We found an antioxidant, proliferating, and antisenescent effects of the fractions deriving from the brewing process on D-dSCs and Caco-2 cells. Finally, our results demonstrated that the bioavailability of polyphenols is greater in beer than in the control standards used, supporting the future clinical application of these compounds as potential therapeutic tools in precision and translational medicine.Beer is one of the most consumed alcoholic beverages in the world, rich in chemical compounds of natural origin with high nutritional and biological value. It is made up of water, barley malt, hops, and yeast. The main nutrients are carbohydrates, amino acids, minerals, vitamins, and other compounds such as polyphenols which are responsible for the many health benefits associated with this consumption of drinks. Hops and malt are one of the raw materials for beer and are a source of phenolic compounds. In fact, about 30% of the polyphenols in beer comes from hops and 70%-80% from malt. Natural compounds of foods or plants exert an important antioxidant activity, counteracting the formation of harmful free radicals. In the presence of an intense stressing event, cells activate specific responses to counteract cell death or senescence which is known to act as a key-task in the onset of age-related pathologies and in the loss of tissue homeostasis. Many studies have shown positive effects of natural compounds as beer polyphenols on biological systems. The main aims of our research were to determine the polyphenolic profile of three fractions, coming from stages of beer production, the mashing process (must), the filtration process (prehopping solution), and the boiling process with the addition of hops (posthopping solution), and to evaluate the effects of these fractions on Dental-derived Stem Cells (D-dSCs) and human intestinal epithelial lines (Caco-2 cells). Furthermore, we underline the bioavailability of beer fraction polyphenols by carrying out the  In toxicology, SIPS can be used to identify xenobiotics inducing premature senescence. However, oxidative stress is the main cause of SIPS activation in normal cells  assay as described in , 37. Briondition , 39. The5 cells/cm2 in 6-well filter support inserts with polyethylene ether phthalate membranes . The medium was changed every alternate day till usage. The cells were differentiated for at least 14 days prior to the transepithelial transport experiments. The integrity of the cell layers was evaluated by transepithelial electrical resistance (TEER) measurements using an epithelial Volt-Ohm Meter . Only the Caco-2 monolayers with TEER values higher than 400 Ω/cm2 were selected for the subsequent experiments. Following the TEER value measurement, the Caco-2 cell monolayers were gently rinsed twice with PBS, and transport medium  was added to the apical (2 mL) and basolateral (3 mL) compartments. Following a 240′ incubation at 37°C, the solution from the apical and basolateral compartments was collected for HPLC analysis.For the transport experiments , 41, theβ-Gal), was detected as previously described [3 cells/cm2. After the time to allow the cell attachment (24 h), the medium was replaced in each well and the cells received H2O2 180 μM for 30′ at 37°C. Subsequently, after H2O2 senescence induction, the three fractions  were added and maintained in the medium for three days before the SA-β-Gal staining assay. In parallel experiments, the three fractions of beer production were added to the cell medium and maintained for three days before assessing the SA-β-Gal staining. The number of senescence-associated SA-β-Gal-positive cells was determined in 100 randomly chosen low-power fields (X 100) and expressed as a percentage of all counted cells.The senescence marker, senescence-associated beta-galactosidase .t-test. A difference between means was considered significant if p ≤ 0.05.The in vitro antisenescence activity data are presented as the means ± standard errors of the means (SEM) of triplicate samples and are the representative of three different experiments. Statistical analysis was performed by one-way analysis of variance (ANOVA) with Bonferroni's corrected The phenolic compounds in beer are related to the composition of the raw materials used in the brewing process. In addition to the genetic characteristics, the fermentation phase may also interfere with the final concentration of polyphenols. Therefore, it is important to evaluate the polyphenol content in fractions coming from different stages of beer production. In particular, we have performed a quantitative and qualitative analysis of the polyphenol content in three fractions, must, prehopping solution, and posthopping solution. The presence of one flavanone as Naringenin (1), one flavan-3-ol as Catechin (2), two flavanols as Quercetin (3), and Rutin (4), one glycosylated hydroquinone as Arbutin (5), and one benzyl-isoquinoline alkaloid as Berberine (6)  have beeThe results expressed in ppm are reported in In the posthopping solution, we found an increase in all the measured components. In particular, the flavonoids Rutin (4), Naringenin (1), and Quercetin (3) show a significant increase in the concentration of about eight (27.39 ± 1.62), three (1.96 ± 0.07), and two (0.58 ± 0.04) times higher than the must solution, respectively. The content of Catechin (2) remains almost constant. However, the most abundant component is the glycosylated hydroquinone Arbutin (5) up to a concentration of 352 ppm in the posthopping solution, about 50% compared to its estimated content in the must (243.59 ± 8.31), while the content of the benzyl-isoquinoline alkaloid Berberine (6) is almost the same than in the must solution and quantitatively comparable to flavonoids (1-3).μL), probably due to a sugar increase in the fraction. High doses (100 μL) of pre- and posthopping solutions did not seem to influence cell growth . This phenomenon was completely abrogated at a high dose (100 μL) of all beer fractions, while the medium doses (50 μL) had different effects depending on the fraction used. In particular, the must and posthopping solution significantly induced an increase in cell proliferation  for 240′ at 37° C. Then, solutions from the apical and basolateral compartments were collected for HPLC analysis. To investigate whether the polyphenols of beer fractions  were bioavailable, Caco-2 cells in monolayer with TEER values higher than 400 in vitro.It is interesting to note that the standards used have a lower permeability than the same polyphenols present in the beer fractions; this is probably due to the fact that in their natural source there are interactions and synergies that are not predictable and replicable The ROS/RNS produced by several endogenous and exogenous processes is neutralized by cellular antioxidant defenses. Imbalance between ROS/RNS and the antioxidant defenses results in cellular oxidative stress , 25. Thiin vivo conditions of tissue injury and regeneration as well as in vitro situations of proliferation and induced differentiation. Polyphenols are found mainly in the peripheral layers of the malt bound to the polysaccharides of the cell wall, in quantities higher than the corresponding varieties of barley. During malting, there is a significant increase in phenolic compound levels thanks to enzymatic processes. Barley polyphenols are very important due to their influence in the different stages of the fermentation process and the overall stability of the beer [Current understanding of polyphenols impacts on adult stem cells mainly comes from the beer . Howeverin vitro. Our analysis about the chemical composition of beer fractions led to the identification and quantification of six polyphenols  from must, pre-, and posthopping solutions during beer production. The results of this study show an increase in posthopping solution of Rutin and Arbutin of about eight and two times, respectively, compared to the must, while Naringenin increases its concentration by about three times compared to the must. Hydroquinone Arbutin is the most concentrated polyphenol in the posthopping solution. Rutin has significant anti-inflammatory and immunomodulatory effects and is a bioactive compound in pharmaceutical products with the action of free radical scavenger [2O2 is naturally produced in the human cell and is widely used as a prooxidant model in the study of oxidative stress. Thus, we tested the antioxidant capacity of the beer fractions by inducing oxidative stress with H2O2. The results reported here indicate that the pretreatment with beer fractions have an antioxidant effect as fractions reverted the oxidative stress induced by H2O2 in D-dSCs and Caco-2 cells.In the current study, fractions deriving from the brewing process were investigated for the evaluation of polyphenols content, antioxidant and proliferating action, antisenescent properties, and bioavailability cavenger . Naringecavenger . Arbutincavenger . Moreovecavenger . Furthercavenger –55. Next2O2 with and without beer fractions and analysed by using β-galactosidase assay. It is based on the expression of the enzyme β-galactosidase, which is overexpressed in cells in the senescence phase and is defined as SA-β-Gal . As expected, exposure to H2O2 resulted in the increase in senescence-associated β-galactosidase activity in both cell models. The addition of must, prehopping solution, and posthopping solution, coming from stages of beer production to the two cellular systems, significantly reduced the senescence induced by oxidative stress.Several compounds have recently been identified that can significantly reverse senescence induced by oxidative stress. Moreover, D-dSCs are considered as a useful model in this translational research area also for their stemness properties –60. To eThe number of reports on the beneficial health effects of polyphenols is increasing speedily but there is still little knowledge about metabolism and bioavailability in humans.Even if some explanation of bioavailability has been suggested, the better appropriate seems to be as that fraction of an ingested nutrient or compound that reaches the systemic circulation and the specific sites where it can exert its biological action –64.The host-related factors affecting the bioavailability can be further subdivided into intestinal factors and systemic factors. The intestinal factors probably represent the most important ones. This activity is of great importance for the biological action of polyphenols, since active metabolites are produced by the colonic microflora, taking into account the great interindividual variability in producing these active metabolites .in vitro system that makes it possible to study the mucosal toxicity of all those substances that are intentionally ingested or accidentally, define their transport mechanism through the intestinal barrier, and then determine their bioavailability in the blood and tissues [The absorption of a substance is to be considered as a limiting factor to the biokinetics of the molecule, and its evaluation is therefore fundamental to define the subsequent toxicological studies. The intestinal absorption model based on Caco-2 cells is a well-characterized  tissues , 66.Indeed, prior to passage into the blood, the polyphenols undergo former structural modifications due to the conjugation process that takes place in the intestine , 62. To In the present study, we tested the bioavailability of beer polyphenols using the Caco-2 human intestinal cell model. The relatively high bioavailability values recorded for almost everyone polyphenols in beer fractions have shown that these compounds can be transported through the Caco-2 monolayer with an intestinal permeability higher than that shown by the relative standards used as control. Often, the benefit of some natural products cannot always be explained with the individual components; there are synergies between the various natural constituents which are more effective than the sum of the individual parts. On the other hand, so far, no one has shown that the health effects of some nutrients can be reproduced by isolating their individual components.Although all these studies have yielded important data for understanding the effects of phenolic beer compounds on cellular systems, much remains to be studied. Further investigation are needed to understand the molecular mechanisms and furthermore human studies as well as experimental animal models are required for a better understanding of the effects of beer, in particular when consumed regularly at moderate doses.In conclusion, polyphenols are very stable molecules and can be found in various concentrations in the different fractions coming from the beer production phases, the mashing process (must), the filtration process (prehopping solution), and the boiling process with the addition of hops (posthopping solution). Our translational results showed a variable polyphenolic content in the analysed beer fractions; moreover, they showed a proliferative effect at low concentrations and an antioxidant effect in two different cell systems, D-dSCs and Caco-2 cells. In addition, the three beer fractions used revealed an antisenescent action. Caco-2 bioavailability assays allowed to record good bioavailability values for all the beer fractions analysed, in particular, the results showed that the bioavailability of polyphenols is greater in beer than the control standards used confirming the synergy existing in nature. Currently, natural compounds as beer as well as several other drinks and foods are considered not only nutritious but also potentially useful bio-based products that can be used to prevent various chronic diseases. Therefore, we can speculate that the use of natural molecules should be able to counteract oxidative stress and cell senescence, supporting the future clinical application of these compounds as potential therapeutic tools for age-related degenerative diseases. However, the mechanisms have yet to be fully characterized. Therefore, further studies are needed to fully understand the role of beer polyphenols in determining conclusive evidence on impacts on human health.


Existing comorbidity measures predict mortality among general patient populations. Due to the lack of outcome specific and patient-group specific measures, the existing indices are also applied to non-mortality outcomes in injury epidemiology. This study derived indices to capture the association between comorbidity, and burden and readmission outcomes for injury populations.Injury-related hospital admissions data from July 2012 to June 2014  for the state of Victoria, Australia were analyzed. Various multivariable regression models were run and results used to derive both binary and weighted indices that quantify the association between comorbidities and length of stay (LOS), hospital costs and readmissions. The new and existing indices were validated internally among patient subgroups, and externally using data from the states of New South Wales and Western Australia.Twenty-four comorbidities were significantly associated with overnight stay, twenty-seven with LOS, twenty-eight with costs, ten with all-cause and eleven with non-planned 30-day readmissions. The number of and types of comorbidities, and their relative impact were different to the associations established with the existing Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Measure (ECM). The new indices performed equally well to the long-listed ECM and in certain instances outperformed the CCI.The more parsimonious, up to date, outcome and patient-specific indices presented in this study are better suited for use in present injury epidemiology. Their use can be trialed by hospital administrations in resource allocation models and patient classification models in clinical settings.The online version contains supplementary material available at 10.1186/s12913-021-06149-1. The ability to quantify the effect of comorbidities for injury patients can assist in predicting outcomes in clinical settings and estimating injury burden in epidemiological research.Hospital-admitted injury patients can experience adverse outcomes during the course of the hospital stay, and comorbidities have the potential to increase that burden. Adverse outcomes include complications, extended hospital stay, readmission to hospital, discharge to long-term nursing care facilities and death. Previous research shows that among hospitalised patients, burden-related outcomes such as readmission to hospital –3, lengtCurrently there are several established methods for quantifying the association between comorbidities and outcomes. These include capturing the presence of at least one, each or a count of all comorbidities, and the use of comorbidity indices such as the Charlson Comorbidity Index (CCI)  and the Factors associated with outcomes for injury patients are different to those for general hospital-admitted patients , 13, 14.The purpose of this study was: (1) to derive and validate new indices to establish the association between comorbidity, and readmission and burden-related outcomes, such as LOS and hospital costs, among hospital-admitted injury patients using Australian administrative datasets; and (2) to compare the performance of the new indices with the CCI and ECM.An analysis of existing morbidity data from the states of Victoria (Victorian Admitted Episodes Dataset (VAED)), New South Wales (NSW) (Admitted Patient Data Collection (APDC)) and Western Australia (WA) ) was carried out. The Victorian dataset was used for deriving injury comorbidity indices and the interstate datasets were used for external validation. Data provision and linkage were undertaken by the Centre for Victorian Data Linkage (CVDL) in Victoria, the Centre for Health Record Linkage (CHeReL) in NSW and the Data Linkage Branch (DLB) in WA.These datasets capture all public and private hospital admissions, and contain patient demographics and morbidity information. The morbidity information includes 40 diagnosis fields for Victoria, 51 for NSW and 78 for WA, consisting of disease, injury and external cause data, coded to the ICD Tenth Revision, Australian Modifications (ICD-10-AM) . MortaliData linkage (using patient-specific identifiers) was performed by CVDL using deterministic data linkage for the Victorian data while CHeReL used probabilistic matching techniques for NSW, and DLB in WA used a multi-faceted process which includes numerous automated and manual sub-processes. CVDL estimates the false positive rate to be between 0.5 to 1%, and the false negative match rates to be between 1 and 2%  and CHeRInjury cases were identified and selected as those with records containing an ICD-10-AM diagnosis code in the range “S00” to “T75” or “T79” in the first appearing diagnosis field in the morbidity datasets; a practice commonly used for national reporting . Case seThe comorbidity indices derivation cohort consisted of adult patients with an index injury admission between 01 July 2012 and 30 June 2014 . They were followed up over a period of two years for subsequent hospital admissions.The same selection and followup process was used as for the Victorian cohort using the APDC  and HMDC . These patients were also followed up in the mortality data to identify patients for censoring; those who died within 30 days of hospital-discharge did not have a possibility for readmission and were excluded from analysis pertaining to readmissions within 30 days .Two outcomes related to LOS, one cost outcome and two outcomes related to readmissions were chosen for index derivation, some details of which has been published before .The LOS days in the morbidity datasets were re-coded as (1) a binary code “0” for those who were discharged on the same day and “1” for those who stayed overnight, and (2) the number of days in hospital as a continuous variable for the index episode for those who stayed at least overnight. Case selection for the second LOS outcome was limited to those who stayed less than 1 month in the index episode (92% of cases).Hospital costs were recorded in Australian dollars (AUD), and were available for Victoria from July 2012 to June 2015 only for those admitted to public hospitals. Costs were standardized to 2012 dollar values using the Australian Consumer Price Index . Due to Two forms of readmissions (any-cause and non-planned) were coded, both with a binary outcome: “1” for patients readmitted within 30 days and “0” otherwise, limited to the first occurring readmission. Patients who died in hospital and those who left against medical advice were excluded.worst injury method [The factors considered were age, sex, body-region, injury type, injury severity, SEIFA (Socio Economic Indexes For Areas), country of birth, geographic region and comorbidity. The coding is similar to that used in our previous studies  , 24. Injy method , a serioy method . The sury method .A combination of the CCI  and ECM A negative binomial regression model for LOS, a linear regression model for log transformed costs and a logistic regression model for the binary outcomes  were fitted using multivariable regression. Socio-demographic variables and injury characteristics were entered in the baseline models.2 and McFadden’s R2 was used to determine predictive ability for the linear and negative binomial models.Predictive power of the logistic regression models was assessed using discrimination (area under the receiver operating characteristic curve) and classification tables. The area under the curve (AUC) ranges from 0 to 1, with a value of under 0.7 representing poor discrimination and anything above that as good discrimination. Classification tables were derived on the basis of using a classification cut-off probability that maximized the combined sensitivity and specificity of the table based on the receiver operating characteristic curve. The adjusted RAfter running the baseline models, comorbidity was added in various forms. Models were compared using the Akaike Information Criterion (AIC) . A diffe2 and McFadden’s R2 for the linear and negative binomial models. If both the AIC statistics change and the improvement to predictive powers were significant, then the interaction terms were retained.The significance of interaction terms was tested using the AIC statistic (for model fit) and the improvement to the predictive power of models using the changes in (i) the AUC statistics for logistic models and (ii) adjusted Rbinary representation of comorbidities significant to the outcome was retained. This model was given the generic name the “Australian Injury Comorbidity Index”. However, depending on the outcome of interest, an extension term describing the outcome is added to the generic name.The final model containing the weighted comorbidity index was derived using the final binary model, weights were computed for each comorbid condition using the following: resulting odds ratios (ORs) for each condition from logistic regression, incident rate ratios (IRRs) for the negative binomial regression; and the exponential of the beta coefficients for the linear regression model. The following rules were applied in allocating weights: the condition was dropped from the index if the weight < 1.2; 1.2 ≤ weight < 1.5 resulted in a score of 1; 1.5 ≤ weight < 2.5 = 2; 2.5 ≤ weight < 3.5 = 3 and so on. These weights were summed up to create the summed weighted score.A Two more indices were also derived: one parsimonious binary injury comorbidity index for burden and one for readmissions. The first was created using only conditions that were associated with LOS and cost, while the second was based on conditions associated with readmissions.2s. Stata 14.0 (StataCorp) was used to analyze the data [Finally, a validation of the newly derived indices was carried out. The indices were internally validated on patient subgroups and externally on NSW and WA datasets. For the internal validations, patient sub-groups were selected based on demographics and/or injury characteristics. The groups were children (< 15 years), adults (> = 65 years), males, females, adults with non-severe injuries, adults with intracranial injuries, adults with blunt trauma, adults with penetrating injuries and hip fracture patients aged 45 years and over. The validation models were the same baseline model identified for each outcome in Victoria with the addition of the binary index, the weighted index, the burden index, the readmission index, CCI, updated CCI and ECM to represent comorbidity. The models with various indices were assessed for predictive ability using the AUC and classification tables and adjusted Rthe data .Nearly a third (30.9%) of the population were 65 years and above, 55.3% were males and approximately 13% had serious injuries. More than half (59.6%) of the patients had a main injury to the extremities and the highest proportion of injuries were fractures (41.3%) (Table The median LOS was 1 day (IQR 1–3) for the entire cohort while those with at least one comorbidity had a higher median LOS of 4 days (IQR 1–17). Over two-thirds required an overnight stay (68.4%). The mean hospital cost was AUD 7457.3 (95% CI 7370.1 − 7544.5) , while the mean cost for those with at least one comorbidity was nearly double ). Excluding patients who died or left against medical advice, 11.4% had at least one any-cause and 7.6% one non-planned readmission to a hospital within 30 days of being discharged for the index admission. These readmissions increased to 18 and 12.7% respectively for those with at least one comorbidity. Overall, comorbidity increased with age, was higher among females, and higher among patients with serious-injuries. An overview of the NSW and WA study populations is also presented in Table Alcohol dependence, cardiac arrhythmia, dementia, depression, diabetes, hypertension without complications and renal disease were the most commonly recorded comorbidities among injured adults (> 15 year of age) Table . After ai). The baseline factors differ for each outcome  and weighted comorbidity indices (iii), existing indices (CCI (iv), updated CCI (v) and ECM (vi)) and the parsimonious indices  (vii)  Table . ResultsInteraction effects between age and sex, comorbidities and age, and comorbidities and sex, were also modelled. The age-sex interaction was selected because of their known interactions with disease , while tThe Australian Injury Comorbidity Index for overnight stay (AICI-os) with 24 comorbidities performed better than the CCI, performed equally to the ECM with the added advantage that it uses fewer comorbidities than the ECM.vi (containing the ECM), followed by model ii (containing the AICI-os). Models containing the CCI (iv) and updated CCI (v) had much poorer fit. Comparing predictive abilities using the AUC and false negative rates ) fits better than the CCI. The AICI-los does not fit as well as the ECM, the only trade-off lies in modelling thirty conditions in the ECM as opposed to only 27 in the AICI-los. In terms of predictive abilities, the differences between models were relatively small. Details of model performance can be found in Appendix Similar to the AICI-los, the Australian Injury Comorbidity Index for costs (AICI-cost) with 28 comorbidities fits better than the CCI and less so than the ECM. Once again, the trade-off between the AICI-cost and the ECM is the number of conditions.2 was best in the model with the ECM (36.6%), followed by AICI-cost (35.9%), while predictive powers of the CCI (32.8%) and updated CCI (32.5%) were lower.The best fit was once again seen in models with the ECM, followed by the AICI-cost while the CCI and updated CCI had a poorer fit. Predictive power in terms of the adjusted RThe Australian Injury Comorbidity Index for all-cause 30-day readmission (AICI-acr) with 10 comorbidities exhibits similar capacity in terms of model fit and predictive power as the existing indices except that it includes fewer conditions than the CCI and ECM.In terms of model fit, the best were models with the ECM, followed by the AICI-acr, while once again the CCI and updated CCI had a poorer fit. The AUC for all models for this outcome was close to but less than 0.7, indicating that the power of the models was somewhat poor and could probably be improved by factors not already adjusted for. Further details can be found in Appendix The best fit was again observed for models with the ECM, followed by the Australian Injury Comorbidity Index for non-planed 30-day readmissions (AICI-npr) with 11 comorbidities and poorer fit with the CCI and updated CCI. There were no significant differences between all the models in terms of AUC statistics and false negative rates. Similar to all-cause readmissions, the AICI-npr has the advantage of having fewer conditions than the CCI and ECM.vii) (Table 2s showed that the two parsimonious indices (AICI-b and AICI-r) can be used for predicting the corresponding outcomes without much loss in predictive capacities (Table A binary index (model i) Table  with 23 es Table .Table The newly derived indices along with the CCI and ECM were validated in subgroups of the Victorian population. A detailed discussion of results can be found in Appendix 2s than Victoria and NSW, and the ECM performed best followed by the AICI and the CCI. For readmissions, AUCs were low (poor predictive power) for all indices and not significantly different, nor were the false negative rates. WA data overall had better predictive power than the other two states.External validations were carried out on the entire adult portion of the interstate cohorts. Comorbidity prevalence for these are presented in Appendix Table ASuggestions on how the indices can be used are provided in Appendix The number and type of comorbidities associated with outcomes vary based on the outcome. The AICIs provide up-to-date, injury and outcome specific parsimonious indices that perform equally well to the long-listed ECM, and in certain instances outperform the widely used CCI.This study shows that pre-existing comorbidities associated with burden and readmission outcomes for injury patients are different for each outcome, enforcing the need for outcome-specific indices. For instance, conditions associated with LOS and costs were somewhat similar  while conditions associated with readmissions were similar but far fewer in number than burden outcomes. The CCI and ECM has been cited extensively  but are often applied to injury populations and non-mortality outcomes when they were originally derived for predicting mortality .The fact that the association between comorbidities and outcomes vary and that the CCI performed better for predicting mortality than LOS and readmissions has been shown in other studies , 35. BotThe new indices allow a more appropriate quantification of the effect of each comorbidity on the outcomes as opposed to using existing indices that are based on older data. More specifically; conditions like HIV/AIDS and metastasis contain the highest risk scores according to the CCI, but the results from this study shows that these conditions have much smaller or no effect on burden and readmission outcomes. Myocardial infarction allocates a certain element of risk in the CCI, but according to the findings here, it only impacted the prediction of costs. Peptic ulcer disease is also allocated a risk element in the CCI but shows association only with LOS and cost and has no association with readmissions in the present study.A study by Moor et al. (2008) further showed that the CCI weights assigned to conditions did not correspond to their study coefficients when assessing mortality for trauma patients  which thA number of studies in the past have indicated the need for study-specific comorbidity indices or weights , 37–40. The only study that derived a comorbidity index for injury populations was by Thompson et al. (2010)  which deAnother strength of this study is the extensive validations of the new and existing indices carried out in subgroups and external populations. It was seen that in most instances the ECM performed best followed by the AICIs and CCI in predicting outcomes. However, the trade-off on the number of comorbidities modelled and the relevance to the outcome could be the deciding point for whether the ECM or AICI is used.The new indices may be more versatile for use than the Multipurpose Australian Comorbidity Scoring System (MACSS) which was derived by Holman et al. (2005) . The MACMoor et.al. (2008)  and TosoThe < 0.7 AUC statistics for assessing readmissions indicate that additional variables are required to explain these outcomes. Readmissions could be due to injuries, comorbidities, complications and health service delivery. Adjusting for the cause of readmission, which was not available in these datasets, may help improve the baseline model.LOS and costs could also be largely driven by hospital facilities as well as the medical and surgical procedures carried out. The distance from a patient’s residence to the hospital could be another important factor, all of which was not part of the data included in this study. LOS can also be dependent on the total patient turnout at a hospital; hospitals with low resources may tend to discharge patients sooner resulting in lower LOS, likewise they could result in higher numbers of readmissions. Accounting for these in the baseline models may improve predictive abilities, but not necessarily the comorbidity indices.injury patients with comorbidities in this study (for Victoria) was 19.5% while previous research for New South Wales, Queensland and South Australia [The capture of comorbidities in this study compare well with a previous study of injury patients that used administrative data in Australia. The proportion of ustralia  found 15The administrative data used in this study does not capture the severity of comorbidities. It may also not capture all comorbidities as the function of most administrative datasets is for informing hospital reimbursement, therefore commodities that are not actively treated within the episode may not be indicated.The inclusion of lookback periods could improve comorbidity capture. However, the new indices are meant for use at the point of hospital admission, and data for lookback periods are generally unavailable at that point; therefore, inclusion of these will be useful only during research and not in clinical settings.Care should be taken when using the new indices in populations where the injury profile or the prevalence of comorbidity are different, as was seen in the subgroup analysis Appendix . FurtherCases selected for 30-day readmissions analysis in the NSW and WA datasets excludes those that died within 30 days. The exclusion of these patients facing a ‘competing risk’ for readmission could incorporate some selection bias as it excludes some serious patients who may have the possibility of a readmission within 30 days. However, this proportion is generally around 1%. This exclusion was not carried out for Victoria as the hospital data in this instance was not linked with mortality data.Epidemiological research and resource use predictions for hospital admitted injury patients will benefit from using the AICIs that have been specifically derived and validated for this group of patients. For example, they can be used by hospitals for planning beds, and by health service administrators when budgeting for future hospital expenditure for injury patients with comorbidities.Another advantage of such indices is that they are less resource intensive given they use information available at point of hospital admission. Clinicians can estimate LOS and the possibility of readmissions for injury patients, adjusting for the effect of comorbidities, and plan the services required accordingly. These indices do not replace clinical knowledge when deciding hospital logistics required for treating patients but maybe used in assisting with the decision-making processes.The AICIs can be further validated in other countries to understand if additional adjustments are required to make them more robust. Another step forward for these indices will be to incorporate comorbidity severity measures, which may improve the indices’ abilities.Comorbidities associated with burden and readmission outcomes vary with the outcome and the method used to measure the outcome. The up-to-date, injury- and outcome-specific AICIs for burden and readmissions, which are similar to the binary index such as the ECM but with fewer conditions, are sufficient for predicting outcomes and does not warrant weighted indices such as the CCI. The AICIs could be further improved by adding more information on comorbidities.Additional file 1: Table S1. Socio Economic Index for Areas (SEIFA) and country of birth details for the Victorian, NSW and WA study populations. Table S2. Performance of selected model fitting strategies in assessing the effect of comorbidity on selected outcome measures (Victoria). Table S3. Performance of new vs existing comorbidity indices using classification tables (Victoria). Table S4. Conditions included in the injury comorbidity indices for burden, readmissions, CCI and ECM. Table S5. Performance of new comorbidity indices vs existing comorbidity indices in injury sub-groups (Victoria). Table S6. Presence of comorbidity with mean LOS and proportion of patients with readmission outcomes in the NSW and WA study populations (> = 15 years). Table S7. Performance of selected model fitting strategies in assessing the effect of comorbidity on selected outcome measures (NSW and WA).Additional file 2: Appendix A1.1. ROC curves for overnight stay (age > = 15 years).Additional file 3:Appendix A1.2. Plots of predicted length of stay (days) vs observed (at least 1 overnight and LOS < =30).Additional file 4: Appendix A1.3. Residual plots for costs.Additional file 5: Appendix A1.4. ROC curves for all-cause 30-day readmissions (age > = 15 years).Additional file 6: Appendix A1.5. ROC curves for non-planned 30-day readmissions (age > = 15 years).Additional file 7: Appendix A2. Interaction plots.Additional file 8:Appendix A3. Model performance details.Additional file 9:Appendix A4. Internal and external validation details.Additional file 10: Appendix A5. Using the Australian Injury Comorbidity Indices.


MultiScan®), with the histological components on the NAFLD-NASH spectrum in a large cohort of cross-sectional data, in order to calibrate the measurement to histology, and to infer what might constitute a clinically meaningful change when related to the FDA’s criteria.Late stage clinical trials in non-alcoholic steatohepatitis (NASH) are currently required by the FDA to use liver biopsy as a primary endpoint. The well-reported limitations with biopsy, such as associated risks and sampling error, coupled with patient preference, are driving investigation into non-invasive alternatives. MRI-derived biomarkers proton density fat fraction (PDFF) and iron-corrected T1 mapping (cT1) are gaining traction as emerging alternatives to biopsy for NASH. Our aim was to explore the correlations between cT1 and PDFF , associations between imaging biomarkers and histology were tested using Spearman’s rank correlation coefficient (rs), and further exploration of the relationships between the imaging variables and histology were performed using linear regression.In a retrospective analysis of data combined from three previously published observational NASH studies, in which adult participants who underwent liver biopsy on suspicion of NAFLD or NASH and had an MRI scan measuring cT1 and PDFF . cT1 was also positively correlated with Kleiner-Brunt fibrosis. Partial correlations, adjusting for steatosis, revealed cT1 correlated with inflammation and fibrosis, whereas PDFF did not, and both were still associated with the NAS, but correlation was weaker with PDFF than cT1. An estimated difference of 88 ms in cT1, or 21% relative difference in PDFF was related to a two-point difference in overall NAS.N = 264 patients with mean age of 54 (SD:9.9), 39% female, and 69% with BMI ≥ 30kg.mThe correlations between cT1 and PDFF with the histopathological hallmarks of NASH demonstrate the potential utility of both cT1 and PDFF as non-invasive biomarkers to detect a pharmacodynamic change in NASH, with cT1 showing superiority for detecting changes in inflammation and fibrosis, rather than liver fat alone. Non-alcoholic fatty liver disease (NAFLD) and its progressive form non-alcoholic steatohepatitis (NASH) have complex histological signatures reflecting coexisting fat deposition, inflammation, hepatocellular injury  and fibrosis, that are each subject to time-dependent and reversible changes. NASH results when fat accumulation in the liver triggers inflammatory signals and reactive oxygen species that can amplify liver injury and stimulate fibrosis . NAFLD iPDFF has an excellent correlation with histologically graded steatosis across the clinical range seen in NASH –11 and hA number of phase II trials are already employing the non-invasive QIBs PDFF and cT1 as diagnostic screening biomarkers and as secondary or exploratory endpoints –31. In oIn order to inform what drives change in cT1 and PDFF, and also to estimate what might constitute a meaningful change in both biomarkers when compared to biopsy, we set out to explore the relationships between both QIBs and the histological features of NASH in a cross-sectional analysis of a large cohort of NAFLD patients with paired biopsies and MRI scans.MultiScan®, FibroScan® liver stiffness measurement (LSM) with controlled attenuation parameter (CAP) and MR Elastography (MRE) were acquired as part of the screening protocol. Participants were invited to undergo core liver biopsy if evidence of steatosis (PDFF ≥ 5%) or fibro-inflammation . Full details have been described elsewhere /NICOLA study enrolled adult patients scheduled for a standard-of-care liver biopsy to investigate known or suspected liver disease from two large tertiary UK liver centres (Oxford and Reading) between March 2011 to May 2015. Similarly, the CALM study (ISCRTN39463479) invited adult patients scheduled for a standard-of-care liver biopsy to investigate known or suspected liver disease from two large tertiary UK liver centres  between February 2014 and September 2015 . Patientlsewhere . All stuAll biopsies were reported by at least two liver histopathologists, and adequacy assessed using the definition of the Royal College of Pathologists, UK. Histology was graded according to the NASH-CRN for Kleiner-Brunt fibrosis, hepatocellular ballooning, lobular inflammation, steatosis, and the composite NAS. All pathologists were blinded to patient characteristics and non-invasive assessment data. Discordance was adjudicated by a third blinded observer. Biopsy scores used for the analysis were those collected as part of the three independent studies and were not re-read centrally.MultiScan® MRI scanning protocol was installed, calibrated, and phantom tested on all the MRI systems in these trials in a standard way (in vivo proton magnetic resonance spectroscopy (MRS), a specialized magnetic resonance technique that measures fat by quantifying the overall volume fraction of lipids in the liver parenchyma. A ShMOLLI sequence was used to derive T1-relaxation. Iron was calculated from the T2* relaxation. To generate cT1 maps, the acquired MOLLI image data were fit using a Bloch equation simulation approach. The resultant cT1 maps were generated according to the latest LiverMultiScan® post-processing algorithms that reflect a measurement that would be expected if the patient had been scanned with a heart rate of 60 bpm, with normal liver iron and on a Siemens 3T scanner. This general approach has been found to yield accurate fitting to MOLLI data in previous work  with an adjusted R2 suggesting that NAS explained 35% of cT1 variability; the residuals of the model satisfied assumptions of the normal distribution. The coefficient of NAS in the linear regression model suggests that a 2-unit increase in NAS score, has a significant increase in cT1 of 88ms. By way of illustration, an 88ms change in cT1 for a patient moving from NAS 5 to NAS 3 would be equivalent to a drop from 921 to 833 ms.The univariable analysis to predict cT1 adjusting for NAS resulted in a regression model  with an R2 suggesting that NAS explained 49% of cT1 variability. Predicted cT1 using the effect estimates of the resultant model indicated an average 44-ms difference in cT1 between two stages of NAS when adjusted for PDFF.The above analysis was repeated controlling for liver fat measured using PDFF. The multivariable analysis to predict cT1 from NAS, adjusted for PDFF, resulted in a regression model , with an R2 suggesting that ballooning explained 22% of cT1 variability. The coefficient of ballooning in the linear regression model suggested that a 1 unit increase in ballooning has a significant increase in cT1 of 81 ms. This remained significant, but the estimated coefficient was reduced to 44 ms if the model was adjusted for PDFF.A multivariable analysis was performed with ballooning as the independent variable and revealed a model to predict cT1 ; however, the residuals of the model did not satisfy assumptions of the normal distribution and thus PDFF data were log-transformed and the model repeated. This resulted in a model  with an R2 suggesting that NAS explained 32% of PDFF variability. The coefficient of NAS in the linear regression model suggests that a 2-unit increase in NAS score has a significant relative increase in PDFF of 21.1%. By way of illustration, a 21.1% relative change in PDFF for a patient moving from NAS 5 to NAS 3 would be equivalent to a drop from 15.6% to 12.4%.The univariable analysis to predict PDFF from NAS resulted in a regression model  with an R2 suggesting that ballooning explained 13% of PDFF variability. The coefficient of ballooning in the linear regression model suggests that a 1 unit increase in ballooning score, has a significant relative increase in PDFF of 16%.A univariable analysis was performed with ballooning as the independent variable and revealed a model to predict PDFF [FOur results show positive correlations between the quantitative image–derived biomarkers (QIBs) of cT1 and PDFF, with all histopathological hallmarks of NASH, and between these histological features themselves. This serves to demonstrate the complex interactions between the nature and timing of the pathology in NAFL and NASH, and to demonstrate how increasing levels of hepatic steatosis are associated with more profound hepatocyte injury, that can ultimately result in fibrosis , the dowWhile PDFF is not strongly associated with disease activity and has an inverted-u shaped relationship with fibrosis, the positive associations between the histopathological measures in non-cirrhotic NASH translate into patients with more steatosis being more likely to exhibit characteristics of advanced features of NASH ; and thuMultiScan reported QIBs, which represent the variation that may be expected across repeated measures were 46 ms for cT1 and 0.8% (absolute) for PDFF. This is lower variation than has been reported previously for PDFF   and subjected to rigorous test-retest performance testing , 18, theimation)  and magnIn terms of the different utility of the QIBs for detecting clinically meaningful change with pharmacological interventions, both the correlation analysis and the literature suggests PDFF  and cT1 Limitations of this study were that the estimates for the difference in biomarker measurement related to histological changes were derived from cross-sectional rather than longitudinal data and that the histological data were obtained independently, without central reads. Given the known large inter-rater variability for granular histologic data such as inflammation and ballooning, there is a possibility for discordance between readers. This however was mitigated to some extent as each study had at least dual read with consensus review. Also, while the combined sample size is substantial, it does not cover all possible combinations of the NAS and fibrosis scores meaning the linear relationships may not be accurately reflected. Despite these limitations, it is encouraging that these data closely reflect the observations from prospective interventional clinical trials using PDFF , 45 and In summary, both cT1 and PDFF show correlations with the histopathological features of NASH and show potential as non-invasive endpoints in NASH trials to detect a relevant pharmacodynamic response. A cT1 difference of 88 or 81 ms is related to a two-point change in NAS and a one-point decrease in ballooning, respectively. Similarly, a relative difference of 21% in PDFF is related to a two-point change in NAS, and a relative difference of >16% to a one-point change in ballooning. As PDFF is largely dominated by steatosis, cT1 shows superiority when the focus is on changes in inflammation and/or fibrosis, and thus using both in combination may provide more granularity for distinguishing specific treatment effects.andrea.dennis@perspectum.com.The data analyzed in this study is subject to the following licenses/restrictions: The data is owned by the sponsors for each study. Requests to access these datasets should be directed to The studies involving human participants were reviewed and approved. All studies were conducted in accordance with the ethical principles of the Declaration of Helsinki 2013 and Good Clinical Practice Guidelines. The RIAL study was approved by the institutional review departments at the University of Oxford and by the National Review Ethics Service . The CALM study was approved by the institutional review departments at the University of Birmingham and by the National Review Ethics Service . The prevalence study was approved by the institutional review at the Brooke Army Medical Centre. The RIAL study was registered with clinicaltrials.gov (NCT01543646) and was sponsored by the University of Oxford. The CALM study was registered with the International Standard Randomized Controlled Trial Number registry (ISRCTN39463479) and the National Institute of Health Research portfolio (15912). The study sponsor was the University of Birmingham. The Prevalence study was registered with clinicaltrials.gov (NCT03142867) and was sponsored by The Geneva Foundation. All participants provided their written informed consent to participate in these studies.AD performed the analysis and wrote the manuscript. MK supported analysis and contributed to the manuscript. CF developed the LMS processing algorithm and supported data analysis. SM contributed to statistical analysis. JF, GH, MP, and SH were all investigators in the trials from which the data were pooled, were involved in study design and data analysis and data interpretation, and reviewed and edited the manuscript. MC, RB, and AS all reviewed and contributed to the manuscript. All authors contributed to the article and approved the submitted version.The RIAL and NICOLA studies were supported by grants from the Oxford NIHR Biomedical Research Centre and the Oxford Health Services Research Committee (OHSRC). The CALM study was academic-led, sponsored by the University of Birmingham and funded by Innovate UK (Enhancing In Vivo Imaging for Stratified Medicine) Award (TSB Ref: 31620-234143). The Prevalence study was sponsored by The Geneva Foundation and funded by EchoSens.®. RB is the CEO and founder of Perspectum. AD, SM, CF, and MK are employees of Perspectum. MP is a shareholder in Perspectum.Perspectum Ltd is a privately funded commercial enterprise that develops medical devices to address unmet clinical needs, including LiverMultiScanThe remaining 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.


SARS-CoV-2 is difficult to contain because many transmissions occur during pre-symptomatic infection. Unlike influenza, most SARS-CoV-2-infected people do not transmit while a small percentage infect large numbers of people. We designed mathematical models which link observed viral loads with epidemiologic features of each virus, including distribution of transmissions attributed to each infected person and duration between symptom onset in the transmitter and secondarily infected person. We identify that people infected with SARS-CoV-2 or influenza can be highly contagious for less than 1 day, congruent with peak viral load. SARS-CoV-2 super-spreader events occur when an infected person is shedding at a very high viral load and has a high number of exposed contacts. The higher predisposition of SARS-CoV-2 toward super-spreading events cannot be attributed to additional weeks of shedding relative to influenza. Rather, a person infected with SARS-CoV-2 exposes more people within equivalent physical contact networks, likely due to aerosolization. The pandemic is rapidly expanding in the United States and is re-emerging focally in many countries that had previous success in limiting its spread  for an individual infected with SARS-CoV-2  while a minority of infected people are super-spreaders . If the average population R0 is greater than 1, then exponential growth of cases occurs in the absence of effective interventions  and frequent super-spreader events associated with SARS-CoV-2 but not influenza. This process included within-host modeling of viral loads, simulations of exposures and possible transmissions based on various transmission dose response curves, testing of various parameter sets against epidemiologic data and exploratory analyses with the best fitting model ,  to genero others . Brieflyo others . Key obsOne limitation of our model is that only half of study participants provided longitudinal viral load data from the very early days of infection when COVID-19 is often pre-symptomatic. Therefore, the model's output is most reliable for later time points. In particular, we have somewhat limited information on viral expansion rate and duration of peak shedding. To impute possible variability, we generated a set of heterogeneous shedding curves in which the viral upslope, the downslope of viral load after peak and the viral load during plateau phase were varied . Overallexposure event in very specific biologic terms as a discrete event consisting of sufficient contact in time and space between a transmitter and one or more uninfected persons (exposure contacts) to allow for the possibility of a successful transmission. An exposure contact is defined as a susceptible person who is exposed to a SARS-CoV-2-infected person for a sufficient period of time and at a close enough distance to allow for the possibility of a successful transmission, which is then determined by the viral load of the infected person. The number of exposure contacts can in theory be reduced by social distancing measures, quarantine, or masking.We defined an Contagiousness is defined as the viral-load-dependent probability of passage of virus-laden droplets or airborne particles from the airways of a potential transmitter to the airway of an exposure contact. Infectiousness is defined as the viral-load-dependent probability of transmission given direct airway exposure to virus in an exposure contact. Transmission risk is the product of these two mechanistic probabilities derived from the ID and CD curves and results is a transmission dose (TD) response curve. Each CD or ID curve is defined by its ID50 (λ) or viral load at which contagion or infection probability is 50% (infectiousness) and obtained qualitatively similar results . Of note, a null model in which there is an assumed fixed probability of infection at all timepoints during infection poorly fit the observed data.We next designed hundreds of dose response curves which separately predict contagiousness (CD curves) and infectiousness (ID curves) at a certain viral dose given an exposure contact. y is 50% , as welly is 50% . The TD5We introduced heterogeneity of exposure contact rates among possible transmitters by randomly selecting from a gamma distribution defined by mean number of exposure contacts per day (θ) and a scaling factor , we randomly selected the incubation period (IP), for both the transmitter and the newly infected person, from a gamma distribution based on existing data , . Given that viral RNA is composed mostly of non-infectious material, we further checked the closeness of the solved ID curve with the observed relationship between viral RNA and probability of positive viral culture from a longitudinal cohort of infected people  and slope (α), along with 408 combinations of the mean exposed contact rate per day ,  mean ser, 2.5]),  cumulati5]), , ,  to explain the lower frequency of observed super-spreader events with this infection. Influenza viral kinetics were modeled using a previously data-validated model . Incubat, 1.5]),  mean ser, 1.5]),  cumulatintervals .7, 0.5, 4, 40]) most closely reproduced empirically observed individual R0 and serial interval histograms  confirming the robustness of the qualitative results of the model.The preciseness of the estimated parameter set was also independently confirmed with the use of Approximate Bayesian Computation (ABC) rejection sampling method,  and a fiSARS-CoV-2 viral load was recently measured with viral RNA levels and mapped to concurrent probability of positive viral culture in a Dutch cohort . Our modThe model also generated super-spreader events with 10,000 simulated transmissions . If supegeneration time, defined as the period between when an individual becomes infected and when they transmit the virus, for all transmission pairs and identified that the mean serial interval (4.4 days) provides an accurate approximation of mean generation time. However, the variance of generation time was considerably lower and by definition does not include negative values. A majority of generation times fell between 4 and 7 days, compared to −5 to 12 days for the serial interval  given an exposure to an infected person with a viral load of <105 SARS-CoV-2 RNA copies. On the other hand, transmission is much more likely (39%) given an exposure to an infected person who is shedding >107 SARS-CoV-2 RNA copies, and 75% given an exposure to an infected person with a viral load of >108 SARS-CoV-2 RNA copies. We obtain similar results (not shown) when we solve our model using the assumption of homogeneous viral load trajectories  as in The optimized ID curve has an ID50 of 10ep slope . The TD5A copies . To asseWe next plotted the probability of infection given an exposure to a transmitter. Under multiple shedding scenarios, the window of high probability transmission is limited to time points around peak viral load, and some heterogeneity in regard to peak infectivity is noted between people . In gene7 SARS CoV-2 RNA copies and a high number of exposure contacts on that day. We observed an inflection point between 106 and 107 SARS CoV-2 RNA copies, after which large increases in the number of daily exposure contacts prominently increases the number of transmissions from a single person  is 40 indicating high variability in day-to-day exposure contact rates  with an e person . The expe person .6 RNA copies or with higher numbers of exposure contacts per day and viral loads exceeding 105 RNA copies. Massive super-spreader events with over 50 infected people almost always occurred at viral loads exceeding 107 RNA copies with high levels of concurrent exposure contacts  and a viral load exceeding 10A copies , which iA copies .0 distribution as shown in 5.0, 0.5, 0.7, 40]) yields good fits to the distribution of individual R0 as well as serial interval , ~100-fold lower than post-lockdown  possibly because infected people were initially less likely to self-isolate after developing symptoms . Therefointerval . Importalockdown . Keepinglockdown  reflect lockdown . The pre0 and serial intervals for influenza: . ID50 values for influenza were lower than SARS-CoV-2, but a direct comparison cannot be made because tissue culture infectious dose (TCID) has been more commonly used for measurements of influenza viral load, whereas viral RNA is used for SARS-CoV-2. Nevertheless, TCID is a closer measure of infectious virus and it is thus reasonable that ID50 based on TCID for influenza would be ~30-fold lower than ID50 based on total viral RNA (infectious and non-infectious virus) for SARS-CoV-2  is unlikely to result in transmission – consistent with the overall rarity of positive cultures at these levels  after coming into contact with SARS-CoV-2 (V) become infected at rate I) produce new virus at a per-capita rate To simulate SARS-CoV-2 shedding dynamics, we employed our previously described viral infection model . In thisIn the model, SARS-CoV-2-specific effector cells rise after two stages from precursors cells , that only some exposure contacts have virus passaged to their airways (contagiousness) and that only some exposed contacts with virus in their airways become secondarily infected (successful secondary infection). Contagiousness and infectiousness are then treated as viral load dependent multiplicative probabilities with transmission risk for a single exposure contact being the product. Contagiousness is considered to be viral load dependent based on the concept that a transmitter’s dispersal cloud of virus is more likely to prove contagious at higher viral load, which is entirely separate from viral infectivity within the airway once a virus contacts the surface of susceptible cells.We next assume that the total exposed contacts within a time step Transmissions within a time step are simulated stochastically using time-dependent viral load to determine infectiousness , we sample the incubation period in the transmitter and in the secondarily infected person from a gamma distribution with a shape described in 0 for SARS-CoV-2. From contact tracing of 391 SARS-CoV-2 cases in Shenzhen, China, 1286 close contacts were identified: the distribution of individual R0 values in this cohort was highly over-dispersed, with 80% of secondary infections being caused by 8–9% of infected people  . The lat0  that we 0 .  for the obtained am from: We visually checked whether our dose-response curve matched the observed probability of positive virus culture . We assu0 and distribution functions of individual R0  rejection-sampling method to estimate 1000 combinations of four parameters .Thank you for submitting your article "Viral load and contact network predict SARS-CoV-2 transmission and super-spreading events" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife". Please let us know if you would like to pursue this option. As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is "in revision at Summary:This work assesses the role of within-host viral shedding dynamics and contact heterogeneity on distribution of transmission events in SARS-CoV-2 and influenza. Using multi-scale modeling, with similar resulting generation time and serial interval distributions to published work, predictions are made on the manner and contribution of super spreading to transmission. Distinctions are seen when comparing to applying a similar modeling framework to influenza.Essential revisions:1) Statistical analysis:The model parameters are estimated using an exhaustive grid search, which yields good fits for the best-fit values, but there is no assessment of statistical certainty in the parameter values. The authors essentially adopted a strategy in the spirit of approximate Bayesian computation (ABC), by proposing parameter values, simulating from a model, and comparing summary statistics of the simulated output to known values from the literature. The analysis would be helped by doing a more formal ABC analysis, as this would provide a better sense of how narrowly constrained the parameter values are given the available data. At minimum, it would be more convincing to consider additional parameter sets grided across a narrowed region of parameter space before selecting an optimal fit.2) Model validationThe state of our knowledge about these infections is limited, both by the short time during with this research has been conducted, and the paper's need to rely on data taken from before the introduction of confounding factors such as social distancing and widespread mask usage. For this reason, in addition to the included sensitivity analysis for the model parameters, a sense of the sensitivity of the model's conclusions to the data set to which it is being fitted is needed. How much would these results change if there are errors in our understanding of the distribution of individual R0 values, or serial intervals?3) Distinction in assumptions for flu and covidThe populations on which the histograms for the two diseases are based are quite different. For SARS-CoV-2, the studies are from China , while those for influenza are from Switzerland. Could cultural differences be relevant? What about seasonal differences, as the time during which the early SARS-CoV-2 studies occurred was necessarily restricted?Furthermore, the explanation for the difference between influenza and COVID is based primarily on differences in contact patterns. While the Discussion clarifies this to be based on the efficiency with which exposures lead to infections (and pre-symptomatic transmission), which does sound like a viral parameter, rather than a social one. These viral factors do seem more believable than having to explain why the patterns of social contact exhibited by influenza patients would differ from those of SARS-CoV-2 patients. More focus on possible mechanistic explanations is warranted.Title: "Contact heterogeneity" rather than "contact network" is more appropriate for the title as no network is considered.eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Aleksandra Walczak as the Senior Editor. The Reviewing Editor has drafted this decision to help you prepare a revised submission.Thank you for submitting your article "Viral load and contact heterogeneity predict SARS-CoV-2 transmission and super-spreading events" for consideration by eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as Revisions:A key definition in the work (as stated in the Discussion) is the exposure contact. However, apart from the parenthetical definition in the Discussion, exposure contact is not clearly defined. This is particularly important as estimates on the exposure contact seem to impact the estimates on the viral load to infectiousness functional relationship. In particular, infectiousness is defined "… as the viral load dependent probability of transmission given direct airway exposure to virus in an exposure contact." Why does the parameterization for this change so significantly pre and post lockdown when the main difference appears to be in exposure contacts? The explanation given refers to "… more prolonged and intense exposure contacts…" This seems to imply an unequal reflection of what an "exposure contact" truly means. Furthermore, how does the "exposed contact rate" compare with exposure contacts.Figure 10. This new figure is confusing. In the text, viral shedding kinetics are referred to as panel (A) and kinetics of infectivity as panel (B), but they are both part of panel (A) in the figure. What is different between the SARS-CoV-2 and Influenza schematics in panel (B). Panel (C) is referred to in the text but is not in the figure. Essential revisions:1) Statistical analysis:The model parameters are estimated using an exhaustive grid search, which yields good fits for the best-fit values, but there is no assessment of statistical certainty in the parameter values. The authors essentially adopted a strategy in the spirit of approximate Bayesian computation (ABC), by proposing parameter values, simulating from a model, and comparing summary statistics of the simulated output to known values from the literature. The analysis would be helped by doing a more formal ABC analysis, as this would provide a better sense of how narrowly constrained the parameter values are given the available data. At minimum, it would be more convincing to consider additional parameter sets grided across a narrowed region of parameter space before selecting an optimal fit.Thank you for this valuable feedback. We agree that a more detailed evaluation of the parameter space would be valuable. In the first version of the manuscript, we demonstrate in Figure 10—figure supplement 3 and Figure 10—figure supplement 4 that there is a fairly narrow parameter space both for the dose response curve  and contact network space  that provides best model fit. Given that we are only exploring 4 parameters, we are confident that our parameter estimates are fairly precise.We appreciate the idea to test the model fit within a tighter parameter space, in proximity to the existing optimal solution. We performed this analysis with λ varied between 6.5 and 7.5 logs at intervals of 0.1, α varied between 0.5 and 1.0 at intervals of 0.05, θ varied between 3 and 5 at intervals of 0.2, and ρ varied between 30 and 50 at intervals of 5, with narrower intervals to allow more precise parameter estimation. The estimates, now reported in the manuscript, only change slightly with this approach.We also performed ABC to gather 1,000 parameter combinations with an error threshold of 0.1. This approach further confirms that our previous parameter estimates are the most likely parameter values given the features of the COVID-19 pandemic see . We have2) Model validationThe state of our knowledge about these infections is limited, both by the short time during with this research has been conducted, and the paper's need to rely on data taken from before the introduction of confounding factors such as social distancing and widespread mask usage. For this reason, in addition to the included sensitivity analysis for the model parameters, a sense of the sensitivity of the model's conclusions to the data set to which it is being fitted is needed. How much would these results change if there are errors in our understanding of the distribution of individual R0 values, or serial intervals?This is an excellent point. We now include data which demonstrates that the serial interval was longer during the initial stages of the pandemic in Wuhan than in subsequent cohorts across the globe (PMID: 31995857). This makes sense because infected people are currently much more likely to self-isolate after developing symptoms than in the very earliest phases of the pandemic. Essentially, a higher proportion of pre-lockdown transmissions occurred during the symptomatic phase compared to post-lockdown. We therefore have additionally re-fit the model to this data and show that the duration of infectivity is predicted to be significantly longer under this assumption. This is now included as Figure 5 in the revised version.We similarly fit the model to presumed data with higher R0 of 2.8-2.9 (see 3) Distinction in assumptions for flu and covidThe populations on which the histograms for the two diseases are based are quite different. For SARS-CoV-2, the studies are from China , while those for influenza are from Switzerland. Could cultural differences be relevant? What about seasonal differences, as the time during which the early SARS-CoV-2 studies occurred was necessarily restricted?This is a very important point as well. We have amended the Discussion to make the more conservative conclusion that the model explains the differences in these two settings for these two viruses. That said, we are extremely confident that our qualitative conclusions can be generalized for global influenza and SARS-CoV-2 for the simple reason that super-spreader events are a fundamental feature of the coronavirus in every country where it has been assessed, whereas these events are very rarely reported for influenza. We make this point in the revised Discussion.We also repeatedly emphasize that the estimate of our 4 parameter values are bound to differ in different countries with different cultural practices and contact matrices.Furthermore, the explanation for the difference between influenza and COVID is based primarily on differences in contact patterns. While the Discussion clarifies this to be based on the efficiency with which exposures lead to infections (and pre-symptomatic transmission), which does sound like a viral parameter, rather than a social one. These viral factors do seem more believable than having to explain why the patterns of social contact exhibited by influenza patients would differ from those of SARS-CoV-2 patients. More focus on possible mechanistic explanations is warranted.Thank you. It is a subtle point but we feel that the contact network parameters that differ between the two viruses are dictated less by social factors and more by critical differences in viral biology. First, both viruses spread in very similar populations. Moreover, as stated in the paper, if a SARS-CoV-2 infected person and an influenza infected person  are placed in an equivalent crowded and poorly ventilated room, the SARS-CoV-2 infected person will have the potential to have exposure contacts with a greater proportion of people with the virus. The most parsimonious explanation is aerosolization for SARS-CoV-2 being much more important than for influenza. We now conclude the paper with a conceptual figure  which illustrates this point.Title: "Contact heterogeneity" rather than "contact network" is more appropriate for the title as no network is considered.We absolutely agree. Thank you for this point.Revisions:A key definition in the work (as stated in the Discussion) is the exposure contact. However, apart from the parenthetical definition in the Discussion, exposure contact is not clearly defined. This is particularly important as estimates on the exposure contact seem to impact the estimates on the viral load to infectiousness functional relationship. In particular, infectiousness is defined "… as the viral load dependent probability of transmission given direct airway exposure to virus in an exposure contact." Why does the parameterization for this change so significantly pre and post lockdown when the main difference appears to be in exposure contacts?Thank you for pointing this omission out. We have now added the following definition: an exposure contact is a susceptible person who is exposed to a SARS-CoV-2 infected person for a sufficient period of time and at a close enough distance to allow for the possibility of a successful transmission. The probability of transmission then becomes dependent on viral load of the infected person. For this reason, the number of exposure contacts may go down under conditions of quarantine or self-isolation upon developing symptoms.The explanation given refers to "… more prolonged and intense exposure contacts…" This seems to imply an unequal reflection of what an "exposure contact" truly means. Furthermore, how does the "exposed contact rate" compare with exposure contacts.We emphasize that exposure contacts were likely different (longer and more intimate) prior to widespread knowledge of the virus in Wuhan allowing for lower viral load transmissions. We eliminate the paragraph with “exposed contact rate” as we agree this is confusing.Figure 10. This new figure is confusing. In the text, viral shedding kinetics are referred to as panel (A) and kinetics of infectivity as panel (B), but they are both part of panel (A) in the figure. What is different between the SARS-CoV-2 and Influenza schematics in panel (B). Panel (C) is referred to in the text but is not in the figure.We fixed the paper such that the text matches the paper.


Half of the specimens were contaminated with saliva and a disclosing silicone and then cleaned either using phosphoric acid, a pumice suspension, air-abrasion with alumina or polishing powder. Surface conditioning was performed by either using a dentin adhesive, a silane containing primer or a composite resin primer, which resulted in 24 unique combinations of 16 specimens per group. Before measuring TBS, half of the specimens of each group were stored in 37 °C water for 3d or were artificially aged for 150 days. Results show that cleaning with pumice or air-abrasion are superior methods compared to using a polishing powder or phosphoric acid. Silane is an inferior conditioning agent compared to composite or dentin primers. Ideally, after contamination, bonding surfaces should be cleaned with a pumice suspension and conditioned with a dentin adhesive. Those surfaces could also be cleaned and conditioned with air-abrasion with alumina particles and a composite resin primer.The objective of this study was to evaluate the effects of different cleaning and conditioning procedures after contamination on the tensile bond strength (TBS) of a luting resin to a core build-up composite resin. Specimens ( Fabrication of indirect dental restorations requires specific procedures including preparation of the tooth following explicit rules ,2,3. DueDuring preparation and try-in procedures of dental restorations, bonding surfaces, i.e., tooth structures, restoration materials and core build-up materials might be contaminated by saliva ,13, blooCleaning and conditioning of contaminated dental composite surfaces prior to adhesive cementation and the investigation of their influence to the bond strength was the objective of various previous studies ,31,32,33To the best knowledge of the authors, cleaning methods of core build-up materials prior to adhesive cementation have not been examined to date. Due to the fact that the ingredients of these build-up materials differ from the composition of other composites used for direct restorations , the best cleaning method after contamination may also differ. The differences in the composites arise from their type (light curing and/or auto curing) and main focus of either mechanical feasibility or aesthetics. The selected build-up materials in this study were light and auto curing and focus mainly on mechanical feasibility because they are covered by other restorative materials.Applicable cleaning methods might include using a pumice suspension with a rotating brush, intraoral airborne particle abrasion, using an air polishing powder or etching with phosphoric acid . ScenariTherefore, the purpose of this study was to investigate the influence of different cleaning and surface conditioning methods on the tensile bond strength of luting resins to a core build-up material after contamination with saliva and a disclosing silicone. This study was designed to test the null hypothesis, that the described (1) cleaning methods and (2) conditioning methods have no influence on the bond strength of a luting resin to a contaminated core build-up material and its durability.n = 384) of a core build-up material  were made. A 2-mL syringe was completely filled with the material of choice to create a composite cylinder with a diameter of 8.8 mm, which was sawed into 4 mm thick pieces after 10 min self-curing time. All specimens were wet polished with a rotating silicon carbide paper down to 600 grit and then stored for 3 weeks in 37 °C tap water to obtain water saturation and almost complete polymerization of the material.Disc-shaped specimens (n = 192) of the specimens were contaminated by placing them with their bonding surfaces facing down into human saliva for one minute. The other half of the specimens remained uncontaminated. The donor of the saliva refrained from eating and drinking for 1.5 h prior to sampling. The saliva was used within 60 min after harvesting. The saliva was then removed from the specimens by spraying water for 15 s and then air drying for 15 s using an air blower with oil-free air. Afterwards, the bonding surfaces were pressed into a disclosing silicone , removed after 5 min with any visible remnants manually detached.Half  was applied to the bonding surface. It was removed after 15 s by spraying water for 15 s and the bonding surface was dried with compressed, oil-free air for 15 s.Pumice powder was mixed with a 0.9% NaCl-solution and was applied onto the bonding surface using a rotating brush for 15 s at a rotation speed of 2000 rpm. Afterwards, the pumice suspension was removed by spraying water for 15 s and then the bonding surface was dried with oil-free compressed air for 15 s.Airborne particle abrasion: the bonding surface was marked with a red marker and then air-abraded with 50 µm alumina particles from a distance of 10 mm and a pressure of 0.5 bar until no colour remnants were visible. The remaining alumina particles were removed with spraying water for 15 s and then the bonding surface was dried with compressed, oil-free air for 15 s.The dry bonding surface was cleaned with a sodium bicarbonate prophylaxis spray  using an air polisher  from a distance of 10 mm and a pressure of 2.5 bar for 15 s. The remaining prophylaxis powder particles were removed by spraying water for 15 s and then the bonding surface was dried with compressed, oil-free air for 15 s.2  from a distance of 10 mm.A dentin adhesive , which is used as follows: The Optibond FL Primer was applied to the bonding surface using a disposable brush. After a dwell time of 30 s, the remaining liquid was removed by using an oil-free air blower for 15 s. Then, the Optibond FL Adhesive was applied using a disposable brush and was blown after 15 s using compressed, oil-free air for another 15 s. Afterwards, the adhesive was polymerized for 30 s with a dental curing light at a light intensity of 650 mW/cmA silane containing primer  was applied to the bonding surface using a disposable brush and after a dwell time of 60 s was dried with compressed, oil-free air for 15 s.2  from a distance of 10 mm.A composite resin primer  was applied using a disposable brush. After a dwell time of 60 s is was gently blown using compressed, oil-free air for 15 s and light-cured for 20 s using a dental curing light at a light intensity of 650 mW/cmThe specimens in each of the four surface cleaning subgroups were randomly assigned to three smaller groups of 16 specimens per group. A different primer was used on each of the three smaller groups, as specified by the manufacturer:2) were filled with dual-curing composite resin . After curing time (5 min), the filled tubes were bonded with a luting composite resin  to the core build-up composite surface using an alignment apparatus under a load of 750 g [2 , then further cured in a light-curing unit  for 90 s, placed at room temperature for 10 min, and then stored in 37 °C tap water after removing the air blocking gel with water spray for 15 s. With regard to contamination presence, the four surface cleaning methods and three surface conditioning primers resulted in 24 test groups. For each test group, 16 specimens were bonded. Half of these bonded specimens were stored in tap water (37 °C) for 3 days and the other half for 150 days with artificial aging, where water storage was interrupted by 37,500 thermal cycles (5 to 55 °C) with a dwell time of 30 s. Composition and batch numbers of the materials are shown in Plexiglas tubes with an inner diameter of 3.2 mm . After sputtering a conductive gold layer with a thickness of approximately 15 nm as measured with a quartz crystal film thickness monitor , three representative samples of each group were examined in a scanning electron microscope  with an acceleration voltage of 15 KeV.At the end of the storage periods, tensile bond strength (TBS) was measured in a universal testing apparatus  at a crosshead speed of 2 mm/min using a chain loop alignment which provided a moment-free axial load application. The fractured interfaces of the debonded specimens were examined using a light microscope  at 30× magnification to calculate the fracture mode of each specimen as either adhesive or cohesive . Fractional allocation measured in percentages of adhesive and cohesive failure mode was possible in case of a mixed adhesive and cohesive failure mode. The arithmetic mean of both failure modes was then determined for each subgroup  lower TBS was detected only in the groups that had been cleaned with air polishing powder and treated with a dentin primer or a composite primer when in a not contaminated environment and any primer in a contaminated environment  higher median TBS than air-polishing with prophylaxis powder, regardless of the contamination status. Moreover, cleaning the surface with phosphoric acid provided statistically significantly lower TBS than pumice suspension in a contaminated environment using a dentin primer. Phosphoric acid also led to a statistically significant lower TBS than air-abrasion in a contaminated environment using either dentin or silane primers. In fact, air abrasion exhibited the highest median TBS (16.2–19.9 MPa) in the contaminated subgroup regardless of the primer used  and scanning electron microscopy (SEM) are shown in Four common cleaning methods were chosen for investigation in this study ,15,41,42After long-term storage simulating the exposition in the oral environment, the bond strength decreased in 16 of the 24 test groups. This might be caused by water saturation and artificial aging, which leads to hydrolytic degradation of the used primers ,46. ConsOnly one statistically significant difference in median TBS between contaminated and not contaminated in the 24 subgroups (air polishing powder cleaning and silane conditioning) could be observed. This is a limitation of our study, which is mainly caused by the low level of significance. Nevertheless, in our application scenario, i.e., bonding after the removal of the provisional restoration, cleaning is always necessary, since the adhesive surface would be contaminated by biofilm and/or remnants of the provisional cement.Our findings compare and reproduce the results of other studies well, e.g., etching a ceramic bonding surface with phosphoric acid was not appropriate to remove remnants of a disclosing silicone and does not provide high TBS long-term. This can be explained by the inability of phosphoric acid to dissolve silicone oils , thus thThe observed lack of potential of a silane to promote sufficient bonding of a luting resin to the core build-up material stands in contrast to the findings in other studies investigating intra-oral repairing procedures of composite fillings . It mighTo sum up, the following conclusions can be drawn from our study: (1) Cleaning a core build-up material bonding surface after contamination with saliva and a disclosing silicone with alumina particle air-abrasion or a rotating brush with pumice suspension resulted in statistically significantly higher TBS than cleaning with an air-polishing device or etching with phosphoric acid. (2) In some cases, using a silane containing primer for conditioning the bonding surface of a core build-up material resulted in statistically significantly lower TBS compared to conditioning with a dentin primer or a composite resin primer. Due to the observed statistically significant differences, the null hypothesis cleaning and conditioning methods have no influence on bond strength, must be rejected. After contamination with saliva and a disclosing silicone, bonding surfaces of a core build-up material should be cleaned with a rotating brush and a pumice suspension and conditioned with a dentin adhesive, which is conveniently required for adhesive bonding to tooth structure nonetheless. Using a less practical way, these surfaces could also be cleaned and conditioned with intraoral air-abrasion with alumina particles and a composite resin primer.