Human embryonic kidney cells (HEK293) were introduced as the expression system to enhance the functionality of the recombinant β2-AR, and the attempt to detect β-agonists in swine urine using such approaches was accomplished unprecedentedly. In this article, a recombinant porcine β2-AR was produced in the inner membrane of HEK293 cells and purified from crude membrane protein by nickel-nitrilotriacetic acid affinity chromatography. After activity identification, the recombinant receptor was used in the development of direct competitive ELRA. Several parameters such as blocking buffer and blocking process were optimized and the performance of the system was determined. The IC50 concentrations of clenbuterol, salbutamol, and ractopamine were 34, 53 and 63 μg/L, and the average recovery rates were 68.2%, 60.3% and 65.5%, respectively. ELRA based on β2-AR shows a series of advantages such as safety, easy operation, and high efficiency, making it promising for the rapid screening of β-agonists in animal urine.A novel enzyme-linked receptor assay (ELRA) based on β Later, these compounds were also found to be efficient repartitioning agents capable of improving muscular mass, inhibiting fat synthesis, and reducing the fat deposition in carcasses at a dose 10 times that of the clinical dosage . HoweverTill date, the commonly used analytical methods of β-agonists are based on chromatographic techniques and immunoassays. There are various chromatographic methods developed for the confirmation of β-agonists, such as ultra-performance liquid chromatography tandem mass spectrometry , gas chr2-adrenergic receptor (β2-AR) is an emerging and powerful alternative screening technique capable of detecting a wide spectrum of similar compounds, including new molecules without any detailed information and low-level cocktails of compounds. β2-AR is a member of the large superfamily of G-protein-coupled receptors, which can be activated by adrenaline and synthetic β-agonists [2-AR has been achieved in all possible expression systems, including Escherichia coli [The receptor assay based on recombinant βagonists . The sitagonists  and the agonists –24 have hia coli –26, yeashia coli , insectshia coli , mammalihia coli –30, and hia coli –32. Howehia coli .2-AR prepared from bovine teat muscles [2-AR expressed in Chinese hamster ovary cells [E. coli [2-AR. The only related research was the one by G. Cheng et al [2-AR expressed in Sf 9 insect cells for the detection of β-agonists. In general, the structure of protein expressed in mammalian cell is similar to that of natural protein when compared with insect cell, which leads to the same performance in activity. To increase the affinity of the core component (receptor), the cell line of the most common mammalian cell, human embryonic kidney cells (HEK293), was used to express β2-AR in vitro. Moreover, the established ELRA was the first attempt to detect β–agonists in swine urine, laying the foundation for practical application and commercialization.At present, few of the radio-receptor assays have already been developed for multiresidue detection of β-agonists using natural membrane-bound β muscles , or recory cells , E. coli[E. coli , and NCB[E. coli . Howeverng et al , who devSV Total RNA Isolation System, Access reverse transcription polymerase chain reaction (RT-PCR) System, and T4 DNA ligase were purchased from Promega . The restriction enzymes of NcoI and XhoI were purchased from NEB . Competent cell NovaBlue, pTriEx-1.1 Hygro vector, and GeneJuice® transfection reagent were supplied by Novagen . Nickel–nitrilotriacetic acid (Ni–NTA) His Bindresin was purchased from Qiagen . HEK293 and horseradish peroxidase (HRP)-β-agonists were supplied by Beijing Kwinbon Biotechnology Co., Ltd . Dulbecco’s modified Eagle’s medium (DMEM) and fetal bovine serum (FBS) were obtained from Gibco . Anti-His monoclonal antibody and HRP-conjugated goat anti-mouse IgG were obtained from Beijing ComWin Biotech Co., Ltd .CBL, SAL, and ractopamine (RAC) were purchased from Sigma-Aldrich . HRP-β-agonists were gifts from Beijing Kwinbon Biotechnology Co., Ltd . All chemicals were of analytical grade without any further purification.2 agonist by API 5000 LC–MS/MS (AB SCIEX) according to the method of Chinese Ministry of Agriculture announcement NO. 1063. Then the supernatant was obtained after centrifugation at 10,000 rpm for 30 minutes, which was used to measure the recovery rates of β-agonists.Liver samples weighing 300 g were routinely collected from the slaughtered pig from a local meat processing factory. Urine samples were obtained from the pigs born into a strictly controlled livestock farm belonging to the China National Cereals, Oils, and Foodstuffs Corporation. They had been proved to be free of any β2-AR was amplified by RT-PCR, and the digestion sites of NcoI and XhoI were arranged at 5′ and 3′ ends of it. Primers were 5′-CATGCCATGGCAGGGCAGCCCGGGAACCGC-3′ (forward) and 5′-CCGCTCGAGTCACAGCATGGAGTCATTTGTACTACAGTTCCTCC-3′ (reverse) designed and synthesized on the basis of the published pig β2-AR gene sequence in Genebank (AF000134). The 1257-bp product that encoded 418 amino acids was inserted into pMD-18T vector to form the cloning plasmid pMD18-T-β2-AR. Recombinant expression plasmid of pTriEx-1.1 Hygro-β2-AR was constructed by restriction digestion and ligation with a C-terminal 6-His fusion tag. The plasmid can be used to test expression in E. coli, insect, and mammalian cells, as it contains the promoters of the above-mentioned 3 expression systems.About 30 mg fresh liver tissues were prepared for total RNA extraction using the Qiagen SV Total RNA Isolation System as per the manufacturer’s instructions. The quality of the isolated RNA was controlled by agarose gel electrophoresis, and concentration and purity were determined by ultraviolet spectrophotometer. The cDNA of porcine β2-AR was transformed into NovaBlue competent cells incapable of “blue/white” screening of recombinants by lacZ α complementation. Thus, selection of transformants was accomplished by plating on medium containing 50 μg/mL ampicillin. The recombinant plasmid was isolated from positive clones and then transiently transfected into HEK293 cells by GeneJuice transfection reagent after verification. The day before transfection, 4-5 × 106 cells were plated in complete growth medium (DMEM + 10% FBS) per 10 cm dish and incubated at 37°C (5% CO2) overnight. When the cell density reached 50-80% confluence, 1 mL of GeneJuice transfection reagent/plasmid mixture (10 μg DNA: 30 μL reagent/mL of DMEM medium) was added dropwise to cells and incubated at 37°C for 4 hours. Subsequently, the medium was replaced with complete growth medium and the incubation time  was optimized for protein expression.The recombinant plasmid of pTriEx-1.1 Hygro-βg for 20 minutes. The membrane pellet was suspended in 7 mL of incubation buffer  and centrifuged again as mentioned above. The final pellets were suspended in 1 mL of the above-mentioned buffer and aliquots of the suspension were frozen at −80°C or in liquid nitrogen.The membrane fraction was isolated from infected HEK293 cells as described earlier –40. Fourg for 30 minutes, after which the pellets were resuspended in hypotonic lysing buffer, and the suspension was centrifuged at 48,000g for 30 minutes again. After weighing, the membrane pellet was resuspended in solubilization buffer  using a homogenizer (30 strokes), and the suspension was mixed by nutation at 4°C for 1 hour. Then the solution was centrifuged at 48,000g for 30 minutes to remove any insoluble particulate material. Subsequent purification of β2-AR by Ni-NTA-affinity chromatography was carried out as per the manufacturer’s protocol. The concentration of midazolein elution buffer was optimized between 75 mM and 250 mM. The fractions were analyzed by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and Western blot  and their protein concentration was determined by bicinchoninic acid assay method. The final receptor was stored at −80°C after it was frozen in liquid nitrogen.Cell pellets were resuspended in10-fold volume of hypotonic lysing buffer, and the suspension was homogenized using a dounce homogenizer (20 strokes). The suspension was centrifuged at 48,000The principle of competitive ELRA was similar to competitive ELISA, which was described earlier . In brie0). The method of recovery was determined at 4 different levels using urine samples spiked with 3 β-agonists  at standards of 1, 10, 50, and 100 μg/L (5 replicates per concentration level), respectively. In trial test, we had tested 5 β-agonists including clenbuterol, salbutamol, ractopamine, carbuterol and terbutaline. The results showed that the aniline-type β-agonists clenbuterol had the highest affinity, next were the phenolic-type β-agonists, such as ractopamine and salbutamol, and yet carbuterol and terbutaline showed the lowest affinity. On the basis of the above study, we chose the 3 β-agonists of clenbuterol, salbutamol and ractopamine with higher affinity as the detection targets. Before adding standards, blank urine samples should be centrifuged for 10 minutes (5000 rpm) to obtain the supernatant.The inhibition concentrations  for inhibition of binding to the receptor by β-agonist were calculated from standard curves fitted by logarithm model, which plotted the β-agonist concentration versus the percentage of binding  was calculated as the mean + 3 standard deviations (SDs) based on 20 blank samples. For the assessment of precision on calibration graphs, coefficients of variation (CV) were determined by the analysis of the spiked CBL at 4 levels for 5 different analyses.Data reported were mean ± SD. Statistical analysis of the data and non-linear curve fitting were performed using Microsoft Excel 2010 and Origin 8.5, respectively.2-ARs is the premise for the development of receptor assay used for the multianalyte detection of β-agonists. In this article, there was increased production of porcine β2-AR in the inner membrane of HEK293 cells, which successfully retained its binding affinity after purification. The full-length cDNA of β2-AR was 1257 bp and was submitted to Genbank under the accession number KF023571.1. Sequencing revealed 4 mutations  in the gene compared with the published gene sequence (GenBank accession number AF000134). The mutation resulted in 3 amino acid changes , which might be attributed to gene diversity of β2-AR in different porcine breeds. Furthermore, all of the amino acids at the ligand binding sites were cloned correctly so as to lay a foundation for ensuring the function of the recombinant receptor.To obtain abundant active β2-AR was constructed successfully after verification of PCR ; however, the expression level indicated by SDS-PAGE and ELISA was very low (data not shown). Consequently, transient expression of β2-AR gene in HEK293 cells was explored and the approximate densities ranged from 17 to 23 pmol/mg of membrane protein. This expression level was still not completely satisfactory, but could basically meet the requirement of its exploration in the development of a new detection method. In order to obtain higher amount of the recombinant receptor, site-specific mutagenesis of β2-AR gene or optimization of expression vector would be useful for futher reseach. Western blot was probed with anti-His tag monoclonal antibody, and the results revealed that β2-AR expressed in HEK293 cells appeared in electrophoresis as diffuse bands with an apparent molecular mass in the range of 47-55 kDa .Recombinant plasmid of pTriEx-1.1 Hygro-β7-55 kDa , lane 1,7-55 kDa , lane 2.ncrement . To reac2+, the recombinant receptor was purified by Ni-affinity chromatography adopted with Ni-NTA produced by Qiagen. Unlike Ni-iminodiacetic acid chelated by Ni2+ with triple bond, the purification filler was chelated by Ni2+ with quadrivalent bond free from abscission in the maximal degree, so as to guarantee the protein of high purity. To realize the best eluting effect, the imidazole concentration in the elution buffer was optimized. In 2-AR was eluted completely when the imidazole concentration reached 250 mmol/L in the elution buffer. The purified β2-AR appeared in electrophoresis as 2 predominant forms of 52 kDa and 47 kDa, respectively  was significantly better than that of the latter (OD450 = 0.672). A possible explanation for this might be that higher temperature might lead to decreased β2-AR activity.The performance of ELRA was always influenced by some parameters, so the system was further optimized according to the OD2-AR. Moreover, different expressions and detection systems might also contribute to it.A typical calibration curve was constructed and illustrated in 2-AR has broad-spectrum binding affinities with β-agonist matter, but the binding affinities were changed along with the difference of structures. Both RAC and SAL exhibited high cross-reactivity to CBL, with values of 54.0% and 64.2%, respectively. Besides, the cross reactivity of RAC with SAL was 84.1%. These findings achieved the expected purpose of multiresidue determination. The LOD was evaluated by analyzing 20 randomly selected swine urine samples that were free of β-agonist. The LOD calculated as the mean + 3 SD was 4 μg/L.In theory, β2-AR-based ELRA system. The urine samples were respectively spiked with 1, 10, 50, and 100 μg/L of each β-agonist after centrifugation at 10,000 rpm for 30 minutes, after which the recovery was analyzed by the assay.The recovery studies were conducted to evaluate the accuracy and precision of the βThe average recovery rates of CBL, RAC, and SAL were 68.2% (varying from 52.2% to 82.4%), 60.3% (varying from 43.8% to 71.2%), and 65.5% (varying from 48.7% to 75.1%), respectively , which wAll β-agonists must not be found in the sample in accordance with relevant Chinese laws. The sensitivity of the commercial ELISA kits for β-agonists were in the range of 0.025–0.1 μg/L, and most of their recoveries were between 70%-110%. In contrast, the results obtained in this study still had a certain gap with them. However, the method showed the capability of the receptor to bind structurally different β-agonists which couldn't be matched by any current rapid detection method. Therefore, with the improvement, this assay would be a valuble tool to be applied for the screening of β-agonists residues in animal production.2-AR expressed in HEK293 cells was developed for simultaneous detection of 3 β-agonists, including CBL, RAC, and SAL. The method had a lower limit detection of 4 μg/L. Although the average recovery rates obtained for the 3 β-agonists (60.3%-68.2%) was unsatisfactory, it revealed the potential of the method to be used to detect known or unknown compounds with agonistic activity in different matrix. To achieve this goal, many aspects should be further investigated such as the increased activity of recombinant receptor as well as the optimization of reaction system and sample pretreatment.A novel receptor assay based on recombinant βIn conclusion, this approach is simple, quick, and environment friendly, showing a potential application for rapid screening of a group of β-agonists for the control of the illegal abuse of β-agonists in animal production.S1 Document2-AR, B. Original date of optimization of the coating buffer, C. Original date of optimization of blocking buffer, D. Original date of establishment of calibration curve, and E. Original date of recovery of β–agonist sdetermined by developed ELRA).This document contains five Tables Click here for additional data file.


Despite the popularity of financial compensation as a means for addressing trust violations, the question whether (more) money can indeed buy trust back remains largely unexplored. In the present research, we focus on the role of violation type and compensation size. The results of a scenario study and a laboratory experiment show that financial compensation can effectively promote the restoration of trust for transgressions that indicate a lack of competence. Conversely, for transgressions which signal a lack of integrity, financial compensation is not an effective tool to repair trust. Moreover, our findings indicate that for both violation types, overcompensation has no positive effects on top of the impact of equal compensation. These findings therefore show that when it comes to trust, money cannot buy everything. The issue of trust has been on the forefront of research agendas across a variety of disciplines in social sciences including psychology, management, organizational behavior, economics, and law –3. This A common understanding has grown that trust is a psychological state comprising the intention to accept vulnerability based upon positive expectations of the intentions or behavior of another . The prePrior research has demonstrated that when trust gets violated, people are motivated to seek explanations for the violation . In thisResearch has revealed that competence and integrity violations are indeed distinct bases for determining trustworthiness –32, whicPrevious studies of perpetrators’ attempts to restore broken trust largely focused on verbal accounts such as apologies, promises, excuses, and denials –28, 34. Prior research has indicated that financial compensation can be an effective tool for restoring a victim’s trust , 36–40. When the violation can be ascribed to a lack of competence, the wrongdoing is not indicative that the perpetrator is a bad person, because anyone can display such a low performance level under certain circumstances –19, 27. On the contrary, when the violation can be attributed to a lack of integrity, the wrongdoing signals that the perpetrator is a bad person, because only people who fall short on certain moral values will display such dishonest behavior –19, 27. Taken together, it can thus be predicted that the relationship between compensation size and the degree of trust repair is moderated by violation type see . SpecifiOur hypotheses were tested in two studies. Study 1 was a scenario study in which participants imagined how they would react to a perpetrator who offered no compensation, equal compensation, or overcompensation to his or her victim after inflicting a competence or an integrity violation. A key benefit of this method is that it allows the manipulation of different violation types and compensation sizes to be easily operationalized. However, because scenario studies are entirely hypothetical, we could solely measure participants trusting intentions. Moreover, an important disadvantage of this method is that reading a scenario is different from actually experiencing a specific event. Therefore, in Study 2 we conducted a laboratory experiment which allowed us to test whether our results could be cross-validated by actual trusting behavior. An advantage of lab experiments is that it is conducted in a well-controlled environment, and therefore more accurate measurements are possible. A downside of this method, however, is that it provides an artificial situation.www.fppw.ugent.be/FACN/AEP_engels.pdf), formal ethical approval was not needed for the conductance of these studies.Both studies were conducted under the supervision of an employee of Ghent University. In accordance with the ethical protocol of Ghent University, the participants gave their written consent, and were debriefed about the purpose and content after the study. Since the present studies were in line with the “General Ethical Protocol for Scientific Research at the Faculty of Psychology and Educational Sciences of Ghent University” , recruited through Amazon Mechanical Turk, completed a scenario study in exchange for $0.5. Fourteen participants (9.9%) failed on our check questions and were thus excluded from further analyses. We employed a 2 (violation type: competence versus integrity) × 3  between-subjects design.A total of 141 US citizens . The scores on these six items were combined into a general measure of trust towards Person A .Participants’ trusting intentions towards Person A were measured using the six item trust scale developed by Desmet and colleagues . Particunot at all, 7 = very much). To investigate whether the compensation size manipulation was successful, we asked participants: “Did Person A offer Person B a financial compensation?” (no / yes) and “If yes, how does this compensation relate to the inflicted loss?” .To examine whether the violation type manipulation was successful, we asked participants: “To what extent could the financial loss be attributed to a lack of competence? (competence manipulation check)” and “To what extent could the financial loss be attributed to a lack of integrity? (integrity manipulation check)” , participated in a lab experiment in exchange for course credits. Again, a 2 (violation type: competence versus integrity) × 3  between-subjects design was employed.A total of 137 undergraduate psychology students at Ghent University, Belgium . In the integrity condition, participants observed Player A solving all puzzles during the first stage, but only a few in the second stage . In either case, this poor performance of Player A in the second stage meant that when the outcomes of the task were unveiled after the completion of both stages, Player A had solved less puzzles for Player B than vice versa. As a result of this poor performance, Player A had inflicted a monetary loss on Player B, as Player B received €3 less than he or she had earned for Player A.In response to this outcome, participants observed electronic communication between the two players, in which Player A blamed the poor outcomes that he or she attained for Player B to “poor skill at this type of task”. In light of Player A’s performance for him- or herself during stage 1 , this claim was truthful in the competence condition (where Player A attained poor outcomes in both stages), making it a competence violation; but false in the integrity condition , making it an integrity violation. Note that the puzzles were equally difficult in both stages. Although making a lesser effort for someone other than oneself can be justified, lying about this makes it a clear integrity violation.Upon completion of the task, the outcomes of both stages were unveiled, exposing Player A’s actual performance level, and the veracity of his or her claim. In response, the manipulation of compensation size was implemented, with Player A providing Player B no compensation (in the no compensation condition), a compensation of €3 , or a compensation of €9 (in the overcompensation condition).Player A / Player B).We employed a behavioral measure of participants’ trust in Player A. Participants learned that after completion of the task, they had to take part in a second study, in which they would perform an (unrelated) dyadic task. It was explicitly stated that this additional task did not require mathematical skills. For this unrelated task, they were offered the choice between two possible interaction partners: Player A, whom they had just observed (choice which reflects trust in Person A) or Player B, the other player who was victimized and subsequently compensated by Player A (choice which reflects no trust in Person A). Specifically, we asked participants: “Which player would you prefer to complete the second study with?” . Next, to investigate whether the compensation size manipulation was successful, we asked participants: “To what extent did Player A offer Player B a lot of extra money?” .To examine whether the violation type manipulation was successful, we asked participants: “To what extent shows Player A’s behavior competence? (competence manipulation check)” and “To what extent shows Player A’s behavior integrity? (integrity manipulation check)” than participants in the integrity condition , F = 236.51, p < .001, η2p = .66. Similarly, participants in the integrity condition attributed the violation more to be a lack of integrity  than participants in the competence condition , F = 351.81, p < .001, η2p = .74. Moreover, a 2 (violation type) × 2 (compensation size) ANOVA on the compensation size manipulation check showed that participants in the overcompensation condition rated the compensation as larger  than participants in the equal compensation condition , F = 394.93, p < .001, η2p = .84. For both the violation type manipulation checks and the compensation size manipulation check, the other main and interaction effects were non-significant .We tested the effectiveness of the violation type manipulation using a 2 (violation type) × 3 (compensation size) ANOVA for both violation type manipulation checks. The results showed that participants in the competence condition attributed the violation more to a lack of competence  = 216.53, p < .001, η2p = .64, and compensation size, F = 26.47, p < .001, η2p = .30. These main effects were qualified by a significant interaction between violation type and compensation size, F = 11.10, p < .001, η2p = .16. To test our hypotheses, this significant interaction effect was further explored using simple effects tests (with Bonferroni correction for multiple comparisons). A 2 (violation type) × 3 (compensation size) ANOVA on the trust scale showed significant main effects of violation type, F = 34.89, p < .001, η2p = .37. As predicted by Hypothesis 1a, after a competence violation, equal compensation  and overcompensation  were both more effective for repairing trust than no compensation . Further, in agreement with Hypothesis 1b, the difference between overcompensation and equal compensation was non-significant after a competence violation (p = .709). Within the integrity condition, there is no significant effect of compensation size, F = 1.85, p = .162, η2p = .03. In agreement with Hypothesis 2a, after an integrity violation, equal compensation  was not more effective than no compensation  to repair trust (p = .25). Moreover, as predicted by Hypothesis 2c (and opposite to the predictions made in the competing Hypothesis 2b), overcompensation  was not more effective than no compensation (p = .416) and equal compensation (p > .999) to repair trust after an integrity violation.Within the competence condition, there was a significant effect of compensation size, M = 3.26, SD = 1.39) than participants in the integrity condition , F = 29.77, p < .001, η2p = .19. Similarly, the analysis on the integrity manipulation check revealed that participants in the integrity condition indicated less that Player A’s behavior demonstrates integrity  compared to participants in the competence condition , F = 16.75, p < .001, η2p = .11. Finally, the analysis on the compensation size manipulation check showed a significant main effect of compensation size, F = 27.74, p < .001, η2p = .30. A post hoc test (Bonferroni) showed that participants indicated more often that Player A offered Player B a lot of extra money in the overcompensation condition  than in the equal compensation condition , as well as in the equal compensation condition compared to the no compensation condition . For all three manipulation checks, the other main and interaction effects were non-significant .We tested the effectiveness of our manipulations using a 2 (violation type) × 3 (compensation size) ANOVA for each manipulation check. The analysis on the competence manipulation check showed that participants in the competence condition indicated less that Player A’s behavior demonstrates competence (p = .044). The percentages of participants who chose to complete the next task with Player A (choice which reflects trust) per condition are displayed in A logistic regression analysis with violation type, compensation size, and the interaction of violation type × compensation size as predictor variables and trusting behavior as dependent variable yielded a significant overall interaction effect , a significant interaction effect between violation type and the dummy contrasting overcompensation to no compensation , and a non-significant interaction effect between violation type and the dummy that contrasted overcompensation with equal compensation . To further explore these interaction effects, we relied on simple slope analyses.Logistic regression analyses using these dummy coded variables yielded an almost significant interaction effect between violation type and the dummy that contrasted equal compensation with no compensation , or an overcompensation compared to no compensation . As predicted by Hypothesis 1b, the difference between equal compensation and overcompensation was non-significant after a competence violation . Moreover, in agreement with Hypothesis 2a, participants in the integrity condition were not more inclined to trust Player A when he or she provided Player B an equal compensation compared to no compensation . Finally, in accordance with Hypothesis 2c (and contrary to the predictions made in the competing Hypothesis 2b), after an integrity violation, participants were not more inclined to trust Player A when he or she provided Player B an overcompensation compared to no compensation , or an overcompensation compared to an equal compensation .In line with Hypothesis 1a, participants in the competence condition were more inclined to trust Player A when he or she provided Player B an equal compensation compared to no compensation . This result is congruent with traditional justice models which suggest that when the harm is unintended − which is often the case with competence violations − financial compensation “evens the score” and is thus viewed as an appropriate way to redress the inflicted harm –51. MoreWith regard to integrity violations, however, equal compensation is not more effective than no compensation (Hypothesis 2a). These results corroborate studies in restorative justice research which revealed that people do not consider the provision of a compensation as satisfactory when the harm is inflicted intentionally –53, whicIn sum, our findings are in line with the cognitive view on trust which holds that the presence of trust is a result of a complex interplay between many different factors –46. MoreAn important strength of the present research is that it provides an experimental method by means of which competence and integrity violations can experimentally be induced in a lab context. Thereby, it provides an important contribution to the trust literature, which until now has solely relied on scenarios to understand the impact of violation type on the restoration of trust. Our research thus provides a much needed and easy applicable paradigm that can actually manipulate violation type experimentally. Moreover, this method can also be employed to study how victims, rather than observers, may respond to trust repair strategies after competence and integrity violations. While this perspective has been overlooked in extant research on such violations –19, 27, A limitation of the present research, however, is that we only focused on situations in which the harm was financially compensable. It could be expected that in other contexts − in which the loss is framed in non-financial terms–financial compensation will even be less effective to promote trust repair. Future research should thus consider the context in which trust is impaired. However, it is important to note that despite the compensation friendly context in our studies, compensation size effects  failed to occur.S1 DatafileData file with raw data of Study 1.(TXT)Click here for additional data file.S2 DatafileData file with raw data of Study 2.(TXT)Click here for additional data file.S1 DatasetDataset with recoded data of Study 1.(SAV)Click here for additional data file.S2 DatasetDataset with recoded data of Study 2.(SAV)Click here for additional data file.S1 QuestionsQuestions given to participants in Study 1.(DOCX)Click here for additional data file.S2 QuestionsQuestions given to participants in Study 2.(DOCX)Click here for additional data file.S1 ScenariosScenarios given to participants in Study 1.(DOCX)Click here for additional data file.S1 SyntaxSyntax file of Study 1.(SPS)Click here for additional data file.S2 SyntaxSyntax file of Study 2.(SPS)Click here for additional data file.


Resources for home care rehabilitation are limited, and many home care clients who could benefit do not receive rehabilitation therapy. The interRAI Contact Assessment (CA) is a new screening instrument comprised of a subset of interRAI Home Care (HC) items, designed to be used as a preliminary assessment to identify which potential home care clients should be referred for a full assessment, or for services such as rehabilitation. We investigated which client characteristics are most relevant in predicting rehabilitation use in the full interRAI HC assessment.We applied two algorithms from machine learning and data mining ― the LASSO and the random forest ― to frequency matched interRAI HC and service utilization data for home care clients in Ontario, Canada.Analyses confirmed the importance of functional decline and mobility variables in targeting rehabilitation services, but suggested that other items in use as potential predictors may be less relevant. Six of the most highly ranked items related to ambulation. Diagnosis of cancer was highly associated with decreased rehabilitation use; however, cognitive status was not.Inconsistencies between variables considered important for classifying clients who need rehabilitation and those identified in this study based on use may indicate a discrepancy in the client characteristics considered relevant in theory versus actual practice. Rehabilitation interventions that target older adults have the potential to generate widespread health benefits. The goal of these interventions is to help individuals restore their functional ability or to maintain their residual functional capacity. A contemporary meta-analysis of randomized controlled trials demonstrated significant short and long term improvements associated with inpatient geriatric rehabilitation related to functional status, admission to nursing homes and mortality . In addiThe elevated risk of hospitalization due to functional decline within this population , 12 has Home care has been described as the “next essential service”  and is aIn order to bridge the gaps between service need, provision and use, it is essential to identify the key factors that predict successful rehabilitation as well as understand how this limited resource is currently being allocated. Due to the challenging nature of older rehabilitation clients and considerable variability even within specific diagnostic categories , 11, 22,Recently, the interRAI Contact Assessment   was creaAn interesting question that arises, therefore, is whether these expert beliefs match actual clinical practices. The aims of our study were to use tools from machine learning and data mining to identify items from the interRAI HC instrument that are most predictive of whether a client will receive rehabilitation services in actual clinical practice, and to compare them with items selected by experts for the RA. By contrasting the items on these two lists, we are able to describe and highlight key differences between expert opinion and actual clinical practice.The present study is a component of a larger multidisciplinary health research program – “InfoRehab” - that aims to improve the understanding and use of health information for musculoskeletal rehabilitation clients . A Ethics clearance was granted for this study from the University of Waterloo Office of Research Ethics (ORE Reference #14795). This research involved a secondary analysis of data collected for clinical and administrative purposes. The RAI-HC and CA data are part of current practice, and are collected as part of the intake process or as part of a routine re-assessment of ongoing needs. The identities of the individuals in the provincial RAI-HC dataset were unknown to the researchers. No individual consent was obtained, as it would be impractical for the province’s publicly-funded home care organizations to attempt to obtain written consent from over 130,000 clients. No personally identifying information was available in the dataset used for analysis. Social Insurance Number or provincial Health Card Number were not available in the dataset, nor any other identifiers that would enable the researchers to identify an individual. The identifiers included in the dataset are provided by the province’s Community Care Access Centres (which coordinate publicly funded home care services in the province) and are of one of two types: either an internal case record number used only for internal tracking, and not a ‘real world’ identifier, or an encrypted health card number. These numbers are consistent so we can track longitudinal cases, but they are not useful in personally identifying individuals or linking to other information that would do so. Results of the research are reported only at an aggregate level, and no individual-level information is reported.The international research consortium interRAI is a 32-country, collaborative, not-for-profit network of researchers and clinicians focused on the development and application of innovative health assessment systems that support evidence-informed decision making at all levels of health and social service delivery. The interRAI consortium has developed 12 comprehensive assessment instruments specifically designed for use with complex populations across the health care continuum , 25, 31.The CA is a standard preliminary assessment developed for use as a screening tool during adult home care intake that guides initial service planning and decision making. It was specifically designed to be completed during initial contact with potential home care clients and includes a subset of items (approximately 50) from the RAI-HC. The purpose of the CA is to identify patients who require a full assessment using the RAI-HC, those with urgent needs for nursing or personal support services, and those for whom a referral for rehabilitation may be appropriate . The devTo inform referrals for rehabilitation, the CA contains a decision tool called the RA. Development of the algorithm was based on case manager ratings of who they felt would be a candidate for rehabilitation services, as well as actual receipt of physiotherapy or occupational therapy following screening. Decision tree models were used to construct a decision support algorithm that combines the findings of various domains into a single summary measure that can be used to inform decision making. It is based on a small number of variables relating to a recent decline in ability to perform activities of daily living, functional and mobility status, and cognitive impairment. Figure This figure and related notes are adapted from the interRAI Contact Assessment (CA) Form and User’s Manual . The RehThe data consisted of initial RAI-HC (version 2.0) assessments for 135,184 newly admitted home care clients evaluated in the community between October 2005 and March 2008. These data were linked to service records that contained information regarding home care service usage during the same period. Rehabilitation (outcome variable) was indicated by whether the client received physiotherapy or occupational therapy within 6 months of their initial assessment, which was coded as a binary indicator.Of the available items in the RAI-HC, a total of 239 were utilized as potential predictors in the analysis. Items were dropped if they were regarded by the authors to be clearly unrelated to rehabilitation. Items that were clear prompts for rehabilitation  were also dropped. The items used were primarily treated as categorical variables as indicated in the instrument; only number of falls (K5), number of visits to the hospital (P4A), emergency room and/or emergent care use (P4B and P4C), number of medications (Q1) and age were treated as continuous variables.The statistical analysis was performed in two main steps.Considering that the CA was designed to be used during first contact with potential home care clients to determine those who would receive a full assessment (and who would not), and that the RAI-HC is used with those who are expected to or have received services for at least 60 days (long stay clients), the clients in the RAI-HC database may be different from the CA target population. Therefore, items deemed most predictive of service utilization based on long stay clients who received the RAI-HC assessment may not serve as an ideal benchmark for the broader CA target population of home care referrals. To address this issue, we created a frequency-matched dataset , by takiThere are several machine learning techniques that can be used for selecting key predictors from large datasets with many variables – recently, these techniques have been utilized in a variety of health-related applications –37. The To describe what the LASSO does, we use standard mathematical notation,yi ∈ {0, 1} is a binary indicator for the outcome; xij is the j -th predictor variable for subject i; and βj is the j -th regression coefficient. Let l denote the log-likelihood function based on modeling each yi as a Bernoulli random variable with parameter, pi≡ Pr (yi = 1), and linking pi to the predictors xi1, xi2, ⋯, xid by the logistic equation,where l, the LASSO accomplishes this by solving the following optimization problem:Unlike classical logistic regression that estimates the regression coefficients by maximizing l is the log likelihood function, andwhere β1, β2, ⋯, βd and forces some of them to become zero. Consequently, only the predictors with regression coefficients that are nonzero will be “selected” by the model that the LASSO produces.is a penalty function that shrinks the regression coefficients Fewer predictors will be selected (more coefficients will become zero) as the non-negative parameter, λ (which controls the amount of shrinkage), is increased. The choice of λ largely controls how many predictors are selected, and therefore must be carefully justified.rank their relative importance. In the toy example of Fig. As we have described elsewhere , to circN = 135,184). We first drew 100 frequency-matched samples (see earlier Section on “Matched Sampling”) from the data set, S1, S2, …, S100, each of size n = 10, 000. Let r denote the rank of variable j based on sample Sk. For each variable j, we then calculated its average rank over the 100 samples,To obtain a more stable ranking, we followed an “ensemble approach” , 44, insσ(j), the standard deviation of as well as σ(j), this time relying on the variable importance measure (VIM) from Breiman’s random forest (RF) algorithm [r. The RF algorithm essentially fits a collection of decision trees, which model interaction effects automatically [marginal evaluations of the would-be deterioration in the model’s overall performance had the values of a predictor been permuted [As an indirect way to account for potential interaction effects, we recalculated lgorithm  to definatically . The varpermuted  ― the rar as described above. The algorithm implemented in “grplasso” is actually a variation of the original LASSO, called the “group LASSO” [We used two R packages, “grplasso” and “randomForest”, to compute p LASSO” . We usedTable Most points were close to the 45° line, which meant that, for the majority of variables, the two sets of average ranks were in good agreement whether the original data or the frequency-matched data were analyzed. This was especially true for the top ranked variables (bottom left corner). There were a small number of outlying points. In particular, the variables - chemotherapy (P2F), cancer present in past five years (J1X) and nurse monitoring less than daily (P2W) - appeared more important on the frequency-matched data set, whereas the variable - caregivers believe client is capable of increased functional independence (H7B) - appeared more important on the original data set. These differences may be attributable to the greater medical complexity expected in longer term home care clients assessed with the RAI-HC. From this point on, we focused on the frequency-matched data set only.Figure Variables in the upper-right region of the plot received relatively low average ranks from both the LASSO and the RF, and therefore were not considered to be important predictors of rehabilitation use in this population. Variables in the upper-left region were more important for the LASSO than for the RF; these included pain intensity disrupts usual activity (K4C), chemotherapy during last 7 days P2F), shopping difficulty (H1FB), and daily nurse monitoring (P2Y). Similarly, those in the lower-right region were more important for the RF than for the LASSO; these included time since last hospital stay (CC4), approximate age (AGE), shopping performance (H1FA), ADL-dressing lower body (H2F), ADL-locomotion outside of home (H2D), and ADL-bathing (H2J). These differences most likely came about because we did not consider any interaction effects in the LASSO approach, whereas in the RF approach interaction effects were automatically considered. The variables in the lower-left region are the most relevant ones to this investigation. They were identified to be strong predictors of rehabilitation service use by both the LASSO and the RF. Results about these variables are summarized in Table F, shoppiTable Six of the ten items identified using the machine learning approach were related to the clients’ ability to ambulate – a client having increased falls frequency (K5), in danger of falling (K6B) or using an assistive device  as their primary mode of locomotion  were all positive indicators of rehabilitation service use. The four additional items selected by the machine learning approach included diagnosis of cancer over the past five years (J1X), client or primary caregiver feels that client would be better off in another living environment (O2B), and nurse monitoring less than once per day (P2W) as negative indicators; and clients belief that they can improve (H7A) as a positive indicator of rehabilitation service use. The list of top ranked variables using the machine learning techniques did not include any items specifically related to the client’s cognitive status.This study builds on our previous explorations using machine learning algorithms to predict and to understand rehabilitation service use , 34, witSince the CA and the RAI-HC are targeted at different populations of home care clients, we sampled a sub-population of the RAI-HC clients in such a way that matched their general characteristics to the population receiving the CA. To investigate the effect of this matching exercise, we repeated the LASSO part of our analysis using the original (unmatched) RAI-HC data. There were no major discrepancies among the top variables identified, other than a few items that may be attributable to the greater medical complexity expected in longer-term home care clients that are typically assessed by the RAI-HC. This matching approach could be used with other health information system databases to compare clients who are on a caseload with those in a screening database.Overall, we found that actual clinical practices and expert opinions do overlap to a certain degree. For example, there is clear agreement that the abilities to climb stairs and to move about are two key factors that determine whether a patient is recommended for rehabilitation services. But we also found some differences. Some of the variables identified by the machine learning algorithms as predictive of rehabilitation use – such as falls frequency, unsteady gait, or limits in going outdoors – could be considered in future versions of the rehabilitation algorithm. This might lead to improved rehabilitation outcomes – for example, rehabilitation interventions that prevent falls could also reduce adverse outcomes such as injury, hospitalization or death.We found that neither the LASSO nor the RF specifically identified cognitive impairment as a strong predictor associated with rehabilitation service utilization. The RA, on the other hand, applies cognitive impairment through the Self Reliance Index – in one of the later splits in the decision tree – which results in higher priority access to rehabilitation for those with cognitive impairment. Others have classified cognitive impairment as a significant negative predictor for rehabilitation potential and/or achievement in older patients . A ratioThe variable, ‘diagnosis of cancer in the past 5 years’, was one of the variables selected in this study as a strong predictor for not receiving rehabilitation in home care. On the other hand, this variable is not included in the decision algorithm on the CA. The fact that cancer was a top predictor for not receiving rehabilitation services may indicate that rehabilitation is seen as inappropriate for some persons with a terminal illness such as cancer. While additional research is needed in this area, especially for older patients with advanced disease , there iAs an indirect way to explore for potential interaction effects, we compared the rank order of the items using two different machine learning techniques. As described in the “A limitation of this study is that, because the full RAI-HC is not administered to all clients who receive the CA, we cannot perform variable selection using data collected for the broader set of clients targeted by the CA at the point of intake into home care. In other words, variables such as the IADLs may have greater discriminant validity in a more heterogeneous population with greater diversity in functional status. We believe that our use of the frequency-matched dataset was the best available strategy to address this potential bias; however, some systematic differences between the two populations may still remain. Nonetheless, we feel these analyses provide useful insight into how actual clinical practices differed from the results of the process used to develop the RA. Research using a larger matched sample and follow-up with patients receiving both the CA and the RAI-HC could be used to further explore the issues discussed in this paper.While there is considerable evidence for the feasibility and effectiveness of home-based rehabilitation for older persons, many who could benefit do not receive needed rehabilitation services. Standardized assessment instruments, such as the RAI-HC used with home care clients in Ontario and other jurisdictions, have the potential to guide appropriate care planning and allocation of limited rehabilitation resources. In this study, we explored patient characteristics that predict rehabilitation services use in home care clients and compared them to what the interRAI consortium has defined as important characteristics to consider when addressing need in this group. We found that, while the two sets of characteristics agreed with each other to a certain extent, there are also some notable differences, especially with regard to cognitive status and cancer diagnosis. Our findings also suggest that data mining methods, such as the LASSO and the random forest, can play an important role in selecting important client characteristics for care planning.


YB-1 gene. MiR-190a contributes the human prostate cancer cell growth through AR-dependent signaling. Moreover, we examined the expression of miR-190a and observed a significant decrease in human prostate cancers. Reduced expression of miR-190a was inversely correlated to AR levels of prostate cancer patients, and patients with higher miR-190a expression in their tumor have improved tumor-free survival. Taken together, our findings identified a biochemical and functional link between miR-190a with reduced expression in advanced prostate cancer, YB-1 and AR signaling in prostate cancer.Prostate cancer at advanced stages including metastatic and castration-resistant cancer remains incurable due to the lack of effective therapies. MiR-190a belongs to the small noncoding RNA family and has an important role in breast cancer metastasis. However, it is still unknown whether miR-190a plays a role in prostate cancer development. Herein, we first observed AR/miR-190a/YB-1 forms an auto-regulatory negative feedback loop in prostate cancer: miR-190a expression was down-regulated by AR activation; YB-1 functions are as an AR activator; miR-190a inhibited AR expression and transactivation through direct binding to 3′UTR of  Prostate cancer is one of the most common malignancies in men in western world, accounting for about 240,000 new cases and 29,000 deaths in the United Stated in 201334Studies have shown that a class of small non-coding RNAs (miRNA) plays a pivotal role in prostate cancer by acting as oncogene or tumor suppressor6In this study, we observed inverse correlation of miR-190a expression and PSA levels in prostate cancer, where patients with higher miR-190a expression in their tumors had improved disease-free survival. In human prostate cancer cells, AR/miR-190a/YB-1 signaling forms an auto-regulatory negative feedback loop, where miR-190a contributes to the human prostate cancer cell growth through AR-dependent signaling. Taken together, our findings reveal a tumor suppressive mechanism for AR/miR-190a/YB-1 negative feedback loop in prostate cancer progression.To determine the crucial miRNAs involved in prostate cancer progression, we first performed Cancer micro-RNA Array that contains 95 different miRNAs in LNCaP and LNCaP/DHT cells. Differentially expressed miRNAs with at least 2-fold alternation were selected . ConsistAndrogen is known to regulate gene expression through binding to AR, which then binds to androgen response element (ARE) in the promoter region of AR target genes. We analyzed the region between 2,000-bp and 1-bp upstream of miR-190a promoter and identified two putative half site for ARE . In ordeNext, we analyzed the levels of miR-190a in several most commonly used prostate cell lines. miR-190a expression in AR-positive cell lines was detected at lower levels  when compared to AR-negative cell lines  . These rIn order to determine whether miR-190a regulates AR signaling, we established stable cell lines  through lentiviral transduction. As shown in A previous study showed how YB-1 promotes AR transcription via binding to the Y-box in the AR promoter regionWhen AR is activated by the endogenous androgenic ligands, the ligand-receptor complex translocates from cytoplasm into the nucleus, and in association with co-regulatory factors, binds to specific androgen responsive elements in the regulatory regions of AR target genesAs shown above, AR-LBD is required for binding to YB-1. Moreover, AR ligand, DHT modulates binding of YB-1 to AR in the nucleus by promoting AR transactivation. Base on these observations, our data confirms function of YB-1 as an AR activator.Our previous study demonstrated that miR-190a inhibited YB-1 and AR expression. In order to validate bioinformatics analysis, which indicated that only YB-1 was a potential target of miR-190a , we perfA previous study showed that YB-1 is upregulated during prostate tumor progression, and it promotes AR transcription via binding to the Y-box in the AR promoter regionTo determine the functional significance of miR-190a in regulating the growth of AR-positive prostate cancer cells, we conducted MTT and colony formation assays. Overexpression of miR-190a in LNCaP, C4-2 and 22Rv1 cells inhibited cell growth .AR has been reported to promote cell proliferation and plays a critical role in the development of prostate cancer5in vivo, we established C4-2 cells stably expressing miR-190a or miR-SC. These cells were implanted subcutaneously into immune-deficient mice and monitored for tumor growth. Overexpression of miR-190a significantly reduced both the tumor size and the tumor weight -miRNA probes we first examined miR-190a expression in 36 cases of human prostate cancer and 8 adjacent normal tissue samples obtained from tissue array. miR-190a exhibited whole cytoplasmic distribution with stronger staining in adjacent normal tissue (n = 8) when compared to prostate cancer tissues (n = 36) (Through (n = 36) . The Kap(n = 36) . Prostatin situ hybridization.Analysis of the abundance of miR-190a and AR by Taqman quantitative real-time PCR (qRT-PCR) in prostate cancer specimens showed a significant decrease of miR-190a levels in prostate cancer tissues (n = 40) when compared to normal prostate tissue (n = 20) . miR-190Androgen receptor (AR), a member of nuclear transcription factor family, promotes tumor growth and survival-related gene transcription via androgen-mediated signaling pathways in castration-sensitive prostate cancer (CSPC)Prior studies have shown that a class of miRNAs plays a pivotal role in prostate cancer by acting as oncogene or tumor suppressor through androgen/AR signaling6miR-190a is known to be downregulated in aggressive neuroblastoma (NBL), and overexpression of miR-190a leads to inhibition of tumor growth and prolonged dormancy periods in fast growing tumorsYB-1 is a member of the DNA- and RNA-binding protein family with an evolutionarily ancient and conserved cold shock domainWhen AR activated by the endogenous androgenic ligands, the ligand-receptor complex translocates from cytoplasm into the nucleus, and in association with co-regulatory factors, binds to specific androgen responsive element in the regulatory regions of AR target genesThe progression of prostate cancer normally advances from castration-sensitive to castration-resistant after long-term androgen deprivation therapy10YB-1 gene. In addition, YB-1 functions as a novel AR activator. Overexpression of miR-190a contributes to prostate cancer growth in CSPC and CRPC. Our results, for the first time, provide compelling evidence further rationalizing the targeting strategy to disengage the functional interaction between miR-190a and AR signaling in clinical practice to treat prostate cancer.In summary, we identified a biochemical and functional link between miR-190a, YB-1 and AR signaling in prostate cancer. AR/miR-190a/YB-1 signaling forms an auto-regulatory negative feedback loop in prostate cancer, where androgen/AR inhibits miR-190a expression through direct binding to the ARE in the miR-190a promoter, and miR-190a inhibits AR expression and activity through binding 3′-UTR of Human prostate cancer cells LNCaP, C4-2, PC-3, DU-145, 22Rv1 and benign prostate epithelial cell line, RWPE-1, were purchased from the American Type Culture Collection (ATCC)in situ hybridization was performed as describeHuman prostate cancer tissue arrays were purchased from Biomax (PR956B). A collection of 40 different kinds of fresh-frozen prostate cancer tumor specimens and 20 tumor normal adjacent tissues were from Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology. All tumor samples were collected immediately after the surgical removal and snap-frozen in liquid nitrogen. Using antisense locked nucleic acid (LNA)-modified probes , Total RNAs were extracted with Trizol reagent according to the manufacturer’s instruction . To determine the mRNA levels of AR, YB-1 and PSA, total RNAs were reversely transcribed by iScript cDNA Synthesis Kit , respectively, according to the manufacturer’s instructionsCells infected with miR-190a and miR-SCR were seeded into 96 well plates in normal growth medium, and cell growth was measured daily by MTT assays using 3--2, 5-diphenyltetrazolium bromide.Cells were plated in triplicates in 3 ml of 0.3% agarose (sea plaque) in complete growth medium overlaid on 0.5% agarose base, also in complete growth medium. Two weeks post cell seeding, colonies were visualized after staining with 0.04% crystal violet in methanol for 1–2 hrs. The colonies more than 50 μm in diameter were counted using an Omnicon 3600 image analysis system.5 cells in a 24-well cell culture plate on the day prior to transfection with Superfect according to the manufacturer’s protocol . For reporter gene assays, a dose-response was determined in each experiment with 50 and 200 ng of expression vector and promoter reporter plasmids (0.5 μg). Luciferase activity was normalized for transfection efficiency using β-galactosidase reporter as an internal control. The fold effect of expression vector was determined with comparison to the value of the empty expression vector cassette and statistical analyses were performed using the t-test35Cells were seeded at a density of 1 × 10Immunoprecipitation (IP) and Western blot assays were conducted in LNCaP, C4-2, 22Rv1 and PC3 and 293 T cells as indicated. Cells were pelleted and lysed in buffer  supplemented with a proteaseinhibitor cocktail (Roche Diagnostics). Antibodies used for Western Blot Analysis. IP and Western blot were: AR (H280) , YB-1, YB-1, c-myc , c-myc , anti-Gal4  and PSA .ChIP assays were performed according to the protocol of the Upstate Biotechnology as described (31). The primer sequence for miR-190a promoter is are P1(−1 ~ −168): 3′CTGGTGCATGTGCTGACAGT-5′, 3′-GAAAAGGCATCCAGGTTTGA-5′; P2(−229 ~ −395): 3′-TCAGGAAGAGTTTGGGGAGA-5′, 3′-ATCCCAGGCAAAAAGTGATG-5; P3(−411 ~ −584): 3′-GCACCAAAATCAGCCAGTCT-5′. 3′-CCCCAGAAGGAACACACATC-5′; P4(−684 ~ −820): 3′-CCATCGTATTAGGAAGGGTGA-5′, 3′-TGCCCTATTAGGCACAAAAA-5′; P5(−834 ~ −988): 3′-GCTGAAGTAGGCCTGTGAGG-5′, 3′-AGGAACCCTCAAACAAAGGA-5′. Polyclonal antibody to AR  was used for IP and normal IgG was used as a negative control. One tenth of original DNA was used as an input control.−ΔCt). The array plate also included the U6 transcript as a normalization signal.Cancer micro-RNA Array assays were conducted as described37C4-2 cells stably expressing miR-190a and miR-SCR were injected subcutaneously into 4-6-week-old castrated mal nude mice purchased from Beijing HFK Bio-Technology.co., LTD. Tumor growth was measured using a digital caliper every 5 days for 4–5 weeks. Tumor weight was measured when mice were sacrificed on day 32 after cell implantation. Immunohistochemical staining under the standard procedure as described before.These studies were carried out in accordance with the guidelines of the Tongji Hospital Institute for Animal Studies. All the experimental protocols were approved by the Institutional Animal Care and Use Committee at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology.in vitro experiments were performed in triplicate, and all data were represented as mean ± SD. Statistical analyses were conducted using Student’s t test and Pearson Correlation Coefficient. The significance of each value was determined when P value was less than 0.05.All statistical analyses were performed using EXCEL 2010 . All How to cite this article: Xu, S. et al. The inhibitory effects of AR/miR-190a/YB-1 negative feedback loop on prostate cancer and underlying mechanism. Sci. Rep.5, 13528; doi: 10.1038/srep13528 (2015).


Diagnosis of acute lung allograft rejection is currently based on transbronchial lung biopsies. Additional methods to detect acute allograft dysfunction derived from plasma and bronchoalveolar lavage samples might facilitate diagnosis and ultimately improve allograft survival. This review article gives an overview of the cell profiles of bronchoalveolar lavage and plasma samples during acute lung allograft rejection. The value of these cells and changes within the pattern of differential cytology to support the diagnosis of acute lung allograft rejection is discussed. Current findings on the topic are highlighted and trends for future research are identified. Lung transplantation is an established treatment option for selected patients with advanced lung disease , 2. HoweA major risk factor for the development of CLAD is the occurrence of repeated episodes of higher grade acute lung allograft rejection (AR) –8. AcuteAcute lung AR is suggested by a decline of lung function that is not explained by other reasons such as infection, left-sided heart failure or weight gain. The gold standard for AR diagnosis is the analysis of serial transbronchial lung biopsies (TBB) . This meVarious attempts have been undertaken to validate alternative diagnostic methods, including clinical and/or radiological criteria. However, these patterns only allow delayed and retrospective diagnosis  and are Alternatively, alterations in bronchoalveolar lavage (BAL) and plasma samples have been examined for specific changes during acute lung AR. Sampling cells by BAL bronchoscopy is less invasive than TBB and allows for repetitive harvesting. Although rare, complications of BAL have been observed and include fever, wheezing or bleeding . Being aSince both BAL and plasma samples have the advantage of being fast and less invasive in comparison with TBB, we reviewed here the evidence for the use of BAL and plasma samples for detection of acute lung AR in lung transplant recipients. This article aims to provide answers to the question whether changes in cell count or percentage of cellularity appear to be specific for acute lung AR and whether they precede clinical symptoms and decline in lung function. An illustrative algorithm for the likelihood of acute AR depending on BAL and plasma cellularity is provided in Fig. More specifically, this review summarises the experimental and clinical evidence on the differential cellularity profiles in BAL and plasma samples during acute lung AR. Current findings on this topic are discussed and areas for future research are identified.We searched the electronic databases Medline , EMBASE  and Web of Science Core Collection . Medical subject heading (MeSH) terms included „bronchoalveolar lavage fluid/cytology“ in combination with “graft rejection” and “lung transplantation” for BAL and “blood/cytology” or “plasma/cytology” in combination with “graft rejection” and “lung transplantation” for data on plasma samples. Publications were eligible if they provided information on cytological changes in BAL or peripheral blood during acute lung AR. We considered articles published in English until June 1, 2015. This included experimental studies, prospective and retrospective clinical studies, review articles and case reports. No other restrictions were applied. We then selected those articles that fulfilled our inclusion criteria. Additionally, we scanned the references of all selected articles to find additional literature that was related to our research question. Finally, 54 papers were eligible to be included in our review. An ordered list of the type and number of articles included is provided in Table We then evaluated the selected articles and compiled an extensive table, listing every cell type, the reference that mentioned these parameters as well as the observed data. While writing the review article more papers were drawn on for background information. Each author reviewed the entire document and provided input before the final manuscript was completed.AR has been recognised as an interacting process of T cell subpopulations, macrophages, granulocytes and B cells , 20. CelBronchoalveolar lavage allows recovering cells and proteins present on the epithelial surface of the lower respiratory tract . OriginaThe technique of retrieving BAL samples might vary between different centres . Differe+ regulatory cells, CD8+ T cells or T cells with other clusters of differentiation have not been included in this review article although findings have emerged on these recently [For this review article, we selected cells that are routinely analysed in clinical diagnostics, including neutrophils, lymphocytes, eosinophils, basophils, monocytes and alveolar macrophages (AM). Natural killer cells (NK cells) and B lymphocytes (B cells) as subgroups of lymphocytes were considered separately. In order to keep this paper close to clinical practice, T cell subgroups such as CD25recently –30. TablNeutrophils are an integral part of innate immunity and constitute the largest portion of circulating white blood cells . They exIn the context of BAL fluid analysis neutrophils have emerged as key mediators of a recently identified CLAD phenotype, which has been defined as neutrophilic reversible allograft dysfunction (NRAD). NRAD is characterised by BAL neutrophilia (≥15 %) without any other signs for infection. These patients respond well to long-term macrolide therapy . A recenClinical data on neutrophils during acute lung AR point toward an increase in neutrophils in BAL samples , 37–40. Moreover, neutrophils in BAL have been shown to correlate with severity of acute AR in a number of studies , 32, 41.In contrast to these findings, other studies found no association between neutrophil percentage and acute lung AR , 43 or aIn addition to these observational data, a gene-based diagnostic classifier for acute lung AR has been proposed. Patil and colleagues found that increased BAL neutrophil gene expression was associated with acute AR. The number of neutrophils in biopsies also increased during rejection episodes, yet this characteristic was no better discriminator than the gene-based classifier .Little data exists on blood neutrophils during acute AR. In one prospective study blood neutrophil count was not associated with acute lung or cardiac allograft rejection. Yet, a lacking increase of neutrophils in the presence of raised eosinophils was considered a specific and early sign for clinically relevant rejection .Early BALymphocytes are mediators of the adaptive and innate immune system and consist of several subtypes including T lymphocytes (T cells), B cells and NK cells. Most studies focusing on lymphocytes make conclusions on lymphocytes in general and do not address the subtypes. T cells have been identified as the most important mediators of acute AR and are thus of special interest in transplantation medicine .Experimental data points towards an increase in BAL lymphocytes during acute lung AR: Data obtained from an experimental rejection model using rats , 48 and p < 0.05) to diagnose acute AR. Specificity for lymphocytes for infection was higher during months 1 to 6 (86 %), while sensitivity remained unchanged [In a clinical setting, all reviewed articles found increased BAL lymphocyte counts during acute AR, however at different time points , 38, 42.nchanged , 38. In However, increased levels of BAL lymphocytes show an insufficient sensitivity to diagnose acute AR , 50, 51.Increased BAL lymphocytes are not exclusively found in acute AR but have been observed during other post-transplant complications , 40. MorThe correlation between BAL lymphocyte count and rejection grade in TBB show inconsistent findings. Several studies found no association between lymphocytosis in BAL and histopathological rejection grade , 50, 51 Data about lymphocytes in peripheral blood differs considerably between studies , 38, 40.Eosinophils are bone marrow-derived granulocytes and account for less than 5 % of circulating leukocytes. Upon stimulation by interleukins (IL) such as IL-4, IL-5 and IL-13 eosinophils produce reactive oxygen species and toxic granule proteins (e.g. eosinophil cationic protein and major basic protein) . EosinopRaised BAL eosinophils were found to be a marker of early lung AR in rats . This reIncreased numbers of eosinophils in peripheral blood precede clinically significant AR in both pulmonary and cardiac transplant recipients. This was first shown in a retrospective study with 58 pulmonary and 56 cardiac allograft recipients. The mean maximum eosinophil count in the three days before treated lung AR was 140/μl, which was significantly higher than when rejection or infection were absent. An increase in blood eosinophils of ≥ 9/μl in allograft recipients had a sensitivity of 72, a specificity of 75 and a modest PPV of 51 % for the detection of acute AR during postoperative month one. Trull and co-workers therefore suggested measuring blood eosinophils daily during the first postoperative month to detect acute AR at an early stage .+ cells > 8 %). If all of these features were absent, acute AR was unlikely with a NPV of > 96 % [Eosinophils have appeared in BAL fluid of patients with good outcome after lung transplantation, making up < 1 % of total cells . Severalf > 96 % . Besidesf > 96 % , 65, 66.A relation between the amount of eosinophils in BAL and severity of acute AR has been reported , 67. In Moreover, eosinophilia ≥ 2 % in BAL seems to correlate with worse outcome after lung transplantation . In a muNot all investigators reported increased BAL eosinophils during acute lung AR , 42, 70.Several mechanisms for the pathogenesis and activation of eosinophils during acute AR exist. Eosinophils injure the graft by releasing cytotoxic agents such as eosinophil cationic protein , 68. ActEosinophils in allografts are not confined to AR but have been found in transplant recipients with viral, fungal and bacterial infection . High eoBasophils are circulating granulocytes representing less than 1 % of blood leukocytes in steady-state conditions of non-transplanted individuals . Upon stThe role of basophilic granulocytes in lung transplant recipients has not yet been elaborated in detail. However, mild peripheral blood basophilia and their presence in BAL fluid have been associated with acute AR in clinical studies , 37, 38.Monocytes are the largest cells in blood and account for 3–8 % of all blood cells. Upon recruitment to different tissues, monocytes undergo maturation into macrophages . AlveolaBAL inflammatory macrophages were raised in an experimental rejection model using rats. While acute AR occurred in all allotransplants on day two after transplantation macrophage number and percentage increased later in the rejection process, on day six .Results on BAL macrophages are not consistent. AM percentage in BAL shows a trend towards reduction during acute AR , 40, 42.AM counts have also been shown to correlate inversely with the histopathological grade of rejection , 50. In NK cells are a subset of lymphocytes with characteristics of both innate and adaptive immunity . Their rFew studies analysed NK cell count during acute lung AR, however these have yielded encouraging results , 85. AddNK cells have been reported to decline in BAL of lung transplant patients during episodes of acute lung AR , 85. NK These findings are in line with newer research on NK cells: NK cells appear to be of pathogenic importance in kidney rejection by responding to missing host MHC ligands , 88 and B cells are lymphocytes expressing immunoglobulin on their surface, among other markers . In theiThe studies included and discussed in our critical review have several limitations. Most studies have a retrospective design and report single centre experience . AdditioMore specifically, relying on one “unique” BAL cellular profile to diagnose acute AR is questionable since BAL cellular patterns differ according to the time-onset of acute AR episodes. As shown in the study by Tikkanen and colleagues, acute AR occurring early (1–180 days) after transplantation was associated with increased lymphocytes whereas elevated neutrophils were found in later-occurring rejection (>180 days after transplantation) .In addition, many studies compared BAL and plasma samples to the histological pattern in TBB. However, whereas a direct relationship between the BAL profile and the severity of grade A (cellular) and B (lymphocytic bronchiolitis) rejection on concurrent TBB samples has been shown , some stFurthermore, in most studies surrogate markers have been analysed separately, impeding further conclusions. Calculating a composite score, which takes into account several inflammatory cells, might provide further insights but is beyond the scope of this descriptive review. Taken together, these factors limit comparability, conclusions and direct translation of these findings in a clinical setting.Specific changes in differential cytology of BAL and plasma samples during acute lung AR have been identified. Data from blood analysis remain weak with only few studies assessing cytological changes in peripheral blood during acute AR.The illustrative algorithm in Fig. BAL differential cytology might be more powerful in the context of CLAD than of AR since the presence of distinct cells (e.g. eosinophils) was found to be associated with worse outcome and shorter CLAD-free survival. Future research should, among others, focus on a composite score that includes several inflammatory cells in order to enhance the diagnostic value of BAL cell composition.In conclusion, when used within the clinical context, BAL and serum samples might be useful to assist in decision-making and alter the likelihood for the presence or absence of acute lung AR. Of note, BAL and serum samples are no substitutes for transbronchial biopsies in the evaluation of lung function decline in lung transplant recipients.AM, alveolar macrophages; AMR, antibody-mediated rejection; AR, allograft rejection; B cells, B lymphocytes; BAL, bronchoalveolar lavage; BOS, bronchiolitis obliterans syndrome; CLAD, chronic lung allograft dysfunction; IFN-γ, interferon gamma; IL, interleukin; IQR, interquartile range; MHC, major histocompatibility complex; NK cells, natural killer cells; NPV, negative predictive value; NRAD, neutrophilic reversible allograft dysfunction; PPV, positive predictive value; T cells, T lymphocytes; TBB, transbronchial lung biopsy; TNF-α, tumour necrosis factor alpha; PPV, positive predictive value; T cells, T lymphocytes; TBB, transbronchial lung biopsy; TNF-α, tumour necrosis factor alpha


Elizabethkingia meningoseptica is a gram-negative, obligate aerobic bacterium in the family Flavobacteriaceae  meningosepticum, and in 1994 it was reclassified in the genus Chryseobacterium . In 2005, it was placed in the new genus Elizabethkingia.Named for Elizabeth O. King, a bacteriologist at the US Centers for Disease Control who studied meningitis in infants, eriaceae . King na


Allergic rhinitis (AR) is primarily caused by a T helper cell (Th)1/Th2 imbalance. In a murine AR model of a previous study, the serum ovalbumin (OVA)-sIgE concentration was high, whereas microRNA (miR)-135a was lowly expressed in the nasal mucosa. The abnormal expression pattern of miR-135a coincided with highly expressed endogenous factors, including GATA binding protein (GATA)-3 and interleukin (IL)-4, and lowly expressed factors, including T-box expressed in T cells (T-bet) and interferon (IFN)-γ. We hypothesized that miR-135a may play an important role in immune regulation in AR mice. In the present study, AR was induced by OVA in the mice. Two groups of the AR mice were treated with a miR-135a mimic and a mimic control, respectively. The serum and nasal mucosa were collected for analysis. Following miR-135a application, the serum OVA-sIgE concentration was significantly reduced. In the nasal mucosa, the expression levels of miR-135a were higher, the mRNA and protein expression levels of GATA-3 and IL-4 were lower, and the expression levels of T-bet and IFN-γ were higher. The miR-135a corrected the Th1/Th2 imbalance in the AR mice. Findings of this study may provide a basis for novel genetic treatments in addressing allergic diseases. The key mechanism which causes AR is the differentiation imbalance of two T-helper cell (Th) subsets, Th1/Th2, which are CD4+ helper T lymphocytes, as Th2 presents a higher immune response than Thl (Allergic rhinitis (AR) is a common disease that affects the quality of life and induces other risk factors, including asthma, termed as than Thl . T-box ethan Thl –13. As Gthan Thl –12,14–15MicroRNAs (miRNAs or miRs) are a class of small molecule RNAs, which play an important role in the regulation of gene expression at the post-transcriptional level . MiRNAs In a previous study, an online bioinformatics program, which predicts and analyzes immune target genes of miRNAs, indicated that a single type of miRNA may be able to control immune regulation through differentiation divergence favoring Th2 cells via GATA-3 target . As yet,ad libitum.Specific pathogen-free male BALB/c mice, aged 5–6 weeks with a weight of 15–18 g, were purchased from the Experimental Animal Center, Renmin Hospital of Wuhan University . The animal experiments were approved by the Animal Ethics Committee, Renmin Hospital of Wuhan University. All mice were maintained under standard conventional conditions including mild light (dark), temperature (18–22°C) and humidity (50–60%), with food and water The mice were injected intraperitoneally for primary sensitization and then they were treated intranasally following primary sensitization for local stimulation and secondary immunization. The AR group mice were injected intraperitoneally with 400 μl saline containing 10 μg ovalbumin (OVA) and 2 mg aluminum hydroxide  on days 1 and 8 to promote drug sensitization. Between days 15 and 21, the mice were administered intranasal drops of 20 μl saline containing 200 μg OVA each day to continuously challenge AR. At the same time, the control group mice were injected intraperitoneally or treated intranasally with an identical dose of saline .The mimic and mimic control group mice were intraperitoneally sensitized and intranasally challenged with OVA, similar to that of the AR group. On days 14, 16, 18 and 20, the mimic group was treated intranasally with 20 μl saline containing 50 μg miR-135a mimic to intervene with the incidence of AR, while the mimic control group was treated under identical conditions with an arbitrary sequence as previously described . The mimBlood was extracted from the orbital venous plexus of the anesthetized mice 24 h after the last nasal cavity challenge. The blood was left to stand for 20 min prior to 10 min centrifugation at 700 × g. The upper serum was collected and stored at −20°C. The serum concentration of OVA-sIgE was detected by an ELISA .TargetScanMouse, an online bioinformatics program, was used analyze the 3′ UTR of GATA-3 and the miR-135a 3′ UTR, and thus to predict the consequential pairing of the target region and miRNA  accordin−ΔΔCT to represent the relative mRNA expression levels of the target genes. The primer sequences used were: F, 5′-CCTGGACCCAACTGTCAACT-3′ and R, 5′-AACTGT GTTCCCGAGGTGTC-3′ for T-bet; F, 5′-CTGGAG GAGGAACGCTAATG-3′ and R, 5′-AGATGTGGCTCA GGGATGAC-3′ for GATA-3; F, 5′-ACTGGCAAAAGGATG GTGAC-3′ and R, 5′-GACCTGTGGGTTGTTGACCT-3′ for IFN-γ; F, 5′-TCAACCCCCAGCTAGTTGTC-3′ and R, 5′-AAATATGCGAAGCACCTTGG-3′ for IL-4; F, 5′-CAC GATGGAGGGGCCGGACTCATC-3′ and R, 5′-TAAAGA CCTCTATGCCAACAC AGT-3′ for β-actin.The anesthetized mice were euthanized by cervical dislocation under sterile conditions. The center seam of the nose from the vertical hard palate of each mouse was excised and sectioned. The nasal septum and mucous membrane of each bilateral nasal cavity lateral wall was eviscerated and placed into sterile vials for storage in liquid nitrogen. The RNA extraction with TRIzol  was performed strictly according to the manufacturer’s instructions, and the product was treated with DNase digestion. In the nasal mucosa of the control, mimic, mimic control and AR groups, Bulge-Loop™ miRNA qPCR was used to detect the miR-135a expression levels, and U6 small nuclear RNA was used as an internal control . SYBR-Green qPCR was used to detect the relative mRNA expression levels of T-bet, GATA-3, IFN-γ and IL-4. β-actin was used as an internal control . The qPCR results used 2Western blot analysis was used to detect the relative protein expression levels of T-bet, GATA-3, IFN-γ and IL-4 within the nasal mucosa of the control, mimic, mimic control and AR groups. The relative protein expression levels of the target genes were indicated on the gray scale ratios between each target gene and GAPDH .Data are presented as the mean ± standard deviation. SPSS statistical software, version 17.0  was used for statistical analysis. Differences between groups were compared using Student’s t-test or analysis of variance. P<0.05 was considered to indicate a statistically significant difference.Following intraperitoneal sensitization and intranasal challenge by OVA, the AR group mice elicited symptoms similar to the clinical occurrence of AR, i.e., nose scratching, sneezing and watery nasal discharge. The control group mice elicited no symptoms. The serum OVA-sIgE concentration in the AR group was significantly elevated compared with that of the control group . The mRNBy using Mouse, it was found that the sequence 5′-AAGCCAU-3′ of the 3′ UTR of GATA-3, at the sites of 222–228, had specific binding sites with the 3′-UUCGGUA-5′ of miR-135a. It was also found that the targets of the miR-135a 3′ UTR included the 3′ UTR of GATA-3. The expression levels of miR-135a in the nasal mucosa of the AR mice were significantly lower than those of the control group .Following intervention with a miR-135a mimic and a mimic control, it was demonstrated that the serum OVA-sIgE concentration in the mimic group was significantly lower than that in the mimic control and AR groups, but with no significant difference to that of the control group. No significant difference in the serum OVA-sIgE concentrations between the mimic control and AR groups was observed. However, the mimic control and AR groups exhibited significantly higher concentrations of serum OVA-sIgE compared with that of the control group .By using a miR-135a mimic and a mimic control in the nasal mucosa of the mice, the expression levels of miR-135a were significantly higher, and the expression levels of GATA-3 and IL-4 were significantly lower in the mimic group than those of the mimic control and AR groups. However, no significant difference in the levels of them between the mimic and control groups was observed. The expression levels of miR-135a, GATA-3 and IL-4 in the nasal mucosa of the mimic control group were not significantly different from those of the AR group. However, the mimic control and AR groups had significantly lower expression levels of miR-135a and higher expression levels of GATA-3 and IL-4 compared with those of the control group .It was also demonstrated that the mRNA and protein expression levels of T-bet and IFN-γ in the nasal mucosa of the mimic group were significantly higher than those of the mimic control and AR groups. Between the mimic and control groups, the difference in the levels of T-bet and IFN-γ was not significant. The mRNA and protein expression levels of T-bet and IFN-γ in the nasal mucosa of the mimic control group were not significantly different compared with those of the AR group. The mimic control and AR groups exhibited significantly lower expression levels than those of the control group .AR is a disease with symptoms including an itchy nose, sneezing and watery nasal discharge. In the present study, AR mice were induced by intraperitoneal injection and intranasal OVA exposure. Compared with those of the control group, the mice in the AR group elicited symptoms of nose itching, sneezing and watery nasal discharge, along with an increase in the concentration of the serum OVA-sIgE. A previous studied showed that the mRNA expression levels of T-bet and IFN-γ were lower, whereas the mRNA expression levels of GATA-3 and IL-4 were higher in the nasal mucosa of AR mice compared with those of normal mice, thus inhibiting the Th1 immune response and enhancing the Th2 immune response, respectively . The mRNTo investigate the immune regulatory effects of miRNAs on Thl/Th2 cell differentiation, a previous study used computer bioinformatics to forecast and analyze possible target genes of miRNAs in the immune system and identified that the Th2-specific transcription factor GATA-3 was a potential candidate, owing to its ability to combine with one or several miRNAs . The resOverall, this study showed that a miR-135a mimic corrected the imbalance of Th1/Th2 in the nasal mucosa of AR mice. The mimic also reduced the serum OVA-sIgE concentration. Thus, treatment with miR-135a was effective in AR and may provide a basis for miRNA application towards novel genetic treatments for allergic diseases.