On Statistical Relationship between ADRA2A Expression and the Risk of Breast Cancer Relapse
The search for novel parameters to predict the risk of relapse in breast cancer was conducted. Significant correlation between the risk of relapse and α-2A adrenergic receptor (ADRA2A) expression was revealed using public microarray datasets. This relationship was confirmed by validation on independent microarray dataset. It was found that when assessing the risk of BC relapse, the accuracy of prediction based solely on the expression of ADRA2A gene is close to that made using OncotypeDX and MammaPrint test systems. In this case, addition of only one or two supplemental prognostic markers (for instance, expression of SQLE gene or SQLE andDSCC1genes) to ADRA2A ensures the accuracy of prediction not inferior to reliability of these test systems.
In the internal medicine wide spectrum the gastroenterology is one of the chapters, less enlightened by the scientific evidence. It does not mean that the practice of the grasntroenterology may ot be improved by the systematic use of the approaches of the evidence based medicine
This study is an attempt to obtain reliable data on the natural history of breast cancer growth. The opportunities for using classical mathematical models (exponential and logistic tumor growth models, Gompertz and von Bertalanffy tumor growth models) were analysed in order to describe growth of the primary tumor and the secondary distant metastases of human breast cancer. Our results suggest a new «Consolidated mathematical growth Model of the Primary tumor and the Secondary distant metastases» (CoMPaS). The CoMPaS is based on exponential tumor growth model and consists of a system of determinate nonlinear and linear equations. The CoMPaS describes correctly the primary tumor growth (parameter T) and the secondary distant metastases growth (parameter M). Also, CoMPaS associates with data of 10–15-year survival in patients with the different tumor stage. Analysis of the metastases «nonvisible period» growth indicate the case of discrepancy between 15-year survival depending on tumor stage. In conclusion, the CoMPaS and supporting computer program were build to improve the accuracy of the forecast on survival of breast cancer and facilitate the optimisation of diagnosing secondary distant metastases. This led to completely original results that show how the growth rate of the metastases can change in relation to the growth rate of the primary tumour, taking into consideration its size and diameter of the tumour.
Genes with significant differential expression are traditionally used to reveal the genetic background underlying phenotypic differences between cancer cells. We hypothesized that informative marker sets can be obtained by combining genes with a relatively low degree of individual differential expression. We developed a method for construction of highly informative gene combinations aimed at the maximization of the cumulative informative power and identified sets of 2–5 genes efficiently predicting recurrence for ER-positive breast cancer patients. The gene combinations constructed on the basis of microarray data were successfully applied to data acquired by RNA-seq. The developed method provides the basis for the generation of highly efficient prognostic and predictive gene signatures for cancer and other diseases. The identified gene sets can potentially reveal novel essential segments of gene interaction networks and pathways implied in cancer progression.
We propose a new mathematical growth model of primary tumor and primary metastases which may help to improve predicting accuracy of breast cancer process using an original mathematical model referred to CoM-IV and corresponding software. The CoM-IV model and predictive software: a) detect different growth periods of primary tumor and primary metastases; b) make forecast of patient survival; c) have higher average prediction accuracy than the other tools; d) can improve forecasts on survival of BC and facilitate optimisation of diagnostic tests. The CoM-IV enables us, for the first time, to predict the whole natural history of primary tumor and primary metastases growth on each stage (pT1, pT2, pT3, pT4) considering only on primary tumor sizes. Summarising: CoM-IV a) describes correctly primary tumor and primary distant metastases growth of IV (T1-4N0-3M1) stage with (N1-3) or without regional metastases in lymph nodes (N0); b) facilitates the understanding of the appearance period and manifestation of primary metastases.
We studied the expression of peroxiredoxin genes (PRDX1, PRDX2, PRDX3, and PRDX6) in human erythroleukemia K652, human breast carcinoma MCF-7, and human ovarian carcinoma SKOV-3 cells during cisplatin resistance development. It was found that drug resistance formation was accompanied by a significant increase in the expression of PRDX1, PRDX2, PRDX3, PRDX6 genes in all cancer cell strains, which confirms the important contribution of redox-dependent mechanisms into the development of cisplatin resistance of cancer cells.
PRIMARY THERAPY OF EARLY BREAST CANCER
Evidence, Controversies, Consensus
N-Methyl-D-aspartate receptors (NMDAr) are involved in multiple physiological functions and neuropsychiatric disorders. Dizocilpine (commonly referred to as MK-801) is a well-known non-competitive NMDAr antagonist with psychotomimetic properties. A combination of electrophysiological and molecular analyses reveals not only the synchrony of baseline oscillations by MK-801, but also more importantly new insight into differential gene expressions in the cerebral cortex, midbrain, hippocampus, ventral striatum, amygdala, and hypothalamus regions after acute low-dose (0.08 mg/kg) MK-801 treatment; only the ventral striatum showed increased gene expression at a high dose (0.16 mg/kg) of MK-801. We believe that our present study will contribute in the understanding of the pathogenic mechanisms of neuropsychiatric disorders.
This paper is devoted to mathematical modelling of the progression and stages of breast cancer. The Consolidated mathematical growth Model of primary tumor (PT) and secondary distant metastases (MTS) in patients with lymph nodes MTS (Stage III) (CoM-III) is proposed as a new research tool. The CoM-III rests on an exponential tumor growth model and consists of a system of determinate nonlinear and linear equations. The CoM-III describes correctly primary tumor growth (parameter T) and distant metastases growth (parameter M, parameter N). The CoM-III model and predictive software: a) detect di erent growth periods of primary tumor and distant metastases in patients with lymph nodes MTS; b) make forecast of the period of the distant metastases appearance in patients with lymph nodes MTS; c) have higher average prediction accuracy than the other tools; d) can improve forecasts on survival of breast cancer and facilitate optimisation of diagnostic tests. The CoM-III enables us, for the rst time, to predict the whole natural history of PT and secondary distant MTS growth of patients with/without lymph nodes MTS on each stage relying only on PT sizes.
Data management and analysis is one of the fastest growing and most challenging areas of research and development in both academia and industry. Numerous types of applications and services have been studied and re-examined in this field resulting in this edited volume which includes chapters on effective approaches for dealing with the inherent complexity within data management and analysis. This edited volume contains practical case studies, and will appeal to students, researchers and professionals working in data management and analysis in the business, education, healthcare, and bioinformatics areas.
This prototype development explains the challenges encountered during the ISO/IEEE 11073 standard implementation process. The complexity of the standard and the consequent heavy requirements, which have not encouraged software engineers to adopt the standard. The developing complexity evaluation drives us to propose two possible implementation strategies that cover almost all possible use cases and eases handling the standard by non-expert users. The first one is focused on medical devices (MD) and proposes a low-memory and low-processor usage technique. It is based on message patterns that allow simple functions to generate ISO/IEEE 11073 messages and to process them easily. MD act as X73 agent. Second one is focused on more powerful device X73 manager, which do not have the MDs' memory and processor usage constraints. The protocol between Agent and Manager is point-to-point and we can distribute the functionality between devices.
Developed both implementation X73 Agent and Manager will cut developing time for applications based on ISO/EEE 11073.