As these undesirable effects can limit patient compliance, an alternative lithium compound with a lesser toxicity profile would dramatically improve treatment efficacy and outcomes. Lithium orotate , a compound largely abandoned since the late 1970s, may represent such an alternative. LiOr is proposed to cross the blood–brain barrier and enter cells more readily than Li2CO3, which will theoretically allow for reduced dosage requirements and ameliorated toxicity concerns. This review addresses the controversial history of LiOr, complete with discussions of experimental and clinical efficacy, putative mechanisms of action, adverse effects, and its potential future in therapy.Bipolar disorder (BD) poses a significant public health concern, with roughly one‐quarter of sufferers attempting suicide. BD is characterized by manic and depressive mood cycles, the recurrence of which can be effectively curtailed through lithium therapy. Unfortunately, the most frequently employed lithium salt, lithium carbonate (Li 2CO3 is an efficacious therapeutic option in BD, but a troublesome side effect profile limits patient compliance. LiOr may represent an alternative form of lithium that can enter the brain more readily than Li2CO3, which would theoretically allow for maintenance of efficacy at reduced dosages, thereby mitigating non‐compliance concerns by minimizing the incidence of dose‐dependent side effects.Li Unsurprisingly, patient non‐compliance with Li2CO3 treatment is a frequently encountered issue . LiOr is most notable for its early use and advocacy by the controversial Hans Nieper in the 1970s, who proposed that orotic acid was a superior carrier compound that could more readily transport inorganic ions across biological membranes  why Li2CO3 rose to prominence, (3) the issues associated with Li2CO3‐based therapy, (4) why research into LiOr stagnated, and (5) how future explorations into LiOr as an option in the management of BD are needed.Considering the lack of literature surrounding LiOr, this review aims to call attention to (1) the history of Li22.1BD is a relatively prevalent mood disorder with an incidence rate exceeding 3% in North America. BD poses a significant health risk, with roughly one quarter of patients attempting suicide  at the catalytic core of each protein  , clay beds, and bedrock mines throughout South America, North America, Australia, and China. Lithium is so abundant in these areas that it frequently makes its way into both food and water supplies. Intriguingly, the absence/decreased levels of lithium in drinking water are associated with increased incidence of a host of harmful mood‐related conditions  original safety concerns surrounding alternative compounds such as lithium chloride, (2) Li2CO3 being the first and subsequently most studied formulation for psychiatric use, and (3) Li2CO3 being the first commercially produced salt for prescription.From these humble beginnings, lithium has gone on to become the arguable gold standard for the management of the manic component of BD and the prevention of mood episode recurrences  across the blood–brain barrier (BBB) and into cells via sodium transporters such as the sodium‐hydrogen antiporter (NHE)  decreases thyroid hormone synthesis and release, leading to elevated serum thyroid stimulating hormone (TSH) Gitlin, , (2) inc62CO3, the very existence of alternative lithium salt compounds demands further exploration of their efficacy and safety. Might an underexplored lithium compound exist that is superior to Li2CO3?The carbonate‐based salt formulation of lithium is among the mainstay treatment options for the prevention of mood‐episode recurrences, but are alternative lithium salt compounds similarly efficacious? Early in its use in medical applications, lithium was administered via a variety of different formulations, such as lithium bromide Hammond,  and lith6.12CO3 is highly bioavailable following oral administration, with an estimated 80–100% of the ingested dose absorbed through the upper intestinal tract  the administration of multiple daily doses (less Li2CO3 ingested per dose) in place of bulk dosing  in the brain than was observed for equivalent doses of Li2CO3  effectively closed the gates on future inquiries into the use of LiOr. While the caution advised is understandable, further research into the effects of LiOr on both kidney function and serum/brain Li+ levels must be conducted across a range of concentrations before LiOr can be ruled out for use in psychiatric applications; after all, the doses employed by both Kling and Smith were quite high  indirectly, through its downstream metabolites uridine, β‐alanine, and carnosine. While speculative in nature, the putative benefits conferred by orotic acid and its metabolites may offer an additional boon to LiOr that is absent in Li7.2Orotic acid‐based mineral salts were presented by Nieper in the 1970s, who noted success in the use of calcium diorotate and LiOr in the recalcification of bone metastases Nieper, , respectWhile the existing literature suggests that orotate salts present an efficacious means by which to deliver minerals, modern research is lacking. Studies on both the potential efficacy of orotic acid as a mineral carrier and its mechanisms of action in accomplishing said goal of mineral delivery are needed.7.32CO3 demonstrated in animal studies  brain Li+ concentrations than what are observed for equivalent doses of Li2CO3 post‐administration . Thus, studies examining the membrane transport mechanisms of LiOr, and any connections they may have to the reported elevations in serum and brain Li+ content relative to Li2CO3, are needed.Shortly thereafter, in 1979, Smith and Schou reported a potential for increased risk of renal toxicity for LiOr relative to Li2CO3. Comparisons of the pharmacokinetics, safety, and efficacy of LiOr and Li2CO3 are sorely lacking in the literature.To date, these studies appear to be the only cases directly contrasting LiOr and Li7.6.22CO3 demonstrates only mild efficacy at therapeutic dosages, that is, >600 mg  discussed previously. While no clinical trials for the use of LiOr in BD have been conducted, LiOr has shown success in promoting cessation of alcohol abuse when administered daily at 150 mg for 6 months; out of the 42 patients involved, 23 remained without relapse for 1–10 years Sartori, . While L8There is one certainty regarding the future of LiOr in psychiatric applications: more research is needed. Orotic acid has shown promise as a mineral carrier for calcium Nieper, , magnesi2CO3, studies exploring its efficacy in both basic science and clinical settings need to be conducted. This need is especially pressing given the nutraceutical status of LiOr, and the consequent wide‐spread use of the compound within the self‐medicating population. Furthermore, some physicians and alternative health practitioners recommend small doses of LiOr to their patients, plausibly for its mood stabilizing properties at such concentrations  following LiOr and Li2CO3 administration may address some of these questions  or in vitro (colorimetric or photoelectric assay of tissue homogenates) assessment of lithium accumulation within various target organs, such as the brain, thyroid or kidney, would effectively address such concerns.Second, it must be determined whether the ease of membrane transmission proposed for LiOr is unique to the BBB, or if organs such as the kidney or thyroid are equally susceptible to accumulation. If they are, then the enhanced membrane transport capacities of orotic acid may still result in toxicity despite reduced dosage requirements. Ex vivo , especially should the enhanced membrane transport capacities of LiOr prove to extend to organs other than the brain.The authors have no conflict of interest to declare.Conception of central ideas and editing: Lane K. Bekar and Anthony G. Pacholko. Writing: Anthony G. Pacholko.All named authors have seen and approved the final version of the manuscript.https://publons.com/publon/10.1002/brb3.2262.The peer review history for this article is available at


A parallel set of titanium screws underwent only the hydroxylation process to present similar acid-etched topography as a control. The presence of the silane on the surface was checked by x-ray photoelectron spectroscopy (XPS), with scanning electron microscopy (SEM) and atomic force microscopy (AFM). A total of 40 titanium screws were implanted in the tibia of ten New Zealand rabbits in order to evaluate bone-to-implant contact (BIC) after 3 weeks and 6 weeks of healing. Silane-coated surface presented higher nitrogen content in the XPS analysis, while micro- and nano-topography of the surface remained unaffected. No difference between the groups was observed after 3 and 6 weeks of healing , although an increase in BIC occurred over time. These results indicate that silanization of a titanium surface with APTES did not impair the bone formation, indicating that this can be a reliable tool to anchor osteogenic molecules on the surface of implant devices.Silane-coating strategy has been used to bind biological compounds to the titanium surface, thereby making implant devices biologically active. However, it has not been determined if the presence of the silane coating itself is biocompatible to osseointegration. The aim of the present study was to evaluate if silane-coating affects bone formation on titanium using a rabbit model. For this, titanium screw implants (3.75 by 6 mm) were hydroxylated in a solution of H Titanium has been widely used as an implant material due to its excellent corrosion resistance, biocompatibility and good mechanical properties such as high tensile strength, high ductility and low density . HoweverTherefore, an implant should allow surface regulation via cellular early-stage attachment for fast reconstruction of the primary implant stability ,12, whicOne particular aminosilane, 3-aminopropyltriethoxysilane (APTES), is frequently used for bioconjugation techniques due to its bifunctional nature. APTES has three alkoxy groups that can attach to titanium hydroxyl groups via siloxane bonds; and on the other side of the molecule, nucleophilic amine groups serve as anchor points for further bonding of bioactive compounds of interest using crosslinking methods ,22. EnhaFor bone healing to occur, numerous cellular and extracellular events take place at the implant/tissue interface. Given that siloxane bonds between APTES and titanium have been shown to be prone to hydrolysis in an aqueous environment ,28, such2SO4/30%H2O2 (dilution ratio 1:1) for 1 h prior to being silanized by dipping in a 10% APTES  in boiling anhydrous toluene  for 4 h under reflux [Dental implants with titanium grade 4, size 3.75 by 6 mm, external hexagon platform  were hydroxylated in a solution of Hr reflux ,19. It ir reflux . BesidesThe titanium implants were subsequently cleaned in toluene and acetone  during 10 min each in an ultrasonic bath to remove unbonded molecules. The implants were dried overnight at 110 °C in a vacuum oven to condensate the hydrogen-bonded silanols into siloxane bonds . The con−10 torr with monochromatic (Al Kα) X-ray radiation as the active source, the chemical composition of each surface was determined.The characterization of the surface was made by scanning electron microscopy (SEM)  at 1500× and 100,000× magnification. Surface topography was evaluated by atomic force microscopy (AFM)  using a 125 nm cantilever in tapping mode . The pictures were digitally processed  to calculate the roughness parameters, including the average height deviation (Sa), the developed interfacial area ratio (Sdr) and the summit density (Sds). By using X-ray photoelectron spectroscopy (XPS)  under 5 × 10The Ethical Committee approved the present study by the protocol number 101443/2012-006, University of Rochester, NY, USA. Moreover, this research was described according to the ARRIVE checklist for the reporting of in vivo experiments. The rabbits were maintained throughout the experimental period in separate cages under conditions of controlled temperature (20 ± 2 °C) and humidity (55% ± 10%). The light-dark-cycle was 12:12 h, and the animals had free access to tap water and a standard pellet rabbit diet.t-test determined a minimum of 9 implants in each group . Given expected attrition or death of animals of 10%, the sample size was defined as 10 implants per group/time point. To reduce the number of animals in the study, 2 implants were randomly applied to each proximal tibia metaphysis for a total of 40 implants inserted in 10 white rabbits, New Zealand race, 9 months old and weighting 3.5–4.0 kg.The sample size was calculated using a priori power analysis based on a previous study that evaluated bone-to-implant contact (BIC) in a similar rabbit model to estimate the effect size . ConsideBefore the insertion of the implants, the rabbits received general anesthesia . Each of the rabbit legs were shaved and treated with disinfectant, chlorhexidine prior to local anesthesia using 1 mL lidocaine at each insertion site. Skin and muscle layers were flapped, and drilling was performed on the bone at low speed with using abundant saline for cooling purposes. After dental implant placement, the fascia layers were sutured using resorbable sutures, and the skin was closed with polypropylene. After the surgery, all animals were transferred to appropriate housing with food and water ad libitum.After 3 weeks and 6 weeks of healing, the rabbits were sacrificed with an overdose of pentobarbital (100 mg/kg) after sedation. All implants and the bone surrounding them were removed and fixed with 4% neutral buffered formaldehyde. Upon dehydration in a graded series of ethanols, the samples were embedded in light-curing resin . A histological section of each implant’s midline, 15 μm thick , was prepared using a cutting and grinding system .t-test at a significance level of 5%.After the histological sections were stained with toluidine blue, BIC values were measured with a microscope  using an imaging software  by a blinded examiner. BIC data were analyzed with an independent − acted as bound water [The titanium screw surfaces that were coated with APTES did not show any changes in surface morphology or roughness . In addind water . In the p > 0.05). After 6 weeks of healing, the newly formed mineralized tissues were found to contain osteocytes and osteoblasts, thus indicating that continuous mineralization of the tissues had occurred (p > 0.05).There was no inflammatory response or adverse reaction observed proximal to any of the implants . The impoccurred . The BICTypically, when bone formation is evaluated before the clinical use of a surface in humans, the tibia and femur of rabbits are the recommended models according to the International Organization for Standardization (ISO 10993-6:2007). These models were used in the present study. Our findings indicate that the coating of a titanium surface with APTES did not affect the morphological surface of the titanium implants tested, and it also did not impair early bone formation. Moreover, similar bone formation starting from the endosteum was observed in both the Ti and APTES-coated groups due to blood vessel disruption. After 6 weeks all the implants were found to be integrated with bone tissue.2 to 5 nmol/cm2 on titanium [Currently, dental implants were manufactured using commercially pure titanium (grade 2 or grade 4) or titanium alloys and receive surface treatments to improve bone formation  Althoughtitanium ,38,39. Ntitanium , assuminThe histological findings of the present study demonstrated no inflammatory infiltrate, bone resorption or foreign body reaction in the periods evaluated, which corroborates titanium’s biocompatibility and indicates the biocompatibility of the APTES-coating. Although it was not statistically significant, the BIC values after 3 weeks of healing indicate lower values for the APTES-coated group, suggesting a slight delay in osseointegration in terms of bone-implant contact rates. However, at 6 weeks, bone formation was equivalent. Unfortunately, there was no other in vivo study for comparison. A previous in vitro study  reported2 group on an APTES-coated surface. When surfaces undergo a curing step at high temperatures, this usually converts any protonated amines to neutral amines, thereby providing more versatility in subsequent applications [In the XPS spectra, the nitrogen peak that was detected indicated that nitrogen is primarily present as an –NHications . Howeverications ,41. Thisications . AlthougThe main drawback of an APTES coating is the hydrolytic instability of the siloxane bond in an aqueous medium, such as blood, leading to a loss of covalently attached silane molecules ,43. As aSilanization of a titanium surface with APTES did not impair bone formation, indicating that this can be a reliable tool to anchor osteogenic molecules on implant devices’ surfaces.


Patients and Methods. A total of 53 patients under treatment with CsA were included. 37 patients with at least one pharmacokinetic profile described with four samples were considered for model building, while the remaining 16 were considered for the assessments of predictive performances. Pharmacokinetic parameter estimation was performed using a nonlinear mixed effect modelling implemented in the Monolix® software ; meanwhile, simulations were performed in R v.3.6.0 with the mlxR package.  To develop a population pharmacokinetic model for Uruguayan patients under treatment with cyclosporine (CsA) that can be applied to TDM. −1 and 0.512 h, respectively; apparent clearance (CL/F) was 30.3 L/h (relative standard error [RSE] ± 8.25%) with an interindividual variability of 39.8% and interoccasion variability of 38.0%; meanwhile, apparent clearance of distribution (Q/F) was 17.0 L/h (RSE ± 12.1%) with and interindividual variability of 53.2%. The covariate analysis identified creatinine clearance (ClCrea) as an individual factor influencing the Cl of CsA. The predictive capacity of the population model was demonstrated to be effective since predictions made for new patients were accurate for C1 and C2 (MPPEs below 50%). Bayesian forecasting improved significantly in the second and third occasions.  A two-compartment model with a first-order disposition model including lag time was used as a structural model. The final model was internally validated using prediction corrected visual predictive check (pcVPC) and other graphical diagnostics. A total of 621 CsA steady-state concentrations were analyzed for model development. Population estimates for the absorption constant (ka) and lag time were 0.523 h A population pharmacokinetic model was developed to reasonably estimate the individual cyclosporine clearance for patients. Hence, it can be utilized to individualize CsA doses for prompt and adequate achievement of target blood concentrations of CsA. With the approval of the calcineurin inhibitor cyclosporine (CsA), a new era in immunopharmacology began. CsA, a cyclic endecapeptide, has been the cornerstone of most immunosuppressive regimens in organ transplantation. In 1994, the Food and Drug Administration (FDA) approved tacrolimus, another calcineurin inhibitor as an effective alternative to CsA. Several studies have shown that the use of tacrolimus is associated with a lower allograft rejection rate compared with CsA . HoweverCsA is extensively metabolized by CYP3A4 and to a lesser extent by CYP3A5 in the liver and in the gastrointestinal tract by CYP3A4, being the content of this enzyme much higher in the intestine than in the liver , 8. CsA T), which has been recognized as the pharmacokinetic metric that better predicts drug response. Several authors proposed monitoring of 2-hour postdose concentration of CsA (C2) as a better surrogate for CsA AUCT [Pharmacokinetic parameters of CsA are highly variable and depend on factors such as age, sex, bodyweight, the pathology of the patient, days posttransplantation, comedication, and creatinine clearance among others –13. In aCsA AUCT –17.Over the past decade, the precision medicine paradigm has emerged, largely supported by technological developments and advances in artificial intelligence methodologies. Under this approach, different strategies are framed to aim the individualization of medical treatments according to the characteristics of the patient. Genetic information, biomarkers, and phenotypic and psychosocial characteristics are used to feed and develop tools that allow distinguishing patients within a population with similar general clinical conditions and therefore adapt the available therapeutic tools to optimize the clinical outcome at the individual level. The use of computational models to support decision-making related to pharmacotherapy is therefore within the precision medicine paradigm. This approach, which belongs to the discipline of pharmacometrics, has been recently addressed as model-informed precision dosing (MIPD) . The goaPharmacometric models provide population pharmacokinetic and pharmacodynamic information, estimating parameters for the dose-dependent mean behavior of drug exposition and effect throughout time and quantifying different levels of variability, mainly the between-subject  and between-occasion  variabilities. During model development, the effects of individual variables on pharmacokinetic and pharmacodynamic processes are recognized and quantified in a covariate model. This framework offers a suitable tool to select the first dose in a new patient according to specific characteristics. Afterwards, when drug concentrations are observed in that patient, data can be included in the model using the population parameters to describe the prior distribution and estimating individual parameters for further dose optimization .The aim of this work was to develop and implement prospectively in the clinical setting a population pharmacokinetic model for the Uruguayan patients under CsA treatment.Pharmacokinetic data from CsA TDM routine of patients under treatment either for organ transplantation or for autoimmune diseases was retrospectively analyzed. Data CsA blood concentrations taken from patients with at least one pharmacokinetic profile described with four samples were included in the analysis for model building. This group of patients was considered for the training data set (Group A).Data including sex, age, bodyweight, medication history, dosage regimen, time of last dose, sampling time, information on concomitant medications, and days posttransplant was collected from the formulary provided by the TDM service. Other relevant information coming from hematological and biochemical tests was obtained from the hospital database system.After the model was developed and internally evaluated, a prospective evaluation of the predictive capacity of the model, in which the above described inclusion criteria were met plus having at least two occasions of routine CsA blood levels monitored over a period of 18 months, was implemented (Group B).Steady-state blood samples were withdrawn at 0, 1, 2, 3, and 4 hours postdosing (C0-C1-C2-C3-C4) and placed into EDTA-containing tubes. Determination of CsA concentrations in whole blood sample was performed using chemiluminescent microparticle immunoassays . The lower limit of quantification was 12.5 ng/mL, and linearity was proven up to 1500 ng/mL. For concentrations located at lower, middle, and higher portions of the calibration range, coefficients of variation (precision) were 4.1, 2.6, and 0.56%, respectively, and relative errors (accuracy) were 4.1, 8.3, and 6.2%, respectively.The population pharmacokinetic analysis was conducted using a Monolix Suite 2019R1 . Briefly, a nonlinear mixed effect model was built through estimation by maximum likelihood using the Stochastic Approximation Expectation Maximization (SAEM) algorithm .Model development was guided with both metrics and graphical diagnostics. Model data fit was assessed with the Akaike information criterion (AIC) and inspection of goodness of fit plots. These included population observations versus model predictions and population residuals and normalized prediction distribution errors (NPDE)  versus tThe uncertainty of the estimated population parameters was calculated via the estimation of the Fisher Information Matrix in Monolix.Different mammillary models were evaluated for the disposition of CsA. Drug transference processes between compartments in all cases were assumed to follow first-order kinetics. Secondly, different modeling strategies for the absorption phase were assessed: immediate first-order absorption; lagged first-order absorption; parallel first-order absorption and transit model absorption.θi is the parameter estimate for the subject i, θpop the typical value for the population, and ηi the subject discrepancy from θ assumed to be normally distributed with mean zero and variance ω2. IIV and IOV were expressed as coefficients of variation (CV), estimated from the respective variance asInterindividual variability (IIV) was tested for each parameter as well as interoccasion variability (IOV) assuming an exponential error model as described below:In the case of IOV, the implementation is analogue, being the gamma of the standard deviation of the interoccasion parameter discrepancies among all patients.Yij stands for the observed concentration and Cij denotes the predicted concentration for the subject i at time j using the pharmacokinetic model described above. Residual error for each observation has therefore an additive  and a proportional  component which are normally distributed with mean of 0 and variances σ, add2 and σ, prop2, respectively.Residual variability was described with a combined error model:Covariate analysis was performed by stepwise forward inclusion and backward deletion, assessing the impact of variables with pharmacological and physiological plausibility of having an impact on CsA pharmacokinetics. Initially, univariate likelihood ratio test was performed for each variable, selecting for inclusion in a full model those variables which reduced the objective function value (OFV) by at least 3.84 points. This magnitude corresponds to a 5% type I error for the null hypothesis of one variable having no effect in the model fit, provided that the compared models are nested. Backward deletion was performed from the full model with a 1% type I error, preserving in the final model those covariates for which exclusion increased the OFV in at least 6.63 points.The impact of continuous and categorical variables over CsA absorption and disposition pharmacokinetic parameters was assessed, including sex, bodyweight, age, comedication, reason of treatment, and creatinine clearance estimated from serum creatinine using the Cockcroft and Gault  formula.m continuous (Covm) and p categorical (Covp) covariates.Typical value of a model parameter (TVP) is described as a function of θn describes the typical parameter value for and ith individual with covariate values  equal to the reference values:  and .m,ref refers usually to the median value across the studied population.Covβm+n and βp+m+n are parameters quantifying the magnitude of the covariate parameter relationship.CSim1) was compared to experimental concentrations (Cexp1) determined by immunoassay analysisCsA blood concentrations were simulated for the first occasion of each patient using population pharmacokinetic parameters obtained in the final model, including patient characteristics for the covariate model. Simulated data , using the population distribution as prior information. This allowed Bayesian forecasting for the CsA whole blood levels in each patient, providing information for dose optimizationCexp2). The individual parameters estimated in step 2 were used to simulate whole blood CsA levels for this second occasion (CSim2). Model performance was then assessed on this second occasion by comparing Cexp2 and CSim2On the second occasion of each Group B patient, a new experimental CsA concentration was obtained at a specific postdosing time  was calculated for all patients as it is shown in equation . The comThese estimators allowed us to evaluate our model and assess the impact of including new experimental data (priors) to update patient individual parameters, comparing model bias and between successive occasion precision not only to evaluate the predictive performance of our model but also to compare the successive occasions.A total of 621 CsA blood concentrations from 37 patients  were included for model building and internal evaluation (training data set). The characteristics of the patients are summarized in The parameters of the final model estimates are summarized in In accordance with previous reports, we found high intra- and intervariability for pharmacokinetic parameters of CsA. For instance, Cl/F showed a high variability both between subjects (IIV = 39.8%) and between occasions (IOV = 38.0%).βClCrea was -0.204, considering that individual Cl can be calculated asi is the individual clearance of CsA, Clpop is the population clearance determined in the PK model, ClCrea is the individual value of creatinine clearance, ClCreapop is the mean value for creatinine clearance (98.62 mL/min), and βClCrea represents how this covariate impacts on the parameter.Age, sex, bodyweight, comedication, and reason of treatment were not identified as significant covariates for CsA clearance. This model was constructed based on retrospective data, and even though hematocrit has been lately related as a covariate affecting CsA population pharmacokinetic models, this information was not available in clinical charts, so it was not included. Only creatinine clearance showed to be a significant covariate for CsA clearance in the PK model. The value of creatinine clearance can be interpreted as a renal function evaluator. The result obtained for The predictive performance data set was comprised by 81 CsA whole blood concentrations from 16 patients included in Group B . The characteristics of the patients are summarized in In this study, a population pharmacokinetic model of CsA was developed and its predictive performance tested, evaluating its routine implementation within the TDM service to make corresponding dose adjustments, therefore maximizing the probability of achieving effectiveness and safety in CsA treatments.Creatinine clearance was the only final significant covariate in the model, showing a negative correlation with CsA apparent clearance, i.e., a lower creatinine clearance is correlated with an increase in CsA apparent clearance. Quantification of this covariate effect is of particular importance to quantify CsA elimination clearance under renal impairment and make proper dose adjustments. Previous studies carried out by our research group on the impact of cardiovascular physiology in pharmacokinetic processes provide background to understand this effect, which we believe affects CsA disposition , 29. In Model implementation in the clinical setting showed interesting results. Predictions made for new patients using only the population pharmacokinetic model and the individual creatinine clearance value were reasonably accurate for C1 and C2, with MPPEs below 50%, which are not too high if we consider CsA pharmacokinetic variability . The estimation of C0 gave poor results for this first occasion. However, this improved remarkably after the inclusion of patient data, reducing the MPPE from 98% to 27% at the third occasion. Accuracy indicators for C1 also improved significantly for the second and third occasions, whereas the prediction of C2 improved also for the second and third occasions. Prediction improvement is shown in Some limitations of the study should be addressed. In our data, the reason of treatment  was analyzed as a covariate resulting in nonsignificance on any of the pharmacokinetic parameters; however, these results may not be conclusive as the number of subjects on each group was small. The lack of multiple validation cohorts is a limitation for the extrapolation of model predictions in other populations; therefore, the reported CYA population pharmacokinetic model should be externally evaluated with target population before implementation in other centers.A population pharmacokinetic model of CsA was developed and implemented to predict CsA whole blood concentrations in the clinical setting for patients under different dosage regimens and pathologies. Creatinine clearance was shown to be the only significant covariate able to partially explain the interindividual variability observed in CsA apparent clearance. The model showed a good predictive performance, which significantly improved with the inclusion of individual patient data and Bayesian forecasting, standing out as a valuable tool to support dose optimization.


We review all the existing published literature on the effect of elevated CO2 (eCO2) on flowering time, nectar and pollen production and plant–pollinator interactions. We also conduct a field experiment to test the effect of eCO2 on bluebells and their pollinators. We found that few studies have assessed the impact of eCO2 on pollination, and our field data found that bluebells flowered on average 6 days earlier under eCO2 conditions. Hoverflies and bumble bees were the main visitors to bluebell flowers, but insect activity was low early in the flowing period. Although we did not find a difference in the number of visits made by insects to bluebell flowers under eCO2, or the amount of seeds those flowers produced, the change in the timing of flowering could mean that a mismatch could develop between bluebells and their pollinators in the future, which would affect pollination success.Climate change is having a profound impact on pollination systems, yet we still do not know to what extent increasing concentrations of carbon dioxide (CO2 (eCO2) on plant–pollinator interactions is poorly understood. This study provides the first systematic review of this topic and identifies important knowledge gaps. In addition, we present field data assessing the impact of eCO2 (150 ppm above ambient) on bluebell (Hyacinthoides non-scripta)–pollinator interactions within a mature, deciduous woodland system. Since 1956, only 71 primary papers have investigated eCO2 effects on flowering time, floral traits and pollination, with a mere 3 studies measuring the impact on pollination interactions. Our field experiment documented flowering phenology, flower visitation and seed production, as well as the abundance and phenology of dominant insect pollinators. We show that first and mid-point flowering occurred 6 days earlier under eCO2, but with no change in flowering duration. Syrphid flies and bumble bees were the dominant flower visitors, with peak activity recorded during mid- and late-flowering periods. Whilst no significant difference was recorded in total visitation or seed set between eCO2 and ambient treatments, there were clear patterns of earlier flowering under eCO2 accompanied by lower pollinator activity during this period. This has implications for potential loss of synchrony in pollination systems under future climate scenarios, with associated long-term impacts on abundance and diversity.The impact of elevated CO Insect-mediated pollination is required by the majority of angiosperm species in order to achieve sexual reproduction . This ec2) are also hypothesised to influence plant–pollinator interactions, e.g., through impacts on plant growth, biochemistry, physiology, and phenology [2 on pollination interactions are limited. This is an important gap in our knowledge because any negative impacts eCO2 confers on these interactions could adversely impact populations of both the plants and their pollinators. This would represent yet another stressor potentially contributing to declining pollinator populations in combination with other factors such as habitat loss and fragmentation, agrochemicals, pathogens and alien species [Elevated concentrations of atmospheric carbon dioxide , temperature (soil and air), nutrient availability  and water availability (precipitation and soil moisture) are also all known to affect flowering time [2 on flowering phenology have used propagated plants in controlled environments, but this approach misses potential interacting effects of other variables such as local environmental/microclimate conditions and species interactions. Given that the scale and direction of the response to eCO2 can vary depending on species and context, further empirical studies are necessary in order to explore these responses in different species and systems, particularly in situ ecosystem (field)-scale experiments.A key mechanism by which eCOand Ward  of studiing time ,15, furt2 could affect pollination is by altering the amount and/or biochemical composition of floral resources. Pollen and nectar are the primary currency in plant–pollinator interactions, and so any changes in the quantity or quality of this resource could have significant impacts on flower-visiting insects. Pollen is an important protein and lipid source for many insect species, including hoverflies , and isita pepo , and a dago spp. . In othesiifolia  and Loblus taeda . Nectar us taeda . There ius taeda , yet datd Hughes  reportedd Hughes  noted ans leaves ,25, but s leaves , as wells leaves . Thus, e2 may affect pollination via other pathways, such as interfering with the production or detection of floral volatiles and thus disrupting plant–pollinator communication [2. Empirical data from in situ, ecosystem-scale experiments are required, therefore, to answer these complex questions.Beside the impact on phenology and floral resources, it is also possible that eCOnication . As poll2 on ecosystem processes such as pollination is particularly difficult in complex ecosystems such as forests. This is due to the difficulty of manipulating CO2 concentrations at an appropriate spatial scale. Free Air Carbon Dioxide Enrichment (FACE) experiments are an invaluable tool in this context, where unenclosed forest/woodland plots are fumigated in situ and ecosystem responses measured to provide vital real-world data [2 since 2012 [2 since 2017, and provides the perfect opportunity to examine the impact of eCO2 on plant–pollinator interactions in this important temperate ecosystem.Exploring the consequences of eCOrld data . There ance 2012 , but thi2 on floral traits and pollination in order to highlight key knowledge gaps for future study, and (2) to assess the impact of eCO2 on plant–pollinator interactions within a mature, deciduous woodland, using the common bluebell (Hyacinthoides non-scripta) as a case study.Against this background, the current study had the following objectives: (1) to undertake the first systematic review of the literature investigating the effect of eCO2, following published guidelines [2 OR “carbon dioxide”) AND ((‘flower* time’ OR ‘flower* phenology’) OR (pollinat* OR nectar OR pollen))). The search was also performed using the same search terms in the online search engine Google Scholar, and the first 80 results, sorted by relevance, were included. The results of the search were assessed against the inclusion criteria by examination of the abstract, and further exploration of the text where this was unclear. The inclusion criteria were set as: (1) the article must report the results of a primary empirical study, (2) the explanatory variables must include eCO2, and (3) the response variables must include either flowering phenology, floral resources or pollination. Studies reporting effects on reproductive allocation, fruit production or seed production were not included. Any article that did not pass all 3 inclusions criteria, or was a duplicate, was excluded.A systematic review was preformed to provide a transparent, comprehensive and objective overview of the quantity and quality of evidence related to pollination under eCOidelines . A comprReview papers included in search results, which passed all other inclusion criteria, were then further examined to identify any additional primary research articles. The final set of articles that had passed the inclusion criteria were read in full and entered into the database by extraction of the relevant data .2 Enrichment (‘BIFoR FACE’) experimental facility, located in Staffordshire, UK  as described in Hart et al. [Quercus robur, as the dominant tree species and an understory comprised mainly of common hazel, Corylus avellana. In brief, 3 experimental arrays fumigate 30 m diameter plots with 150 ppm above ambient CO2, with 3 control arrays which fumigate with ambient air. Fumigation commenced on 3 April 2017, which means the woodland system had been exposed to eCO2 for a period of 2 years prior to the experiment.The field experiment was conducted at the Birmingham Institute for Forest Research Free-Air COt et al. . The facHyacinthoides non-scripta, Asparagaceae) is a widespread spring-flowering bulbous perennial that occurs throughout Atlantic Western Europe. It is an ideal case study species to explore how field-layer flowering plants within temperate woodlands might respond to eCO2 due to its abundance, floral composition, flowering phenology and insect-mediated pollination. The species is locally abundant throughout the experimental site in both eCO2 and ambient arrays. Typically, 7–20 flowers are produced on a raceme that opens in an acropetal sequence, each lasting 2–3 weeks. This species reproduces vegetatively by budding and sexually by seed. Insect-mediated cross-pollination is required to produce a full seed set, with self-pollinated flowers producing fewer fruits and seeds, conferring a degree of ‘effective self-incompatibility’ [2 (SE = ±1.3) in the ambient arrays and 8.2 m2 (SE = ±3.8) in the treatment arrays  and patch-level illuminance (LUX) were recorded. Soil moisture was measured using CS655 probes  and recorded on a Campbell Scientific CR300 series datalogger. Mean monthly soil moisture and temperature were calculated for the three years preceding this study. Mean daily soil moisture and temperature were also recorded throughout the duration of the flowering period. Illuminance was recorded for each patch throughout the flowering period using a smartphone light meter application .To determine whether other environmental variables differed significantly among experimental arrays, soil temperature (°C), soil moisture  were used to monitor the flowering phenology of the bluebells throughout the 2019 flowering period. Cameras were installed facing each experimental patch at a height of ~50 cm and took photographs twice a day. From these photographs, the date of specific flowering stages  was determined. Total flowers in bloom were counted weekly during in-person surveys.A 30 min flower visitation survey was conducted at each patch every two weeks throughout the bluebell flowering period (3 time points). The flowering period was subsequently divided into three time windows around these survey points  to facilitate analysis. Surveys of all patches were performed in succession on the same day between 11:00 and 14:00, in a random order. Surveys were conducted on days when air temperature, wind, precipitation and cloud cover were as similar as possible. During each survey, every visit made by an insect to a bluebell flower within the patch was recorded and the insect identified to species level, or genus level for taxa where this is not possible from field identification (or family level for Ichneumonidae). A ‘visit’ was defined as each individual event when an insect entered/landed on a flower, potentially coming into contact with the floral reproductive organs (cf. ).After all flowering was completed, 60 racemes were collected from each patch. The number of flowers produced and fruits that developed were recorded. The total number of seeds developing within 3 fruits from each raceme was then counted. One early-, one mid- and one late-flowering fruit were selected. This was determined by their position on the raceme, which corresponds to the period in which they flowered.The impact of the treatment on first flowering date, mid-flowering date and flowering duration was assessed by ANOVA. Pearson’s product-moment correlations were performed between each of the flowering date measures , and each environmental variable during the flowering period . Comparisons of mean monthly soil moisture, soil temperature and mean light intensity during flowering period between treatment and ambient arrays were performed using Wilcoxon rank-sum tests.2 treatment on number of visits per unit area of each patch, a Wilcoxon rank-sum test was used. The effect of time period on number of visits was tested using a Kruskal–Wallis rank sum test. The effects of both treatment and time period on seed set were tested by fitting generalised least squares model and applying the varIdent weights term to control for the heterogeneity in the sample period using nmle package version 3.1-144 [The relationship between number of visits to a patch and patch size was tested with a linear regression. To analyse the effect of eCO 3.1-144 . The ana 3.1-144 .The search process returned a total of 189 articles—74 articles from Web of Knowledge, 80 from Google Scholar and 35 from examination of reference lists within review articles. Of these, 73 articles passed the inclusion criteria, with publication years ranging from 1956 to 2020 .2 for these 73 studies was 730 ppm, with a mean control concentration of 360 ppm. More than 118 plant species from 32 families were investigated with 146 individual species level responses reported. There is a strong bias in the geographic location of the studies, with over 72% performed in North America (53%) or Europe (19%). There were two or fewer studies from Africa, Central America or the Middle East, and none from South America.The mean treatment concentration of CO2 on flowering phenology , Solanaceae (−11.67) and Euphorbiaceae (−9), whereas the greatest mean delay in flowering was by Geraniaceae (+1.88), Amaranthaceae (+2.03) and Cucurbitaceae (+10.8).Fifty-five articles investigated the impact on eCOhenology , SM2. Fl2 effects on pollen  in eCO2 arrays and 14.86 m3/m3 (±1.26) in ambient arrays  and 3420 lx (±491) in ambient arrays .Over the course of the proceeding three years mean soil temperature did not differ significantly between treatment and control arrays with an overall mean of 9.7 °C (±0.65) and 9.5 °C (±0.64), respectively . The duration of flowering was not significantly different between treatment and control , with a mean of 46 days under eCO2 and 45 days for ambient patches , mid-flowering date  and flowering duration   .p = 0.0436), with the mean number of visits per patch rising to 5.8 and 4.8 in the ‘mid’ and ‘late’ flowering periods, respectively . There were no significant differences in visitation per area between treatment and control arrays (p = 0.6866).The overall number of visits under eCOPlatycheirus spp. made the greatest number of visits, peaking in the mid-flowering period. Rhingia campestris made the second highest number of visits of any hoverfly species, with 88% of these occurring in the late flowering period. Bumble bees (Apidae: Bombus) represented 22.9% of total flower visits, with B. pratorum workers the most common bumble bee observed. For all other Bombus species, visits were made exclusively by queens.A total of 18 species/species groups visited bluebell flowers during the surveys , of whicp = 0.0526). There was a significant correlation between total number of flower visits recorded and mean number of seeds per fruit produced from flowers that bloomed during the corresponding period (p = 0.0348).Seed set followed a similar temporal pattern , with an2 on pollination. The systematic review of the literature revealed here indicates that the majority of publications examining pollination under eCO2 reported on the impacts on flowering time (75%), which is likely due to this variable being easier to measure from direct observation. This covered a wide range of flowering species from a reasonable phylogenetic spread of families, although the importance of insect pollination to their pollination ecology varied considerably. For example, the large proportion of studies reporting the effect on Poaceae (10), which are largely wind-pollinated [Compared with the number of studies that examine the effect of other climate change variables, such as temperature and precipitation patterns , there illinated .2 by 3.73 days. This conforms with the general findings of previous studies assessing large numbers of species  and addic rates . The phy2 on floral resources, which is likely due to the additional methodological steps required to sample and measure these properties. Where this was measured the results are equally mixed, i.e., no consistent response across species. Direct measurements of floral traits such as flowering time and floral resources do not provide a direct quantification of pollination, however, and therefore can only be used to infer the impact of eCO2 on this interaction.There were substantially fewer studies examining the impact of eCO2 upon it. Only 4% of the studies found by this review directly measured an aspect of the effect of eCO2 on pollination, revealing an important knowledge gap. Furthermore, none of these studies were performed in situ, therefore potentially missing the effects of important interactions that cannot be replicated in controlled environments. Empirical studies addressing this specific area are urgently needed to improve our predictions of plant–pollinator interaction changes under climate change.Insect-mediated pollination is a complex interaction between multiple species, which is perhaps why so few studies to date have directly measured the impact of eCO2 on pollination directly in situ and the first assessment of a pollination interaction in a FACE experiment. Flowering phenology of common bluebell was found to advance by a mean of 6 days under eCO2  only active in the late period. The advance in flowering phenology driven by eCO2 observed in this study, therefore, is potentially shifting peak flowering (and thus seed production) away from the peak flight period of key pollinators. Other insect species may of course ‘step in’ to provide a pollination service, but recent evidence from alpine environments suggests altering pollinator communities can have significant negative effects on plant reproductive success [Bombus species observed, only queens were recorded visiting bluebells. Thus, this flower likely represents an important resource for queens emerging, somewhat nutrient-deprived, from their overwintering diapause. Any reduction in the availability of this resource, e.g., resulting from a phenological mismatch, could reduce the success of queens subsequently establishing a colony.Over 90% of all insect visits to bluebells occurred during the mid- and late-flowering periods, with many important (based on number of visits) species . If this pattern continues under future eCO2 scenarios, then greater phenological mismatches may occur than predicted by temperature-based models alone, with the main flowering period of several plant species potentially losing synchrony with the peak flight period of key pollinators. This could lead to a shift in the plant–pollinator network, resulting in a declining forage resource for certain pollinators, as well as a decrease in in plant seed set. Importantly, this impact is likely to be greater for plant species with short flowering periods and/or very specialised plant–pollinator relationships.eCO


Linear regression was used to estimate coefficients and 95% confidence intervals with a false discovery rate (FDR) of 0.10. Of the 218 metabolic measures, 173, 103, 71 and 6 were associated with BMI, fruits and vegetables, alcohol consumption and physical activity. Notable findings included negative associations between glycoprotein acetyls, an inflammation-related metabolite with lower BMI and greater fruit and vegetable consumption, a positive association between polyunsaturated fatty acids and fruit and vegetable consumption and positive associations between high-density lipoprotein subclasses with lower BMI. These findings provide insight into metabolic alterations in the context of cancer prevention and the diverse biological pathways they are involved in. In particular, behaviors related to BMI, fruit and vegetable and alcohol consumption had a large metabolic impact.Metabolomics can detect metabolic shifts resulting from lifestyle behaviors and may provide insight on the relevance of changes to carcinogenesis. We used non-targeted nuclear magnetic resonance to examine associations between metabolic measures and cancer preventive behaviors in 1319 participants  from the BC Generations Project. Behaviors were dichotomized: BMI < 25 kg/m Over the coming decades, this burden is projected to grow substantially2. In the words of Christopher Wild, former director of the International Agency for Research on Cancer (IARC) “we cannot treat our way out of the cancer problem”3. Modifiable factors contribute substantially to the overall risk of cancer. Increased body weight, poor diet, physical inactivity and alcohol consumption accounted for approximately 18% of cancer cases in the United States in 2014, the highest contributing factors other than cigarette smoking4. Recent estimates from a Canadian population, suggest 34% of cancer in men and 33% of cancers in women are attributable to preventable risk factors5.The global burden of cancer is substantial, with an estimated 18.1 million new cancer cases and 9.6 million cancer deaths in 2018 alone7. Recommendations include maintaining body weight within the healthy range and avoiding weight gain in adulthood, being physically active at levels similar to national guidelines, limiting sedentary time, eating a healthy diet which includes wholegrains, fiber, fruits and vegetables, limited red meat, little if any processed meat, limited fast foods and sugary drinks and limiting or avoiding consumption of alcohol. Despite recommendations, few individuals engage in preventive behavior. For example, epidemiologic studies have reported 11% of women meet recommendations for fruit and vegetable consumption and less than 1% of men and women meet all preventive recommendations9. A greater understanding of mechanisms linking lifestyle behaviors and cancer is important to identify individuals who may be at greater risk, identify or refine strategies for preventive efforts and provide insight on biological processes that could be targets for new preventive approaches.A number of guidelines and recommendations for cancer prevention have been put forth from government and nonprofit health organizations including the American Cancer Society and the World Cancer Research Fund/American Institute for Cancer Research12. Findings suggest obesity is associated with elevated branched-chain amino acid (BCAA), and saturated fatty acid metabolites, suggestive of insulin resistance as well as elevated levels of inflammation and sex hormone-related metabolites. Prior pilot research from our group reported several notable differences in metabolite profiles between people (n = 120) who did and did not follow recommendations for body weight, physical activity and alcohol consumption. For example, not meeting recommendations was associated with lipid metabolite profiles indicative of poor metabolic health including lower levels of high-density lipoprotein (HDL) metabolites, and higher levels of low density lipoprotein (LDL) metabolites13. The aim of the current study was to expand upon our prior effort to study associations between cancer preventive behaviors and metabolic profiles and to determine relevance of metabolic associations with cancer-related pathways in a much larger sample of Canadian adults.Metabolomics, the study of small molecules that participate in metabolism provides an integrated view of upstream genetic, transcriptomic and proteomic variation. As such, metabolomics can provide detailed characterization of metabolic phenotypes and corresponding alterations in metabolism that may precede disease. For example, a number of studies have examined metabolic changes associated with obesity2, approximately 5% reported prevalent diabetes, 1% reported prevalent heart disease as defined by a history of stroke or myocardial infarction and 30.2% reported a family history of cancer. Aside from half of the population being men, the demographics align with the overall BC Generations Project (BCGP) cohort14.A sample from one participant failed during metabolomics analysis, and thus the study included 1319 participants. Characteristics of these participants are shown in Table Associations between lifestyle behaviors categorized according to cancer prevention guidelines and metabolic measures are shown in Figs. −11) and cholesterol in large HDL . The largest negative coefficients were observed for glycoprotein acetyls , the ratio of triglycerides to phosphoglycerides  and lipoprotein measures, particularly larger average size of very low density lipoproteins (VLDL) subclasses.Of the metabolic measures associated with an overweight/obese BMI, 13 were fatty acids, three were apolipoproteins , eight were amino acids including inverse associations with BCAAs isoleucine, valine and leucine, two were fluid-balance related , one was inflammation-related (glycoprotein acetyls), and the balance were glycerides, phospholipids and lipoprotein measures. Specific metabolic measures, their coefficients and p-values are shown in Fig. −7), the ratio of polyunsaturated fatty acids to total fatty acids  and ratio of omega-3 fatty acids to total fatty acids .Of the 103 metabolic measures associated with fruits and vegetables, 10 were fatty acids, one was an apolipoprotein (the ratio of ApoB to ApoA1), one was inflammation-related (glycoprotein acetyls), four were trigylcerides, and one was a ketone body (acetate) with the balance comprised of cholesterol, and lipoprotein subclasses. The coefficients and p-values are shown in Fig. −10), and various HDL metabolic measures e.g. lipids in medium HDL , as shown in Fig. The metabolic measures associated with alcohol consumption were predominately subclasses of lipoproteins or cholesterol metabolites N = 63), two were apolipoproteins, three were glycerides and phospholipids, 3 were fatty acids, 1 was inflammation-related, two were amino acids and one was a fluid balance related. The largest coefficients and most significant associations between metabolic measures and alcohol consumption were observed for ApoA1  to total fatty acids, glycoprotein acetyls, the ratio of triglycerides to phosphoglycerides and various lipoprotein measures. Notably, the association patterns for BMI and fruit and vegetable consumption were generally consistent with respect to direction (both positive or both negative) but were opposite of associations with alcohol consumption. For example, the coefficient of glycoprotein acetyls for fruits and vegetables and BMI were − 0.20 and − 0.66 while for alcohol consumption it was 0.19.The interrelationships of the top 25 BMI, fruit and vegetable and physical activity-associated metabolic measures are shown in the GGM  and the ratio of ApoB1/ApoA1 (negative association). Each of these metabolic measures were also associated with at least one other behavior and it is thus possible that they are markers of overall lifestyle and not specifically dietary intake, although all three associations remained statistically significant in models mutually adjusted for physical activity, alcohol consumption and BMI.Similar to BMI, associations were observed between fruit and vegetable consumption and metabolic measures of lipoprotein subclasses, however, the largest coefficients were observed for polyunsaturated fatty acids, omega-3 fatty acids and unsaturated fatty acids, which were positively associated with fruit and vegetable consumption. As may be expected, given their biological and chemical properties, these metabolic measures formed a small correlated cluster. Our findings align with those from a large cohort of British men and women which examined metabolite profiles associated with diet quality evaluated as adherence to the alternative healthy eating index (AHEI). The AHEI is scored according to intake of foods and nutrients that can prevent chronic disease. Fruit and vegetable consumption contribute up to 20 points out of a total score of 87.56) included metabolic measures that promote inflammation, but not those that promote cardiometabolic health. For example, for low alcohol consumers we observed lower concentrations of A1, numerous subclasses of HDL metabolites, and DHA, and greater concentrations of glycoprotein acetyls and subclasses of large LDL. This finding replicates our prior study of metabolic profiles and cancer preventive behaviors13 and supports prior research that, for some cancers, any amount of alcohol can increase risk27. Conversely, moderate alcohol consumption has been associated with lower risk of atherosclerosis29. The underlying mechanism is hypothesized to be increased transport rate of ApoA1 and ApoA11 leading to raised HDL cholesterol30. In our study, ApoA1, and HDL subclasses were lower in participants meeting guidelines for cancer prevention which emphasis little to no alcohol compared to those who consumed more than one drink per day for women or two drinks per day for men. Our findings also align with a prior metabolic profiling study of alcohol consumption in nearly 10,000 adults31 that similarly reported metabolic profiles that reflected a ‘double-edged’ effect of alcohol on cardiometabolic risk factors including increased HDL subclass concentrations and LDL subclasses.Metabolic profiles of adherence to cancer prevention alcohol guidelines  may be more impactful. Of the behaviors assessed, the greatest metabolic shifts were observed for BMI, fruits and vegetables and alcohol consumption, suggesting these may be particularly attractive targets. The diverse pathways represented by these metabolic measures provide additional insight into mechanisms linking lifestyle behavior and cancer development, confirming a role of role of obesity-related insulin resistance, and suggesting cholesterol transport pathways as a potentially promising line of inquiry. However, prospective studies of lifestyle behaviors and incident cancer are needed to confirm findings.43. The BCGP began in 2009 with the goal of learning more about how environment, lifestyle and genes contribute to cancer and other chronic disease. By 2015, 29,767 participants aged 30 to 74 years had enrolled14. Participants completed questionnaires on demographics, health history and lifestyle behaviors and provided biospecimens. A proportion (56%) underwent in-person physical measurements. For this study, the same inclusion criterion were applied as our previous study13 to facilitate pooling of data. A random sample of 1200 participants were selected that were 50% men, reported no current or former smoking at baseline, had no personal history of cancer, were not pregnant, attended an in-person assessment and provided a blood sample. This study was approved by the Institutional Review Board at the University of British Columbia (REB number: H17-00004). All research was performed in accordance with relevant guidelines/regulations. All participants provided informed consent.The BCGP is part of the national Canadian Partnership for Tomorrow’s Health (CanPath), a prospective, longitudinal cohort study18.Participants reported average daily servings of fresh, frozen, canned or cooked fruits and vegetables. They also reported the number, amount and type of alcoholic drinks consumed in a typical week. One drink was defined as 12 oz of beer, 5 oz of wine or 1.5 oz of 80-proof liquor/spirits. Weight was measured with bioelectrical impedance . Time spent physically active and sedentary in the prior week was assessed by the short-form International Physical Activity Questionnaire (IPAQ-SF). The IPAQ-SF is a validated, widely used questionnaire that captures different domains of physical activity at work, at home, active transportation and leisure time, time spent sitting and the physical effort of physical activity6 that were assessed in the BCGP. This included body mass index (BMI), fruit and vegetable consumption (servings/day), moderate to vigorous physical activity, and alcohol consumption (drinks/week). Behaviors were considered as continuous measures, and using scores (0 and 1) for adherence to a given component, as per prior approaches that have operationalized preventive guidelines13. Preventive behavior was defined as BMI < 25 kg/m2, 5 or more servings of fruits and vegetables per day, no more than 2 drinks/day for men or 1 drink/day for women and at least 30 min of moderate or vigorous physical activity on 5 or more days of the week.We focused on the main cancer prevention guidelines pertaining to body weight, diet, physical activity and alcohol intake22. Participants were not required to fast. Samples were stored at 4 °C before shipment to the processing laboratory the following day. Samples were processed according to BCGP’s Standard Operating Procedure24. EDTA vacutainer tubes were centrifuged for 10 min at 1300g/4 °C/with brake on to separate out the plasma fraction. Plasma was transferred to a cryovial and stored at − 80 °C between 19 to 24 h after laboratory blood collection. All samples were noted to be free of hemolysis and lipidemia.Participants had blood drawn at a community medical laboratory using standard blood collection techniques26 and methodological details have been previously published27. The platform captured lipoproteins, fatty acids and fatty acid composition, ketone bodies, fluid balance related  metabolites, an inflammation related metabolite, amino acids, glycolysis and gluconeogenesis-related metabolites. The same NMR setup and software library was used for metabolic quantification for the pilot and larger study. Of the 225 metabolic measures analyzed, glucose values were lower while lactate values were higher than those commonly observed in samples processed by Nightingale Health Ltd. The levels were indicative of glycolysis, with prolonged processing time at the mobile collection centers, rather than shipment conditions (verified by shipment temperature logs). Both metabolic measures were subsequently excluded from analyses along with five metabolic measures  phospholipid, concentration of extremely large VLDL cholesterol, concentration of extremely large VLDL cholesterol ester, concentration of extremely large VLDL free cholesterol, and concentration of extremely large VLDL triglycerides) that were below the limit of detection for > 20% of the population, leaving a total of 218 metabolic measures for analysis. As variation in levels of lactate and glucose were modest it is unlikely to have a measurable effect on other metabolites. Overall, samples were deemed to be high quality as metabolite distributions matched those observed in other cohort studies, there were no indications of irregularities or signs of degradation. The coefficient of variation determined from duplicate samples (5% of sample) was < 5% for the vast majority of metabolic measures.Metabolic assessment on all samples was carried out by Nightingale Health Ltd. . The nuclear magnetic resonance (NMR) platform has been applied in a number of epidemiologic studies44 and Stata version 14.045. Metabolic measures were log-transformed and centered to a mean of 0 and standard deviation of 1. When the concentration of a metabolic measure was below the limit of detection, values were set to half of the minimum value of that metabolic measure in the total population13. Missing covariate  and predictor data  were multiple imputed using the Multivariate Imputations by Chained Equation (MICE) package in R46. Age, sex, self-reported body mass index (BMI), self-perceived health, and diet quality were included as auxiliary variables in the multiple imputation47. Convergence was achieved with 30 iterations and 40 imputations.Statistical analyses were conducted using R version 4.0.342. All models were adjusted for age, sex and a variable to indicate whether samples were part of the pilot or the larger sample. Beta coefficients, SE and p-values were reported. To account for multiple testing, false discovery rates were computed using the q value for each individual exposure28. Statistical significance was set at p < 0.05 and q < 0.10. Linear regression models were also mutually adjusted for cancer preventive behaviors to determine if associations were independent. Sensitivity analyses were performed with further adjustment for demographic and health variables .Linear regression was used to evaluate associations between quantified metabolic measures modelled as the outcome and health behaviors modeled as the exposure. Health behaviors were categorized and dummy coded 0 or 1) according to cancer prevention guidelines. The reference group was those not meeting recommendations, and therefore the regression coefficient refers to the change in mean standardized value of a metabolic measure between those with a healthy behavior and those without. Associations between behaviors as continuous variables and concentrations of metabolic measures were also examined using linear regression  approach using the Cytoscape app within Metscape48. The GGM is visualized as metabolic measures (nodes) connected by edges that represent conditional correlations. For the sake of clarity, the network was constructed using only the edges for which the conditional correlation was in the top 10%. Metabolic measures were grouped into apolipoproteins, cholesterol, fatty acids, glycerides and phospholipids, inflammation and lipoprotein subclasses to facilitate interpretation of subnetworks.Pearson pairwise correlations between all metabolic measures significantly associated with alcohol consumption, BMI, and fruits and vegetables were determined to evaluate the potential independence of metabolic associations. Physical activity related metabolites were not included due to the small identified associations. Pairwise correlations for the 25 top-ranked metabolic measures for each of alcohol, BMI, and fruits and vegetables were additionally evaluated after conditioning on other top-ranked metabolic measuresSupplementary Information.


Safflower extract is commonly used as a traditional Chinese medicine to promote blood circulation and remove blood stasis. The antioxidant and anticancer properties of safflower extracts have been extensively studied, but their antiaggregative effects have been less analyzed. We found that safflower extract inhibited human platelet aggregation induced by ADP. In addition, we further analyzed several safflower extract compounds, such as hydroxysafflor yellow A, safflower yellow A, and luteolin, which have the same antiaggregative effect. In addition to analyzing the active components of the safflower extract, we also analyzed their roles in the ADP signaling pathways. Safflower extract can affect the activation of downstream conductors of ADP receptors , thereby affecting the expression of activated glycoproteins on the platelet membrane and inhibiting platelet aggregation. According to the results of this study, the effect of safflower extract on promoting blood circulation and removing blood stasis may be related to its direct inhibition of platelet activation. Carthamustinctorius L., a plant of the Asteraceae family, by removing impurities, drying in the shade, or drying under low heat. Safflower is often used as a traditional Chinese medicine to promote hormone balance, remove blemishes, regenerate new cells, circulate blood, remove bruises, and relieve pain [Safflower, also known as thorn flower, is a type of chrysanthemum. The leaves are linear, thin, and long, with a special aroma and a bitter taste. Safflower can be grown in large areas and is less likely to die , so its eve pain ,3,4.The biological activity of the extract of safflower is related to the various compounds in the extract. Previous studies have indicated that the extract of safflower could be divided into lipophilic compounds and hydrophilic compounds due to different extraction methods . The lipPlatelets are formed from the cytoplasm of mature megakaryocytes in the bone marrow. Each megakaryocyte can produce 2000–7000 platelets. Platelets have a half-life of approximately 7 to 9 days and are cleared mainly by macrophages in the spleen. Under normal circumstances, platelets are biconvex, disc-shaped, can extend out a foot process, and appear irregular after stimulation ,18,19. TThe processes of platelet adhesion, activation, and aggregation appear almost simultaneously after platelet activation. The adhesion of platelets refers to the adhesion of platelets and nonplatelet surfaces. The main substances involved in this process are the glycoproteins on the platelet membrane, the Von Willebrand factor in the plasma, and the collagen component in the subcutaneous tissue ,23. AggrMany of the above studies have pointed to safflower’s role in anti-inflammation and inhibiting cancer cell growth. However, safflower is often used in traditional Chinese medicine to promote blood circulation and remove blood stasis. The mechanism of safflower in platelet aggregation remains unclear. In this study, we confirmed that safflower has antiplatelet aggregation activity by using ADP-induced human platelet aggregation experiments. In addition, we further analyzed the active compounds of the safflower extract and the effect of the safflower extract on the signal transduction of ADP-induced platelet aggregation.The results in Since safflower extract inhibits platelet aggregation, we next wanted to explore which safflower extract compounds play an important role, and we selected three compounds available for further analysis. When human platelets were treated with 25 μM hydroxysafflor yellow A, safflower yellow A, or luteolin, ADP-induced platelet aggregation was significantly inhibited by 70.9%, 42.3%, and 74.9%, respectively. When platelets were treated with each high concentration (100 μM) of compound found in the safflower extract, almost no platelet aggregation reaction was found . These aADP induces platelet aggregation mainly through P2Y1 and P2Y12 receptors on the platelet membrane . Next, wThe results of experiments using the abovementioned antagonists found that safflower extract and its compounds inhibit the activation of P2Y1 and P2Y12 receptors on the platelet membrane. Next, we wanted to explore whether safflower extract and its compounds truly affect the downstream activation pathways involved in P2Y1 and P2Y12 receptors. We directly analyzed the safflower extract, and its compounds affected the activation of calcium influx induced by the P2Y1 receptor and cAMP production regulated by the P2Y12 receptor. ADP induced an increase in intracellular calcium ions in platelets by 1.84-fold, while safflower extract and its compounds inhibited the increase in calcium ion concentration induced by ADP by 31.3–46.4% A. When pAfter participating in calcium ion activation and regulation of cAMP production on the platelet membrane through the ADP receptor, the phospholipid on the membrane is then degraded, and AA is released, which then generates TXA2 and induces the formation of glycoprotein on the membrane to participate in the aggregation of platelets . TherefoGlycoprotein IIb/IIIa  on the platelet membrane is mainly produced after stimulation of platelets by ADP and can combine with fibrinogen to cause platelet aggregation. In addition to the above intraplatelet signal transduction tests, we finally wanted to investigate whether safflower extract and its compounds caused changes in glycoprotein expression on the platelet membrane. In order to simulate the living conditions, whole blood was used for drug treatment and staining analysis, and platelets were selected on a flow cytometer for membrane protein analysis. Although the platelet cell population accounts for less than 1% of blood samples, these glycoproteins on the platelet membrane can still be found to be altered by ADP stimulation . In wholOur results indicated that safflower extract could inhibit platelet aggregation mainly in the following aspects: ADP receptor transduction and expression of PAC-1 glycoprotein on the platelet membrane, calcium ion activation, and the cAMP, AA, and TXA2 contents regulated by ADP in intracellular platelets were all significantly inhibited by safflower extract . These rAlthough safflower has the effect of promoting blood circulation and removing blood stasis, in vivo experiments were mostly conducted with various compounds of safflower extract as materials. Oral feeding carthamins yellow at 100 mg/Kg and 200 mg/kg in living rats can significantly reduce blood fluidity . In ratsThe compounds found in safflower extract used in this study, whether hydroxysafflor yellow A, safflower yellow A, or luteolin, are all converted into flavonoids during metabolism. Flavonoids can be found in many green vegetables or fruits, and their effects have been extensively studied. Because of their unique benzene ring structure, flavonoids are believed to have a strong antioxidant effect. Studies have shown that they inhibit the metabolic pathway of AA (including PLA2 and COX2) and modulate the activation of transcription factors involved in inflammation, such as NF-κB, GATA-3, and STAT-6 ,34. In tIn addition to their anti-inflammatory and anticancer effects, flavonoids have also been shown to directly bind to TXA2 receptors to directly inhibit platelet activation . Based oIn summary, we found that safflower extract and several safflower extract compounds can inhibit ADP-regulated human platelet aggregation. The production of calcium ions, cAMP, AA, and TXA2 regulated by ADP receptors, whether P2Y1 or P2Y12, would be inhibited by safflower extract or compounds found in safflower extract. We further found that the formation of the PAC-1 complex, a glycoprotein involved in the aggregation of platelet membranes, was also inhibited by safflower extract or several safflower extract compounds. Therefore, the use of safflower extract in traditional Chinese medicine as a way to promote blood circulation and remove blood stasis may be related to its inhibition of platelet aggregation.Safflower powder was purchased from Shun Ten Pharmaceutical Co., Ltd. . A total of 10 g of safflower powder was dissolved in 100 mL of distilled water, boiled, and filtered. The water-soluble safflower extract was sterilized by an autoclave and then stored at 4 °C.g at room temperature for 20 min to obtain PRP. The PRP was centrifuged at 1500× g for 15 min, and the PPP and isolated platelets were obtained in the supernatant and sediment, respectively. The isolated platelets were resuspended in modified calcium-free Tyrode buffer for subsequent tests.Human blood was obtained from healthy adults through venipuncture in a vacuum tube containing sodium citrate. All participants who participated in this study provided informed consent, and this study was approved by the Taipei Tzu Chi Hospital Institutional Review Board. The obtained blood was centrifuged at 180× The platelet processing of safflower extract and the measurement of platelet aggregation were performed using the light transmittance aggregometer platform  with adjustable temperature and continuous stirring. PRP was treated with safflower extract, hydroxysafflor yellow A , safflower yellow A (ChemFaces), or luteolin (ChemFaces) at 37 °C for 1 h. After setting the reference value of each sample with PPP, PRP was treated with 100 μM A2P5P  or clopidogrel (Santa Cruz Biotechnology) for 3 min. Finally, 10 μM ADP  was added, and the aggregation reaction was monitored for 6 min.PRP was treated with safflower extract or compounds found in safflower extract at 37 °C for 1 h, after which 3 μM Fura-2AM  was added for 45 min, and the isolated platelets were obtained by centrifugation. The method of measuring calcium ions has been described previously . For thePRP was treated with safflower extract or compounds found in safflower extract for 1 h, and isolated platelets were obtained by centrifugation. Then, 3 μM fibrinogen and 10 μM ADP were added and placed at 37 °C for 3 min for stimulation. The sample was then placed in dry-ice-cooled ethanol to quickly freeze the sample to stop the reaction. The content of TXB2 in each sample was measured in accordance with the manufacturer’s instructions . Alternatively, isolated platelets obtained by the treatment of safflower extract or compounds found in safflower extract with 10 μM ADP and fibrinogen stimulation for 30 min were centrifuged, and the supernatant was obtained to measure the content of AA .The obtained blood was diluted with PBS at a ratio of 1:2. The diluted blood was treated with safflower extract or compounds found in safflower extract for 1 h and then treated with fluorescent antibodies  with 20 μM ADP at room temperature for 20 min. The diluted blood samples were immediately fixed with 400 μL of 1% formaldehyde/PBS, and the expression of surface antigen was analyzed by flow cytometry .p-value of less than 0.5 between groups was considered significant.Each experimental data point was obtained from at least three samples. The experiment was performed with at least three replicates and presented as ±SD of the mean. Statistical analysis was performed using one-way ANOVA and Dunnett’s posttest. A


The Joint United Nations Programme on HIV/AIDS (UNAIDS) projections of paediatric HIV prevalence and deaths rely on the International epidemiology Databases to Evaluate AIDS (IeDEA) consortium for mortality estimates among children living with HIV (CHIV) receiving antiretroviral therapy (ART). Previous estimates, based on data through 2014, may no longer be accurate due to expanded paediatric HIV care and treatment eligibility, and the possibility of unreported deaths in CHIV considered lost to follow‐up (LTFU). We therefore estimated all‐cause mortality and its trends in CHIV (<15 years old) on ART using extended and new IeDEA data.n = 221).We analysed (i) IeDEA observational data from CHIV in routine care globally, and (ii) novel data from an IeDEA tracing study that determined outcomes in a sample of CHIV after being LTFU in southern Africa. We included 45,711 CHIV on ART during 2004 to 2017 at 72 programmes in Africa, Asia‐Pacific and Latin America. We used mixed effects Poisson regression to estimate mortality by age, sex, CD4 at ART start, time on ART, region and calendar year. For Africa, in an adjusted analysis that accounts for unreported deaths among those LTFU, we first modified the routine data by simulating mortality outcomes within six months after LTFU, based on a Gompertz survival model fitted to the tracing data  and 9.4 (6.3 to 13.4) deaths per 100 person‐years in the routine and tracing data, respectively. We found strong evidence of higher mortality at shorter ART durations, lower CD4 values, and in infancy. Averaging over covariate patterns, the adjusted mortality rate was 54% higher than the unadjusted rate. In unadjusted analyses, mortality reduced by an average 60% and 73% from 2005 to 2017, within and outside of Africa, respectively. In the adjusted analysis for Africa, this temporal reduction was 42%.Mortality rates among CHIV have decreased substantially over time. However, when accounting for worse outcomes among those LTFU, mortality estimates increased and temporal improvements were slightly reduced, suggesting caution in interpreting analyses based only on programme data. The improved and updated IeDEA estimates on mortality among CHIV on ART support UNAIDS efforts to accurately model global HIV statistics. FurtThe UNAIDS Spectrum model (Spectrum)  generateHowever, paediatric HIV programmes have evolved over time , 7, 8, 9In this study, we estimated all‐cause mortality rates and investigated their temporal trends among CHIV younger than 15 years on ART, during 2004 to 2017, in multiple regions within and outside of Africa. Unique to this analysis are the two distinct data sources: for all regions, we used routine‐care IeDEA observational data from participating treatment programmes; for southern Africa, we additionally used novel IeDEA data from a recent tracing study that determined outcomes in a sample of CHIV considered LTFU , 12. The22.1The IeDEA collaboration collects and harmonizes HIV/AIDS data globally , 15, 16.We also utilized data from a recent IeDEA tracing study in six treatment programmes in five countries  in southern Africa. From the traced sample of 3256 persons classified as LTFU by participating programmes , 12, we Within IeDEA , each pa2.2Analyses were performed in R version 3.6.3  and StatWe performed two distinct analyses to produce: (i) “unadjusted” estimates of mortality, based on the routine data only; and (ii) for the African regions, “adjusted” estimates that incorporated the tracing study data. Secondary sensitivity analyses were also performed to investigate the impact of selected analysis decisions. All analyses are described below.2.3We analysed counts of deaths in the routine data using a multivariable Poisson mixed effects regression model (log link function). A child's time at risk started at the later of programme enrolment and ART initiation, and either ended at death (if recorded before age 15) or was censored at the earliest of turning 15 years old, being transferred to another programme, and last being seen alive, based on recorded clinic, laboratory and pharmacy visits. We excluded data beyond programme's centre‐specific “database closure dates”, from which we consider records incomplete.Covariates included were consistent with how CHIV are distinguished in Spectrum . These wTo describe temporal trends in mortality, we included calendar year as a linear spline with a single knot in 2010 (Web Appendix E in Appendix 3). From our IeDEA data, we extracted the CD4 measurement closest to, and within six months before and two weeks after, ART initiation. CD4 was modelled as pseudo‐continuous, using the (base 10) logarithm of the midpoint of the CD4 interval. In Spectrum [For the CD4 at ART initiation covariate, CD4 percentage measures were used in children younger than five years , and CD4 counts in CHIV aged at least five years  tests, we allowed for distinct covariate effects by ART duration (less than vs. at least one year), for each of CD4, region and the temporal trend.2.4It is anticipated that a substantial number of child deaths remain unrecorded in the routine data because ART programmes in Africa do not generally link patient records to vital registration systems or use active follow‐up . The adjTo generate adjusted mortality estimates, we applied an approach similar to that used for adult on‐ART mortality rate inputs for Spectrum   if neither death nor transfer to another treatment programme were recorded, and the child did not access care within the six months preceding the centre database closure date. Simulation was restricted to six months after LTFU as it becomes increasingly uncertain whether a child remains on ART as this horizon is extended. The simulation and analysis were repeated 20 times, and results combined using Rubin's rules .The simulation model was obtained by fitting a Gompertz proportional‐hazards survival model to the tracing study data, though restricting the number of parameters due to the small sample of 221 CHIV  were different from those in the tracing study (2013 to 2017). To reduce potential biases, the simulation model assumed that the temporal trends in mortality fitted to the unadjusted routine data also apply after LTFU  simulating outcomes for 90 days or one year after LTFU, rather than six months; (ii) not including a time trend in the simulation model; (iii) moving all centre closure dates one year earlier (in case records are incomplete from earlier than indicated); and (iv) the multiple imputation of missing CD4 values.33.1Characteristics of the 45,711 CHIV in the routine data (after any data exclusions) are summarized in Table The fitted Poisson regression models are described in Table 3.2The 221 CHIV included in the tracing study data analysis, and their mortality rates, are described in Table The survival model fitted to the tracing study data  of CHIV LTFU from the southern African region used to adjust mortality estimates for unreported deaths in all sub‐Saharan African regions, and the assumption that fitted temporal trends in mortality before LTFU hold after LTFU, incorporated without accounting for uncertainty. Other researchers have cautioned against generalizing programme‐specific results [Limitations of this study include that IeDEA treatment programmes may not be representative of their region, though inclusion of multiple programmes and the use of random effects in analyses to describe heterogeneity mitigates this concern. Further limitations are the small sample ; the National Heart, Lung, and Blood Institute (NHLBI); the National Institute on Alcohol Abuse and Alcoholism (NIAAA); the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK); the Fogarty International Center (FIC); the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD); the National Cancer Institute (NCI); the National Institute of Allergy and Infectious Diseases (NIAID); the National Institute of Mental Health (NIMH); the Office of the Director, National Institutes of Health (OD); and the National Library of Medicine (NLM). The IeDEA regions are supported by grants U01AI069907 , U01AI069923 , U01AI096299 , U01AI069911 (East Africa), U01AI069924 (Southern Africa) and U01AI069919 (West Africa). Informatics resources are supported by the Harmonist project (R24AI124872).The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Additional funding acknowledgements, site investigators and cohorts are listed in the Acknowledgements document in Appendix The authors declare that they have no competing interests.MD, LFJ and CTY conceived the study. RK designed and performed the data analysis, with contributions by LFJ and MD RK drafted the article, with inputs from all authors. AE, TS, JP, DLD CT AK JGC and CK contributed to the routine data collection. EZ, LFJ and RK collated the routine data. MB and BC led the tracing studies and collated the tracing study data. All authors have read and approved the final version of the article.Appendix S1. Additional outputs and technical detailsClick here for additional data file.Appendix S2. Lists of additional funding acknowledgements, site investigators and cohorts, as referenced in Acknowledgements of the article.Click here for additional data file.