Waiting times for organs in the United States are long and vary widely across regions. Donor registration can increase the number of potential donors, but its effect on the actual number of organ transplants depends upon several factors. First among these factors is that deceased donor organ donation requires both that death occur in a way making recovery possible and that authorization to recover organs is obtained. We estimate the potential donor death rate and donor authorization rate conditional on potential donor death by donor registration status for each state and for key demographic groups. With this information, we then develop a simple measure of the value of a new donor registration. This combined measure using information on donor authorization rates and potential death rates varies widely across states and groups, suggesting that focusing registration efforts on high-value groups and locations can significantly increase the overall number of donors. Targeting high-value states raises 26.7 percent more donors than a uniform, nationwide registration effort. Our estimates can also be used to assess alternative, but complementary, policies such as protocols to improve authorization rates for non-registered potential donors. As of July 1, 2019, there were 113,312 candidates for an organ transplantation on waiting lists in the United States. The time on waiting lists can be quite long and varies by organ type. For example, in 2017, only 47 percent of kidney transplant candidates first listed in 2007 had received a transplant [The need for more organ donations is widely recognized and many different types of efforts have been used to increase the pool of available organs. For example, donor registration efforts attempt to increase awareness of the need for organ donors and to expand the pool of individuals who have authorized organ donation –6. Laws With this paper, we focus on the measurement of the efficiency of the donor registration process. Donor registration is the most visible method for increasing organ donation in the U.S. But there are large differences across states in registration rates, offering the possibility that perhaps some states have significant room for improvement. Donor registration drives are also a costly endeavor. Using data from Pennsylvania, researchers estimated that promotional and outreach efforts cost $455 per new registrant . But theImportantly, it is also unclear how donor registration actually links to donated organs, with increasing actual donations and transplants being the fundamental policy objective. Related to this point, some research suggests that registered donors have a lower death rate than unregistered individuals and are therefore less likely to become a potential donor .In this paper we develop a new method to estimate the value of new donor registrations on organ donations. Though we use data from the United States, our method can be adapted for any country. Specifically, we measure value as the probability of a new donor registration leading to a new organ transplant donor. We use a simple model of organ donation that separates the probability of donating into its component conditional probabilities. The key probabilities include 1) the probability of becoming a potential donor, and 2) the probability of donor authorization conditional on becoming a potential donor. Using standard probability theory measures that can be linked to observable data, we create a measure of the value of a new donor registrant. This measure is then computed for various subpopulations, including state, age and gender. These statistics based on observable population-level data can then be used as a guide for directing donor registration efforts and funds toward groups or regions of highest potential value. We perform policy simulations to compare the value of different targeting strategies. We find that targeting certain states using our measure of value raises 26.7 percent more donors than a uniform, nationwide registration effort, while targeting those states with low registration rates is actually less effective than a uniform approach. We also use the estimates to show that a program to raise donor authorization rates among the non-registered population is a promising alternative.We use the Standard Transplant Analysis and Research (STAR) files provided by the United Network for Organ Sharing (UNOS) as contractor for the Organ Procurement and Transplantation Network (OPTN). All computations are based on OPTN data as of March 31, 2019 . The “ElAn organ donation occurs when Authorization is granted for organs to be recovered and transplanted from a potential deceased donor. OPTN/UNOS uses the term “Eligible Death” to refer to a deceased donor declared to be legally dead based on neurological criteria (“brain dead”) along with other specified criteria . OPOs arR and NR to denote Registered and Non-Registered donor status. Authorization for transplantation is A. Let E denote Eligible Death. PD, including those deficient on some element required for Eligible Death status as well as donors after circulatory death. The two inner ovals are Eligible Deaths, E, and Authorized Donors, A. We know the number of Eligible Deaths and the number of Authorized Donors from the Eligible Death and Deceased Donor files from OPTN.We use pi and qi defined asThe probability of donating is broken down as follows:Breaking the overall probability of donating into component conditional probabilities allows us to estimate these separately and better understand variation in donation probability across states and demographic groups. We can then simulate the effects of changes in the parameters.M are either Registered (R) or Non-Registered (NR). If there is homogeneity within each group, total (expected) donors, T, can be written asNext we model the effect of policy interventions on the total number of organs donated. Assume people in a population of size R by one and decreases NR by one, yielding a net change in total expected donors of p1p2 − q1q2. The change will be larger when there is a large difference in the conditional authorization rates (p2 and q2), or when the death rates (p1 and q1) differ substantially between registered and non-registered populations.A simple way to model the change would be to assume that donor registration increases the size of the registered population and decreases the size of the non-registered population. That is, a new donor registration increases T accounts for the offsetting reduction from the unregistered, it implicitly assumes that registration converts the registrant from one type to the other, changing not just the conditional probability of Donor Authorization but also the probability of becoming a Potential Donor. A more realistic assumption is that registration changes only the conditional Authorization rate p2, leaving the other parameters unchanged. Then the expected increase in the number of donors from a new registration isWhile this modeling of the effect of registration on We can then compute Δ by state and for various demographic subgroups. Estimation of Δ is straightforward given suitable data: simply estimate the conditional probabilities –4). We . We 4). The second difficulty is that we do not know the number of Potential Donors. Potential Donors is a smaller set than the total number of deaths. It is the set of individuals who meet the medical criteria for having at least one donatable organ upon death. In practice this number is not collected or reported for the whole population.all deaths (e.g. including deaths from circulatory system failure), then we can estimate the number of registered and non-registered Potential Donors based on the observed number of actual Authorized donors from each group. We also account for the statistical uncertainty of our estimates and assumptions. Specific details for the computations are found in the Appendix.However, we can estimate it based on the data from Eligible Deaths. The set of Eligible Deaths is well defined on neurological criteria and a set of other restrictions. From these data we can estimate the Authorization rate for Eligible Deaths. If we make the plausible assumption that the Authorization rate is the same for Ideally, we would also like to compute the value of a new registration, Δ, for demographic characteristics such as age, gender, ethnicity, and religious affiliation. However, state-level data for donor registries with demographic details are not generally available. Unlike donor registries in other countries, no information is collected on the religious affiliation of deceased organ donors or new donor registrants in the United States. Likewise, at the most common location to register as a organ donor in the United States, each state’s Department of Motor Vehicles, no information on ethnicity is recorded. We do have demographic data on age and gender for a subset of 12 states  plus Puerto Rico and the District of Columbia from a 2015 study commissioned by Donate Life America (DLA). We use these estimates to do a more detailed analysis on the available subset of states , 26.p1 , p2 (Registered Donor Authorization rate), q1  and q2 (Non-Registered Donor Authorization rate) for each state are found in Tables p1, q1 and Δ to rates per 100,000 population. Figs We can compute the value of a new registration, Δ, using Estimated values of Δ should be large when non-registered Potential Donor death rates are high and when non-registered Authorization rates are low. A glance at For the U.S. as a whole, the value of Δ is 2.06 per 100,000, meaning that 100,000 new registrants should produce an increase of 2.06 donors per year. State estimates of Δ range from lows of 0.32 and 0.62 in North Dakota and Idaho to highs of 6.95 and 5.31 in Delaware and Missouri. Point estimates suggest that the relative value of a new registrant is 21.7 times higher in Delaware than in North Dakota (6.95/0.32). The ratio of the 75th percentile  to the 25th percentile  is 2.26. Similarly, the relative value of a new registrant in Connecticut is 67 percent higher than that of one in neighboring Massachusetts (2.79 vs. 1.67). The precision of estimation improves for more populous states. The largest states by population are California ), Texas ), Florida ) and New York ).p1 or q1) is higher for the non-registered than for the registered, and this pattern holds for most states. What matters for the value of Δ is the non-registered Potential Donor Death rate q1. This ranges from a high of 18.41 in Delaware to a low of 2.30 in New Hampshire. However, high Donor Authorization rates for the non-registered (q2) reduce the effect of higher Potential Donor Death rates because the non-registered Potential Donor Deaths are already highly likely to become donors. For example, Pennsylvania’s Potential Donor Death rate rate is high (6.06) but its Δ is lower than average ) because it has a relatively high Donor Authorization rate of 0.64 ). In contrast, New York—with a similar Δ of 2.20—has a lower Potential Donor Death rate and Donor Authorization rate for the non-registered. These Donor Authorization rates vary from a high of 81 percent in Puerto Rico to a low of 26 percent in Vermont, while the national average rate is 56 percent.We now look closely at the influence of the component probabilities on the magnitude of Δ. The national donor registration rate is 46 percent and state rates range from 21 percent in Puerto Rico to 77 percent in Colorado. The four largest states  have relatively low registration rates. The national Potential Donor Death rate (q2) is slightly higher for men (62 vs 57 percent). The dominant factor here is that the non-registered Potential Donor death rate is 46 percent higher for men (6.53 vs. 4.48). Therefore, the estimated Δ for men is 2.40 and only 2.00 for women, or 20 percent higher.Males are less likely to be registered than Females (46 vs. 51 percent), but the estimated non-registered Donor Authorization rate (q2) fall from 65 percent for the 18-29 age group to 45 percent for the 55-74 age group. The estimated values for Δ rise markedly from 1.72 for the 18-29 group to 2.98 for ages 30-54 and 3.40 for ages 55-74. These estimates are by necessity short-run computations that ignore many complexities such as migration between states and the difference in the number of years on a donor registry for the middle-aged versus young adults. Since our estimate is the value per year, it may understate the benefit of registering younger donors.Estimates for age groups show similar patterns. Registration rates are fairly constant across age groups other than the youngest group, but non-registered Donor Authorization rates  and a portion of Virginia in order to match the registration number. Simulations 3-5 repeat the exercise for different targeting criteria. The spreadsheets used to perform the calculations are included in the Supplemental Materials. Readers can use the data to perform other simulations as needed.As mentioned, Simulation 2 targets high Δ states. Using Targeting states with low registration rates is also a natural choice. Simulation 3 increases the number of new donor registrations by the same amount as in Simulations 1 and 2, but raises the number of additional donors by only 299. This surprising result highlights the problem of focusing solely on registration numbers to the exclusion of other key variables.q2) and high Potential Donor Death rates is more effective than focusing on low registration rates, raising donors by 384 and 406, respectively.Simulations 4 and 5 follow the same pattern as Simulations 1-3, but focus on other components of Δ. Simulation 4 shows that targeting states with low non-registered Donor Authorization rates (T = Rp1p2+ (M − R)q1q2 q1q2  to compuq2 by 5 percentage points, state by state. This raises the number of expected donors by 463. Simulation 7 repeats the exercise, but uses a 10 percentage point increase in q2. This doubles the change in the number of expected donors to 926. Simulation 8 simulates a targeted effort to raise q2 to 60 percent in all states that are below that level. This simulation estimates the number of expected donors to increase by 541.Donor Authorization rates among the non-registered range widely across states, from 22 percent in Rhode Island to 82 percent in Puerto Rico. Simulation 6 increases In this paper we present estimates of potential donor death rates and donor authorization rates by registration status. Although our estimates are for the U.S., the approach can be used in any country if modified to fit local definitions and data constraints. We have shown that these probabilities vary markedly across states and by age and gender. We also derive a simple measure of the value of a new registered donor and find that it, too, varies across states and groups.Our results suggest that there are likely to be significant gains from targeted registration efforts. Since donated organs can move across state lines for transplantation, it seems wise to spend registration resources where they will do the most potential good. As an example, registration funding could be allocated to those states in which potential donor death rates are high and donor authorization rates are low among the non-registered population.et al. estimate that for every additional donor, 1.55 kidneys, 0.37 hearts, and 0.76 livers are transplanted. Taking the case of kidneys, this implies from 463 to 1,435 additional kidneys for transplantion. Accounting for the value of all donated organs, they estimate that the net economic value for a new organ donor is approximately $1 million. The simulations therefore imply a potential net economic gain from retargeting to be from about $300 million up to nearly $1 billion.The simulations suggest that for approximately the same cost of recruiting a new donor registrant, between 299 and 926 additional organ donors would result if registrations were targeted more purposefully. Mendeloff Our estimates also highlight the importance of using other methods to increase the number of transplants. For example, we have simulated the gains from increasing donor authorization rates. Our estimates suggest that this may be more productive than donor registration efforts. As an example, approximately $1 million dollars a year was spent on promoting donor registries 2010—2012 in the OPO that covers most of West Virginia and part of Pennsylvania . Part ofq2; therefore, the increase in the donor authorization rate (p2 − q2) for a new registrant will be smaller than for a randomly-selected unregistered person. In this case Δ should be considered an upper bound. For this reason, we believe that it is more useful to think of Δ as a relative value when comparing across states and subgroups.One weakness of this analysis is that it assumes that registrants are drawn at random from the unregistered population. If, as seems likely, those willing to register are from families that naturally favor organ donation, the registration propensity will be positively correlated with We believe that it is worthwhile to pay more attention to the underlying determinants of organ donation rates, especially potential donor death rates, eligible death rates and donor authorization rates for the registered and non-registered populations. The resources used for registration of new donors will be better allocated when policy-makers have better and more precise data. At present we still lack accurate and up-to-date estimates of the demographics of the registered and non-registered.x1 − x10 refer the number in each set. We know x5, x6, x9 and x10 because these are Eligible Deaths listed in the UNOS Eligible Death file. We know x7 and x8 because we know the count of donors who are listed in the UNOS Deceased Donor file but who are not in the Eligible Death file. We seek to estimate x3 and x4, the numbers of registered and non-registered Potential Donor Deaths who are neither Authorized Donors nor Eligible Deaths.Referring to x5 through x10 and estimates of x3 and x4. The remaining values x1 and x2 are then estimated using total population numbers. The formulas for the probabilities are given in We assume that the Donor Authorization rates vary only with registration status, not on physical characteristics distinguishing Eligible Death Status. Then Authorization rates are the same for registered donors who are Eligible Deaths and for those who are in the larger group of Potential Donor Deaths. Then our estimate of the Donor Authorization rates among registered and non-registered Eligible Deaths are p1 = P(PD|R) or q1 = P(PD|NR), depending on registration status. The analogous Donor Authorization probabilities are p2 = P(A|PD ∩ R) and q2 = P(A|PD ∩ NR). We assume each individual is an independent Bernoulli trial. For each parameter, denote the trial for individual i from a population of size N be Xi, so that Xi∼B(θ), where E(Xi) = θ and Var(Xi) = θ(1 − θ). Then the maximum likelihood estimator We seek estimators for the four probabilities and for our statistic, Δ. For Potential Donor Deaths, we assume the probability of an individual becoming an Potential Donor is either q1(p2 − q2), is a non-linear function of three of these parameters. To obtain its variance we use the so-called “delta method”:p1 − q2, q1, − q1). We assume zero correlation across estimators so that Σ is a diagonal matrix with the variances of q1, p1 and q2 on the diagonal.Our measure of the expected increase in donors from a new registrant, Δ = S1 File(XLSX)Click here for additional data file.


New surface electromyography (sEMG) feature extraction approach combined with Empirical Mode Decomposition (EMD) and Dispersion Entropy (DisEn) is proposed for classifying aggressive and normal behaviors from sEMG data. In this study, we used the sEMG physical action dataset from the UC Irvine Machine Learning repository. The raw sEMG was decomposed with EMD to obtain a set of Intrinsic Mode Functions (IMF). The IMF, which includes the most discriminant feature for each action, was selected based on the analysis by Hibert Transform (HT) in the time-frequency domain. Next, the DisEn of the selected IMF was calculated as a corresponding feature. Finally, the DisEn value was tested using five different classifiers, such as LDA, Quadratic DA, k-NN, SVM, and Extreme Learning Machine (ELM) for the classification task. Among these ML algorithms, we achieved classification accuracy, sensitivity, and specificity with ELM as 98.44%, 100%, and 96.72%, respectively.


To determine the presence and spatial distribution of different macrophage phenotypes, governed by granulocyte macrophage colony‐stimulating factor (GM‐CSF) and macrophage colony‐stimulating factor (M‐CSF) skewing signals, in giant cell arteritis (GCA) lesions.n = 11) from treatment‐naive GCA patients, aorta samples from GCA‐related aneurysms (n = 10) and atherosclerosis (n = 10) were stained by immunohistochemistry targeting selected macrophage phenotypic markers, cytokines, matrix metalloproteinases (MMPs) and growth factors. In vitro macrophage differentiation (n = 10) followed by flow cytometry, Luminex assay and ELISA were performed to assess whether GM‐CSF and M‐CSF are drivers of macrophage phenotypic heterogeneity.Temporal artery biopsies (TABs, +/MMP‐9+ macrophages were located at the site of tissue destruction, whereas FRβ+ macrophages were located in the inner intima of arteries with high degrees of intimal hyperplasia. Notably, this pattern was also observed in macrophage‐rich areas in GCA aortas but not in atherosclerotic aortas. Flow cytometry showed that GM‐CSF treatment highly upregulated CD206 expression, while FRβ was expressed by M‐CSF‐skewed macrophages, only. Furthermore, localised expression of GM‐CSF and M‐CSF was detected, likely contributing to macrophage heterogeneity in the vascular wall.A distinct spatial distribution pattern of macrophage phenotypes in TABs was identified. CD206+/MMP‐9+ macrophages and FRβ+ macrophages in GCA linked to tissue destruction and intimal proliferation, respectively. We suggest that these distinct macrophage phenotypes are skewed by sequential GM‐CSF and M‐CSF signals. Our study adds to a better understanding of the development and functional role of macrophage phenotypes in the pathogenesis of GCA and opens opportunities for the design of macrophage‐targeted therapies.Our data document a distinct spatial distribution pattern of CD206 +/MMP‐9+ macrophages were located at the site of tissue destruction, whereas FRβ+ macrophages were located in the inner intima of arteries with high degrees of intimal hyperplasia. We suggest that these distinct macrophage phenotypes are skewed by sequential GM‐CSF and M‐CSF signals.In this study, we revealed a distinct spatial distribution of macrophage phenotypes in GCA‐affected vessel walls. CD206 Giant cell arteritis (GCA) is an inflammatory disorder and the most frequent form of vasculitis. GCA affects the medium and large vessels and occurs exclusively in elderly individuals.highCD16− cells, intermediate CD14highCD16+ cells and non‐classical CD14dimCD16+ cells.The pathology of GCA is characterised by a granulomatous infiltrate in the vessel wall, which mainly consists of T cells and macrophages.Macrophages are the main producers of proinflammatory cytokines, growth factors and tissue‐destructive molecules, including matrix metalloproteinases (MMPs),+ macrophages have been associated with fibroblast activation and proliferation in rheumatoid arthritis.Macrophages are incredibly plastic cells that can switch phenotypes and functions depending on environmental cues. The growth factors, granulocyte macrophage colony‐stimulating factor (GM‐CSF) and macrophage colony‐stimulating factor (M‐CSF), were shown to skew macrophages into different phenotypes.in vitro. In addition, we assessed the expression of GM‐CSF and M‐CSF in GCA lesions.Although macrophages are one of the dominant inflammatory cellular infiltrates in GCA lesions,Transmural inflammation was found in all GCA‐positive TABs, whereas no leucocyte infiltrates were found in the non‐inflamed control TABs . We observed significantly higher expression of M‐CSF transcripts in GM‐MØs than in M‐MØs Figure . GM‐CSF As CD206 expression was observed to be higher in GCA GM‐MØs and M‐MØs than in their HC counterparts, we reasoned that per‐cell expression of the GM‐CSF and M‐CSF receptors might be modulated in GCA monocytes. This, however, did not appear to be the case in peripheral blood monocyte subsets from HCs and GCA patients, in which no differences were found Figure .+ macrophages is likely to prime adjacent macrophages to express FRβ.Taken together, our data suggest that the expression pattern of GM‐CSF and M‐CSF in GCA lesions is different and may contribute to the spatial distribution of macrophage phenotypes in GCA lesions. M‐CSF produced by CD206FRβ positivity was found in the inner intima of the TAB, this is the region where intimal proliferation occurs. To determine whether the extent of FRβ positivity was associated with the severity of intimal hyperplasia, we divided luminal occlusion in GCA‐affected TABs into mild or massive occlusion based on the intimal thickness score Figure  and rela+ macrophages could be a source of PDGF, we performed macrophage differentiation and activation experiments followed by ELISA for PDGF‐AA in the culture supernatant. Our results showed that M‐MØs produced significantly higher concentrations of PDGF‐AA compared to GM‐MØs  could contribute to intimal hyperplasia. To investigate whether FRβs Figure , in bothIn this study, we revealed a distinct spatial distribution of macrophage phenotypes in GCA‐affected vessel walls and provide evidence that macrophage phenotypic heterogeneity is influenced by the growth factors GM‐CSF and M‐CSF. Moreover, distinct macrophage phenotypes were associated with tissue destruction and intimal hyperplasia. Although it has been suggested previously that macrophages have different functions in different compartments of the inflamed vessel wall in GCA,+/MMP‐9+ macrophages were mainly found in the media and media borders along the sites of lamina elastica degradation which is in line with a previous report that MMP‐9‐producing macrophages are located in the media borders.+ macrophages.Our data demonstrate that macrophages in GCA‐affected TAB show a distinct expression pattern of surface markers depending on their location in the tissue. Macrophages with a proinflammatory phenotype, including expression of Th1‐ or Th17‐skewing cytokines, were detected throughout the vessel wall. CD206in vitro differentiation data confirmed that GM‐MØs indeed have high expression of CD206 and lack expression of FRβ. In contrast, FRβ expression was exclusively upregulated in M‐MØs. Additionally, GM‐CSF has been reported to diminish FRβ expression by macrophages even when differentiated in the presence of both GM‐CSF and M‐CSF.+ macrophages in the media and media borders are mainly primed by GM‐CSF, while FRβ+ macrophages adjacent to CD206+ macrophages are primed by M‐CSF. This also implies a gradient of GM‐CSF and M‐CSF production in different layers of the vessel wall that may be responsible for the distinct macrophage phenotype distribution observed. GM‐CSF expression was found highest in the adventitia and was mainly expressed by endothelial cells and infiltrating leucocytes, presumably activated T cells and B cells.+/MMP9+ macrophages. GM‐CSF can induce M‐CSF production, as previously demonstrated in monocytes+ and are located at the site of medial destruction.Our data suggest a role for GM‐CSF and M‐CSF in macrophage phenotypic heterogeneity in GCA lesions. Previously, CD206 and FRβ were found to be markers for GM‐CSF‐ and M‐CSF‐differentiated macrophages, respectively.+ macrophages.+ M‐MØs in the inner intima may contribute to intimal hyperplasia by increased growth factor production such as PDGF‐AA.+ macrophages in GCA remains to be elucidated.We found significantly higher expression of FRβ in the inner intima in TABs with a higher degree of intimal hyperplasia. This finding suggests that FRβ macrophages may play a role in myofibroblast activation, migration and proliferation, leading to intimal hyperplasia. Indeed, improvement of pulmonary fibrosis was shown by depleting FRβet al.+ macrophages. These GM‐MØs then migrate to the media and media borders, exerting their tissue‐invasive, digestive and proangiogenic effects. Additionally, these CD206+ GM‐MØs release large amounts of M‐CSF, which in turn primes the macrophages surrounding them to express FRβ. These FRβ+ macrophages then boost myofibroblast migration and proliferation, possibly by higher production of growth factors (such as PDGF‐AA). This process eventually leads to luminal occlusion.Macrophage heterogeneity in GCA lesion could be influenced by step‐wise GM‐CSF and M‐CSF signals. Recently, Watanabe in vitro differentiation model. Indeed, the GCA tissue environment is much more complex and enriched with a multitude of cytokines. Cytokines such as IFNγ, which is highly expressed in GCA lesions,In contrast to our tissue staining results, MMP‐9 production was found to be higher in M‐MØ than in GM‐MØ. This result may be explained by the limitations of the + macrophages in GCA, whereas concomitant CD206/FRβ expression was shown in atherosclerosis macrophages. The Th2 cytokine IL‐4, highly expressed in atherosclerotic lesions but to a limited extent in GCA lesions, can upregulate CD206 expression on FRβ macrophages.A distinct macrophage distribution pattern was also observed in GCA‐affected aortas but not in atherosclerotic aortas. In contrast to TABs, a distinct macrophage distribution pattern was observed only within the media of the GCA aortas. The variation in this distribution pattern between TABs and the aorta may be caused by differences in vessel wall size and anatomical build‐up, as aortas have thicker media with multiple lamina elastica layers. In contrast to GCA, but confirming previous reports, macrophages in atherosclerotic aortas were found mainly in the intima surrounding the atherosclerotic plaques.We observed that circulating monocytes and monocyte‐derived macrophages from GCA patients display a GM‐CSF signature compared to HCs. This finding was reflected by lower FRβ expression on monocytes and higher CD206 expression after differentiation into macrophages. This also implies that monocytes from GCA patients have a stronger response to GM‐CSF. However, we found no difference in GM‐CSF receptor expression between the groups. Additionally, our previous study showed no elevation of GM‐CSF in the serum of GCA patients,in vitro differentiation studies. We are aware that our in vitro model does not fully capture the events in the tissue, as a plethora of cytokines that can lead to further skewing and activation of macrophages were not explored. Future studies should focus on expanding the proposed model to incorporate additional activating cytokines and in vivo intervention models.The major strength of our study is the comprehensive analysis of multiple markers of inflammation and tissue remodelling, which allows the identification of distinct macrophage phenotypes. Our biopsy tissues were obtained from treatment‐naive patients to exclude the potential effects of GCs on macrophage phenotypes. Future studies should, however, address the impact of GCs on the skewing of lesional macrophage phenotypes. Finally, we also included atherosclerotic aortas for comparison and found that the roles of macrophages in the pathogenic processes leading to these two diseases are indeed different. We identified a possible role for GM‐CSF and M‐CSF in the local skewing of macrophage phenotypes in GCA and substantiated this finding with Our study may aid in expanding current GCA pathogenic models and identifying markers for targeted therapy. Currently, a GM‐CSF receptor‐blocking antibody, mavrilimumab (NCT03827018), is being evaluated in a phase 2 clinical trial for the treatment of GCA. Our findings add to the rationale for targeting the GM‐CSF receptor in this disease. Additionally, reduced inflammation was shown in a rheumatoid arthritis cartilage explant model with a CD64‐targeted immunotoxin.Taken together, vascular lesions of GCA patients display a distinct spatial distribution pattern of macrophage phenotypes associated with tissue destruction and intimal hyperplasia that are likely influenced by local expression of M‐CSF and GM‐CSF. These findings contribute to improved insights into the pathogenesis of GCA and lay the foundation for designing new macrophage‐targeted therapies and imaging tracers.The data supporting the findings of this study are available from the corresponding author on reasonable request.18F‐fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET‐CT) for GCA. In addition, non‐inflamed TAB tissue from patients who had (PET‐CT‐proven) GCA, isolated PMR patients, and individuals who had neither GCA nor PMR (n = 5 each) were included as controls. PMR was diagnosed by a positive PET‐CT for PMR. Clinical and laboratory data for these patients were collected as part of our prospective cohort study. The study was approved by the institutional review board of University Medical Center Groningen (METc2010/222). Written informed consent was obtained from all study participants. All procedures were in compliance with the Declaration of Helsinki.Eleven inflamed TAB tissue samples of histologically proven GCA collected before the start of GC treatment were studied Table . The dian = 10) and age‐matched atherosclerotic controls (n = 10) were retrospectively obtained after aortic aneurysm surgery (Supplementary table Aorta tissues from GCA patients n = 0 and agen = 10) and age‐ and sex‐matched healthy controls  participating in the prospective cohort study were used for in vitro studies  from treatment‐naive GCA patients n = 0 and ageFormalin‐fixed, paraffin‐embedded tissues were cut into sections of 3 µm. The sections were deparaffinised and rehydrated, followed by antigen retrieval in a 95°C water bath : 0 = no positive cells, 1 = occasional positive cells (0–1% estimated positive), 2 = small numbers of positive cells (> 1–20%), 3 = moderate numbers of positive cells (> 20–50%), 4 = large numbers of positive cells (more than 50%). An average score was calculated from assessments by two independent investigators. Tissues were scored in representative areas that contained infiltrating cells, as GCA can contain skip lesions.Paraffin sections were deparaffinised and rehydrated, followed by antigen retrieval in Tris‐EDTA buffer (pH 9) in a 95°C water bath for 45 min. Tissues were incubated for 5 min with TrueBlack Lipofuschin autofluorescence blocker  in the dark at room temperature. Next, tissues were incubated with a cocktail of primary antibodies  to generate GM‐MØs or 100 ng mL−1 M‐CSF (Peprotech) to generate M‐MØs. The medium was replaced on the second and fourth day. On day 7, after collecting the supernatants for Luminex assay, monocyte‐derived macrophages were harvested using citrate saline  for 15 min at 37°C. For macrophage activation experiments, monocytes were cultured for 7 days in the same medium in the presence of GM‐CSF/M‐CSF. The medium was replaced on the third and fifth days. LPS (100 ng mL−1)  was added to the culture during the fifth day medium change. On day 7, supernatants were collected for ELISA.Giant cell arteritis and HC monocytes were isolated from thawed PBMCs by negative selection using the EasySep monocyte enrichment kit , which does not deplete CD16Phenotyping of monocytes and monocyte‐derived macrophages was performed by flow cytometry using fluorochrome‐conjugated monoclonal antibodies specific for HLA‐DR , CD14 , CD16 , CD64 , CD86 , CD206  and FRβ . The expression of the GM‐CSF receptor  and the M‐CSF receptor  on monocyte subsets was analysed by a separate flow cytometry panel . Cells were measured on LSR‐II (BD) flow cytometer. For comparison of the mean fluorescence intensity between experiments, the LSR‐II flow cytometer was calibrated for each run using FACSDiva CS&T research beads (BD). Data were analysed using Kaluza software (BD). Monocytes and macrophages were gated by FSC/SSC, doublets were excluded, and dead cells were excluded using Zombie dye (Biolegend). To exclude contaminating lymphocytes in the monocyte gate, cells negative for both HLA‐DR and CD14 were gated out. Monocyte subsets were gated based on CD14 and CD16 expression.−1 were included and assigned the value of 1.75 pg mL−1. Supernatant levels were corrected for the macrophage cell count at the time of harvesting and are expressed as ng mL−1 per 50 000 cells.Supernatants from the GM‐MØ and M‐MØ cultures (non‐activated and activated) were stored at −20°C until further use. In supernatants of non‐activated cultures, levels of IL‐1β, IL‐6, IL‐10, IL‐12, IL‐23 and MMP‐9 were measured with Human premix Magnetic Luminex screening assay kits  according to the manufacturer's instructions and read on a Luminex Magpix instrument . Data were analysed with xPONENT 4.2 software (Luminex). PDGF‐AA concentrations in the supernatant of activated cultures were measured with the Human PDGF‐AA Duo set ELISA  according to the manufacturer's instruction. For calculation and graphing purposes of the PDGF data, data points below the detection limit of 3.5 pg mLTotal RNA was extracted from healthy donor‐derived GM‐MØs and M‐MØs using the RNeasy® Mini Kit . Total RNA was reverse transcribed with SuperScript III reverse transcriptase  with random hexamers . Real‐time qPCR was conducted with a ViiA™ 7 Real‐Time PCR System with TaqMan™ probes (Thermo Fisher) targeting M‐CSF  and GM‐CSF . Amplification plots were analysed with QuantStudio™ Real‐Time PCR software v1.3 . Relative gene expression was normalised to β‐actin  as an internal control.in vitro study, nonparametric Mann–Whitney U‐tests (two‐tailed) were used. Differences in FRβ expression scores for the inner intima between patients with low and high vessel occlusion scores were also assessed by Mann–Whitney U‐tests. Differences between the results of GM‐MØ and M‐MØ raised from the same donor were analysed with the paired Wilcoxon signed‐rank test.To analyse the differences between HC and GCA, GM‐MØ and M‐MØ in the The authors declare no conflict of interest. AMHB was a consultant for Grünenthal Gmbh until 2017. EB and KvdG as employees of the UMCG received speaker/consulting fees from Roche which were paid to the UMCG. PH, WHA and EB have received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement 668036.William F Jiemy: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Validation; Visualization; Writing‐original draft; Writing‐review & editing. Yannick van Sleen: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Validation; Visualization; Writing‐original draft; Writing‐review & editing. Kornelis SM van der Geest: Data curation; Project administration; Resources; Software; Supervision; Validation; Writing‐review & editing. Hilde A ten Berge: Data curation; Formal analysis; Investigation; Visualization; Writing‐review & editing. Wayel H Abdulahad: Formal analysis; Investigation; Supervision; Validation; Writing‐review & editing. Maria Sandovici: Supervision; Validation; Writing‐review & editing. Annemieke MH Boots: Conceptualization; Funding acquisition; Methodology; Resources; Supervision; Validation; Writing‐review & editing. Peter Heeringa: Conceptualization; Funding acquisition; Methodology; Project administration; Resources; Supervision; Validation; Writing‐review & editing. Elisabeth Brouwer: Conceptualization; Funding acquisition; Methodology; Project administration; Resources; Supervision; Validation; Writing‐review & editing. Click here for additional data file.


Lithobates chiricahuensis). Habitat preferences of this frog species are not well understood. We sought to better understand fine‐scale habitat selection, to inform conservation of Chiricahua leopard frogs.Habitat degradation and fragmentation are major drivers of amphibian declines. The loss of environmental features that allow for movement between water sources may be particularly detrimental for amphibians in arid environments. Climate changes will increase the importance of microhabitats to amphibians. Enhancing areas to facilitate movement may be a necessary conservation strategy for many animal species that depend on wetlands, including federally threatened Chiricahua leopard frogs . We assessed fine‐scale habitat selection by comparing characteristics at each frog location and a random location 5 m away using conditional logistic regression.Frogs preferred features that likely reduce desiccation, even after accounting for the presence of water. Frogs selected areas with more low‐lying cover, especially aquatic vegetation and woody debris, a tree overstory, and a mud substrate.We recommend managing potential movement corridors for Chiricahua leopard frogs by ensuring the presence of muddy creek bottoms, woody debris, riparian overstory, low‐lying ground cover, and pools. Microclimates created by these features seem especially valuable given warming temperatures and modified precipitation regimes, resulting in decreased surface water, soil moisture, and vegetation cover. Retaining or creating preferred habitat features and microclimates in areas between water sources may increase connectivity among isolated populations of Chiricahua leopard frogs and could improve persistence and recovery of other water‐obligate species in arid landscapes. Retaining or creating preferred habitat features and microclimates in areas between water sources may increase connectivity among isolated populations of Chiricahua leopard frogs and could improve persistence and recovery of other water‐obligate species in arid landscapes. We studied the habitat preferences of Chiricahua leopard frogs as they moved between water sources and present those selection results in order to better inform management of habitat for this threatened amphibian. Our focal sites for trapping frogs were two earthen‐pond tanks in the Seco Creek drainage , which provides habitat for 33% of the known populations of Chiricahua leopard frogs in New Mexico , a closely related species of a similar size  to frogs following the method of Muths  location, and several ecological characteristics Table . As Chir2.5Prior to analysis, we examined the distribution of possible explanatory variables. We condensed substrate and cover types that we documented infrequently to an “other” category. We maintained separate categories of overstory type for juniper and willow, which were used commonly, but condensed all other tree species into a general “tree spp.” category. More than half of all locations were in areas without overstory cover , limiting our ability to make inference across the range of this covariate. As such, we converted the percentage of overstory cover at the location into a categorical variable with two levels, little to no overstory cover (≤10%) or greater overstory cover (>10%), which we included as a potential covariate.coxme package in R  or where a model failed to converge.We selected an inferential model based on the stepwise variable selection method of Hosmer and Lemeshow . This me3We tracked 44 Chiricahua leopard frogs  using radio telemetry, locating frogs a total of 1,036 times. The duration of the tracking period varied among individuals . Frogs preferentially selected locations based on presence of water, percent cover (quadratic relationship), and type of cover available to a frog, type of overstory cover, and substrate Table  but did 3.1Frogs were 2.92 (95% CI = 2.78–3.07) times more likely to select a location with water, compared to one without.3.2Compared with a location with no cover, frogs were 19.49 4.08–93.22) times more likely to select a site within a 1 m circle that provided more low‐lying cover. Selection probability increased to a maximum of 70% cover, although uncertainty around this estimate was high , a closely related species found in similar arid environments, selected pools with more vegetation, more overstory cover, and greater heterogeneity around the edges of pools  experiencing greater desiccation risk in areas with less herbaceous groundcover and moving through more densely covered areas when available ; data curation (lead); formal analysis (lead); investigation (lead); methodology ; writing – original draft (lead); writing – review and editing . Andrea R. Litt: Conceptualization ; funding acquisition ; investigation ; methodology ; project administration ; resources ; supervision ; writing – original draft (supporting); writing – review and editing . Magnus McCaffery: Conceptualization ; funding acquisition ; investigation ; methodology ; project administration ; resources ; supervision ; writing – original draft (supporting); writing – review and editing (supporting).


Origanum vulgare L.) and thyme (Thymus vulgaris L.) have long been known for their organoleptic properties. Both plants are widely used in cuisine worldwide in fresh and dried form and as a pharmaceutical raw material. The study aimed to assess if the type of cultivation influenced chosen chemical parameters , antimicrobial activity  and shaped cytotoxicity (with L929 mouse fibroblasts cell line) in water macerates of dry oregano and thyme. Polyphenols content and antimicrobial activity were higher in water macerates obtained from conventional cultivation (independently from herb species), unlike the pigments in a higher amount in macerates from organic herbs cultivation. Among all tested macerates stronger antimicrobial properties  and higher cytotoxicity (abilities to diminish the growth of L929 fibroblasts cytotoxicity) characterized the conventionally cultivated thyme macerate.Oregano ( Origanum vulgare L., also known as wild marjoram), and thyme (Thymus vulgaris L.) are popular and widely used in cuisines and the cosmetic industry. Oregano and thyme are native species of the Mediterranean region , which is one of the tests recommended by the Food and Drug Administration (FDA) and ISO as previously described [The metabolic activity of the L929 cells was tested after application of water extracts obtained from dried plants. Cells in culture medium were seeded in 96-well plates (2 × 10escribed .p = 0.05). Obtained results are presented results are shown as the mean value of six replications. The statistical significance of the cytotoxicity results was determined by Kruskal-Wallis test (p < 0.05). Data are presented as mean values ± SD. For statistical analysis, the STATISTICA 12 and 13 PL software was used .Statistical analysis over chemical parameters was performed with Duncan’s test  and are rich in pigments (organic). It is worth pointing that “conventional” thyme macerate exhibited strong activity against the strains of Thyme “conventional” macerates diminished the growth of L929 fibroblasts cytotoxicity, and “organic” oregano increased the ability of L929 cells to reduce MTT. Cultivation type had shaped influence on the ability of L929 cells to reduce MTT—both organic herbs macerates had significant higher abilities to increase this property; however, conventional cultivated herbs (water macerates) had higher antimicrobial activity and higher polyphenols content. Extracts, isolated from ecological plant cultures, may be used in higher concentrations in formulations for medical use due to their lower cytotoxic activity. This allows achieving a higher concentration of biologically active substances while maintaining biological safety, and faster achievement of the therapeutic and cosmetic effect.


Phascolarctobacterium faecium is a strict anaerobe belonging to the Firmicutes phylum that is found abundantly in the human gastrointestinal tract. Here, we report the complete genome sequence of P. faecium G 104, a strain isolated from a fresh stool sample from a healthy lean donor. Phascolarctobacterium faecium is a strict anaerobe belonging to the Firmicutes phylum that is found abundantly in the human gastrointestinal tract. Here, we report the complete genome sequence of P. faecium G 104, a strain isolated from a fresh stool sample from a healthy lean donor. Phascolarctobacterium faecium is a Gram-negative, nonsporulating, and strictly anaerobic bacterium that was first isolated from a healthy human donor  . The single-molecule real-time (SMRT) Link software v.6.0.0 was used to filter and assemble the subreads. The HGAP4 algorithm (https://github.com/sanger-pathogens/circlator) was used for circularization and removal of the overlaps, as described in the PacBio assembly sequencing pipeline  fermentation into butyrate and three genes encoding a bile acid sodium symporter and bile acid-inducible protein F were detected but not functionally determined.an donor  and was an donor . This baan donor  and posian donor  and to lan donor . P. faecum (IBM)  supplemelgorithm  was usedlgorithm  (https:/pipeline . ContigsCM 30894 . The genP. faecium G 104 was deposited in GenBank under accession number CP061002.1, BioProject accession number PRJNA659610, BioSample accession number SAMN15921107, and SRA accession number SRR13005647.The complete genome sequence of


Spinal cerebrospinal fluid (CSF) leak can lead to intracranial hypotension and is an important differential diagnosis to consider in patients with sudden-onset chronic daily headaches. Pars interarticularis (PI) fracture is a potential rare cause of suspected spinal CSF leak.This is a retrospective case series of 6 patients with suspected spinal CSF leak evaluated between January 2016 and September 2019. All patients received a magnetic resonance imaging (MRI) of the brain with and without gadolinium, MRI whole spine and full spine computed tomography (CT) myelogram. Targeted epidural patches with fibrin sealant were performed. Treatment response at return visit (3 months post-patch) was documented.Six patients  were diagnosed with a suspected spinal CSF leak and PI fracture. Mean age at the time of headache onset was 39 years old, and a range from 32 to 50 years old. Mean time to targeted epidural patches with fibrin sealant was 4.5 years. All 6 patients had PI fractures identified on CT myelogram and received targeted epidural patches with fibrin sealant at the site of the PI fracture. All patients had significant improvement in their headache intensity.Our study highlights: 1) the importance of PI fracture as a possible culprit of suspected spinal CSF leak in patients with intracranial hypotension; 2) the added benefit of CT imaging for detecting bony abnormalities such as fractures in patients with intracranial hypotension; and 3) the successful treatment of suspected spinal CSF leak when targeting the fracture site. Spinal cerebrospinal fluid (CSF) leak can lead to intracranial hypotension and is an important differential diagnosis to consider in patients with sudden onset chronic daily headaches. This entity can often be confused with other chronic headache disorders, such as new daily persistent headache and chronic migraine. The most common clinical manifestation of a spinal CSF leak is orthostatic headache (worse upright) . HoweverIn addition to orthostatic headache, patients with spontaneous intracranial hypotension (SIH) can experience a broad clinical spectrum of symptoms, including cochleovestibular dysfunction , 17, craSpinal CSF leak is commonly precipitated by traumatic events such as motor vehicle accidents, sports injuries, and medical procedures such as dural or epidural punctures . HoweverSix cases of suspected spinal CSF leak secondary to PI fracture were identified retrospectively. All patients were evaluated between January 2016 to September 2019 at the Stanford CSF leak headache clinic. All cases were presented at the neuroradiology/headache neurology CSF leak conference with headache specialists, anesthesiologists and neuroradiologists in attendance. All patients received an MRI of the brain with and without gadolinium, MRI whole spine and full spine CT myelogram. No bedside lumbar puncture was performed. These studies were interpreted by board-certified neuroradiologists. All targeted epidural patches with fibrin sealant were performed by a team member board-certified in Headache, Anesthesiology, and Pain Medicine (IC). Treatment response at return visit (3 months post-patch) was documented based on a pain intensity scale from 0 to 10, 10 being the most severe. This study received IRB approval  from Stanford University.This is a descriptive case series study only, so no statistical analysis was utilized.Six patients  had suspected spinal CSF leak based on their clinical and/or radiological findings. All 6 patients complained of daily unremitting orthostatic headaches. Patient 3, 4, and 5 had migraine features  with their headaches. Patient 1, 2, and 6 had severe occipital pressure pain with their headaches. They had a number of concomitant symptoms including blurry vision, tinnitus, ear fullness, altered taste, vertigo, neck pain, interscapular pain, autonomic dysfunction and/or cognitive dysfunction , but these have no localizing value . Spinal Patient 1 demonstrated a transient and mild relief from his non-targeted lumbar epidural patches, which suggests a possibility that prior treatments were coincidentally injected at or near the site of PI fracture since the lumbar region is often a standard location for non-directed epidural blood patches. Limited information was available from chart review on the other patients’ prior treatment response.Patients 1 and 4 developed suspected rebound intracranial hypertension 6 months post-patch based on a change in their headache characteristics (headache worse lying supine). This information was relayed to our center from their primary providers. This illustrates a potential risk of spinal CSF leak treatment long term. Our patients had a prolonged clinical course before the PI fracture was identified as the suspected leak site. It is possible that contributed to an increased risk of rebound intracranial hypertension .As for the PI fracture, the treatment is usually nonsurgical (rest and bracing). Surgery (laminectomy or posterior lumbar fusion) may be required if symptoms persist or remain bothersome . PatientSome limitations were seen with this study: Two out of the 6 patients (Patient 1 and 2) met the diagnostic criteria of “headache attributed to low CSF pressure” as per the International Classification of Headache Disorders Third Edition . The othThe diagnosis and management of spinal CSF leak causing SIH remains a challenge, especially in cases where the site of suspected CSF leak is difficult to detect. There are limitations to each imaging modality and often a combination is required to confirm and localize a CSF leak. Our study highlights: 1) the importance of PI fracture as a possible culprit of suspected spinal CSF leak in patients with intracranial hypotension; 2) the added benefit of CT imaging for detecting bony abnormalities such as fractures in patients with intracranial hypotension; and 3) the successful treatment of suspected spinal CSF leak when targeting the fracture site. If no clinical improvement is observed after adequate conservative measures  or non-targeted epidural patches in the context of a non-localizing spinal CSF leak, further testing may be warranted to explore an osseous pathology, such as occult fractures. No conclusions can be drawn regarding the frequency with which PI fractures are complicated by CSF leaks. Future research in a larger cohort of patients is required to explore the likelihood of detecting PI fracture in patients with non-localizing spinal CSF leak and to identify any risk factors associated with both PI fracture and spinal CSF leak. Long term follow-up data are required to explore the effectiveness and potential complication  from targeted patches to the fracture site.


Drug addiction is a chronic relapsing disease leading to allostatic alterations in the brain’s reward, stress, and executive function systems . The etiBidirectional interactions between the nervous system and immune system, collectively known as the “neuroimmune system”, regulate a wide range of physiological and pathological processes . While sGranulocyte-colony stimulating factor (G-CSF) is a ~20-kDa protein produced by bone marrow stromal cells, endothelial cells, macrophages, and fibroblasts, as a peripheral hematopoietic growth factor . Both G-In a series of rodent experiments, Kutlu et al.  investigex vivo fast-scan cyclic voltammetry to assess dopaminergic function in the nucleus accumbens (NAc)—a region heavily implicated in reward processing. Repeated systemic administration of G-CSF increased electrically-evoked dopamine release in the NAc, without altering dopamine uptake or transport. Acute application of G-CSF onto the brain slice failed to alter NAc dopamine transmission, indicating that G-CSF does not act directly at dopaminergic terminals in the NAc, but perhaps on their cell bodies in the ventral tegmental area (VTA) or other converging circuits . Finally, quantitative polymerase chain reaction revealed that repeated G-CSF treatment suppressed pro-inflammatory cascades in the NAc, as demonstrated by reduced TNF-α gene expression, while acute G-CSF increased the expression of several dopaminergic genes   . CollectA previous study by the same group delineated the link between G-CSF and the biobehavioral response to cocaine. Specifically, Calipari et al.  found thFuture work is warranted to determine the extent to which G-CSF influences behavioral correlates of drug and natural rewards, as well as its regulatory effects in other addiction-related brain regions. As a first pass, Mervosh et al.  used an As a whole, the aforementioned data elucidate novel mechanisms whereby peripheral administration of an anti-inflammatory cytokine (G-CSF) enhances the activity of a central reward circuit (mesolimbic dopamine system) and modulates both motivational and cognitive aspects of reward processing. The sucrose threshold task used in this study should be highlighted as this behavioral economics self-administration protocol allows for the dissociation of A) the animal’s preferred level of consumption when effort is low (i.e. preferred intake level) and B) the maximal effort the animal will expend to maintain its preferred intake level (i.e. motivation to consume). The researchers determined that G-CSF increased both sucrose intake and motivation. As demonstrated by the reversal learning task, cognitive flexibility was also enhanced by repeated, but not acute, G-CSF injections. This is an important finding given that cognitive deficits play substantial roles in many neuropsychiatric diseases, including addiction, and most of the currently available pharmacotherapies do not address these impairments . A singlMedication development for neuropsychiatric disorders, especially addiction, is a complex, lengthy, and expensive process with low success rates. Therefore, the notion that G-CSF may represent an effective therapeutic target is of particular relevance and interest. Recombinant G-CSF is already approved by the FDA and has been tested in clinical trials for other indications—an advantage which may facilitate translation of the present preclinical findings into human research. As an initial bench-to-bedside step, proof-of-concept human laboratory studies may provide valuable information on the effects of G-CSF administration in patients with SUDs. As demonstrated by Calipari et al. , G-CSF dMF and FV were responsible for the conceptualization of this paper. MF, AB, HK, LH, and FV developed the rationale, wrote the first draft, critically reviewed the content, and approved the final manuscript for publication.This work was supported by NIH intramural funding ZIA-AA000218 , jointly supported by the NIDA Intramural Research Program and NIAAA Division of Intramural Clinical and Biological Research (MF); NIH Center on Compulsive Behaviors, funded by the NIH Deputy Director for Intramural Research (DDIR) Innovation Award (MF); NIAAA K99 AA027576 grant (LH); NIAAA K99 AA025408 grant (FV); and Developmental Exposure Alcohol Research Center (DEARC) at Binghamton University, P50 grant AA017823 (FV). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the funders, which had no role in the development of this article.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Brain-derived neurotrophic factor (BDNF) is implicated in the survival of striatal neurons. BDNF function is reduced in Huntington’s disease (HD), possibly because mutant huntingtin impairs its cortico-striatal transport, contributing to striatal neurodegeneration. The BDNF trophic pathway is a therapeutic target, and blood BDNF has been suggested as a potential biomarker for HD, but BDNF has not been quantified in cerebrospinal fluid (CSF) in HD. We quantified BDNF in CSF and plasma in the HD-CSF cohort (20 pre-manifest and 40 manifest HD mutation carriers and 20 age and gender-matched controls) using conventional ELISAs and an ultra-sensitive immunoassay. BDNF concentration was below the limit of detection of the conventional ELISAs, raising doubt about previous CSF reports in neurodegeneration. Using the ultra-sensitive method, BDNF concentration was quantifiable in all samples but did not differ between controls and HD mutation carriers in CSF or plasma, was not associated with clinical scores or MRI brain volumetric measures, and had poor ability to discriminate controls from HD mutation carriers, and premanifest from manifest HD. We conclude that BDNF in CSF and plasma is unlikely to be a biomarker of HD progression and urge caution in interpreting studies where conventional ELISA was used to quantify CSF BDNF. It causes behavioural, cognitive, and motor dysfunctions and no disease-modifying treatment has yet demonstrated efficacy2. BDNF is a growth factor implicated in neuronal survival, development, and synaptic plasticity. HD post-mortem brain tissue has been shown to have reduced BDNF3. Murine studies demonstrate that the absence of BDNF leads to early striatal neuronal death and HD phenotypes5. BDNF concentration is lower in the striatum of R6/1 and zQ175 mice compared to the wild-type counterparts7. This lowered concentration may have been influenced by a wild-type huntingtin-mediated loss of function mechanism. Wild-type huntingtin regulates the transport of BDNF and indirectly promotes the genetic expression of BDNF9 and BDNF overexpression produced phenotypic recovery in a murine model10. BDNF and its trophic pathway are targets for therapeutic development11.In HD, the selective vulnerability and degeneration of striatal neurons may be caused by the depletion of brain-derived neurotrophic factor (BDNF)13, and restoration of BDNF may ameliorate behavioural deficits and neuronal loss in AD models12. Similarly, BDNF deficiency in serum has been observed in HD patients14.BDNF concentration in cerebrospinal fluid (CSF) or blood could be an accessible means of quantifying dysfunction of this pathogenic pathway, could be a useful monitoring or prognosis biomarker, and could elucidate target engagement by therapeutics expected to restore the pathway. BDNF in these fluids has been studied in several neurodegenerative diseases: patients with Parkinson’s and Alzheimer’s disease (AD) have reduced serum BDNF levels15 so its concentration in blood-derived products, such as plasma and serum, may not be an accurate reflection of the CNS due to platelet activation and degranulation. Moreover, all previous reports of BDNF levels in CSF in neurodegeneration have used conventional enzyme-linked immunosorbent assays (ELISAs) and have found concentrations below the linear range of the assay, raising doubt as to the validity and accuracy of the reported disease differences18.However, BDNF is stored in platelets19 and no comparison between CSF and plasma levels has been performed using a suitable ultra-sensitive assay.To our knowledge, BDNF has not been successfully quantified in CSF in HD patientsWe therefore compared several immunoassays and quantified BDNF in blood and CSF from HD mutation carriers and healthy controls both cross-sectionally and over a 2-year longitudinal period to determine whether BDNF is a potential biomarker for HD.https://doi.org/10.5522/04/11828448.v1)20. This was a single-site study affiliated with the HDClarity study with added optional magnetic resonance imaging (MRI) and 6-week repeated sampling, and with a 24-month longitudinal follow-up. It was conducted according with the Declaration of Helsinki and was approved by the London–Camberwell St Giles Research Ethics Committee. All participants gave written informed consent.Participants , and 40 manifest HD) were recruited from the National Hospital for Neurology and Neurosurgery/University College London HD Multidisciplinary Clinic as part of a longitudinal CSF collection initiative  diagnostic confidence level (DCL) of 4 and HTT CAG repeat count ≥ 36. PreHD participants had CAG ≥ 40 and DCL < 4. Healthy controls were age- and gender-matched to gene expansion carriers, mostly spouses or gene-negative siblings of HD gene expansion carriers and with no neurological signs or symptoms. Manifest HD participants were staged according to UHDRS total functional capacity22. Briefly, participants underwent clinical assessments including the UHDRS subscales. Blood samples were collected into lithium heparin tubes after an overnight fast and were centrifuged at 1300 g for 10 min at 4 °C to produce normal plasma (as opposed to platelet-poor plasma)23 and aliquoted on ice and stored at − 80 °C using a standardised protocol. T1-weighted MRI data were acquired on a 3 T Siemens Prisma scanner as previously described20. Brain volume change was expressed as change per year and adjusted to total intracranial volume.The inclusion and exclusion criteria, clinical assessments, blood and CSF collection and processing, and MRI acquisition and processing are described in the online protocol and previous reportsBDNF was quantified in duplicate in 20 CSF and serum samples from deidentified individuals from a clinical neurochemistry laboratory to compare the sensitivity of commercially available immunoassays: BDNF Emax ImmunoAssay System , Human BDNF ELISA , and Single molecule array (Simoa) Human BDNF Discovery Kit . POLARstar Omega  was used to measure the fluorescent products in the standard ELISAs, whilst an HD-1 Analyzer  was used for the Simoa kit. For Promega and Sigma-Aldrich ELISAs, the limit of detection (LOD) and quantification (LOQ) were calculated from raw absorbance data.Plasma and CSF BDNF from the HD-CSF cohort were quantified in duplicate using the Simoa kit on an HD-1 Analyzer according to manufacturer guidance. Plasma samples were measured at 1:640 and CSF at 1:4 dilution. The lower LOQ and lower LOD were 0.0293 pg/mL and 0.0026 pg/mL, respectively. All measurements were above both. The intra-assay coefficients of variation  was 8.30% for CSF (n = 84) and 3.26% for plasma (n = 91). The inter-assay CV  was 4.39% for CSF (n = 2) and 2.74% for plasma (n = 4). Four (4.8%) CSF samples were only measured once. Quantification was blinded to disease status.For the HD-CSF cohort, BDNF distribution was assessed for normality, and plasma and CSF BDNF values underwent square root and inverse square transformation, respectively.General linear models and Pearson’s chi-squared or Fisher’s exact test were performed to assess intergroup differences at baseline and at the 24-month follow-up characteristics.27. All included gender, BMI, anti-depressant, anti-psychotic medication, and age. For plasma we also included sample storage duration and platelet count. For CSF we also included CSF erythrocyte count. As a relevant contributor to HD natural history, CAG repeat length was included in the models.Potential confounders including age, gender, body mass index (BMI), medication, serum platelet counts, and CSF blood contamination (hemoglobin and erythrocytes), storage duration, and short problem behaviour assessment (PBA-s) subscores were examined through general linear models, Pearson’s correlations or ordinal logistic regressions. Those expected to have a relevant impact on BDNF, either based on reports from the literature, or directly seen in our sample, were included as covariates for subsequent modelsBDNF intergroup differences were investigated with general linear models. Association in HD mutation carriers with clinical and imaging measures were investigated with Pearson’s partial correlations with bootstrapped bias corrected and accelerated 95% confidence intervals (95%CI). Receiver operating characteristic (ROC) curves were drawn for plasma and CSF BDNF.To investigate short term stability, two-way mixed effects models intraclass correlations (ICC) were performed between the baseline and optional 6-week samples.Annualised rates of change in BDNF and clinical measures were calculated by subtracting baseline from follow-up value and dividing by time between samplings. Intergroup differences and associations in HD mutation carriers with clinical and imaging measures were investigated as above.To study BDNF longitudinal trajectories, we used mixed effects models with age and potential confounders as fixed effects, and random effects for participant (intercept) and age (slope), generated independently for controls and mutation carriers.p value < 0.05. No methods for multiplicity correction were used, so any statistically significant outcomes would need to be considered in the context of related statistical tests.All analyses were performed with Stata 15.1 (StataCorp). The significance level was defined as p < 0.0001; Simoa and Sigma-Aldrich: r = 0.9428, p < 0.0001; Promega and Sigma-Aldrich: r = 0.9175, p < 0.0001). In CSF, Simoa and Sigma-Aldrich showed a weaker association . The Simoa assay quantified BDNF concentration in blood and CSF within the linear range, and above the LOD and LOQ in all samples. This assay was therefore used for all subsequent analyses in both biofluids.Serum BDNF levels in 20 test samples were in the linear range for tested assays. However, CSF levels were below the LOD in all 20 samples on the Promega assay, while 16 (80%) out of 20 samples were between the LOD and the LOQ on the Sigma-Aldrich assay. In plasma, all tested assays were correlated (Simoa and Promega: r = 0.9200, Demographic and clinical characteristics are summarised in Table Plasma and CSF BDNF did not differ by gender Figure . The plaBDNF levels in baseline plasma samples were substantially higher than in the 24-month follow-up samples Fig. . This wap = 0.015; Figure p = 0.032; Figure Neither plasma nor CSF BDNF differed significantly between groups Fig. . There wPlasma and CSF BDNF showed poor ability to distinguish controls from HD mutation carriers (plasma AUC = 0.494, CSF AUC = 0.602; Fig. CSF BDNF concentrations significantly fluctuated between the baseline and 6-week follow-up period (CSF ICC = − 0.126, Fig. The rate of change in CSF BDNF did not differ between any HD stages Fig. A,B. LongThe conventional ELISAs we tested lacked the sensitivity required to accurately quantify CSF BDNF. With an ultra-sensitive assay, BDNF levels were measurable in both CSF and plasma but did not reflect HD or its progression and were not associated with the examined clinical and imaging measures.It was notable that the ELISA assays used in previous reports of altered CSF BDNF in neurological disease appear insufficiently sensitive for the purpose. This emphasizes the importance of ensuring the analyte in question lies in the assay’s linear range and urges caution when interpreting previous reports of CSF BDNF as a disease biomarker. To our knowledge, this is therefore the first report of BDNF levels in human CSF using an assay capable of reliably quantifying them.23.Unexpectedly, we also found that the storage duration is the major influence upon BDNF level in plasma but not CSF. This diminishes the utility of stored frozen samples for BDNF quantification in plasma and suggests time in storage should be controlled for in future biomarker studies. BDNF is known to be enriched in platelets, and our centrifugation procedure was designed to produce normal as opposed to platelet-poor plasma. The latter may show less influence from platelet-derived BDNF and therefore be a better surrogate for brain BDNFBDNF had no properties suggesting it is a biomarker for HD progression in CSF or plasma. Its level did not differ significantly between controls and HD mutation carriers, or between preHD and HD groups. It had poor classification ability in our ROC analysis and lacked associations with clinical and MRI brain volumetric measures. In the context of a clear lack of groupwise differences, the small number of associations seen among many tests is likely a chance finding and not of biological significance. Neither baseline BDNF level, nor its rate of change, were prognostic for subsequent change in clinical or imaging outcomes. We therefore conclude that BDNF in CSF or plasma is unlikely to be a valid biomarker of HD, in the ‘diagnostic’, ‘monitoring’ or ‘prognostic’ categories according to the FDA’s BEST categorisation.28.These negative findings contrast starkly with the performance of other CSF and plasma analytes, most notably neurofilament light and mutant huntingtin proteins, which do differ between groups, rise with progression, and predict subsequent clinical and imaging change9. Moreover, if striatal BDNF loss is significant, other gene promotors of the BDNF gene that are not directly modulated by HTT could possibly compensate for the mHTT-induced deficit of BDNF in the cortex29.It should be noted that the absence of detectable changes in CSF or plasma does not exclude an important role for BDNF or its trophic pathway in the pathogenesis of HD. The decrease of BDNF might be very region-specific and therefore overwhelmed by generally unaltered BDNF in other regions. Several studies found reduced BDNF in striatum but not cortex in HD modelsIn the present study, we did not investigate BDNF isoforms, including its precursor, pro-BDNF. Studying the isoforms of BDNF, including pro-BDNF, could still reveal disease-related changes. Unlike some previous reports, we studied plasma rather than serum, but since plasma is prepared by centrifugation rather than clotting, we reasoned that it was less likely to contain artefactual signals from platelets.Finally, it is important to note that even though we found BDNF to lack utility as a biomarker of HD in the natural history setting, it is still theoretically possible that a therapeutic intervention could produce detectable and meaningful changes in CSF or plasma BDNF. Our findings may be of value for the design of such trials in which BDNF would serve as a pharmacodynamic biomarker.Supplementary Information.


In the setting of hematopoietic stem cell transplantation, donor-patient HLA matching is the prime donor selection criterion. Matching algorithms provide ordered lists of donors where the probability of a donor to be an HLA match is calculated in cases where either donor or patient HLA typing information is ambiguous or incomplete. While providing important information for the selection of suitable donors, these algorithms are computationally demanding and often need several minutes up to hours to generate search results. Here, we present a new search kernel implementation for Hap-E Search, the haplotype frequency-based matching algorithm of DKMS. The updated search kernel uses pre-calculated information on donor genotypes to speed up the search process. The new algorithm reliably provides search results in <1 min for a large donor database (>9 Mio donors) including matching and mismatching donors, even for frequent or incomplete patient HLA data where the matching list contains several thousand donors. In these cases, the search process is accelerated by factors of 10 and more compared to the old Hap-E Search implementation. The predicted matching probabilities of the new algorithm were validated with data from verification typing requests of 67,550 donor-patient pairs. Matching of donor and patient human leukocyte antigens (HLA) is a primary factor for patient outcome after hematopoietic stem cell transplantation –3. Over In order to overcome this problem of missing or ambiguous data, several prediction tools—Haplo Stats at NMDP  and EasyWMDA has developed a framework for the implementation of HLA matching software  and carrOne drawback of standard matching algorithms is the poor runtime performance for large donor databases and haplotype datasets. It is desirable to accelerate the search process to make the donor selection workflow more efficient.Here, we present the completely revised search kernel for Hap-E Search that meets the challenges of searching more than 9 million registered potential stem cell donors in an efficient way.Like HapLogic and OptiMatch, Hap-E Search uses standard formulae describing relations between haplotypes and genotypes in incompletely typed individuals to determine matching probabilities. Details are given in the Since 2011, the haplotype frequency-enhanced search algorithm Hap-E Search has been used at DKMS , 18. TheNevertheless, with a continuously growing donor database and larger haplotype datasets, the tree-based approach is no longer sufficient to provide search results in adequate time, i.e., <1 min. Especially for donor searches that result in several thousand donors, search time increases substantially. With the new search kernel, we want to tune the performance of the search kernel and optimize user experience by providing search results in <1 min even for computationally demanding donor searches.Our basic approach to improve the performance of Hap-E Search is to reduce the computational load during each donor search: the workload is transferred to a preparation step that has to be performed once before the search kernel gets operational. In this preparation step as much information as possible is stored and can then be re-used in every search, thus further reducing the time a user has to wait for search results. This approach is applied to potential donor genotypes based on HLA typing, as they are required in every search and change in rare cases only. In the preparation step before the new search kernel gets operational, we compute and store genotype information for all donors in the database. Afterwards, the donor information is constantly kept updated to the current state of the donor database.*01:01 and A*01:01:01G would be assigned to different groups even though they are identical on antigen recognition domain (ARD) level. For our database, this grouping reduces the number of donors in the search kernel by 36%. More sophisticated grouping on ARD level was not implemented, as it improves the grouping effect only slightly (donor number reduction by 40% for our database) at much higher computational cost. For each representative, potential genotypes according to HLA typing results and underlying haplotype data are calculated  matching probabilities are calculated for all 10/10 (9/10) matched representative donors. The matching probabilities can be calculated efficiently see  by interIn a last step, results for representative donors are expanded to the full donor set.For donors or patients with HLA typing that cannot be represented by the used haplotype data, we approximate matching probabilities by assuming minimal frequencies for the “unknown” genotypes. The minimal frequency is dependent on the sample size used for calculation of population haplotype data. For ambiguous typing results, all possible “unknown” genotypes are weighted equally. Details are given in the There is an ongoing discussion whether HLA mismatches of loci that are homozygous for both patient and donor should be counted either as one or two mismatches. Therefore, this parameter can be changed in our search algorithm via a control variable. The default is one mismatch as specified in WMDA guidelines .*01:04N, A*34:01:01G matches A*01:01:01G, A*34:01:01G, as A*01:04N is part of the G-code A*01:01:01G , this “null portion” of the multi-allele code is treated as homozygous for matching. This means that in addition to the standard matching of expressed alleles, the typing A*03:01+A*02:MRVF will also match like homozygous A*03:01+A*03:01 through its null portion A*03:01+A*02:125N , 1 million donors and 12,407 haplotypes (setting 2), and 1 million donors and 34,071 haplotypes (setting 3). All haplotypes in these settings were from the German population.All tests were executed on an Oracle Database (Oracle Version 11.2) hosted on an Intel Xeon E5620 CPU .On our productive system, searches can be processed in parallel on five dedicated oracle databases.The new search kernel was validated both on simulated data provided by WMDA and on real donor-patient data from verification typing (VT) at our donor center, which is the patient-driven high-resolution retyping of donors upfront of an HSCT.WMDA has developed a cross-validation set for the validation of haplotype based search algorithms . It consMatch predictions of the new search kernel were validated by comparing probability forecasts and actual match status of 67,550 donor-patient pairs from VT requests. Only donor-patient pairs where high resolution typing at all five match loci was available for both patient and donor after VT were used for validation. Donor typing at time of request and after VT were required to be consistent, such as to exclude typing errors from the validation samples.10/10 and 9/10 matching probabilities were calculated with the new Hap-E Search kernel using the donor typing at time of VT request and German haplotype frequencies. Matching probabilities were rounded to integer percentage values and grouped into 22 classes in probability interval steps of 5%. Samples with a predicted 10/10 (9/10) probability of 0 or 100% were grouped into separate classes, as they account for most of the cases. The portion of VT confirmed matches within each class was then compared to the predicted value. For evaluation of the 10/10 matching probabilities, samples were excluded where the donor already had a confirmed mismatch to his patient prior to VT. This left 54,065 donor-patient pairs for validation in the 10/10 match category.Preparation of donor data has to be performed once for the existing donor database before launch of the new search kernel. We compared data preparation for three different settings . In bothDepending on the setting, the time required for initial preparation ranged from 6 h 39 min to 28 h 53 min. For the settings tested, preparation time scales approximately proportional with size of donor database and number of haplotypes: For five times as many donors, preparation time increases by a factor of 4.3, while a 2.7-fold extension of haplotypes leads to an increase in preparation time by a factor of 2.3.After launch of the search kernel, the genotype data must be kept updated permanently. In all three settings, about the same time (≈200 ms) is needed per updated or added donor for this process. Preparation time per donor is longer for the update step than in the initial preparation due to database administration overhead like table constraints, indices, etc.Assuming a total of 1,000,000 new or updated donors per year , the cost of genotype data update would be <9 min per day in all three settings.We compared the performance of the new vs. the old search kernel for 50 searches as described in section comparison with the original Hap-E Search kernel. A direct comparison is difficult, as the two algorithms do not produce the same extent of search results: the new search kernel provides matching probabilities for all potential 9/10 and 10/10 matching donors, whereas the old search kernel excludes potential 9/10 donors where typing information is only available for loci HLA-A and HLA-B due to runtime issues. Therefore, the number of search results is considerably higher for the new compared to the old search kernel.Compared to the former search kernel that used a tree-based approach, the match process is speeded up considerably . The morWe compared the average search duration for 15 arbitrary patients with 5-locus high-resolution HLA typing in the three settings described in section performance in different settings . All seaAs expected, preparation of patient genotype information  is indepTotal search duration scales favorably with size of the donor database and number of used haplotypes: on average, it is doubled when the search is performed for a five times larger donor database. Average search duration increases by 50% when haplotypes are extended by a factor of 2.7. This makes it feasible to apply our approach to large and growing donor databases, and to improve calculated matching probabilities by using larger haplotype datasets.We performed the matching validation on the simulated WMDA data  to evaluate our new algorithm. Matching probabilities for all 10,000,000 donor-patient pairs were identical to the consensus data, except one. For the pair P000656–D009637 we observed a deviation of 1% in the 9/10 probability (87 instead of 88%) due to rounding, which is within tolerance of the validation criteria. Average search duration per patient within this predefined donor-haplotype setting was 4.3 s with a standard deviation of 1.5 s and a median of 3.9 s. Maximum search duration was 17.7 s, whereas the fastest search took 2.7 s.The results for the validation of matching probabilities predicted by Hap-E Search on actual donor-patient data from VT is shown in For the 10/10 match category , 93% of The data confirm the validity of our predicted matching probabilities: 21/22 data points are within 95% confidence interval for the 10/10 match category, and 17/22 data points for the 9/10 match category.This work focuses on improving the software side of the donor selection process: we present a new approach that tuned the performance of our matching algorithm Hap-E Search for a more efficient workflow within standard donor center and registry activities at DKMS.We tested data preparation and search performance for three different settings to estimate the behavior of the algorithm depending on the number of donors and haplotypes. Data preparation time scales approximately proportional to both parameters, which is beneficial especially for large donor databases. However, the duration of initial data preparation is of minor importance (as long as it does not deteriorate): it needs only to be performed once before the launch of the search kernel or after changes of the haplotype basis. This preparation can be performed in the background so that the search service does not need to be interrupted in the meantime.More importantly, the time needed for the constant update of the database was negligible with <9 min per day  in all settings.The duration of donor searches scales favorably with the number of donors in the database as well as the number of used haplotypes, which makes it feasible to apply our approach to large and growing donor databases, and to improve calculated matching probabilities by using larger haplotype datasets. The algorithm is programmed in a flexible way, so that haplotype data can easily be extended to improve the accuracy of matching probabilities.The new search kernel has been operational since September 2018 and provides fast and robust search results. Currently, the donor database contains over 9 million donors from six different DKMS entities in Germany, Poland, United Kingdom, United States, Chile and India. At the moment, we run over 3,000 new searches per month, of which ~500 are registry searches and 2,500 are searches connected to donor center activities. The majority of searches in the registry are triggered via the European Marrow Donor Information System (EMDIS) , 20. NinFor all active searches, search results are updated on a daily basis to report changes in the matching list. At the moment, we run search updates for about 2,200 registry searches (mostly EMDIS) and 180 donor center searches each night. These search updates are processed within 4.5 h.The performance improvement achieved by the new search kernel directly improves the day-to-day work of search coordinators, who are the main users of the Hap-E Search algorithm. The reduced waiting time until search results are available facilitates a smoother and more effective work flow.With the improvements of Hap-E Search 2.0, we now have a fast, reliable, and flexible matching algorithm that meets the challenges of a large and further growing donor database.The datasets generated for this study are available on request to the corresponding author.CU and JH: development of the search algorithm. CU: manuscript. AS: supervision, advise, and sponsorship.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.