Search Results (10)

Background/Objectives: Renal cell carcinoma (RCC) accounts for 2–3% of all cancers, with approximately 25% of patients being detected at the metastatic stage. This study aimed to determine the prognostic significance of co-evaluating two risk parameters: one, the HALP score based on haemoglobin, albumin, lymphocyte, and platelet counts, and the other, albumin-to-alkaline phosphatase ratio (AAPR) in patients with metastatic RCC treated with targeted therapy. Methods: This retrospective cohort study included 147 patients with metastatic RCC. The HALP score and AAPR values were calculated from pre-treatment blood test results, and followingly, the cut-off value was determined by using ROC analysis. Patients were categorised into three groups with a low, moderate or high combined risk score based on this cut-off value. The effect of these risk groups on survival was evaluated. Results: The mean age of patients was 64.1 ± 11.9. The median follow-up time was 38.3 months, and the mortality rate was 53.7% in all groups. Kaplan–Meier survival analysis showed a statistically significant difference between the combined scores of the risk groups: the median survival time was 51.6 months in the low-risk group, 20.7 months in the medium-risk group, and 10.4 months in the high-risk group (p < 0.001), with 5-year survival rates being 38.8% in the low-risk group, 30% in the intermediate-risk group, and 19% in the high-risk group. When compared to the low-risk group, Cox regression analysis revealed that the mortality risk, i.e., HR (hazard ratio), was 2.42 times higher in the intermediate-risk group and 3.76 times higher in the high-risk group. A nephrectomy operation decreased the mortality risk (HR = 0.24) by 4.16 times. Conclusions: This new combined risk scoring, obtained from co-evaluating the HALP score and AAPR, was found to be an independent prognostic factor in metastatic RCC patients. This combined risk scoring is expected to help clinicians in treatment decisions. Full article

Background and Objectives: This study aimed to evaluate the prognostic significance of the pre-treatment albumin-to-alkaline phosphatase ratio (AAPR) in early-stage colorectal cancer patients and to compare it with the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) within the same patient cohort. Materials and Methods: This retrospective study included 540 patients who were followed up after a diagnosis of early-stage colorectal cancer and whose albumin (ALB), alkaline phosphatase (ALP), neutrophil, platelet, and lymphocyte values were measured before treatment. Results: In the receiver operating characteristic (ROC) curve analysis for overall survival (OS), the AAPR index Area Under Curve (AUC):0.560, (p = 0.018), NLR index (p = 0.079), and PLR index (p = 0.692) were evaluated. In the ROC analysis for OS, a cut-off value of the AAPR index of ≤0.423 was found. In the AAPR-low group, the relapse and death rates were higher than in the AAPR-high group (p = 0.004 and p = 0.001, respectively). As the AAPR index decreased, the NLR and PLR indexes increased (p = 0.027 and p = 0.003, respectively). Median disease-free survival (DFS) was worse in the AAPR-low group (128 versus 156) months (p = 0.015). The AAPR index significantly affected OS with hazard ratio (HR):0.42 (95%CI, 0.18–0.97) (p = 0.044). At 5 years, 68% of the patients in the AAPR-low group and 79% of the patients in the AAPR-high group were alive (p = 0.005). In a multivariate analysis, low AAPR, patient age at diagnosis, TNM stage, and recurrence status were independent factors affecting OS (p = 0.022, p < 0.001, p = 0.002, and p < 0.001, respectively). Conclusions: In early-stage colorectal cancer patients, the OS was worse in the AAPR-low group than in the AAPR-high group. The AAPR index demonstrated significant prognostic value for OS compared to the NLR and PLR in the same patient cohort. Full article

Background and Objectives: The Albumin-to-Alkaline Phosphatase ratio (AAPR) is an easily applicable and cost-effective marker investigated as an outcome predictor in solid cancers. Preliminary evidence in breast cancer suggests that a low AAPR correlates with a poor response to neoadjuvant chemotherapy (NAC) in primary tumors. However, data regarding the axillary response are lacking. This study aims to evaluate whether the AAPR can predict the axillary response in initially nodal-positive (cN+) breast cancer patients undergoing NAC. Materials and Methods: Clinical and biochemical variables of cN+ breast cancer patients undergoing NAC were collected. Pre-NAC albumin and alkaline phosphatase serum values were utilized in the AAPR calculation. Fisher’s exact test was performed to identify differences between the two groups of patients (high and low AAPR according to the cut-off reported in the literature). The primary outcome was the nodal pathologic complete response (pCR) rate in the two groups of patients. Results: Nodal pCR was achieved in 20/45 (44.4%) patients. A total of 36/45 (80%) patients had an AAPR > 0.583. Among patient and tumor characteristics, the only statistically significant difference between the two groups was the axillary pCR between the low and high AAPR groups (p-value = 0.03, OR = 0.129, 95% CI = 0.00–0.835). Conclusions: This pilot study suggests that the pre-treatment AAPR might be an early predictor of axillary response in cN+ breast cancer patients undergoing NAC. This result justifies further investigation in larger prospective trials to validate this finding. Full article

Background and Objectives: To investigate the role of preoperative albumin-to-alkaline phosphatase ratio (AAPR) in predicting pathologic node-positive (pN+) disease in penile cancer (PC) patients undergoing inguinal lymph node dissection (ILND). Materials and Methods: Clinical data of patients with squamous cell carcinoma (SCC) PC + ILND at a single high-volume institution between 2016 and 2021 were collected and retrospectively analyzed. An AAPR was obtained from preoperative blood analyses performed within 30 days from their scheduled surgery. A ROC curve analysis was used to assess AAPR cutoff, in addition to the Youden Index. Logistic regression analysis was utilized for an odds ratio (OR), 95% confidence interval (CI) calculations, and an estimate of pN+ disease. A p value < 0.05 was considered to be as statistically significant. Results: Overall, 42 PC patients were included in the study, with a mean age of 63.6 ± 12.9 years. The AAPR cut-off point value was determined to be 0.53. The ROC curve analysis reported an AUC of 0.698. On multivariable logistic regression analysis lymphovascular invasion (OR = 5.38; 95% CI: 1.47–9.93, p = 0.022), clinical node-positive disease (OR = 13.68; 95% CI: 4.37–43.90, p < 0.009), and albumin-to-alkaline phosphatase ratio ≤ 0.53 (OR = 3.61; 95% CI: 1.23–12.71, p = 0.032) were predictors of pN+ involvement. Conclusions: Preoperative AAPR may be a potentially valuable prognostic marker of pN+ disease in patients who underwent surgery for PC. Full article

The estimation of the formation permeability is considered a vital process in assessing reservoir deliverability. The prediction of such a rock property with the use of the minimum number of inputs is mandatory. In general, porosity and permeability are independent rock petrophysical properties. Despite these observations, theoretical relationships have been proposed, such as that by the Kozeny–Carmen theory. This theory, however, treats a highly complex porous medium in a very simple manner. Hence, this study proposes a comprehensive ANN model based on the back propagation learning algorithm using the FORTRAN language to predict the formation permeability from available well logs. The proposed ANN model uses a weight visualization curve technique to optimize the number of hidden neurons and layers. Approximately 500 core data points were collected to generate the model. These data, including gamma ray, sonic travel time, and bulk density, were collected from numerous wells drilled in the Western Desert and Gulf areas of Egypt. The results show that in order to predict the permeability accurately, the data set must be divided into 60% for training, 20% for testing, and 20% for validation with 25 neurons. The results yielded a correlation coefficient (R²) of 98% for the training and 96.5% for the testing, with an average absolute percent relative error (AAPRE) of 2.4%. To validate the ANN model, two published correlations (i.e., the dual water and Timur’s models) for calculating permeability were used to achieve the target. In addition, the results show that the ANN model had the lowest mean square error (MSE) of 0.035 and AAPRE of 0.024, while the dual water model yielded the highest MSE of 0.84 and APPRE of 0.645 compared to the core data. These results indicate that the proposed ANN model is robust and has strong capability of predicting the rock permeability using the minimum number of wireline log data. Full article

Objective: To determine if preoperative albumin-alkaline phosphatase ratio (AAPR) is predictive of clinical outcomes in patients with degenerative lumbar diseases undergoing lumbar fusion. Method: 326 patients undergoing posterior lumbar decompression and fusion were retrospectively analyzed. The cumulative grade was calculated by summing the Pfirrmann grades of all lumbar discs. Grouping was based on the 50th percentile of cumulative grade. The relationship between AAPR, intervertebral disc degeneration (IDD) severity, and fusion rate was explored using correlation analyses and logistic regression models. Meanwhile, the ROC curve evaluated the discrimination ability of AAPR in predicting severe degeneration and non-fusion. Results: High AAPR levels were significantly negatively correlated with severe degeneration and non-fusion rate. A multivariate binary logistic analysis revealed that high preoperative AAPR was an independent predictor of severe degeneration and postoperative non-fusion (OR: 0.114; 95% CI: 0.027–0.482; p = 0.003; OR: 0.003; 95% CI: 0.0003–0.022; p < 0.001). The models showed excellent discrimination and calibration. The areas under the curve (AUC) of severe degeneration and non-fusion identified by AAPR were 0.635 and 0.643. Conclusion: The AAPR can help predict the severity of disc degeneration and the likelihood of non-fusion. Full article

Carbon geo-sequestration (CGS), as a well-known procedure, is employed to reduce/store greenhouse gases. Wettability behavior is one of the important parameters in the geological CO₂ sequestration process. Few models have been reported for characterizing the contact angle of the brine/CO₂/mineral system at different environmental conditions. In this study, a smart machine learning model, namely Gene Expression Programming (GEP), was implemented to model the wettability behavior in a ternary system of CO₂, brine, and mineral under different operating conditions, including salinity, pressure, and temperature. The presented models provided an accurate estimation for the receding, static, and advancing contact angles of brine/CO₂ on various minerals, such as calcite, feldspar, mica, and quartz. A total of 630 experimental data points were utilized for establishing the correlations. Both statistical evaluation and graphical analyses were performed to show the reliability and performance of the developed models. The results showed that the implemented GEP model accurately predicted the wettability behavior under various operating conditions and a few data points were detected as probably doubtful. The average absolute percent relative error (AAPRE) of the models proposed for calcite, feldspar, mica, and quartz were obtained as 5.66%, 1.56%, 14.44%, and 13.93%, respectively, which confirm the accurate performance of the GEP algorithm. Finally, the investigation of sensitivity analysis indicated that salinity and pressure had the utmost influence on contact angles of brine/CO₂ on a range of different minerals. In addition, the effect of the accurate estimation of wettability on CO₂ column height for CO₂ sequestration was illustrated. According to the impact of wettability on the residual and structural trapping mechanisms during the geo-sequestration of the carbon process, the outcomes of the GEP model can be beneficial for the precise prediction of the capacity of these mechanisms. Full article

The albumin-to-alkaline phosphatase ratio (AAPR) is a novel promising prognostic marker in cancer patients. However, the evidence for its significance in lung cancer is scarce. Therefore, we assessed the prognostic value of the AAPR in a large cohort of lung cancer patients. Data on lung cancer patients diagnosed from January 2009 to June 2018 were extracted from the Danish Lung Cancer Registry and combined with data on the pretreatment serum AAPR level extracted from the clinical laboratory information system (LABKA). AAPR tertiles were applied as cutoffs. Cox proportional hazard models assessed the prognostic value of the AAPR. In total, 5978 non-small cell lung cancer (NSCLC) patients and 1099 small cell lung cancer (SCLC) patients were included. Decreasing AAPR level was significantly associated with declining median overall survival (OS) in NSCLC patients (medium vs. low AAPR, adjusted HR = 0.73 (95% confidence interval (CI) 0.68–0.79); high vs. low AAPR, adjusted HR = 0.68 (95% CI 0.62–0.73)) and in SCLC patients (medium vs. low AAPR, adjusted HR = 0.62 (95% CI 0.52–0.74); high vs. low, adjusted HR = 0.59 (95% CI 0.50–0.70)). In conclusion, the AAPR was an independent prognostic factor in NSCLC and SCLC patients. The correlation seems to be level dependent, with reducing survival found to be associated with decreasing AAPR level. Full article

Carbon dioxide-based enhanced oil-recovery (CO₂-EOR) processes have gained considerable interest among other EOR methods. In this paper, based on the molecular weight of paraffins (n-alkanes), pressure, and temperature, the magnitude of CO₂–n-alkanes interfacial tension (IFT) was determined by utilizing soft computing and mathematical modeling approaches, namely: (i) radial basis function (RBF) neural network (optimized by genetic algorithm (GA), gravitational search algorithm (GSA), imperialist competitive algorithm (ICA), particle swarm optimization (PSO), and ant colony optimization (ACO)), (ii) multilayer perception (MLP) neural network (optimized by Levenberg-Marquardt (LM)), and (iii) group method of data handling (GMDH). To do so, a broad range of laboratory data consisting of 879 data points collected from the literature was employed to develop the models. The proposed RBF-ICA model, with an average absolute percent relative error (AAPRE) of 4.42%, led to the most reliable predictions. Furthermore, the Parachor approach with different scaling exponents (n) in combination with seven equations of state (EOSs) was applied for IFT predictions of the CO₂–n-heptane and CO₂–n-decane systems. It was found that n = 4 was the optimum value to obtain precise IFT estimations; and combinations of the Parachor model with three-parameter Peng–Robinson and Soave–Redlich–Kwong EOSs could better estimate the IFT of the CO₂–n-alkane systems, compared to other used EOSs. Full article

Prediction models in mobility and transportation maintenance systems have been dramatically improved by using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well as their hybrids, i.e., Levenberg–Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through using four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE) and standard error (SE). The CMIS model outperforms other models with the promising results of APRE = 2.3303, AAPRE = 11.6768, RMSE = 12.0056 and SD = 0.0210. Full article