Nebivolol Ameliorates Cisplatin‐Induced Nephrotoxicity in Rats

Treatment with cisplatin is associated with dose‐limiting side effects, mainly nephrotoxicity. On the other hand, nebivolol, a β₁‐adrenoceptor antagonist, exhibits vasodilatory and antioxidative properties. This study aimed to determine whether nebivolol possesses a protective effect against cisplatin nephrotoxicity and explore many mechanisms underlying this potential effect. Nephrotoxicity was induced in Wistar rats by a single intraperitoneal injection of cisplatin (6 mg/kg) on day 2. Nebivolol (10 mg/kg) was administered orally for 7 consecutive days. Nebivolol showed a nephroprotective effect as demonstrated by the significant reduction in the elevated levels of serum creatinine and urea as well as renal levels of malondialdehyde, nitric oxide products (nitrite/nitrate), inducible nitric oxide synthase, tumour necrosis factor‐alpha, caspase‐3, angiotensin II and endothelin‐1 with a concurrent increase in renal levels of reduced glutathione and endothelial nitric oxide synthase compared to untreated rats. Histopathological examination confirmed the nephroprotective effect of nebivolol. Pre‐treatment with N_ω‐nitro‐L‐arginine methyl ester, the non‐specific nitric oxide synthase inhibitor, partially altered the protection afforded by nebivolol. In conclusion, nebivolol protects rats against cisplatin‐induced nephrotoxicity that is most likely through its antioxidant, anti‐inflammatory and antiapoptotic effects as well as by abrogation of the augmented angiotensin II and endothelin‐1 levels.     

角质形成细胞的培养及其表皮生长因子受体表达

目的:在成功分离人皮肤角质形成细胞的基础上,观察表皮生长因子受体在人皮肤角质形成细胞中的表达情况。方法:实验于2006-3/10在北京大学深圳医院中心实验室进行。采用dispase Ⅱ-trypsin两步消化法获取表皮基底层细胞,用小鼠皮肤成纤维母细胞滋养层和黄素腺嘌呤二核苷酸培养液进行培养。小鼠皮肤成纤维母细胞的预处理:向对数生长期的小鼠皮肤成纤维母细胞培养液中加入丝裂霉素C至终浓度为4mg/L,37℃下培养4h,弃去培养液,用D-Hank’s液洗3次,加入浓度为0.25g/L的胰蛋白酶消化,分离出细胞,离心(200g,5min),用黄素腺嘌呤二核苷酸培养液悬浮细胞,计数,以5.0×104/cm2的密度种于培养皿内,37℃、体积分数0.05的CO2培养箱下培养。角质形成细胞的培养:将分离的角质形成细胞悬浮在黄素腺嘌呤二核苷酸培养液中,以2.0×104/cm2的密度接种在前1天经丝裂霉素C处理的小鼠皮肤成纤维母细胞滋养层上,37℃、体积分数0.05的CO2培养箱下培养。24h换液,以后每3d换1次液。采用免疫细胞化学的方法检测表皮生长因子受体的表达,采用复合逆转录聚合酶链反应检测角质形成细胞中表皮生长因子受体mRNA的表达。结果:采用dispaseⅡ消化法分离了真皮和表皮,获得较多的角质形成细胞,可以避免真皮成纤维细胞的污染。人皮肤角质形成细胞在黄素腺嘌呤二核苷酸培养液中培养5d可见明显的集落,约10d可长满单层。免疫细胞化学显示表皮生长因子受体在细胞表面有明显的表达,复合逆转录聚合酶链反应显示表皮生长因子受体mRNA有明显的表达。结论:用小鼠皮肤成纤维母细胞滋养层和黄素腺嘌呤二核苷酸培养液可以较好地培养原代人皮肤角质形成细胞,表皮生长因子受体在细胞表面有明显的表达,这些结果为与表皮生长因子受体相关的皮肤病(如银屑病)的研究奠定了基础。     

程序变温法确定药物降解反应级数

通过电子计算机模拟程序升温加速试验,从理论上阐明了常规的程序升温法不能确定药物降解反应级数的原因是因为同一组数据可由不同的反应级数和活化能的组合所拟合;解决这一问题的关键是在一个变温程序中包含升温和降温部分;据此提出了一种新的程序变温方法(程序升降温法)。利用这种方法,可以真正做到只通过一次程序变温加速试验,就获得包括反应级数在内的药物降解的动力学参数,且确定反应级数的能力与恒温法相近似。     

Intraperitoneal infusion of homocysteine increases intimal hyperplasia in balloon-injured rat carotid arteries.

Hyperhomocysteinemia is a significant risk factor in atherosclerosis and thrombosis. However, its role in the development of intimal hyperplasia after arterial reconstructive procedures remains uncertain. We therefore studied the effect of homocysteine on intimal hyperplasia in a rat model of carotid artery balloon injury. Twenty-four Sprague-Dawley rats were divided into three groups: control (saline infusion), and low dose (0.14 mg/day) and high dose (0.71 mg/day) homocysteine delivered continuously via osmotic pumps implanted intraperitoneally. All animals underwent left common carotid artery balloon denudation with sacrifice after 14 days. Plasma homocysteine levels, intimal hyperplasia, and cell proliferation of rat carotid arteries were determined. In vitro rat smooth muscle cell (SMC) proliferation with homocysteine treatment was also performed. Plasma homocysteine levels at sacrifice were 1.80+/-0.35, 2.65+/-0.05 and 3.50+/-0.22 microM in three groups, respectively. Intimal hyperplasia developed in all balloon-injured arteries in both control and homocysteine-treated animals. The intimal area and intima/media area ratio were increased by 92% (P<0.05) and 105% (P<0.05), respectively, in the high dose-homocysteine-treated animals as compared to the control animals. Homocysteine (high dose) also significantly promoted the intimal cell proliferation (bromodeoxyuridine incorporation) by 2.2-fold as compared to controls. Furthermore, homocysteine treatment in the cell culture study showed a concentration-dependent increase of rat SMC proliferation. These data demonstrate that the continuous intraperitoneal administration of homocysteine significantly increases intimal hyperplasia and SMC proliferation after carotid artery balloon injury in the rat as well as in vitro SMC proliferation. This study suggests that, following arterial reconstructive procedures, elevated plasma homocysteine may increase the complications of clinical restenoses that are associated with intimal hyperplasia.     

Coupling of M2 muscarinic receptor to L-type Ca channel via c-src kinase in rabbit colonic circular smooth muscle

BACKGROUND & AIMS: The L-type Ca(2+) channel is a major pathway for Ca(2+) influx in colonic smooth muscle and is modulated by endogenous levels of nonreceptor tyrosine kinase, c-src. Tyrosine kinases are also activated by G-protein-coupled receptors (GPCR). This study determined whether muscarinic receptor couples to Ca(2+) channels via c-src kinase. METHODS: Currents were measured in rabbit colonic smooth muscle cells and in transfected HEK293 cells by patch-clamp technique. Tyrosyl phosphorylated proteins were detected by Western blots and the interaction of c-src with the c-terminus of alpha subunit of Ca(2+) channel was determined by a GST pull-down assay. RESULTS: Methacholine (10 micromol/L) enhanced Ca(2+) channel currents by 30% under conditions whereby the M(3) receptor pathway was blocked by either 4-DAMP or by intracellular dialysis with anti-Galphaq antibody. Similar effects were observed by blocking intracellular Ca(2+) release with heparin. Enhancement was abolished by intracellular anti-Galphai antibody and by the c-src inhibitor, PP2 but unaffected by the inactive analog PP3. Immunoblot with anti-src antibody revealed increased src phosphorylation by muscarinic receptor stimulation. Purified c-src directly associated with the c-terminus of alpha1c subunit of the Ca(2+) channel. In M(2) receptor transfected HEK293 cells, currents were enhanced 2-fold by carbachol. CONCLUSIONS: These studies demonstrate stimulation of Ca(2+) current in colonic smooth muscle cells by M2 receptor coupled to Galphai-G protein and c-src activation. They also suggest a central role of c-src kinase in the cross-talk between tyrosine kinase receptor and GPCR.     

Prediction of Zika-confirmed cases in Brazil and Colombia using Google Trends

Zika virus infection in humans has been linked to severe neurological sequels and foetal malformations. The rapidly evolving epidemics and serious complications made the frequent updates of Zika virus mandatory. Web search query has emerged as a low-cost real-time surveillance system to anticipate infectious diseases’ outbreaks. Hence, we developed a prediction model that could predict Zika-confirmed cases based on Zika search volume in Google Trends. We extracted weekly confirmed Zika cases of two epidemic countries, Brazil and Colombia. We got the weekly Zika search volume in the two countries from Google Trends. We used standard time-series regression (TSR) to predict the weekly confirmed Zika cases based on the Zika search volume (Zika query). The basis TSR model – using 1-week lag of Zika query and using 1-week lag of Zika cases as a control for autocorrelation – was the best for predicting Zika cases in Brazil and Colombia because it balanced the performance of the model and the advance time in the prediction. Our results showed that we could use Google search queries to predict Zika cases 1 week earlier before the outbreak. These findings are important to help healthcare authorities evaluate the outbreak and take necessary precautions.Key words: Brazil, Colombia, Google Trends, prediction, Zika

Zika virus infection in humans has changed in character from an endemic self-limited mild illness to an epidemic disease [1]. Developing accurate tools to predict Zika infection spread is required for early prevention of the disease [2]. The purpose of this analysis is to explore whether web-based query could effectively predict Zika virus spread.On 2 October 2016, Pan American Health Organisation (PAHO) released an epidemiological report of Zika virus in different countries [3]. Each report contains the number of confirmed and suspected cases in each country as reported by Ministry of Health in these countries. For our analysis, we selected Colombia and Brazil because there was continuous monitoring for both confirmed and suspected cases. In addition, both countries were considered as most epidemic countries in South America. PAHO report for Brazil included both suspected and confirmed cases from January 2016 to 9 July 2016, which corresponds to the first epidemiologic week of 2016 till the 27th epidemiologic week of 2016. For Colombia, the report had data from 9 August 2015 to 21 May 2016, which corresponds to the 32nd epidemiologic week of 2015 to the 20th epidemiologic week of 2016. We used Webplotdigitiser software to extract the weekly confirmed Zika cases of Brazil and Colombia [4]. We only extracted confirmed cases not suspected nor reported cases to avoid overestimation of the epidemic. That is because the case definitions for Zika suspected included rashes with one of the following symptoms: fever, usually <38.5 °C, conjunctivitis (non-purulent/hyperaemic), arthralgia, myalgia and peri-articular oedema with the history of travelling to one of the epidemic areas [5]. These criteria are similar to many infectious diseases that caused reporting of a huge number of Zika cases, mainly suspected cases, while confirmed Zika cases represented only a minimum of these numbers. This can be proved by epidemiological reports released by PAHO in October 2016 in which we noticed a big difference between reported and confirmed cases. We used only confirmed cases to avoid overestimation of the epidemic because we have noticed a big difference between confirmed and reported cases that will affect our results.To get the web search volume for the word Zika in this specific time period, we used Google Trends (https://trends.google.com/trends/) to get the weekly search volume for word ‘Zika’, termed Zika query. We did not use other words for signs and symptoms of Zika because it was similar to other diseases that can cause misjudgement of search volume. The steps of searching the Google Trends and processing the query data for the analysis are explained in the Supplementary video 1.We used a standard time-series regression (TSR), particularly the Poisson distributed lag model (PDLM) to examine the association between weekly Zika cases (i.e. the outcome) and weekly Zika query (i.e. the predictor). A quasi-Poisson distribution of the outcome is assumed to account for the overdispersion (the presence of expected increasing variance among the data). We also considered important features of the application of TSR to infectious diseases, such as the lag association (e.g. the last week Zika query could be associated with this week Zika cases), the strong auto-correlations and the controlling for the long-term trend. These features are discussed in detail in Imai et al. [6]. This model has been considered the best in the prediction of dengue cases when compared with other models including standard multiple regression model (SMR) and seasonal autoregressive-integrated moving average model (SARIMA) [7].The general model is specified as follow: 1where Y_t is the weekly Zika count on week t, μ_t is the mean parameter of the Poisson distribution, alpha (α) is the intercept, and Lag E_t−k is the Zika query in week t minus lag k (k = 0, 1, 2, 3).Time is a variable that takes consecutive numbers ranging from 1 on the day on which observations began to 27 on the final day of the observation period in Brazil data, and to 41 in Columbia data. The time variable was used to control the long-term trend in Zika cases (assumed an increase linear trend) following Bhaskaran et al.’s method [8]. AC stands for the auto-correlation term. We invite the reader to refer Imai et al. for the nature of the technical details of this model [6].We used R software version 3.4.3 for all the described analyses [9]; we used Epi [10], tsModel [11] and bbmle [12] packages.In total, seven different models were constructed, and the performance of them was validated based on the dispersion value, which was used for the evaluation of the model as reported by Imai et al.[6] (i.e. the smaller the dispersion value, the better the model in predicting Zika cases). The seven constructed models for each country with their dispersion values are described in Supplementary Tables S1 and S2.In addition, we also conducted a sensitivity analysis to determine whether the results were dependent on modelling choices. We replaced the time variable by the peak indicator variable (i.e. two values: 1 indicates high-peak weeks, 0: otherwise). The high-peak weeks were defined as the weeks containing Zika case counts greater than the median value of Zika case counts of the whole study period.The best model in predicting Zika cases in Brazil was the model with basis TSR, including lag zero of Zika query plus lag one of Zika cases as controlling for auto-correlation (i.e. TSR lag (Zika, 0) + AC: lag (log (Y + 1), 1)) (Supplementary Table S1). Whereas the model with basis TSR, including lag one of Zika query plus lag one of Zika cases as controlling for auto-correlation came into second (i.e. TSR lag (Zika, 1) + AC: lag (log (Y + 1), 1)). Similarly, the best model in predicting Zika cases in Colombia is TSR lag (Zika, 0) + AC: lag (log (Y + 1), 1), and the model TSR lag (Zika, 1) + AC: lag (log (Y + 1), 1) took second place (Supplementary Table S2).For the real application, the model that can predict Zika cases in future would be preferable. Therefore, in this study, we would recommend using the model TSR lag (Zika, 1) + AC: lag (log (Y + 1), 1) in predicting Zika cases in Brazil and Colombia because it balanced the performance of the model and the advance time of prediction. The pattern of observed Zika cases and predicted Zika cases using the model TSR lag (Zika, 1) + AC: lag (log (Y + 1), 1) in Brazil and Colombia is shown in the (Fig. 1). The correlation coefficients are 0.986 and 0.918 in Brazil and Colombia, respectively, indicating a good predictive capacity of the models. The results of sensitivity analysis were consistent with the results of the original models, suggesting that our results are robust and not likely affected by modelling choices. Open in a separate windowFig. 1.The figure shows the pattern of observed Zika cases and predicted Zika cases using the model TSR lag (E, 1) + AC: lag (log (Y + 1), 1) in Brazil (a) and Colombia (b). (a) Brazil, basis TSR model with lag one of Zika query as a predictor and the lag one of log value of Zika case as controlling for the auto-correlation. (b) Colombia, basis TSR model with lag one of Zika query as a predictor and the lag one of log value of Zika case as controlling for the auto-correlation. The vertical line defines years 2015 and 2016.Our study explored the possibility to use Google Trends as a low-cost available Zika bio-surveillance system in developing countries. Our model was robust for the prediction of Zika in the two countries 1 week in advance, which can help to activate timely vector control by local authorities, and community-based preventive measures. It has been shown that Zika followed the same time period and geographic distribution of dengue and Chikungunya viruses in Brazil [13, 14, 15]. This is because of the concurrent transmission of these viruses by the same vector. In addition, the model can be used for monitoring other arboviral diseases. After current tropical urbanisation, increasing global transportation and global warming, there is a spread of Aedes spp. to other regions in the world [16]. With the presence of these vectors plus the circulating arboviruses in human blood, this will be adequate for another arboviral-emerging disease [16]. More arboviral diseases are expected in the literature to be the next global outbreak including Venezuelan equine encephalitis virus, Mayaro and Oropouche [17]. Venezuelan equine encephalitis virus had the same symptoms of Zika including rash, fever, headache, myalgia and arthralgia. The similarity between the symptoms of Zika, Chikungunya and Mayaro virus can lead to misdiagnosis of these diseases as Zika.[18] Theoretically, the similarity between viruses can result in an abnormal increase in search volume or at least change in the trend which will give an initial overview of the state of arboviral circulation. Hence, the model can reflect the status of arboviruses in these two countries. Yet, more research is needed to confirm this theory. With no research tool to discover the epidemic potential of these arboviruses, monitoring Zika can help predicting the status of arboviruses.Prediction of Zika cases using Google Trends was investigated in previous papers [19, 20]. They used the suspected cases of Zika, and correlated the Zika-related Google searches, Twitter microblogs and HealthMap news reports with the suspected cases of Zika in Colombia, El Salvador, Honduras, Venezuela and Martinique. In our study, however, we used the confirmed Zika cases for correlation and prediction which will give more reliable and consistent results. Another point of our study is the source of data. Our data were directly extracted from PAHO reports, which is considered far more reliable than Twitter microblogs and HealthMap. We tried these data before and we found an overlapped and duplicate data that were immediately discarded, and we decided to depend only on official reports provided in PAHO. For the statistical model, McGough et al. used LASSO regression model for prediction whereas, we used the PDLM. Phung et al. [7] validated the three different models comprising: SMR, SARIMA and PDLM for the prediction of dengue cases and they found that PDLM was the most accurate for prediction.In conclusion, we could use Zika query to predict Zika cases 1 week in advance, which provides a useful tool for monitoring and controlling Zika outbreaks.     

Does the Surgeon’s Experience Impact on Radiocephalic Fistula Patency Rates?

Establishing successful long-term hemodialysis access remains a major challenge. The primary aims of this study were to determine whether primary success and primary and secondary patency rates of a series of consecutive radio-cephalic fistulae (RCF) were affected by the experience of the surgeon. The secondary aims were to assess complications, and to compare results with patency rates from the literature. All native fistulae (AVF) created in our unit between January 1, 2002 and December 31, 2005 were analyzed retrospectively. The RCF were identified and divided into group A (RCF fashioned by the consultant surgeon), and group B (fashioned by the junior surgeons within the unit). Demographic characteristics, risk factors, primary success rate (patent fistula at discharge), and primary and secondary patency rates were compared between each group using chi-squared test. During this period, 552 AVF were created. Of the 195 RCF, there were 153 fistulae in group A and 42 in group B. Median follow-up was 22 months for both groups. There was no difference with regards to age, sex ratio, prevalence of diabetes, and cardiovascular disease. The primary success rate in group A and B was 94.2% and 81%, respectively (p < 0.01). Primary and secondary patency rates at 22 months were 80%, 93%* and 74%, 81%* in group A and B, respectively (*p < 0.025). Even within group B, these results compare very favorably with the published literature. These results suggest that the placement of a RCF should be performed by the most experienced member of a team dedicated to vascular access creation or at least under his supervision.     

Comparison of laparoscopic and open pyeloplasty in 100 patients with pelvi-ureteric junction obstruction

Background This study aimed to determine whether the morbidity and outcome rates for laparoscopic transperitoneal dismembered pyeloplasty are different from those for dismembered pyeloplasty, to analyze the learning curve of laparoscopic pyeloplasty, and to determine whether preoperative stent placement affects outcome. Methods For this study, 49 laparoscopic pyeloplasties (period 2000–2005) and 51 open pyeloplasties (period 1992–2003) were reviewed. Results Compared with open procedures, laparoscopic procedures were associated with a longer mean operating time (159 vs 91 min; p < 0.001), a shorter mean time to normal diet (38 vs 72 h; p < 0.001), and a similar mean hospital stay (5 days; p = 0.6). The operative complication rates were 17% for primary laparoscopic pyeloplasties and 24% for primary open pyeloplasties. The rates were higher for secondary procedures. The success rates for primary and secondary procedures were, respectively, 98% (41/42) and 57% (4/7) for laparoscopy and 96% (46/48) and 67% (2/3) for open surgery. Failed procedures showed no improvement in loin pain or obstruction. At the 6-month follow-up evaluation, 29% of the open surgery patients but none of the laparoscopic surgery patients reported wound pain. Conclusions The efficacy of laparoscopic pyeloplasty is equivalent to that of open pyeloplasty, with less wound pain at 6 months. The outcome for secondary procedures is inferior. There was a trend toward a reduction in complications and the conversion rates with time, suggesting that there may be a learning curve of approximately 30 laparoscopic pyeloplasty cases. Preoperative stent insertion did not seem to affect any objective measures of outcome for laparoscopic pyeloplasty.