抗心磷脂抗体、抗β_(2)糖蛋白Ⅰ抗体与不明原因胚胎反复种植失败相关性分析

目的探讨抗心磷脂抗体(ACA-IgM;ACA-IgG)、抗β_(2)糖蛋白Ⅰ抗体(A-β_(2)GPI-IgM;A-β_(2)GPI-IgG)与不明原因胚胎反复种植失败的相关性。方法回顾性分析北部战区总医院2019年1—12月收治的不明原因胚胎反复种植失败的218例患者(B组)和接受胚胎移植且临床确定妊娠的321例孕妇(A组)的临床资料。记录两组研究对象的一般资料,并比较ACAIgM、ACA-IgG、A-β_(2)GPI-IgM、A-β_(2)GPI-IgG阳性率。结果A组ACA-IgM、ACA-IgG、A-β_(2)GPI-IgM、A-β_(2)GPI-IgG阳性率分别为3.11%(10/321)、4.67%(15/321)、1.87%(6/321)、1.56%(5/321),B组分别为6.88%(15/218)、8.25%(18/218)、3.66%(8/218)、3.21%(7/218)。两组ACA-IgM、ACA-IgG、A-β_(2)GPI-IgM、A-β_(2)GPI-IgG阳性率比较,差异均无统计学意义(P>0.05)。结论抗心磷脂抗体、抗β_(2)糖蛋白Ⅰ抗体与不明原因胚胎反复种植失败无明显相关性。     

维生素B12和叶酸干预对绝经后骨质疏松症妇女的骨代谢及同型半胱氨酸水平的影响

目的观察叶酸和维生素B12对绝经后骨质疏松症妇女的骨代谢及同型半胱氨酸水平的影响。方法 80例绝经后骨质疏松症妇女参加研究。所有参与者随机接受叶酸和维生素B12(n=40)或安慰剂(n=40)治疗。在干预前的基线及干预后3个月和6个月,测量两组患者血清同型半胱氨酸、维生素B12和骨代谢标志物的水平。结果治疗前,两组患者血清同型半胱氨酸、维生素B12和骨代谢标志物水平比较差异无统计学意义(P0.05)。治疗后,两组患者同型半胱氨酸均下降,但比较差异无统计学意义(P0.05)。6个月后各组间血清维生素B12、骨钙素、CTX的变化均有显著改变且差异有统计学意义(P0.05)。结论绝经后骨质疏松症妇女补充叶酸和维生素B12可以一定程度改善同型半胱氨酸及骨代谢指标水平。     

灯盏乙素介入IP3R-Ca2+途径抑制β淀粉样蛋白诱导的神经细胞凋亡

目的：观察灯盏乙素（Scu）对β淀粉样蛋白（Aβ）诱导的细胞模型中1，4，5-三磷酸肌醇受体（IP₃R）-Ca²⁺途径的影响，探讨其在阿尔茨海默病（AD）病程中可能发挥的积极作用。方法：选用神经母细胞瘤细胞分为对照组、Scu处理组、Aβ处理组、Aβ+Scu （高、中、低）处理组及Aβ+IP₃R拮抗剂组，用CCK-8法筛选药物浓度并检测各组细胞生存率；用酶联免疫吸附法检测各组细胞中1，4，5-三磷酸肌醇（IP₃）的含量；用蛋白印迹和实时荧光定量聚合酶链反应方法检测各组细胞IP₃R和凋亡相关因子Caspase-3、Bcl-2、Bax的蛋白及mRNA的表达水平；用激光共聚焦显微镜观察各组细胞内Ca²⁺浓度的变化；用AnnexinV/PI双染法测定各组细胞的凋亡率。结果：与对照组和Scu处理组相比，Aβ处理组细胞存活率下降，IP₃含量升高，IP₃R、Bax和Caspase-3的蛋白及mRNA表达上调，Bcl-2蛋白及mRNA的表达下调，细胞胞浆内Ca²⁺浓度及细胞凋亡率升高；Aβ+Scu处理组细胞中各检测指标的变化与Aβ处理组的结果正好相反，IP₃R通道下游指标的变化与Aβ+IP₃R拮抗剂组基本一致。结论：Scu能够下调通路蛋白IP₃、IP₃R的表达，抑制Aβ介导的Ca²⁺内流所致的细胞凋亡，可能通过对IP₃R-Ca²⁺途径的调控来影响AD病程。     

2015年版《中国药典》一部收载中药材及饮片测定信息的分析

《中国药典》对中药材及饮片质量控制的最终目标是"明真伪,辨优劣",这2个目标是通过基源、性状、鉴别、含有量测定等所有项目检测结果的综合评定实现的。本文对2015年版《中国药典》一部收载中药材及饮片与含有量测定相关的信息进行分析,发现诸多问题,如有三分之一无该项目、许多所需标准物质有重复等。因此,现阶段对成分复杂的中药材、饮片、中成药需要采用源头控制、过程控制与结果控制相结合的方式。     

气血同治法治疗胃息肉验案1则

胃息肉在消化道息肉中最常见,疾病早期多无明显症状,即使有临床症状也无特异性,主要表现为上腹疼痛和腹胀等症,极易造成漏治和误治,在很大程度上影响患者的生活质量。于春泉主任总结多年临床经验,在治疗胃息肉方面有独到的见解,以气血同治为治疗法则,通过补气、行气、活血和养血等治法扶正祛邪,恢复脏腑气血阴阳动态平衡,标本兼顾,临床效果可观。     

Geographical Origin and Level Identification of Frankincense Based on Hyperspectral Image

Background: As the demand for traditional Chinese medicinal materials increases in China and even the world, there is an urgent need for an effective and simple identification technology to identify the origin and quality of the latter and ensure the safety of clinical medication. Mineral element analysis and isotope finger-printing are the two commonly used techniques in traditional origin identification. Both of these techniques require the use of stoichiometric methods in the identification process. Although they have high accuracy and sensitivity, they are expensive and inefficient. In addition, near-infrared spectroscopy is a fast, nondestructive, and widely used identification technique developed in recent years, but its identification results are susceptible to samples’ states and environmental conditions, and its sensitivity is low. Hyperspectral imaging combines the advantages of imaging technology and optical technology, which can simultaneously access the image information and spectral information which reflect the external characteristics, internal physical structure, and chemical composition of the samples. Hyperspectral imaging is widely applied to agricultural product inspection, but research into its application in origin and quality identification of TCM materials is rare. Methods: In this study, the algorithm framework discriminative marginalized least squares regression (DMLSR) was used for feature extraction of frankincense hyperspectral data. The DMLSR with intraclass compactness graph and manifold regularization can efficiently learn the projective samples with higher separability and less redundant information than the original samples. Then, the discriminative collaborative representation with Tikhonov regularization (DCRT) was applied for classifying the geographical origin and level of frankincense. DCRT introducesthe discriminant regularization term and incorporates SID, which is more sensitive to the spectrum as the measurement method and is more suitable for the frankincense spectral data compared with SVM. Results: For the origin classification task, samples of all levels from each origin were, respectively, selected for three?way classification. We used 10-fold cross-validation to select a model parameter in the experiment. When obtaining the optimal parameters, we randomly selected the training set and testing set, where the training set accounts for 70% and the training set for 30%. After repeating this random process 10 times, we obtained the final average classification accuracy, which is higher than 90%, and the standard deviation fluctuation is usually small. For the level classification task, samples of each level from three origins were separately selected for multiclassification. We randomly selected the training set and testing set from each origin. The level classification results of the three origins are good on D4350 data, and the classification accuracy of each level is basically above 80%. Conclusion: Experiments and analysis show that our algorithm framework has excellent classification performance, which is stable in origin classification and has potential for generalization. In addition, the experiments show that in our algorithm framework, different classification tasks need to combine different data sources to achieve better classification and recognition, as the origin classification task uses frankincense’s D3000 data, and level classification task uses frankincense’s D4350 data.