Regression Diagnostics with Predicted Residuals of Linear Model with Improved Singular Value Classification Applied to Forecast the Hydrodynamic Efficiency of Wave Energy Converters

Synergism of red blood cells and tranexamic acid in the inhibition of fibrinolysis

BACKGROUND

Postpartum hemorrhage (PPH) is the leading cause of maternal death worldwide. The World Maternal Antifibrinolytic trial showed that antifibrinolytic tranexamic acid (TXA) reduces PPH deaths. Maternal anemia increases the risk of PPH. The World Maternal Antifibrinolytic-2 trial is now assessing whether TXA can prevent PPH in women with anemia. Low red blood cell (RBC) counts promote fibrinolysis by altering fibrin structure and plasminogen activation.

OBJECTIVES

We explored interactions between RBCs and TXA in inhibiting fibrinolysis.

METHODS

We used global fibrinolytic assays (ball sedimentation and viscoelasticity) to monitor the lysis of fibrin containing plasminogen and tissue-type plasminogen activator. We applied a fluorogenic kinetic assay to measure plasmin generation in fibrin clots and scanning electron microscopy to study fibrin structure.

RESULTS

According to parallel-line bioassay analysis of the fibrin lysis-time data, the antifibrinolytic potency of 4-128 μM TXA was increased in the presence of 10% to 40% (v/v) RBCs. Global fibrinolysis assays showed that the joint effect of RBCs and TXA was about 15% larger than the sum of their individual effects in the inhibition of fibrinolysis. In plasminogen activation, TXA added the same increment of inhibition to the effect of RBCs at any cell count in the fibrin clot. Regarding fibrin structure, TXA thickened fibrin fibers, which impaired plasminogen activation, whereas RBCs promoted fine fibers that were more resistant to plasmin.

CONCLUSIONS

The antifibrinolytic potency of TXA is enhanced in fibrin formed in the presence of RBCs through inhibition of plasminogen activation and fibrin lysis, which correlates with modifications of fibrin structures.

Machine Learning Predictive Models for Evaluating Risk Factors Affecting Sperm Count: Predictions Based on Health Screening Indicators

In many countries, especially developed nations, the fertility rate and birth rate have continually declined. Taiwan's fertility rate has paralleled this trend and reached its nadir in 2022. Therefore, the government uses many strategies to encourage more married couples to have children. However, couples marrying at an older age may have declining physical status, as well as hypertension and other metabolic syndrome symptoms, in addition to possibly being overweight, which have been the focus of the studies for their influences on male and female gamete quality. Many previous studies based on infertile people are not truly representative of the general population. This study proposed a framework using five machine learning (ML) predictive algorithms-random forest, stochastic gradient boosting, least absolute shrinkage and selection operator regression, ridge regression, and extreme gradient boosting-to identify the major risk factors affecting male sperm count based on a major health screening database in Taiwan. Unlike traditional multiple linear regression, ML algorithms do not need statistical assumptions and can capture non-linear relationships or complex interactions between dependent and independent variables to generate promising performance. We analyzed annual health screening data of 1375 males from 2010 to 2017, including data on health screening indicators, sourced from the MJ Group, a major health screening center in Taiwan. The symmetric mean absolute percentage error, relative absolute error, root relative squared error, and root mean squared error were used as performance evaluation metrics. Our results show that sleep time (ST), alpha-fetoprotein (AFP), body fat (BF), systolic blood pressure (SBP), and blood urea nitrogen (BUN) are the top five risk factors associated with sperm count. ST is a known risk factor influencing reproductive hormone balance, which can affect spermatogenesis and final sperm count. BF and SBP are risk factors associated with metabolic syndrome, another known risk factor of altered male reproductive hormone systems. However, AFP has not been the focus of previous studies on male fertility or semen quality. BUN, the index for kidney function, is also identified as a risk factor by our established ML model. Our results support previous findings that metabolic syndrome has negative impacts on sperm count and semen quality. Sleep duration also has an impact on sperm generation in the testes. AFP and BUN are two novel risk factors linked to sperm counts. These findings could help healthcare personnel and law makers create strategies for creating environments to increase the country's fertility rate. This study should also be of value to follow-up research.

Research on Prediction Method of Hydraulic Pump Remaining Useful Life Based on KPCA and JITL

Hydraulic pumps are commonly used; however, it is difficult to predict their remaining useful life (RUL) effectively. A new method based on kernel principal component analysis (KPCA) and the just in time learning (JITL) method was proposed to solve this problem. First, as the research object, the non-substitute time tac-tail life experiment pressure signals of gear pumps were collected. Following the removal and denoising of the DC component of the pressure signals by the wavelet packet method, multiple characteristic indices were extracted. Subsequently, the KPCA method was used to calculate the weighted fusion of the selected feature indices. Then the state evaluation indices were extracted to characterize the performance degradation of the gear pumps. Finally, an RUL prediction method based on the k-vector nearest neighbor (k-VNN) and JITL methods was proposed. The k-VNN method refers to both the Euclidean distance and angle relationship between two vectors as the basis for modeling. The prediction results verified the feasibility and effectiveness of the proposed method. Compared to the traditional JITL RUL prediction method based on the k-nearest neighbor algorithm, the proposed prediction model of the RUL of a gear pump presents a higher prediction accuracy. The method proposed in this paper is expected to be applied to the RUL prediction and condition monitoring and has broad application prospects and wide applicability.