Exploring the relationship between the speed-resolved perfusion of blood flux and HRV following different thermal stimulations using MSE and MFE analyses

Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning

BACKGROUND

The prediction of drug sensitivity plays a crucial role in improving the therapeutic effect of drugs. However, testing the effectiveness of drugs is challenging due to the complex mechanism of drug reactions and the lack of interpretability in most machine learning and deep learning methods. Therefore, it is imperative to establish an interpretable model that receives various cell line and drug feature data to learn drug response mechanisms and achieve stable predictions between available datasets.

RESULTS

This study proposes a new and interpretable deep learning model, DrugGene, which integrates gene expression, gene mutation, gene copy number variation of cancer cells, and chemical characteristics of anticancer drugs to predict their sensitivity. This model comprises two different branches of neural networks, where the first involves a hierarchical structure of biological subsystems that uses the biological processes of human cells to form a visual neural network (VNN) and an interpretable deep neural network for human cancer cells. DrugGene receives genotype input from the cell line and detects changes in the subsystem states. We also employ a traditional artificial neural network (ANN) to capture the chemical structural features of drugs. DrugGene generates final drug response predictions by combining VNN and ANN and integrating their outputs into a fully connected layer. The experimental results using drug sensitivity data extracted from the Cancer Drug Sensitivity Genome Database and the Cancer Treatment Response Portal v2 reveal that the proposed model is better than existing prediction methods. Therefore, our model achieves higher accuracy, learns the reaction mechanisms between anticancer drugs and cell lines from various features, and interprets the model's predicted results.

CONCLUSIONS

Our method utilizes biological pathways to construct neural networks, which can use genotypes to monitor changes in the state of network subsystems, thereby interpreting the prediction results in the model and achieving satisfactory prediction accuracy. This will help explore new directions in cancer treatment. More available code resources can be downloaded for free from GitHub ( https://github.com/pangweixiong/DrugGene ).

Diagnosis of Skin Vascular Complications Revealed by Time-Frequency Analysis and Laser Doppler Spectrum Decomposition

Nowadays, photonics-based techniques are used extensively in various applications, including functional clinical diagnosis, progress monitoring in treatment, and provision of metrological control. In fact, in the frame of practical implementation of optical methods, such as laser Doppler flowmetry (LDF), the qualitative interpretation and quantitative assessment of the detected signal remains vital and urgently required. In the conventional LDF approach, the key measured parameters, index of microcirculation and perfusion rate, are proportional to an averaged concentration of red blood cells (RBC) and their average velocity within a diagnostic volume. These quantities compose mixed signals from different vascular beds with a range of blood flow velocities and are typically expressed in relative units. In the current paper we introduce a new signal processing approach for the decomposition of LDF power spectra in terms of ranging blood flow distribution by frequency series. The developed approach was validated in standard occlusion tests conducted on healthy volunteers, and applied to investigate the influence of local pressure rendered by a probe on the surface of the skin. Finally, in limited clinical trials, we demonstrate that the approach can significantly improve the diagnostic accuracy of detection of microvascular changes in the skin of the feet in patients with Diabetes Mellitus type 2, as well as age-specific changes. The results obtained show that the developed approach of LDF signal decomposition provides essential new information about blood flow and blood microcirculation and has great potential in the diagnosis of vascular complications associated with various diseases.

Pilot Study of Blood Perfusion Changes at PC4 and Its Surrounding Points Induced by Acupuncture and Moxibustion

Acupuncture and moxibustion are widely used in clinical practice; however, the differences between their mechanisms are unclear. In the present study, the response of blood perfusion resulting from acupuncture or moxibustion at Ximen (PC4) and its surrounding points was explored. Using the wavelet method, the differences in the frequency interval of blood flux were observed. Furthermore, the correlations between these points were analyzed. The results suggested that moxibustion could significantly improve blood flow perfusion at PC4 compared to acupuncture; however, there was no significant difference around PC4. The response of blood flux at PC4 to different stimulations was related to the frequency V (0.4–1.6 Hz) component. However, a difference in response at other points was not observed. Correlation analysis showed that both acupuncture and moxibustion could cause a decline in the correlation of blood flux signals at these recorded points, but there was no significant difference between these techniques. The results suggested that, at least in the forearm, the acupuncture or moxibustion only influenced the level of blood perfusion locally.

The Relationship Between Blood Perfusion in the Lower Extremities and Heart Rate Variability at Different Positions

Previous studies have explored the relationship between the complexity of local blood flow signals and heart rate variability (HRV) under different thermal stimulations. However, the relationship between the complexity of local blood flow signals and HRV in different positions is not clear. In this study, healthy participants were placed in different body positions. The bilateral blood flux and ECG were monitored, and refined composite multiscale entropy (RC MSE) and refined composite multiscale fuzzy entropy (RC MFE) were used to measure the complexity of the local blood flux. The sample entropy was calculated to evaluate the HRV complexity. The change of body position did not affect the time domain or frequency domain of HRV, but did reverse the blood flux laterality of the lower extremities. Furthermore, there was a negative correlation between the complexity of right-side blood flux and sample entropy of HRV when the participant was in the -10 degrees position. These results provide a new perspective of the relationship between skin blood flux signals and cardiac function.

Laser Doppler Spectrum Analysis Based on Calculation of Cumulative Sums Detects Changes in Skin Capillary Blood Flow in Type 2 Diabetes Melitus

In this article, we introduce a new method of signal processing and data analysis for the digital laser Doppler flowmetry. Our approach is based on the calculation of cumulative sums over the registered Doppler power spectra. The introduced new parameter represents an integral estimation for the redistribution of moving red blood cells over the range of speed. The prototype of the device implementing the technique is developed and tested in preliminary clinical trials. The methodology was verified with the involvement of two age groups of healthy volunteers and in a group of patients with type 2 diabetes mellitus. The main practical result of the study is the development of a set of binary linear classifiers that allow the method to identify typical patterns of the microcirculation for the healthy volunteers and diabetic patients based on the presented diagnostic algorithm.

Impact of local thermal stimulation on the correlation between oxygen saturation and speed-resolved blood perfusion

The physiologically important relationship between oxygen saturation and blood flow is not entirely understood, particularly with regard to the multiple velocity components of flow and temperature. While our previous studies used classic laser Doppler flowmetry combined with an enhanced perfusion probe to assess local blood flow following thermal stimulation, oxygen saturation signals were not assessed. Thus, the current study used multiscale entropy (MSE) and multiscale fuzzy entropy (MFE) to measure the complexity of oxygen saturation signals following thermal stimulation in healthy subjects. The results indicate that thermal stimulation increases oxygen saturation and affects the measured signal complexity in a temperature-dependent fashion. Furthermore, stimulus temperature not only affects the correlation between speed-resolved blood perfusion and oxygen saturation, but also the correlation between the complexity area indices (CAI) of the two signals. These results reflect the complexity of local regulation and adaptation processes in response to stimuli at different temperatures.