Background: According to the WHO, anemia is a highly prevalent disease, especially for patients in the emergency department. The pathophysiological mechanism by which anemia can affect facial characteristics, such as membrane pallor, has been proven to detect anemia with the help of deep learning technology. The quick prediction method for the patient in the emergency department is important to screen the anemic state and judge the necessity of blood transfusion treatment.Method: We trained a deep learning system to predict anemia using videos of 316 patients. All the videos were taken with the same portable pad in the ambient environment of the emergency department. The video extraction and face recognition methods were used to highlight the facial area for analysis. Accuracy and area under the curve were used to assess the performance of the machine learning system at the image level and the patient level.Results: Three tasks were applied for performance evaluation. The objective of Task 1 was to predict patients' anemic states [hemoglobin (Hb) <13 g/dl in men and Hb <12 g/dl in women]. The accuracy of the image level was 82.37%, the area under the curve (AUC) of the image level was 0.84, the accuracy of the patient level was 84.02%, the sensitivity of the patient level was 92.59%, and the specificity of the patient level was 69.23%. The objective of Task 2 was to predict mild anemia (Hb <9 g/dl). The accuracy of the image level was 68.37%, the AUC of the image level was 0.69, the accuracy of the patient level was 70.58%, the sensitivity was 73.52%, and the specificity was 67.64%. The aim of task 3 was to predict severe anemia (Hb <7 g/dl). The accuracy of the image level was 74.01%, the AUC of the image level was 0.82, the accuracy of the patient level was 68.42%, the sensitivity was 61.53%, and the specificity was 83.33%.Conclusion: The machine learning system could quickly and accurately predict the anemia of patients in the emergency department and aid in the treatment decision for urgent blood transfusion. It offers great clinical value and practical significance in expediting diagnosis, improving medical resource allocation, and providing appropriate treatment in the future.

Apremilast is a phosphodiesterase 4 (PDE4) inhibitor used in the treatment of psoriasis and several other inflammatory diseases. Interest has been expressed in seeking out therapies that address both psoriasis and atherosclerosis. In the present study, we explored the effects of apremilast in human aortic endothelial cells (HAECs) exposed to oxidized low-density lipoprotein (ox-LDL) to simulate the atherosclerotic microenvironment in vitro. Our findings indicate that apremilast may reduce the expression of lectin-like oxidized-low-density-lipoprotein receptor-1 (LOX-1), the main ox-LDL scavenging receptor. Apremilast also inhibited the expression of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and interleukin-8 (IL-8), which are deeply involved in the chronic inflammatory response associated with atherosclerosis. Interestingly, we found that apremilast inhibited the attachment of U937 monocytes to HAECs by reducing the expression of the chemokine monocyte chemotactic protein 1 (MCP-1) and the cellular adhesion molecule vascular cell adhesion molecule-1 (VCAM-1). This effect was found to be mediated through the rescue of Krüppel like factor 6 (KLF6) expression, which was reduced in response to ox-LDL via increased phosphorylation of c-Jun N-terminal kinase (JNK). These findings suggest a potential role for apremilast in the treatment of atherosclerosis.

Pipeline risk assessment is crucial for pipeline safety management and operation. The aim of this study is to develop a comprehensive assessment model that accurately evaluates pipeline risks and ensures the safe and reliable operation of the pipeline system. The model is based on multisource spatial data and is primarily applicable to long-distance oil and gas pipelines that traverse complex geological conditions in mountainous areas. The research is conducted using the example of the Jinliwen natural gas pipeline in Zhejiang Province, China. By analyzing the geological data of the study area and the potential risks that the pipeline may encounter, a comprehensive risk assessment indicator system for the pipeline was developed using slope units to divide pipeline sections. The pipeline risk levels are classified using the K-means clustering-entropy weighted-random forest algorithm. The model is evaluated using accuracy (Acc), precision (Pre), recall (R), F1-score, and the ROC curve. The results show that the model has an accuracy of 0.917, a precision of 0.92, a recall of 0.916, an F1-score of 0.914, and an AUC (Area Under Curve) of 0.93, indicating its strong predictive capability. The risk assessment results demonstrate a strong consistency when compared with actual incident events. This indicates that the constructed model effectively reflects the influencing factors of pipeline risk, providing a basis for pipeline risk assessment and disaster prevention and mitigation efforts in similar regions.

Flexible pressure sensors, vital for medical, human-machine interaction, and intelligent recognition applications due to their high-sensitivity, excellent-linearity, and broad-pressure response, face challenges in achieving a harmonious balance among these attributes. Inspired by the gradient modulus in human skin layers, we proposed a pioneering method to adjust the gradient elastic modulus of amino trimethylene phosphonic acid (ATMP)-assisted polyvinyl alcohol (PVA) hydrogel through the Hofmeister effect, introducing micro-pyramid electrodes. This innovative approach successfully constructs a bio-gradient gel iontronic sensor (BGGITS) with an ultra-wide-range perception. The BGGITS exhibits a linear high-sensitivity of 700 kPa-1 within a broad-pressure detection range of up to 800 kPa. The composite design, integrating gradient gel and microstructure electrodes, demonstrates exceptional pressure resolution and mechanical stability. This biomimetic skin pressure sensor holds promise for achieving linear high-sensitivity across a broader pressure range simultaneously and may find applications in electronic skin for health monitoring and tactile perception in the future.

Hyaluronic acid (HA) is an important macromolecular polymer widely existing in skin, which can lock water molecules in the skin.Due to the barrier effect of skin stratum corneum, high molecular weight HA can not enter the deep skin, while low molecular weight HA has ability limited to maintian water.Nowadays, HA injection is widely used as a supplement method, which brings many disadvantages due to its invasiveness.Therefore, in this study, the ionic liquid induction technology was used to transform the macromolecular HA into the supermolecular state. Through in vitro transdermal experiment and human skin moisturizing experiment, it was proved that the supermolecular state HA has better transdermal and skin moisturizing ability, which provide a new potential method for non-invasive exogenous supplementation of HA.