Survival Models in Breast Cancer Patients

Background: Breast cancer is the most prevalent malignancy among Iranian women. Five and ten year survival is one of the indicators used for evaluation of the quality of care after surgery. In this study, we used several survival models to determine risk factors, survival times and life expectancies of different types of surgery. Methods: This study was performed on 310 patients who underwent surgery during a ten years period. Logistic regression and Cox regression models were used to analyze the factors leading to death. The Kaplan-Meier method (non-parametric) was used to estimate the survival rate. The log-rank test was used to compare survival in different groups. To compare life expectancy of different types of surgery, we used the actuarial life table method. Results: Logistic regression showed that stage, grade, age and history of benign malignancy had significant relationship with death. Log-rank test showed that there was a significant difference between survival for patients with different stages, age and history of benign tumors. Cox regression model demonstrated that the variables of stage, grade, age and benign problems were the major risk factors. Actuarial life table model showed that the life expectancy for all patients was 10.03 years. This life expectancy in early stages of breast cancer for mastectomy and lumpectomy were 8.99 and 8.35 years, respectively, which was not significant. Conclusion: It can be concluded that the higher stage, grade, age and history of benign tumor were, the most important risk factors were correlated to mortality in breast cancer patients. This study showed that there was no significant difference between life expectancies of mastectomy and lumpectomy surgery

Survival Models in Breast Cancer Patients

Abstract Background: Breast cancer is the most prevalent malignancy among Iranian women. Five and ten year survival is one of the indicators used for evaluation of the quality of care after surgery. In this study, we used several survival models to determine risk factors, survival times and life expectancies of different types of surgery

Survey of both hepatitis B virus (HBsAg) and hepatitis C virus (HCV-Ab) coinfection among HIV positive patients

<p>Abstract</p> <p>Background</p> <p>HIV, HBVand HCV is major public health concerns. Because of shared routes of transmission, HIV-HCV coinfection and HIV-HBV coinfection are common. HIV-positive individuals are at risk of coinfection with HBV and HCV infections. The prevalence rates of coinfection with HBV and HCV in HIV-patients have been variable worldwide depending on the geographic regions, and the type of exposure.</p> <p>Aim</p> <p>This study aimed to examine HBV and HCV coinfection serologically and determine the shared and significant factors in the coinfection of HIV-positive patients.</p> <p>Methods</p> <p>This descriptive, cross-sectional study was carried out on 391 HIV-positive patients including 358 males and 33 females in Lorestan province, west Iran, to survey coinfection with HBsAg and anti-HCV. The retrospective demographic data of the subjects was collected and the patients' serums were analyzed by ELISA kits including HBsAg and anti-HCV. The collected data was analyzed with SPSS software (15) and Chi-square. Fisher's exact test with 5% error intervals was used to measure the correlation of variables and infection rates.</p> <p>Results</p> <p>The results of the study indicated that the prevalence of coinfection in HIV-positive patients with hepatitis viruses was 94.4% (370 in 391), out of whom 57 (14.5%) cases were HBsAg positive, 282 (72%) cases were anti-HCV positive, and 31 (7.9%) cases were both HBsAg and anti-HCV positive.</p> <p>Conclusion</p> <p>There was a significant correlation between coinfection with HCV and HBV and/or both among HIV-positive patients depending on different variables including sex, age, occupation, marital status, exposure to risk factors.(p < 0.001).</p

Minimal Size of Cell Assemblies Coordinated by Gamma Oscillations

In networks of excitatory and inhibitory neurons with mutual synaptic coupling, specific drive to sub-ensembles of cells often leads to gamma-frequency (25–100 Hz) oscillations. When the number of driven cells is too small, however, the synaptic interactions may not be strong or homogeneous enough to support the mechanism underlying the rhythm. Using a combination of computational simulation and mathematical analysis, we study the breakdown of gamma rhythms as the driven ensembles become too small, or the synaptic interactions become too weak and heterogeneous. Heterogeneities in drives or synaptic strengths play an important role in the breakdown of the rhythms; nonetheless, we find that the analysis of homogeneous networks yields insight into the breakdown of rhythms in heterogeneous networks. In particular, if parameter values are such that in a homogeneous network, it takes several gamma cycles to converge to synchrony, then in a similar, but realistically heterogeneous network, synchrony breaks down altogether. This leads to the surprising conclusion that in a network with realistic heterogeneity, gamma rhythms based on the interaction of excitatory and inhibitory cell populations must arise either rapidly, or not at all. For given synaptic strengths and heterogeneities, there is a (soft) lower bound on the possible number of cells in an ensemble oscillating at gamma frequency, based simply on the requirement that synaptic interactions between the two cell populations be strong enough. This observation suggests explanations for recent experimental results concerning the modulation of gamma oscillations in macaque primary visual cortex by varying spatial stimulus size or attention level, and for our own experimental results, reported here, concerning the optogenetic modulation of gamma oscillations in kainate-activated hippocampal slices. We make specific predictions about the behavior of pyramidal cells and fast-spiking interneurons in these experiments.Collaborative Research in Computational NeuroscienceNational Institutes of Health (U.S.) (grant 1R01 NS067199)National Institutes of Health (U.S.) (grant DMS 0717670)National Institutes of Health (U.S.) (grant 1R01 DA029639)National Institutes of Health (U.S.) (grant 1RC1 MH088182)National Institutes of Health (U.S.) (grant DP2OD002002)Paul G. Allen Family FoundationnGoogle (Firm

Protein patterns and their association with photosynthetic pigment content, agronomic behavior, and origin of purslane accessions (Portulaca oleracea L.)

In this study, the proteomic, morphometric, and photosynthetic pigment data of purslane (Portulaca oleracea) accessions were combined together to show their impact on genetic variation in order to establish a relationship between protein patterns and phenotypic behavior of the plant. Seeds of 18 collected purslane accessions were cultivated based on a completely randomized design with three replicates. Before the flowering stage, the data on morphology, photosynthetic pigment content, and seed proteins were obtained. The results showed a significant difference among purslane accessions in terms of the most studied agronomic characteristics and the content of photosynthetic pigments and proteins. The cluster analysis of the 18 purslane accessions based on agronomic data, and photosynthetic pigment content, and protein pattern data produced three main clusters. Moreover, the seed protein analysis revealed that the two polymorphic protein bands of size 40 kDa (protein “a”) and 30 kDa (protein “b”) effectively diversified the agronomic, photosynthetic pigment, and phylogenetic relationships among the purslane accessions. Interestingly, protein “a” was produced in plants growing in low altitude areas and played a suppressive role for TDW, while protein “b” was produced in plants growing in high altitude areas and functioned as an activator agent for this trait. Overall, the outcomes of the present study indicated the presence of high genetic variability (77.6%) among the purslane accessions. These findings suggest that these proteins should be sequenced for further proteomic analyses and can be used for hybridization to generate useful recombinants in segregating generations and improve breeding varieties of P. oleracea

Improving growth indices and productivity of phytochemical compounds in lemon balm (Melissa officinalis L.) through induced polyploidy

The induction of polyploidy using mutagenic chemicals is one of the plant breeding methods to enhance the production of secondary metabolites. In the current research, to induce polyploidy in lemon balm (Melissa officinalis) plants, seeds were treated with different concentrations of colchicine for various exposure times. A factorial experiment was performed using a randomized complete block design with two factors: colchicine concentrations with four levels (control, 0.05%, 0.1%, and 0.2%) and exposure times with three levels (24, 48, and 72 h) and three replicates. The physiological and phytochemical traits of plants were measured at a 4–6 leave stage. The results indicated that different concentrations of colchicine had a significant effect on the chlorophyll a (Chl.a), chlorophyll b (Chl.b), carotenoid, phenol, flavonoid, and rosmarinic acid contents. The exposure times of colchicine also caused significant changes in Chl.a, Chl.b, carotenoid, phenol, flavonoid, and rosmarinic acid amounts (P # 0.01). Increasing the colchicine concentration significantly increased the physiological and phytochemical traits at 0.05% and 0.1% concentration in comparison to the control (P # 0.01). In contrast, the interaction of colchicine concentration and exposure time had a significant effect on Chl.a, Chl.b, carotenoid, and rosmarinic acid amounts. The findings of this study indicate that one of the effective methods in primary screening of polyploidy plants in the polyploidization breeding program is the estimation of the physiological changes, the contents of chlorophyll a and b, and the total amount of chlorophyll and secondary metabolites. Flow cytometry is recommended to be used for the accurate identification of the ploidy level in M. officinalis

Data-driven combustion modeling for a turbulent flame simulated with a computationally efficient solver

The use of machine learning (ML) for modeling is on the rise. In the age of big data, this technique has shown great potential to describe complex physical phenomena in the form of models. More recently, ML has frequently been used for turbulence modeling while the use of this technique for combustion modeling is still emerging. Gene expression programming (GEP) is one class of ML that can be used as a tool for symbolic regression and thus improve existing algebraic models using high-fidelity data. Direct numerical simulation (DNS) is a powerful candidate for producing the required data for training GEP models and validation. This paper therefore presents a highly efficient DNS solver known as HiPSTAR, originally developed for simulating non-reacting flows in particular in the context of turbomachinery. This solver has been extended to simulate reacting flows. DNSs of two turbulent premixed jet flames with different Karlovitz numbers are performed to produce the required data for training. GEP is then used to develop algebraic flame surface density models in the context of large-eddy simulation (LES). The result of this work introduces new models which show excellent performance in prediction of the flame surface density for premixed flames featuring different Karlovitz numbers

Annihilation events topology and their generated sound in turbulent premixed flames

The Combustion Institute This paper studies the contribution of flame annihilation events to the sound radiated by turbulent, premixed flames. Previously published direct numerical simulation (DNS) datasets of stoichiometric and lean (ϕ=0.7) flames (Haghiri et al. 2018) are first examined using an efficient formulation of the method of Griffiths et al. (2015) to identify the annihilation events. Four classes of annihilation event are observed. Three of these - pocket burn-out, tunnel closure and tunnel formation - were defined by Griffiths et al. A ‘multi-feature’ event is also defined in this paper as any combination of the other three annihilation events occurring close enough such that their radiated sound can be considered as originating from a single event. Further post-processing of these stoichiometric and lean datasets shows that the fluctuations in heat release rate associated with these 4 observed types of annihilation events are responsible for the broadband sound radiated by both flames. This, in turn, suggests that flame annihilation is the physical mechanism by which air-fuel ratio affects the radiated sound amplitude at high frequencies. This result is supported by previous works which have shown that the sound radiated from individual annihilation events scales with the laminar flame speed and the temperature ratio