Healthcare industry players make payments to medical providers for non-research expenses. While these payments may pose conflicts of interest, their relationship with overall healthcare costs remains largely unknown. In this study, we linked Open Payments data on providers’ industry payments with Medicare data on healthcare costs. We investigated 374,766 providers’ industry payments and healthcare costs. We demonstrate that providers receiving higher amounts of industry payments tend to bill higher drug and medical costs. Specifically, we find that a 10% increase in industry payments is associated with 1.3% higher medical and 1.8% higher drug costs. For a typical provider, for example, a 10% or $25 increase in annual industry payments would be associated with approximately $1,100 higher medical costs and $100 higher drug costs. Furthermore, the association between payments and healthcare costs varies markedly across states and correlates with political leaning, being stronger in more conservative states. Industry payments to healthcare providers for non-research costs are common in the US. Linking Medicare and Open Payments data, the authors find that providers receiving more industry payments tend to bill higher drug and medical costs, and this association is stronger in more conservative states. A global conversation is underway regarding the potential influence of financial players on decision-making related to public policy and social systems. Although much of this conversation has focused on the influence of money in politics, these concerns also extend to other aspects of society. In the United States, healthcare providers, in particular, are allowed to sustain financial relationships with corporations that sell medical devices and pharmaceuticals. At the same time, the United States is also dealing with the highest healthcare costs in the world9. Concern has been put forth about the extent and manner of the industry’s influence on the behavior of individual healthcare providers and the implications for healthcare costs10. As of today, the full extent of such influence is not well understood.Human behavior and decision-making are subjective and can be influenced by political, economic, and even social factors, such as norms12. In aggregate, healthcare costs amounted to $3.3 trillion or 17.9% of the US Gross Domestic Product (GDP) in 2016 and are expected to grow to almost 20% of GDP by 202613. In response to this growing problem, academics, policymakers, and the general public are striving to understand the drivers of and ultimately reduce healthcare costs14.In 2016, healthcare consumers in the United States paid more for less, spending about twice as much per capita  than people in other developed countries, while falling short of those countries in terms of performance measures, such as health outcomes and access9. The US Department of Health and Human Services reported that drug-related costs are among the largest cost drivers in Medicare13, accounting for ~17% of total individual healthcare spending ($457B) or $1443 per capita in 201615. It has also been noted that pharmaceuticals are a major source of the differences in healthcare costs between the United States and other similar countries9. As for utilization of services, it is established that medical providers in the US bill for more medical services and charge a higher cost per service versus those in comparable countries16. While previous work has examined these two cost drivers in isolation, the role of pharmaceutical companies in healthcare costs is complex and involves potential conflicts of interest17. For example, in the United States, healthcare industry players are allowed to pay for medical providers’ general and research expenditures, and providers are allowed to directly own and invest in the healthcare industry19. This relationship and its association with healthcare costs are the focus of this article.Two expenditure categories are the primary drivers of healthcare costs in the United States: pharmaceuticals and healthcare service utilization20. As a result of the ACA, medical device corporations  are required to report payments to all medical providers participating in Medicare21. Extant work examining OP data have demonstrated the large scale of the financial ties between pharmaceuticals and medical providers. For example, in 2016, 2005 medical device corporations spent $9.15B collectively on 904,922 physicians across the United States22. It is also estimated that US pharmaceuticals spend more on advertising and marketing than on R&D23.To help address concerns about this potential conflict of interest between medical providers and the healthcare industry, the 2010 Affordable Care Act (ACA) established the Open Payments (OP) system. OP aims to provide a more transparent and accountable healthcare system by publishing data on the financial ties between industry and medical providers24. For example, higher payments to medical providers by the healthcare industry have been linked to the prescription of brand-name drugs, even when equally effective generic substitutes are available30. In a more pernicious context, previous work demonstrated a relationship between payments to medical providers from opioid manufacturers and an increase of opioid prescriptions, which may have contributed to the ongoing opioid epidemic31. These findings have also been documented in the press by Propublica, a nonprofit investigative news organization, in their Dollars for Docs report32. Although most of these studies have focused on the healthcare industry’s influence over drug prescriptions10, medical spending on services  represents a much larger fraction of overall healthcare costs than drug spending. For example, in the 2016 Medicare data, we report on below, allowed medical costs amounted to over $82B, or over five times the total drug costs of $15B.Substantial research has focused on the impact of industry payments and more generally medical marketing on providers’ attitudes and behavior in the US24. To address this difficulty, we programmatically match over 90% of the NPIs in the 2016 OP data set. We use the NPI-matched OP data for all the analyses in this paper, which we make openly available for the research community to facilitate future research.There is a paucity of research on the association between industry payments and overall healthcare costs. This may be in part owing to the challenge in linking OP to healthcare data on individual providers, because the National Provider Identifier (NPI) is missing from the OP data set35, we also examine the extent to which these financial ties vary geographically and investigate potential political, economic and social drivers of such differences. The results we present below contribute to a better understanding of the pervasive association between industry payments to providers and healthcare costs in the United States.The objective of our study is to quantify the relationship between payments to providers from the pharmaceutical industry and healthcare costs. Rather than focusing on specific procedures or drugs, our scope is total Medicare costs, broken down by medical services and drugs. Although our goal is not to establish a direct causal link between industry payments and healthcare costs, we include several important controls to estimate this association robustly. Further, as healthcare delivery varies across US statesWe present analyses based on the merging of two primary open data sources: the OP system, which contains information on healthcare industry general payments to individual medical providers; and the Medicare Provider Physician and Other Supplier (MPOS) data from the Centers for Medicare and Medicaid Services (CMS), which contains data on providers’ Medicare services and procedures, including the cost  of each procedure. Unlike the CMS data, the OP system does not include the NPI, a unique provider identifier. Without this linking variable, it is difficult to connect the two data sets to study the relationship between industry payments in the OP system and medical costs in the CMS data. To overcome this, we attempted to determine the NPI of providers in the OP system by querying the NPI Public Registry. Based on name and address, we found exact matches for 90.4% of the unique providers appearing in the 2016 OP data, 374,766 of which also appear in the 2016 MPOS data. The linked payment and cost data on these providers form the basis of all subsequent analyses.Figure β1 in Model 1 (see Methods section) of 0.131 ). Note that this association is the same when considering providers’ costs per beneficiary as the response variable, as the number of beneficiaries is included as a control in the model.To quantify the association between providers’ industry payments and medical  costs, we fit a log–log regression model with costs as the response variable . We considered only providers within the middle 50% of the payment distribution , as the relationship is approximately linear within this range . To further test the robustness of the new association with medical costs, we re-estimated the association controlling for the provider’s total drug costs (Model 3). In this model, the estimate of β1 was 0.091 ), so that a 10% difference in industry payments was associated with a 0.91% difference in medical costs, given fixed drug costs. This test served three primary purposes. First, it established that the association between industry payments and medical costs was not spurious or owing to the correlation between providers’ medical and drug costs (Spearman rank correlation = 0.41 among providers with non-zero drug costs). Second, it showed a significant association between industry payments and medical costs above and beyond the association between such payments and drug costs suggested by previous work. Finally, it provided an additional control to establish an association between industry payments and medical costs among physicians that are comparable in terms of several key factors . We note that this robustness test complements, rather than replaces, the original model having a 1.3% effect size, as drug and medical costs tend to increase together in practice9.The association reported above is distinct from that between industry payments and prescribing behavior that has been previously reportedTo put a 1.3% difference in total medical costs in context, consider a typical provider within the middle range of industry payments, with industry payments equal to the median of $250 and total Medicare medical costs equal to the median of $90,646. A 10% difference of $25 in industry payments would then be associated with an expected difference of ~$1,100 in total medical costs. Controlling for drug costs, the expected difference in medical costs would be 0.91%, or $825. Such differences in medical costs are much larger than the differences in industry payments with which they are associated. Therefore, even a small numerical difference in payments is associated with a much larger difference in expected medical costs. Further, although the estimated effect size of 1.8% for drug costs is larger than the 1.3% estimated effect size for medical costs, physicians tend to have much higher medical costs than drug costs. In fact, 61% of the providers in this analysis had no drug costs. Among providers within the middle range of industry payments, the median non-zero drug costs were $5,862, so that a 10% difference in industry payments would be associated with a difference in drug costs of 1.8% or ~$100.We investigated how the association between industry payments and medical costs varied geographically within the United States. First, we fit a log–log regression model with a state-specific slope to quantify the association between industry payments and medical costs within each state (Model 4). Indeed, the estimated state-level log–log regression slope varied markedly, ranging from  approximately 0.06 in Maine and Nevada to over 0.22 in Louisiana , followed by high school graduation rate (−0.46). Figure The geographic differences in the payments–costs relationship observed in Fig. rk against all of the measures listed above, which controls for possible confounding among the various political, economic, and social factors (Model 5). Note that this model is not concerned with the amount of medical costs or industry payments, but rather the strength of the association between providers’ industry payments and medical costs  after controlling for other key provider-level factors.To jointly examine potential drivers of the geographical variation in the strength of the payments–cost association, we turned to a model-based approach. Specifically, we fit a multiple linear regression model of rk. The McKinsey rankings were grouped into upper, middle and lower thirds, and the model includes a coefficient associated with each group. The coefficient estimates and normal 99% confidence intervals for each predictor variable are displayed in Fig. p values based on a two-sided t test against the null hypothesis that each coefficient equals zero are reported in Supplementary Table rk among all continuous variables , indicating a strong association between a state’s conservative leaning and the strength of the payments–costs relationship after controlling for other important state-level factors. Besides this political measure, no other economic or social factors were significantly associated with rk, with the exception of log population size . The McKinsey & Co. health rankings were not statistically significant when considered jointly using an F test (degrees of freedom = 43 and 3) against the null hypothesis that all three coefficients are zero , though individually some groups showed effects trending toward significance  and providers  mandatory. These data have allowed researchers to begin measuring and tracking the financial ties between the healthcare industry and providers. Despite this new transparency, however, these ties remain substantial and do not seem to be slowing down. In fact, reported payments more than doubled between 2013 and 2016, from $4.33 billion to $8.81 billion, according to OP. Furthermore, a recent survey showed that very few Americans were aware that this information is publicly available, and only 5% knew whether their doctor had received payments40. Therefore, transparency alone, without effective communication to consumers or adoption of evidence-based policies, has not been sufficient to curtail the payments of large medical corporations to medical providers. These limits of transparency have been noted previously, most notably by Weil et al.39, who concluded, “Consumers may not understand or be interested in new information, may not have the capacity to choose when given information, or may not act in accord with policymakers’ aims”.In response to potential conflicts of interest that may harm the public, the common response from academics and policymakers is calls for increased transparency32. Several possible strategies for improved use of OP data to empower healthcare consumers have been suggested, including flagging unusually high payments21, going beyond disclosure to incentives41, educating patients on how to interpret OP data42, and expanding the reach of the OP system43. A suggestion we would like to put forth is to provide OP information to the public at the point of service, a strategy that has been utilized effectively in other contexts, including restaurant hygiene, auto safety ratings and appliance energy efficiency metrics39. For example, restaurant hygiene scorecards have led to important decreases in foodborne illness44. By giving the public digestible information on physicians’ industry ties at the point of service, patients would be put in charge of deciding how much they value the independence of their providers from the healthcare industry. An important first step, however, is to investigate how consumers can accurately gauge and interpret the information in the OP system and how they may act in response.To make actionable use of the transparency provided by the OP system, we believe more work is needed to help patients access and interpret the data. As a 2017 JAMA article argued, “Transparency regarding public disclosure of payments to physicians from industry, and the relationship between payments and prescribing is still in its infancy. Going forward, as additional data become available, it will be imperative to continue improving the tools available for consumers and provide information and context on how to use them”45.We hope our study will spur future research on the implications of industry payments in the healthcare system, with the ultimate goal of impacting policy making. Such research is currently limited by the exclusion of the NPI from the OP system. Inclusion of the NPI in OP would allow researchers to link data on industry payments directly to the other data sets provided by CMS. Although we were able to match 90.4% of the providers in the 2016 OP data programmatically, the lack of a complete linkage introduces the possibility of selection bias. Such linkage is also time-consuming and requires substantial effort and expertize. This is a fundamental limitation of the OP system in its current state. Interestingly, this problem was foreseen and reported on prior to the rollout of OP in 201132. Future research is needed to determine the causal mechanisms relating industry contributions to costs.This study is subject to several other limitations. We focused our analysis on the category of general payments in the OP data set and did not study payments to providers for research purposes. Future studies should focus on understanding the differences between general payments and research payments in terms of their relationships with healthcare costs. In addition, this study was based on Medicare data and does not consider non-Medicare healthcare costs. Finally, our analysis is subject to the limitations of observational studies, namely that it does not establish a causal effect of industry payments on healthcare costs. Given the nature of the data, previous studies investigating the relationship between industry ties and prescribing behavior have also been limited to correlational analysesIn the context of our analysis, one possible alternative explanation of the observed association between total industry payments and total healthcare costs is that of reverse causality. That is, providers with high levels of spending may be targeted by the medical device industry as most likely to result in a return on investment. Yet, this suggests that these corporations expect that on average, providers receiving payments will in turn increase spending in ways that will benefit those corporations. Disentangling such a causal feedback loop would require more granular and detailed data on payments and healthcare expenditures than is currently available to the public.22.Although establishing causality remains a worthy goal and may be ultimately more effective in provoking changes to policy, public opinion, and provider behavior, to date this has proven elusive. Yet, some have contended that establishing causality may not be a necessary goal. Steinbrook argued that the financial relationships between physicians and the industry, resulting from payments are problematic in and of themselves, stating that “The American Medical Association’s Code of Medical Ethics is clear that ‘[g]ifts to physicians from industry create conditions that carry the risk of subtly biasing—or being perceived to bias—professional judgment in the care of patients.”35 (Data set 4), and data on state-level economic and social factors compiled from several sources (Data set 5). Data set 1 includes information from manufacturers of drugs and devices  on payments they make to medical providers participating in Medicare. The linkage to Medicare provider utilization and costs in Data set 2 enriches the data and enables us to investigate the effect industry payments may have on healthcare utilization and cost. Linkage with measures of political ideology in Data set 4 allow us to capture how state-level differences in political ideology may shift the relationship between industry payments and provider utilization. Finally, Data set 5 provides state-level controls, such as population and income. All of the data used for both the provider- and state-level analyses are cross-sectional.The data sources used in the study included the Open Payments (OP) from the Centers for Medicare and Medicaid Services (CMS) (Data set 1), the Medicare Provider Utilization and Payment Data from CMS (Data set 2), the NPI Public Registry from CMS (Data set 3), the Gallup poll on conservative ideologyhttps://openpaymentsdata.cms.gov/) requires manufacturers and group purchasing organizations (GPOs) to disclose payments and other transfers of value to medical providers and hospitals every year. One of the stated objectives of the program is that it “helps to identify relationships that can both lead to the development of beneficial new technologies and wasteful healthcare spending”. Although OP is the only national data set of its type linking the industry and medical providers, it does not make any claims on whether the financial relationships are beneficial or whether they cause conflicts of interest. We focus on general payments  for the analyses in the paper.Data set 1: Industry Payments. The Open Payments system (https://www.cms.gov) to examine the costs of care based on Medicare payment amounts. Specifically, we used the Medicare Provider Physician and Other Supplier (MPOS) data, which include information on services and procedures provided to Medicare beneficiaries by individual medical providers. The MPOS contains information on utilization, payment , and submitted charges for each provider, identified with a unique National Provider Identifier (NPI). We examined two types of cost: allowed medical services and allowed prescription drugs for 2016, which is the most recent year available. The 2016 MPOS data include 991,362 individual medical providers performing 2.33 billion individual services, which adds to $107.05 billion in Medicare allowed amounts.Data set 2: Resource Utilization and Costs of Care. We used the Medicare Provider Utilization and Payment data (https://npiregistry.cms.hhs.gov), we queried each provider present in OP to obtain the NPI, which we then linked to the MPOS data set. This allowed us to aggregate data from the OP and MPOS data sets at the individual provider level. Of the 636,871 unique providers appearing in the 2016 OP data set, we found exact matches for 576,144 (90.4%), whose collective payments represented 91.8% of the total 2016 general payments. The matched and unmatched providers did not differ substantially in terms of the amount or number of payments, or in the types of providers represented. Of the matched providers, 374,766 also appeared in the MPOS 2016 data set. This is the set of providers we used for the main analysis of the study.Data set 3: NPI Public Registry. The OP and MPOS data sets both contain identity information about the individual providers, such as the provider name, geographical location, and gender. However, there is no direct link between the data sets, as unlike MPOS, OP does not contain the NPI of each provider. Using the NPI Public Registry from CMS  living within the 50 US states and the District of Columbia. The data contains a measure of conservative ideology for each state.Data set 4: Gallup Poll on Conservative Ideology. Data pertaining to political orientation were obtained from a poll by Gallup on conservative ideology46; the McKinsey & Co. Leading States Health Care Index, a composite score based on access, quality, population health34; and the KFF Professionally Active Specialist Physicians by Field data set for 2016, which contains the number of medical providers and specialists by state47.Data set 5: State-level control variables. To control for state-level economic and social factors, we used several data sets: the 2016 American Community Survey (ACS) by the US Census Bureau, an annual survey providing social, economic, housing, and demographic information about US populationTwo primary analyses were carried out during this study. First, we investigated the relationship between industry payments to healthcare providers  and the cost of services and drugs billed to Medicare by these providers. Second, we assessed how this relationship varied geographically across the US states and investigated potential drivers of this difference related to state-level political, economic, and social measures. All analysis code is made available for reuse.We first performed a series of exploratory analyses to examine the shape of the data distribution across the linked individual healthcare providers. We computed skewness and Pearson’s measure of kurtosis for both medical costs and industry payments. The skewness and kurtosis of a Normal distribution are 0 and 3, respectively. The distribution of medical costs had skewness of 8.8 and kurtosis of 214, whereas the distribution of payments had skewness of 179 and kurtosis of 43,139, indicating that both distributions were highly right-skewed. After log transformation, the medical costs distribution had skewness of −0.4 and kurtosis of 3.1, and the payments distribution had skewness of 0.6 and kurtosis of 3.5, indicating that both distributions were approximately log-normal before transformation. Given the extreme right-skewed distribution of both medical costs and industry payments, we performed all analyses on log-transformed data.https://ggplot2.tidyverse.org/) using the default parameter values and methods of the R loess function, including a span of 0.75  and degree of 2 for the local polynomial regression models. Both plots displayed in Fig. To visually investigate the relationship between industry payments and medical costs, given the large number of observations in our data set we performed loess smoothing of the scatterplot of log payments versus log medical costs  within each state Fig. . The curi belonging to state ki, we related medical costs (Mi) to industry payments (Pi) and number of beneficiaries (Bi) with the modelWe observed a consistent nonlinear increasing relationship between payments and medical costs, with an approximately linear segment between the 25th quantile ($77) and 75th quantile ($940) of industry payments, indicated by the shaded band in Fig. Mi is log-normally distributed with mean μi and variance σ2, where μi = E[log(Mi)] = β1log(Pi) + β2log(Bi). Thus, E[Mi]  = exp(μi + σ2/2) = exp(2/2). Note that the relative expected medical costs given a 1% change in payments, holding number of beneficiaries and state constant, is β1/100 for moderate values of β1, e.g., between −5 and 5). Therefore, the interpretation of the coefficient β1 in this model is the percentage difference in expected medical costs associated with a 1% difference in industry payments. To fit Model 1, we utilized data from 159,389 providers within the middle range of industry payments with observed medical costs.That is, we assume that Di be the drug costs for provider i, this model is given by:We also evaluated two alternative models to assess the robustness of our main model examining the association between medical costs and industry payments. First, we fit a model using drug costs, rather than medical costs, as the response variable. To fit this model, we utilized data from the 61,574 providers within the middle range of industry payments with observed, non-zero drug spending. Letting Second, we fit a model similar to Model 1, but including drug costs as an explanatory variable. To fit this model, we utilized data from the 159,389 providers within the middle range of industry payments with observed medical and drug costs. This model is given by:β1 is the coefficient of interest, with the following interpretations:In all three models, β1 is estimated to be 0.131 ) and represents the percentage difference in expected medical costs associated with a 1% difference in industry payments, given a fixed state and number of beneficiaries.In Model 1, β1 is estimated to be 0.180 ) and represents the percentage difference in expected drug costs associated with a 1% difference in industry payments, given a fixed state and number of beneficiaries.In Model 2, β1 is estimated to be 0.091 ) and represents the percentage difference in expected medical costs associated with a 1% difference in industry payments, given a fixed state, number of beneficiaries, and level of drug costs.In Model 3, i belonging to state ki, we fit the modelTo better understand how the relationship between industry payments and medical expenses varies by state, we refit Model 1 within each state. Again considering the middle 50% of industry payments, for observation β1 in Model 1, but is specific to providers in state ki. Estimates and Normal 95% confidence intervals for the βk1 for each state k are given in Supplementary Table The interpretation of mentclass2pt{minimrk for state k. The rk values for each state are given in Supplementary Table The log–log regression slope provides a highly interpretable measure of association between industry payments and medical costs and, importantly, allows us to estimate the percent difference in medical costs associated with a percent difference industry payments. However, it has two drawbacks. First, it is limited to the middle 50% of industry payment values, as the relationship is more linear within this range than across the entire range of payment levels Fig. . Second,rk values of each state, showing differences in the strength of the correlation between industry payments and medical costs of providers. We then investigated how state-level political, economic, and social factors may help explain these differences. We considered the following measures: conservative advantage, population size 16 years and older , unemployment rate, median household income, high school graduation rate, percentage of people without health insurance, percentage of medical providers classified as specialists, and the McKinsey Leading States Health Care Index, grouped into thirds.Figure rk against all of the measures listed above. Note that we fit Model 5 using rk on its original scale rather than Fisher transforming the values, as they are all small to moderate and therefore virtually unchanged by transformation. All continuous variables including rk were centered and scaled; the coefficients therefore represent the expected standard deviation increase in rk associated with a 1 s.d. increase in each predictor variable, holding the other variables constant. The McKinsey index was grouped into thirds  and indicator variables for each group were included in the model, so that the coefficients indicate the expected value of rk after centering and scaling associated with belonging to each group, given mean values for each continuous variable. Specifically, we fit the following model:To control for possible confounding factors among the various political, economic and social factors, we fit a multiple linear regression model of k, Consk is conservative advantage, Popk is population size, Unempk is unemployment rate, Inck is median household income, HSk is high school graduation rate, Uninsk is percentage without health insurance, Speck is the percentage of specialist medical providers, and Healthkg is an indicator variable equal to 1 if the state belongs to the upper (g = 1), middle (g = 2) or lower (g = 3) third of states in terms of health, according to the McKinsey rankings. The coefficient estimates and p values based on a two-sided t test against the null hypothesis that each coefficient equals zero for Model 5 are given in Supplementary Table entclass1pt{minimaAll statistical analyses were conducted in R version 3.5.1. The Institutional Review Board at Indiana University exempted this study for approval because all patients in the CMS databases had been de-identified.Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


The discovery of the tumor suppressor p53, through its interactions with proteins of tumor-promoting viruses, paved the way to the understanding of p53 roles in tumor virology. Over the years, accumulating data suggest that WTp53 is involved in the viral life cycle of non-tumor-promoting viruses as well. These include the influenza virus, smallpox and vaccinia viruses, the Zika virus, West Nile virus, Japanese encephalitis virus, Human Immunodeficiency Virus Type 1, Human herpes simplex virus-1, and more. Viruses have learned to manipulate WTp53 through different strategies to improve their replication and spreading in a stage-specific, bidirectional way. While some viruses require active WTp53 for efficient viral replication, others require reduction/inhibition of WTp53 activity. A better understanding of WTp53 functionality in viral life may offer new future clinical approaches, based on WTp53 manipulation, for viral infections. Almost 40 years ago, while studying tumor induction by small DNA tumor viruses, several groups have “bumped” into a nonviral protein with an apparent molecular mass of 53KDa, which co-immunoprecipitated with Simian Vacuolating Virus 40 (SV40) large T-antigen protein ,2,3,4,5.Indeed, WTp53 is a well-known tumor suppressor protein, which is activated by varied stress signals. When activated, WTp53 regulates various cellular pathways that determine the cell fate, such as cell cycle arrest, differentiation, senescence, and apoptosis ,9,10. AsIn this review, we discuss the different viral strategies used to modulate the p53 defensive responses.The small DNA viruses, Adenoviruses and SV40, utilize the DNA replication machinery of the host cell by elegant and diverse strategies. In the host cell, transcription regulation of cell cycle genes is achieved by the binding of the Retinoblastoma protein (Rb) to the transcription factor E2F. Apparently, both Adenoviruses and SV40 are able to manipulate the host cell by the binding of their viral proteins, E1A and SV40 T-antigen, to the Rb, respectively ,17,18,19Another association between SV40 and p53 is noticed on the transcription level. Studies suggested that T-antigen uses “DNA mimicry” ; T-antigDrayman et al. provided another view on the relationship between p53 and SV40. Interestingly, immediately after SV40 infection, activated p53 is noticed only in cells that do not express SV40 viral proteins, suggesting a p53-dependent decision between abortive and productive infection. These p53 host defense mechanisms did not rely on apoptosis, cell cycle arrest, or induction of interferon-stimulated genes, but rather on binding competition between p53 and Sp1, a host protein, which is essential for SV40 assembly ,25.The human papilloma viruses (HPVs) are double-stranded DNA viruses with over 60 identified types, which are divided by their oncogenic capacity into two groups, high-risk and low-risk HPVs. While low-risk HPVs are associated with benign hyperplasia, high-risk HPVs contain 12 known virus types that are associated with anogenital carcinomas. Although the majority of cervical cancer cases are caused by the high-risk HPV-16 and HPV-18 infections, other cancer types are also associated with high-risk HPVs such as head and neck, mouth, vulva, vagina, penis, and anus cancers ,27. In oIn addition, recent accumulated data have shown that not only E6 but also the viral protein E7 are able to affect p53 function by their binding to the DREAM complex. p53 regulates the DREAM complex in order to inhibit the expression of plethora of genes. It was found that E7 is able to associate directly with DREAM components p107 and p130, leading to disruption in p53 regulation ,34. TakeYatapoxvirus genus [The smallpox and vaccinia viruses, which are members of the poxviruses family, have a large DNA of 191 kb , thus thus genus . Infectius genus . AccordiFlavivirus in the family Flaviviridae, originally identified in Uganda in 1947 [The Zika virus (ZIKV) is a mosquito-borne, single-stranded, positive-sense RNA virus belonging to the genus  in 1947  and whic in 1947 . ZIKV in in 1947 ,42,43. Z in 1947 . The mec in 1947 . In anot in 1947 .The West Nile virus, which may cause different severe morbidities and even death , is anotOrthomyxovirdae family of RNA viruses and the primary cause of respiratory tract infections [Influenza A virus (IAV) is a member of the fections . IAV is fections ,51. Indefections ,53. To ofections . In addifections . Both infections . It seemfections . In agrefections . Anotherfections . Furtherfections . These dfections . Taken tp21 and BAX, leading to host cells’ death [The human immunodeficiency virus type 1 (HIV-1) is a lentivirus (a subgroup of retrovirus) which causes a progressive loss in CD4 lymphocyte numbers and function, resulting in the immunodeficiency associated with AIDS . Variouss’ death . Additios’ death . Interess’ death . Additios’ death . This HIThe human herpes simplex virus-1 (HSV-1) genome is a linear double-stranded DNA virus . Accumulp53 regulation was found to play a crucial role in different infection stages of various viruses. Therefore, it is rational to believe that novel treatments, based on targeting and reactivating p53, may lead to beneficial therapeutic outcomes not only for cancer therapy but also for infectious diseases at large. Indeed, super p53 mice, harboring an extra copy of p53, showed better tumor resistance as well as antiviral activity . AccordiAll in all, it seems that the incessant effort for developing drugs that will be able to target and activate WTp53, such as Nutlin, is crucial not only for cancer therapy but also to a greater extent for different antiviral infections.Viruses are dependent on their cellular host for their prosperity, thus they have adopted different strategies to recruit the host cell machinery for their own needs. p53, being an important cellular transcription factor, orchestrates main cellular pathways which determine the cell fate. Therefore, it is not surprising that WTp53 is a prominent candidate for viral targets . Viruses


Economic modelling analysis.To determine lifetime direct and indirect costs from initial hospitalisation of all expected new traumatic and non-traumatic spinal cord injuries (SCI) over 12 months.United Kingdom (UK).Incidence-based approach to assessing costs from a societal perspective, including immediate and ongoing health, rehabilitation and long-term care directly attributable to SCI, as well as aids and adaptations, unpaid informal care and participation in employment. The model accounts for differences in injury severity, gender, age at onset and life expectancy.Lifetime costs for an expected 1270 new cases of SCI per annum conservatively estimated as £1.43 billion (2016 prices). This equates to a mean £1.12 million (median £0.72 million) per SCI case, ranging from £0.47 million (median £0.40 million) for an AIS grade D injury to £1.87 million (median £1.95 million) for tetraplegia AIS A–C grade injuries. Seventy-one percent of lifetime costs potentially are paid by the public purse with remaining costs due to reduced employment and carer time.Despite the magnitude of costs, and being comparable with international estimates, this first analysis of SCI costs in the UK is likely to be conservative. Findings are particularly sensitive to the level and costs of long-term home and residential care. The analysis demonstrates how modelling can be used to highlight economic impacts of SCI rapidly to policymakers, illustrate how changes in future patterns of injury influence costs and help inform future economic evaluations of actions to prevent and/or reduce the impact of SCIs. The United Kingdom (UK) experiences around 16 new cases per million population in traumatic spinal cord injuries  and 2–3 These SCIs can have devastating, life-changing impacts on those injured and their families. Economic evaluation can help make the case for investing in actions to reduce these injuries, as well as better manage and support people living with SCI. It can help determine the incremental cost-effectiveness of actions compared to usual care.A pre-requisite for any economic evaluation is to quantify the economic costs of SCI to different sectors/stakeholders. These include National Health Service (NHS) costs for immediate and ongoing healthcare use, not only in dealing with SCI and its many chronic complications, including pressure sores, respiratory, cardiovascular and urinary/bowel problems , but alsDespite the importance of economic evidence in health policymaking in the UK and many countries, there are surprisingly few estimates of SCI costs. While UK studies on the cost-effectiveness of interventions for SCI and complications have been published , no detaA Microsoft-Excel simulation model estimated net present value lifetime costs for all new hospital presenting cases of SCIs in the UK in 1 year. This incidence-based costing approach is powerful as it allows subsequent economic evaluations to illustrate potentially avoidable long-term costs if interventions are effective in preventing and/or reducing the impacts of any health problem . It has All costs are in 2016 UK pounds, where necessary using the International Monetary Fund’s World Economic Outlook Purchasing Power Parity 2016 implied inflation and conversion factors. Costs beyond 1 year are discounted at the UK HM Treasury Green Book recommended rate of 3.5% per annum.https://www.spinalunit.scot.nhs.uk/) has treated all new cases of traumatic SCI in patients aged >12 years in Scotland for >20 years [Given the absence of a single source of UK-wide incidence data, the model used data from the Queen Elizabeth National Spinal Injuries Unit (QENSIU). QENSIU . This economic analysis uses this data to group traumatic injuries at admission between more severe AIS-ABC grades for both paraplegia and tetraplegia and all AIS-D grade injuries. Conservatively, it assumes that all reported non-traumatic injuries are AIS-D grade even though at least a third of these injuries could be more severe and costly . UK OffiMany different approaches have been used to estimate short- and long-term impacts on mortality of SCIs . Here coTable Rates of rehospitalisation for discharged rehabilitation patients with SCI can be high, in excess of 50% of patients with more severe trauma may be readmitted at least once within 1 year –19. CommThe need for home/vehicle modifications depends on injury severity. A Swiss survey of 482 people with SCI reported 85% had at least one home adaptation , most coThe model also accounts for immediate/ongoing costs following discharge to specialist residential care. This is conservative as the increased likelihood of subsequent transfer to residential care after 1 year is not included. Although it is suggested that 20% of UK SCI patients could be discharged to residential care , we use The analysis includes costs of long-term home-based attendant/nursing care. This varies depending on care needs, from 24-h 7-day-a-week care to little or no care. An evaluation of specialist neuro-rehabilitation in the north of England simply estimated costs for 168 and 14 h per week care due to lack of data . We idenThe model assumes on average individuals with paraplegia or tetraplegia ABC grades living at home receive 30.87 and 58.1 h of paid care per week, respectively. This is more conservative than the 80-h per week estimate in the English analysis we identified. These values are based on a breakdown of care use in the United States  that we Few estimates of informal family care provided to people with SCI have been published. We were unable to find UK costs, so we used estimates from Australia on informal care received by individuals with tetraplegia and paraplegia . There iEmployment rates came from an English survey of 1700 working age people who had used specialist SCI centre care; only 40.2% of paraplegia ABC injuries and 31.4% of tetraplegia ABC injuries were employed . This coThe model estimates 1270  new SCI cases per annum, of which 13% would be non-traumatic. Table Fifty-one million pounds in specialist hospital-based healthcare service costs is likely to be conservative; these are greatest for tetraplegia ABC cases, accounting for >2.9% of their lifetime costs. This figure does not include further substantive costs in hospital/home for the most complex cases of SCI, including ventilation. Anecdotally, we are aware of annual costs in excess of £320,000 per annum for 24/7 home care for a person with SCI requiring ventilation.Figure Sensitivity analyses explored impacts of changing key model assumptions. The maximum initial tariff for non-elective traumatic complex spinal injuries used was £20,448, with much lower tariffs used for less severe injuries; even with rapid transfer to specialist SCI centres, care costs for some can be much higher, especially if requiring long-term ventilation. A French study reported 6.5% of new traumatic SCI cases required long-term ventilation . If 6.5%The model is sensitive to professional home care assumptions. If all individuals with tetraplegia ABC injuries received 7.3 home care hours per day  rather than 12.3 h per day, then overall costs fall to £1.11 billion and mean lifetime costs per individual with tetraplegia ABC injuries to £1.15 million. If total hours of professional care are not reduced to take account of expected mean informal care received, costs increase to £1.78 billion. If the 6.5% of people with tetraplegia ABC who might require long-term ventilation were also to need 24-hour daily home care, then total lifetime costs would rise to £1.83 billion, with mean lifetime costs per tetraplegia ABC case increasing to £2.56 million.The home care hourly rate also impacts on care costs. If a lower hourly rate for personal care of £14.28  is used,An incidence-based costing approach provides policymakers with information on long-term impacts; new UK cases of SCI per annum may incur lifetime costs exceeding £1.43 billion or £1.12 million per case, with more than two thirds of costs potentially falling on the public purse.This estimate is conservative. For instance, £21.4 million is spent annually on spinal cord rehabilitation services in England (HRG code VC08Z) , but we International studies point to substantive costs but estimates vary greatly and interpretation is difficult due to differences in health/social care systems. Studies use a variety of methodologies, define SCIs in different ways and focus on different elements of cost. Few include non-traumatic SCIs.Several used incidence-based costing approaches. A model for traumatic SCIs in Australia reported lifetime costs of £2.73 million and £5.2 million per paraplegia or tetraplegia case, respectively. These costs are higher than our analysis , mainly because the Australian model includes the monetary value for lost quality of life due SCI disability. It estimated that each year of full quality life lost would cost £91,750 [A Canadian model estimated lower lifetime costs  between a mean £0.89 million for incomplete paraplegia to £1.82 million for complete tetraplegia [In a US model health, rehabilitation and long-term care mean lifetime costs were between £0.58 million and £4.20 million depending on age and injury severity . In anotIn our analysis, mean initial year costs per case for hospitalisation, aids and adaptation, as well as any residential or home care use, were £39,846, £73,393 and £8,246 for paraplegia ABC, tetraplegia ABC and AIS-D, respectively. In Australia, mean initial costs of hospitalisation for traumatic SCI, including initial rehabilitation as well as acute care, were estimated as £11,652 and £15,264 for individuals aged up to 64 and aged ≥65 years, respectively . Costs iWe conclude that, despite the magnitude of costs, this first analysis of UK SCI costs is conservative. Further analyses of UK costs, making use of registry data on long-term resource use, are needed. This would allow analysis to account for SCIs converting from one ASIA grade to another . Work isOur analysis shows how models play a powerful role in highlighting economic impacts of SCI to policymakers and illustrating how changes in patterns of cause, type, age at injury and mortality risk influence future costs. This is important given trends towards higher age at injury, for instance linked to falls rather than traffic accidents. In our model, age at injury substantially impacts on costs, for instance with mean lifetime cost of traumatic tetraplegia in a man aged 36–45 years being £2.63 million compared with £1.30 million if aged between 56 and 65 years (Fig. https://www.spinal-research.org/cost-spinal-cord-injury.The Excel model used to estimate these costs is available on request from the corresponding author. It is also available to download from


The response rate on patient-reported outcome measurements (PROMs) necessary to adequately evaluate a treatment and improve patient care is unknown. Hospitals generally aim for the highest possible response rate without insight into the increase in costs involved. This study aimed to investigate which PROMs response rate is achievable in relation to the costs in an orthopaedic practice.In an observational study, patients planned for orthopaedic surgery were asked to participate per surgical procedure . Patient-reported outcomes (PROs) collection with a digital online automated PROMs collection system  was compared to a combined automated system and manual collection . Response rate was calculated preoperative and at two postoperative time points separately, and on all three time points together. Costs were calculated for the study period, per year and per surgical procedure. Calculations were executed for all surgical procedures and for three subgroups: knee arthroplasty, hip arthroplasty and anterior cruciate ligament reconstruction (ACLR).Using maximal effort the response rate increased for all surgical procedures compared to minimal effort; the preoperative response rate from 86% to 100% and the postoperative response rates from 55% to 83% (3 or 6 months) and 53% to 83% (12 months). Concerning the response at all three time points for all surgical procedures, minimal effort resulted in 44% response rate and increased to 76% with maximal effort. Lowest postoperative response rates were found in the ACLR group for both maximal and minimal effort. A costs difference of €5.55–€5.98 per surgical procedure between maximal and minimal effort was found.A two times higher PROMs response rate for patients responding at all three time points (44% versus 76%) is achievable with maximal effort compared to the use of an automated PROMs collection system only. Manual collection adds a cost of €5.5–€6 per surgical procedure to automated PROMs collection alone. It is debatable if these additional costs are justifiable from a value-based health care perspective as the response rate for adequate evaluation of a treatment is still unknown. From a patient’s perspective, implant survival may not be the best measure of surgery success. Instead, pain reduction, functional improvement and quality of life are important –4. With To achieve the goal of evaluating treatments and improving patient care a certain level of response rate is necessary to ensure generalizability and to minimize selection bias of the collected PROs . UnfortuAlthough PROs are an important component of health outcome and several authors have reported tips and tricks regarding PROs collection –15, evenTherefore, a clear understanding is needed of which response rate is achievable and at what costs. The aim of this study was to investigate which PROMs response rate is achievable in relation to the costs for PROs collection in an orthopaedic practice.2, were followed.PROs collection was performed in a medium–sized-orthopaedic hospital . Between January 2014 and June 2015, 5300 orthopaedic patients that underwent in total 5769 surgical procedures, characterised by aged 12 years and older, American Society of Anaesthesiologists (ASA) classification of I or II, and body mass index (BMI) ≤35 kg/mPatients were informed and asked by their surgeon’s receptionist to participate in PROs collection and to allow further scientific analysis using their anonymised data. All included patients signed the informed consent form. PROMs sets were based on the type of surgery performed and included those that were mandatory as set out by the NOV . All setPatients registered and completed their preoperative PROMs on a computer using a web-based survey of a digital, online, automated system for collecting PROs  directly after consultation in the hospital. In case they needed assistance or could not handle a computer, an (admission administrator) employee was available to provide instructions or hand out paper forms. Before surgery, completeness of the PROMs was checked by the PROMs administrator and in case of incomplete PROMs, a paper form was handed out to the patient at the day of surgery to collect the missing PROs (response check). After surgery the PROMs administrator manually entered the date of surgery in the automated system; by doing so, postoperative PROMs were automatically sent by email 3 or 6, and 12 months after surgery. In case of non-response an automatic reminder was sent after 7 days. In case no email address was registered, the PROMs were sent by postal service and included an invitation letter and a stamped self-addressed envelope. This was all done by the PROMs administrator who received a notification by the automated system to execute this. If the patient did not respond after two invitations by email, the PROMs administrator automatically received a notification by the system to send a third invitation per postal service. All returned forms were manually entered in the automated system by the PROMs administrator. All questions in the automated system were mandatory. In total, per surgical procedure the patient was invited to complete the PROMs at three time points: preoperatively, at 3 or 6 months postoperatively, and at 12 months postoperatively.After data collection, per surgical procedure and per time point patients were allocated to two groups: the minimal effort or the maximal effort group. Patients for which PROs were collected only using the automated system were included in the minimal effort group. For this group, additional manual labour was only needed for entering the date of surgery. The maximal effort group included all patients where extra manual labour was needed: response check, PROMs sent by postal service, third invitations sent by postal service and remaining tasks. These remaining tasks consisted of answering patients phone calls or emails, or correcting administrative errors such as wrong email addresses.Response rate was calculated by dividing the number of returned questionnaires completed partly or totally by the number of surgical procedures minus the number of surgical procedures of patients who were deceased ) . ReasonsBetween January 2014 and June 2015, all 5300 patients planned for 5769 surgeries were included of which only 2 times a patient declined participation, therefore 5767 surgical procedures (100%) of 5298 patients were available for participating PROMs  and 53% to 83% 12 months)  and maximal effort (€34.42) compared to the other subgroups . These additional costs of €6 per surgical procedure were found for all surgical procedures as well as in the subgroups. Regarding these subgroups, lowest response rates and highest costs were found in the ACLR group with both maximal and minimal effort.The only two previous orthopaedic studies that use a digital online automated PROMs collection system reported 43% response 6 months after knee surgery for patellar instability, ligament, cartilage, or meniscus injury  and 92% A recent study among trauma and orthopaedic surgeons concluded that one of the two most important constraints against implementing PROMs was costs . PreviouLittle is known about the costs made to collect PROs in relation to the benefit of collecting PROs. The present study shows the considerable costs to achieve high response rates; knowing that these costs are even without costs for data analysis and improvement strategies, which is expected to result in reducing costs. From a value based health care perspective, it is questionable if the costs made to collect PROs, and the additional costs for improving the response rate, are justifiable. The most important question might not be how many response is needed, but how representative the respondents are for the hospital or patient group in question . It coulTo the authors knowledge, this is the first study clarifying the achievable response rate on PROMs versus the associated health care costs in a medium sized orthopaedic practice. It provides other hospitals insights into what costs they might expect for collecting PROs in their hospital setting or patient groups using minimal and maximal effort. A limitation of this study was that the amount of time needed for all specific manual tasks in the collection process was not exactly measured but was estimated.A two times higher PROMs response rate for patients responding at all time points is achievable with maximal effort compared to the use of a digital online automated PROMs collection system only for PROs collection in an orthopaedic practice. Manual collection adds a cost of €6 per surgical procedure to automated PROMs collection alone. As the response rate for adequate evaluation of a treatment is still unknown it is questionable if these additional costs are justifiable from a value-based health care perspective.


N-hydroxysuccinimides).Several inorganic materials are potentially suitable for enzymatic covalent immobilization, by means of several different techniques. Such materials must meet stringent criteria to be suitable as solid matrices: complete insolubility in water, reasonable mechanical strength and chemical resistance under the operational conditions, the capability to form manageable particles with high surface area, reactivity towards derivatizing/functionalizing agents. Non-specific protein adsorption should be always considered when planning covalent immobilization on inorganic solids. A huge mass of experimental work has shown that silica, silicates, borosilicates and aluminosilicates, alumina, titania, and other oxides, are the materials of choice when attempting enzyme immobilizations on inorganic supports. More recently, some forms of elemental carbon, silicon, and certain metals have been also proposed for certain applications. With regard to the derivatization/functionalization techniques, the use of organosilanes through silanization is undoubtedly the most studied and the most applied, although inorganic bridge formation and acylation with selected acyl halides have been deeply studied. In the present article, the most common inorganic supports for covalent immobilization of the enzymes are reviewed, with particular focus on their advantages and disadvantages in terms of enzyme loadings, operational stability, undesired adsorption, and costs. Mechanisms and methods for covalent immobilization are also discussed, focusing on the most widespread activating approaches (such as glutaraldehyde, cyanogen bromide, divinylsulfone, carbodiimides, carbonyldiimidazole, sulfonyl chlorides, chlorocarbonates,  T. T152]. 2OH moiety is introduced on the support. This can be activated in many ways  under mvide infra vide infraThe commercially available 3-isocyanatopropyl-trialkoxysilanes allow inserting in a given inorganic carrier, the isocyanate function which is reactive towards primary amines under mild conditions. Therefore such silanes could be regarded as activating agents 2) and with some catechol derivatives, such as DOPA . This peculiar behavior of titania opens the way to its derivatization, for in principle any catechol derivative, unless excessively crowded with bulky substituents in the ortho-position relative to the two phenolic hydroxyls could be used to anchor the functions of choice to titania . U. UN-cyan2-containing carriers. It is the cyclic trimer of cyanogen chloride, being quite less toxic and less expensive than BrCN. Electron-poor carbon atoms of TCT can undergo nucleophilic attack from both silanol –OH, and –NH2 of amino-functionalized silicas or other supports , which however is not a true cyanylating agent, is often treated together, as it can activate both –OH and –NHsupports . Lysine At 4 °C, 1 g of support is suspended in aqueous buffer or in organic solvent  mixture with 0.15–1.5 g cyanuric chloride ,179. TCT2Cl are commonly used) are able to convert alcohols into the corresponding sulfonyl esters, which in turn act as alkylating agents towards several nucleophiles -trimethoxysilane) or primary alcohols  are preferred , not always compatible with enzyme biological activity [The electron-withdrawing effect of sulfone function activates the vinyl double bond towards Michael-type addition by a wide range of nucleophiles. Primary amines react even too easily, possibly leading to unwanted support reticulation. Accordingly, secondary amines , 5%–10% solution of divinylsulfone are allowed to react with the chosen support for a period of time depending on reactivity of functionalizing functions  [Both in alkaline aqueous buffer or organic solvent (erature) ,167,202.The formed bond is quite stable, at least at acid/neutral pH values . A quitep-benzoquinone [2 functionalized support can be used in this technique, yielding an activated support able to couple mainly with –NH2 and –SH groups from proteins (p-benzoquinone solution (in ethanol 20% v/v) [Vinyl functions also undergo Michael-type additions when adjacent to a carbonyl, such as in the case of oquinone . Thus, tproteins . Activat20% v/v) ,206. Aft20% v/v) ,205,207)2) is another bifunctional agent able to activate aminated-supports with isothiocyanate functions (R-NCS), which in turn can couple with protein nucleophiles [Thiophosgene  changed the residual activity significantly. In fact, para-analogues resulted in a decreased interfacial steric hindrance.Isothiocyanate functions can be inserted also with more manageable bifunctional agents . In a reN-hydroxysuccimide esters, containing different spacers between the two reactive groups . For instance, N,N'-disuccinimidyl suberate can activate alkylamine inorganic supports, introducing a quite long molecular spacer, and then couple with protein –NH2  and mixed with a ratio 1:10/1:5 w/w with –NH2 functionalized support [N-succinimide-activated support can be alternatively dried and stored in desiccator at room temperature for several months with negligible loss of active groups [Disuccinimidyl suberate is dissolved in anhydrous organic solvent  (EGS) immobilized proteins can be released with mild hydroxylaminolysis . Signifw yields .N-succinimidyl moiety on one side, and N-maleimidyl on the other. For instance N-succinimidyl-3-maleimidopropionate (SMP) or succinimidyl-4-cyclohexane-1-carboxylate (SMCC) activate alkylamine supports as seen above, but the coupling occurs with protein –SH, as reported in N-maleimidyl moieties, yielding a stable thioether linkage with the insertion of a quite long molecular spacer. The protocol is identical to the one described above for support activation , wh dioxane ).i.e., phosphate buffer pH 8) SPDP previously dissolved in methanol is suspended with a ratio about 1:10 w/w with –NH2 functionalized support. After 60’ stirring and exhaustive washes, protein coupling occurs about at neutrality [The same pattern of reaction can be accomplished with succinimidyl-3-(2-pyridyldithio)propionate (SPDP), enabling similarly the coupling between alkylamine inorganic supports and protein –SH . In a slutrality . In thisi.e., ≈8). Extensive washes are necessary prior to further reaction [i.e., iron and copper) contamination.Thiol-functionalized supports enable specific activation methods, involving coupling with protein –SH . Since thiol functions are very prone to oxidation during storage, a reducing treatment before any activation is preferable. In a typical protocol, 30’ incubation with 0.03 M dithiothreitol, and 1 mM EDTA is carried out at pH slightly alkaline (reaction . EDTA is− form of the support [2-2'-Dipyridyldisulfide (DPDS) is probably the most used homobifunctional crosslinker for this kind of immobilization procedures. DPDS is very prone to thiol–disulfide exchange because of electron-withdrawing effect by pyridyl nitrogen. A slight alkaline pH (≈8) is necessary to allow the presence of thiolate –S support . ReleaseA very short molecular spacer is inserted. The advantage of this approach is the reversibility of the formed bond, enabling the regeneration of costly supports. In a typical protocol, 1.5 mM DPDS is used for support activation at pH 8 in the presence of 1 mM EDTA. After 30’ and exhaustive washes with 1 mM EDTA, coupling with proteins occurs at pH 8 .Strong and irreversible coupling between carrier and protein –SH can be instead obtained using 1,6-bismaleimidohexane (BMH). The α–β double bond of this crosslinker undergoes nucleophilic addiction in turn by carrier and protein thiols, yielding stable thioether linkages . In thisi.e., acetone), and then added to a suspension of the support in a neutral buffer containing 1 mM EDTA [BMH requires to be preventively dissolved in a compatible organic solvent  can be also activated with 1,1'-carbonyldiimidazole  [i.e., tetrahydrofuran, acetone or dioxane) excess 1,1'-carbonyldiimidazole reacts with the support for 2 h at room temperature [Silanol and primary alcoholic –OH groups (riazole) . This isperature . Crowleyperature .2 groups are usually involved in the final carbamate bond between protein and support , requiring often several hours . Lysine  support . Such a ortho position relative to the phenolic function), but histidine and tryptophan also react, although to a lesser extent [When lysine and/or cysteine are crucial for enzyme native state (and therefore should not be affected by the immobilization procedure), an interesting alternative involves diazotization of the support and coupling with aromatic aminoacid residues. The tyrosine side chain is the most reactive function . Since this polymer is commercially available with various degrees of polymerization, it is possible to obtain a molecular spacer with any length, according to the specific desires of operators .2 functionalized supports, that in turn react with protein –NH2 groups  is synthesized by aqueous reaction between an excess of epichlorohydrin and a proper length poly(ethylenglycol) for 12 h at 25 °C . The bifIn a novel technique, Wu and coworkers recently used titania microspheres to covalently immobilize the hemoenzyme catalase through strong coordinative bond among Ti-OH functions and catechol groups . The enz3 led to high immobilization yields and significant stabilization of the enzyme.In some immobilization procedures, however, functionalized supports are able to couple with proteins without any activation. Amino-functionalized supports, for instance, can be used for one-step immobilization without activation or cross-linking agents, using genetically modified enzymes. In their recent paper, Wang and coworkers inserted a genetically encoded CHO–tag into recombinant lipase . Simple 2SO4 at 85 °C, exhaustive washings, and 1 h incubation with 0.4 M NaIO4 (5 mL per gram of support) at room temperature [2 and carrier [The same reaction between amines and aldehydes can be used for –CHO functionalized supports. The so-called glyoxyl functions  can be inserted by acidic hydrolysis of epoxy groups: standard protocol requires 2 h treatment with 0.1 M Hperature . Then si carrier ,226,227. carrier . Unfortu2 groups can react with glyoxyl functions, so a high density of lysines is required to allow efficient and stable immobilization, which represents the main disadvantage of such an approach. This can be overcome using multimeric proteins , or inserting additional amino functions via genetic manipulation or chemical amination [N-aminoethyl-aminopropyl silica. The support is recognized as a substrate by the enzyme, which oxidizes the diamine function thus creating aldehyde groups. These quickly react with the enzyme (which is a glycoprotein) giving rise to irreversible coupling. A satisfactory activity retention is observed [Long coupling times are reported, allowing firstly a physical adsorption. In fact, only physically adsorbed proteins react covalently with –CHO, generating an intense multipoint attachment ,227. Thimination ,169. An observed .i.e., mercaptoethanol, ethanolamine, glycine) [2, but also –SH, phenol, and imidazole [2 moieties can be used).The main reactive moiety able to couple with proteins without activation, however, is the epoxy function. Its reactivity along covalent immobilization and the effect on the tuning of enzyme activity have been recently reviewed ,228. Alsglycine) ,225,229.midazole ,228. AccA classical protocol requires suspension of epoxy-activated support in pH 9 buffer for 48 h . An alkaAmong all immobilization carriers available for researchers operating in the field of enzyme technology, inorganic supports stand out. Some peculiar features, in fact, enable their unmatched application. Microbial resistance, possibly mesoporosity, mechanic and thermal strength are for instance unique characteristics of inorganic supports. In this review, we have encompassed the most widespread supports and the most used approaches for both their functionalization and activation, to give a complete and updated overview of all disposable options. In our opinion, operators should carefully evaluate every feature of both supports and functionalization/activation methods. Only in this way, in fact, covalent immobilization can display all its potential, in the perspective of not random, but instead site-specific procedures aimed to targeted tuning of enzyme properties. With this paper, easy and updated comparison of available resources/procedures is possible, helping operators to make more rational choices.


To determine their potential role as a source of human infection, we tested domestic dogs (urban) and wild coatis (wild) in Brazil for vaccinia virus. Our findings of positive neutralizing antibodies and quantitative PCR results for 35/184 dogs and 13/90 coatis highlight a potential public health risk.  Swab sahttp://www.megasoftware.net). Statistical analyses were conducted by using Epi Info software version 7.2.1.0 (https://www.cdc.gov/epiinfo); χ2 and Fisher exact tests were applied with significance set at 5%. We also calculated relative odds ratios (ORs) and 95% CIs.To detect VACV DNA from serum and anal swab samples, we performed real-time PCR targeting the C11R or A56R gene  and in 13 coatis  . UnivariSamples from all seropositive animals were submitted for quantitative PCR (qPCR) to detect VACV DNA . OverallAlignment of the amplified A56R fragments showed high similarity to the homologous gene of VACV isolates from Brazil  Figure. ,We assessed VACV exposure of 2 interacting species of animal: domestic dogs from an urban area and coatis from a bordering wild area. In contrast to VACV infections, human cowpox virus infections have mostly occurred in urban areas of Europe. Cowpox virus is transmitted to humans mainly by domestic cats that are in contact with rodents, the natural cowpox virus reservoirs (The seroprevalence of orthopoxvirus neutralizing antibodies in dogs in Brazil has been described. Peres et al. found that, along with other farm animals, 22.8% of 114 dogs tested were seropositive for orthopoxviruses (We also detected orthopoxvirus neutralizing antibodies in wild coatis, which is consistent with results of a previous study that described the seroprevalence of orthopoxviruses in procyonids from Mexico (,,To impart information about the role of domestic animals and wildlife in the natural cycle of VACV, we developed a hypothetical model based on previous studies ,,. BecauseNucleotide sequences of vaccinia virus A56R gene from domestic dogs and wild coatis, Brazil, 2013–2015, compared with sequences from other orthopoxviruses.


Since 2015, severe outbreaks of hepatitis-hydropericardium syndrome (HHS), caused by hypervirulent fowl adenovirus serotype 4 (FAdV-4), have emerged in several provinces in China, posing a great threat to poultry industry. So far, factors contributing to the pathogenesis of hypervirulent FAdV-4 have not been fully uncovered. Elucidation of the pathogenesis of FAdV-4 will facilitate the development of effective FAdV-4 vaccine candidates for the control of HHS and vaccine vector. The interaction between pathogen and host defense system determines the pathogenicity of the pathogen. Therefore, the present review highlights the knowledge of both viral and host factors contributing to the pathogenesis of hypervirulent FAdV-4 strains to facilitate the related further studies. Aviadenovirus genus, the Adenoviridae family, is a non-enveloped, double-stranded DNA virus. Its genome is approximately 45 kb and encodes a number of structural and nonstructural proteins. The FAdV-4 capsid consists of three main exposed structural proteins, hexon, fiber and penton base, with the fiber being noncovalently linked to the penton base. The FAdV-4 capsid is characterized by two separate fiber proteins (fiber-1 and fiber-2) associated with each penton base [Fowl adenovirus serotype 4 (FAdV-4), belonging to the FAdV-C species of the ton base ,7,8. HHSton base , and theton base , quails ton base , ducks [ton base , and manton base . The preton base ,20,21,22The genome of FAdV-4 is approximately 45 kb in length and encodes a number of structural and nonstructural proteins. Three main exposed structural proteins, hexon, fiber and penton base, constitute the viral capsid. Hexon contains group-, type- and subtype-specific antigenic determinants, against which antibodies are produced . The fibOwing to the limited available information on the complete genome sequences of FAdV-4 strains with different pathogenicity, the difficulty in manipulating the large size genome of FAdV-4 and a shortage of reverse genetic systems for FAdV-4, the factors determining FAdV-4 pathogenicity profiles remain unclear. Recent studies of fully sequenced genomes of pathogenic and non-pathogenic FAdV-4 isolates implied potential genomic variations associated with virulence ,28.Sequence alignment revealed that comparing to non-pathogenic strains, hypervirulent FAdV-4 isolated in China have a unique 1966 bp nucleotide deletion on the right region of the genome, resulting in the absence of ORF19, ORF27 and ORF48. Meanwhile, multiple distinct amino acid substitutions were identified on fiber-1, fiber-2, penton and hexon . AccordiPrevious study suggested that chicken embryo lethal orphan (CELO) virus, belonging to FAdV-1, might bind to the coxsackievirus and adenovirus receptor (CAR) via fiber-1, but initiate the accessory infection through fiber-2 , indicatNumerous studies have confirmed that viral proteins can make full use of host factors to facilitate replication and pathogenicity during infection. Research showed that hexon hijacked T-complex polypeptide 1 subunit eta (CCT7) to contribute hypervirulent FAdV-4 replicating in LMH cells, an immortalized chicken liver cell line. Further analysis showed that as a cytosolic chaperone protein, CCT7 might be involved in the folding of hexon protein or play a role in the assembly of FAdV-4 capsid proteins . AdditioApart from structural proteins, the role of nonstructural proteins in virus assembly, stability and pathogenicity cannot be negligible. The late phage nonstructural protein 100K assists chimeric hexon in trimerization to generate recombinant HAdV-5 vectors and improve proliferation efficiency ; HAdV-5 Robust innate immune responses can inhibit virus replication and limit viral spread. Conversely, virus has evolved various defense mechanisms to control cell death and repress host antiviral response. The replication cycle of HAdV consists of an early stage and a late stage, and some of early gene products  have shown to play important roles in host immunosuppression. HAdV-5 E1A Rb-binding domain markedly repressed NF-κB-dependent defense against TNF-α , and E1A+ and CD8+ T-lymphocytes in thymus [According to our and others’ research data, hypervirulent FAdV-4 infection causes disintegration of lymphocytes in bursa of Fabricius, severe reduction and necrosis of lymphocytes in the spleen , decreasn thymus , suggestNaturally, many chicken flocks will have co-infection with immunosuppressive viruses such as chicken infectious anemia virus (CIAV), Marek’s disease virus (MDV), infectious bursal disease virus (IBDV) and Newcastle disease virus (NDV) ,44,45. IHost immune systems have developed multiple ways to recognize viral pathogens. Prompt activation of sensing mechanisms in the host targets each step of infection to detect and eliminate the pathogen. Hypervirulent FAdV-4 infection is initially recognized via pattern recognition receptors (PRRs) . DownstrRecent studies have demonstrated that most of the hypervirulent FAdV-4 strains isolated in China are fatal to chickens but not ducks. After infected with HLJDAd15 (duck-isolated strain)  or SD082In addition, broilers are the dominant hosts for FAdV-4, and once infected, the animals are most likely to develop HHS and other complications. The incidence of HHS and mortality is also higher in broilers than in breeding and laying flocks after infected with FAdV-4 virus ,56. So fCytokines are thought to be important signaling molecules in the induction and regulation of immunoinflammatory responses. Although these molecules play critical roles in eliminating the invading pathogens, exuberant immune responses are positively associated with liver damage . After aAs mentioned above, many chicken flocks often naturally co-infect with immunosuppressive viruses. It is worthy to point out that immunosuppression could actually reduce the effects of a cytokine storm and therefore reduce the mortality associated with these infections. In addition to lysis of important cell types Marek’s disease virus can also interact with inflammatory mediators to change the immune response ,67. ThusHAdV-5 vector-based vaccines are widely used to prevent infectious diseases, but the main problem is its accumulation in liver . As a heApart from liver injury, hydropericardium is also a typical clinical sign triggered by hypervirulent FAdV-4 infection . After iPrevious studies indicated that the outcome of experimentally induced HHS was dependent on the age of birds ,74,75. Min ovo changes and there is a progressive increase in the number of birds with specific maternal antibody. Chicks hatching from infected eggs may excrete virus from the time of hatching, but more typically chicks do not excrete virus until two to four weeks of age. Presumably reactivation of latent virus does not occur until maternal antibody declines [Vertical transmission is important in the spread of FAdV-4, and consequently, in induction of HHS. A typical outbreak of avian adenovirus is the result of infection occurring in the unvaccinated breeder birds with subsequent vertical transmission to the broilers . As the declines . PrimariThe pathogenicity of hypervirulent FAdV-4 is dependent on the function of viral proteins and on host immune responses, implying the important roles of both viral factors and the host immune system in the FAdV-4pathogenesis. Up to now, FAdV-4 pathogenesis-related studies are few and extensive researches urgently need to be carried out. Exploring virulence-related genes, the potential acting mechanism of these genes, and the roles of exacerbated immune response in tissue damage could assist in the development of new strategies to combat hypervirulent FAdV-4 infections.


Highlighted Research Paper:Moment-to-Moment Fluctuations in Neuronal Excitability Bias Subjective Perception Rather than Strategic Decision-Making, by Luca Iemi and Niko A. BuscheNeuro publication, Spontaneous alpha-band oscillations represent ongoing fluctuations in neuronal excitability, with periods of strong oscillations indicating neuronal inhibition . Visual They used variations of a two-interval forced choice task, in which subjects were asked to report in which of two intervals a critical stimulus had appeared. The rationale was based on two assumptions. First, bias should facilitate performance on trials in which excitability in the to-be-reported interval happens to exceed excitability in the other interval. Second, while a decision bias  should have that effect irrespective of what the decision is about, a perceptual bias  should affect performance only in decisions about stimulus presence versus stimulus absence.Indeed, Iemi and Busch (2018) found that weak EEG alpha oscillations (strong excitability) before the stimulus-present interval and strong alpha oscillations (weak excitability) before the stimulus-absent interval biased subjects to correctly detect the stimulus in the appropriate interval. No effect of ongoing oscillations was found when the decision was not about stimulus presence versus absence, but instead was about stimulus orientation. This finding implies that fluctuations of neuronal excitability due to ongoing neuronal oscillations in the alpha band amplify sensory signals as well as internal noise, thereby amplifying the subjective impression of seeing something, even when nothing had been presented.Conceptually, this perceptual bias can be compared to the effect of the ISO setting  . With loThese results bring up an exciting follow-up question: do these ongoing fluctuations of excitability and perceptual bias, caused by fluctuations in the strength of neuronal oscillations, affect performance in real-world detection tasks such as radiology or airport security screenings?


Prions cause transmissible infectious diseases in humans and animals and have been found to be transmissible by blood transfusion even in the presymptomatic stage. However, the concentration of prions in body fluids such as blood and urine is extremely low; therefore, direct diagnostic tests on such specimens often yield false-negative results. Quantitative preanalytical prion enrichment may significantly improve the sensitivity of prion assays by concentrating trace amounts of prions from large volumes of body fluids. Here, we show that beads conjugated to positively charged peptoids not only captured PrP aggregates from plasma of prion-infected hamsters, but also adsorbed prion infectivity in both the symptomatic and preclinical stages of the disease. Bead absorbed prion infectivity efficiently transmitted disease to transgenic indicator mice. We found that the readout of the peptoid-based misfolded protein assay (MPA) correlates closely with prion infectivity in vivo, thereby validating the MPA as a simple, quantitative, and sensitive surrogate indicator of the presence of prions. The reliable and sensitive detection of prions in plasma will enable a wide variety of applications in basic prion research and diagnostics. C, into a misfolded and aggregated form, PrPSc that accumulates in the brain in the form of plaques or smaller plaque-like deposits  of 109.23 units ml-1 was calculated using the Reed-Muench formula [10-fold serial dilutions  transgenic mice, whether the PSR1 beads were able to capture quantitatively prion infectivity from plasma samples. To that purpose, we used pools of plasma from 263K prion-infected hamster at presymptomatic or symptomatic stages. Plasma samples from symptomatic hamsters were collected at the stage of onset of clinical signs showing the typical disease characteristics of ataxia, loss of appetite and poor grooming. The individual pools  were inc50’s of 2.8 ± 0.3 to 1.9 ± 0.5 units ml-1Most other mice inoculated with beads treated with the different plasma pools from symptomatic hamster showed mean incubation periods between 74 ± 3 and 94 ± 5 dpi  mice.The calculated titers are slightly higher than the apparent titer of 1.25 LD disease , 48, fortga20 mice i.c. inoculated with PSR1 beads treated with a 10−9 dilution of a 10% (w/v) RML6 brain homogenate and sacrificed at 253 dpi  as well as in urine and feces see  Tables. Sc that were only barely detectable by standard methods [−6 dilution, LOD estimated to be < 40 pg/ml of aggregated PrP) [Previously, we have reported that the MPA could detect prion aggregates with high sensitivity in the brain of a patient with a familial prion disease . Crucial methods . The anated PrP) . These fSc, as prions in plasma were proposed to consist of more PK sensitive conformations of PrPSc [Sc on the bead surfaces might also be caused from PrPSc being protected against degradation on the bead, e.g. by limiting exposure to proteolysis.Our data showed that the PSR1 beads were able to enrich low concentrations of infectivity from the plasma of hamsters in symptomatic and even presymptomatic stages of the disease. Although we have not performed a direct injection of plasma pools from 263K prion-infected hamster at presymptomatic or symptomatic stage to estimate the infectivity titers, our findings suggest that the beads are capable of capturing prion infectivity from plasma at concentrations around the threshold of detection identified earlier in rodent prion infectivity experiments for presymptomatic cases , 18. Altof PrPSc . The appTransmission of infectivity from solid-state surfaces might occur either by dissociation of infectivity or by the transport of these surfaces to different brain areas where they initiate infection , 54. We Sc conformers, known as prion strains [Prions have been shown to form structurally distinct PrP strains . Those s strains . The siz strains , with di strains . This gi3O4 metal surface [3O4 metal surface beads [The ability of PSR1-coated beads to efficiently capture and enrich prions from plasma directly suggests the feasibility of solid-state prion-capture matrices for both clinical and research uses. For example, the beads might be applied for depletion, removal and clearance of prions from biological fluids and biopharmaceutical products. The number of beads can easily be increased and their magnetic properties should allow the capture of prions even in high volumes. In a further embodiment, the beads might also be applied in concert with the scrapie cell endpoint assay (SCEPA)  and/or w surface , 64, sol surface  or magne surface , 65. Magce beads , 64 havece beads , 67. Simce beads ; most prce beads . The bince beads , 54. Thece beads , 30, 31,ce beads , 30, 31.ce beads . MultivaFinally, the PSR1 beads might also be used together with a sensitive detection method to diagnose prions. Prion bioassays report the infectious titer and do not directly reflect the total number of PrP molecules, whereas the MPA reports the amount of PrP in aggregated conformations. Because each aggregate can be composed of a vast number of PrP molecules, the subsequent disaggregation can lead to the exposure of many epitopes for sensitive immunodetection. A sensitive prion detection assay based on the MPA technology could be easily automatable and optimized for high-throughput applications, eventually paving the way to early and sensitive blood diagnosis of prion diseases in humans and to the prevention of transmission of prions through economical and reliable screening of blood and blood products.S1 ARRIVE(PDF)Click here for additional data file.S1 Fig(A) Brain sections of a tga20 mouse inoculated with PSR1-beads scarified at 8 dpi. (B) Brain sections of a tga20 mouse inoculated with PSR1-beads and terminated at 31 dpi. Vacuolation was again visualized by HE staining, astrocytic gliosis by staining the GFAP protein with a GFAP-specific antibody, and microgliosis by the activated microglial marker Iba-1. Activation of astrocytic gliosis and microgliosis was detected near the injection. .Coronal sections at low magnification show that the outflow of the CSF through the lateral ventricles is not affected. (TIF)Click here for additional data file.S2 Fig(A) show no evidence of vacuolation, PrPSc deposition or gliosis. Mice inoculated with 263K prion-infected hamster brain homogenate (B), inoculated with PSR1 beads incubated with plasma pools from symptomatic hamster at 117–118 dpi (C) and at 143 and 154 dpi (D) show vacuoles in the HE stained section, PrPc and PrPSc deposition is visualized by the PrP antibody SAF84 and astrocytic gliosis is evidenced by an antibody directed against GFAP. .Non-inoculated mice (TIF)Click here for additional data file.S3 Fig(A) show no evidence of vacuolation, PrPSc deposition or gliosis. Mice inoculated with 263K prion-infected hamster brain homogenate (B), inoculated with PSR1 beads incubated with plasma pools from symptomatic hamster at 117–118 dpi (C) and at 143 and 154 dpi (D) show vacuoles in the HE stained section, PrPc and PrPSc deposition is visualized by the PrP antibody SAF84 and astrocytic gliosis is evidenced by an antibody directed against GFAP. .Non-inoculated mice (TIF)Click here for additional data file.S4 Fig−9 dilution) and euthanized at 253 dpi. HE stained sections of brain, spleen, spinal cord ganglion and spinal cord out nerve are shown. Beads are indicated with yellow arrows. .The mouse shown was inoculated with RML6 brain homogenate (10(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S1 TableYes: beads were present in these body fluids. No: no beads were observed. n.a.: non-analyzed. dpi: days post infection. The analysis was only performed for the indicated mice.(PDF)Click here for additional data file.S2 TableYes: beads were found in these homogenates. No: no beads were observed. n.a.: non-analyzed. dpi: days post infection. The analysis was only performed for the indicated mice.(PDF)Click here for additional data file.S3 TableParaffin sections of various organs were analysed for the presence of the beads. Yes: beads were found in these organs. No: no beads were observed. n.a.: non-analyzed.?: not clearly identified. BM: bone marrow. dpi: days post infection. The analysis was only performed for the indicated mice.(PDF)Click here for additional data file.S4 TableAnimals were monitored 3 times per week after RML6 or 263K inoculation for clinical signs including gait, grooming, activity, rough hair coat, limb paresis and ataxia. Once the animals showed the first sign of scrapie (grade 1), they were monitored every day and wet food was supplied in the cage. When the animals reached score grade 2 that hindered them reaching the water bottle, they were either euthanized with 3% isofluorane/L O2 (hamster) or methoxyflurane (mice), followed by decapitation.(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.