Exploration of Curriculum Ideology and Politics Integrating Medical Humanities Cultivation Model to Enhance Medical Core Values

Purpose:To explore the application effect of the cultivation mode of Curriculum Ideology and Political Integration of Medical Humanities to enhance the core values of medicine in the clinical internship stage of medical students.Methods:Students in the clinical internship stage of the First Affiliated Hospital of Baotou Medical College were selected,and a total of 156 students in one random class in each grade were taken as the observation group and received the integration cultivation mode;148 students were taken as the control group and employed the traditional mode.The teaching effect of interns in the two groups was analyzed.Results:The teaching performance of students in both groups after clinical internship teaching was improved compared with that before admission;the teaching performance,teaching effect,teaching evaluation,and teaching satisfaction of the observation group were higher than that of the control group.Conclusion:The integration of the medical humanities training model with curriculum ideology and politics in the clinical internship of medical students is conducive to the improvement of teaching performance,teaching evaluation,teaching satisfaction of teachers and students,and the development and improvement of core values of medical students,which is of good value for teaching application.

基于标准加入法和多水平的药代动力学综合分析疏血通注射液中不稳定的次黄嘌呤(潜在的质量标志物)

[目的]作为一种中药注射制剂,舒血通注射液用于治疗缺血性脑卒中。次黄嘌呤是其潜在的质量标志物之一。本研究的目的是通过对次黄嘌呤的定量和药代动力学行为分析为疏血通注射液的质量控制奠定基础。[方法]应用液相色谱-串联质谱(LC-MS/MS)首次建立了基于标准加入法的疏血通注射液中次黄嘌呤的定量方法。另一方面,也成功建立了基于LC-MS/MS的大鼠给药后血浆样品中次黄嘌呤的测定方法。[结果]对于相同批次的疏血通注射液,采用常规的液质联用技术测出的次黄嘌呤的浓度大于LC-MS/MS联合标准加入法测出的次黄嘌呤的浓度。单次静脉注射疏血通注射液的低、中、高剂量比为1:2:4,AUC0-t分别为(848.34±324.53)μg·h/L、(1483.94±497.74)μg·h/L,(3074.84±910.29)μg·h/L,剂量呈良好的线性依赖关系。[结论]标准加入法的引入可校正目标化合物的浓度,进而消除内源性物质的影响。别嘌呤出可抑制次黄嘌呤的转化以保证药代动力学研究中检测的准确性。通过空白血浆前处理获得的“空白生物基质”成功区分了内源性和药源性的次黄嘌呤。静脉注射次黄嘌呤血药浓度与给药剂量呈良好的线性关系。同样,多次给药中剂量组也没有药物蓄积,这与单次给药中剂量组的药代动力学特征是相似的。

创业板IPO首日溢价影响因素LASSO回归分析

中国证券市场经过十几年的发展,成为了全球最大的新兴市场。但高速发展的同时,也存在许多问题。我国新股上市首日的溢价程度远远高于其他国家证券市场的水平。尤其是创业板,自2009年创业板开板至今,从最初的36家发展到现在的918股,总市值达到64476亿元,有效解决了中小企业融资难问题,但同时也存在高溢价现象。新股首日高溢价的长期存在造成诸多的危害。不利于整个证券市场的健康发展。因此,研究这一问题,找出影响新股首日溢价的原因,对于新股发行制度、监管制度的改革具有重要意义。本文拟用LASSO降维的方法,排除影响溢价的不重要因素,寻求对创业板溢价影响最大的因素[Y2]。

Heterointerface engineering of assembled CoP_(2) on N-modified carbon as efficient trifunctional electrocatalysts for Zn-Air batteries and overall water splitting

Enhancing catalytic activity through modulating the interaction between N-doped carbon and metal phosphides clusters is an effective approach.Herein,the electronic structure modulation of CoP_(2) supported N-modified carbon(CoP_(2)/NC)has been designed and prepared as efficient electrocatalysts for oxygen reduction reaction(ORR),oxygen evolution reaction(OER),and hydrogen evolution reaction(HER).Notably,CoP_(2)/NC-1 catalyst exhibits impressive performance in alkaline media,with an ORR half-wave potential of 0.84 V,as well as OER and HER overpotentials of 290 and 129 mV(at 10 mA·cm^(−2)),respectively.In addition,CoP_(2)/NC-1 produces a power density as high as 172.9 mW·cm^(−2),and excellent reversibility of 100 h at 20 mA·cm^(−2) in home-made Zn-air batteries.The experimental results demonstrate that the synergistic interactions between N modified carbon substrate and CoP_(2) material significantly enhance the kinetics of ORR,OER,and HER.Density functional theory(DFT)calculations reveal the strong electrons redistribution of CoP_(2) induced by high-density N atoms at the interface,thus optimizing the key intermediates and significantly lower the energy barrier of reactions.These electronic adjustments of CoP_(2) greatly enhance its kinetics of ORR/OER/HER,leading to faster reactions.This study provides profound insights into the specific modification of CoP_(2) by N-doped carbon,enabling the construction of efficient catalysts.

Rejuvenating LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2) cathode directly from battery scraps

Battery recycling is indispensable for alleviating critical material shortages and enabling sustainable battery applications.However,current methods mostly focus on spent batteries,which not only require sophisticated disassembly and material extraction but also have unknown chemistries and states of health,resulting in high costs and extreme challenges to achieve regeneration.Here,we propose the direct recycling and effective regeneration of air-degraded LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2)(NCM523)cathode directly from battery scraps generated during battery manufacturing.The NCM523 shows surface degradation only a few nanometers deep and accordingly can be regenerated without adding Li,achieving restored properties(170 mAh g^(-1) at 0.1 C,92.7%retention after 1000 cycles)similar to those of fresh commercial materials.EverBatt analysis shows that scrap recycling has a profit of$1.984 kg^(-1),which is~10 times higher than conventional recycling,making it practical and economical to rejuvenate slightly degraded electrode materials for sustainable battery manufacturing.

Machine learning in polymer informatics

Polymers have been widely used in energy storage,construction,medicine,aerospace,and so on.However,the complexity of chemical composition and morphology of polymers has brought challenges to their development.Thanks to the integration of machine leaming algorithms and large data resources,the data-driven methods have opened up a new road for the development of poly-mer science and engineering.The emerging polymer informatics attempts to accelerate the performance prediction and process optimization of new poly-mers by using machine learning models based on reliable data.With the grad-ual supplement of currently available databases,the emergence of new databases and the continuous improvement of machine learning algorithms,the research paradigm of polymer informatics will be more efficient and widely used.Based on these points,this paper reviews the development trends of machine learning assisted polymer informatics and provides a simple introduc-tion for researchers in materials,artificial intelligence,and other fields.

Degradation mechanism analysis of LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2) single crystal cathode materials through machine learning

LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2)(NCM523)has become one of the most popular cathode materials for current lithium-ion batteries due to its high-energy density and cost performance.However,the rapid capacity fading of NCM severely hinders its development and applications.Here,the single crystal NCM523 materials under different degradation states are characterized using scanning transmission electron microscopy(STEM).Then we developed a neural network model with a two-sequential attention block to recognize the crystal structure and locate defects in STEM images.The number of point defects in NCM523 is observed to experience a trend of increasing first and then decreasing in the degradation process.The space between the transition metal columns shrinks obviously,inducing dramatic capacity decay.This analysis sheds light on the defect evolution and chemical transformation correlated with layered material degradation.It also provides interesting hints for researchers to regenerate the electrochemical capacity and design better battery materials with longer life.