Impact of Pre-Treatment NLR and Other Hematologic Biomarkers on the Outcomes of Early-Stage Non-Small-Cell Lung Cancer Treated with Stereotactic Body Radiation Therapy

Introduction: We evaluated the association of pre-treatment immunologic biomarkers on the outcomes of early-stage non-small-cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT). Materials and methods: In this retrospective study, all newly diagnosed early-stage NSCLC treated with SBRT between January 2010 and December 2017 were screened and included for further analysis. The pre-treatment neutrophil-lymphocyte ratio (NLR), monocyte lymphocyte ratio (MLR), and platelet-lymphocyte ratio (PLR) were calculated. Overall survival (OS) and recurrence-free survival (RFS) were estimated by Kaplan–Meier. Multivariable models were constructed to determine the impact of different biomarkers and the Akaike information criterion (AIC), index of adequacy, and scaled Brier scores were calculated. Results: A total of 72 patients were identified and 61 were included in final analysis. The median neutrophil count at baseline was 5.4 × 109/L (IQR: 4.17–7.05 × 109/L). Median lymphocyte count was 1.63 × 109/L (IQR: 1.29–2.10 × 109/L), median monocyte count was 0.65 × 109/L (IQR: 0.54–0.83 × 109/L), median platelet count was 260.0 × 109/L (IQR: 211.0–302.0 × 109/L). The median NLR was 3.42 (IQR: 2.38–5.04), median MLR was 0.39 (IQR: 0.31–0.53), and median PLR was 156.4 (IQR: 117.2–197.5). On multivariable regression a higher NLR was associated with worse OS (p = 0.01; HR-1.26; 95% CI 1.04–1.53). The delta AIC between the two multivariable models was 3.4, suggesting a moderate impact of NLR on OS. On multivariable analysis, higher NLR was associated with poor RFS (p = 0.001; NLR^1 HR 0.36; 0.17–0.78; NLR^2 HR-1.16; 95% CI 1.06–1.26) with a nonlinear relationship. The delta AIC between the two multivariable models was 16.2, suggesting a strong impact of NLR on RFS. In our cohort, MLR and PLR were not associated with RFS or OS in multivariable models. Conclusions: Our study suggests NLR, as a biomarker of systemic inflammation, is an independent prognostic factor for OS and RFS. The nonlinear relationship with RFS may indicate a suitable immunological environment is needed for optimal SBRT action and tumoricidal mechanisms. These findings require further validation in independent cohorts.

Optimization of Exploration Prospects Based on Ant Colony Algorithm and XGBoost Combined Optimization Model

Traditional exploration prospect optimization is uncertain due to human factor, the primary reason of that problem is the complex nonlinear relationship between trap quality and related geological factors. Some researchers proposed use artificial neural network (ANN) to solve the problem of the comprehensive geological evaluation of traps, because ANN can describe the nonlinear relationship of multiple geological factors. Considering ANN has some drawbacks, such as it is need lots of parameters for training, and the learning process can not be observed. In this paper we proposed a combined optimization model to accomplish optimization of exploration prospects, and express the affinity order between the prospects and its related geological factors, also can provide the data support for exploration. Based on trap data of an oilfield in Africa, there are 12 geological factors related to trap quality, including trap coefficient, trap depth, trap scale, trap area, Reservoir coefficient, Preservation coefficient, hydrocarbon source coefficient, resources etc.. The ant colony algorithm is used for feature selection, and irrelevant and redundant features are eliminated through multiple iterations, making it suitable for model processing and improving training speed. Based on ant colony algorithm, we get the key parameters for XGBoost model training, namely trap area, reservoir coefficient, preservation coefficient, resource, and the key features are used in XGBoost model for training and prediction. Finally, we compared our prediction results with expert prediction, the error is 0. In this paper, we proposed a combined optimization model based on ant colony algorithm and XGBoost for exploration prospect optimization. We recognized the key geological factors and different characteristic rules for exploration prospect optimization, in the process of optimization, ant colony discards the bad features that interfere with classification and recognition, and retains the features that contribute greatly to classification. In comprehensive geological evaluate of trap, the proposed combined optimization model is suitable for complicated nonlinear geological relationship, and express the affinity order between the prospects, the proposed method can work as an auxiliary way in petroleum exploration, also the proposed method can provide decision support for exploration prospect optimization, and finally can fulfill cost decreasing and benefit increasing.

Parametric study of cone angle influence on bubble vortex breakdown onset in laminar conical flow at various swirl numbers

The current work studies a swirling laminar viscous pipe flow with a controllable swirl number and varying pipe divergence cone angle. Such flows are widely used in various engineering applications. When a certain level of flow swirl is reached, a phenomenon called vortex breakdown occurs, the characteristics of which depend on the intensity of swirling of the flow and the Reynolds number. However, in addition to these two parameters, an important influence is exerted by the pipe opening angle, which often does not allow generalizing the results obtained in the pipe flow with even slightly different angles. Since experimentally it is quite difficult and expensive to change the pipe angle, especially considering the water as working fluid, this issue could be solved using CFD techniques. Using the design study, 63 different combinations of S and α are considered. The effect of the pipe divergence angle on the position of the bubble vortex breakdown and its properties is demonstrated. It is shown that there is a nonlinear relationship between the position of the bubble breakdown onset and the minimum value of the axial velocity at the axis depending on the opening angle of the cone.

Relationship between Driving Pressure and Mortality in Ventilated Patients with Heart Failure: A Cohort Study

Background. Heart failure (HF) is a leading cause of mortality and morbidity worldwide, with an increasing incidence. Invasive ventilation is considered to be essential for patients with HF. Previous studies have shown that driving pressure is associated with mortality in acute respiratory distress syndrome (ARDS). However, the relationship between driving pressure and mortality has not yet been examined in ventilated patients with HF. We assessed the association of driving pressure and mortality in patients with HF. Methods. We conducted a retrospective cohort study of invasive ventilated adult patients with HF from the Medical Information Mart for Intensive Care-III database. We used multivariable logistic regression models, a generalized additive model, and a two-piecewise linear regression model to show the effect of the average driving pressure within 24 h of intensive care unit admission on in-hospital mortality. Results. Six hundred and thirty-two invasive ventilated patients with HF were enrolled. Driving pressure was independently associated with in-hospital mortality (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.06–1.18; P < 0.001 ) after adjusted potential confounders. A nonlinear relationship was found between driving pressure and in-hospital mortality, which had a threshold around 14.27 cmH2O. The effect sizes and CIs below and above the threshold were 0.89 (0.75 to 1.05) and 1.17 (1.07 to 1.30), respectively. Conclusions. There was a nonlinear relationship between driving pressure and mortality in patients with HF who were ventilated for more than 48 h, and this relationship was associated with increased in-hospital mortality when the driving pressure was more than 14.27 cmH2O.

Pseudotime analysis reveals exponential trends in DNA methylation aging with mortality associated timescales

The epigenetic trajectory of DNA methylation profiles has a nonlinear relationship with time, reflecting rapid changes in DNA methylation early in life that progressively slow. In this study, we use pseudotime analysis to determine these trajectories. Unlike epigenetic clocks that constrain the functional form of methylation changes with time, pseudotime analysis orders samples along a path based on similarities in a latent dimension to provide an unbiased trajectory. We show that pseudotime analysis can be applied to DNA methylation in human blood and brain tissue and find that it is highly correlated with the epigenetic states described by the Epigenetic Pacemaker. Moreover, we show that the pseudotime nonlinear trajectory can be modeled using a sum of two exponentials with coefficients that are close to the timescales of human age-associated mortality. Thus, for the first time, we can identify age-associated molecular changes that appear to track the exponential dynamics of mortality risk.