Changes of porcine pancreas α‐amylase in activity and secondary conformations under inhibition of tea polyphenols

To investigate two‐sided functions of tea polyphenols (TP) in antinutrition and energy balance modulation, TP were extracted from Chinese green tea and used to complex porcine pancreas α‐amylase (PPA). Changes of PPA in activity and secondary conformations were analysed. Porcine pancreas α‐amylase was found sensitive to TP treatment. Tea polyphenols exhibited IC₅₀ at 0.41 mg mL^?1 against PPA and maximum inhibitory rate (98.17%) at 3.0 mg mL^?1. Tea polyphenols inhibition was concluded as noncompetitive pattern based on its unchanged Km value (0.98 mg mL^?1) for soluble starch substrate. Tea polyphenols inhibition arose from pH 1.5 to 10.14, covering gastric and intestinal environments inside body. Circular dichroism spectra analysis revealed regular changes of PPA in secondary conformations (increased proportions of α‐helix and β‐sheet) prior to its inactivation at low TP concentrations. Tea polyphenols‐inhibited PPA had distinct double‐negative peaks at 204 nm and 208 nm. Porcine pancreas α‐amylase was inactivated by TP in ways of complexation and modification of secondary conformations.     

乙醇溶液中Tweens对猪胰脂酶的修饰作用

采用Tween系列表面活性剂在乙醇-水溶液体系中对猪胰脂酶进行修饰,以它们催化酯交换的交换量为指标评价修饰的效果,并且对修饰酶与未修饰酶的热稳定性进行了研究。结果表明:40℃下30 min即可完成Tweens对猪胰脂酶的修饰,但不同Tween的最佳条件并不相同。Tween 20修饰脂肪酶的最佳条件是无水乙醇与水的比例为1∶2(v/v),Tween 20用量为5%;Tween 40和Tween 65修饰猪胰脂酶的最佳条件是无水乙醇与水的比例为1∶2(v/v),用量均为15%;Tween 60修饰猪胰脂酶的最佳条件为无水乙醇与水的比例为1∶2(v/v),用量为20%;Tween 80修饰脂肪酶的最佳条件是无水乙醇与水的比例为1∶3(v/v),用量为5%;Tween 85修饰脂肪酶的最佳条件是无水乙醇与水的比例为1∶3(v/v),用量为15%。修饰后酶的热稳定性实验研究表明,在40～80℃间,修饰酶的催化反活性都高于未修饰酶。Tweens修饰猪胰脂酶能有效提高酶的活性及其热稳定性。     

固相萃取-高效液相色谱-柱后衍生荧光法测定奶粉中维生素B11含量

建立了固相萃取-高效液相色谱-柱后衍生荧光法用于测定奶粉中维生素B11含量。奶粉酶解样液经固相萃取小柱纯化后,在ZORBAX SB-C18色谱柱上,以pH=3.5、浓度为0.05mol/L磷酸二氢钾-乙腈体系为流动相,流速为1.0mL/min,进样量20μL,以0.5%过硫酸钾溶液为衍生试剂,反应器温度为60℃,流速0.5mL/min,经荧光检测器(Ex=365nm,Em=440nm,增幅值100,衰减值16)检测定量。结果表明:在0.02～1.76μg/mL范围内该方法线性良好,R2=0.9999,检测限为4ng/mL,定量限为12ng/mL,灵敏度足以满足奶粉检测的需要,方法回收率在92.5%～95.7%之间,变异系数为1.32%。     

基于3D路径聚合高分辨率网络的胰腺分割

目的 胰腺的准确分割是胰腺癌识别和分析的重要前提。现有基于深度学习的主流胰腺分割网络大多是编码—解码结构,对特征图采用先降低再增加分辨率的方式,严重丢失了胰腺位置和细节信息,导致分割效果不佳。针对上述问题,提出了基于3D路径聚合高分辨率网络的胰腺分割方法。方法 首先,为了捕获更多3D特征上下文信息,将高分辨率网络中的2D运算拓展为3D运算;其次,提出全分辨特征路径聚合模块,利用连续非线性变换缩小全分辨率输入图像与分割头网络输出特征语义差异的同时,减少茎网络下采样丢失的位置和细节信息对分割结果的影响;最后,提出多尺度特征路径聚合模块,利用渐进自适应特征压缩融合方式,避免低分辨率特征通道过度压缩导致的信息内容损失。结果 在公开胰腺数据集上,提出方法在Dice系数（Dice similarity coefficient,DSC）、Jaccard系数（Jaccard index,JI）、精确率（precision）和召回率（recall）上相比3D高分辨率网络（3D high-resolution net,3DHRNet）分别提升了1.41%、2.09%、2.35%和0.49%,相比具有代表性编码—解码结构的胰腺分割方法,取得了更高的分割精度。结论 本文提出的3D路径聚合高分辨率网络（3D pathaggregation high-resolution network,3DPAHRNet）具有更强的特征位置和细节信息的保留能力,能够显著改善在腹部CT（computed tomography）图像中所占比例较小的胰腺器官的分割结果。开源代码可在https：//github.com/qiuchengjian/PAHRNet3D获得。     

羊肉部分下脚料的利用与开发

介绍了从羊肉下脚料羊血、羊心、胰脏提取的一些生化制品的性质和功能,包括凝血酶、超氧化物歧化酶、血红素、细胞色素C、胰酶,并对羊肉下脚料的合理利用提出了建议.     

多肽胰岛素正常转运经淋巴而非门脉途径IGMEI胰组织注射SEM观察

探讨胰岛素等多肽激素在细胞外的正常转运途径或规律,对免疫金标记外源性胰岛素（IGMEI）注射的大鼠胰组织冷冻切片,进行了二次电子和背散射电子图像（SEI／BEI）的扫描电镜（SEM）示踪性观察,SEM观察SEI显示胰腺小叶和小叶间结缔组织,血管和淋巴管,胰腺导管和胰岛的结构特点清晰可见,根据管腔内是否存在红细胞和内皮细胞的结构特点,可以区别血管或淋巴管。SEM观察BEI表明,较强的背散射电子出现在胰结缔组织间隙,淋巴管或毛细淋巴管内;然而,胰的血管或毛细血管内,无标记金颗粒的背散射电子或BEI微弱,结果提示,注射或释放入胰组织液中的胰岛素等多肽激素或分泌颗粒,其正常转运途径或规律,可能通过淋巴而非肝门静脉途径转运进入血液。     

Model Predictive Control of Glucose Concentration Based on Signal Temporal Logic Specifications with Unknown-Meals Occurrence

Abstract

The glycemia regulation is a significant challenge in the Artificial Pancreas (AP) scenario. Several control systems have been developed in the last years, many of them requiring meal announcements. Therefore, if the patients skip the meal announcement or make a mistake in the estimation of the amount of carbohydrates, the control performance will be negatively affected. In this extended version of our previous work, we present a Model Predictive Controller (MPC) for the AP in which the meal is treated as a disturbance to be estimated by an Unknown Input Observer (UIO). The MPC constraints are expressed in terms of Signal Temporal Logic (STL) specifications. Indeed, in the AP some requirements result in hard constraints (in particular, absolutely avoid hypoglycemia and absolutely avoid severe hyperglycemia) and some other in soft constraints (avoid a prolonged hyperglycemia) and STL is suitable for expressing such requirements. The achieved results are obtained using the BluSTL toolbox, which allows to synthesize model predictive controllers with STL constraints. We report simulations showing that the proposed approach, avoiding unnecessary restrictions, provides safe trajectories in correspondence of higher unknown disturbance.