2种中药化学对照品的多晶型现象初探

化学药品的晶型研究十分广泛,但中药中活性化学成分的多晶型现象尚未引起足够重视。该文以无水的脱水穿心莲内酯和含水的升麻素苷为代表,采用显微、熔点、差热分析和红外光谱技术探讨了不同晶型中药化学对照品的差异。结果表明晶型不同会引起熔点、热行为和红外光谱的改变。提示在应用中药化学对照品时如得到不同的指标测定结果,须考虑多晶型的存在。不同晶型的中药活性成分的化学性质差异尚待深入研究。     

Cardioprotection of PLGA/gelatine cardiac patches functionalised with adenosine in a large animal model of ischaemia and reperfusion injury: A feasibility study

The protection from ischaemia‐reperfusion‐associated myocardial infarction worsening remains a big challenge. We produced a bioartificial 3D cardiac patch with cardioinductive properties on stem cells. Its multilayer structure was functionalised with clinically relevant doses of adenosine. We report here the first study on the potential of these cardiac patches in the controlled delivery of adenosine into the in vivo ischaemic‐reperfused pig heart. A Fourier transform infrared chemical imaging approach allowed us to perform a characterisation, complementary to the histological and biochemical analyses on myocardial samples after in vivo patch implantation, increasing the number of investigations and results on the restricted number of pigs (n = 4) employed in this feasibility step. In vitro tests suggested that adenosine was completely released by a functionalised patch, a data that was confirmed in vivo after 24 hr from patch implantation. Moreover, the adenosine‐loaded patch enabled a targeted delivery of the drug to the ischaemic‐reperfused area of the heart, as highlighted by the activation of the pro‐survival signalling reperfusion injury salvage kinases pathway. At 3 months, though limited to one animal, the used methods provided a picture of a tissue in dynamic conditions, associated to the biosynthesis of new collagen and to a non‐fibrotic outcome of the healing process underway. The synergistic effect between the functionalised 3D cardiac patch and adenosine cardioprotection might represent a promising innovation in the treatment of reperfusion injury. As this is a feasibility study, the clinical implications of our findings will require further in vivo investigation on larger numbers of ischaemic‐reperfused pig hearts.     

AI-based spectroscopic monitoring of real-time interactions between SARS-CoV-2 and human ACE2

The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), invades a human cell via human angiotensin-converting enzyme 2 (hACE2) as the entry, causing the severe coronavirus disease (COVID-19). The interactions between hACE2 and the spike glycoprotein (S protein) of SARS-CoV-2 hold the key to understanding the molecular mechanism to develop treatment and vaccines, yet the dynamic nature of these interactions in fluctuating surroundings is very challenging to probe by those structure determination techniques requiring the structures of samples to be fixed. Here we demonstrate, by a proof-of-concept simulation of infrared (IR) spectra of S protein and hACE2, that time-resolved spectroscopy may monitor the real-time structural information of the protein−protein complexes of interest, with the help of machine learning. Our machine learning protocol is able to identify fine changes in IR spectra associated with variation of the secondary structures of S protein of the coronavirus. Further, it is three to four orders of magnitude faster than conventional quantum chemistry calculations. We expect our machine learning protocol would accelerate the development of real-time spectroscopy study of protein dynamics.

The ongoing pandemic of COVID-19, a highly infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed tremendous threat to human health and well-being by having affected tens of millions of people and killed more than 1 million affected since December 2019 (1). It has spurred enormous efforts in biological and biomedical research to search for a solution to this fatal disease, which rapidly advance our knowledge about it, including the identity of the pathogen (i.e., SARS-CoV-2), the genome sequence of the virus, and the structural basis for coronavirus recognition and infection (2–5). SARS-CoV-2 recognizes human angiotensin-converting enzyme 2 (hACE2) as the entry receptor to host cells using its surface spike glycoprotein (S protein) (1). The interactions of S protein with hACE2 have been subjected to intensive investigations by several groups (6–10), which laid the foundation for comprehensive understanding of the invasion of SARS-CoV-2 into the human body at the atomic scale (11), helps the search for intermediate hosts of the coronavirus (12), and will guide the design of therapeutics and vaccines (11, 13). Since the physiological environment in which S protein and hACE2 interact is always fluctuated due to the dynamic nature of water, a dynamic picture of the interactions between them is needed for precise mechanistic understanding that will inspire modulation and application (14). Unfortunately, such information relies on real-time tracking of protein conformations, which cannot be achieved by powerful structure characterization techniques with atomic precision like X-ray diffraction and cryoelectron microscopy, because they require fixed structures in samples. It motivates us to develop alternative approaches to resolve the issue.Recently, time-resolved infrared (IR) spectroscopy techniques have realized successful monitoring of changes of secondary structure with time (15), signaling the feasibility of real-time observation of protein dynamics in ambient conditions using spectroscopy. However, to facilitate the monitoring of specific peptide fragments in a secondary structure typically requires isotope labeling (e.g., C=O in the amide of protein backbone is replaced with ¹³C=O or C=¹⁸O) in the preparation of samples, which is, unfortunately, tedious and expensive for systematic investigation on conformation changes in protein dynamics. Therefore, it is desirable to develop isotope labeling-free spectroscopy to accelerate structure study of proteins for biological and biomedical sciences. To achieve this goal, one needs to employ quantum chemistry calculations to complete spectra signal assignment and structure determination. In fact, it relies on computer simulations of various possible conformers to nail the job, which is, unfortunately, very expensive for macromolecules like proteins. One of the biggest bottleneck problems in spectroscopic measurement of proteins is lack of rapid theoretical interpretation that can timely translate spectra signals into structural information. As a result, it is nearly impossible for an experimental spectroscopic study to monitor continuous structural changes associated with protein functions. Developing a cost-effective spectra simulation protocol is a pressing task to advance the real-time spectroscopy study of protein structures.Machine learning (ML), a collection of statistics-based methods which gain prediction power from the learning of big data, has emerged as a powerful toolkit to reduce the barrier to revealing the structure−property relationship (16). It has been increasingly popular in the study of molecules and materials, such as predicting chemical reaction routes (17) and accelerating discovery of materials (18). Especially, neural networks (NN), a subclass of ML algorithms, are well recognized for handling complex nonlinear problems. NN established a predictive model for desired properties by iterative optimization of a complex high-dimensional function in a virtually infinite space of parameters. This feature makes it a transferrable tool for predicting protein spectra (19).In this article, we developed and applied a cost-effective ML protocol, to predict the IR spectra along with the kinetic process of a COVID-2019 virus (SARS-CoV-2) protein binding to hACE2. The efficient simulation of IR signals of different states of the coronavirus associated with the changes in its secondary structure is very encouraging for studying dynamic interactions between S protein of SARS-CoV-2 and human ACE2 with the help of ML techniques. This will enable a real-time spectroscopic monitoring of protein structure evolution for this deadly virus, providing valuable information for understanding its molecular mechanism, as well as developing cures and vaccines. ML should provide a cost-effective tool for simulating optical properties of SARS-CoV-2.     

老年高血压病患者血清真胰岛素、C肽水平及替米沙坦的干预作用

目的通过对老年高血压非糖尿病患者血清真胰岛素、C肽、血糖的观察,探讨高胰岛素血症、胰岛素抵抗(IR)与老年高血压的关系,以及替米沙坦干预的影响。方法测定20例非肥胖型老年高血压病患者(NHT组),15例肥胖型老年高血压病患者(OHT组)及20例健康老年人(对照组)的空腹血糖、真胰岛素、C肽、血脂、血尿酸和IR指数、胰岛β细胞功能指数。高血压病患者予以替米沙坦(80 mg,1次/d口服)治疗,4 w后复查,观察治疗前后血压和上述指标的变化。结果本研究的3组患者年龄、性别、空腹血糖状况相匹配。OHT组和NHT组血清真胰岛素、C肽水平及IR指数(HOMA-IR)和胰岛β细胞功能指数(HOMA-islet)均高于健康对照组。高血压病患者经替米沙坦治疗后血压、真胰岛素、C肽水平及HOMA-IR和HOMA-islet指数较治疗前均明显下降,差异具有显著性(P<0.05)。结论老年高血压非糖尿病患者,特别是伴有肥胖者,存在IR、高胰岛素血症及代偿性胰岛β细胞功能增高。替米沙坦除了具有强效降低血压的作用外,还具有改善IR和胰岛β细胞功能的作用。     

Study on the Clinical and Endocrine Characteristics of Poly- cystic Ovary Syndrome with Different Ovarian Morphology

Objective To evaluate the differences of the clinical manifestation and endocrine situation in patients with different ovarian morphology of polycystic ovary syndrome(PCOS).Methods A total of 234 PCOS patients were enrolled according to the ovary morphology and divided into three groups: 112 patients with B-polycystic ovary morphology(both two ovaries were PCOM, B-PCOM), 50 with U-PCOM(only one ovary was PCOM) and 72 with N-PCOM(none was PCOM). There were 39 infertile women without PCOS as control group. Data were analyzed by using SPSS 15.0 software.Results There was no statistical difference in body mass index(BMI) among the three groups of PCOS. The endometrial thickness increased in patients with B-PCOM and decreased with N-PCOM. The levels of testosterone, androstenedione and luteinizing hormone increased in PCOS groups, especially in N-PCOM patients. HOMA-IR increased, HOMA-β, disposition index(DI) and △I60/△G60 decreased in patients with NPCOM compared with in B-PCOM and U-PCOM groups. Higher level of total cholesterol(TC) and lower level of high-density lipoprotein(HDL)-C existed in PCOS patients,especially in N-PCOM. There were positive correlations between oligo-anovulation,endometrial thickness, LH/FSH ratio, fasting insulin(FINS), the area under curve of glucose(AUCGLU) and PCOM, while there was a negative correlation between HOMAIR and PCOM.Conclusion There are relationships among hyperandrogenism, hyperinsulinemia,insulin resistance(IR) and ovary morphology in PCOS patients. PCOS patients without PCOM have more serious IR and hyperandrogenism.     

复方贞术调脂方对HepG2细胞胰岛素抵抗的作用及机制研究

目的 观察复方贞术调脂方（FTZ）对胰岛素抵抗（IR） HepG2细胞的作用,为开拓FTZ的治疗范围,阐明FTZ调节糖脂代谢的机制提供实验依据.方法 用高浓度胰岛素诱导HepG2细胞使其产生胰岛素抵抗,用高、中、低3个剂量的FTZ干预后,葡萄糖氧化酶法检测HepG2细胞培养液上清液中葡萄糖的含量,实时荧光定量PCR检测HepG2细胞胰岛素信号PI-3Kp85 mRNA的表达,Western blot检测HepG2细胞胰岛素信号转导蛋白IRS1表达.结果 模型细胞培养基上清液中葡萄糖含量高于正常细胞（P＜0.05）.给予FTZ（1、25、100μg/mL）后,HepG2细胞培养基上清液中葡萄糖含量均低于模型细胞葡萄糖含量（P＜0.05）.胰岛素抵抗细胞与正常细胞相比PI-3Kp85 mRNA和IRS1的蛋白表达显著降低（P＜0.05）.给予FTZ干预后,与胰岛素抵抗细胞相比PI-3Kp85 mRNA和IRS1的蛋白表达显著增加（P＜0.05）.结论 FTZ可改善胰岛素抵抗HepG2细胞对葡萄糖的摄取.其改善胰岛素抵抗的作用机制之一可能是通过上调胰岛素信号PI-3Kp85 mRNA和IRSl蛋白质在胰岛素抵抗HepG2细胞的表达.     

电针对非酒精性脂肪性肝炎大鼠胰岛素抵抗指数水平的影响简

目的 采用电针刺激大鼠腧穴,观察其对高脂饮食所致的非酒精性脂肪肝（NASH）大鼠胰岛抵抗指数（HO-MA-IR）水平的影响.方法 40只SD大鼠随机分为正常对照组、模型组、电针组和西药组,每组10只,在16周后隔夜空腹,以2％戊巴比妥钠（45mg/kg体重）麻醉,经下腔静脉采血,分离血清.采用放射免疫分析法测定空腹血清胰岛素（FINS）,计算HOMA-IR.结果 空腹血糖（FBG）正常组与其他各组比较,差异无统计学意义（P＞0.05）;胰岛素抵抗指数水平模型组比正常组显著增高（P＜0.01）;模型组比电针组增高（P＜0.05）;电针组较之西药组有下降趋势,但二者比较无明显差异（P＞0.05）.结论 电针可能通过改善NASH大鼠HOMA-IR而发挥治疗NASH的作用.     

Treatment Patterns,HealthCare Resource Utilization and Costs in Patients with Bipolar Disorder,Newly Treated with Extended Release or Immediate Release Quetiapine Fumarate using US Healthcare Administrative Claims Data

Background

Differences in treatment patterns, health care resource use, and costs are expected among patients newly treated with quetiapine extended release (XR) or quetiapine immediate release (IR).

Objective

To compare treatment patterns, health care resource use, and costs in patients with bipolar disorder newly treated with quetiapine XR or quetiapine IR.

Methods

This was an observational, retrospective cohort study that used HealthCore Integrated Research Database–identified patients (age range, 18-64 years) with an International Classification of Disease, Ninth Revision diagnosis of bipolar disorder and ≥1 pharmacy claim for quetiapine XR or quetiapine IR between October 2, 2008, and July 31, 2010. Outcomes were as follows: patient characteristics at the index date (first claim for quetiapine XR or quetiapine IR); 12-month preindex clinical characteristics, health care resource use, and costs; and 12-month postindex treatment patterns, health care resource use, and costs, assessed using generalized linear models (adjusted for index date and preindex patient demographic characteristics, clinical characteristics, health care resource use, and costs).

Results

In total, 3049 patients with bipolar disorder were analyzed (651 in the quetiapine XR group and 2398 in the quetiapine IR group). Of patients initiating treatment with quetiapine XR, 8.8% had no change in or discontinuation of their index therapy compared with 5.7% of patients treated with quetiapine IR (adjusted odds ratio, 1.44; 95% confidence interval, 1.03-2.00; P = 0.0317). The average daily dose (adjusted mean) of quetiapine XR was higher than quetiapine IR (225 vs 175 mg/d, P < 0.0001). An average daily dose of 300 to 800 mg was reached sooner (15.6 vs 30.8 days, P = 0.0049) and in more patients (44.2% vs 27.2%, P < 0.0001) who were taking quetiapine XR compared with patients taking quetiapine IR. No differences in total health care costs were found between the cohorts; however, patients taking quetiapine XR were less likely to be hospitalized for mental health–related reasons (12.1% vs 18.3%, P = 0.0022) and incurred lower mental health–related costs (US $6686 vs US $7577, P = 0.0063) compared with patients taking quetiapine IR.

Conclusions

Treatment patterns and dosing differ in patients with bipolar disorder treated with quetiapine XR compared with those treated with quetiapine IR. Mental health–related hospitalizations and costs may be reduced in the 12 months after patients initiating treatment with quetiapine XR compared with initiating treatment with quetiapine IR.