Bioengineered 3D Human Kidney Tissue,a Platform for the Determination of Nephrotoxicity

The staggering cost of bringing a drug to market coupled with the extremely high failure rate of prospective compounds in early phase clinical trials due to unexpected human toxicity makes it imperative that more relevant human models be developed to better predict drug toxicity. Drug–induced nephrotoxicity remains especially difficult to predict in both pre-clinical and clinical settings and is often undetected until patient hospitalization. Current pre-clinical methods of determining renal toxicity include 2D cell cultures and animal models, both of which are incapable of fully recapitulating the in vivo human response to drugs, contributing to the high failure rate upon clinical trials. We have bioengineered a 3D kidney tissue model using immortalized human renal cortical epithelial cells with kidney functions similar to that found in vivo. These 3D tissues were compared to 2D cells in terms of both acute (3 days) and chronic (2 weeks) toxicity induced by Cisplatin, Gentamicin, and Doxorubicin using both traditional LDH secretion and the pre-clinical biomarkers Kim-1 and NGAL as assessments of toxicity. The 3D tissues were more sensitive to drug-induced toxicity and, unlike the 2D cells, were capable of being used to monitor chronic toxicity due to repeat dosing. The inclusion of this tissue model in drug testing prior to the initiation of phase I clinical trials would allow for better prediction of the nephrotoxic effects of new drugs.     

Biomechanics of the Cornea Evaluated by Spectral Analysis of Waveforms from Ocular Response Analyzer and Corvis-ST

Purpose

In this study, spectral analysis of the deformation signal from Corvis-ST (CoST) and reflected light intensity from ocular response analyzer (ORA) was performed to evaluate biomechanical concordance with each other.

Methods

The study was non-interventional, observational, cross-sectional and involved 188 eyes from 94 normal subjects. Three measurements were made on each eye with ORA and CoST each and then averaged for each device. The deformation signal from CoST and reflected light intensity (applanation) signal from ORA was compiled for all the eyes. The ORA signal was inverted about a line joining the two applanation peaks. All the signals were analyzed with Fourier series. The area under the signal curves (AUC), root mean square (RMS) of all the harmonics, lower order (LO included 1^st and 2^nd order harmonic), higher order (HO up to 6^th harmonic), CoST deformation amplitude (DA), corneal hysteresis (CH) and corneal resistance factor (CRF) were analyzed.

Results

The device variables and those calculated by Fourier transform were statistically significantly different between CoST and ORA. These variables also differed between the eyes of the same subject. There was also statistically significant influence of eyes (left vs. right) on the differences in a sub-set of RMS variables only. CH and CRF differed statistically significantly between the eyes of subject (p<0.001) but not DA (p = 0.65).

Conclusions

CoST was statistically significantly different from ORA. CoST may be useful in delineating true biomechanical differences between the eyes of a subject as it reports deformation.     

雌性大鼠结膜、睑板腺组织中肥大细胞的分布及形态学研究

目的:研究雌性大鼠结膜、睑板腺组织中肥大细胞的形态及分布.方法:用HE染色和改良甲苯胺蓝染色方法.结果:肥大细胞在穹隆结膜和睑结膜的固有层均有分布,细胞形态多样,大小不等,以圆形、卵圆形为多见,尚可见少量梭形、锥形;穹隆结膜的肥大细胞数量较睑结膜多.睑板腺组织中肥大细胞分布在被膜和相邻腺泡间质内,被膜内的肥大细胞以梭形、卵圆形为主,细胞长轴沿被膜平行排列;相邻腺泡间质中的肥大细胞以圆形、卵圆形或不规则形为主.可见少数相邻肥大细胞借胞体的突起互相连结.结论:为进一步探讨肥大细胞在结膜、睑板腺组织中的生物学作用以及眼表疾病的发生机制奠定了基础.     

Mathematical Programming Models for Determining the Optimal Location of Beehives

Farmers frequently decide where to locate the colonies of their domesticated eusocial bees, especially given the following mutually exclusive scenarios: (i) there are limited nectar and pollen sources within the vicinity of the apiary that cause competition among foragers; and (ii) there are fewer pollinators compared to the number of inflorescence that may lead to suboptimal pollination of crops. We hypothesize that optimally distributing the beehives in the apiary can help address the two scenarios stated above. In this paper, we develop quantitative models (specifically using linear programming) for addressing the two given scenarios. We formulate models involving the following factors: (i) fuzzy preference of the beekeeper; (ii) number of available colonies; (iii) unknown-but-bounded strength of colonies; (iv) probabilistic carrying capacity of the plant clusters; and (v) spatial orientation of the apiary.     

Extracellular BCL2 Proteins Are Danger-Associated Molecular Patterns That Reduce Tissue Damage in Murine Models of Ischemia-Reperfusion Injury

Background

Ischemia-reperfusion (I/R) injury contributes to organ dysfunction in a variety of clinical disorders, including myocardial infarction, stroke, organ transplantation, and hemorrhagic shock. Recent investigations have demonstrated that apoptosis as an important mechanism of cell death leading to organ dysfunction following I/R. Intracellular danger-associated molecular patterns (DAMPs) released during cell death can activate cytoprotective responses by engaging receptors of the innate immune system.

Methodology/Principal Findings

Ischemia was induced in the mouse hind limb by tourniquet or in the heart by coronary artery ligation. Reperfusion injury of skeletal or cardiac muscle was markedly reduced by intraperitoneal or subcutaneous injection of recombinant human (rh)BCL2 protein or rhBCL2-related protein A1 (BCL2A1) (50 ng/g) given prior to ischemia or at the time of reperfusion. The cytoprotective activity of extracellular rhBCL2 or rhBCL2A1 protein was mapped to the BH4 domain, as treatment with a mutant BCL2 protein lacking the BH4 domain was not protective, whereas peptides derived from the BH4 domain of BCL2 or the BH4-like domain of BCL2A1 were. Protection by extracellular rhBCL2 or rhBCL2A1 was associated with a reduction in apoptosis in skeletal and cardiac muscle following I/R, concomitant with increased expression of endogenous mouse BCL2 (mBCL2) protein. Notably, treatment with rhBCL2A1 protein did not protect mice deficient in toll-like receptor-2 (TLR2) or the adaptor protein, myeloid differentiation factor-88 (MyD88).

Conclusions/Significance

Treatment with cytokine-like doses of rhBCL2 or rhBCL2A1 protein or BH4-domain peptides reduces apoptosis and tissue injury following I/R by a TLR2-MyD88-dependent mechanism. These findings establish a novel extracellular cytoprotective activity of BCL2 BH4-domain proteins as potent cytoprotective DAMPs.     

Mass Transfer in the Cornea: II. Ion Transport and Electrical Properties of a Series Membrane Tissue

The electrical and active transport properties of isolated rabbit cornea are investigated by computer experimentation. The tissue is modeled as a series membrane system and the passive ion fluxes through it are described by the frictional formulation of irreversible thermodynamics. From short-circuit current (SCC) data, it is found that the epithelial sodium pump rate (P) is not appreciably changed when much of the sodium in the solution bathing the anterior corneal surface (concentration = c₁₁) is replaced by choline, with choline-free medium posteriorly. Simulations of open-circuited corneas, using the mean P computed from the SCC data, yield corneal and stromal potentials in agreement with experiment. The stromal fluid is calculated to become more hypotonic as c₁₁ is diminished, a result consistent with posttest measurements of the sodium content of experimental stromata. The apparent decrease in “bound sodium” which accompanies the reduction of c₁₁ is a result of the associated changes in steady stromal hydration; the epithelial sodium pump does not contribute to corneal deturgescence. The inclusion of a simple epithelial structure in the computations changes the value of P but affects neither its constancy nor the calculated behavior of the cornea under open-circuit conditions. A general algebraic relation among pump rates and ion fluxes in short-circuited series membrane systems bathed in complex media is derived and used to construct a relation between P and SCC for the cornea. This equation yields pump rates in good agreement with the computer results and is used to show that (a) P is independent of c₁₁ if d(SCC)/dc₁₁ is a constant related to the over-all corneal permeability to sodium, and (b) a Lineweaver-Burke plot of 1/SCC vs. 1/c₁₁ can appear to be linear at constant P.     

Graph-Based Data Selection for the Construction of Genomic Prediction Models

Efficient genomic selection in animals or crops requires the accurate prediction of the agronomic performance of individuals from their high-density molecular marker profiles. Using a training data set that contains the genotypic and phenotypic information of a large number of individuals, each marker or marker allele is associated with an estimated effect on the trait under study. These estimated marker effects are subsequently used for making predictions on individuals for which no phenotypic records are available. As most plant and animal breeding programs are currently still phenotype driven, the continuously expanding collection of phenotypic records can only be used to construct a genomic prediction model if a dense molecular marker fingerprint is available for each phenotyped individual. However, as the genotyping budget is generally limited, the genomic prediction model can only be constructed using a subset of the tested individuals and possibly a genome-covering subset of the molecular markers. In this article, we demonstrate how an optimal selection of individuals can be made with respect to the quality of their available phenotypic data. We also demonstrate how the total number of molecular markers can be reduced while a maximum genome coverage is ensured. The third selection problem we tackle is specific to the construction of a genomic prediction model for a hybrid breeding program where only molecular marker fingerprints of the homozygous parents are available. We show how to identify the set of parental inbred lines of a predefined size that has produced the highest number of progeny. These three selection approaches are put into practice in a simulation study where we demonstrate how the trade-off between sample size and sample quality affects the prediction accuracy of genomic prediction models for hybrid maize.DESPITE the numerous studies devoted to molecular marker-based breeding, the genetic progress of most complex traits in today''s plant and animal breeding programs still heavily relies on phenotypic selection. Most breeding companies have established dedicated databases that store the vast number of phenotypic records that are being routinely collected throughout the course of their breeding programs. These phenotypic records are, however, gradually being complemented by various types of molecular marker scores and it is to be expected that effective marker-based selection schemes will eventually allow current phenotyping efforts to be reduced (Bernardo 2008; Hayes et al. 2009). The available marker and phenotypic databases already allow for the construction and validation of marker-based selection schemes. Mining the phenotypic databases of a breeding company is, however, quite different from analyzing the data that is generated by a carefully designed experiment. Genetic evaluation data is often severely unbalanced as elite individuals are usually tested many times on their way to becoming a commercial variety or sire, while less performing individuals are often disregarded after a single trial. Furthermore, the different phenotypic evaluation trials are separated in time and space and as such, subjected to different environmental conditions. Therefore, ranking the performance of individuals that were evaluated in different phenotypic trials is usually a nontrivial task.Animal breeders are well experienced when it comes to handling unbalanced genetic evaluation data. The best linear unbiased predictor or BLUP approach (Henderson 1975) presented a major breakthrough in this respect, especially when combined with restricted maximum-likelihood or REML estimation of the needed variance components (Patterson and Thompson 1971). Somewhat later on, this linear mixed modeling approach was also adopted by plant breeders as the de facto standard for handling unbalanced phenotypic data. The more recent developments in genomic selection (Bernardo 1995; Meuwissen et al. 2001; Gianola and van Kaam 2008) and marker-trait association studies (Yu et al. 2006) are, at least partially, BLUP-based and are therefore, in theory, perfectly suited for mining the large marker and phenotypic databases that back each breeding program. In practice, however, the unbalancedness of the available genetic evaluation data often reduces its total information content and the construction of a marker-based selection model is limited to a more balanced subset of the data.As phenotypic data are available, genotyping costs limit the total number of individuals that can be included in the construction of a genomic prediction model. The best results will be obtained by selecting a subset of individuals for which the phenotypic evaluation data exhibits the least amount of unbalancedness. In this article we demonstrate how this phenotypic subset selection problem can be translated into a standard graph theory problem that can be solved with exact algorithms or less-time-consuming heuristics.In most plant and animal species, the number of available molecular markers is rapidly increasing, while the genotyping cost per marker is decreasing. Nevertheless, as budgets are always limited, genotyping all mapped markers for a small number of individuals might be less efficient than genotyping a restricted set of well-chosen markers on a wider set of individuals. One should therefore be able to select a subset of molecular markers that covers the entire genome as uniformly as possible. We demonstrate how this marker selection problem can also be translated into a well-known graph theory problem that has an exact solution.The third problem we tackle by means of graph theory is more specific to hybrid breeding programs where the parental individuals are nearly or completely homozygous. This implies that we can deduce the molecular marker fingerprint of a hybrid individual from the marker scores of its parents. As the phenotypic data are collected on the hybrids, genotyping costs can be reduced by selecting a subset of parental inbreds that have produced the maximum number of genetically distinct offspring among themselves. Obviously, the phenotypic data on these offspring should be as balanced as possible.Besides solving the above-mentioned selection problems by means of graph theory algorithms, we demonstrate their use in a simulation study that allows us to determine the optimum trade-off between the number of individuals and the size of the genotyped molecular marker fingerprint for predicting the phenotypic performance of hybrid maize by means of ɛ-insensitive support vector machine regression (ɛ-SVR) (Maenhout et al. 2007, 2008, 2010) and best linear prediction (BLP) (Bernardo 1994, 1995, 1996).     

Comparative Study of Injury Models for Studying Muscle Regeneration in Mice

Background

A longstanding goal in regenerative medicine is to reconstitute functional tissus or organs after injury or disease. Attention has focused on the identification and relative contribution of tissue specific stem cells to the regeneration process. Relatively little is known about how the physiological process is regulated by other tissue constituents. Numerous injury models are used to investigate tissue regeneration, however, these models are often poorly understood. Specifically, for skeletal muscle regeneration several models are reported in the literature, yet the relative impact on muscle physiology and the distinct cells types have not been extensively characterised.

Methods

We have used transgenic Tg:Pax7nGFP and Flk1^GFP/+ mouse models to respectively count the number of muscle stem (satellite) cells (SC) and number/shape of vessels by confocal microscopy. We performed histological and immunostainings to assess the differences in the key regeneration steps. Infiltration of immune cells, chemokines and cytokines production was assessed in vivo by Luminex^®.

Results

We compared the 4 most commonly used injury models i.e. freeze injury (FI), barium chloride (BaCl₂), notexin (NTX) and cardiotoxin (CTX). The FI was the most damaging. In this model, up to 96% of the SCs are destroyed with their surrounding environment (basal lamina and vasculature) leaving a “dead zone” devoid of viable cells. The regeneration process itself is fulfilled in all 4 models with virtually no fibrosis 28 days post-injury, except in the FI model. Inflammatory cells return to basal levels in the CTX, BaCl₂ but still significantly high 1-month post-injury in the FI and NTX models. Interestingly the number of SC returned to normal only in the FI, 1-month post-injury, with SCs that are still cycling up to 3-months after the induction of the injury in the other models.

Conclusions

Our studies show that the nature of the injury model should be chosen carefully depending on the experimental design and desired outcome. Although in all models the muscle regenerates completely, the trajectories of the regenerative process vary considerably. Furthermore, we show that histological parameters are not wholly sufficient to declare that regeneration is complete as molecular alterations (e.g. cycling SCs, cytokines) could have a major persistent impact.     

Clinical Risk Scoring Models for Prediction of Acute Kidney Injury after Living Donor Liver Transplantation: A Retrospective Observational Study

Acute kidney injury (AKI) is a frequent complication of liver transplantation and is associated with increased mortality. We identified the incidence and modifiable risk factors for AKI after living-donor liver transplantation (LDLT) and constructed risk scoring models for AKI prediction. We retrospectively reviewed 538 cases of LDLT. Multivariate logistic regression analysis was used to evaluate risk factors for the prediction of AKI as defined by the RIFLE criteria (RIFLE = risk, injury, failure, loss, end stage). Three risk scoring models were developed in the retrospective cohort by including all variables that were significant in univariate analysis, or variables that were significant in multivariate analysis by backward or forward stepwise variable selection. The risk models were validated by way of cross-validation. The incidence of AKI was 27.3% (147/538) and 6.3% (34/538) required postoperative renal replacement therapy. Independent risk factors for AKI by multivariate analysis of forward stepwise variable selection included: body-mass index >27.5 kg/m² [odds ratio (OR) 2.46, 95% confidence interval (CI) 1.32–4.55], serum albumin <3.5 mg/dl (OR 1.76, 95%CI 1.05–2.94), MELD (model for end-stage liver disease) score >20 (OR 2.01, 95%CI 1.17–3.44), operation time >600 min (OR 1.81, 95%CI 1.07–3.06), warm ischemic time >40 min (OR 2.61, 95%CI 1.55–4.38), postreperfusion syndrome (OR 2.96, 95%CI 1.55–4.38), mean blood glucose during the day of surgery >150 mg/dl (OR 1.66, 95%CI 1.01–2.70), cryoprecipitate > 6 units (OR 4.96, 95%CI 2.84–8.64), blood loss/body weight >60 ml/kg (OR 4.05, 95%CI 2.28–7.21), and calcineurin inhibitor use without combined mycophenolate mofetil (OR 1.87, 95%CI 1.14–3.06). Our risk models performed better than did a previously reported score by Utsumi et al. in our study cohort. Doses of calcineurin inhibitor should be reduced by combined use of mycophenolate mofetil to decrease postoperative AKI. Prospective randomized trials are required to address whether artificial modification of hypoalbuminemia, hyperglycemia and postreperfusion syndrome would decrease postoperative AKI in LDLT.     

On the Importance of Comprehensible Classification Models for Protein Function Prediction

The literature on protein function prediction is currently dominated by works aimed at maximizing predictive accuracy, ignoring the important issues of validation and interpretation of discovered knowledge, which can lead to new insights and hypotheses that are biologically meaningful and advance the understanding of protein functions by biologists. The overall goal of this paper is to critically evaluate this approach, offering a refreshing new perspective on this issue, focusing not only on predictive accuracy but also on the comprehensibility of the induced protein function prediction models. More specifically, this paper aims to offer two main contributions to the area of protein function prediction. First, it presents the case for discovering comprehensible protein function prediction models from data, discussing in detail the advantages of such models, namely, increasing the confidence of the biologist in the system's predictions, leading to new insights about the data and the formulation of new biological hypotheses, and detecting errors in the data. Second, it presents a critical review of the pros and cons of several different knowledge representations that can be used in order to support the discovery of comprehensible protein function prediction models.     

对淤血再灌注肠神经组织损伤机制的探讨

目的:建立大鼠肠淤血再灌注动物模型,探讨淤血再灌注肠神经组织损伤的机制,为临床相关疾病的诊断、治疗提供理论依据.方法:成年Wistar大鼠60只,雌雄不限,随机分为正常组、对照组和实验组,每组20只.实验组采用阻断门静脉1h后开放的方法建立大鼠小肠淤血再灌注模型,对照组只进行同样腹部手术操作但不夹闭门静脉,正常组不手术.6小时后取各组下腔静脉血,测定血清中超氧化物歧化酶(SOD)活性和丙二醛(MDA)的含量,然后处死动物,取距回盲部15厘米处肠管1厘米,采用伊红-苏木素(HE)染色观察肠粘膜组织形态学变化;用免疫组织化学方法观察正常组、对照组和实验组小肠壁肠神经组织中微管相关蛋白2(MAP-2)的表达情况.结果:HE染色可见,正常组、对照组为正常肠道管壁结构,实验组肠壁各层有比较明显的淤血、出血,小肠绒毛固有层水肿,黏膜上皮有脱落、坏死;实验组MAP-2的表达明显低于正常组及对照组(P＜0.05);与正常组及对照组相比较,实验组SOD活性明显降低(P＜0.05),MDA的含量则明显升高(P＜0.05).结论:肠淤血再灌注可能导致肠道神经元数量减少,其机制可能与肠淤血再灌注造成的自由基损伤和脂质过氧化有关.     

Uric Acid and the Prediction Models of Tumor Lysis Syndrome in AML

We investigated the ability of serum uric acid (SUA) to predict laboratory tumor lysis syndrome (LTLS) and compared it to common laboratory variables, cytogenetic profiles, tumor markers and prediction models in acute myeloid leukemia patients. In this retrospective study patients were risk-stratified for LTLS based on SUA cut-off values and the discrimination ability was compared to current prediction models. The incidences of LTLS were 17.8%, 21% and 62.5% in the low, intermediate and high-risk groups, respectively. SUA was an independent predictor of LTLS (adjusted OR 1.12, CI95% 1.0–1.3, p = 0.048). The discriminatory ability of SUA, per ROC curves, to predict LTLS was superior to LDH, cytogenetic profile, tumor markers and the combined model but not to WBC (AUCWBC 0.679). However, in comparisons between high-risk SUA and high-risk WBC, SUA had superior discriminatory capability than WBC (AUCSUA 0.664 vs. AUCWBC 0.520; p <0.001). SUA also demonstrated better performance than the prediction models (high-risk SUAAUC 0.695, p<0.001). In direct comparison of high-risk groups, SUA again demonstrated superior performance than the prediction models (high-risk SUAAUC 0.668, p = 0.001) in predicting LTLS, approaching that of the combined model (AUC 0.685, p<0.001). In conclusion, SUA alone is comparable and highly predictive for LTLS than other prediction models.     

Pathophysiological Responses in Rat and Mouse Models of Radiation-Induced Brain Injury

The brain is the major dose-limiting organ in patients undergoing radiotherapy for assorted conditions. Radiation-induced brain injury is common and mainly occurs in patients receiving radiotherapy for malignant head and neck tumors, arteriovenous malformations, or lung cancer-derived brain metastases. Nevertheless, the underlying mechanisms of radiation-induced brain injury are largely unknown. Although many treatment strategies are employed for affected individuals, the effects remain suboptimal. Accordingly, animal models are extremely important for elucidating pathogenic radiation-associated mechanisms and for developing more efficacious therapies. So far, models employing various animal species with different radiation dosages and fractions have been introduced to investigate the prevention, mechanisms, early detection, and management of radiation-induced brain injury. However, these models all have limitations, and none are widely accepted. This review summarizes the animal models currently set forth for studies of radiation-induced brain injury, especially rat and mouse, as well as radiation dosages, dose fractionation, and secondary pathophysiological responses.