Centrifugal blood pumps are usually designed with secondary flow paths to avoid flow dead zones and reduce the risk of thrombosis. Due to the secondary flow path, the intensity of secondary flows and turbulence in centrifugal blood pumps is generally very high. Conventional design theory is no longer applicable to centrifugal blood pumps with a secondary flow path. Empirical relationships between design variables and performance metrics generally do not exist for this type of blood pump. To date, little scientific study has been published concerning optimization and experimental validation of centrifugal blood pumps with secondary flow paths. Moreover, current hemolysis models are inadequate in an accurate prediction of hemolysis in turbulence. The purpose of this study is to optimize the hydraulic and hemolytic performance of an inhouse centrifugal maglev blood pump with a secondary flow path through variation of major design variables, with a focus on bringing down intensity of turbulence and secondary flows. Starting from a baseline design, through changing design variables such as blade angles, blade thickness, and position of splitter blades. Turbulent intensities have been greatly reduced, the hydraulic and hemolytic performance of the pump model was considerably improved. Computational fluid dynamics (CFD) combined with hemolysis models were mainly used for the evaluation of pump performance. A hydraulic test was conducted to validate the CFD regarding the hydraulic performance. Collectively, these results shed light on the impact of major design variables on the performance of modern centrifugal blood pumps with a secondary flow path.

In recent years, blood pumps have become the bridge to heart transplantation for patients with heart failure. Portability and wearability of blood pumps should be considered to ensure patient satisfaction in everyday life. To date, the focus has been on the development of portable and wearable peripheral components, little attention has been paid to the portable and wearable performance of the blood pump itself. This study reported a novel design of a wearable and portable extracorporeal centrifugal blood pump. Based on an in-house centrifugal maglev blood pump, the wearable and portable blood pump was designed with parallel inlet and outlet pipes to improve the wearable performance. A ring cavity was set at the inlet to convert the circumferential velocity of the inlet pipe to an axial velocity. The hydraulic and hemolytic performance of the baseline and portable blood pumps were analyzed and compared. Compared with the baseline pump, the hydrodynamic and hemolytic performance of the portable pump has been maintained without serious degradation. The results of this study will improve the life quality of patients with heart failure, and enhance the clinical benefits of artificial heart.

The characteristics of the lesion areas in medical images are complex, and existing fully connected layer-based neural networks still cannot address the issue of linear kernels, making them inadequate for handling the nonlinear classification problem of medical image data. This paper investigates a multi-scale feature joint prediction Kolmogorov-Arnold Network convolutional network combined with a spatial attention mechanism for medical image classification applications. In this article, we propose the Multi-Scale Feature prediction network combined with Kolmogorov-Arnold Network. In this model, convolutional blocks are concatenated to output feature maps of different scales. These feature maps are then passed through a spatial attention module, and the output is weighted and summed using learnable weights in the Kolmogorov-Arnold Network layer for classification. The model is trained, tuned, and tested on three publicly available medical image datasets, including the skin cancer image dataset, COVID-19 lung CT dataset, and brain tumor MRI dataset. Experimental results show that the proposed model demonstrates strong performance in classification tasks across four datasets, achieving an accuracy of 87.27% for binary skin cancer classification, 94.12% for the three-class COVID-19 CT classification, and 97.48% for the four-class brain tumor CT classification. This performance outperforms the classification accuracy of the selected comparison methods. Additionally, we applied a perturbation rate of 0.5% and 1.0% using the FGSM method on the test sets of the three datasets and re-tested, finding that our method achieved higher accuracy than the comparison methods. Comprehensive experimental results validate the effectiveness of our proposed method, demonstrating its applicability and robustness in different medical scenarios. This approach is expected to enhance the performance of medical image-assisted classification.

A method for analysis of psychotropic drugs in urine is investigated using a novel single-drop liquid-liquid-liquid microextraction (SDLLLME) apparatus as an electrospray emitter in ion mobility spectrometry (IMS). In this method, ketamine and pethidine are back-extracted into the acceptor phase (water and methanol) from the water and methanol immiscible organic phase. Sensitivity of extraction is improved as it does not require further methanol-adding procedure. Factors affecting the extraction of psychotropic drugs are characterized, including organic solvent type, extraction time, and concentration of NaOH/Ac in the donor/acceptor phase. The best extraction sensitivity is obtained with 600 μL dodecane as the organic phase, 30 minutes extraction time, and 10 mL donor phase with 0.01 M sodium hydroxide (NaOH) and 3 μL acceptor phase with 0.5 M acetic acid (Ac). Using this method, the two analytes can be extracted and analyzed simultaneously, showing this method is valuable for field application.

A medical device will emit electromagnetic radiation to its surrounding environment either actively or passively. However, clinicians are unaware as to whether the ambient electromagnetic radiation will exceed the human body’s endurance capacity. In this paper, the mathematical model of electromagnetic parameters devoted to Specific Absorption Rate (SAR) testing of medical devices was established using a Debye Model. Body liquids featuring dielectric properties including the conductivity and permittivity of tissues at various body parts were simulated on the basis of results derived from the model. A simplified anthropomorphic phantom for the SAR test was founded on the basis of geometric parameters by following the principles of resemblance and consistent conductivity. A full-band electromagnetic mathematical model of brain, muscle, heart, lungs, stomach, and kidneys was set up. Electromagnetic radiation levels of a wearable Electrocardiograph monitoring device were measured and found that the maximum electric field intensity was up to 30 V/m, and the electromagnetic radiation SAR value was 0.96 W/kg, which were equivalent to the electromagnetic radiation exposure of the occupational group. The results established that electromagnetic radiation of certain medical devices exceeded the allowed values specified by the World Health Organization (WHO). Therefore, further studies within the field of medicine are required to decide whether additional evaluation measures should be required.