明目地黄丸中牡丹皮、白芍的含量测定

目的:建立紫外分光光度法测定明目地黄丸中牡丹皮、白芍两味中药总丹皮酚含量.方法:按<中国药典>2000年版一部收载六味地黄丸中牡丹皮含量测定方法,以水蒸气蒸馏丹皮酚,通过测定274nm最大吸收波长处紫外吸收进行含量测定.结果:该方法的线性范围2.00～10.00μg/ml(r=0.9996,n=5),平均回收率为99.7%RSD=0.8%(n=5).结论:本法简便,准确,灵敏度高,重现性好,结果稳定可靠.     

明目地黄丸中牡丹皮白芍的含量测定

目的:建立紫外分光光度法测定明目地黄丸中牡丹皮、白芍两味中药总丹皮酚含量。方法:按《中国药典》2000年版一部收载六味地黄丸中牡丹皮含量测定方法,以水蒸气蒸馏丹皮酚,通过测定274nm最大吸收波长处紫外吸收进行含量测定。结果:明目地黄丸总丹皮酚的紫外图谱与六味地黄丸中丹皮酚的紫外图谱完全一致,且阴性对照吸收度可忽略不计。结论:本法简便、快速、专属性强、重现性好。     

茶树油的提取工艺优化试验研究

目的:以茶树油提取率为考察指标,研究茶树油的提取工艺.方法:采用正交试验进行优选,气相色谱法测定4-羟基萜烯含量.结果:影响茶树油提取率的主要因素是提取时间,对茶树油的提取有显著影响;其次是加水量,对茶树油的提取有较大影响;浸泡时间的影响最小.各因素对4-羟基萜烯含量无影响.结论:最佳的提取工艺为浸泡时间2 h,提取时间6 h,加水800 ml.     

慢性肾脏病3-4期糖尿病肾脏疾病患者进展到终末期肾病的危险因素分类树模型分析

【目的】分析糖尿病肾脏疾病（DKD）患者进展到终末期肾病（ESRD）的相关危险因素,筛查进展到ESRD的高风险人群,以早期预防。【方法】收集本院231例糖尿病肾脏疾病患者的临床资料,随访3年,据是否出现ESRD分为未进展到ESRD（133例）、ESRD组（98例）。使用分类树模型分析进展到ESRD相关危险因素,通过节点增益分析筛选进展到ESRD的高风险人群。【结果】从预测变量中筛选到4个重要解释变量：载脂蛋B（ApoB）、性别、糖尿病视网膜病变、收缩压;ApoB升高是DKD进展的重要的危险因素;ApoB＞1.14mmol/L的慢性肾脏病（CKD）3～4期DKD患者,3年进展到ESRD的概率是75.0%,如合并糖尿病视网膜病变,有79.7%的概率进展到ESRD。【结论】分类树模型能有效筛选并分析进展到ESRD危险因素,并识别高风险人群特征,有利于早期防治。     

老年睑板腺功能障碍蠕形螨感染率及茶树精油联合氟米龙的疗效

目的：研究老年睑板腺功能障碍(MGD)蠕形螨感染率及茶树精油联合氟米龙的疗效。

方法：回顾性病例对照研究。选择2017-09/2018-03在宁波市眼科医院门诊就诊的老年MGD患者59例118眼。将睫毛蠕形螨镜检为阳性的38例患者76眼进行症状评分、蠕形螨数量、泪膜破裂时间(BUT)、角膜荧光素染色(FL)、Schirmer I试验(SIaT)等检查,并按不同治疗方法分成3组,A组使用0.02%氟米龙滴眼液滴眼24眼; B组使用茶树精油(TTO)湿巾敷眼26眼; C组用0.02%氟米龙滴眼液滴眼联合TTO湿巾敷眼26眼,随访4wk。

结果：118眼中76眼(64.4%)睫毛蠕形螨镜检为阳性。睫毛蠕形螨阳性的三组患者治疗后的主观症状评分均较治疗前有改善(P<0.05)。治疗后A组蠕形螨数量较治疗前无显著差异(P=0.11); B、C组较治疗前明显减少(均P<0.01)。三组患者BUT治疗前后比较,A组BUT无显著差异(P=0.22); B、C组BUT明显延长(均P<0.05)。治疗后C组的BUT较A、B组明显延长(均P<0.05)。三组患者治疗前后的FL评分均较治疗前明显改善(P<0.05)。

结论：老年MGD患者睫毛蠕形螨感染率较高,睑板腺热疏通后TTO湿巾联合0.02%氟米龙滴眼液能有效驱螨、缓解MGD患者局部症状。     

妥布霉素地塞米松眼膏联合茶树油清洁湿巾治疗蠕形螨睑缘炎

目的：探讨妥布霉素地塞米松眼膏联合缘螨净茶树油清洁湿巾治疗蠕形螨睑缘炎的治疗效果。

方法：前瞻性临床研究。选取蠕形螨睑缘炎患者72例,随机分为3组,试验组给予妥布霉素地塞米松眼膏联合缘螨净茶树油清洁湿巾治疗,对照1组给予妥布霉素地塞米松眼膏联合茶树油眼膏治疗,对照2组给予茶树油眼膏治疗。治疗30d后,对患者进行症状评分、拔睫毛螨虫计数及裂隙灯下观察临床体征评分。比较三组治疗前后数据。

结果：三组治疗前后蠕形螨计数均有差异(P<0.01),症状总评分差值有差异(F=20.05,P<0.01),体征总评分差值有差异(F=8.10,P<0.01)。

结论：妥布霉素地塞米松眼膏联合缘螨净茶树油清洁湿巾治疗蠕形螨睑缘炎疗效显著,茶树油清洁湿巾具有便携、易保存、不易变质的特点。     

Dietary Administration of Asimina triloba. (Paw Paw) Extract Increases Tumor Latency in N.-Methyl-N.-nitrosourea–Treated Rats

Abstract

The paw paw tree, Asimina triloba. (L.) Dunal (Annonaceae), contains more than 50 bioactive components, primarily annonaceous acetogenins. Some therapeutic activities have been associated with this material, but the potential to mediate a cancer chemopreventive effect has not been reported. In this study, a standardized extract from the twigs, in which bullatacin, asimicin, and trilobacin represent the most potent and major bioactive acetogenins, was tested in the N.-methyl-N.-nitrosourea–induced mammary carcinogenesis model. With Sprague-Dawley rats given a diet containing paw paw extract (1250 and 2500 mg/kg diet; based on maximum tolerated dose studies), mammary tumor latency was increased from 55 to 66 days. However, mammary tumor incidence and multiplicity were not affected by extract consumption.     

Risk stratification for mortality in cardiovascular disease survivors: A survival conditional inference tree analysis

Background and aimsEfficient analysis strategies for complex network with cardiovascular disease (CVD) risk stratification remain lacking. We sought to identify an optimized model to study CVD prognosis using survival conditional inference tree (SCTREE), a machine-learning method.Methods and resultsWe identified 5379 new onset CVD from 2006 (baseline) to May, 2017 in the Kailuan I study including 101,510 participants (the training dataset). The second cohort composing 1,287 CVD survivors was used to validate the algorithm (the Kailuan II study, n = 57,511). All variables (e.g., age, sex, family history of CVD, metabolic risk factors, renal function indexes, heart rate, atrial fibrillation, and high sensitivity C-reactive protein) were measured at baseline and biennially during the follow-up period. Up to December 2017, we documented 1,104 deaths after CVD in the Kailuan I study and 170 deaths in the Kailuan II study. Older age, hyperglycemia and proteinuria were identified by the SCTREE as main predictors of post-CVD mortality. CVD survivors in the high risk group (presence of 2–3 of these top risk factors), had higher mortality risk in the training dataset (hazard ratio (HR): 5.41; 95% confidence Interval (CI): 4.49–6.52) and in the validation dataset (HR: 6.04; 95%CI: 3.59–10.2), than those in the lowest risk group (presence of 0–1 of these factors).ConclusionOlder age, hyperglycemia and proteinuria were the main predictors of post-CVD mortality.Trial registrationChiCTR-TNRC-11001489.     

Clustered patterns of species origins of nature-derived drugs and clues for future bioprospecting

Many drugs are nature derived. Low drug productivity has renewed interest in natural products as drug-discovery sources. Nature-derived drugs are composed of dozens of molecular scaffolds generated by specific secondary-metabolite gene clusters in selected species. It can be hypothesized that drug-like structures probably are distributed in selective groups of species. We compared the species origins of 939 approved and 369 clinical-trial drugs with those of 119 preclinical drugs and 19,721 bioactive natural products. In contrast to the scattered distribution of bioactive natural products, these drugs are clustered into 144 of the 6,763 known species families in nature, with 80% of the approved drugs and 67% of the clinical-trial drugs concentrated in 17 and 30 drug-prolific families, respectively. Four lines of evidence from historical drug data, 13,548 marine natural products, 767 medicinal plants, and 19,721 bioactive natural products suggest that drugs are derived mostly from preexisting drug-productive families. Drug-productive clusters expand slowly by conventional technologies. The lack of drugs outside drug-productive families is not necessarily the result of under-exploration or late exploration by conventional technologies. New technologies that explore cryptic gene clusters, pathways, interspecies crosstalk, and high-throughput fermentation enable the discovery of novel natural products. The potential impact of these technologies on drug productivity and on the distribution patterns of drug-productive families is yet to be revealed.