Enterobacteriaceae. We use blaCTX-M (the most widespread ESBL gene family) and 16S rRNA  abundance data from 833 rectal swabs from 133 ESBL-positive patients followed up in a prospective cohort study in three European hospitals. We find that cefuroxime and ceftriaxone are associated with increased blaCTX-M abundance during treatment , while treatment with meropenem, piperacillin-tazobactam, and oral ciprofloxacin is associated with decreased blaCTX-M . The model predicts that typical antibiotic exposures can have substantial long-term effects on blaCTX-M carriage duration.Antibiotic-induced perturbation of the human gut flora is expected to play an important role in mediating the relationship between antibiotic use and the population prevalence of antibiotic resistance in bacteria, but little is known about how antibiotics affect within-host resistance dynamics. Here we develop a data-driven model of the within-host dynamics of extended-spectrum beta-lactamase (ESBL) producing  Bacteria that are resistant to antibiotics are a growing global health crisis. One type of antibiotic resistance arises when certain bacteria that can produce enzymes called extended-spectrum beta-lactamases (or ESBLs for short) become more common in the gut. These enzymes stop important antibiotics, like penicillin, from working. However, exactly which antibiotics and treatment durations contribute to the emergence of this antibiotic resistance remain unknown.Now, Niehus et al. find certain antibiotics that are associated with an increase in the number of gut bacteria carrying antibiotic resistance genes for ESBL enzymes. First, rectal swabs collected from 133 patients from three European hospitals were analysed to measure the total gut bacteria and the number of genes for ESBL enzymes. These samples had been collected at several time points including when the patient was first admitted to hospital, then every two to three days during their stay, and finally when they were discharged.Combining the analysis of the samples with details of the patients’ charts showed that treatment with two antibiotics: cefuroxime and ceftriaxone, was linked to an increase in ESBL genes in the gut bacteria. Other antibiotics – namely, meropenem, piperacillin-tazobactam and oral ciprofloxacin – were associated with a decrease in the number of bacteria with ESBL genes. Niehus et al. then performed further analysis to see if different treatment regimens affected how long patients were carrying gut bacteria with ESBL genes. This predicted that a longer course of meropenem, 14 days rather than 5 days, would shorten the length of time patients carried ESBL-resistant bacteria in their guts by 70%, although this effect will likely depend on the location of the hospital and the local prevalence of other types of antibiotic resistance.This analysis reveals new details about how antibiotic treatment can affect ESBL resistance genes. More studies are needed to understand how antibiotics affect other antibiotic resistance genes and how resistant bacteria spread. This will help scientists understand how much specific antibiotic regimens contribute to antibiotic resistance. It may also help scientists develop new antibiotic treatment strategies that reduce antibiotic resistance. Enterobacteriaceae amongst patients receiving cephalosporin-based prophylaxis prior to colorectal surgery is associated with a more than two-fold increase in risk of surgical site infection . Previously, using a subset of these data, Meletiadis et al. demonstrated a statistical association between exposure to ceftriaxone and increases in blaCTX-M normalised by total bacterial load. Here, we addressed some broader questions. We studied the effects of a range of different antibiotics on the abundance of blaCTX-M and of 16S rRNA, and we aimed to fully characterise the within and between host variation of blaCTX-M and 16S rRNA and their within-host dynamics. For this purpose we developed a novel dynamic model, a state-space model that we fit to fine-grained patient-level measurements and antibiotic exposure data. By incorporating hidden-state dynamics our model allowed us to dissect and quantify different types of data variability, such as noise from qPCR measurement or from the DNA extraction process, and to separate this from the within-host processes. In this way we directly estimated ecologically important parameters such as strength of resistance amplification during antibiotic treatment or the rate of decline of blaCTX-M. We then used our model to make counterfactual predictions about how alternative choices of treatment would impact blaCTX-M carriage duration. The development of this data-driven within-host model and its use in exploring the impact of antibiotic treatment on amplification and loss of resistance is an important step in furthering our quantitative mechanistic understanding of how antibiotic use drives changes in the prevalence of resistance in a population.In this work, we analysed sequential rectal swabs (n = 833) from 133 ESBL positive hospitalised patients from three hospitals  to study the dynamics of antibiotic resistance gene abundance. Both blaEnterobacteriaceae at admission, and 133 patients (12%) gave consent to be included in the study: 51 (38%) from Romania; 52 (39%) from Serbia; and 30 (23%) from Italy. The median age was 59 years (range of 23–88), and 46% were female. The median length of hospital stay was 15 days (maximum of 53 days). All patients apart from one had two or more rectal swabs taken, with a median of five swabs per patient (range of 1–15). 114 out of 133 (86%) enrolled patients received antibiotics during their stay and 85 of these 114 (75%) received two or more different antibiotics, which were given both in mono- and combination therapy , fluoroquinolones (18%), penicillins (9%), nitroimidazole derivatives (metronidazole) (9%), glycopeptides (8%), carbapenems (5%), and others (26%). Two thirds of antibiotic treatment days were from intravenously administered antibiotics and one third from oral administration. Details on individual antibiotics are given in CTX-M abundance exhibits a diverse range of dynamic patterns, including monotonic increases and decreases, as well as highly variable non-monotonic behaviour   see .CTX-M data in particular were found to also contain a strong process component signal, indicating that a median estimate of 36%  of the variability in the qPCR outcomes was due to underlying within-host dynamics  we found 16S rRNA gene abundance to be two orders of magnitude higher than blaCTX-M , with an estimated coefficient of variation (ratio of standard deviation to the mean) of 5.5 for 16S rRNA and 32.1 for blaCTX-M. Between-patient abundance of blaCTX-M showed substantially more variability than within-patient abundance . In contrast, 16S rRNA gene abundance had similar between-patient and within-patient variability   . We note.4,1.7])  indicateCTX-M abundance divided by 16S rRNA gene abundance, was only slightly elevated in time intervals where antibiotics were given compared to those where they were not  was associated with a modest decrease in blaCTX-M abundance  was associated with substantially higher increases in relative blaCTX-M abundance , we found that cefuroxime and ceftriaxone were associated with increases in both absolute blaCTX-M abundance  and relative blaCTX-M abundance   and 16S rRNA gene abundance , although uncertainty was large (CTX-M/16S rRNA) also appeared to be negative . Intravenously administered ciprofloxacin did not show these effects. Imipenem and meropenem had similar effects on blaCTX-M abundance, while no clear effects were evident for amikacin, metronidazole, and amoxicillin-clavulanic acid (Fitting a dynamic model of blactively) . Piperacas large . Their enic acid . The outnic acid  . Below this threshold the gene may either be lost from the bacterial community, or it may exist in very small reservoirs for example in persister cells  for cefuroxime and 120%  for ceftriaxone versus no exposure  , and each reduces blaCTX-M carriage even more relative to a 7 day course of combined ceftriaxone plus amikacin    . A typicctively) . Finally%, 89%]) .CTX-M resistance genes. The largest effects were found for exposures to the second and third generation cephalosporins, cefuroxime and ceftriaxone, both of which were associated with increases in blaCTX-M abundance. Forward simulations indicated that if these associations are causal, exposure to typical courses of these antibiotics would be expected to more than double the carriage duration of blaCTX-M. Both cefuroxime and ceftriaxone have broad-spectrum killing activity . This observation can be explained by a general reduction of bacterial biomass that leads the blaCTX-M abundance to drop below detection levels. In line with this, our simulations suggested that a typical course of meropenem or of piperacillin-tazobactam would reduce blaCTX-M carriage duration relative to no treatment by about 40%, and each course reduces blaCTX-M carriage duration by about 70% relative to a combined course of ceftriaxone plus amikacin. Pharmacokinetic models suggest that bacteriocidal serum concentrations of ceftriaxone persist for relatively short time periods after treatment , but it does not allow for more complex fitness effects due to, for example, synergies between antibiotics  project . The study enrolled adult (>18 y) inpatients of medical and surgical wards, excluding pregnant patients. Enrolled patients were screened at admission for carriage of ESBL-producing Enterobacteriaceae with rectal swabs . Patients who tested positive for ESBL producing Enterobacteriaceae carriage (details below) were included in the follow-up cohort. The target cohort size was calculated to be 400 patients, based on the number of patients that would be required to detect one log difference in resistance abundance with 90% certainty. However, patient recruitment we slower than anticipated. For all follow-up patients (n = 133) rectal swabs were taken every two to three days (as per study protocol) during hospitalisation, which includes one swab at admission and one at discharge. The swabs were stored at −80 degrees Celsius and sent to a central laboratory for processing. Using patient charts, the study also collected information on antibiotics treatment, including antibiotic type, duration, and route of administration. See Participants were recruited as part of an observational, prospective, cohort study that included three hospitals , with known high endemic prevalence of antibiotic resistance in bacterial infections. The hospitals were serving a general urban population. The study was conducted over two years from January 2011 to December 2012 as part of the multi-centre SATURN , replated on blood agar and incubated overnight in air. ESBL status was then confirmed with the double disk diffusion method according to CLSI guidelines . These methods were performed in the laboratories on the respective hospitals . The above methods are not specific to any single bacterial species, but instead identify ESBL producing specimens of the Enterobacteriaceae family, including Escherichia coli, Klebsiella pneumoniae, or Enterobacter cloacae and others. According to the standard definition by the Centers of Disease Control  to test for ESBL producing  Control , samplesCTX-M gene family with primers CTX-M-A6 (TGGTRAYRTGGMTBAARGGCA) and CTX-M-A8 (TGGGTRAARTARGTSACCAGAA)  and one targeting a conserved bacterial 16S rRNA gene region bacteria using the following primer set, 16S_E939F (GAATTGACGGGGGCCCGCACAAG) and 16S_1492R (TACGGYTACCTTGTTACGACTT)  to assess total bacterial quantity. Each singleplex qPCR run targeted either 16S gene or blaCTX-M, and included reaction tubes with negative controls and tubes containing a standard (16S or blaCTX-M depending on the run) of different concentrations (eight different dilutions), which also served as a positive control. For details on reaction mix and cycling conditions see DNA was extracted from the swab samples using QIAampDNA Stool Mini Kit (Qiagen) and a fixed volume (4 µl) of DNA solution was used as a template for quantitative PCR (qPCR) assays. Two singleplex qPCR assays were conducted, one to assess quantity of blaCTX-M and 16S rRNA. To get reliable estimates of autocorrelation, we selected only patients with more than five time points. Separately for the blaCTX-M and 16S rRNA gene data, we computed the first-order autocorrelation (disregarding varying spacing between time points) for each patient, and we averaged these values across the patients. We then simulated serially uncorrelated ‘white noise’ time series, again separately for blaCTX-M and 16S rRNA, with the same length as the patient data and with identical time series mean and variance. Similar to the real data, we computed mean autocorrelations for the simulated data and show their distribution for a large number of such simulations  together with the observed autocorrelation  for blarelation . We alsoCTX-M or 16S rRNA. The term CTX-M and 16S rRNA), +) for the the noise parameters CTX-M and 16S rRNA measurements. The posteriors of the three noise parameters are shown in To estimate the amount of observation noise and process noise in the time series we constructed a Bayesian state-space model that included qPCR noise, swab noise, and biological noise. This model is given through:CTX-M or 16S gene) and patient +). We fitted the model using Stan (v2.19.1), with 80,000 posterior draws after a burn-in period of 500 iterations. The R̂ statistic (<1.01) and MCMC rank histogram plots  as a marker of natural selection. First, we computed the changes in relative resistance for every pair of adjacent time points and for each antibiotic we used a binary variable indicating whether or not a given antibiotic was administered between these time points. When an antibiotic treatment was on the same day as a swab, this treatment was allocated to the time interval between this day and the next swab. We first looked at how changes in relative resistance are associated with courses of any antibiotics, then with courses of anti-enterbacteriaceae antibiotics to which carriage of blaCTX-M does not confer direct resistance , and finally with antibiotics that have a broad-spectrum activity and to which blaCTX-M does confer resistance . Results are shown in To study the association between antibiotic treatment and resistance we looked at relative abundance of resistance  from observation noise  and also to account for different spacing between measured time points.We extended previous modelling approaches to extracting ecological parameters from microbial ecosystem dynamics  by applyCTX-M and 16S rRNA measurements are expected to be proportional to the absolute abundance of the resistance gene and bacterial load respectively, the ratio blaCTX-M/16S is a measure of the relative abundance of the blaCTX-M gene in the gut microbiota. Positive or negative selective effects by antibiotics on blaCTX-M mediated resistance are expected to cause shifts in bacteria carrying blaCTX-M versus non-carriers. As a result they affect blaCTX-M/16S, but quantifying their effects on absolute blaCTX-M abundance is important for predicting extinction and persistence of the blaCTX-M gene. Under the assumption that 16S rRNA gene abundance is independent of antibiotic treatment, variation in 16S rRNA would be caused mainly by the swab procedure and DNA extraction (and other steps in the protocol), and it could be used to normalise blaCTX-M abundance. However, as we found in CTX-M/16S, from which the effects on blaCTX-M could then be computed.We analysed antibiotic treatment separately by type and route of administration, only including treatments that occurred in five or more patients (amoxicillin-clavulanic acid (iv), piperacillin-tazobactam (iv), cefuroxime (iv), ceftriaxone (iv), meropenem (iv), imipenem (iv), ciprofloxacin (iv), ciprofloxacin (or), amikacin (iv), metronidazole (iv)). Since the blaCTX-M or 16S rRNA, and CTX-M/16S rRNA) or 16S rRNA. Then, CTX-M or 16S rRNA). The qPCR standard deviation for sample CTX-M and to 16S, such that this error cancels out when computing the ratio of blaCTX-M and 16S rRNA CTX-M/16S ratio and of 16S are given through CTX-M/16S rRNA, on the log-scale. Exponentiating this variable gives the copy numbers (or copy number ratio) on the real scale. Therefore, summing all effects on the scale of Studying the standard deviation between qPCR measurement repeats as a function of the mean, we observed that qPCR variation remained relatively stable over five orders of magnitude of the mean measurement , but it increased quickly for lower magnitudes . In the rvations . The ecoCTX-M/16S rRNA to the parameters In this model, +), and on the antibiotic effects  that achieved the same median extinction time. We then used this model to repeatedly (2000 times) simulate blaCTX-M carriage durations, with each simulation using a new draw from the parameter posterior. The resulting distribution of carriage times contains both the uncertainty in the parameter estimates and uncertainty from the Markov process (CTX-M carriage durations repeatedly (200 times) with the same parameters, and taking the median carriage duration to remove Markov process uncertainty. We repeated this for 300 draws of parameters ; Lulla Opatowski (Reviewer #3).Thank you for submitting your article "Quantifying antibiotic impact on within-patient dynamics of extended-spectrum β-lactamase resistance" 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.Summary:CTX-M) and on the duration of carriage of resistance. Overall, the manuscript represents a potentially important contribution to the field. However, there is some T data overlap in a previous study by the authors. and we have identified a few essential revisions to resolve this issue and that need to be addressed as well.This work asks what the dynamic consequences of antibiotic perturbations are to the human gut flora, and addresses this question with a novel mathematical model that is fit to data. The authors use the model to estimate the impact of antibiotic treatment on the abundance of resistance  variability, Figures 1, 2; (2) effect of treatment on relative resistance; (3) effect of treatment on abundance and (4) the model with fitting, and consequently the estimates re duration of carriage. Each has limitations.Please set these results, with the novelty and limitations, in context.The modelling is clearly new and I think it is important. In that direction, the clarity should be improved. I particularly noted the very long paragraph in subsection “Dynamic within-host model”, and the benefit that explaining the model and its assumptions (and limitations) for non-statisticians, non-modellers would likely bring.Please clarify re interpretation of 16s, abundance as noted by reviewers.Please improve Figures 1 and 2. While "hieroglyphics" may go a bit far, I agree that Figure 1 is visually striking but hard to see and interpret. Perhaps in Figure 1 you could group the time curves by similarity, or present an expanded view of some representative curves, with the full set in an appendix. Or something like that.Reviewer #1:Enterobacteriaceae and the blaCTX family of resistance genes, that are the focus of their study.The motivation for this study and its goals are certainly important and timely. The collateral effects of antibiotic treatment on the distribution of bacteria species and strains in the enteric flora (microbiome) of humans and the incidence of antibiotic resistance genes and resistance encoding plasmids are subjects of considerable importance, epidemiologically as well as clinically. In their Introduction, the authors do a fine job of presenting this and particularly so for the extended-spectrum β-lactamase (ESBL)-producing CRX-M genes, their study is correlative rather than mechanistic. From the results presented, it's not clear how the different modes and purpose of treatment the within-patient dynamics ESBL resistance. From what they say in their Discussion, the authors are very much aware of this limitation of this study.This investigation certainly has the virtue of being extensive; fecal samples were taken sequentially from 133 hospitalized patients from three countries  for a median of five samples from each patient. This virtue does have a downside; these patients were hospitalized for different reasons and treated orally of intravenously with 10 different antibiotics. The results presented suggest that some antibiotics are more likely than others to affect the distribution and abundance of these enteric bacteria and the blaCRX-M genes could have epidemiological and clinical implication, they don't really consider these potential implications of their results. It's also not clear how this information can be used. Presumably the choice of antibiotics, mode and frequency of administration in these hospitalized patients is based on the nature of the infection and information about the susceptibility of the target bacteria to the drugs employed for treatment. While in an ideal world, consideration should to the collateral effects of treatment; we are far from that ideal world. This is particularly so for hospitalized patients. It would be of some interest to do an analogous study for the treatment of community-acquired infections (about 90% of antibiotic use in humans), where information about the collateral effects of treatment with different antibiotics may have broader and implications that could be implemented.Although their observation some antibiotics are more likely than others to affect the enteric microbiome and the frequency of blaReviewer #2:CTX-M. The gene increase is then normalized to 16s abundance for overall internal QC for extraction efficiency and bacterial content. The work appears to be very well done and the methods and approach to analysis appear to support the conclusions of the authors. The figures are easy to interpret and additive to the manuscript . Also the discussion includes a portion focused on relative biomass and bacterial presence in the gut of patients on antimicrobials but it is not clear they would be able to infer this information from the variability in swab collection and extraction efficiency. Would favor revising the discussion to address this issue.This is an interesting and important piece of work demonstrating the correlation between certain antimicrobial exposure in patients and associated increase in abundance of blaCTX-M abundance with 16s. I think it would be helpful for the authors to further explain how this differs from prior work and is additive to the literature. The conclusions from that manuscript were almost identical as well. Would favor a re-write to address this existing work and contrast the submitted manuscript.One concern is the amount this manuscript overlaps with another previously published manuscript . The authors state in the Introduction section that they used a subset of the data but in reviewing the other manuscript it appears that there are 133 patients in this analysis and 122 from the other description. They also used very similar methodology by normalizing the blaReviewer #3:Enterobacteriaceae carriers. The data comes from 133 patients from Romania, Serbia and Italy and consists in longitudinal series of rectal swabs (with a median of 5 swabs per patient). Swabs were analysed in order to quantify the abundance of blaCTX-M and 16SrRNA. For both indicators, the within- and between-host variabilities, and the within-host dynamics are analysed using Bayesian state-space models. Parameters associated to the selection exerted by different classes of antibiotics on the dynamics of blaCTX-M, 16SrRNA and blaCTX-M/16SrRNA are estimated.Niehus and colleagues present an original study in which they investigate the impact of different antibiotics on ESBL resistance within-host in This is a very exciting and well written article. This is, to my knowledge, the first study attempting to analyse, using hypothesis-driven mechanistic models, the dynamics of within-host resistance genes and the impact of various classes of antibiotics. I believe that the study and results presented here represent a major contribution to the field.CTX-M/16SrRNA would more or less be interpreted as a decrease in resistance rate. However, it is not clear to me how important is the contribution of Enterobacteriaceae species to the global quantity of 16SrRNA.– It would be good for the reader to detail and clarify how each indicator should be interpreted. My understanding is for example that a decrease in bla– My main comment is related to the simulation study. It would be good to provide a validation of the model before running simulations, for example by doing out of fit assessment? Is that possible? Would you have enough power?– It would be interesting to estimate whether the antibiotic classes have different delays of impact.– I am not an expert but my understanding is that the aptitude to induce degradation of the flora may not be fully characterized by the "broad spectrum" characteristic. Other characteristics define whether the antibiotic destroys or not non-pathogenic (and anaerobic?) bacteria. It would be good to discuss more that aspect.eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Wendy Garrett as the Senior Editor.Thank you for submitting your article "Quantifying antibiotic impact on within-patient dynamics of extended-spectrum β-lactamase resistance" 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.Summary:Enterobacteriaceae conferred by extended-spectrum β-lactamase (ESBL) production. Such ESBL-producing organisms are responsible for a high and increasing burden of disease globally. The authors aimed to determine the effects of typical antimicrobial exposures in hospitalized patients on the dynamics of resistance gene abundance and total bacterial load. The authors asked: if such effects exist, how do they vary between different treatment regimens, and what is the predicted impact of antibiotic exposure on persistence of patient colonization with ESBL-producing organisms?Antimicrobial resistance is recognised by the World Health Organization and others as one of the most pressing threats to global health. However, our understanding of the ecological processes that link antimicrobial use to the emergence and spread of resistance is limited. In particular , a quantitative understanding of the within-host processes relating clinical antibiotic exposure to within-patient resistance abundance is lacking. For bacteria that colonize the human gut, such within-host processes are likely to play a major role in mediating the relationship between antimicrobial use and the prevalence of resistance in the wider population. This paper focuses on resistance in Essential revisions:The manuscript remains of high interest because of the conceptual innovation of this work related to the model.However, reviewers still do not fully understand the dynamical model, which in our view was one of the key points of novelty of this work because it makes the link between mechanistic processes and the noisy observations. Accordingly, further revisions of this section should be completed before the paper can be accepted for publication.It would be helpful to link the data to the notation so we know which symbols correspond to data. For clarity, even after doing this please note which are the hidden-state variables .Technically, the notation a, b refers to a closed interval so infinity should not be listed in this form. We would suggest simply the phrase "improper uniform prior".Equation 4 and text: it appears that you haven't "added them on a log scale" because the sum that you have computed goes into the mean of the normal distributions in Equation 4.t=1 = xt=1a0c1c2 have to do with any of the terms in (4) ? The text in remains inscrutable compared to the equations and notation. There is mention of the model "looping" through time points – how does this work? Please expand the text and be explicit about the likelihood at each stage, what the looping steps are, whether the terms are added to the mean or are multiplicative, and so on. Please define xi,j,g=ratio, xi,j,g=16S and all notation.What does xWhen you say that you "fitted the model with STAN" please give the posterior decomposition, and the likelihood. State which parameters are estimated. The notation of the model should be connected to Figure 4 and 5 where the model's inferences are shown.Despite the response to review where you agree that it will be difficult to assess "abundance", you then include the following statement. It seems that the authors agree with the limitations around abundance in the response to review but then do not fully incorporate into the discussion. Please consider revising. Example: “Surprisingly, despite the relatively broad antibacterial spectrum of cefuroxime and ceftriaxone, there was no evidence that exposure to these antibiotics reduced 16S rRNA abundance .” Essential revisions:The reviewers brought up the previous publication, Meletiadis, J. et al., 2017, in which a good portion of the cases were reported, and where some of the same conclusions were reached.The authors should clarify the novel contributions of this work in light of the previous manuscript. It seems that the key claimed results here are (1) variability, Figures 1, 2; (2) effect of treatment on relative resistance; (3) effect of treatment on abundance and (4) the model with fitting, and consequently the estimates re duration of carriage. Each has limitations.Please set these results, with the novelty and limitations, in context.CTX-M normalised by total bacteria? Our study addresses a set of related but different questions and makes use of the full, uncollapsed data. First we are concerned with the differential effects of a range of antibiotics (not only one) both on blaCTX-M and on total bacterial load. Second, our focus is on developing a mechanistic understanding of the impact of antibiotics on the dynamics of both blaCTX-M and 16S rRNA, rather than considering static statistical associations. Third, through formulating and fitting a dynamic model to the data we aimed to estimate ecologically important parameters  which are of interest in themselves. Finally, we sought to make predictions about the impact of different antibiotic treatment regimens on persistence of blaCTX-M. We have now changed the final paragraph of our Introduction to clarify how our goals are different from the previous work. We also added a new section in the Discussion that highlights the methodological novelties and advantages where we previously only stated the limitations.This is an important point. As we mention in our Introduction, a subset of the data we use were also used in a publication by Meletiadis et al., in 2017. Their work used standard statistical methods  on collapsed data (each patient’s time series was converted to a zero or one) to ask, is there a statistical association between ceftriaxone treatment and blaThe modelling is clearly new and I think it is important. In that direction, the clarity should be improved. I particularly noted the very long paragraph in subsection “Dynamic within-host model”, and the benefit that explaining the model and its assumptions (and limitations) for non-statisticians, non-modellers would likely bring.We very much agree with this point. In response, we have rewritten the “Dynamic within-host model” section by breaking it into clearer sections, and by more clearly explaining the novelty of the model and more clearly listing the model assumptions. We also make sure to use language that is accessible for non-statisticians and by explaining some mathematical terminology.Please clarify re interpretation of 16s, abundance as noted by reviewers.As noted by reviewer 1, it would be interesting to validate 16S as an appropriate measure of cell density. Our study did not include an internal validation of the 16S rRNA measurements, however other studies have previously shown the ability of qPCR to quantify bacterial amounts in fecal samples . We now mention this previous validation and the limitation that we do not include an internal validation in the “Quantitative PCR” section. We further added in our Discussion a comment on newer methods that quantify specifically viable bacterial cells , which may guide future studies like ours.Related to this, reviewer 2 notes that it may be difficult to draw conclusions about antibiotic effects on 16S rRNA given variability in DNA extraction efficiency. We agree that the variability of DNA extraction is expected to be large. Therefore our model was specifically designed to directly estimate variability that affects repeat measurements (through noise in the swab efficiency and DNA extraction) and to separate this variability from real effects on the underlying true 16S rRNA quantity. We realised that in our previous manuscript we only mentioned the swab procedure as a source of noise affecting the DNA concentration in the qPCR machine. We correct this now by being more explicit in various places for our new manuscript:· “noise through the swab process and gene extraction”· “observation noise ”· “variation in 16S rRNA would be caused mainly by the swab procedure and DNA extraction (and other steps in the protocol)”· “error introduced by the swab procedure, DNA extraction etc.”· “noise from qPCR measurement or from the DNA extraction process” “observation uncertainty – for instance through the swab procedure, DNA extraction, or qPCR process”Reviewer 2 also states that one would anticipate antibiotics to cause shifts in relative species abundances as opposed to reductions in overall cell numbers. We fully share this expectation, in particular for antibiotics that target only a small subset of species present in the microbiome. Indeed, almost all of our inferred 16S effects support this hypothesis. We now take this up more explicitly in our Discussion by saying: The stable 16S abundance could be due to rapid overgrowth of non-susceptible strains which replace bacteria killed by these antibiotics , leading to shifts in species composition rather than total species abundance.Please improve Figures 1 and 2. While "hieroglyphics" may go a bit far, I agree that Figure 1 is visually striking but hard to see and interpret. Perhaps in Figure 1 you could group the time curves by similarity, or present an expanded view of some representative curves, with the full set in an appendix. Or something like that.We have now changed Figures 1 and 2 to make them easier to interpret. In particular, we followed reviewer 1’s comment on Figure 2 and made it more concise by removing the additional bars of the lower part that indicated between and within patient time series variability. They were confusing as they showed a variability on the same scale as the sequence abundance, and between and within patient variability can easily be summarised and reported as numbers, which is what we do now.For Figure 1, we have now produced a new version that contains a random selection of 50% of the study patients only. We additionally improved the previous figure with all patients by choosing a colourblind-friendly colour scheme and by increasing the plot area so that gene dynamics can be seen more clearly. We do agree with reviewer 2 (“this figure is interesting to look at the variability across the data set and would favor keeping”) that this version of the figure is a nice way to show the entire dataset. As reviewer 1 mentions, through eyeballing it appears impossible to spot waxing and waning in response to antibiotic treatment or to spot differences between treated and untreated patients, and this is exactly the purpose of this figure i.e. to show how noise is dominating the dynamics. Because we think that both are effective ways of presenting the data we attach both versions of Figure 1 for the editor to make the final decision on which one to include should the manuscript be accepted.CTX-M resistance gene abundance across patients. For visualisation purpose, a subset of 49 patients was selected uniformly at random from all 132 patients with more than one rectal swab. The x-axis scale is identical across panels, the length of one week is given for scale in the top-left corner. Timelines are ordered by length. The y-axis scale differs between panels, with the space between vertical grey lines representing a 10-fold change in the absolute blaCTX-M gene abundance (measured in copy numbers). The left-hand side shows patients who received antibiotic treatment (n=113), and the two right-hand side columns are patients without antibiotic treatment (n=19). For clarity, we show only the twelve most frequently used antibiotics in distinct colours and other antibiotics in light grey.”For ‘Figure 1 alternative’ the adjusted caption is: “Time series plots demonstrating the diverse range of dynamical patterns of blaReviewer #1:The motivation for this study and its goals are certainly important and timely. The collateral effects of antibiotic treatment on the distribution of bacteria species and strains in the enteric flora (microbiome) of humans and the incidence of antibiotic resistance genes and resistance encoding plasmids are subjects of considerable importance, epidemiologically as well as clinically. In their Introduction, the authors do a fine job of presenting this and particularly so for the extended-spectrum β-lactamase (ESBL)-producing Enterobacteriacae and the blaCTX family of resistance genes, that are the focus of their study.CRX-M genes, their study is correlative rather than mechanistic. From the results presented, it's not clear how the different modes and purpose of treatment the within-patient dynamics ESBL resistance. From what they say in their Discussion, the authors are very much aware of this limitation of this study.This investigation certainly has the virtue of being extensive; fecal samples were taken sequentially from 133 hospitalized patients from three countries  for a median of five samples from each patient. This virtue does have a downside; these patients were hospitalized for different reasons and treated orally of intravenously with 10 different antibiotics. The results presented suggest that some antibiotics are more likely than others to affect the distribution and abundance of these enteric bacteria and the blaIndeed, we are aware of the limitations of our observational data regarding causal conclusions, including a potential for selection bias and confounding through patient differences that affect both the treatment decision and resistance dynamics, as well as time-varying confounding. We now add a statement on this in the Discussion.CRX-M genes could have epidemiological and clinical implication, they don't really consider these potential implications of their results. It's also not clear how this information can be used.Although their observation some antibiotics are more likely than others to affect the enteric microbiome and the frequency of blaCTX-M dynamics.Even though our Figure 5 shows the impacts of different alternative antibiotic choices on the duration of resistance carriage  we refrain from drawing any clinical conclusions or recommendations from our results. This is because: 1) our study only looks at the effects on a single type of resistance; and 2) it is not clear what the individual patient or the community effects are of the observed changes to the blaPresumably the choice of antibiotics, mode and frequency of administration in these hospitalized patients is based on the nature of the infection and information about the susceptibility of the target bacteria to the drugs employed for treatment. While in an ideal world, consideration should to the collateral effects of treatment; we are far from that ideal world. This is particularly so for hospitalized patients. It would be of some interest to do an analogous study for the treatment of community-acquired infections (about 90% of antibiotic use in humans), where information about the collateral effects of treatment with different antibiotics may have broader and implications that could be implemented.This point is well taken and the obvious reason for studying hospital patients is the relative ease of following up patients. There are however existing studies on the way that explore similar questions in a community setting.Reviewer #2:CTX-M. The gene increase is then normalized to 16s abundance for overall internal QC for extraction efficiency and bacterial content. The work appears to be very well done and the methods and approach to analysis appear to support the conclusions of the authors. The figures are easy to interpret and additive to the manuscript . Also the discussion includes a portion focused on relative biomass and bacterial presence in the gut of patients on antimicrobials but it is not clear they would be able to infer this information from the variability in swab collection and extraction efficiency. Would favor revising the discussion to address this issue.This is an interesting and important piece of work demonstrating the correlation between certain antimicrobial exposure in patients and associated increase in abundance of blaCTX-M abundance with 16s. I think it would be helpful for the authors to further explain how this differs from prior work and is additive to the literature. The conclusions from that manuscript were almost identical as well. Would favor a re-write to address this existing work and contrast the submitted manuscript.One concern is the amount this manuscript overlaps with another previously published manuscript (1). The authors state in the Introduction section that they used a subset of the data but in reviewing the other manuscript it appears that there are 133 patients in this analysis and 122 from the other description. They also used very similar methodology by normalizing the blaThis is the first major point raised by the editor and we addressed this above.Reviewer #3:CTX-M and 16SrRNA. For both indicators, the within- and between-host variabilities, and the within-host dynamics are analysed using Bayesian state-space models. Parameters associated to the selection exerted by different classes of antibiotics on the dynamics of blaCTX-M, 16SrRNA and blaCTX-M/16SrRNA are estimated.Niehus and colleagues present an original study in which they investigate the impact of different antibiotics on ESBL resistance within-host in Enterobacteriaceae carriers. The data comes from 133 patients from Romania, Serbia and Italy and consists in longitudinal series of rectal swabs (with a median of 5 swabs per patient). Swabs were analysed in order to quantify the abundance of blaThis is a very exciting and well written article. This is, to my knowledge, the first study attempting to analyse, using hypothesis-driven mechanistic models, the dynamics of within-host resistance genes and the impact of various classes of antibiotics. I believe that the study and results presented here represent a major contribution to the field.CTX-M/16SrRNA would more or less be interpreted as a decrease in resistance rate. However, it is not clear to me how important is the contribution of Enterobacteriaceae species to the global quantity of 16SrRNA.– It would be good for the reader to detail and clarify how each indicator should be interpreted. My understanding is for example that a decrease in blaCTX-M / 16S rRNA. As the reviewer suggests, we expect the blaCTX-M / 16S rRNA ratio to be approximately propotional to the fraction of bacterial cells harbouring the blaCTX-M gene. We then model antibiotic effects on the ratio blaCTX-M / 16S rRNA (representing effects on the relative fitness of blaCTX-M carriers) and effects on the total bacterial abundance as approximated by 16S. But our model also gives the resulting dynamics of absolute blaCTX-M abundance, which is important for predicting its extinction and persistence.This is an interesting and important point, and our previous version of the manuscript was not clear enough about why we chose to model effects on bla– My main comment is related to the simulation study. It would be good to provide a validation of the model before running simulations, for example by doing out of fit assessment? Is that possible? Would you have enough power?We fully agree that model validation is important. In our study we therefore performed model comparison in which we compared our model with a model without antibiotic effects – we find that the data supports effects of antibiotics. For this we now performed a leave-one-out cross-validation approximation. We approximate because the number of patients treated with the same antibiotics is small. We have revised the manuscript so that we explain this more clearly now.– It would be interesting to estimate whether the antibiotic classes have different delays of impact.This is an interesting point that is on our (long) list of possible extensions to our model. However, while interesting, we consider this to be outside the focus of this study and leave this question to further work on this and similar datasets.– I am not an expert but my understanding is that the aptitude to induce degradation of the flora may not be fully characterized by the "broad spectrum" characteristic. Other characteristics define whether the antibiotic destroys or not non-pathogenic (and anaerobic?) bacteria. It would be good to discuss more that aspect.This a great point: the term “broad”-spectrum describes an antibiotic’s killing-spectrum in terms of known clinical pathogens. This may or may not be related to the extent of impact on the entire microbiota biomass. We now clarify this in the Discussion (first paragraph).Essential revisions:The manuscript remains of high interest because of the conceptual innovation of this work related to the model.However, reviewers still do not fully understand the dynamical model, which in our view was one of the key points of novelty of this work because it makes the link between mechanistic processes and the noisy observations. Accordingly, further revisions of this section should be completed before the paper can be accepted for publication.It would be helpful to link the data to the notation so we know which symbols correspond to data. For clarity, even after doing this please note which are the hidden-state variables .We have rewritten major parts of the dynamic model description. First, we have explained all symbols in Equation 4 within the paragraph below (previously symbols where explained in two separate parts). Second, we have included a new paragraph that lists all the symbols that represent data, and all the symbols representing estimated variables. We have been explicit about which symbols denote hidden-state variables.Technically, the notation a, b refers to a closed interval so infinity should not be listed in this form. We would suggest simply the phrase "improper uniform prior".This is a good point, and we now use the phrase “improper uniform prior” instead.Equation 4 and text: it appears that you haven't "added them on a log scale" because the sum that you have computed goes into the mean of the normal distributions in Equation 4.t=1 = xt=1a0c1c2 have to do with any of the terms in (4) ? The text in remains inscrutable compared to the equations and notation. There is mention of the model "looping" through time points – how does this work? Please expand the text and be explicit about the likelihood at each stage, what the looping steps are, whether the terms are added to the mean or are multiplicative, and so on. Please define xi,j,g=ratio, xi,j,g=16S and all notation.What does xf(abx) work, and we explain each likelihood term in our model, and also give the full posterior distribution (see below). Finally, we have defined all notation, including xi,j,g=ratio, xi,j,g=16S.We agree that this part was not well explained. In response, we have substantially changed this part of our model description. The new description is explicit about which parameters are on a log-scale, and what the real scale reflects (gene copy numbers). To clarify the way antibiotic effects work, we give a numerical example, in which we used the same symbols as our Equation 4, instead of introducing new ones. We have also clarified how the sums of the definition of When you say that you "fitted the model with STAN" please give the posterior decomposition, and the likelihood. State which parameters are estimated. The notation of the model should be connected to Figure 4 and 5 where the model's inferences are shown.https://arxiv.org/pdf/1510.04163.pdf) refer to as posterior decomposition, we now (in addition to Equation 4) give the full posterior distribution as the product of different terms (Equation 5). But in addition we also explain our three different likelihoods in terms of decomposing the total amount of variation in the data into variation attributable to different processes. We finally also added the marginal posterior distributions (together with prior distributions) of the antibiotic effect parameters  as an additional Supplementary Figure. To address the second point, we now explicitly list all parameters of Equation 4 that are estimated. Finally, to link methods to figures, we added an explanation of how the posterior draws of cz,b were transformed before plotting them in Figure 4. We added a reference to Figure 4 at that point, and we added two references to Figure 5 into the description of the posterior predictions.It was not entirely clear to us what was meant by ‘posterior decomposition’. Based on what Neiswanger et al. .”Based on this comment, we have now decided to remove any interpretation of antibiotic effects on 16S gene abundance. We think that this also helps the discussion be more focused on the CTX-M resistance gene.


High-performance self-consolidating concrete is one of the most promising developments in the construction industry. Nowadays, concrete designers and ready-mix companies are seeking optimum concrete in terms of environmental impact, cost, mechanical performance, as well as fresh-state properties. This can be achieved by considering the mentioned parameters simultaneously; typically, by integrating conventional concrete systems with different types of high-performance waste mineral admixtures  and ultra-high range plasticizers. In this study, fresh-state properties , hardened-state properties , and cost of high-performance self-consolidating concrete (HPSCC) prepared with waste mineral admixtures, were examined and compared with three different reference mixes, including normal strength-vibrated concrete (NSVC), high-strength self-compacted concrete (HSSCC), and high-performance highly-viscous concrete (HPVC). Then, a multi parameter analytical approach was considered to identify the optimum concrete mix in terms of cost, workability, strength, and durability. Self-consolidating concrete (SCC), also referred to as self-compacted concrete, is an innovative construction material with favorable rheological behavior that does not require vibration for placing and compaction. It can flow under its weight, filling in formworks, and achieving full compaction, even in the presence of complex-shaped concrete members with highly congested reinforcement ,2,3,4. BHigh-performance concrete (HPC) is engineered to meet specific needs of a project, including mechanical, durability, or constructability properties. The demand for HPC has been continuously increasing due to its superior mechanical and durability properties ,15. WhenAbundant research can be found in the literature on the properties of SCC. Most of the previous works have tested the fresh SCC mixes for common workability tests in order to prove self-consolidation of the concrete. The investigated properties were flowability, deformability and passing-ability, through slump-cone flow, J-Ring, V-funnel, and L-box tests ,28,29. TA study reported that the elastic modulus, creep and shrinkage of SCC did not differ significantly from the corresponding properties of normal strength concrete (NSC) . Some ofRegarding the novelty of this work, it can be clearly seen in the literature that the HPSCC has been investigated in the past two decades, but its practical application is still limited. This is due to the fact that its consolidated technical performance of HPSCC  is not fully understood, and there are often insufficient statements concerning its exact overall behavior. Existing research provides information only about the improvements in the properties of HPSCC mixes through the variation in the composition or addition of materials, but it does not inform what will happen to other parameters such as its consolidated economic and engineering performance. In the view of this understanding, this research was designed to present a comprehensive study regarding HPSCC’s overall properties and comparing with three common reference concrete types, i.e., normal strength-vibrated concrete (NSVC), high-strength self-compacted concrete (HSSCC), and high-performance highly-viscous concrete (HPVC). The individual comparisons are based on the strength, workability and durability through 14 different types of tests. A new analytical approach has been proposed for a multi-parameter comparison between different types of concrete.2 particles (up to 93.1%). The fly ash was type F, primarily consisting of silica, alumina, iron, and calcium oxides. The chemical and the physical properties of binders are shown in The materials used for the concrete mixes were cement, silica fume, fly ash, fine and coarse aggregates, water, and superplasticizer. The cement was ordinary Portland cement type CEM-I 42.5R  was recorded and when the concrete flow is stopped, the diameter of the spread at right angles is then measured and the mean is the slump flow . The data for economic assessment considerably vary between regions. This is because local conditions highly affect the cost of labor, and the market costs for recovered materials, as well as the transportation scenarios. In this study, the most probable case scenario for the city center  was considered to estimate the cost of the concrete mixes. The distance between the concrete plant and the raw materials, namely cement and aggregates was 184 km and 110 km, respectively. Besides, the other raw materials are imported from Turkey.The settlement time of NSVC was different from that of HPVC since the latter had contained the superplasticizer; it outspread at a slower rate until 8 s after the lifting of the cone, while NSVC was stable within 3 s. The slump test results were shown in The slump flow test results were shown in The flow of 740–900 mm is used as a conformity limit for highly congested structures, and the conformity criteria of 2–5 s is used for the T500 when the improvement of segregation-resistance is necessary. T500 of less than 2 s is applied for very congested structures, better surface finishing, and risk of bleeding or segregation. From a practical point of view, increasing the initial flow head can also increase the flow energy necessary to transport coarse aggregate ,56. The Results of the J-Ring test were shown in Concrete density is an important property that is used in the design of concrete-structures through calculating the self-weight of the members. Results of the water absorption test are shown in The compressive strength results shown in No considerable differences were observed at 180-days. The water/binder ratio has a great influence on the compressive strength of SCC and VC, whereas, the subject is still controversial and the authors got different conclusions. Some studies on the mechanical behavior of SCC showed that for the same w/b ratio, SCC has generally lower mechanical strengths than traditional vibrated concrete . HoweverThe average results of three 100 mm × 200 mm cylinders that tested for splitting tensile strength are shown in The average test results of three 75 mm × 75 mm × 350 mm prisms at age of 90 days were presented in The response of the concrete against stresses is different for the four mixes; when the strength of the concrete is higher, the strains were smaller for the same load level. Stress-strain relationships for the concrete mixes were shown in f′c was 4.49 and 4.27 for HPSCC and NSVC mixes respectively. The recorded ratio by [Determination of modulus of elasticity was based on the 40% of ultimate load and 0.000050 strain level. The obtained results were arranged in ratio by  was 4.20ratio by . Researcratio by ; other aratio by , while iratio by .Concrete is a composite material that derives properties from its multiphase and multi-scale ingredients. These ingredients are thermally inconsistent and during fire conditions, start to dissociate, leading to degradation in its strength and durability. Although, the behavior of HPSCC subjected to fire has not been extensively studied and thus remains largely unknown . The heaWhen concrete is exposed to a gradually increased temperature, the heat was transferred from the outer face of the concrete to its core, this process requiring less time for NSC and more time for HPC. The weak point of HSC classes is in that the heat was restricted by the dense microstructure, which leads to an explosion and spalling of concrete corners due to pore pressure, but the root cause of the failure was the cracking of concrete due to thermal tensile stresses, and the specimens with higher tensile strength can resist more pore pressure and spalling stresses. The relatively loose microstructure of NSC leads to absorption of heat to the concrete core and disintegrating its structure mainly due to the pore pressure build-up and the development of thermal stresses. Gravel particles can easily pull out and the burned paste is similar to dust, crushable with fingers, as shown in Subjecting of concrete specimens directly to the fire is different from oven heating, regarding the distribution of the heat around concrete faces. Fire test results on +500 °C for 45 min were shown in The test results of the freeze-thaw cycles were evaluated through the mass loss of concrete and the residual compressive strength, as presented in The changes in concrete surface and the corresponding number of freeze-thaw cycles were shown in Water absorption is a key parameter in the investigation of the durability of concrete. Because of its low w/b ratio of 0.20–0.45, it is widely believed that HPSCC should be highly resistant to both scaling and physical breakup due to freezing and thawing. Research showed that non-air-entrained HPC with w/b 0.22–0.31 could be extremely resistant to freeze-thaw damage and it was suggested that air-entrainment and supplementary cementitious materials are not needed. Among six mixtures tested; only the silica fume concrete with w/b 0.22 was frost resistant . The wei3 and the aggregate particles appeared in early stages on the entire surface of the concrete; in HSSCC, initially, the first layer of concrete surface wore at earlier stages, but later, the degradation of the concrete surface was almost stopped or it was wearing very slowly so that the total weight loss after 50 cycles reached 27.2 kg/m3; both HPSCC and HPVC mixes were durable, showed no scaling, and a negligible loss of weight by having 0.387 and 0.286 kg/m3 respectively, as shown in 3 after 50 cycles is recommended for HPC. Rating of specimens was performed as in ASTM C672 [The concrete specimens were exposed to 50 cycles of freezing and thawing. NSVC lose weight of 372.6 kg/mSTM C672 . Scale r2 is obtained after 40 cycles of scaling, for 3 concrete types with an air content of 2, 4, and 6% and w/c ratio of 0.25–0.50.When comparing the scaling test results and the results of freezing and thawing test cubes, NSVC had lost 15.8% of its weight in the scaling test, but the loss was 82.6% in concrete cubes. This difference can be justified by the that the cubes were entirely submerged in water and attacked all sides, but the scaling pans were exposed to freeze-thaw cycles only at the top surface. Gagne et al. tested 27 mixes using silica fume with w/b of 0.23, 0.26, and 0.30, and a wide range of air–void systems. All specimens performed exceptionally well in salt scaling, confirming the durability of HPC. Also in [An increased upfront cost must be expected for HPSCC, especially for better performance, enhanced quality control, along with better-quality formwork to withstand higher pressures. On the other hand, the incorporation of industry by-products increased productivity, reduced labor, and energy consumption, and fewer post-construction repairs can balance the final cost. The HPSCC is much more economical in certain applications on a basis of the original cost, and also in a point of view the durable upkeep, and more ecological than the usual concrete. Moreover, the lifespan of HPSCC is estimated at two or three times than on a usual concrete. The major obstacles that prevent a wider implementation of HPSCC in construction are its high cost (+25 to 50%), and the lack of knowledge of the properties. In 3 of concrete. The average results of multi-parameter assessment scale (MPAS) are showing that the HPSCC is the best option (MPAS = 196.6) in selecting a mix among the tested classes  can hardly or never be found. Therefore, it is of great interest to investigate such a type of comprehensive experiments. Based on the results and discussion, the following conclusions can be drawn:3 of concrete. Therefore, further studies on the mentioned parameters must be done by considering the real structural application such as beam and column.Even though this study considered the main parameters of concrete such as technical properties and cost, further studies are still required on this path since the optimization  was made based on 1 m


Streptococcus pneumoniae, whereas oral cephalosporin is preferred in AOM due to Moraxella catarrhalis and Haemophilus influenzae. As for the second and third line antimicrobial treatments, there is a wide variety of suggested antibiotic classes with variations in duration and posology. The decision to prescribe antimicrobial treatment as a first-line choice is based on the severity of the symptoms in 16 of the guidelines included in this review.Acute otitis media (AOM) in children represents a public health concern, being one of the leading causes of health care visits and antibiotic prescriptions worldwide. The overall aim of this paper is to unravel the major current insights into the antibiotic treatment of AOM in children. Our approach is three-fold: 1. a preclinical evaluation of antibiotics in animal models of AOM stressing on the advantages of different species when testing for different schemes of antibiotics; 2. an overview on the new antimicrobial agents whose efficacy has been demonstrated in refractory cases of AOM in children; and 3. an analysis of the different guidelines stressing on the differences and similarities between the various schemes of antibiotic treatment. The preferred therapeutic agents remain amoxicillin and the amoxicillin-clavulanate combination for AOM caused by  Acute otitis media (AOM) is a multifactorial disease representing the second most common cause of family physician visits in the pediatric population, following upper respiratory infections ,2, with Our aim is to unravel the major insights into the antibiotic treatment of AOM in children. We will start by presenting a preclinical evaluation of antibiotics in animal models of AOM stressing on the advantages (characteristics) of different species when testing for different schemes of antibiotics. In the second part of our paper, we will provide an overview on the new antimicrobial agents whose efficacy has been demonstrated in refractory cases of AOM in children. Therapeutic failures in AOM in children are usually due to inadequate antimicrobial treatment, non-identification of the pathogen, or antimicrobial resistance ,11,12. TAnimal models have been and will continue to be a powerful way to explore the mechanisms of diseases, with a significant impact on the development of new compounds and optimal therapeutic regimens . PreclinMoraxella catarrhalis, Staphylococcus aureus, Streptococcus pneumoniae and Haemophilus influenzae) and viral pathogens  [AOM represents one of the most often cited reasons for the antibiotic prescription in children under 3 years of age . This coa virus) ,34,35. Aa virus) . Thus, aA careful assessment of these studies in which these drugs were evaluated together with detailed analysis of pharmacodynamics and pharmacokinetic aspects of each of them could differentiate the various compounds and recognize those with best tolerability, cost/efficacy ratio, and safety.S. pneumoniae, whereas oral cephalosporin’s (cefixime) is preferred in AOM due to M. catarrhalis, and H. influenzae [M. catarrhalis, S. pneumoniae and H. influenzae were identified in the secretions from the middle ear. The curing of AOM was confirmed 14 days after the start of treatment for both groups. Therapeutic failure was recorded in 25 children, 16 of 58 being under 2 years of age. In the group treated with amoxicillin + clarithromycin, a smaller number of therapeutic failures were registered. Despite the proven efficacy of the conventional treatment, antibiotic resistance is an increasing phenomenon nowadays. A meta-analysis of the studies on the antimicrobial resistance in AOM in children performed during 1980–2017 [S. pneumoniae) to penicillin but not to amoxicillin, the recommendation being to continue using this antibiotic as a first choice in treating children with otitis media [S. pneumoniae and H. influenzae, both of which were insensitive to first-line therapy. M. catarrhalis has demonstrated beta-lactamase production and amoxicillin resistance. In cases of penicillin allergy, erythromycin was used, for which S. pneumoniae showed high resistance. However, all three pathogens were susceptible to the amoxicillin-clavulanate combination, a combination that is normally recommended as second-line therapy [The preferred therapeutic agents remain amoxicillin and the amoxicillin-clavulanate combination for AOM caused by fluenzae . The efffluenzae . The 10-fluenzae . Kono etfluenzae  have com980–2017 , detecteis media ,40. The  therapy . Brook e therapy  have rep therapy .S. pneumoniae strains isolated in 62 children, aged between 1 and 16 years, during the episodes of AOM (including recurrent/treatment failure AOM and post-treatment visits) [A ten-year retrospective analysis revealed that the rate of antibiotic-resistant ear infections has doubled over the last decade. Ampicillin had the highest overall resistance rate (88.5%), followed by ceftriaxone 84.5%), amoxicillin (81.9%), and tetracycline (74.5%) . Althoug visits) . Bacteri.5%, amox visits) . The phe visits)  releasedIn order to understand the mechanism of bacterial resistance to beta-lactam antibiotics, we will shortly discuss the main mechanisms of action of penicillin-type antibiotics. These are antibiotics with a beta-lactam structure that selectively interfere with the synthesis of the bacterial cell wall, a wall made of a polymer called peptidoglycan. More specifically, it acts in the last stage of the synthesis of the bacterial cell wall, as it is a bactericidal process and consists in the irreversible binding of the beta-lactam ring in the antibiotic structure to the penicillin binding proteins (PBPs) located in the membrane, which serve as specific receptors for beta-lactams. All beta-lactam antibiotics, which inhibit glutamine synthetase, bind to PBPs and have been shown to catalyze a number of reactions involved in the process of synthesizing cross-linked peptidoglycan necessary to maintain the integrity of the bacterial cell. This is an irreversible reaction and inactivates the enzyme. These proteins (transpeptidases) are bacterial enzymes that bind penicillin, and their exposure to antibiotics prevents the synthesis of the cell wall and causes structural changes or even cell lysis. Penicillins inhibit this transpeptidase-catalyzed reaction, thus blocking cell wall synthesis and the accumulation of “Park nucleotide” or uridine diphosphate (UDP) -acetylmuramyl-pentapeptide. Transpeptidases or penicillin-binding proteins, in its presence, form the penicilloyl-enzyme complex with impaired deacetylation reaction catalyzed by transpeptidase with lysis and cell death, hence the quality of bactericidal agents . AnotherThe most effective therapies for beta-lactamase producing pathogens include clindamycin, chloramphenicol and metronidazole in combination with a macrolide, or the combinations of amoxicillin/clavulanate  or piperH. influenzae was the most prevalent bacteria, followed by S. pneumoniae and M. catarrhalis. The antimicrobial susceptibility testing for the major bacterial strains revealed bacterial resistance for S. pneumoniae and H. influenzae, especially in patients with AOM, with a frequency that was not influenced by gender, age, geographical area, severity of the disease, hospital institution, and previous medication. The authors do not clearly set up in the end a first-line antimicrobial treatment, but they recommend amoxicillin or amoxicillin-clavulanate , cefditoren, or cefditoren-pivoxil for moderate cases only. Mild cases should receive only symptomatic treatment.Suzuki et al.  evaluateS. pneumoniae, new compounds such as Tebipenem pivoxil as the first carbapenem with oral administration available for children could be considered [For resistant nsidered ,50,51.S. pneumoniae and H. influenzae isolates, thus indicating a favorable transfer of tebipenem to the overflow of the ear. The carbapenem efficiency has been proven and confirmed also by the low incidence of relapses and the high rate of the number of cases cured [The efficacy of tebipenem-pivoxil in AOM in children has been demonstrated by Sugita et al.  in a clies cured .H. influenzae. Garenoxacin and Moxifloxacin are recommended only in adults [H. influenzae isolates have been proved resistant and can only survive at the maximum therapeutic concentration of tosufloxacin [The therapy with non-beta-lactam compounds (macrolides and quinolones) has been widely used as an alternative to beta-lactams. The use of new quinolones such as tosufloxacin has increased due to macrolide resistance. Tosufloxacin has been used in children with high antimicrobial activity against n adults . Howevern adults  H. influfloxacin . Severalfloxacin ,55,56.H. influenzae, regardless of β-lactamase production [Cefditoren pivoxil, a third-generation cephalosporin, is administered as an oral prodrug, absorbed and metabolized by hydrolysis reaction in the active compound cefditoren in the intestine, distributed in the blood until the site of action, and then eliminated by the kidneys . It has oduction ,61.In order to reduce the potential complications  and the economic burden associated with AOM, various professional guidelines and consensus papers have been created. In the United States of America, Sweden, United Kingdom, France, Spain, and Italy, the introduction of guidelines was associated with a reduction of up to 12% of unnecessary prescriptions and an increase of up to 58% in the accuracy of the type and dosage of the prescribed antibiotic . AccordiA systematic review of the literature for guidelines published between January 1989  and September 2020 was conducted. We performed a systematic search on PubMed and EMBASE databases using keywords: “acute otitis media” AND “children” OR “pediatric” AND (“guideline” OR “consensus”). National or international clinical practice guidelines and guidance documents with recommendations for the management of AOM in children were eligible for inclusion. A total of 263 papers have been initially found. The following items were excluded: withdrawn or superseded guidelines, clinical trials or systematic reviews not part of clinical practice guidelines, and types of publications other than guidelines or consensuses . Websites of national pediatric associations and bibliographies of all included guidelines were examined in order to identify further relevant resources.A total of 20 AOM clinical practice guidelines were included in this review. The following guidelines were analyzed and compared regarding antibiotic treatment options: World Health Organization (WHO), Finland, U.S., Australia, Czech Republic, France, Italy, Spain, Denmark, Poland, Portugal, U.K., Belgium, Germany, Ireland, Luxembourg, Netherlands, Norway, Sweden, and Switzerland. All guidelines emphasize the need for accurate diagnosis, which is the main condition in order to establish the adequate treatment.Two types of approaches towards antibiotic administration have been identified: a watchful waiting approach and immediate antibiotic prescription. WHO guideline recommends antibiotics to all children with confirmed AOM. Immediate antibiotics for any AOM can be considered according to Finland, U.S. and Czech Republic guidelines, whereas the rest of the guidelines mentioned above (16/20) encourage the watchful waiting approach. The indications for immediate antibiotic treatment are also based on the presence of severe symptoms  and the recurrence of AOM episodes . The advSeventy-five percent (15/20) of the guidelines include young age as a criterion for immediate antimicrobial treatment; 57% (8/14) of them recommend antibiotic therapy for children younger than 24 months old. Only 20% (4/20) of the guidelines recommend antibiotics as a first-choice treatment in children with unilateral AOM, whereas 75% (14/20) of guidelines support immediate antibiotic treatment in cases of bilateral AOM in children aged less than 2 years. The decision to prescribe antimicrobial treatment as a first-line choice is based on the presence of severe symptoms  in 85% (17/20) of the guidelines included in this review.Recommendations regarding the management of recurrent AOM (three or more episodes of AOM in the previous 6 months or four or more episodes in the last 12 months) are included in 40% (8/20) of the guidelines included in this review. Long-term antibiotics are not recommended routinely. The Australian guidelines encourage treatment with long-term antibiotics  for 3–6 months in children <2 years of age who are at risk of developing chronic suppurative otitis media. Antibiotic stewardship is promoted by providing clear diagnostic criteria  and by clearly specifying the moment when to initiate antimicrobial treatment (20/20). Almost all the guidelines  clearly specify the duration of antibiotic regimens. Amoxicillin is universally accepted as first-line antibiotic therapy in all included guidelines . There iAfter the introduction of national AOM clinical practice guidelines the antibiotic prescription rates decreased by 12% with an increase of up to 58% for the recommended first choice antibiotic . Since tS. pneumoniae and H. influenzae remain the most important pathogens in the determination of AOM, being the bacteria against which antimicrobial treatment should be targeted. The rat, mouse, gerbil, guinea pig, and chinchilla are the preferred animals for experimental AOM models with individual advantages and disadvantages. Antibiotic resistance is rising in all parts of the world due to the misuse and overuse of the antimicrobial treatment. In response of this worrying phenomenon, new antibiotics have been proven efficient in AOM in children: Tebipenem-pivoxil, Tosufloxacin, and Cefditoren pivoxil. Overall evidence suggests that guideline adherence is effective in reducing antibiotic prescriptions. A total of 20 AOM clinical practice guidelines were included in this review: World Health Organization (WHO), Finland, U.S., Australia, Czech Republic, France, Italy, Spain, Denmark, Poland, Portugal, U.K., Belgium, Germany, Ireland, Luxembourg, Netherlands, Norway, Sweden, and Switzerland. The current guidelines on AOM management include two types of approaches towards antibiotic administration: watchful waiting approach and immediate antibiotic prescription. Amoxicillin is proposed as first-line antimicrobial therapy in all included guidelines, whereas cephalosporin, amoxicillin-clavulanic acid, or Trimethoprim-sulfamethoxazole are among the second-line antibiotics. Recommended treatment duration varied from 5 to 10 days. A higher dosage than usual is recommended for amoxicillin and amoxicillin-clavulanic acid in order to overcome resistant strains. Sixteen of the guidelines recommend immediate antibiotic treatment in case of severe symptoms . Fifteen of the 20 included guidelines include age as a criterion for immediate antimicrobial treatment, eight of them recommending immediate antibiotic therapy for children younger than 24 months old. Judicious use of antibiotics is a priority, as antimicrobial resistance represents a serious threat to global public health and the adherence to guideline may reduce the treatment failures, which are usually related to antibiotic resistant strains.


Reactive powder concrete (RPC), typically with higher compressive strength, is particularly attractive to structural engineers to apply them in infrastructures for enhancing their resistance under severe environments and loads. The main objective of the initial study presented in the paper was to investigate the behavior of two types of these new cementitious materials differing in the nature of microfibers. The RPC mixes were reinforced with steel and then with basalt microfibers. To evaluate the structural performance of developed unconventional materials, properties were investigated experimentally and compared with the control normal concrete mix. Mechanical tests indicated that dispersing fine fibers for making RPC, a mean compressive strength of 198.3 MPa and flexural strength 52.6 MPa or 23.2 MPa, respectively, were developed after 28 days of standard curing at ambient temperatures. In composite structures consisting of steel girders and a concrete slab, it is necessary to prevent the relative slip at the steel and concrete interface using shear connectors. The very high RPC strength enabled a material saving, weight-reduced application of precast construction, and particularly effective joint to steel beams. The investigation of such shear connection efficiency, in the case of the higher strength concrete deck, using standard push-out test specimens was executed. Finite element numerical models were developed. The outputs of the studies are presented in the paper. Reactive powder concrete (RPC) is one of the most advanced cement-based composite materials in the group of ultra-high performance concretes (UHPC). Such composites can provide a compressive strength above 140 MPa, usually about 200 MPa, as already observed by Richard . In thesA new group of RPC composites is reinforced with short, thin steel fibers, usually 0.2 mm in diameter and 12 mm long emerged, in a quantity of approximately 2% by volume. As a result of further research in the 1990s, one of the first composite, according to , was cal2.The favorable properties of RPC composites permit to create thin profiles and to overspan considerable structural areas. Aïtcin , Perry aBuildings constructions are often in need of spanning greater distances between vertical supports. Composite steel beams cooperating with the concrete flooring deck, placed on the upper flange, represent a preferable solution. The interiors of the building can be better used without restrictive vertical columns. In particular, trusses may allow the placement of various actually required several installations into the free space between the cords. Besides, steel-concrete composite bridges are used as an alternative to ordinary structural systems because of their ability to adapt their geometry to design constraints and the possibility of using favorable properties of both structural materials . HoweverThanks to its properties, RPC is increasingly being used in widely understood composite structures in which layers of concrete of different classes are joined together. This allows for the strengthening of existing structures  but also2/kg as well as a very high initial strength. Silica quartz powder 0–0.2 µm from Łaziska ironworks and silica sand 0–0.4 mm from Osiecznica stone quarry were also used as additives. This study involved two different RPC materials. At first, the steel fibers 12 mm long and 0.2 mm in diameter were spread in the mixture for the development of RPC. Basalt fibers of 12 mm length with 18 µm thickness were used for the other innovative testing. A polycarboxylate superplasticizer was added at a ratio of 2.5% of cement weight to fluidify the mixture. The chemical composition of cement, silica fume, quartz powder, and quartz sand is given in For this introductory study, three groups of mixes were prepared. The control concrete series (C) examined the samples’ behavior without fibers. Two new RPC sets contained steel fibers (S) or unconventional basalt fibers (B). Portland cement CEM I 52.5 R was produced in Polish cement plant Rejowiec. Moreover, ingredients and admixtures were added for achieving a specific surface area 410 mThe grading of fine materials in dry conditions used for the entire mixtures is shown in The control concrete samples without fibers (C) and RPC sets containing steel fibers (S) or unconventional basalt fibers (B) composition is shown in The studies of concrete and RPC mixtures consistency were performed according to CEN-EN 1015-3 standard , based o3 as given in The concrete density tests were carried out in accordance with the standard EN 1015-10 . The denThe flexural and compressive strength were determined according to the EN 1015-11 standard . The 40 2 to 0.0007 kg/m2. In the case of RPC, this value was negligibly higher. When basalt fibers were used, the mass loss appeared even to be smaller. This allowed the frost resistance of RPC concrete to be assessed as very good material according to the criteria adopted by the standard, regardless of the type of fibers used. The freeze-thaw resistance of concrete in the presence of deicers (3% NaCl) was determined according to the rules given in the CEN/TS 12390-9 document . The resThe porosity of concrete samples was measured using the mercury PoreMaster 60 porosimeter under pressure in the range from 1 to 400 MPa . The anaAs can be seen from the data in For assuring the connection of steel beams and a concrete slab, steel stud connectors are normally applied. Potential employment of the RPC to attach particularly precast concrete decks to steel superstructure was the subject of the subsequent study. The shear capacity and the load-slip relations are the most important characteristics for the design of this type of composite structure. The suitable method to find the shear connection properties could be full-scale composite beam tests, but they are quite time-consuming and rather costly . TherefoThe experimental work started in mixing, casting, and testing the mechanical properties of ordinary and high-strength concrete. Consequently, thus, three sets of specimens for mechanical properties testing were prepared to determine the compressive and flexural strength as well as elastic modulus of ordinary concrete but also RPC with the addition of steel or basalt fibers. The mechanical performance of the produced RPC was primarily considered by compressive strength, evaluated by exchanging different fibers according to EN 12390-3 . Three s2. The studies even confirmed that incorporating microfibers into the mixtures resulted in higher early compressive strength. The mean compressive strengths of six specimens produced from both categories of RPC, as well as the values of three ordinary concrete samples, are summarized in The cube specimens were also used to determine other material characteristics. Although a different aspect ratio of the samples compared to the cubes would be more appropriate to exclude the influence of the dimensions. They were instrumented with four strain gauges on the opposite sides. Two of them were used for vertical strain measurement and the further two for transversal horizontal strain recording. During the compressive test, the load history was measured by a universal testing machine, while the strains of the specimen were obtained by averaging two sideways gages values, as shown in E computed with the longitudinal strains on the elastic part of the curve in The Young’s modulus The tensile behavior could be determined from the load-deflection curve obtained by applying a center-point load on a simply supported notch prism. It tested the ability of unreinforced concrete beam to withstand failure in bending. The test specimens were two prisms per composition, totaling six ones conforming to EN 12390-1 , with st2. The application of fibers in conventional concrete as supplementary cementitious material to an appropriate amount was seen and exhibited better mechanical behavior.The experimental force-deflections curve for specimens reinforced with steel microfibers consisted, generally from the linear-elastic part of the diagram, up to 75% of the applied load. The progressive tensile damage beyond this point made the curve diverge from the straight line. Nevertheless, microfibers reinforcement could redistribute the stress between initiated microcracking, which did not lead to precipitate failure of the specimen. But multiple propagated microcracks ended at the peak of the load-deflection curve. The strains were localized in a single crack opening in the mid-span, where the final failure occurred. The average flexural strength on all RPC standard beams specimens was 25.46 N/mmHEB 260B and two concrete slabs attached to the flanges of the steel beam. The concrete slabs were 650 mm long, 600 mm wide, attached using four stud connectors with a shank diameter of 16 mm and 75 mm in height to each flange. The longitudinal distance between the connectors was 96 mm, and the transversal pitch 56 mm. Two test specimens, C1 and C2, included both slabs of the ordinary concrete C25/30. In the case of specimens S1 and S2, the RPC mortar was added, a blend of micro smooth steel fiber. The specimens B1 and B2 were prepared with the addition of basalt fibers. As shown in The push-out investigation was executed in accordance with EN 1994 ,31. As iPush-out specimens were kept over a steel base plate. The contact surfaces of steel and concrete were cleaned and smoothened to avoid eccentric loading. The specimens were tested using a universal testing machine until complete failure. Dial and strain gauges were mounted at critical locations, as shown in At each load or displacement increments, the slip history between two concrete slabs and the steel beam was measured by linear variable transformers and recorded. Its average value was plotted against the load per connector.C1 and C2 remained in good condition without any cracking. In both push-out specimens, first of all, the interface cracks on the sides were observed at about 300 kN, thus around 55% of the ultimate failure loads. Cracks along the width of the concrete slab started appearing at the level of shear connectors beyond the load of 400 kN. Gradually, the cracking of the concrete slab caused the failure of stud shear connectors by fracture at an ultimate load of 543 kN, as illustrated in The cracking patterns during each load increment were carefully observed to identify the failure mechanism. Interface slip between the steel flange and concrete slab was measured using dial gauges mounted by keeping the tip in a vertical direction. It was noted that during the initial 25 cyclings between 40 kN ens 200 kN, both specimens In the second set of four RPC specimens, the failure of concrete slabs started due to the spalling of ordinary concrete at the corner of slabs, as shown in The push-out tests remained also the time-consuming and costly option. Therefore, numerical simulation was developed as a tool for the investigation of the behavior of the connection, using the least possible resources, to achieve an adequate relationship with reality. In this study, the numerical simulation process employed ADINA software, based on a finite element method. The tri-dimensional modeling of the specimen was adopted, thanks to the favorable geometrical representation provided by the software, which was consistent with the experimental test layout. Load increments were applied at small intervals, where the size of them was automatically selected by the software code, based on numeric convergence conditions. In order to model the steel strain-stress behavior, a bilinear behavior was adopted. The concrete damage plasticity model was employed, which was implemented in the ADINA code. This software offers a large program library with solid space elements with different typologies. The finite elements to be used for the discretization of concrete slabs and steel parts are illustrated in For both types of RPC push-out specimens, the experimental slips, similarly, initially were increasing proportionally to the slightly higher loading level of 273 kN, as shown in An adequate consistency between numerical and experimental results could be observed and proved by employing previously calibrated numerical simulation. Consequently, the use of the finite element model was validated. As a result of this study, it can be concluded that numerical modeling and experimental tests are complementary research tools.The effects of processing additions were analyzed based on the work conducted. The composition of the RPC mix was analyzed using X-ray fluorescence spectroscopy. The data shown in The data generated from compressive tests of ordinary and high-strength concrete reported in The connector and the concrete strength are the main factors affecting the behavior of shear connections at the steel-concrete interface in composite member. As actual design methods are based on the test results of studs embedded in normal strength concrete, the load-slip behavior and the shear capacity in higher strength fiber-reinforced concrete were necessary to evaluate the appropriate design. In this study, the tests were conducted by employing six specimens, which differed in material properties of the concrete and stud, as shown in The results of the finite element models were compared with push-out tests and values provided by testing. The graphical overviews in Two reactive powders concrete reinforced with the steel or basalt fibers and control normal concrete specimens were investigated for structural applications in this study. High performance fiber-reinforced reactive powder concrete revealed its exceptional mechanical and cracking resistances. Thus, the RPC using fine steel or basalt fiber aggregates could meet the mechanical properties requirements for practice in structural applications after being cured in the ordinary curing condition.A maximum mean compressive strength of 198.3 MPa at 28 days curing age was produced using finely dispersed steel fibers. Compared to the control mix of normal concrete, a 33% larger compressive strength was achieved. Nevertheless, basalt fiber reinforcement of concrete with correspondingly mean values of 135.6 MPa had a less significant impact on the compressive RPC strengths, but with better resistance in an aggressive environment. The flexural strengths of the RPC were, in all cases, greater than those of normal concrete. Fiber-reinforced concrete is characterized by a dense microstructure and thus very high strength as well as excellent durability properties. This enables a material saving and weight-reduced, slim construction method that opens up completely new possibilities and areas of application predominantly in precast constructions. But RPC is today still relatively expensive. Besides, a combination with normal concrete may minimize the construction cost. Especially, the shear connection of composite systems can be designed using two types of concrete. The deck slabs could be cast as commonly of normal strength concrete, while the adjacent space of connector location could use higher performance concrete. Executed push-out specimen testing proved to describe suitably shear connection behavior as well as the resistance of shear studs and concrete slab. Besides, post-cracking tensile capacity and pseudo-ductility with work-hardening behavior, accompanied by multiple micros-cracks, were observed in experimental and theoretical studies. This new hybrid connection provided outstanding strength and resistance to transfer shear at the steel-concrete interface. This type of innovative shear connection at the steel-concrete interface could be attractive, especially in the case of precast decks of building or bridge composite structures. The mutual assembly of two different structural parts was simplified and executed faster.Numerical models making use of the finite element method were developed to simulate the behavior of shear connection in composite structures of steel and concrete of different forms. The comparison with experiments confirmed that these analytical tools were able to describe the real behavior of composite structures, including concrete cracking, crushing, and reinforcement yielding. Therefore, the above procedures may potentially provide more powerful means in practical design.