Romboutsia and Met-T2D status. Other bacterial taxa were consistent with previous observations in T2D, including reduced butyrate producers such as Anaerostipes hadrus. Hence, our results highlighted both shared and unique gut microbial biomarkers of Met-T2D in individuals from different ethnicities but living in the same geographical area. Future research using higher-resolution shotgun sequencing is needed to clarify the role of ethnicity in the association between T2D and gut microbiota composition.It is currently unknown whether associations between gut microbiota composition and type 2 diabetes (T2D) differ according to the ethnic background of individuals. Thus, we studied these associations in participants from two ethnicities characterized by a high T2D prevalence and living in the same geographical area, using the Healthy Life In Urban Settings (HELIUS) study. We included 111 and 128 T2D participants on metformin (Met-T2D), 78 and 49 treatment-naïve T2D (TN-T2D) participants, as well as a 1:1 matched group of healthy controls from, respectively, African Surinamese and South-Asian Surinamese descent. Fecal microbiome profiles were obtained through 16S rRNA gene sequencing. Univariate and machine learning analyses were used to explore the associations between T2D and the composition and function of the gut microbiome in both ethnicities, comparing Met-T2D and TN-T2D participants to their respective healthy control. We found a lower α-diversity for South-Asian Surinamese TN-T2D participants but no significant associations between TN-T2D status and the abundance of bacterial taxa or functional pathways. In African Surinamese participants, we did not find any association between TN-T2D status and the gut microbiome. With respect to Met-T2D participants, we identified several bacterial taxa and functional pathways with a significantly altered abundance in both ethnicities. More alterations were observed in South-Asian Surinamese. Some altered taxa and pathways observed in both ethnicities were previously related to metformin use. This included a strong negative association between the abundance of  Type 2 Diabetes (T2D) is considered as a major global public health concern . DiffereIn addition to its known role in several fundamental processes in the host, including nutrient metabolism and the host immune response, the gut microbiome is indeed also proposed to be involved in host glycemic control and associated with insulin resistance ,13,14,15From the above observations, the question arises if different gut microbiome profiles in T2D are indeed observed in subjects from different ethnic descent. Insights herein could improve future applications of the gut microbiome in personalized prevention, diagnostic tools or therapeutic strategies. Therefore, in the ongoing HELIUS study, we analyzed the differences in gut microbiome composition and function between healthy and T2D subjects of South-Asian Surinamese and African Surinamese descent, two ethnic minority groups with a high prevalence of T2D living in Amsterdam (NL), taking into account their metformin medication status. The HELIUS study is an ongoing study, at baseline including 18–70 years old residents of Amsterdam, the Netherlands. Participants were randomly recruited from the municipal registry, stratified by their ethnic origin, being of either Dutch, South-Asian Surinamese, African Surinamese, Turkish, Moroccan or Ghanaian descent (mainly first-generation immigrants). A detailed description of the study design, study population and rationale is provided elsewhere ,30. In tRecruited participants completed a questionnaire including items related to sociodemographic characteristics, lifestyle  and health status. They underwent a physical examination, including measurements of anthropometric characteristics  and blood pressure, and a review of current medication. Overnight fasted blood samples were drawn and used for determination of HbA1c, glucose and lipid profile. Furthermore, a subset of participants collected their own stool sample at home using a collection tube. They were asked to bring the sample to the research location within 6 h after collection or otherwise to store it in the freezer overnight and bring it the next day. At the research location, samples were temporarily stored at −20 °C, before daily transport to the AMC, where they were checked for conformity and stored at −80 °C. Information regarding participants’ antibiotic use in the past three months, probiotic use and whether they had diarrhea in the past week was recorded when participants handed in the sample. A subsample of the HELIUS cohort also provided detailed dietary intake data from ethnic-specific food frequency questionnaires (FFQs), as previously described ,32.−1 BSA, 5% dimethylsulfoxide, 200 nM of each primer and 20 ng of genomic DNA. PCR was carried out under the following conditions: initial denaturation at 94 °C for 47 min; followed by 25 cycles of denaturation at 94 °C for 45 s; annealing at 52 °C for 60 s; elongation at 72 °C for 90 s; and a final elongation step at 72 °C for 10 min. Duplicates were combined, purified with the PCR Clean-Up kit  and the NucleoSpin Gel, and quantified using the Quant-iT PicoGreen dsDNA kit . Purified PCR products were diluted to 10 ng/μL and pooled in equal amounts. To remove short amplification products, these pooled amplicons were purified again using Ampure magnetic purification beads . Negative controls were included for each sample. Gel electrophoresis was used to confirm the absence of detectable PCR products in these negative controls. Positive controls were not included in these runs. However, the protocol used to analyze the samples was optimized using mock samples. Libraries for sequencing were prepared by mixing the pooled amplicons with PhiX control DNA purchased from Illumina. This input DNA contained 15% PhiX and had a concentration of 3 pM and resulted in the generation of about 700 K clusters/mm2 and an overall percentage of bases with quality score higher than 30 (Q30) higher than 70%. All analytical procedures were blinded for ethnicity (but not randomized).Sequencing of the stool samples was performed at the Wallenberg Laboratory . Total genomic DNA was extracted from a 150 mg fecal sample aliquot using a repeated bead beating method . Fecal mn = 24) were excluded at this rarefaction stage, resulting in a final complete dataset of 6032 samples with 22,532 ASVs. Lastly, this rarefied ASV table was inputted to PICRUSt2 (v 2.2.0_b) with default parameters to predict MetaCyc pathway abundances per sample [USEARCH  was user sample .Type 2 diabetes was defined based on either fasting glucose levels ≥7.0 mmol/L, fasting HbA1c levels ≥48 mmol/mol or use of antidiabetic medication. To account for the known confounding effect of metformin on the gut microbiome ,22,23,24Healthy controls were included if they were normoglycemic (both fasting glucose levels <5.6 mmol/L and fasting HbA1c levels <39 mmol/mol), did not self-report diabetes and did not fulfil more than 1 of the following metabolic syndrome criteria: high blood pressure (systolic blood pressure ≥130 mmHg or diastolic blood pressure ≥85 mmHg or use of blood pressure lowering medication), high blood triglycerides (≥1.7 mmol/L or lipid lowering medication), reduced HDL cholesterol , <1.29 mmol/L  or lipid lowering medication) or high fasting plasma glucose (≥5.6 mmol/L or glucose lowering medication). The central obesity component , ≥90 cm  and ≥94 ) was not taken into account due to its high prevalence. In case of missing data for a particular criterion, this criterion was considered as fulfilled. Participants were excluded if they had taken antibiotics in the past three months or if this variable was unknown. In addition, healthy controls were also excluded in case they had missing values for at least one of the following values: fasting glucose levels, fasting HbA1c levels, antidiabetic medication use, metabolic syndrome status, age, sex or BMI.n = 111, TN-T2D: n = 78) and South-Asian Surinamese . Healthy controls from the same ethnic group were selected for Met-T2D and TN-T2D cases using a 1:1 propensity score matching based on age, sex and BMI. . The maSince T2D cases were matched to controls based on only a limited number of general variables , and we t-tests or Wilcoxon rank-sum tests, respectively. Counts (%) are provided for categorical variables and compared with a Fisher exact test or Chi-squared test.Anthropometric and clinical values are summarized with mean ± standard deviation or median (Inter Quartile Range) for normally and non-normally distributed variables, respectively. Differences in these variables between controls and diabetes subjects were compared with two sample We obtained dietary patterns scores for a subset of individuals by performing a principal components analysis with VARIMAX rotation  on 44 nThe diversity of gut microbiota for each individual was assessed with several α-diversity indices calculated at the ASV level, namely richness , Faith’The interindividual dissimilarities in gut microbiota composition (β-diversity) were assessed with both the unweighted and weighted UniFrac distances (function ‘UniFrac’ from phyloseq v 1.30.0 R package ) and theAll of the above-described analyses regarding α- and β-diversity were performed for each ethnicity separately and stratified by T2D status . Interactions between T2D status and ethnicity were tested by introducing the product of T2D status and ethnicity in a model already including T2D status and ethnicity. p-values were corrected for multiple comparisons using Benjamini–Hochberg correction (p. adjust) [Within each ethnicity, read counts of individual ASVs were compared between T2D cases  and their controls to identify differentially abundant ASVs. Only ASVs with on average ≥ 3 reads per sample (≥0.02%) in the specific dataset under consideration were included in the analysis. Since ASV abundance is not normally distributed and no golden standard for ASV analysis is available, we applied a sensitivity analysis with different models and transformation methods (data not shown) to select significant differentially abundant ASVs. While we observed that results were dependent on the chosen model or transformation in some cases, we decided to use the Wilcoxon rank-sum test on the untransformed read counts to compare the abundances, since this model uses the least assumptions about the distribution.  adjust) ).For each T2D status, ASVs that remained significant after this correction in either of the two ethnicities were further analyzed with a sensitivity analysis to disentangle the effect of potential confounders. Therefore, linear regression models with arcsin square-root transformed relative abundances of each ASV as output were run for each ethnicity separately, using the same type of adjustments as for the α- and β-diversity measures . To verify if T2D had a different effect on the abundance of the identified ASVs for a specific ethnicity, we tested the interaction effect between T2D and ethnicity on the abundance of the selected ASVs by introducing the product of T2D status and ethnicity in a model already including T2D status and ethnicity.To compare the predicted functional differences of the gut microbiome between T2D cases and controls, PICRUSt2 read counts were normalized to relative abundances using total sum scaling. Only pathways with an average abundance ≥ 0.02% in the specific dataset under consideration were included in the analysis. We conducted the same analysis as those conducted for ASV. However, in the sensitivity analysis, pathway abundances were log-transformed after addition of a pseudocount (0.01%).p-values ≤ 0.05 (either Benjamini–Hochberg corrected or for terms in (un)adjusted linear models) were considered to be statistically significant for single terms, whereas for interaction terms a criterion of p-value ≤ 0.20 was used. Since in the sensitivity analysis of the ASV and pathway abundances p-values were no longer corrected for multiple comparisons, ASVs and pathways were only considered as significantly associated with T2D in a specific ethnicity if they were significant in both the Benjamini–Hochberg corrected Wilcoxon analysis and the linear regression models. For all statistical analysis, R v.3.6.2  was usedWe built machine learning models to predict T2D status (T2D vs. matched controls) based on microbiome profile for both Met-T2D and TN-T2D status for each ethnicity separately. Models were built on the same features included in the Wilcoxon analyses described above. All models were constructed with the same stability selection procedure to ensure robustness of the results and prevent overfitting.  For thisFinal model performance, SHAP feature importance and permutation feature importance were obtained by averaging the scores across the different shuffles. Features were selected as important to distinguish T2D cases and healthy controls if they were both present in the top 20 of the average SHAP importance and in the top 20 of the average permutation importance. The machine learning pipeline was implemented in Python v3.7.6, using the scikit-learn (v 0.22.1) package.In total, 189 African Surinamese T2D cases (111 Met-T2D and 78 TN-T2D) and 177 South-Asian Surinamese T2D cases (128 Met-T2D and 49 TN-T2D) were included. After matching, 130 African Surinamese and 128 South-Asian Surinamese were selected as healthy controls, some of them being matched to both a Met-T2D and a TN-T2D case. Since both metformin use and ethnicity are known confounders for the gut microbiome ,23,24,26p = 0.000716), Shannon index (p = 0.0027), inverse Simpson index (p = 0.0169) and phylogenetic diversity . After adjusting for age, sex, BMI and medication use, both South-Asian Surinamese Met-T2D and TN-T2D cases were characterized by a significantly lower richness and diversity  compared to their healthy controls. In African Surinamese, we only observed a significantly lower phylogenetic diversity in Met-T2D cases (p = 0.0324) compared to their controls, which remained significant after adjusting for age, sex and BMI. In addition, a significant interaction effect between Met-T2D and ethnicity was observed for the Shannon index and richness, supporting the difference in observed associations between ethnic groups. In the subsample with available dietary information, T2D status was not associated with α-diversity in any of the models  and unweighted UniFrac . Only South-Asian Surinamese Met-T2D cases had a significantly different weighted UniFrac dissimilarity compared to their healthy controls (p = 0.0013), supported by a significant interaction effect between Met-T2D and ethnicity. However, only the effect of Met-T2D on Bray–Curtis dissimilarity in the African Surinamese remained significant after adjusting for age, sex, BMI and medication use. The same analyses in the diet subsample revealed either no significant effects of T2D or the effect vanished after adjusting for the dietary patterns , several ASVs assigned to Clostridium sensu stricto 1, and one ASV assigned to Escherichia/Shigella were associated with Met-T2D in both ethnicities  was only identified in African Surinamese, while several ASVs assigned to members of the Lachnospiraceae, Lactobacillaceae, Christensenellaceae, Erysipelotrichaceae and Ruminococcaceae families were unique for the South-Asian Surinamese.Several ASVs assigned to the ties see A, with aLachnospiraceae family) in both “covariate-adjusted” models and “covariate-and-medication-adjusted” models. For most of these ASVs, the Met-T2D effect was uniquely significant for the South-Asian Surinamese, with significant interactions between Met-T2D status and ethnicity. In the subgroups with available dietary information, African Surinamese Met-T2D cases still showed a significant lower abundance for several ASVs belonging to the Peptostreptococcacceae family and Clostridium sensu stricto 1 after adjusting for age, sex, BMI and dietary patterns compared to their controls, and for Romboutsia also after adjusting for medication use. In South-Asian Surinamese, Met-T2D cases had a lower abundance of Bifidobacterium compared to their controls after adjusting for age, sex, BMI, medication use and dietary patterns, whereas the abundance of Romboutsia and Clostridium sensu stricto 1 was only significantly lower after adjusting for age, sex, BMI and dietary patterns  for all ASVs that were significantly associated with Met-T2D in at least one ethnicity after multiple testing correction in the previous analyses see B. For roerns see .Escherichia/Shigella, Clostridium sensu stricto 1, Romboutsia and Intestinibacter bartlettii were important for classifying Met-T2D cases in both ethnicities  and the South-Asian Surinamese TN-T2D cases vs. matched controls (average AUC 0.52 ± 0.12). Prediction performance of the model in the South-Asian Surinamese Met-T2D vs. matched controls was only modest, as indicated by the average AUC of 0.7 ± 0.06.In this study, we showed differences in gut microbiome α- and β–diversity measures, as well as in abundances of bacterial taxa (ASVs) and functional pathways between individuals with diabetes on metformin treatment and their healthy controls from two ethnic minority groups (South-Asian Surinamese and African Surinamese) living in the same geographical area and characterized by a high T2D prevalence. Furthermore, we showed that Met-T2D cases could be separated from their healthy controls based on their gut microbiome composition in both ethnicities using machine learning classifiers. Some of the Met-T2D-associated ASVs and pathways showed overlap between ethnicities. The gut microbiome of South-Asian Surinamese displayed more alterations between Met-T2D cases and controls, indicated by lower α-diversity measures and more significantly differentially abundant ASVs and pathways than the gut microbiome of the African Surinamese, when the same Met-T2D status was considered. By contrast, except for a lower richness and phylogenetic diversity in South-Asian Surinamese TN-T2D cases compared to their controls, we did not observe any other relation between TN-T2D and the gut microbiome in both ethnicities.Our statistically nonsignificant results in the ASV and pathway analyses for TN-T2D status in both ethnicities are in line with some previous studies ,21. HoweClostridium sensu stricto 1 [Peptostreptococcaceae Romboutsia [Terrisporobacter [Intestinibacter bartlettii [Escherichia [Romboutsia was identified as a robust and strong biomarker in the relation between Met-T2D and the gut microbiome in both ethnicities. Besides its potential relation to metformin use [Romboutsia was also previously negatively correlated with the abundance of Enterobacteriaceae, in line with the observed higher abundance of Escherichia/Shigella in Met-T2D cases in our study [Clostridium sensu stricto 1, Peptostreptococcaceae Terrisporobacter and Intestinibacter bartlettii, and Escherichia/Shigella after all adjustments in African Surinamese, while those significant effects vanished in South-Asian Surinamese after adjustment for other medication use (often not addressed in the previous studies). While interactions between Met-T2D and ethnicity were significant for Peptostreptococcaceae Terrisporobacter and Intestinibacter bartlettii, they were not for Clostridium sensu stricto 1 and Escherichia/Shigella, indicating that associations in South-Asian Surinamese could not be excluded for these ASVs. By contrast, South-Asian Surinamese Met-T2D cases had a significantly higher abundance of Lachnoclostridium, also previously related to metformin use [Proteobacteria abundance, Akkermansia muciniphila, and butyrate and propionate production potential. [A. Muciniphila, this was replicated in our study in both ethnicities, although represented by different ASVs and often only as important biomarkers in the machine learning analyses. The functional analysis revealed the same pattern. Almost all pathways that overlapped between both ethnicities have been related to metformin use before, including an increase in pathways corresponding to fermentation of pyruvate to butanoate or related to menaquinol (vitamin K2) biosynthesis and to TCA cycle. [Romboutsia [Intestinibacter bartlettii [Lachnoclostridium [Contrary to TN-T2D status, we did identify statistically significant results according to Met-T2D status. Those cases could express a more severe diabetic phenotype compared to the untreated diabetes cases. However, previous studies have reported that metformin use itself affects gut microbiome composition, even in healthy normoglycemic individuals ,24,59,60tricto 1  and Peptrobacter , and esprtlettii ,23,60 weherichia ,23,24 shrmin use ,62, Rombur study . As for rmin use ,23 whichtential. ,22,23 ExA cycle.  However,A cycle. ,21. HoweA cycle. ,19, inclmboutsia , Intestistridium ; therefoAnaerostipes hadrus were indeed significantly and robustly negatively associated with Met-T2D [Lachnospiraceae, a family containing several butyrate producers [Clostridium sensu stricto 1 and Peptostreptococcaceae [Besides potential metformin-driven effects, we also observed patterns previously related to type 2 diabetes or other cardiometabolic parameters in our Met-T2D analyses. For instance, a lower abundance of butyrate producers has been reported in diabetes ,10, whic Met-T2D . This sp Met-T2D . Likewisroducers , were sioccaceae ,69. ASVsFinally, functional predictions revealed a large number of significantly different pathways in South-Asian Surinamese Met-T2D cases compared to controls, but only a small number in the African Surinamese, with significant interactions between Met-T2D and ethnicity for most pathways. This could relate to a more disturbed functional capacity of the gut microbiome but also to a better representation of South-Asian Surinamese taxa in the PICRUSt database. However, machine learning analyses did not reveal a clear distinction between Met-T2D cases and controls based on these pathways. South-Asian Surinamese Met-T2D cases in our study showed a reduced biosynthesis potential of several amino acids, including branched chain amino acids (BCAAs). Remarkably, BCAAs are often positively associated with insulin resistance or T2D, and BCAA synthesis was even enriched in samples after addition of metformin to an in vitro gut simulator ,13,22. FEscherichia in treated T2D cases and a higher abundance of Lactobacillus in both treated and untreated T2D cases [Lachnospiraceae or Ruminococcaceae families were often enriched in controls compared to T2D cases. These results are in line with ours. By contrast, we did not identify Prevotella as a biomarker, and results regarding α-diversity were inconsistent across studies [Lachnoclostridium, but not Escherichia/Shigella, was observed in metformin-treated diabetes subjects compared to untreated ones in this Indian subpopulation [Clostridiaceae and Peptostreptococcaceae showed a lower abundance in T2D subjects  in two studies compared to controls [Anaerostipes in T2D subjects. These are all in agreement with our study. However, they reported a higher α-diversity in T2D subjects.Previous sequence data of the gut microbiome in individuals with African and South-Asian genetic backgrounds have already revealed taxa associated with type 2 diabetes in those ethnic groups ,71,72,732D cases ,17,73. T studies ,17,73. R studies  analyzedpulation . While wcontrols ,71. In acontrols  mentioneWhile several other studies have compared T2D subjects to controls from different ethnicities ,9,21,25,Romboutsia and was consistently less abundant in Met-T2D cases compared to controls in both ethnicities. Since this taxon was previously related to metformin use, as were some other identified markers, this may indicate a metformin-driven signal. Other identified taxa were previously related to diabetes, blood pressure or lipid profiles but were often represented by different ASVs in different ethnicities. Identifying metformin-unrelated, true type 2 diabetes patterns in the gut microbiome is thus complex. More research, preferably using shotgun sequencing at higher resolution in prospective studies, is necessary to investigate if different strains or species are indeed implicated in type 2 diabetes in both ethnicities to guide future prevention, diagnostic or therapeutic approaches.In conclusion, we examined ethnic specific type 2 diabetes-related alterations in the gut microbiome of two ethnic groups (South-Asian Surinamese and African Surinamese) living in the same geographical area. No statistically significant biomarkers were identified between controls and treatment-naïve subjects in both ethnicities. However, significant differences in gut microbiome composition and function were identified in metformin-treated T2D individuals compared to controls and more so in South-Asian Surinamese. The ASV with the strongest association was assigned to


Staphylococcus aureus agents and anti-pseudomonal agents. We compared the weekly numbers of initiations and discontinuations of antibiotic prescription on each day of the week or on the days after a holiday. There was no statistical difference in the number of antibiotic initiations on both weekdays and the day after a holiday. However, antibiotic discontinuation was significantly higher from Tuesday onward than Monday and from the second day than the first day after a holiday. Similar trends were observed regardless of the categories of antibiotics, hospital and admission ward, and AST activity. This study suggests that broad-spectrum antibiotics tend to be continued during weekends and holidays and are most likely to be discontinued on Tuesday or the second day after a holiday. This was probably due to behavioral factors beyond medical indications, requiring further antimicrobial stewardship efforts in the future.To encourage and guide antimicrobial stewardship team (AST) activity and promote appropriate antibiotic use, we studied the impact of day of the week on the initiation and discontinuation of antibiotic administration. This was a multicenter observational study conducted at 8 Japanese hospitals from April 1 to September 30, 2019, targeting patients who underwent treatment with broad-spectrum antibiotics, such as anti-methicillin-resistant  Following the approval of the AMR Global Action Plan in May 2015 by the World Health Organization2, we were strongly urged to promote our antimicrobial stewardship program. In Japan, the National Action Plan on AMR was developed in April 2016, raising the issue of AMR to be tackled by our society as a whole3.One of the most pressing concerns for global public health is antimicrobial resistance (AMR) undermining our healthcare system. The emergence of highly drug-resistant bacteria has been attributed to the excessive and inappropriate use of existing antibiotics4. The management of and mortality due to myocardial infarction and cerebral stroke is worse among patients admitted on weekends than among those admitted on weekdays6. In patients hospitalized on weekends for sepsis, in-hospital mortality, admission to the intensive care unit (ICU), and intubation can be worse than in those admitted on weekdays7. With regard to antibiotic prescription, inappropriate or excessive use may increase over the weekend in emergency centers8 and in primary care settings11, which cannot simply be explained by the prevalence or incidence of disease. Social or situational factors, such as limited staff, rather than simply medical indications, may have influenced the antibiotic prescription behavior observed in these studies. In a single-center observational study, the in-hospital use of broad-spectrum antibiotics  over weekends and holidays was found to be more than was necessary, which may have been due to behavioral factors beyond medical indications12.In general, hospital functionality is lower on weekends than on weekdays because of fewer medical and laboratory staff available. There are several reports on the association between day of the week and health service outcomes. For example, mortality rates for patients electively admitted on weekends may be worse, specifically in surgery, internal medicine, and genecology and obstetricsIn this study, we aimed to identify the differences of initiation and discontinuation in the use of broad-spectrum antibiotics between weekdays and weekends in multiple medical institutions.The selection of the study patients is shown in Fig. During the study period, scheduled and unscheduled admissions numbered 714 (32.5%) and 1483 (67.5%), respectively. Almost all the scheduled admissions occurred during weekdays , and there were fewer cases of admissions during weekends . In addition, unscheduled admissions were more frequent on weekdays  than on weekends . There were 1889 (86.0%) patients in the general wards and 308 (14.0%) patients in the ICUs. The number of patients newly administered with anti-MRSA drugs, carbapenems, and PIPC/TAZ were 538 (24.5%), 785 (35.7%), and 874 (39.8%), respectively. The total numbers of days of administration were 5376 days for anti-MRSA drugs, 6186 days for carbapenems, and 7101 days for PIPC/TAZ.p = 0.204) and on the day after a holiday .The numbers of broad-spectrum antibiotic initiations on each day of the week and on the day after the holiday are shown in Fig. p-value was < 0.001 between Monday and Tuesday, between Monday and Wednesday, and between Monday and Thursday, and p-value was < 0.005 between Monday and Friday , or on the second day than on the first day after the holiday (p < 0.05). In the ICUs, there were no significant differences among the weekdays, but there was a significant difference between the first day and second day after a holiday (p < 0.05). We performed an analysis of the relationship between the day of the week and the duration of treatment. There was no significant difference in the duration of treatment depending on the day of the week or the day after the holiday , hospitals , and ward categories . And there was no difference between the day of the week and the duration of treatment. The onset of infectious disease is not biased toward any particular day; therefore, the termination of antibiotic administration should be evenly distributed over the week. However, our results showed a particular variation within a week, suggesting that there are other factors apart from medical indications. Multiple factors, such as staffing shortages, may influence antibiotic prescription, as shown in previous reports12. Sub-analysis for each of the university and community hospitals in this study found similar patterns. In regional hospitals with few clinicians, they may be busy on weekdays with outpatient duties, which may delay the decision on the treatment plan for inpatients. Although AST interventions were effective in terms of appropriate use of antibiotics14, the results were similar even when AST activities and consultation with specialists were available. The fact that ASTs and specialists are generally working only on weekdays may have influenced the present study. The relatively increased number of antibiotic discontinuations on weekdays after Tuesday and after the second day of the holiday may also have been attributed to AST activity that starts up on Monday or the first day of the week.There are several possible explanations for the tendency of antibiotic discontinuation. A single-center observational study at Osaka University Hospital, Japan, reported significantly more discontinuation of antibiotics on Tuesday or the second day after a holiday, similar to the results of the present study. The AST at the hospital reviewed broad-spectrum antibiotics prescribed on a daily basis throughout the weekdays, but there was an uneven discontinuation of antibiotics. In academic medical institutions, it takes a full day for young clinicians to review laboratory data, discuss laboratory results with their attending physicians, and finally decide to discontinue or change antibiotic treatments. This may be one of the reasons for the discontinuation of antibiotics throughout the week15. Up-to-date notes on treatment left for the weekend staff can reduce their burden and make physicians responsible for decision-making. By assessing patients properly on a daily basis, we believe that antibiotic use can be further optimized. The decision to prescribe antibiotics is also influenced by the physician’s background, including their medical careers and specialty16. These personal trends in antibiotic prescription should also be well managed by the activities of the AST14. Additional hospital staffing during the holidays could improve antibiotic use and would be well worth the additional expenses. A hospitalist system could be a solution, as it would allow staff to work shifts and focus solely on ward duties, regardless of weekdays and holidays17.Solutions to disproportionate antibiotic prescription should be discussed. Recent British literature on hospital quality improvement reports that it would be valuable to conduct a review of antibiotic administration before the weekendThis study has several limitations. First, the study did not analyze patient outcomes, which are important in assessing the effectiveness of AST activity. Second, it did not analyze the organs that are infected. The diagnosis of infection can also affect the duration and timing of antibiotic initiation and discontinuation. Third, attending physicians prescribing antibiotics have not been analyzed. The specialty and experience of medical doctors can influence the pattern of antibiotic prescription.In summary, the study found a bias in discontinuing broad-spectrum antibiotics throughout the week, regardless of the types of facilities, wards, and AST activities. This result is expected to encourage and guide AST activity, leading to the appropriate use of antibiotics in this age of growing AMR. In particular, with regard to the use of broad-spectrum antibiotics, AST activity is expected even on weekends and holidays. Modifying daily AST activities, such as directing and scheduling the use of antibiotics prior to weekends, is needed to ensure that ward staff are able to cope with weekends and holidays when staffing is short. Differences in staffing between weekdays and weekends are common worldwide; thus, similar phenomena to those reported in this study may be observed elsewhere. Further research in different medical cultures to confirm the generalizability of the results observed in this study will be needed.We conducted a multicenter retrospective observational study on the in-hospital use of broad-spectrum antibiotics (defined below) at Okayama University Hospital (OUH) and 7 community hospitals. Two of these hospitals  were associated with the endowed course of Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, which conducts clinical research and social implementation trials for the education of general practitioners focusing on regional characteristics in the Setouchi Marine Area. Other facilities included Tsuyama Chuo Hospital (TCH), Okayama City Hospital (OCH), Yonago Medical Center (YMC), Okayama Kyokuto Hospital (OKH), and Okayama Memorial Hospital (OMH). These hospitals do not require pre-prescription authorization to initiate antibiotic treatment, and both attending physicians and residents can process orders for antibiotics. At OUH and TCH, infectious disease specialists work full-time, and they are available to discuss antimicrobial prescription with the attending physicians. At OUH, TCH, OCH, YMC, and OKH, the infection control team conducts prospective audits and feedback as an AST activity on weekdays, but not on weekends. This feedback is provided in the medical record alone, but at TCH and OCH, the attending physicians are contacted directly by AST members if necessary.The study period was from April 1 to September 30, 2019 (6 months). A collection of public holidays on the Japanese calendar  was excluded from the study because during this period, antibiotic prescription patterns were different from those on regular weekdays and weekends. All hospitalized patients who received broad-spectrum antibiotics during this period were included in the study. The data were obtained from the electronic medical records. The information about enrollment in the study was disclosed on each hospital’s website and by poster notice at each hospital with an opportunity for a denial of participation, and we provided a contact point for participants’ opt-out. A need of informed consent from each participant was waived and the study protocol was approved by the Ethical Committees of OUH (No. 2001-013) and adhered to the tenets of the Declaration of Helsinki and the Ethical Guidelines for Medical and Health Research Involving Human Subjects.Broad-spectrum antibiotics were defined as follows: (i) anti-MRSA drugs, including vancomycin, teicoplanin, linezolid, daptomycin, and arbekacin, and (ii) anti-pseudomonal drugs, such as carbapenems  and PIPC/TAZ.The weekdays were defined as Monday to Friday and weekends as Saturday and Sunday. Holidays were defined as national days off. Routine care was usually provided on weekdays. Hospital staff functionality, both numerically and qualitatively, was reduced during weekends and holidays, but not during weekdays. In general wards, there are usually no patient handoffs among attending doctors. We analyzed the initiation and discontinuation of antibiotic prescription on each day of the week. In addition, we investigated these patterns on the days after holidays to evaluate the influence of holidays on antibiotic prescription.post-hoc procedure. Analyses were performed using the EZR, version 3.5.2, which is a graphical user interface for R 18. Statistical significance was set at a p-value < 0.05.For treatment initiation, we compared the weekly numbers of antibiotic prescriptions that started on each day of the week. For treatment discontinuation, we compared the weekly numbers of antibiotic prescriptions discontinued on each day of the week or on the days after a holiday. We also performed sub-analysis individually between anti-MRSA and anti-pseudomonal drugs, between the university hospital (OUH) and the other regional hospitals, between general wards and ICUs, two hospitals with AST activity (OCH and TCH), and two hospitals with full-time infectious disease specialists (OUH and TCH). For the comparison of continuous variables, we applied the Kruskal–Wallis test and the Mann–Whitney U test with Bonferroni correction as a Supplementary Information 1.Supplementary Information 2.


The variety of approaches to tackle climate change reflects the size of this global problem. No technology will act as a panacea to cure the greenhouse gas emissions problem, but new building materials with byproducts or even wastes have the potential to play a major role in reducing the environmental impacts of the building sector. In this study, three potential solutions of concrete with dispersed reinforcement in the form of recycled fibers  were examined. The aim is to present a detailed analysis of the thermal properties of new building materials in an experimental approach. Concrete mixtures were prepared according to a new, laboratory-calculated recipe containing granite aggregate, a polycarboxylate-based deflocculant, Portland cement (52.5 MPa) and fibers. This experimental work involved three different contents of each fiber , and all tests were carried after the complete curing cycle of concrete (28 days). The topic of industrial development is being more and more often taken up together with topics of sustainable development and a circular economy as indisputably related. These discussions have led to the creation of many projects aimed at providing environmentally friendly solutions that can be used in the industry ,2,3,4,5.2, which comes during lime formation in the limestone heating and decarbonizing process, including all phases, namely raw material preheating, calcination, clinker burning and clinker cooling [2 from the cement manufacturing process. Therefore, both cement substitutes [Responsibility for upward of 5% of anthropogenic greenhouse gas emissions is for the cement industry to bear as its main building material nowadays ,9,10. Th cooling ,12,13. Astitutes ,15,16,17stitutes ,19,20,21stitutes ,23,24,25stitutes . Thus, istitutes ,28,29. The thermal properties of materials have been studied widely in recent years ,31,32,333 amount), they obtained around a 16% increase in thermal conductivity and around a 50% increase in specific heat compared to plain concrete. A similar increase in values of fiber concrete thermal conductivity was also obtained by Zhang et al. [3) allowed achieving thermal diffusivity in the range of 0.18–0.20 mm2/s, thermal conductivity in the range of 0.23–0.31 W/mK and specific heat in the range of 1.19–1.70 MJ/m3K, depending on the amount of sodium silicate, sodium hydroxide and fly ash used for concrete mixtures.The thermal properties of fiber-reinforced concrete have been studied by many authors in recent years ,51,52. Cg et al.  arrived g et al.  and Zhang et al. . The decg et al. . The higThe main goal of this study is to examine the effect of recycled fiber addition on the thermal properties of concrete, such as thermal diffusivity, thermal conductivity and specific heat. Three types of recycled fiber were tested, polypropylene, glass and steel, all fully obtained during the waste recycling process and have not been tested in the available literature so far. Thus, the presented results make an important contribution to this area of research and partly decrease the environmental damage caused by the construction industry.The materials used in this study are Portland cement (CEM I 52.5R), according to EN 197-1:2012 , and tapU = 5.72 and CC = 0.96) and well compacted [Crushed granite aggregate with a fraction of 0.063–2.000 mm was used as a filler in all concrete mixtures. Upper and lower sieve curves were determined in accordance with the EN 12620+A1:2010  standardompacted .An increase in the workability of the mixtures was accomplished, with a significantly reduced amount of water, by adding a chemical admixture to fiber-reinforced concrete. A water/cement ratio (w/c) of 0.3 was able to be maintained because of a low-alkaline liquid chemical admixture added. It was based on an aqueous solution of modified polycarboxylic ethers (melamine and silanes/siloxanes) and free of chlorine. The chemical composition of the admixture is presented in Polypropylene fiber (PF) was made fully from plastic packaging waste and modified by the extruder to increase its adhesion to the cementitious mix a,b. The Glass fiber (GF) obtained from waste materials was used in this study c,d. The The steel fiber (SF) used in this study was obtained by the process of recycling old tires e,f. AfteFour different types of concrete mixtures (one without fibers and the rest with different contents of fibers) were produced. The fiber content was 0.50, 0.75 and 1.00 wt.% (by mass of the cement), marked in this article as 0.50X, 0.75X or 1.00X, where X is the type of fiber (polypropylene (PF), glass (GF) or steel (SF)). The base recipe (without fiber) was marked as BM. In total, to investigate the effect of fiber content on the thermal properties of the cementitious mix, ten different mixtures were produced see . A constAll dry components, with fiber addition, were mixed together for 2 min. After that, wet components were added to the concrete mix. The whole mixing process lasted for 5 min. All samples were produced in laboratory conditions (21 °C temperature and 50% humidity) and stored in water according to EN 12390-2:2019-07 .The initial and final setting times were measured with the Vicat apparatus . Immediately after the mixing process was over, the Vicat mold was well compacted and filled with cement paste. Next, it was placed in the Vicat apparatus, and the plunger was removed to penetrate the paste. The initial time was measured when the penetration of the 1 mm diameter needle was just above 5 mm from the bottom of the mold base. After that, the 1 mm diameter needle was changed to a needle with an enlarged 5 mm hollow cylindrical base. The setting time was noted when this needle made an impression on the surface of the concrete but did not penetrate it.The pH value was determined using a pH meter . Tests were carried for a fresh mix with 10 mL of liquid phase as a 10% water solution of mortar according to PN-B-01810:1986 . After tThe viscosity test was carried out with a DV3T viscometer . The maximum speed of 200 RPM and the cycle duration of 20 s were taken as the boundary conditions of the procedure. Each concrete mixture, with and without fiber addition, was measured three times, and the average values were calculated. 3, from each series and each mix were used for testing. The actual dry mass of the samples was weighed and recorded. Then, they were put into a water bath at a temperature of 20 °C and a density of 998 kg/m³. After complete immersion, the samples were left in the water until the weight changes over 24 h were less than 0.2%. After removing the samples, they were wiped with a damp cloth, and the weight of the water-saturated sample was determined. After that, the samples were dried in a laboratory dryer at a temperature of 80 °C, and then the dry weight of the samples was measured. Based on the obtained results and the difference between the weights of dry and wet samples, the porosity was calculated.Three cubic samples, with dimensions of 150 × 150 × 150 mm3 standard cubes as per EN 12390-7:2019-08 [The density of hardened concrete samples was investigated using 150 × 150 × 150 mm:2019-08 . Ten spe3) of each concrete mix were tested this way, and the final result was calculated as the measurements’ average.Thermal conductivity, thermal diffusivity and specific heat were included in the scope of the research. Based on the temperature response analysis to the heat flow impulses of the material, all measurements were made. To do so, an ISOMET2114 analyzer  was used. Heat flow was excited by the resistor heater electrical heating inserted into the probe, which was in direct contact with the tested specimen. Probes with a 60 mm diameter and evaluated material with a minimum 25 mm thickness were used. The thermal conductivity evaluation and heat capacity volume were based on temperature records as a function of time taken periodically within an unlimited medium heat propagation. Five measurements in total of each sample were taken at different locations, and the temperature ranged between −15 and 60 °C. Ten cubes  are presented in The surfaces of PF, GF and SF are without roughness and smooth , therefoThe pH values of all recipes exhibit the alkaline reaction see . All pH The workability of all concrete mixtures is presented in Based on the test results , it can A slight influence of fiber addition in the density of concrete compared to the base sample can be observed . The hig3) showed about a 3.3% and 10.0% thermal conductivity increase depending on the plain concrete recipe. This, however, was indicated by the type of concrete that they tested (autoclaved aerated concrete), which had very low thermal conductivity, in the range of 0.11–0.13 W/mK, and high porosity. As the fibers took air void places, thermal conductivity rose, which can be expected in foam or aerated concrete. The tested polypropylene fiber concretes showed a decrease in thermal conductivity as well. For fiber additions of 0.50%, 0.75% and 1.00%, values lowered by 4.3%, 5.3% and 8.5%, respectively, compared to BM. The lowest thermal conductivity was reported for 1.00PF, which was equal to 1.72 W/mK. Zhang et al. [3 addition of steel fibers (an increase by 10%). Liu et al. [The thermal conductivities of the tested samples with different fiber types are shown in g et al.  reportedg et al.  as well.u et al.  studied 3K), and its value was about 30% lower than plain concrete, see 3K, 1.85 MJ/m3K and 1.80 MJ/m3K were lower by about 4%, 7% and 9% compared to BM, respectively. Steel fiber concrete, however, did not continue the decreasing specific heat trend presented above, and opposite phenomena were observed. Compared to plain concrete, samples containing steel fibers showed an increase in specific heat. The highest increase was reported for the highest number of fibers (about 2.5%). The same trend was reported by Algourdin et al. [3, an equivalent of 12.6% cement mass used in their mixtures.The specific heat measured in this study, presented in n et al. , who pren et al. , which w2/s (about 57% higher than plain concrete). Due to an increase in this property by 42.5% and 28.7% for 0.50GF and 0.75GF samples, respectively, it can be concluded that the higher the glass fiber content, the higher the thermal diffusivity of concrete. This trend applies also to concrete reinforced with polypropylene fibers. As this study shows, samples with a 0.50%, 0.75% and 1.00% addition of polypropylene fibers obtained a thermal diffusivity of 1.04 s, 1.11 and 1.16 µm2/s, respectively. It is worth mentioning that the highest increase in this modification was equal to 23.4%. Opposite phenomena were reported for steel fiber concretes as their thermal diffusivity decreased with increasing fiber content. The lowest values of thermal diffusivity (0.89 µm2/s) were noted for 1.00SF samples, which was about 5.3% less compared to BM. Fu and Chung [The thermal diffusivities of the tested samples are presented in nd Chung  also repNo significant influence on the initial and final setting time, pH values and density of concretes was determined after the addition of polypropylene, glass and steel fibers;The workability of all concrete mixtures was not affected by fiber addition, which was confirmed by conducted viscosity tests;All tested samples showed increased values in porosity compared to the base mixture. The highest porosity was reported for glass fiber concretes: 5.9%, 6.7% and 7.2% for 0.50GF, 0.75GF and 1.00GF samples, respectively. For concrete with 1.00% glass fiber, the obtained porosity was over three times higher compared to the reference sample;Thermal conductivity tests yielded values from 1.65 to 1.98 W/mK. The lowest thermal conductivity was reported for 1.00GF, which showed a decrease in values by 12% compared to BM. The highest thermal conductivity was reported for 1.00SF samples, as their results were 5.3% higher than BM;3K;A decreasing specific heat trend with increasing polypropylene and glass fiber dosage was reported. An opposite trend was defined for concretes reinforced with steel fibers. Specific heat values were in the range of 1.39–2.03 MJ/m2/s), and 1.00GF samples showed the highest thermal diffusivity (1.48 µm2/s). For polypropylene and glass fiber concrete, an increase in thermal diffusivity was observed with increasing fiber amount. Steel fiber concrete showed a contrary trend, as the values were much lower compared to BM.1.00SF samples showed the lowest thermal diffusivity  on the thermal conductivity, specific heat and thermal diffusivity of fiber-reinforced concretes. As there was no agglomeration process observed during mixing, it is possible to utilize recycled waste in fiber form by adding it to concrete mixtures. The tested materials can be applied in a precast concrete wall, as they require high thermal properties. On the basis of this study, it has been proven that the thermal properties of fiber-reinforced concrete depend on the type of fiber used in the manufacturing process. The obtained results of the fiber-reinforced concretes, through our experimental research, are as follows:This research is part of a wider study of sustainable solutions that can be introduced to the building sector and contribute to reducing environmental damage caused by the construction industry. The large-scale deployment of waste materials as mixed components could make the building sector more environmentally friendly and target the Paris Agreement levels.


Results: More than 70% of the attitudes were statistically different (p < 0.05) between hospital physicians (HPs) and primary care physicians (PCPs). When compared to PCPs, HPs showed higher agreement with antibiotic resistances being a public health problem and ascribed more importance to microbiological tests and to the influence of prescription on the development of resistances. On the other hand, PCPs tended to agree more regarding the negative impact of self-medication with antibiotics dispensed without medical prescription and the need for rapid diagnostic tests. Seven out of nine sources of knowledge’s usefulness were statistically different between both settings, with HPs considering most of the knowledge sources to be more useful than PCPs. Conclusions: Besides the efforts made to improve both antibiotic prescribing and use, there are differences in the opinions between physicians working in different settings that might impact the quality of antibiotic prescribing. In the future, these differences must be considered to develop more appropriate interventions.Background: Antibiotic resistance is a worldwide public health problem, leading to longer hospital stays, raising medical costs and mortality levels. As physicians’ attitudes are key factors to antibiotic prescribing, this study sought to explore their differences between primary care and hospital settings. Methods: A survey was conducted between September 2011 and February 2012 in the center region of Portugal in the form of a questionnaire to compare hospital ( Antibiotic resistance is a worldwide public health problem, leading to longer hospital stays and raising medical costs and mortality levels ,2,3,4,5.To improve the effectiveness of antimicrobial stewardship interventions, their design should be multifaceted, multidisciplinary and based on the characteristics of each specific setting ,13,14. HAttitudes are the key factors in the antibiotic prescribing process ,15,16. HA survey was conducted in Portugal’s Centre Regional Health Administration (ARS-Centro) , from September 2011 to February 2012. Determinants of prescribing were assessed in all PCPs working in the National Health Service facilities of the ARS-Centro and hospital care physicians working in internal medicine departments within the hospitals of the ARS-Centro.This study was approved by Portugal’s Centre Regional Health Administration (Permit No. 015650/2011), by the hospital’s administration and by the Portuguese Data Protection Authority  (Permit No. 2886/2013). The personally addressed, reply-paid self-administered questionnaires were sent by post mail to PCPs up to four times to nonrespondents. In the case of HPs, the questionnaires were hand-delivered to the director of each hospital/medical service, who distributed them among the HPs. The answers were then collected by the administrative services of the department. The respondents did not receive any incentives. Nine hundred and fourteen questionnaires were delivered to PCPs and two hundred and twenty-seven questionnaires to HPs. All the data concerning PCPs’ knowledge and attitudes has been previously published [Instructions to complete the questionnaire;“Antibiotics and Resistance”: 17 statements regarding the knowledge and attitudes towards antibiotic prescribing, antibiotic use and antimicrobial resistance. To each of these statements, an attitude was attributed;“In the treatment of respiratory infections, how would you rate the usefulness of each of these sources of knowledge?”: 9 statements regarding the importance of having several sources of knowledge, which can help comprehend the sources of knowledge underlying antibiotic mis-prescription;Sociodemographic and professional data ;Open box for additional comments.All physicians were asked to fill a previously validated , twoThe measurement of the agreement with the questions included in U test. Differences were established as statistically significant at p < 0.05. The statistical analysis was performed using SPSS 25  and MS Excel  software.As the variables did not follow a normal distribution, nonparametric tests were conducted. Differences in the results between HPs and PCPs were evaluated using the Mann–Whitney In order to assess whether the differences observed were influenced by the fact that some physicians reported to work on both settings, a sensitivity analysis was conducted, in which these physicians were excluded.The overall response rate was 47.8%: of the PCPs; 421 answers were obtained, which corresponded to an overall response rate of 46.1%; 124 of the HPs invited accepted to participate in the study, corresponding to an overall response rate of 54.6%.The process of distributing and collecting the questionnaires is summed up in the Statistically significant results were obtained for the attitudes such as ignorance, responsibility of others, fear, complacency and indifference statements. Regarding ignorance, hospital physicians showed a higher agreement with resistance being a public health problem (S1), the importance of microbiological tests (S2) and the influence of prescriptions on the development of resistances (S4) compared to PCPs.About the responsibility of others, HPs appear to be less convinced that the development of new antibiotics will solve the resistance problem (S5). On the other hand, PCPs tend to agree more regarding the negative impact of self-medication with antibiotics and dispense without medical prescription (S12 and S13). Furthermore, PCPs expressed a higher agreement about the need of rapid diagnostic tests (S3).PCPs agreed more with the prescription of antibiotics in situations of fear and uncertainty (S7 and S8), as well as about having a complacent attitude with their patients (S10).The differences found in the usefulness of sources of knowledge were all related to HPs considering some of the sources evaluated more helpful. Differences were found for clinical practice guidelines, documentation from the industry and from medical information officers, continuous educations, contribution of specialists, peers and the internet.Overall, after the sensitivity analysis, the differences between PCPs and HPs, have remained constant in almost all dimensions. However, the attitudes of fear (S7) and responsibility of others—other professionals (S13) were no longer significant. Furthermore, despite that the distribution remained the same as the one reported in This study shows that, besides an overall increasing of both apprehension and knowledge regarding health professionals and patients, there are still differences in the knowledge and attitudes that may be important to tackle in future healthcare interventions ,19. FurtRegarding the attitudes underlying antibiotic prescribing, statistically significant differences were found for the responsibility of others, fear, indifference, complacency and ignorance in 12 of the 17 statements evaluated. These constitute somewhat unsettling results, as it reveals how different the attitudes concerning resistance between both settings can be.Most HPs and PCPs agreed that antibiotic resistance is an important public health problem in their settings, reflecting the knowledge regarding antibiotic resistance is a distressing worldwide public health problem, increasing medical costs and mortality levels ,5,20,21.The development of new antibiotics is also a point of discordance between PCPs and HPs: the first are more convinced that new antibiotics will be developed, and the literature shows promising lines of research . HoweverSignificantly different answers were also obtained regarding the usefulness of the microbiology results in deciding which treatment to provide and the availability of the diagnostic techniques. The results of blood cultures help to reduce antibiotic use and narrow antibiotic therapy, thus reducing the costs , and theDifferences were also found regarding the responsibility of patients or pharmacies, where PCPs agreed more that patients may manage to obtain antibiotics even without a prescription and/or self-medicate. This difference in physicians’ perceptions might be related to the proximity between primary care and community pharmacy practices. Nevertheless, the evidence is published about the dispense without a prescription ,33 and sFear about future bacterial resistances was also an attitude where physicians showed differences. PCPs agreed more about prescribing broad-spectrum antibiotics, in the case of diagnostic uncertainty or the impossibility of following-up patients. This might be explained with the unavailability of diagnostic tools to aid physicians’ diagnosis in primary care settings and is concordant with the previous research ,35,36,37The statistically significant opinions in which physicians showed the least agreement were related to insistence from the patient, patient trust and time constraints. Again, differences were found, and PCPs seem to be more complacent with patients than the HPs. There is data indicating that both physicians consider patient satisfaction is crucial, so it is vital to manage patients’ expectations ,38, nameHPs tended to show higher agreement about the usefulness of the sources of knowledge. With exception to the courses promoted and organized by the pharmaceutical industry and previous clinical practices, all the other sources of knowledge evaluated were considered more useful by HPs. The reasons behind these differences are complex to understand, but the need to be continuously updated in a hospital care context might be associated with the results found. Furthermore, as the HPs tended to be younger, they might feel the need to resort to other sources of knowledge to compensate for a lower clinical practice experience when compared to PCPs, who tend to be older. Perhaps younger doctors rely more on sources of information based on scientific evidence, while older doctors rely on their clinical experience.Considering the fact that the vast majority of antibiotics are prescribed in primary care , the ideThis study is accompanied by some limitations. Despite the statistically significant differences in attitudes, we cannot assure it triggers different antibiotic prescribing practices between settings. Considering the geographical limitations, the low response rate and specific characteristics regarding physicians’ clinical practice and the Portuguese Health System, the extrapolation of the results to other countries might not be adequate, as it can compromise its external validity. Nevertheless, the internal validity has been assured, as the questionnaire has been previously validated ,45. The To conclude, the results of the study revealed that there are differences in opinions between physicians working in hospital and primary care that might impact the quality of antibiotic prescribing. Therefore, interventions to improve the antibiotic prescription quality should be tailored to each setting, especially considering the more evident differences between primary care and HP attitudes  for a more effective tackling of this global concern.


The aim of this research is to recommend a set of criteria for estimating the compressive strength of concrete under marine environment with various saturation and salinity conditions. Cylindrical specimens from three different design mixtures are used as concrete samples. The specimens are subjected to different saturation levels  on water and water–NaCl solutions. Three parameters  of concrete are measured using two NDT equipment in the laboratory while two parameters (density and water-to-binder ratio) are obtained from the design documents of the concrete cylinders. Three different machine learning methods, which include, artificial neural network (ANN), support vector machine (SVM) and Gaussian process regression (GPR), are used to obtain multivariate prediction models for compressive strength from multiple parameters. Based on the R-squared value, ANN results in the highest accuracy of estimation while GPR gives the lowest root-mean-squared error (RMSE). Considering both the data analysis and practicality of the method, the prediction model based on two NDE parameters  and one design parameter (water-to-binder ratio) is recommended for assessing compressive strength under marine environment. In many general design codes [cf is needed in the new construction sites to determine the timing of critical site works such as reshoring, demolding and post-tensioning and to achieve quality control and quality assurance (QA/QC) [cf has a good correlation with durability indices  [cf in structures to better understand the structural integrity and durability of new and existing structures.Concrete in buildings and civil infrastructure systems is employed to mainly resist compressive stress in structural members under various external loadings. Consequently, compressive strength of concrete, gn codes ,2, compr (QA/QC) ,4,5. It ffusion) ,7,8. Thecf and UPV in samples of mortars with 25% of pozzolanic content.There are several measurements methods for evaluation of compressive strength of concrete in the laboratory and the filed practices. First, compressive strength of concrete can be typically assessed using a destructive method on regularly shaped concrete samples in construction sites and/or core samples directly collected from various sections of concrete members . While tcf. However, P-wave velocity of concrete is affected by many factors such as smoothness of contact surface during test, temperature, moisture content, material and mix proportion and presence of reinforcing steel or fiber [Until now, P-wave velocity calculations have been mostly used in the laboratory and in the field practice to assess UPV of concrete and to estimate or fiber ,17 that or fiber ,19,20. Ror fiber ,22,23. HSome researchers argued the importance of the use of multiple NDE parameters that complement the use of UPV. Each individual nondestructive test (NDT) evaluates a different set of parameters such as UPV for wave velocities ,25,26, rThere have been several data fusion methods that relate multiple sensor data to estimate compressive strength of concrete. Regression analysis is one of the common methods that has been used for estimation of mechanical properties of concrete, specifically, its compressive strength. For predicting associated tasks, regression techniques are the simplest and most efficient; however, the performance of the regression analysis results is strongly dependent on a relationship between various parameters (independent parameters) and compressive strength of concrete (dependent variable) that is predefined before the regression analysis. Therefore, it is necessary to establish consolidated knowledge background on the variables of interest in regression techniques. Another method of data fusion is through machine learning. Machine learning algorithms employ computational methods to “learn” information directly from data rather than relying on a model based on a predetermined equation. As the number of samples available for learning grows, the algorithms adjust their performance. Examples of this methods are the artificial neural network (ANN), support vector machines (SVM) and Gaussian process regression (GPR). Most studies that use regression analysis for prediction of compressive strength of concrete compare the results with ANN analysis ,41,42,45While data fusion has been widely used in estimating the properties of concrete, it is seldom that the effect of water saturation and other environmental factors, such as presence of sodium chloride in concrete, are included in estimating the concrete compressive strength. Concrete, a porous and heterogenous material, comprises several types of voids   that canThe objective of this study is to estimate the compressive strength of concrete using the combination of different NDT parameters, ultrasonic velocities (P- and S-waves) and ER of concrete, and two physical parameters of concrete, density and water-to-binder ratio of concrete. Experimental and data fusion materials and methodology of this study is discussed in the succeeding section. The concrete specimens used in the experimental part were commercially sourced from a batching plant and the details of material properties are described in Sample cylinders, with 200 mm height and 100 mm diameter, were manufactured for all the tests that were done for the study. Three concrete mixes were used, with different water-to-binder ratios—MIX 1, MIX 2 and MIX 3. The properties and quantities of the samples are presented in SD represents the estimated degree of saturation in a unit of % at time t after an oven-dry concrete cylinder is immersed in water, i is index of the constants. Saturation curves were developed to use as reference for the tests on saturated concrete cylinders, both in water and water–NaCl solution. Five target saturation levels were considered for this study—standard saturations (oven-dry and saturated-surface dry), 25%, 50% and 75%. The variables for this procedure were the proportion of the mix and the time spent immersed in the water. Three examples from each design mix were utilized in this approach, for a total of nine cylindrical specimens. After being cured in water for at least 150 days, the specimens were dried in an electronic oven  for at least 72 h at a constant temperature of 105 °C. The specimens’ mass was measured thirty minutes after they were removed from the oven. The specimens were then placed in small tanks in groups, and tap water was gently added to ensure that they remained submerged in water after the initial water absorption of the concrete. For the first ten hours, the mass of the specimens was recorded every 30 min. Excess water was wiped off the specimens with a moist cloth when they were removed from the tanks. The cylinders were then returned to the water for continuous immersion once the mass was recorded. Then, the mass was continuously measured every 24 h until the tenth day of immersion time. During testing, the actual saturation was an estimate of the target saturation from the reference curve. The actual readings in this study are extremely close to the target saturation levels, demonstrating the procedure’s efficiency. After actual saturation of the cylinders, nondestructive and uniaxial compressive tests were performed. The inclusion of saturating concrete specimens with NaCl was studied to consider the effect of other environmental factors in the properties of concrete.pV, was determined by dividing the travel distance by the travel time of the waved is the distance between transducers, at is the time of first wave arrival and dt is the delay time, calculated during calibration of the probes. Delay time was determined when time for the first arrival wave was registered when the two transducers were positioned against each other. The first arrival time of the P-waves, at, was determined by the modified threshold method [A total of fifteen groups were assembled for this experiment. For each design mix, five specimens were prepared for the measurement from each saturation level, giving a total of 75 specimens. The standard test procedure according to ASTM C 597/C597M-16 was used to assess the P-wave velocity of concrete cylinders with five different saturation levels [d method .The S-wave velocity of concrete was measured using the P-wave velocity method described in the previous section but using a different pair of transducers . The S-wave transducer has a weight of 340 g with dimensions of 114 mm (length) by 84 mm (diameter), which is portable. Its eight dry point shear wave sensor array does not require extra coupling agent (such as a sticky and viscous coupling gel). This minimizes the influence of coupling conditions between the concrete surface and the transducer. Moreover, the shear wave sensors’ dry contact function substantially improves test speed while ensuring accurate and consistent data gathering. Electrical resistivity (ER) of concrete was measured by a commercially available four-point Wenner probe with an electrode spacing of 38 mm. Four electrodes are aligned linearly at equidistant with each other see . The devAfter measuring the three NDE parameters (P- and S-wave velocities and ER), the compressive strength of the cylindrical specimens were measured using a 2000 kN universal testing machine . The specimens were at the age of around 226 to 263 days at the time of testing. The compressive strengths were tested in accordance with ASTM C39/C39M-20  under diBefore combining different parameters for estimation purposes, it is important to pre-process the data to be used for the different data fusion methods. Statistical analysis was used to verify the experimental data to be used for the study. Coefficient of variation was used to determine the variability of the NDT measurements as well as the compressive strength of the concrete specimens. The Pearson correlation was also calculated to determine the strength of association between the different parameters and to the dependent variable, compressive strength of concrete. Outliers were also determined to improve the estimation analysis. When there is an outlier, the corresponding parameter for that outlier data was also removed from the analysis. This is done so that there is a balanced amount of data for all the parameters used in the analysis.A computer software (SPSS Statistics from IBM) was used for regression analysis and the nonlinear regression analysis were considered for evaluation. In this software, the nonlinear function model was defined by the user, and the coefficients and constant were estimated by doing many iterations until the differences between the previous and current estimates were minimal. Different functions were defined, depending on the number of combinations used, with different initial estimates for the coefficients and constants. The function defined is additive with each term characterized depending on the relationship of the compressive strength with a particular parameter. Previous studies have established that the relationship between wave velocities and compressive strength is exponential ,81,82,83S is the S-wave velocity in km/s, P is P-wave velocity in km/s, D is the density in g/cm3, ER is the electrical resistivity in kΩcm and WB is the water-to-binder ratio. B1, B2, B3, B4, B5, B6, B7, A1 and A2 are the coefficients and constants estimated by the software. Depending on the number of parameters being combined, the number of the constants and the coefficients would change. In the equation presented, all five parameters were considered. Finally, the effectivity of the statistical method was compared to the results of the machine learning analysis.In general, the function that was defined in the program is presented below:iy is the weighted sum in the ith neuron, ix is the input in the ith neuron, ijw is the weight between the ith and jth neurons, bj is the bias in jth neuron and f is the activation function.For artificial neural network (ANN) method, one of the Machine Learning Toolbox from MATLAB software was used. The data set used for analysis was subdivided into three groups: training set, validation set and test set. Each group had a corresponding number of data that can be selected inside the toolbox. For the present study, the data distribution is as follows: 70% of data set for training, 15% for validation and 15% for testing. The analysis was done with one hidden layer selected for the ANN architecture as shown in Another one of the machine learning applications from MATLAB software was used, which is the regression learner (RL) method. For this study, only the models support vector machine (SVM) and Gaussian process regression (GPR) were considered after initial analysis with the other methods in MATLAB software. The general method used for regression training is shown in x, y) where x is the input vector, y is the target value and N is the size of the dataset acquired by the mapping of x into a high-dimensional feature space:Support vector machine (SVM) has been used widely for classification and recently, also been used for regression ,19,20,21The goal of the SVM is to make the function as flat as possible, i.e., to minimize the structural risks in the model.The nonparametric, Bayesian approach to regression known as Gaussian process regression (GPR) is making waves in the field of machine learning. GPR has many advantages, including the ability to work with small datasets and provide uncertainty measurements on forecasts. Because GPR is nonparametric , rather than computing the probability distribution of parameters of a single function, GPR computes the probability distribution of all admissible functions that fit the data. Few studies have already used GPR in estimating properties of concrete ,62,63,64It is important to analyze and verify the experimental data obtained from this study before doing the data combination. When investigating the consistency and reliability of the test methods used for this study, experimental variability was examined. For this research, the coefficient of variation (COV) was calculated and used as a means for evaluating the experimental variability of the NDT measurements as well as the compressive strength of the concrete cylinders. Moreover, outliers were determined by the quartile method. The summary of the statistical analysis is presented in The COV of the density ranges from 1.53% to 3.12% for concrete cylinders with different mixture proportions and saturation levels. The water-to-binder ratio was not included in the analysis as the data set was subdivided with respect to the different mix proportions. The COV for P-wave velocity ranges from 4.76% to 6.39% and for S-wave velocity, the range is from 1.43% to 2.17%. One outlier data was removed from the set of S-wave velocity before analysis. These values show that the P-wave velocity are more affected by the water saturation than the S-wave velocity, regardless of the type of liquid they were saturated with. For electrical resistivity (ER), the values of COV are very large because ER is greatly affected by the amount of water present in the concrete. The values from the oven-dried concrete specimens were not recorded since the values exceeded the capacity of the equipment. As for the compressive strength, the COV ranges from 11.55% to 20.22%. This can be explained by the levels of saturation that the concrete cylinders were exposed in. There were two outliers excluded from the analysis from this parameter.cf. p-values.Using the SPSS software from IBM, the different parameters were tested on their correlation with each other, as well as to the dependent variable, All five parameters: P, S, ER, D, WB.P, WB, ER.S, WB, ER.P, WB.S, WB.P, ER.S, ER.P, S.P.S.Based on the calculated Pearson correlation coefficients, the most influential parameter that can affect the compressive strength is the S-wave velocity and the water-to-binder ratio. Analysis of this combination was also checked together with the electrical resistivity to consider the saturation degree and effect of the presence of NaCl in the environment. The combinations were chosen according to the correlation coefficients between parameters and the target variable, which is the compressive strength, and the practicality and ease of data collection, e.g., ease of use of NDT equipment. The combination of parameters considered for this study is listed as follows:c,testf and c,predf, respectively, using different combinations of the five parameters  from multivariate regression analysis. c,testf and c,predf. For these equations, c,predf is in MPa, P and S are in km/s, ER is in kΩcm and D is in g/cm3. As can be seen from 2, ranging from 0.818 to 0.930 with the exception of using P-wave velocity alone, with R2 of 0.440, and P-wave velocity and electrical resistivity with R2 of 0.118. Using ER alone was not included in the summary since from initial analysis, it gave an unacceptable of R2. This can be explained by the results obtained when the concrete specimens were under the oven-dried conditions and the high variability of the values across all saturation conditions. Using the combination of P-wave velocity and ER gave the lowest R2 of 0.118 among the 10 combinations considered. This can be explained by the effect of water saturation in both the P-wave velocity and ER values. On the other hand, using S-wave velocities alone already gave an acceptable value of coefficient of determination equal to 0.838.2 value calculated by the nonlinear analysis is equal to 0.844. For the combination of P-wave velocity and water-to-binder ratio, the R2 value from the nonlinear analysis is equal to 0.886. As discussed from It can be observed from While regression analysis is easy to interpret, it entails a background in statistical training, and are frequently constrained by rigorous normality, variable independence, one-pass approximation, linearity, dimensionality, among others. In addition, it includes a lengthy and difficult computation and analytical technique since initial value must be assumed for each parameter. Background on the relationships and/or correlations of each estimator to the compressive strength must also be known to make the analysis and iterations faster.2, of eight sets of combinations in compressive strength estimation.For this research, MATLAB was used with its integrated application of Neural Net Fitting. Seventy percent of the experimental data were allotted for training and fifteen percent each for validation and testing analysis. A few rounds of training were done with different configurations to determine the optimal model that can predict the compressive strength of concrete. The training algorithm used in this study is the Lavenberg–Marquardt (LM) network since it requires less time but still requires more memory. The same sets of combinations from the multi-variate regression were used for ANN analysis. ANN tool in MATLAB do not analyze single parameter for data fusion. For this analysis, the coefficient of determination was used as a criterion in determining the best combination of parameters in estimating the compressive strength of concrete. c,testf and c,predf, respectively, based on ANN analysis from the neural network fitting application in MATLAB. Predicted results from the toolbox can be also stored after the analysis to compare with the actual values. Based on the R2 values, the best combination from ANN analysis is the combination of five parameters. This is derived from ANN analysis with 10 hidden neurons. The ranking with respect to the best combination of parameters is the same from the multi-variate regression analysis. The R2 values from the different combinations are relatively close to each other. In this case, based on ANN analysis, all the combinations are sufficient to estimate compressive strength of concrete. Playing with different number of hidden layers of neurons, it was observed that increasing or decreasing the number would not greatly affect the overall R2 value of the combinations. It is better to use lesser number of hidden neurons since large number of neurons may result to overfitting the data. If that happens, it might mean that the model did not learn the trend and thus will not be able to generalize to new available data. While it is suggested to use fewer hidden layers for ANN, it is still best to be cautious because too few might yield to an underfitting or bias model that would not be able to fit new data as well.As can be seen from From this analysis, although the combination of all five parameters gives the highest coefficient of determination, other combinations tested are sufficient to estimate the compressive strength of concrete. The practicality of gathering the parameters might be one criterion to determine which is the optimum combination to use in the estimation. Moreover, while ANN is considered more accurate than multi-variate regression analysis, the relative importance of the various parameters is not provided by the ANN. ANN also requires a large training data set to accurately predict a property. Moreover, the neural network being considered as a “black box”, its approximation will not provide any insight into the shape of the function. There is no straightforward relationship between the weights and the estimated function. Even determining which input feature is irrelevant is a challenge.c,testf and c,predf, respectively, using different combinations of the five parameters from SVM and GPR in the regression learner application in MATLAB. The correlation results from SVM and GPR are similar to those obtained from ANN in 2) values from SVM and GPR for the prediction of compressive strength of concrete using 10 different combinations of the five input parameters . Kernels and predefined models used in this study are also presented in 2 values from both methods are almost similar. Both methods gave the highest R2 to the combination of all five parameters while the lowest R2 came from using only P-wave velocities. The R2 values from SVM and GPR were comparable to the values from ANN if same input parameters are used.The effectivity in estimating the compressive strength of the concrete using different methods were compared in terms of their R-squared. The calculation of root mean square error (RMSE) is another way of comparing the different methods used for this study. The coefficient of determination, R-squared, is helpful when trying to rationalize what considerations might be driving the fundamental process of interest for the dependent variable. RMSE, on the other hand, gives an indication of how close the estimated values are to the actual observed data. This is useful in a range of applications to comprehend the accuracy of the model’s predictions. 2 and RMSE, among the three methods from machine learning used, GPR is the most promising giving the good values for both R2 and RMSE. However, it should be noted that all four methods gave good values of R2 and RMSE as shown in Based on the results of both the statistical criteria, R2 = 0.44 and RMSE = 18.04). This shows that P-wave velocity is greatly affected by the saturation condition of concrete. Adding another NDT parameter to both wave velocities may improve their performance. In this case, electrical resistivity, which is a parameter that is also easy to measure, was combined to the wave velocities. The results for both combination  improved but not significantly for S-wave velocity. In terms of coefficient of determination, addition of ER to S-wave velocity improved its performance by only 6.98% but to P-wave velocity, the improvement was 70.45% except when the conventional regression analysis. Adding another property to the NDT parameters might improve the performance significantly. In this study, water-to-binder ratio, which is a concrete property available through the design documents, is added to the ultrasonic wave velocities. As can be seen from Using only the single parameter, S-wave velocity, gives good values for the statistical criteria with values equal to 0.86 for coefficient of determination and 8.462 for RMSE. However, estimating concrete’s compressive strength using S-wave velocity needs more analysis and study since gathering of experimental and/or data is not easy and there is still limited research on this topic. On the other hand, the use of only P-wave velocity did not result in good values for the statistical criteria . The first three components are NDE parameters that can be measured in situ using the available NDT equipment while the last two components are generally available from the design documents. Among the four regression methods used, ANN gave the highest R-squared value equal to 0.97 while the use of GPR gave the lowest RMSE equal to 4.292. Based solely on the data analysis and on the values of RThe analysis from RMSE calculations and an additional statistical parameter  velocity, electric resistivity and water-to-binder ratio—are sufficient to estimate the compressive strength of concrete when it is exposed to wet condition or marine environment. However, when choosing between P-wave and S-wave velocity measurement, it is more practical to use P-wave as it is easier to measure than the S-wave velocity.This study also recommends to further investigate the potential use of S-wave velocity in estimating concrete under a saturated condition. This recommendation is based on the observations on the accuracy of using S-wave velocity, together with other parameters, in terms of the R-squared and RMSE values.In the end, using only one NDT parameter is not enough in estimating the compressive strength of concrete under a saturated condition. Considering the practicality and ease of NDT measurement, the combination of P-wave (or S-wave) velocity, water-to-binder ratio and electrical resistivity might be good enough to estimate the compressive strength of concrete exposed in different saturation environments.