Recently, stimuli-responsive slippery surfaces with controllable wettability have attracted considerable interest due to their potential applications in diverse fields such as droplet manipulation, microfluidics, and self-cleaning. However, most existing anisotropic slippery materials are limited to a single response mode, often failing to address the practical requirements for dual or synergistic stimulation in complex environments. A key challenge also lies in achieving in situ adhesion modulation and sophisticated droplet manipulation. Here, we report the design of a responsive anisotropic solid-liquid switching slippery surface (RASWS) is designed and fabricated by utilizing ultrafast laser-induced graphene (LIG) as the structural framework, which is subsequently infused with a paraffin-based ferrofluid. Droplets experience nearly an order of magnitude lower sliding resistance on pitcher plant-mimetic gradient surfaces. Notably, both in situ adhesion modulation and droplet manipulation are achieved using only light or thermal stimulus. Furthermore, droplets can be driven to slide against gravity on the anisotropic surface under magnetic actuation. Moreover, applying thermal or light stimuli enables in situ switching from solid to liquid lubrication, leading to a marked enhancement in friction-reduction performance. Therefore, this work not only overcomes critical challenges of single-response limitations and multifunctional integration but also paves the way for a new paradigm in developing intelligent interfaces with integrated stimuli-responsiveness, anisotropic lubrication, and programmable droplet control.

Recently, various slippery liquid-infused porous surfaces (SLIPS) have been fabricated for the protection of various materials. However, these SLIPSs are limited by their underlying storage structure and superficial lubricant layer, showing poor durability. Herein, inspired by the high-strength structure of Shell nacre’s “brick-mud” layer, we fabricated an all-inorganic composite coating by using wet chemically etched MXene as a brick and an aluminum phosphate binder (AP) as mud. Then, a series of microwell-array structures were designed and prepared on the coating via nanosecond ultrafast laser writing ablation technology. Subsequently, the textured surface was modified by a silane coupling agent. Vinyl-terminated polydimethylsiloxane (PDMS) was tightly grafted onto the porous surface through a thiol-ene click reaction to obtain lubricant grafted texture surface (LGTS). The prepared LGTS showed good lubrication properties for multiple phases, including various liquids, ice crystals, and solids. It exhibits excellent chemical stability and mechanical durability under deionized water impact, centrifugal test, strong acid solutions, anti/de-icing cycles, and high-intensity friction. Thus, the proposed strategy for constructing robust LGTS will greatly promote theoretical research on super wetting interfacial materials and their applications in the fields of antifouling, anti/de-icing, and lubricating protection.

In recent years, slippery liquid infused porous surfaces (SLIPS) renowned for their exceptional liquid repellency and anti-fouling properties, have garnered considerable attention. However, the instability of both structural integrity and the oil film severely restricts their practical applications. This study is inspired by superwetting biological surfaces, such as fish scales, seashells, and Nepenthes, to design and fabricate a multiplex biomimetic and robust lubricant-infused textured surface (LITMS) using laser-coating composite processing technology. The influence of morphological structure and chemical composition on oil stability, wettability, and lubricating properties are systematically investigated. The LITMS exhibits remarkable repellency toward multiphase materials, including liquids, ice crystals, and solids, demonstrating exceptional omniphobicity, anti-icing, and anti-friction properties. Thus, this preparation strategy and construction methodology for SLIPS provide new insights into interfacial phenomena and promote advancements in applications for engineering material protection and machinery lubrication.

Laser texture has been widely used in the fields of mechanical production， precision medical treatment， and material surface modification， because of its high precision， high speed， and low environmental pollution.Over the past 30 years， related researches have entered a stage of vigorous development， with the emergence of ultrafast laser texture technology.However， due to the wide variety of geometric topography and various arrangements and combinations，problems such as poor universality of parameter design in different materials still hinder the development of laser textured friction reduction research.Therefore， this work reviewed the development history of laser texture friction reduction researches from the aspects of geometrictopography， dimensional parameters， laser parameters， lubrication conditions， etc.The influences of the laser texture’s geometry topography， parameters and working conditions on the tribological properties were discussed， and the friction reduction mechanism of the laser texture under different lubrication conditions was summarized.Furthermore， the current problems of laser texturing technology were put forward， and its future development direction was prospected.

Introduction: Biomimetic lubricant-infused porous surfaces are developed and applied for omniphobicity and corrosion protection, which exhibit great advantages compared to superhydrophobic surfaces. Methods: Herein, superhydrophobic Fe@E-Zn@PFOA was prepared via the electrodeposition of laminated Zinc coating, further vapor etching, and post-modification with perfluoro caprylic acid. The facile, inexpensive, and environment-friendly water vapor etching process can form a porous honeycomb-like structure. Moreover, the perfluoropolyether lubricant was wicked into the porous and superhydrophobic surfaces, obtaining lubricant-infused surfaces of Fe@E-Zn@PFOA@PFPE. Results and discussion: The influences of the textured roughness and chemical composition on the surface wettability were systematically investigated. The Fe@E-Zn@PFOA@PFPE performs omniphobicity with small sliding angles and superior corrosion resistance compared with the superhydrophobic surface, owing to their multiple barriers, including infused lubricant, hydrophobic monolayers, and compact Zn electroplating coating. Thus, the proposed lubricant-infused surface may provide insights into constructing protective coatings for the potential applications of engineering metal materials.

Boundary condition settings are key risk factors for the accuracy of noninvasive quantification of fractional flow reserve (FFR) based on computed tomography angiography (i.e., FFRCT). However, transient numerical simulation-based FFRCT often ignores the three-dimensional (3D) model of coronary artery and clinical statistics of hyperemia state set by boundary conditions, resulting in insufficient computational accuracy and high computational cost. Therefore, it is necessary to develop the custom function that combines the 3D model of the coronary artery and clinical statistics of hyperemia state for boundary condition setting, to accurately and quickly quantify FFRCT under steady-state numerical simulations. The 3D model of the coronary artery was reconstructed by patient computed tomography angiography (CTA), and coronary resting flow was determined from the volume and diameter of the 3D model. Then, we developed the custom function that took into account the interaction of stenotic resistance, microcirculation resistance, inlet aortic pressure, and clinical statistics of resting to hyperemia state due to the effect of adenosine on boundary condition settings, to accurately and rapidly identify coronary blood flow for quantification of FFRCT calculation (FFRU). We tested the diagnostic accuracy of FFRU calculation by comparing it with the existing methods (CTA, coronary angiography (QCA), and diameter-flow method for calculating FFR (FFRD)) based on invasive FFR of 86 vessels in 73 patients. The average computational time for FFRU calculation was greatly reduced from 1-4 h for transient numerical simulations to 5 min per simulation, which was 2-fold less than the FFRD method. According to the results of the Bland-Altman analysis, the consistency between FFRU and invasive FFR of 86 vessels was better than that of FFRD. The area under the receiver operating characteristic curve (AUC) for CTA, QCA, FFRD and FFRU at the lesion level were 0.62 (95% CI: 0.51-0.74), 0.67 (95% CI: 0.56-0.79), 0.85 (95% CI: 0.76-0.94), and 0.93 (95% CI: 0.87-0.98), respectively. At the patient level, the AUC was 0.61 (95% CI: 0.48-0.74) for CTA, 0.65 (95% CI: 0.53-0.77) for QCA, 0.83 (95% CI: 0.74-0.92) for FFRD, and 0.92 (95% CI: 0.89-0.96) for FFRU. The proposed novel method might accurately and rapidly identify coronary blood flow, significantly improve the accuracy of FFRCT calculation, and support its wide application as a diagnostic indicator in clinical practice.

在“新医科?新工科”背景下，对医学生的培养提出了更高的要求。MRI设备学课程是培养医学影像技术专业的重点课程之一，为了提高MRI设备学课程的教学质量，提升教学效果，本文构建MRI设备学课程线上线下混合式教学的新理念、推进MRI设备学课程教学设计的改革与创新，优化MRI设备学课程的教学内容，重构混合式教学的课程评价等方面，开展了MRI设备学课程的线上线下混合式教学探索与研究。研究发现线上线下混合式教学的有机融合，有效地激发了学生的兴趣和自主学习的能动性，丰富了教学资源，拓展了教与学的空间，实现了“以学生为中心”的新型教学模式，满足“新医科?新工科”背景下高等医学人才的培养目标。

Background and Objectives: The diameter stenosis of the coronary artery exhibited by coronary angiography (CA), as the input data, has been widely applied to predict fractional flow reserve (FFR). However, in at least 29% of patients, the results of using diameter stenosis≥50% to predict FFR were inconsistent with invasive FFR. Therefore, based on multiple independent risk factors, we proposed the novel input data to significantly improve the diagnostic accuracy for FFR prediction. This study aims to propose novel input data to improve the diagnostic accuracy for FFR prediction.Methods: A meta-analysis involving a large number of clinical cases was used to quantitatively identify multiple independent risk factors for FFR prediction. Then, the improved analytic hierarchy process (IAHP) was used to quantitatively analyze and optimize the weighted values of independent risk factors for FFR prediction. Next, we proposed the novel input data, based on seven independent risk factors with the highest weighted values, to predict FFR. Later, the novel input data was used to predict FFR in 331 patients with coronary stenosis by using six machine-learning algorithms. Finally, based on the random forest algorithm, we tested the diagnostic accuracy of the novel input data for FFR prediction in 331 patients, by comparing it with previous input data (including CA≥50% and simplified input data).Results: In addition to diameter stenosis, the input data should include minimum diameter, age, hypertension, diabetes mellitus, gender, and LAD stenosis, which had the highest weighted values for FFR prediction. The diagnostic accuracy of the novel input data for FFR prediction was at least 91% using six machine-learning algorithms. The diagnostic accuracy was higher than CA≥50% (67%) and simplified input data (77%) using the random forest algorithm.Conclusions: The novel input data, including multiple independent risk factors, significantly improves the diagnostic accuracy for FFR prediction, effectively assists the clinical diagnosis of myocardial ischemia caused by coronary stenosis, and supports the widespread application as the reference indicates in clinical practice. In addition, the novel input data is not limited to using the random forest algorithm to predict FFR. It provides a reference for the input data of other machine-learning algorithms, to significantly improve the diagnostic accuracy for FFR prediction.

Introduction: Hemodynamic diagnosis indexes (HDIs) can comprehensively evaluate the health status of the cardiovascular system (CVS), particularly for people older than 50 years and prone to cardiovascular disease (CVDs). However, the accuracy of non-invasive detection remains unsatisfactory. We propose a non-invasive HDIs model based on the non-linear pulse wave theory (NonPWT) applied to four limbs. Methods: This algorithm establishes mathematical models, including pulse wave velocity and pressure information of the brachial and ankle arteries, pressure gradient, and blood flow. Blood flow is key to calculating HDIs. Herein, we derive blood flow equation for different times of the cardiac cycle considering the four different distributions of blood pressure and pulse wave of four limbs, then obtain the average blood flow in a cardiac cycle, and finally calculate the HDIs. Results: The results of the blood flow calculations reveal that the average blood flow in the upper extremity arteries is 10.78 ml/s (clinically: 2.5–12.67 ml/s), and the blood flow in the lower extremity arteries is higher than that in the upper extremity. To verify model accuracy, the consistency between the clinical and calculated values is verified with no statistically significant differences (p < 0.05). Model IV or higher-order fitting is the closest. To verify the model generalizability, considering the risk factors of cardiovascular diseases, the HDIs are recalculated using model IV, and thus, consistency is verified (p < 0.05 and Bland-Altman plot). Conclusion: We conclude our proposed algorithmic model based on NonPWT can facilitate the non-invasive hemodynamic diagnosis with simpler operational procedures and reduced medical costs.

OBJECTIVE:Pulse wave has been considered as a message carrier in the cardiovascular system (CVS), capable of inferring CVS conditions while diagnosing cardiovascular diseases (CVDs). Clarification and prediction of cardiovascular function by means of powerful feature-abstraction capability of machine learning method based on pulse wave is of great clinical significance in health monitoring and CVDs diagnosis, which remains poorly studied.METHODS:Here we propose a machine learning (ML)-based strategy aiming to achieve a fast and accurate prediction of three cardiovascular function parameters based on a 412-subject database of pulse waves. We proposed and optimized an ML-based model with multi-layered, fully connected network while building up two high-quality pulse wave datasets comprising a healthy-subject group and a CVD-subject group to predict arterial compliance (AC), total peripheral resistance (TPR), and stroke volume (SV), which are essential messengers in monitoring CVS conditions.RESULTS:Our ML model is validated through consistency analysis of the ML-predicted three cardiovascular function parameters with clinical measurements and is proven through error analysis to have capability of achieving a high-accurate prediction on TPR and SV for both healthy-subject group (accuracy: 85.3%, 86.9%) and CVD-subject group (accuracy: 88.3%, 89.2%).DISCUSSION:The independent sample t-test proved that our subject groups could represent the typical physiological characteristics of the corresponding population. While we have more subjects in our datasets rather than previous studies after strict data screening, the proposed ML-based strategy needs to be further improved to achieve a disease-specific prediction of heart failure and other CVDs through training with larger datasets and clinical measurements.CONCLUSION:Our study points to the feasibility and potential of the pulse wave-based prediction of physiological and pathological CVS conditions in clinical application.

The interventional treatment of cerebral aneurysm requires hemodynamics to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in calculating cerebral aneurysm hemodynamics before and after flow-diverting (FD) stent placement. However, the complex operation (such as the construction and placement simulation of fully resolved or porous-medium FD stent) and high computational cost of CFD hinder its application. To solve these problems, we applied aneurysm hemodynamics point cloud data sets and a deep learning network with double input and sampling channels. The flexible point cloud format can represent the geometry and flow distribution of different aneurysms before and after FD stent (represented by porous medium layer) placement with high resolution. The proposed network can directly analyze the relationship between aneurysm geometry and internal hemodynamics, to further realize the flow field prediction and avoid the complex operation of CFD. Statistical analysis shows that the prediction results of hemodynamics by our deep learning method are consistent with the CFD method (error function <13%), but the calculation time is significantly reduced 1,800 times. This study develops a novel deep learning method that can accurately predict the hemodynamics of different cerebral aneurysms before and after FD stent placement with low computational cost and simple operation processes.

针对钢铁在熔炼过程中产生的废石墨砖,探讨一条废石墨砖资源再利用而代替增碳剂的生产工艺新途径,提高工业资源的利用率,减少工业废物对环境的影响.

垫板是大型机床的重要部件的组成部分,多年来因其铸造成品率较低,不能满足生产的需求.通过优化垫板的铸造工艺,改进浇注系统,合理的设置补缩冒口,对容易出现缺陷的部位进行有效的补缩,提高了铸件成品的合格率,降低了生产成本.

目的 探究居民对中医药的认知、认同、需求及关注度在新冠肺炎疫情期间的变化,并对居民对中医药文化的认知情况进行分析.方法 采用整群抽样的方法,抽取泰安市泰山区、岱岳区、新泰市、宁阳县4个区县,以社区为单位对社区居民进行线上问卷调查以及线下入户调查.结果 疫情后居民对中医药的疗效认可程度有显著性增高(P<0.05).年轻人对中医的改观较大,女性改观程度稍高于男性,高学历人群改观程度不明显.结论 中医药在新冠疫情的救治中发挥重要作用,居民对中医药的认知、认同、需求及关注度均有改观.

为有效解决超细/纳米WC-Co在热喷涂时容易脱碳的相关问题,制备耐磨性与耐腐蚀性良好的涂层粉末,并切实广泛应用于工业领域.基于原位合成技术批量化制备的WC-Co粉末为原料,在保持既有喷涂喂料粉末的前提下,通过超音速火焰喷涂工艺(HVOF)规模化制备超细结构WC-Co涂层.试验结果表明,WC-Co涂层粉末的耐磨粒磨损性能与耐腐蚀性能较好.

Based on the noninvasive detection indeices and fuzzy mathematics method, this paper studied the noninvasive, convenient and economical cardiovascular health assessment system. The health evaluation index of cardiovascular function was built based on the internationally recognized risk factors of cardiovascular disease and the noninvasive detection index. The weight of 12 indexes was completed by the analytic hierarchy process, and the consistency test was passed. The membership function, evaluation matrix and evaluation model were built by fuzzy mathematics. The introducted methods enhanced the scientificity of the evaluation system. Through the Kappa consistency test, McNemer statistical results ( P = 0.995 > 0.05) and Kappa values (Kappa = 0.616, P < 0.001) suggest that the comprehensive evaluation results of model in this paper are relatively consistent with the clinical, which is of certain scientific significance for the early detection of cardiovascular diseases.

The relationship between simultaneous limbs blood pressure differences and Ankle–Brachial Index (ABI) is controversial. This paper aims to investigate the association of limbs blood pressures differences with ABI as the current non-invasive diagnosis method in clinical primary care. A cross-sectional study was performed to analysis the relationship between them. The results showed that [Formula: see text] was independently associated with inter-arm (OR, 15.469; CI, (1.776–134.773); [Formula: see text]) and inter-ankle (OR, 7.189; CI, (1.010–51.179); [Formula: see text]) when the systolic blood pressure difference [Formula: see text][Formula: see text]mmHg. Therefore, the simultaneous measurement of four limbs blood pressure differences can provide an aid for the non-invasive detection method of PAD in clinical primary care.

Owing to the diversity of pulse-wave morphology, pulse-based diagnosis is difficult, especially pulse-wave-pattern classification (PWPC). A powerful method for PWPC is a convolutional neural network (CNN). It outperforms conventional methods in pattern classification due to extracting informative abstraction and features. For previous PWPC criteria, the relationship between pulse and disease types is not clear. In order to improve the clinical practicability, there is a need for a CNN model to find the one-to-one correspondence between pulse pattern and disease categories. In this study, five cardiovascular diseases (CVD) and complications were extracted from medical records as classification criteria to build pulse data set 1. Four physiological parameters closely related to the selected diseases were also extracted as classification criteria to build data set 2. An optimized CNN model with stronger feature extraction capability for pulse signals was proposed, which achieved PWPC with 95% accuracy in data set 1 and 89% accuracy in data set 2. It demonstrated that pulse waves are the result of multiple physiological parameters. There are limitations when using a single physiological parameter to characterise the overall pulse pattern. The proposed CNN model can achieve high accuracy of PWPC while using CVD and complication categories as classification criteria.

For patients with type 2 diabetes, the evaluation of pulse waveform characteristics is helpful to understand changes in arterial stiffness. However, there is a lack of comprehensive analysis of pulse waveform parameters. Here, we aimed to investigate the changes in pulse waveform characteristics in patients with type 2 diabetes due to increased arterial stiffness. In this study, 25 patients with type 2 diabetes and 50 healthy subjects were selected based on their clinical history. Age, height, weight, blood pressure, and pulse pressure were collected as the subjects' basic characteristics. The brachial-ankle pulse wave velocity (baPWV) was collected as an index of arterial stiffness. Parameters of time [the pulse wave period (T), the relative positions of peak point (T-1) and notch point (T-2), and pulse wave time difference between upper and lower limbs (T-3)] and area [the total waveform area (A), and the areas of the waveform before (A(1)) and after (A(2)) the notch point] were extracted from the pulse wave signals as pulse waveform characteristics. An independent sample t-test was performed to determine whether there were significant differences between groups. Pearson's correlation analysis was performed to determine the correlations between pulse waveform parameters and baPWV. There were significant differences in T-3, A, A(1), and A(2) between the groups (p<0.05). For patients with type 2 diabetes, there were statistically significant correlations between baPWV and T-3, A, A(1), and A(2) (p<0.05). This study quantitatively assessed changes in arterial pulse waveform parameters in patients with type 2 diabetes. It was demonstrated that pulse waveform characteristics (T-3, A, A(1), and A(2)) could be used as indices of arterial stiffness in patients with type 2 diabetes.

Objects: To investigate the association of simultaneously measured limbs blood pressures with Ankle-Brachial Index as the current non-invasive diagnosis method of peripheral artery disease in clinical primary care. Methods: 228 subjects (61 males, mean age, 63.92±10.72 years; 167 females, mean age, 59.47±7.33 years) were enrolled. Limbs blood pressure measurements were simultaneously performed using a blood pressure and pulse monitor device in the supine position. Data were statistically analyzed with SPSS 15.0. Results: The mean age of the 229 subjects was 60.66±8.58 years. Variance analysis presented that RABI and LABI have significant differences with inter-arm difference in SBP (≥10 mmHg VS < 10 mmHg,≥10 mmHg VS ≥15 mmHg and≥15 mmHg VS < 10 mmHg). RABI have significant differences with inter-ankle difference in DBP (≥15 mmHg VS < 10 and≥10 mmHg VS ≥15 mmHg). Multinomial logistic regression analysis presented that LABI (<0.9; OR, 10.028; CI, (1.109-90.682); P=0.040) was independently associated with inter-arm SBP difference ≥ 10mmHg; LABI (<0.9; OR, 15.469; CI, (1.776-134.773); P=0.013) and RABI (0.90-1.00; OR, 4.231; CI, (1.205-14.860); P=0.024) were independently associated with inter-arm SBP difference ≥ 15mmHg. RABI (<0.9; OR, 7.189; CI, (1.010-51.179); P=0.049) and RABI (0.90-1.00; OR, 6.273; CI, (1.783-22.077); P=0.004) were independently associated with inter-ankle SBP difference≥ 15mmHg. LABI (0.90-1.00; OR, 4.331; CI, (1.039-14.330); P=0.016) was independently associated with inter-ankle DBP difference of ≥ 10mmHg. After excluding 99 hypertension patients, LABI (<0.9; OR, 246.330; CI, (5.442-11191.384); P=0.005) was still independently associated with inter-arm SBP difference ≥ 15mmHg. Conclusion: LABI <0.9 was independently associated with inter-arm SBP difference ≥ 15mmHg, while these differences still existed after excluding 99 hypertensive patients. In addition, the cut off (0.90-1.00) of ABI was independently associated with inter-arm SBP difference ≥ 15mmHg and inter-ankle DBP difference ≥ 10mmHg or ≥ 15mmHg. Hence, detection of limbs blood pressure difference with simultaneous measurement may provide an aid for the non-invasive diagnostic method of peripheral artery disease in clinical primary care.