.
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Dissertations / Theses on the topic "Multimarker model"

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Alzaabi, Adhari Abdullah. "Identification and Characterization of Serum Biomarkers Associated with Breast Cancer Progression." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/6452.

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Despite the recognized advances in the treatment of breast cancer, it still accounts for 15% of all cancer-related deaths. 90% of breast cancer deaths are due to unpredicted metastasis. There is neither successful treatment for metastatic patients nor a specific test to predict or detect secondary lesions. Patients with primary tumor will be either over-treated with cytotoxic side effects or under-treated and risk recurrence. This necessitates the need for personalized treatment, which is hard to offer for such heterogeneous disease. Obstacles in treating breast cancer metastasis are mainly due to the gaps exist in the understanding of the molecular mechanism of metastasis. The linear model of metastasis is supported by several observations that reflect an early crosstalk between the primary and secondary tumor, which in turn makes the secondary microenvironment fertile for the growth of disseminated cells. This communication occurs through circulation and utilizes molecules which have not been identified to date. Identifying such molecules may help in detecting initial stages of tumor colonization and predict the target organ of metastasis. Furthermore, these molecules may help to provide a personalized therapy that aims to tailor treatment according to the biology of the individual tumor. Advances in proteomics allows for more reproducible and sensitive biomarker discovery. Proteomic biomarkers are often more translatable to the clinic compared to biomarkers identified using other omics approaches. Further, protein biomarkers can be found in biological fluids making them a non-invasive way to treat or investigate cancer patients. We present in this manuscript our study of the use of a proteomic approach on blood serum samples of metastatic and non-metastatic patients using LC-MS/MS quantitative analysis machine to identify molecules that could be associated with different stages of breast cancer metastasis. We focused on the deferential expression of low molecular weight biomolecules known to reflect disease-specific signatures. We manually analyzed 2500 individual small biomolecules in each serum sample of total of 51 samples. Comparisons between different sample types (from stage I and III Breast Cancer patients in this case) allows for the detection of unique short peptide biomarkers present in one sample type. We built a multi-biomarker model with more sensitivity and specificity to identify the stage of the tumor and applied them on blinded set of samples to validate prediction power. We hope that our study will provide insights for future work on the collection, analysis, and understanding of role of molecules in metastatic breast cancer.
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Wason, James Maurice Stephen. "The use of multimarker models in genome-wide association studies." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608810.

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Zhitlukhin, Mikhail Valentinovich. "Stochastic dynamics of financial markets." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/stochastic-dynamics-of-financial-markets(4eb80d2a-e90a-4ab0-b9e2-ad930c8a4d94).html.

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This thesis provides a study on stochastic models of financial markets related to problems of asset pricing and hedging, optimal portfolio managing and statistical changepoint detection in trends of asset prices. Chapter 1 develops a general model of a system of interconnected stochastic markets associated with a directed acyclic graph. The main result of the chapter provides sufficient conditions of hedgeability of contracts in the model. These conditions are expressed in terms of consistent price systems, which generalise the notion of equivalent martingale measures. Using the general results obtained, a particular model of an asset market with transaction costs and portfolio constraints is studied. In the second chapter the problem of multi-period utility maximisation in the general market model is considered. The aim of the chapter is to establish the existence of systems of supporting prices, which play the role of Lagrange multipliers and allow to decompose a multi-period constrained utility maximisation problem into a family of single-period and unconstrained problems. Their existence is proved under conditions similar to those of Chapter 1.The last chapter is devoted to applications of statistical sequential methods for detecting trend changes in asset prices. A model where prices are driven by a geometric Gaussian random walk with changing mean and variance is proposed, and the problem of choosing the optimal moment of time to sell an asset is studied. The main theorem of the chapter describes the structure of the optimal selling moments in terms of the Shiryaev–Roberts statistic and the posterior probability process.
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chang, liu ching, and 劉慶昌. "The Determinative Model Of Strategy Interaction-An Empirical research Of Multimarket Contact And Resource Similarity." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/29896616577309168189.

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碩士
中原大學
企業管理學系
88
Our research discusses the relation between multimarket contact and the type of strategy interaction. We start from RBV to discuss how resource similarity influences the firm’s competitive action and exam the effect of strategy group analysis. It makes the relation between multimarket contact and performance perfectly and we want to understand the common relation across different industries. In our research, beside second data, we also collect major data by using questionnaire. The industries we choose are photoelection product , car and motorcycle. The nine industries in our research include desktop PC, notebook, monitor, scanner, modem, mainboard, cd-rom, car and motorcycle. About our research method, we first use reliability analysis for our questionnaire . Second , we use ANOVA and regression analysis in multimarket contact , resource similarity and strategy similarity to get empirical evidence. Our research distinguish the types of strategy interaction into cooperate strategy and competitive strategy. And we also distinguish the competitive strategy into price competitive strategy and non-price competitive strategy. Mutual forebearance hypothesis suggest that Multimarket contact makes competitive intensity lower, and the deterrence effect also make firm take cooperate strategy as high multimarket contact . And golden rule suggest when firms take competitive strategy, they also tend to take non-price strategy. When the degree of resource similarity or strategy similarity is higher, the firms do not want to take competitive strategy and want to take cooperate strategy because of high understand degree. And the higher degree of resource similarity , the higher degree of strategy similarity. Because the higher degree of resource similarity between firms, the higher degree of strategy driver similarity and make the higher degree of strategy similarity. Although our empirical evidence analysis suggest that multimarket contact and resource similarity do not influence firm’s cooperate strategy, we can know multimarket contact , resource similarity and strategy similarity indeed influence firm’s non-price competitive strategy. And we can know there is a clear relation between resource similarity and strategy similarity. But out of mind , all multimarket contact , resource similarity and strategy similarity do not influence firm’s price competitive strategy. Our research provide some influence variables how influence firm’s taking strategy. But our research do not compare cooperate strategy , price competitive strategy and non-price competitive strategy and we can not know why firms tend to take price competitive strategy. And these are future research direction.
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Books on the topic "Multimarker model"

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Hammer, Jeffrey S. A multimarket model for Turkish agriculture. Washington, DC (1818 H St., NW, Washington 20433): Agriculture and Rural Development Dept., World Bank, 1989.

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Böhnlein, Barbara. Multimarket contact, collusion, and market structure. Florence: European University Institute, 1994.

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Book chapters on the topic "Multimarker model"

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Diao, Xinshen, Michael Johnson, and Hiroyuki Takeshima. "Appendix G. Agricultural Sectors included in the Economywide Multimarket Model." In The Nigerian Rice Economy, edited by Kwabena Gyimah-Brempong, Michael Johnson, and Hiroyuki Takeshima. Philadelphia: University of Pennsylvania Press, 2016. http://dx.doi.org/10.9783/9780812293753-020.

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Conference papers on the topic "Multimarker model"

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Budde, P., H.-D. Zucht, T. Witte, M. Schneider, and P. Schulz-Knappe. "PS1:2 Development of a multimarker model for the detection of systemic lupus erythematosus based on new and traditional autoantibodies." In 11th European Lupus Meeting, Düsseldorf, Germany, 21–24 March 2018, Abstract presentations. Lupus Foundation of America, 2018. http://dx.doi.org/10.1136/lupus-2018-abstract.51.

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Corchero, Cristina, F. Javier Heredia, and Eugenio Mijangos. "Efficient solution of optimal multimarket electricity bid models." In 2011 European Energy Market (EEM). IEEE, 2011. http://dx.doi.org/10.1109/eem.2011.5953017.

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