Escherichia coli in turkey flocks, building on the Canadian Integrated Program for Antimicrobial Resistance Surveillance methodology for farm-level AMU and AMR data integration. Two analytic approaches were used: (1) multivariable mixed-effects logistic regression models examined associations between AMU  summarized as the number of defined daily doses in animals using Canadian standards  and AMR and (2) multivariable mixed-effects Poisson regression models studied the linkages between AMU and the number of classes to which an E. coli isolate was resistant (nCRE. coli). A total of 1,317 E. coli isolates from a network of 16 veterinarians and 334 turkey producers across the five major turkey-producing provinces in Canada between 2016 and 2019 were used. Analysis indicated that AMR emerged with the use of related antimicrobials , however, the use of unrelated antimicrobial classes was also impacting AMR . As for studying AMU-nCRE. coli linkages, the most robust association was between the parenteral aminoglycosides use and nCRE. coli, though in-feed uses of four unrelated classes  appear to be important, indicating that ongoing uses of these classes may slow down the succession from multidrug-resistant to a more susceptible E. coli populations. The analysis of AMU (route and disease-specific)-AMR linkages complemented the above findings, suggesting that treatment of certain diseases  are influential in the development of resistance to certain antimicrobial classes. The highest variances were at the flock level indicating that stewardship actions should focus on flock-level infection prevention practices. This study added new insights to our understanding of AMU-AMR linkages in turkeys and is useful in informing AMU stewardship in the turkey sector. Enhanced surveillance using sequencing technologies are warranted to explain molecular-level determinants of AMR.Antimicrobial resistance (AMR) in enteric bacteria continues to be detected in turkey flocks and retail products worldwide, including in Canada. However, studies assessing linkages between on-farm antimicrobial use (AMU) and the development of AMR are lacking. This study aims to identify AMU characteristics that impact the development of AMR in the indicator bacteria  Antimicrobial resistance (AMR) is a global health threat that has limited the options for the treatment of bacterial infections in animals and humans . A recenAntimicrobials have been used effectively to treat, control, and prevent bacterial infections in poultry for decades; however, AMU  enhancesGlobally, Canada is among the top 10 turkey producing countries in terms of value in agricultural production . CanadiaEscherichia coli are part of the intestinal flora of humans and animals and are broadly used in many surveillance systems as indicator bacteria to study the emergence, transmission, and spread of AMR determinants in the ecosystem as they can be isolated efficiently and cost-effectively from fecal and environmental samples  investigate associations between AMR outcomes  and the corresponding flock-level AMU , (2) investigate associations between the two AMR outcomes previously described, and any AMU via a specific-administration route  to treat or prevent a specific disease .The Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) has recently reported a decrease in total AMU that corresponded with a decrease in multidrug-resistant (MDR) bacteria ,b in turA schematic diagram with the detail of the study methodology is shown in At each farm sampling visit, veterinarians administered a questionnaire to producers to collect data regarding on-farm AMU practices throughout the turkeys’ grow-out period. The selection criteria of participating turkey farms and flocks, protocols for on-farm fecal sampling, and laboratory procedures are described elsewhere . BrieflyE. coli colonies were assessed using Simmons citrate and indole tests. Colonies that were negative on the indole test were further confirmed using API® 20E bacterial identification kit.A 25 g sample from each pooled fecal sample was mixed with 225 mL of buffered peptone water and incubated at 35 ± 1°C for 24 h. One drop from this incubated mixture was streaked onto a MacConkey agar plate and was incubated at 35°C for 18–24 h. Suspect colonies that fermented lactose were transferred onto Luria-Bertani agar. Presumptive E. coli isolate per positive fecal sample was tested for antimicrobial susceptibility using a broth microdilution method with the Sensititre Antimicrobial Susceptibility Testing System  and the CMV4AGNF National Antimicrobial Resistance Monitoring System (NARMS) plate comprised of 14 antimicrobials. Isolates were classified as susceptible, intermediate, or resistant to a specific antimicrobial by evaluating breakpoints of their minimum inhibitory concentration (MIC) values following the Clinical and Laboratory Standards Institute (CLSI) M7-A8 guidelines where possible. No CLSI interpretive criteria for Enterobacteriaceae were available for azithromycin or streptomycin therefore breakpoints were based on the distribution of MIC values and were harmonized with those of the NARMS. For quality control, Escherichia coli ATCC 25922 strain was used.One The data analyzed in this study (2016–2019 surveillance years) were extracted from the CIPARS sentinel-farm surveillance AMU database. The CIPARS farm AMU questionnaire (Supplement in Reference #7) generates high-resolution AMU data comprised of flock-level demographics to derive various input parameters for the count-, weight- and dose-based AMU indicators, AMU by route of administration, and syndromic/animal health information. For our study, the AMU measurement chosen was the exploratory indicator, the number of defined daily doses using Canadian standards (nDDDvetCA)/1,000 kg-animal days at risk , which wThe defined daily dose for an antimicrobial class was determined by combining the average of all approved unique doses (for prevention and treatment purposes), based on the Canadian Compendium of Veterinary Products  and Company–routes), (2) route-specific AMU, also in nDDDvetCA/1,000 kg-animal days at risk (AMUroute–specific), and (3) route and disease-specific AMU , defined as the use of any antimicrobial by a specific administration route to treat or prevent a specific disease (AMUroute–disease–specific).Three AMU predictor variables, summarized at the antimicrobial class level, were defined (1) the quantity of AMU aggregated across all routes of administration measured in nDDDvetCA/1,000 kg-animal days at risk  by individual antimicrobial agents and by class were extracted from the CIPARS AMR dataset. The following were the AMR outcomes used for this study: (1) seven antimicrobial classes that were included in the NARMS Gram-negative panel including aminoglycosides , beta-lactams , folate pathway inhibitors , macrolides (azithromycin), quinolones/fluoroquinolones/ , phenicols (chloramphenicol), and tetracyclines (tetracycline); (2) composite AMR outcome variable nCRAll statistical analyses and data visualization were performed using the R  softwareE. coli isolates resistant to each of the seven antimicrobial classes described above was calculated, by dividing the number of isolates resistant to the antimicrobial class by the total number of isolates tested. The proportion of antimicrobial classes used on farms was calculated by dividing the number of farms that used the antimicrobial class by the total number of farms. Antimicrobial resistance and antimicrobial use variables with proportions < 5% or > 95% were excluded from further analysis to keep enough variability in the models. Tetracyclines, aminoglycosides, folate pathway inhibitors, and beta-lactams were the only classes with resistance in > 5% of the isolates and therefore included as homologous outcome variables of interest. The six antimicrobial classes: tetracyclines, aminoglycosides, folate pathway inhibitors, beta-lactams, bacitracin, and streptogramins were used in a minimum of 5% of the turkey flocks and were used in the AMU predictor variables created.The proportion of As described in any–routes. The outcome variable was the class-specific resistance for the logistic mixed-effect analysis and the nCRE. coli, a composite AMR indicator for the Poisson mixed-effect regression analysis. The AMU was summarized by the nDDDvetCA/1,000 kg-animal days at risk for each antimicrobial class across all routes of administration. The purpose of this analysis was to explore if AMU regardless of route of administration  had an impact on AMR.AMR-AMUroute–specific. This approach is similar to the above exercise, but the AMU component was disaggregated by the route of administration. The purpose of this analysis was to characterize if an exposure to an antimicrobial class via a given route could impact AMR differently. This analysis was important for providing context on potential shifts in AMU practices  as it relates to the turkey sector’s AMU strategy (The turkey farmers of Canada).AMR-AMUroute–disease–specific. In addition to the route of administration, disease indication of use was an additional attribute of the AMU component in this analysis. The purpose of this exercise was to assess if certain routes of administration for treating a given disease condition could also alter AMR differently. As above, the AMR indicator was individual class level resistance for the logistic models and nCRE. coli for the Poisson models.AMR- AMUTo account for the time-at-risk in the Poisson models, the natural log-transformed age of the birds at the time of sampling was used as an offset. To evaluate the overdispersion of any of the models, a goodness-of-fit chi-squared test was performed.Each 4-level mixed-effect regression model accounted for clustering and included the contact network of the veterinarians (“sampling network” variable which represents the participating sentinel farms within the veterinary practice), year, and flock as random intercepts. The hierarchical structure of the 4-level nested models included isolates at the lowest, flocks at the second, sampling year at the third, and sampling network at the highest level.p-value (p < 0.20) were offered to a multivariable model. To assess collinearity between AMU variables, a Pearson correlation analysis was performed and examined the coefficients. When highly correlated (rho = 0.8) variables were identified, the variable with the smaller p-value was included in the multivariable model.The model building consisted of two steps. First, univariable 4-level regression models were built, and variables with a liberal p-value ≤ 0.05) predictor variables were retained in the final multivariable models. When eliminating non-significant variables, the whole and reduced models were compared by assessing the AIC and BIC values, and models with smaller values were considered a better fit. Each final multivariable model was checked for influential observations, normality of residuals, homogeneity of variance, and collinearity among predictor variables using the sjplot package in R software. The normality of random effects was also evaluated using the same package. The Poisson models were checked for overdispersion and inflated zeros. Interaction terms between predictor variables significant in the final multivariable models have been evaluated.In the second step, a multivariable 4-level regression model was built using a manual stepwise backward elimination method. All significant , an OR of < 1 indicated that the probability of resistance decreased with decreased AMU, and if > 1 then the probability of resistance increased with increased AMU. An IRR of < 1 indicated a decrease and > 1 indicated an increase in the nCRE. coli with each additional unit increase in AMU .For all model outcomes (odds ratio (OR) for the logistic regression models and incidence rate ratio (IRR) for the Poisson models), 95% confidence intervals and 2÷ 3 = 3.29;whereπ = 3.14  of the model, assuming that level 1 (isolate) variance on the logit scale was:ππ = 3.14 . The ranE. coli isolates recovered from pooled fecal samples collected between 2016 and 2019 from 334 turkey flocks by 16 veterinarians across the five major turkey-producing Canadian provinces. Of the 334 flocks, two E. coli isolates were recovered from one flock, three isolates each from 17 flocks, and four isolates each from the remaining 316 flocks. Four antimicrobial classes with at least a 5% prevalence of resistance, including tetracyclines (61.7%), aminoglycosides (45.0%), folate pathway inhibitors (30.4%), and beta-lactams (31.2%) were included in the analysis. Out of 1,317 E. coli isolates, 363 were susceptible to all the 7 tested antimicrobial classes, whereas 237 isolates were resistant to one, 301 isolates to two, and 416 (31.6%) isolates resistant to at least 3 or more antimicrobial classes. Resistance to antimicrobial classes in E. coli isolated from flocks with no antimicrobial use during the study was detected. In these flocks, a total of 46.73% of isolates were resistant to tetracyclines, 31.55% to aminoglycosides, 21.43% to folate pathway inhibitors, and 17.86% to beta-lactams, respectively , and the three most frequently occurring patterns were the use of bacitracin (n = 55), aminoglycosides-streptogramins (n = 36), and bacitracin-aminoglycosides (n = 32)  .The six antimicrobial classes: tetracyclines, aminoglycosides, folate pathway inhibitors, beta-lactams, bacitracin, and streptogramins were used in a minimum of five percent of the turkey flocks and therefore were included in the regression analysis as predictor variables.E. coli outcome was the Poisson model.Tetracyclines, aminoglycosides, folate pathway inhibitors, and beta-lactams were the only classes with resistance in > 5% of the isolates and were therefore included as outcome variables of interest in the regression analysis. The transformations of the continuous variables did not improve the model estimates; therefore we used the untransformed continuous AMU variables in all of our models. The best-fitting model for the nCRResults of the univariable mixed-effects logistic regression models are presented in The final multivariable mixed effect models are presented below. No interaction terms among the variables included in the multivariable models were significant.any route predictors are shown in E. coli, and AMUany route are shown in The results of the multivariable mixed-effects logistic regression models evaluating associations among the homologous AMR outcome and the AMUE. coli isolate being resistant: to aminoglycosides would decrease by 0.017, to beta-lactams would decrease by 0.015, and to folate pathway inhibitors would decrease by 0.024.Significant associations were observed between aminoglycosides resistance and the use of two unrelated classes of antimicrobials (folate pathway inhibitors and bacitracin), however, effect estimates (ORs and 95% CI’s) were very small. Similar effect estimates were noted in other AMR-AMU pairs examined ,b, Two hE. coli. As with the above analysis, the IRR estimates were relatively similar across the 4 AMU-AMR pairs modeled and approached an IRR of 1. The predicted marginal effects of antimicrobial use on nCRE. coli of turkeys showed an increasing trend with the increase in the quantity of AMU  significantly increased nCRy of AMU . Again, The model diagnostics for the Poisson model, including normality of residuals, influential observations, collinearity among predictor variables, and homogeneity of variance are presented in Analysis of the unexplained variance components residing at each level of the mixed-effects regression models indicated that the sampling network have a negligible effect in all of the models, except for the model of resistance to aminoglycosides ; whereasE. coli.The results of the multivariable mixed-effects logistic regression models assessing the linkages between AMU via specific administration routes and AMR are presented in The odds of resistance to tetracyclines were increased by using injectable aminoglycosides, in-feed use of folate-pathway inhibitors, and tetracyclines. The highest magnitude of OR estimates was observed for aminoglycoside use (aminoglycosides > folate pathway inhibitors > tetracyclines). A significant association was noted between the unrelated AMR-AMU pairs , however, OR is slightly more than 1  in these models. It also indicates that if there is a 25% decrease in the mean use of injectable aminoglycosides , the odds of resistance to tetracyclines will decrease by 0.06 given other variables remain constant. A significant association was observed for the homologous pair (folate pathway inhibitors resistance-folate pathway inhibitor use via feed) but OR estimate similarly approached 1.E. coli significantly increased by the injectable use of aminoglycosides (IRR = 2.585), and in-feed use of four antimicrobial classes  but their effect estimates approached 1  to treat or prevent a specific disease are presented in E. coli.The results of multivariable mixed-effects logistic regression models that evaluated associations of individual class resistances and nCRAs shown in The highest variances were observed at the isolate and flock levels. The flock-level variances were 0.78 (standard deviation: 0.88) for aminoglycosides, 1.15 (1.07) for beta-lactams, 0.65 (8.06) for folate pathway inhibitors, and 0.34 (0.58) for tetracyclines models.E. coli significantly increased in turkey flocks treated with antimicrobials via water for enteric diseases and via feed for late septicemia and enteric diseases. Similar to the previous models, there were small variances at the sampling network and year levels , year, and flock levels, the nCRE. coli increased gradually with the increase in the antimicrobial quantity used in turkey flocks. This study identified specific classes of antimicrobials and certain exposure characteristics (disease indications and route of administration) that potentially contribute to AMR emergence. It further highlights the importance of high-resolution AMU data collected from the end-user  and the complementarity of different AMR indicators (class-specific and nCRE. coli) for understanding AMU-AMR linkages.This study builds on previous CIPARS experiences in AMU-AMR data integration and analysis to further explore the relationships between AMU and AMR in indicator Comparing our study results to previous studies is difficult because one needs to consider differences in study design , analytical approaches , sample size , AMU indicator used , health status (disease present in a flock or not), husbandry , sampling procedures , and antimicrobial susceptibility testing .E. coli isolates to the same or unrelated antimicrobial classes. However, the odds ratios were positive and slightly more than one in most models, indicating that substantial changes in antimicrobial use may still result in small changes in resistance. The diversity of AMU patterns signifies various AMU practices in turkey flocks ranging from zero or no use , single antimicrobial class to multiple classes that might entail simultaneous or concurrent AMU exposures in turkey flocks. Exposures to > 1 antimicrobial class could be a routine practice or could occur in an outbreak situation ; it could also be associated with the use of combination products. In our previous analysis, the odds of being a high user of antimicrobials in turkeys were significantly higher for those that used any antimicrobial via water and those that used folate pathway inhibitors, bacitracin, and tetracyclines. These findings highlight the potential role of co-selection for resistance or selection for fitness of specific AMR strains and could be explained by the co-location and transmission of antimicrobial resistance genes on mobile genetic elements   . Anothertegrons)  interfertegrons) .E. coli isolates can persist in the environment (Enterococcus faecium (GRE) in pigs were detected and observance of an abrupt decline only occurred upon the phasing out of other antimicrobials such as macrolides . Previous use of injectable gentamicin may have exerted co-selection of AMR in E. coli resulting in tetracycline resistance. The treatment uses of combination products via water (neomycin-oxy-/tetracycline combination products), in part, could also play a role. In response to the Turkey Farmers of Canada’s AMU reduction policy, the preventive use of gentamicin was no longer allowed by 2019. Having only 1 year of post-AMU intervention data in the study timeframe, the long-term impact of gentamicin use on the gut and environmental flora of turkeys warrants ongoing monitoring of AMU and AMR. With the elimination of injectable gentamicin use, the potential for a compensatory increase in aminoglycosides administered via other routes will need to be monitored through surveillance.A robust association was noted between aminoglycosides use (mainly gentamicin) through injections and tetracycline-resistant It is important to identify the most common turkey infectious diseases that initiated the use of different antimicrobial classes and consequently affect the development of AMR. Our previous analysis indicated an increase in enteric and neonatal diseases (colibacillosis and its sequelae) consistent with the timing of the removal of the preventive use of certain antimicrobial classes intended for these syndromes . This prA recent study has shown the impact of short-term AMU on the development of AMR . In our E.coli that signified the number of antimicrobial classes to which an E. coli isolate was resistant. Moreover, we accounted for the time of exposure by including the natural log-transformed age of turkeys at the time of sampling as an offset. Previous studies also demonstrated the effect of simultaneous use of more than one antimicrobial on the development of MDR bacteria , the Poisson model allowed us to use an outcome variable nCRbacteria .E. coli isolates. Besides, the odds ratios of AMU predictor variables were close to 1 indicating that an overall reduction in AMU across multiple antimicrobial classes might be needed to decrease AMR. There were negligible to moderate variance components residing at the year level, suggesting that AMU practices did not differ substantially over time. However, the data used include only one-year post-AMU reduction intervention (2019) and thus requires the reevaluation of farm data in subsequent years to fully assess the impact of the turkey sector’s AMU strategy. Despite our study showing a negligible to low effect of sampling network on the emergence of AMR in E. coli isolates of turkey flocks, veterinarians have an important role in implementing antimicrobial stewardship programs to mitigate the emergence of antimicrobial-resistant bacteria.To identify intervention targets, we evaluated the unexplained variance components residing at each level  of our mixed-effects regression models. Flocks had the highest variances in all of the models, suggesting flock-level AMU interventions might have the highest impact on reducing the emergence of AMR in E. coli isolates. Future studies should evaluate the genetic determinants of AMR and identify genetic elements associated with the co-selection of AMR. Our study also identified AMR in E. coli isolated from flocks with no previous antimicrobial use history , stressing the importance of future studies to identify additional risk factors besides AMU  that impact the development of AMR. In addition, future studies should evaluate the effectiveness of antimicrobial alternatives    to reducE. coli isolates. At the turkey industry level, reassessment of the AMU reduction strategy on flock health, particularly, trends in the diagnosis of diseases and shifts from preventive to therapeutic AMU would inform additional stewardship measures in addition to an ongoing AMU/AMR surveillance.This study showed that the flock-level use of antimicrobial classes impacted the emergence of AMR to the same or unrelated antimicrobial classes, highlighting that mechanisms independent of AMU are playing a role . This study further highlights the utility of a dose-based AMU indicator in AMU-AMR association studies; the indicator accounted for the average daily doses specific to the antimicrobial, the exposure period, and the weight of birds, which is an ideal method to evaluate the impact of AMU on the prevalence of AMR over the lifetime of turkeys. The use of aminoglycosides administered via injections had the highest impact on homologous resistances. In-feed use of bacitracin, streptogramins, folate pathway inhibitors, and tetracyclines appear to be playing a role in the perpetuation of resistance in turkey flocks. Given that these antimicrobial classes are indicated for specific disease syndromes (enteric disease and late septicemia), infection prevention control measures for the reduction of these diseases are necessary to offset the need for AMU. Flocks had the highest variances in all of the models; from an AMU stewardship perspective, interventions at the producer level might have the highest impact on reducing the emergence of AMR in agnes.agunos@phac-aspc.gc.ca.The datasets presented in this article are not readily available because data requests should be sent to the Public Health Agency of Canada. Requests to access the datasets should be directed to AA, Ethical review and approval was not required for the animal study because there were no animal experiments conducted for this research. An informed consent form was administered by the veterinarian to their producers before the flock visit.RS: study design, data analysis, writing – prepared initial draft, review, editing, and visualization. AA: writing – review, editing, resources, and project administration. SG and AD: writing – review and editing, and resources. CV: study design, data analysis, writing – prepared initial draft, review, editing, project administration, and supervision. All authors contributed to the article and approved the submitted version.


Yet, the optimal location for chest compressions is unclear. It was hypothesized that compared with the standard chest compression (SCC) location, left ventricle chest compressions (LVCCs) would result in greater ETCO2 (in-line sampling), BP , and CBV  were measured during the pre-CA, untreated-CA, and CPR-treated phases.Female Landrace swine  underwent two mins of untreated asphyxiated cardiac arrest (CA). Thereafter, swine were treated with three 2-min cycles of either SCC or LVCC mechanical basic life support CPR (LUCAS 3). ETCO2, BP, and CBV were similar between groups at pre- and during untreated-CA (P ≥ 0.188). During CPR, ETCO2 , mean arterial BP , and CBV  were significantly greater in the LVCC versus SCC group. Moreover, a greater proportion of animals obtained targets for ETCO2  versus 100 % (32/32), P < 0.001) and diastolic BP  versus 97 % (48/49), P = 0.020) in the LVCC versus SCC group.ETCOIndicators of cardiac output, BP, and cerebral perfusion during CPR were greatest in the LVCC group, suggesting the quality of chest compressions during BLS CPR may be improved by performing compressions over the left ventricle compared to the centre of the chest. Cardiac arrest (CA) occurs over 500,000 times annually in the United States.Current CPR guidelines define the surface landmark of standard chest compression (SCC) as the centre of the chest on the lower half of the sternum.172 (established indicator of cardiac outputLeft ventricle chest compression (LVCC) during CPR may offer hemodynamic benefits compared to SCC.2, BP and CBV) were monitored.This prospective interventional trial was approved by the University of Saskatchewan Animal Research Ethics Board (#20200042). Briefly, animals underwent asphyxiated CA and received either SCC or LVCC BLS CPR while hemodynamics  were sedated with an intramuscular injection of 20–30 mg/kg of ketamine . Swine were intubated and mechanically ventilated . Anesthesia depth was maintained with 1–3 % isoflurane . Body temperature was maintained at ∼ 38 °C . An intravenous (IV) catheter was inserted into an auricular vein for fluid maintenance  and medication delivery.2 range of 35–40 mmHg.21Animals were placed supine in a V-shaped holder with the limbs and head secured to prevent displacement during CPR.2, heart rate, BP) and CBV were recorded using a digital-analog converter  interfaced with a data collection software program  during baseline, CA and CPR.Following instrumentation, transthoracic echocardiography  was used to locate and mark the surface site for either SCC . Employing a modified version of the ARRIVE guidelines’ simple randomization procedure,Random.org) and thereafter animals were rotated systematically  to achieve the necessary sample size. Potential nuisance variables were balanced between groups with strict standardized animal care, housing, feeding interaction and handling procedures, using the same investigators for animal instrumentation and experimentation, and using animals of the same sex and similar mass.Owing to the blatant difference in chest compression locations, experimenters could not be blinded during CPR administration. However, data analysis was completed by a researcher blinded to experimental condition. Data during the entire 2 mins of baseline, untreated CA and each round of BLS CPR were binned independently and averaged for each period. Erroneous waveforms that exceeded 1 standard deviation from the mean were removed. Statistical analyses were completed using SigmaPlot 14.0 . Sample size was estimated using ETCOA priori between group pairwise comparisons at baseline, CA, and round 1, round 2 and round 3 of BLS CPR were completed using a post hoc Tukey’s test. Within group differences were not explored. Grouped data are presented as mean with individual data points. Cohen’s d effect size analysis  were calculated to compare the magnitude and directional effects of LVCC versus SCC on systemic and cerebral hemodynamic status during each round of CPR.2 (≥20 mmHg)Indices of hemodynamic status were compared using a group (SCC versus LVCC) × time  mixed model ANOVA. 2 data was missing from n = 3 in the SCC and n = 4 in the LVCC group , and CBV data was missing from n = 2 in each group . After the initial 20 experiments, LVCC group had a greater number of incomplete data sets; thus, the alternation order was revised to attain necessary sample sizes . Final samples sizes are listed in each figure legend and individual data points are shown.To ensure statistical power for all measures and to account for incomplete data, 32 animals underwent the CPR protocol . There was incomplete data for three animals in the LVCC group (ROSC). Further, BP data was missing from n = 1 in the SCC group , ETCOThere was a significant group × time interaction effect (P < 0.001) for systolic CBV values. Subsequent pairwise comparisons  reveal groups were similar at baseline and during untreated CA (P ≥ 0.506), but greater in the LVCC versus SCC group in all three rounds of CPR  during all three circulation checks. Swine that did achieve ROSC (n = 3) presented with asystole during untreated CA and/or circulation checks prior to regaining independent circulation. The occurrence of ROSC was not dependent on chest compression location . Similar2 values. Pairwise comparisons reveal groups were similar at baseline and during untreated CA (P ≥ 0.226), but greater in the LVCC versus SCC group in all three rounds of CPR  for ETCOhout CPR . Moreovea priori comparisons, between group comparisons were made at each time point. Pairwise comparisons reveal DBP was similar at baseline and during untreated CA (P ≥ 0.188), but greater in the LVCC versus SCC group in all three rounds of CPR  for SBP values. Pairwise comparisons reveal groups were similar at baseline and during untreated CA (P ≥ 0.277), but greater in the LVCC versus SCC group in all three rounds of CPR .In the present study, both systolic CBV and mean CBV were greater during LVCC compared to SCC. This was likely the result of increased stroke volume and potential redistribution of blood flow to the brain during compression systole. Both ETCOGiven the increased cardiac output, it follows that there would be a concomitant increase in BP. Improvements in BP with LVCC versus SCC were in concordance with previous swine work using a model of ventricular fibrillation arrest.2 (≥20 mmHg) was reached in merely 52 % of SCC versus 100 % of LVCC rounds. The AHA target for DBP (≥25 mmHg) was reached in 82 % of SCC versus 98 % of LVCC rounds throughout BLS CPR. AHA targets were met more routinely with LVCC, raising the possibility that LVCC improves the effectiveness of CPR in real-world settings.In the present study, the AHA target for ETCOIn the current study, although more swine achieved ROSC in the LVCC (n = 3) versus SCC group (n = 0), the prevalence of ROSC was not different between groups. However, the current BLS-only study was likely underpowered to assess differences in this secondary outcome. Anderson and colleagues reported ROSC in 69 % of LVCC versus 0 % of SCC swine, and all during advanced life support CPR.2 is a surrogate for cardiac output. Thus, more invasive preclinical work and human trials are requisite to confirm these findings.Data from swine models of resuscitation must be interpreted cautiously and confirmed in the clinical setting. Compression of the heart and underlying structures may differ between species. Underlying structures during CPR were not tracked and may have shifted. This was not a randomized control trial, and after the first animal was randomized, subsequent animals were assigned using a systematic rotation, alteration protocol. This approach was employed to obtain a sufficient number of complete data sets in both groups, using the fewest number of animals.The present study provides novel insight into the benefits of LVCC versus SCC on hemodynamic performance, demonstrating that LVCC improves systemic and cerebral hemodynamic status in a swine model of asphyxiated CA and CPR. Given the observed benefits of LVCC and vitality of cerebral preservation to ROSC and survival, further studies are warranted to determine the translational relevance of these discoveries.


Follow-up univariate analyses revealed that amygdala binding potential and age were both independently associated with choice inflexibility. The findings suggest that individual differences in dopamine function may be associated with financial risk taking in healthy adults.Enhancing dopamine increases financial risk taking across adulthood but it is unclear whether baseline individual differences in dopamine function are related to risky financial decisions. Here, thirty-five healthy adults completed an incentive-compatible risky investment decision task and a PET scan at rest using [11C]FLB457 to assess dopamine D2-like receptor availability. Participants made choices between a safe asset (bond) and a risky asset (stock) with either an expected value less than the bond (“bad stock”) or expected value greater than the bond (“good stock”). Five measures of behavior  and beliefs  were computed and D2-like binding potential was extracted from four brain regions of interest . We used canonical correlation analysis to evaluate multivariate associations between decision-making and dopamine function controlling for age. Decomposition of the first dimension ( Why do people take risks? There are many potential contributions to risk preferences from biases in perceptions of risk, beliefs about benefits, differential weighting of costs and benefits, misunderstanding of risk or low numeracy in general, among many other factors . Some haPharmacological modulation of the dopamine system has been shown to increase financial risk taking in everyday life for some individuals. The most well-known examples are that some middle-aged and older individuals with Parkinson’s Disease (PD) who are treated with dopamine agonists develop novel gambling addictions and engage in other excessively risky behaviors . RelatedBoth learning abilities and financial risk-taking vary across individuals of different ages. Studies of aging and reinforcement learning show that older adults learn more slowly , but stuND) with decision-making behavior. Multivariate methods may help better guide analysis of these naturally multivariate data sets and produce a more comprehensive understanding of brain-behavior associations in general.Along with scientific research in general, the reproducibility and generalizability of studies of brain-behavior individual difference effects have been critiqued and questioned recently . This cr11C] FLB 457 radioligand, partial-volume-corrected (PVC) dopamine D2-like receptor non-displaceable binding potential (BPND) was estimated from four regions of interest: midbrain, amygdala, anterior cingulate, insula. These regions of interest were identified based on previous research documenting critical contributions to learning  , and behavior. We predicted that there would be an association between the set of brain variables and the set of task-related behavioral variables such that higher BPND would be associated with better performance (i.e. fewer task-related errors).The goal of this study was to examine multivariate associations between dopamine function and learning-based financial decision-making in an adulthood sample. Using the methyl]-3-methoxy-2-(methoxy-11C) benzamide was synthesized in the Yale PET Center radiochemistry laboratory in the Yale School of Medicine. PET scans were acquired on a high resolution research tomograph . [11C] FLB 457 (median specific activity: 7.80 mCi/nmol) was injected intravenously as a bolus  over 1 min by an automated infusion pump . Prior to each scan, a six-minute transmission scan was performed for attenuation correction. Dynamic scan data were acquired in list mode for 90 min following the administration of [11C]FLB 457 and reconstructed into 27 frames  with corrections for attenuation, normalization, scatter, randoms, and dead time using the MOLAR (Motion-compensation OSEM List-mode Algorithm for Resolution-Recovery Reconstruction) algorithm. Event-by-event, motion correction was applied using a Polaris Vicra optical tracking system  that detects motion using reflectors mounted on a cap worn by the subject throughout the duration of the scan. After decay correction and attenuation correction, PET scan frames were corrected for motion using SPM8 . T1- weighted high-resolution anatomical scans  were obtained for each participant. These structural scans facilitated co-registration and spatial normalization of the PET data.ND) was calculated for each of these ROIs , amygdala , insula  and anterior cingulate .Both MRI and PET data were parcellated into 62 bilateral cortical, 12 bilateral subcortical, 3 posterior fossa, 5 ventricle, and 1 white matter regions of interest  using the Hammers atlas . Followiese ROIs : midbraiTo test for an association between dopamine receptor availability measures and decision-making task measures we performed a canonical correlation analysis (CCA). CCA is a general statistical method that measures the relationship between two multidimensional variable sets. More specifically, given two datasets X and Y, CCA seeks to find a linear combination of X that maximally correlates with a linear combination of Y. The linear combinations of X and Y are synthetic variables, called canonical variates. The canonical variates together are a canonical pair and the correlation between the canonical variates is called a canonical correlation. The possible number of canonical pairs is limited to the number of variables in the smallest dataset with the constraint that each successive pair is uncorrelated with all previous ones.P < 0.05. Analysis was conducted in R  and age as correlates of the five financial decision-making task performance variables . The CCA evaluated the multivariate shared relationship between the two variable sets . The analysis produced five functions with squared canonical correlations (R2c) of 0.581, 0.351, 0.072, 0.028, and 0.005. The full model across all five functions was statistically significant using the Wilks’s = 0.24 test criterion, F = 1.747 P = 0.029. The effect size for the complete set of five canonical correlations was 2 =r 0.581, showing that the full model explained 58% of the shared variance between the two variable sets. A dimension reduction analysis showed that the second through fifth functions did not explain statistically significant amounts of shared variance as seen in A canonical correlation analysis (CCA) was conducted using four dopamine D2-like receptor availability variables  for the criterion variables suggest that choice inflexibility, suboptimal investment, and optimism are the relevant contributors to the decision-making variate. The structure coefficients for inflexibility and suboptimal investment have the same sign, indicating that they were positively related. Optimism was inversely related to the other criterion variables.From the predictor variables, structural coefficients suggest that age and amygdala dopamine binding potential were the relevant contributors to the age/brain variate. Age was positively related to each of the relevant task-related variables except for optimism. Amygdala binding potential was negatively related to all the task-related variables except for optimism. These general findings were robust to variations in the statistical model showing similar patterns when age was removed as an individual variable  = 2.44, P = 0.02, and negative association between dopamine receptor availability within the amygdala and choice inflexibility b = −0.04, t(33) = −2.15, P = 0.04, controlling for age. This negative correlation indicated that higher levels of dopamine receptor availability were associated with better performance (less inflexible or more flexible choice). Age explained a significant proportion of variance in choice inflexibility, R2 = 0.15, P = 0.02. Amygdala dopamine receptor availability also explained a significant proportion of variance in choice inflexibility, R2 = 0.12, P = 0.03.Follow-up exploratory univariate analyses were conducted to examine the effects of dopamine receptor availability within individual ROIs and age on choice inflexibility using more traditional analysis methods see . UnivariThe present study evaluated whether baseline dopamine receptor availability was related to individual differences in learning-based risky financial decision-making across adulthood. In a relatively small sample of healthy human adults, we identified preliminary multivariate associations between risky investment decision-making and dopamine receptor availability. Using multivariate CCA, the study reveals general associations between dopamine receptor availability and risky decision-making. In light of the mixed suggestions from pharmacological studies about the effects of dopamine on risky behavior, the present data suggest that baseline levels of dopamine across adulthood are directly related to risky financial decision-making. Thus, it is important to understand an individual’s baseline dopamine status when evaluating how manipulations of the dopamine system might affect decision-making.Decomposition of the CCA and follow-up univariate analyses demonstrated that the effects were driven by negative correlations between amygdala D2-like receptor availability and choice inflexibility controlling for age. Individuals with lower levels of D2-like receptor availability in the amygdala were more likely to persist in choosing the asset that they chose in the first trial of the block when this asset was objectively the less optimal choice. Individuals with higher levels of D2-like receptor availability in the amygdala were less likely to make these perseverative errors. Functional neuroimaging studies have identified contributions of amygdala function to risky decision-making in general  as well The effects in this study are novel in both the specific implication of dopamine function in the amygdala and the specific contributions of learning to risky choice. Individuals with low amygdala D2 receptor availability seem to be less sensitive to feedback when their initial choice ends up being a worse option. In this phase of the task, they would be better off choosing the safe asset, the bond, but instead persist in choosing a losing stock. Although this task is inspired by investment decision-making in everyday life, we did not collect measures of individuals real-life reactions or choices after negative events in the stock market for example. The task was incentive-compatible but the financial stakes were relatively small. Although suggestive, it is unclear whether these effects would extend to perseveration on poor initial choices in everyday risk taking.The associations between amygdala receptor availability and choice inflexibility held controlling for age in univariate analyses. However, age was also correlated with choice inflexibility. The strength of the independent associations between choice inflexibility and both age and amygdala receptor availability were similar with each accounting for 12–15% of the variance in perseveration on the first choice made in a block after trial-related feedback revealed that it was a bad choice. These effects suggest that older adults may be less likely to adjust their choice behavior after receiving feedback that they are losing money. The suggestion that older adults might be more likely to make perseverative errors in investment decision-making would be consistent with a wealth of research on aging and perseveration in general . Althougn = 200 for the 10 variables examined in this study. However, more recent guidance for examining brain-behavior relationships recommends simply having more participants than the number of variables in either domain  because it was part of a larger multimodal neuroimaging study examining adult age-related differences. Although the expanded age range of our sample increases the generalizability of our findings, the age distribution of this sample was not uniform. Second, with only 37 participants, this is a relatively small sample for multivariate analysis. It has been proposed that the number of participants per variable should be 20/1 , or n = -powered , althoug-powered . UnfortuIn summary, the study provides novel, preliminary evidence for multivariate associations between dopamine receptors and risky learning-based investment decision-making across adulthood. Given the small sample size, the replicability and generalizability of the effects is not yet clear. However, the study does provide suggestive evidence for the utility of using a multivariate approach to examining brain-behavior associations in adulthood. Given how frequently behavioral tasks can produce multiple performance-related measures  and the number of data points collected in typical brain imaging studies, multivariate methods are ideally suited for identifying brain-behavior associations. Discussions of questionable research practices have regularly identified experimenter degrees of freedom as a potential source of bias in the literature . InitialMikella Green: Methodology, Formal Analysis, Writing—Original Draft, Visualization. Jennifer Crawford: Formal Analysis, Investigation, Data Curation, Project administration. Camelia Kuhnen: Conceptualization, Formal Analysis, Writing—Review & Editing. Gregory Samanez-Larkin: Conceptualization, Resources, Writing—Review & Editing, Visualization, Supervision, Funding acquisition. Kendra Seaman: Software, Data Curation, Writing—Original Draft, Writing—Review & Editing, Supervision.This study was supported by National Institute on Aging (grant numbers R00-AG042596 to G.R.S.-L. and T32-AG000029 to K.L.S.).Conflict of interest statement: None declared.https://osf.io/yjqpb/.Data and code used in the manuscript: dalearningCCA_supp_tgad008Click here for additional data file.


PROK1 was greater on Days 12 and 14 of pregnancy compared to Day 9. PROK1 protein expression during pregnancy increased gradually and peaked on Day 14, when it was also significantly higher than that on Day 14 of the estrous cycle. Prokineticin receptor 1 (PROKR1) mRNA abundance increased on Days 12 and 14 of pregnancy, whereas PROKR2 elevated on Day 14 of the estrous cycle. PROK1, acting via PROKR1, stimulated the expression of genes involved in progesterone synthesis, as well as progesterone secretion by luteal tissue. PROK1–PROKR1 signaling reduced apoptosis and increased the viability of luteal cells. PROK1 acting through PROKR1 stimulated angiogenesis by increasing capillary-like structure formation by luteal endothelial cells and elevating angiogenin gene expression and VEGFA secretion by luteal tissue. Our results indicate that PROK1 regulates processes vital for maintaining luteal function during early pregnancy and the mid-luteal phase.Prokineticin 1 (PROK1) is a pleiotropic factor secreted by endocrine glands; however, its role has not been studied in the corpus luteum (CL) during pregnancy in any species. The present study aimed to investigate the contribution of PROK1 in regulating processes related to porcine CL function and regression: steroidogenesis, luteal cell apoptosis and viability, and angiogenesis. The luteal expression of  Progesterone synthesis is controlled by steroidogenic acute regulatory protein (STAR); side-chain cleavage cytochrome P450 (CYP11A1); and hydroxy-delta-5-steroid dehydrogenase, 3 beta- and steroid delta-isomerase 1 (HSD3B1).The corpus luteum (CL) is an important endocrine gland for pregnancy establishment in mammals2. The lack of fertilization results in CL regression on days 14–16 post ovulation. During pregnancy, CL function is maintained by luteoprotective and luteotrophic factors such as estradiol-17β and prostaglandin E23. Previous reports have indicated a high rate of embryo mortality in pigs during early pregnancy; however, the etiology of this phenomenon is not yet fully understood4. CL dysfunction has been suggested as one of the reasons for increased embryonic mortality in pigs5.The proper function of the CL is very important in pigs because it is a major source of progesterone during the entire pregnancy7. Angiogenesis in luteal tissue is tightly regulated during different phases of the CL lifespan. The main angiogenic factor in the CL is vascular endothelial growth factor A (VEGFA)8. However, prokineticin 1 (PROK1), known as endocrine gland-derived vascular endothelial growth factor (EG-VEGF), may also stimulate angiogenesis11. It is worth highlighting that PROK1 does not share sequence similarity with VEGFA12. Prokineticin 1 in mammals exerts pleiotropic, tissue-dependent effects, but the most recognized are the mitogenic effects on the endothelial cells of microvessels proximal to endocrine glands and on smooth muscle contractivity in intestines14. This short-amino acid chain protein belongs to the AVIT family and acts by binding to one of two receptors: prokineticin receptor 1 (PROKR1) and 2 (PROKR2)15. However, PROK1 has a higher affinity for PROKR1 than for PROKR216. Both receptors act through the recruitment of different G-proteins  and can induce the signaling pathway of protein kinase C, mitogen-activated protein kinase, phosphoinositide 3 kinase or protein kinase B17. Nonetheless, PROK1 acting through one of its receptors can have different effects; for example, PROK1-PROKR1 signaling affects the growth of fetal endothelial cells, while PROK1-PROKR2 signaling induces endothelial cell permeability18.Several processes are involved in CL formation, luteal lifespan, and CL regression, including tissue remodeling, apoptosis, and angiogenesis associated with endothelial cells23. The expression of PROK1 mRNA in the human endometrium increases during the peri-implantation period24. Our recent studies indicate that PROK1 and PROKR1 gene and protein expression in the endometrium and PROK1 gene expression in the trophoblast are upregulated during implantation and early placentation in pigs25. PROK1, acting through PROKR1, is an embryonic signal mediator in the endometrium that participates in early pregnancy establishment by promoting angiogenesis, trophoblast cell proliferation, and adhesion and regulating the expression of genes involved in embryo–maternal interactions25. Moreover, PROK1 and its receptors (PROKRs) are expressed in ovarian follicles and corpora lutea in humans, cows, and rats28. Although PROK1 expression in the CL during the estrous cycle is dynamic29, the main role of PROK1 in the CL is still not fully understood. PROK1 effector cells in the corpus luteum are luteal endothelial cells and luteinized cells20. Based on the aforementioned studies, it may be supposed that PROK1 stimulates angiogenesis in the CL. However, the expression of PROK1 is strongest in the bovine and human CL during the late luteal stage or corpus luteum regression when angiogenesis declines29. Until now, only the expression profiles of the PROK1 gene, not the protein, have been evaluated in the corpus luteum during the estrous/menstrual cycles in cattle and humans. Moreover, there is a lack of data concerning the expression and role of PROK1 in the CL during early pregnancy in any species. PROK1 expression profiles and function in the porcine CL have not been studied until now.The role of PROK1 in female reproductive function has been studiedWe hypothesized that PROK1 regulates processes involved in controlling the luteal lifespan in pregnancy and the estrous cycle in pigs. Our objectives for the present study were (1) to evaluate PROK1 and its receptors’ (PROKR1 and PROKR2) gene and protein expression profiles in porcine corpora lutea collected during the luteal phase of the estrous cycle and corresponding days of pregnancy; (2) to determine the localization of PROK1 and its receptors’ protein expression in the porcine corpus luteum; and (3) to study the effects of PROK1 in the corpus luteum on major functions such as progesterone synthesis, luteal cell apoptosis and viability, and angiogenesis.PROKR1 and PROKR2 genes , the beginning of luteolysis on Day 14 of the estrous cycle and the corresponding days of pregnancy . PROK1 gene expression was greater on Days 12 and 14 of pregnancy than on Day 9 of pregnancy  and Day 14 of pregnancy , Day 14 of the estrous cycle (p < 0.05), and Day 14 of pregnancy . In addition, none of the treatments used decreased the tissue explant viability after 18 h of incubation compared to the control Fig. . The antIsolated primary luteal cells were characterized  and on Day 14 of the estrous cycle and pregnancy  and late-luteal  phases of the estrous cycle and the corresponding days of pregnancy, including the period of luteal rescue from regression in response to embryonic estradiol (Days 12 and 14 of pregnancy). We combined our ex vivo approach with an in vitro model of ultrathin luteal tissue explants (180 µm), which is more physiological than using one particular population of cells isolated from the CL. We used pigs as models for studying the mechanisms underlying CL physiology because, among others, in the pig as a multiparous species, several CL are usually formed in each ovary, providing an excellent source of material for multiple analyses in tissue studies. Moreover, the cellular mechanisms involved in the regulation of reproductive processes can be common among species with different types of placentation despite of species-specific differences in the establishment of pregnancyPROK1 mRNA is elevated in the mid-luteal phase of the estrous cycle, which aligns with the findings of previous studies on the bovine and human corpora lutea29. However, in the late luteal phase, the gene expression of PROK1 was decreased in the porcine CL, whereas in cattle, it is still elevated27. It has been suggested that the increased expression of the PROK1 and PROKR1 genes in the bovine CL at the late luteal phase may be related to the involvement of these genes in recruiting monocytes to the regressing CL27. Therefore, these interspecies discrepancies may be the difference in the stage of regression between these two species. The exact stage of regression is difficult to assess without administration of exogenous prostaglandin F2α and collection of timed luteal samples. However, we did not perform such an analysis because we decided to show the expression of PROK1 and its receptors under physiological conditions (the estrous cycle and early pregnancy). Nonetheless, our results are consistent with those of earlier studies showing that stress conditions such as cell starvation, hypoxia and high levels of tumor necrosis factor, which also occur during luteolysis, result in markedly elevated levels of PROKR2 mRNA and do not affect the expression of PROK1 in bovine endothelial cells of CL origin26. Interestingly, PROK1 acting through PROKR2 may exert antiapoptotic effects on endothelial cells in the bovine CL26.Our results revealed that luteal expression of PROKR1 and PROKR2 mRNA was regulated during the estrous cycle and pregnancy, but PROKR1 and PROKR2 protein expression did not change significantly in the studied periods. The discrepancies between the mRNA and protein abundances for PROK1 and its receptors in the porcine CL may have resulted from the fact that transcription and translation do not always have a linear relationship33. Protein synthesis from a certain copy number of mRNA molecules may be either enhanced or repressed depending on different mechanisms, as described elsewhere34. Moreover, discrepancies between mRNA and protein expression can be caused by the time shift between mRNA and protein synthesis. Most likely, the maximum PROK1 mRNA expression on Day 12 resulted in a peak PROK1 protein expression slightly later on Day 14 of pregnancy. However, because of the luteolytic events occurring in the porcine CL shortly after Day 12 of the estrous cycle, the increase in PROK1 mRNA could not be observed on Day 14 of the estrous cycle. Regarding PROKR2, it is also possible that the increase in PROKR2 mRNA abundance observed on Day 14 of the estrous cycle could have resulted in elevated protein expression later than on Day 14 of the estrous cycle. Another explanation for this discrepancy may be the fact that Western blotting is less accurate than qPCR, and subtle changes may have been hidden.Until now, only gene expression for the PROK1–PROKR system has been reported for the CL in other species. In our study we demonstrated the protein expression of PROK1 and its receptors. The profiles of PROK1 mRNA and protein expression were relatively parallel, with somewhat of a shift in protein expression. Both the mRNA and protein levels were, however, greater on Day 14 of pregnancy than on Day 9 of pregnancy. The expression of 22 and women36.Although there are numerous reports on PROK1 in the CL in other species, our study is the first concerning PROK1 and its receptor gene and/or protein expression profiles in the CL during early pregnancy in any species. The abundance of the PROK1 transcript and protein was elevated in the luteal tissue during pregnancy at the maternal recognition of pregnancy and implantation period. This finding suggests that the role of PROK1 in the corpus luteum may be especially significant during pregnancy in luteoprotective/antiluteolytic mechanisms, ensuring prolonged CL function. Our results correspond with reports of increased PROK1 expression in the pregnant endometrium at the time of embryo implantation in pigs7, has been primarily associated with luteal regression37. However, apoptosis is also an important process for developing CL, in which dynamic changes in tissue remodeling occur7. Moreover, the significant role of the apoptotic process in controlling the immune response  has been described38. Accordingly, the antiapoptotic effect of PROK1 has been demonstrated in different cell types—endothelial cells of CL origin in cattle26.We demonstrated that PROK1 prevented porcine luteal cells from undergoing apoptosis; therefore, PROK1 supports the function of the corpus luteum during early pregnancy, when elevated levels of PROK1 are observed. Apoptosis, a type of programmed cell death27 and human corpora lutea29. Colocalization of PROK1 and PROKRs in blood vessels and steroidogenic cells implies the significant effect of PROK1 on crucial processes related to corpus luteum function: steroidogenesis and angiogenesis.The expression of PROK1 and its receptors PROKR1 and PROKR2 was localized in porcine steroidogenic cells and blood vessels in the porcine CL during the mid- and late luteal phases and early pregnancy. This finding suggests that PROK1 acts in an autocrine and/or paracrine manner in porcine luteal tissue. Similar results for PROK1 localization have been described for bovineHSD3B1, STAR, and CYP11A1) was found to be upregulated by PROK1 only on Day 12 of pregnancy. This could have been caused by measurement of the gene expression and P4 secretion at the same time-point. The increase in P4 might have occurred later in the tissue collected on these specific days. Another explanation is that PROK1 regulates the synthesis of P4 in the CL on Day 14 of pregnancy/or the estrous cycle via mechanisms other than direct regulation, such as cholesterol or LDL uptake or storage, progesterone metabolism or action of PROK1-induced VEGFA. Another possible explanation for the increased P4 secretion by the luteal tissue collected from the regressing CL on Day 14 of the estrous cycle is that the potential effect of PROK1 was maintained when the tissue was separated from the luteolytic signals present in vivo. The lack of suppression of P4 secretion by luteal tissue collected on Day 14 of the estrous cycle may indicate a limitation of this in vitro model; hence, our conclusions on the role PROK1 in porcine CL during luteolysis should be interpreted with caution. However, the results on gene expression and P4 secretion were consistent for luteal tissue collected on Day 12 of pregnancy. Our results show the effects of PROK1 on the expression of genes involved in P4 synthesis and on P4 production by luteal tissue, which thus far have not been studied in any species. A previous study using the luteinized human granulosa cell line SVOG implied that steroidogenesis and the increased transcript abundance of the PROK1 gene could be positively correlated; however, no direct experiments have been performed to demonstrate this relationship20. Our findings are consistent with the increased abundances of STAR and HSD3B1 mRNA in the porcine CL on Day 14 of pregnancy39.We found that prokineticin 1 stimulated the secretion of P4 by CL explants collected on Day 12 of pregnancy and on Day 14 of pregnancy and the estrous cycle. On the other hand, the expression of genes involved in steroidogenesis  or close to the interstitial space (29%) because luteal cells consume two to six times more oxygen per unit weight than the liver, the kidneys, or even the heart1. During the mid-luteal phase and early pregnancy, when PROK1 expression in the porcine CL is high, angiogenesis is an intensive process that ensures luteal tissue with nutrients and substrates for P4 synthesis. Our findings agree with previous reports indicating PROK1 as a mitogen for endothelial cells41. In endocrine glands, it has been shown that PROK1 regulates microvessel formation by supporting endothelial cell survival, migration, and fenestration18. Moreover, an in vivo study confirmed the angiogenic role of PROK1 because administering PROK1 into rat ovaries intensified angiogenesis9. Our findings also suggest that PROK1 regulates angiogenesis indirectly by increasing the luteal gene expression of the angiogenic factor ANG and the secretion of VEGFA by porcine luteal tissue. In the porcine CL, the abundance of VEGFA mRNA and protein is elevated during the mid-luteal phase of the estrous cycle and drops at the onset of luteolysis8. Similarly, in bovine CL, the levels of VEGFA mRNA tend to decrease during the late luteal phase and in the regressing CL27. However, the results of the present study showed that the protein abundance of PROK1 in the porcine CL was not changed in the analyzed days of the estrous cycle, whereas in the bovine CL, the mRNA levels of PROK1 increase at the onset of luteolysis27. Intriguingly, our results showed that in cyclic pigs, PROK1 increased VEGFA secretion by porcine luteal tissue at the onset of luteolysis, but in pregnant pigs, PROK1 stimulated VEGFA secretion by luteal tissue at both the time corresponding to the luteal phase (Day 12) and the time corresponding to the onset of luteolysis (Day 14). This, in turn, may suggest different roles for PROK1 depending on the physiological state. PROK1 may contribute to processes controlling luteolytic events in the regressing CL, whereas during pregnancy, PROK1 may support mechanisms regulating angiogenic processes in the functioning CL. Moreover, our results are consistent with previous reports indicating that PROK1 increases VEGFA mRNA expression in bovine luteal steroidogenic cells26. In addition, a similar relationship between PROK1 and VEGFA secretion has been reported for endometrial explants in pigs22.The present results indicated that PROK1 increased capillary-like structure formation by luteal endothelial cells, which stimulated angiogenesis in the porcine CL. Interestingly, angiogenesis in the CL is more intensive than in any other organ and similar to that in a rapidly growing tumorSTAR, HSD3B1, and CYP11A1), and elevates P4 synthesis by luteal tissue. PROK1–PROKR1 signaling also stimulates capillary-like structure formation by luteal endothelial cells and promotes angiogenesis by increasing VEGFA secretion and angiogenin mRNA expression in luteal tissue. Therefore, we concluded that PROK1 is an important regulator of porcine CL function during the mid-luteal phase of the estrous cycle and early pregnancy.In conclusion, our study thoroughly elucidates the role of PROK1 in the porcine corpus luteum during the mid- and late luteal phase of the estrous cycle and early pregnancy. Our results indicate that PROK1, which is abundantly expressed in the porcine corpus luteum at the mid-luteal phase of the estrous cycle and early pregnancy, participates in processes related to CL function, such as progesterone synthesis, luteal cell apoptosis and viability, and angiogenesis Fig. . Our resThe ovaries used in our research were collected from animals bound for commercial slaughter and meat production. The use of animals was in accordance with the Act of 15 January 2015 for the Protection of Animals Used for Scientific or Educational Purposes, Directive 2010/63/EU of the European Parliament and the Council of 22 September 2010 on the protection of animals used for scientific purposes and conducted in accordance with the national guidelines for agricultural animal care, and the ARRIVE guidelines. Experiments were not carried out on human tissue or cells samples.Gilts of similar age and genetic background from one commercial herd were observed for the onset of estrus. After two natural estruses, the animals were randomly divided into two groups: pregnant and cyclic. Gilts assigned to the pregnant group were artificially inseminated at 12 and 24 h after the onset of estrus (Day 0). Animals were slaughtered in a local slaughterhouse on Day 9, 12, or 14 of pregnancy or the estrous cycle . Reproductive status (cyclic/pregnant) was assessed based on the presence and morphology of conceptuses and the concentration of progesterone in plasma from jugular vein blood  were immunolocalized in the porcine corpora lutea on Days 12 and 14 of the estrous cycle and pregnancy  as described previously42, with some modifications. Briefly, the ovaries were collected from gilts on Days 12 and 14 of the estrous cycle or pregnancy , placed in medium M199  supplemented with antibiotics  and 0.1% (wt/vol) bovine serum albumin  and transported to the laboratory on ice. Then, a corpus luteum was separated from the rest of the ovary and cut out using a tissue coring tool. Luteal tissue was transferred onto a Krumdick Tissue Slicer . Tissue explants of similar dimensions and weight  were cut and collected in a Petri dish filled with medium M199. The luteal explants were then transferred to 6-well plates . For one hour, the explants were preincubated in medium with or without a PROKR1 antagonist (2-(5-(4-fluorobenzyl)-1-(4-methoxybenzyl)-1,4,5,6-tetrahydro-4,6-dioxo-1,3,5-triazin-2-ylamino)-ethyl)-guanidine; PC7, 10 μM). Then, the medium was changed, and the explants were incubated for 18 h in a humidified atmosphere containing 95% air and 5% CO2 at 37 °C with gentle shaking with either vehicle  or PROK1  in the presence or absence of a PROKR1 antagonist . The IC50 for PROKR1 is 36 nM, whereas that for PROKR2 is 4400 nM43. The amino acid sequence similarity of PROKR1 between pigs and mice (the species for which the antagonist was tested for the first time) was 83.46%. The antagonist was kindly donated by Dr. Gianfranco Balboni from the University of Cagliari in Cagliari, Italy. The doses of PROK1 and PC7 were chosen based on previous studies44. Following incubation, the luteal explants and culture media were snap-frozen in liquid nitrogen and transferred to -80 °C for future analyses. The expression of genes involved in P4 synthesis  in the luteal explants was investigated by qPCR, as described in the section “Gene expression analyses” provided in the The effects of PROK1 on the expression of genes involved in steroidogenesis as well as on progesterone synthesis by luteal tissue explants were studied using an in vitro model of precision-cut luteal slices as described previouslyTo investigate the effects of PROK1 on the apoptosis/viability of luteal cells, we isolated steroidogenic cells from porcine CL. Ovaries were collected from gilts on Day 12 of the estrous cycle . Luteal tissue was dissected from the surrounding ovarian tissue and mechanically fragmented using a scalpel. Subsequently, the tissue was enzymatically digested in 0.07% (w/v) collagenase  solution in M199 medium supplemented with 1% BSA for 70 min at 37 °C. The total cell suspension contained luteal and endothelial cells. The post-digested cell suspension was then filtered through gauze. Luteal cells were separated from the obtained supernatant through a series of centrifugations . The homogeneity of the isolated cells was confirmed by immunodetection of the luteal cell marker HSD3B1. Detailed information on luteal cell characteristics is provided in the 5 in M199 medium with 10% newborn calf serum . After 24 h, the cells were treated with vehicle or 40 nM PROK1 in the presence or absence of 1 µM PC7 for 20 h. After incubation, the cells were detached enzymatically using Accutase solution , and apoptosis was assessed using the flow cytometer FACS Aria II  and a FITC Annexin V/Dead Cell Apoptosis Kit  according to the manufacturers’ instructions. The cell populations were classified into three groups: live cells, apoptotic cells, and dead cells. The experiment was repeated 11 times .Luteal cells were seeded into 6-well plates at a concentration of 4 × 10To determine whether PROK1 participates in angiogenesis in the porcine corpus luteum, an in vitro model of a capillary-like structure formation assay and a model of precision-cut luteal slices were used.2 at 37 °C. The homogeneity of the isolated cells was confirmed by examining the cell cultures for the presence of the endothelial cell marker vWF. Detailed information on the endothelial cell characteristics is provided in the Endothelial cells were isolated during the luteal cell isolation procedure (described in Experiment 4). For isolation of endothelial cells, the supernatant obtained during the first three centrifugations was collected and kept on ice. The collected supernatant was filtered (filter with a pore diameter of 15 µm) and centrifuged at 300 × g for 10 min. Subsequently, the cell precipitate was suspended in culture medium MCDB 131 , including 10% NCS and 50 μg/mL endothelial cell growth supplement , and cells were seeded onto a sterile plate. The cells were cultured in a humidified atmosphere containing 5% CO45. Briefly, isolated endothelial cells from the corpora lutea (3 × 105 cells/mL) were suspended in MCDB-131 medium with or without PC7 (1 μM) and preincubated for 20 min in a humidified atmosphere containing 5% CO2 at 37 °C. Subsequently, the cells were treated with PROK1 (40 nM) or vehicle (the control). The cells were then plated onto μ-Slide angiogenesis plates  covered with growth factor-reduced Matrigel  and incubated for 5 h at 37 °C in a humidified atmosphere containing 95% air and 5% CO2 in a Zeiss Axio Observer System (Carl Zeiss Microscopy). Single wells were photographed at 30-min intervals. The photographs were analyzed using ImageJ software with an angiogenesis plug-in46. This software is able to measure and quantify morphological structures formed by endothelial cells. The parameters describing the formation of the capillary-like networks were defined as described earlier47. Pixels with at least three neighbors were defined as nodes. Junctions were referred to as nodes or fused nodes. Elements limited by two junctions/nodes were defined as the segments. The branches consisted of elements delimited by a junction and one extremity, whereas the pieces of three, limited by two junctions (but not exclusively associated with one branch), were considered master segments. At least three linked master segments were defined as master junctions. Areas limited by the segments or master segments were considered meshes. The experiment was repeated 10 times  in duplicate (each treatment was duplicated).A capillary-like structure formation assay was performed as described previouslyANG), angiopoietin 2 (ANGPT2), fibroblast growth factor 2 (FGF2), fms-related receptor tyrosine kinase 1 (FLT1), and kinase insert domain receptor (KDR), and the secretion of VEGFA in response to PROK1 treatment were evaluated. The level of VEGFA in the culture medium was determined by enzyme-linked immunosorbent assay . The expression of genes associated with angiogenesis, including angiogenin . All statistical analyses were conducted using GraphPad PRISM v. 9.0.0 software .The results obtained in Experiment 1 were assessed using two-way ANOVA, followed by Bonferroni multiple comparison post-test. The results from Experiment 3, studying the effect of PROK1 on the expression of angiogenic genes and VEGFA secretion (Experiment 5) and assessment of P4 concentration in jugular vein blood plasma collected from cyclic and pregnant gilts were evaluated by two-way ANOVA, followed by Tukey’s post-test. The results obtained from the apoptosis assay (Experiment 4), capillary-like structure formation assay (Experiment 5) and luteal explants viability after treatment were assessed using one-way ANOVA, followed by Tukey's multiple comparisons post-test. The results from luteal explants viability before and after incubation were analyzed using T-tests. Differences were considered statistically significant at a 95% confidence level (Ovaries used in our research were collected from animals bound for commercial slaughter and meat production. The use of animals was in accordance with the Act of 15th of January 2015 on the Protection of Animals Used for Scientific or Educational Purposes, Directive 2010/63/EU of the European Parliament and the Council of 22nd of September 2010 on the protection of animals used for scientific purposes and conducted in accordance with the national guidelines for agricultural animal care, and the ARRIVE guidelines. Experiments were not carried out on human tissue/cells samples.Supplementary Information.


GPR151 is a kind of protein belonging to G protein-coupled receptor family that is closely associated with a variety of physiological and pathological processes.The potential use of GPR151 as a therapeutic target for the management of metabolic disorders has been demonstrated in several studies, highlighting the demand to explore its activators further. Activity prediction serves as a vital preliminary step in drug discovery, which is both costly and time-consuming. Thus, the development of reliable activity classification model has become an essential way in the process of drug discovery, aiming to enhance the efficiency of virtual screening.We propose a learning-based method based on feature extractor and deep neural network to predict the activity of GPR151 activators. We first introduce a new molecular feature extraction algorithm which utilizes the idea of bag-of-words model in natural language to densify the sparse fingerprint vector. Mol2vec method is also used to extract diverse features. Then, we construct three classical feature selection algorithms and three types of deep learning model to enhance the representational capacity of molecules and predict activity label by five different classifiers. We conduct experiments using our own dataset of GPR151 activators. The results demonstrate high classification accuracy and stability, with the optimal model Mol2vec-CNN significantly improving performance across multiple classifiers. The svm classifier achieves the best accuracy of 0.92 and F1 score of 0.76 which indicates promising applications for our method in the field of activity prediction.The results suggest that the experimental design of this study is appropriate and well-conceived. The deep learning-based feature extraction algorithm established in this study outperforms traditional feature selection algorithm for activity prediction. The model developed can be effectively utilized in the pre-screening stage of drug virtual screening. G protein-coupled receptors (GPCR) are the largest family of membrane protein receptors in the mammalian genome, widely distributed in the central nervous system, immune system, cardiovascular and other organs and tissues. They are involved in both physiological and pathological processes including nociception. In recent years, GPCR research is highly sought after by pharmaceutical companies due to the potential of GPCRs as candidate targets in the search for new therapeutics. GPR151 is an orphan receptor belonging to the class A GPCR family that is highly enriched in receptor nuclei neurons. It plays a role in regulating mood, stress, nicotine withdrawal and preventing obesity. GPR151 is abundantly expressed in the dorsal root ganglia and is closely associated with nociception, making it a potential drug target for treating a variety of psychiatric, neurological, and metabolic disorders. Xia et al.  found thz et al.  demonstrz et al.  showed tTraditional screening of molecular activity is accomplished through high-throughput screening experiments. Existing techniques for investigating GPR151 activators are usually cell-based experimental studies which are expensive in research progress. Drug virtual screening technology has emerged as a cost-effective and efficient method for modern drug development, offering a new way to reduce costs and increase the probability of drug discovery. Despite the advantages of virtual screening methods, they still face certain challenges. Firstly, when the active pocket of a target protein is unknown or cannot be determined, it may be necessary to traverse the protein structure space to obtain optimal docking results, which can decrease the accuracy of docking results and lead to computational inefficiencies. Secondly, traditional molecular docking-based virtual screening can be time-consuming and labor-intensive. The resource and time consumption are still significant and unaffordable for many users even when using high-throughput screening to optimize performance.The PubChem database has incorporated four high-throughput screening experimental datasets for small molecule activators of GPR151 since 2020. The comprehensive dataset provides a valuable resource for conducting broader molecular activity studies of GPR151 activators. The recent surge in machine learning and deep learning techniques has accelerated the development of intelligent systems in the field of molecular research. Artificial intelligence-assisted drug design (AIDD) , 7. CurrMolecular features can be SMILES sequences , moleculThis paper proposes a data collection method aimed at assisting molecular docking computations, thereby facilitating the rapid virtual screening of large molecular databases for drug discovery. The method incorporates molecular docking computation and high-throughput experimental data to generate consistent GPR151 activator datasets. Additionally, we propose an improved algorithm which utilizes the idea of bag-of-words model in natural language processing to densify the sparse fingerprint vector. We also systematically compare the performance of various classical feature selection algorithms, deep learning models and traditional classifiers for molecule activity prediction tasks and find out the best model Mol2vec-CNN. To assess generalization performance, an experiment based on ZINC sub database is performed for well-performing deep learning models. The activity prediction labels of the trained model show an agreement of over 70% with results obtained from molecular docking software. These findings have a significant impact on the efficiency of screening active compounds from large molecule databases.A molecular fingerprint is a condensed representation of a molecule that encodes its structural features into fixed-size arrays of bits for comparison. The typical process is to extract the molecular fragments and then hash them to generate bit variables, where each bit relates to a molecular fragment. This study employs two molecular fingerprint algorithms: topological fingerprint (RDKFP) and morgan fingerprint (MorganFP). RDKit version 2022.3.5  is applinth feature corresponding to code n is an integer). Each compound is seen as a sentence and the bit marked as 1 in the molecular fingerprint indicates the presence of the word in the sentence, with the corresponding code recorded. All valid bit numbers in the 2048-dimensional fingerprint are traversed to acquire a coding vector, then the code matrix is padded and input into the embedding layer of the neural network to transform the number matrix into dense feature vector. The Mol2vec features  are processed using the open library (https://github.com/samoturk/mol2vec) and fed into different deep models for further feature extraction.In this paper, we use traditional feature selection methods and deep learning methods to carry out further extraction for molecular fingerprint and Mol2vec to enhance model characterization capability. The study employs three traditional feature selection methods, namely principal component analysis (PCA)  linear dAfter using traditional algorithms and deep neural network for molecular feature extraction, this study employs five basic machine learning classifiers, namely logistic regression (LR), k-nearest neighbor (KNN), random forest (RF), decision tree (DT), and support vector machine (SVM) for activity classification. These classifiers are also compared with the softmax classifier of the deep neural network. The key flow is illustrated in Fig. fs steps, following equilibration at NVT and NPT ensemble for adequate sampling through energy fluctuations, Root-Mean-Square-Deviation (RMSD) and Root-Mean-Square-Fluctuation (RMSF). The modeled GPR151 receptor is found to have an average energy of The available conformation of the GPR151 receptor has an influence on molecular docking results, with the receptor’s activation state also playing an important role . PreviouAfter obtaining a reasonably relaxed three-dimensional structure, a large active pocket is selected based on the common structural characteristics of the seven helices of GPCRs.The helical region is enclosed and molecular docking calculations are performed using AutoDock Vina  and AutoThe hardware environment for molecular dynamics and molecular docking calculations is the “ORISE” supercomputer with a single node equipped with 32 core x86 processors and 4 GPU accelerators at a base frequency of 2.0 GHz. AutoDock Vina calculations are performed on 32 CPU cores with 8 cores running in parallel intranode. AutoDock GPU utilizes 4 GPU cards to perform 4 tasks simultaneously intranode.https://pubchem.ncbi.nlm.nih.gov/bioassay/), as shown in Table The GPR151 activator molecular datasets in this paper are gathered from PubChem Bioassay , false negative(FN), false positive(FP) and true negative(TN). Accuracy represents the proportion of all records with correct predictions out of the total. Precision is the percentage of true predictions in all predicted positive results. Recall is the proportion of correctly predicted positive molecules to all actual positive ones. The Precision and Recall rate are interdependent, with one affecting the other. F1 score represents the average of them, with higher values indicating better model quality. These metrics are calculated as follows:For GPR151 activator activity prediction task, this paper first compares the performance of three raw molecular features  and five basic classifiers  using the test set’s performance metrics as the benchmark result. The results are shown in Table To extract and learn better features, we perform the preprocessing operation shown in Fig. Table zinc20.docking.org) in 2020, which contains over 1.4 billion compounds. After consistent collection, we select 300 active and 500 inactive molecules from the AID1508602 dataset as the parent samples. Similar molecules of GPR151 activator are obtained from ZINC20-ForSale-22Q1-1.6B database and further filtered according to the values of ecfp4 and daylight, which are both greater than the value of 0.5. After de-duplication, the final number of molecules is about 10k.Professor John J. Irwin of UCSF  releasedTo validate the efficacy of the model in this study, we conduct experiments to compare the activity prediction results of the model with the docking computational results in the set of above molecules from above mentioned ZINC database. Molecular docking computation is performed using AutoDock GPU with the same environment and parameter settings as reported in the docking preparation section. The consistency between docking results and the ideal three predicted models  is evaluated through three metrics: Consistency, Active-Recall and Inactive-Recall which can be calculated by the following equations:As shown in previous results, traditional feature selection algorithms have shown certain advantages compared to initial molecular features. On the other hand, deep learning algorithms exhibits strong learning abilities in high-dimensional features and performs well in the molecular field. From the results section, it appears that the deep model are valid and applicable on different types of molecular features. The encoding and padding operations of molecular fingerprints make the features more dense and lay the foundation for feature input of neural networks. Mol2vec calculates the molecular substructure vector and leverages neural networks to extract highly effective features that can enhance prediction accuracy.To enhance the interpretability of the model, we attempt to use t-SNE dimensionality reduction and visualization methods. By employing t-SNE, we can effectively visualize the high-dimensional data in a lower-dimensional space, making it easier to explore the correlations and distributions of the features. The feature extraction steps on RDKFP, MorganFP are visualized as shown in Figs. The time complexity of the proposed method primarily involves two tasks: calculating molecular features and training the model. While the calculation time for molecular fingerprints and Mol2vec increases linearly with the number of input molecules, the input features of deep neural networks remain constant, resulting in consistent calculation time during model training. The spatial complexity of our method is independent of the number of input molecules, as the dimensions of the molecular fingerprint and Mol2vec features remain fixed at 2048 and 100, respectively.However, our model still face several challenges. Figures To summarize, the prediction performance of the test set can generally be improved by implementing molecular feature representations after neural network feature extraction. The LSTM model is more suitable for molecular fingerprints, while the CNN model is more appropriate for Mol2vec features. The performance of different classifiers varies, with svm yielding the best result for Mol2vec features extracted by CNN. This model can be applied to screen active molecules from massive databases. Our designed GPR151 activator classification model achieves over 70% accuracy in screening active molecules in large molecular datasets. Furthermore, deep learning model significantly accelerates the screening speed, thereby reducing time consumption. The molecular activity prediction model is executed on a single CPU of 12th Gen Intel(R) Core(TM) i7-12700 while AutoDock Vina and AutoDock GPU are executed on the “ORISE” supercomputers, with calculation time of 109 h on 32 CPUs and 1 h on 4 GPUs to process 10,000 molecules, respectively. However, the prediction time of our deep learning model is controlled within a few minutes, which is ten times faster than traditional molecular docking. This provides a novel method for rapidly enriching potential active compounds for large-scale virtual screening in drug discovery.In this paper, we propose a molecular fingerprint enhancement algorithm that preprocesses bit-sparse fingerprint features using the idea of bag-of-words model in natural language processing. By combining this encoding step with neural network models for feature extraction, we can better extract effective information from molecules than traditional feature selection algorithms such as PCA, resulting in improved classifier performance. Moreover, we perform experiments on different types of molecular features, neural networks, and classifiers to systematically compare the adaptability of multiple network structures and classifiers for molecular features. Our optimal model, Mol2vec-CNN-SVM, achieves an accuracy and F1 score of 92% and 76%, respectively. Our model’s effectiveness is demonstrated through generalization experiments on large-scale databases. The model is capable of narrowing down the range of potential compounds in the initial stages of virtual screening through activity classification, which assists molecular docking in rapidly identifying active molecules. In our future research, we will focus on improving the encoding rules and exploring state-of-art artificial intelligence architectures to optimize our proposed method. We will continue to explore molecular property prediction model to provide more reliable results for virtual screening.