The success of antimicrobial stewardship is dependent on how often it is completed and which antimicrobials are targeted. We evaluated the impact of an antimicrobial stewardship program (ASP) in three non-ICU settings where all systemic antibiotics, regardless of spectrum, were targeted on the first weekday after initiation.C. difficile infection (CDI), mortality, readmission, and length of stay were evaluated using univariate analyses.Prospective audit and feedback (PAAF) was initiated on the surgical, respiratory, and medical wards of a community hospital on July 1, 2010, October 1, 2010, and April 1, 2012, respectively. We evaluated rates of total antibiotic use, measured in days on therapy (DOTs), among all patients admitted to the wards before and after PAAF initiation using an interrupted time series analysis. Changes in antibiotic costs, rates of Time series modelling demonstrated that total antibiotic use decreased (± standard error) by 100 ± 51 DOTs/1,000 patient-days on the surgical wards (p = 0.049), 100 ± 46 DOTs/1,000 patient-days on the respiratory ward (p = 0.029), and 91 ± 33 DOTs/1,000 patient-days on the medical wards (p = 0.006) immediately following PAAF initiation. Reductions in antibiotic use were sustained up to 50 months after intervention initiation, and were accompanied by decreases in antibiotic costs. There were no significant changes to patient outcomes on the surgical and respiratory wards following intervention initiation. On the medical wards, however, readmission increased from 4.6 to 5.6 per 1,000 patient-days (p = 0.043), while mortality decreased from 7.4 to 5.0 per 1,000 patient-days (p = 0.001). CDI rates showed a non-significant declining trend after PAAF initiation.ASPs can lead to cost-effective, sustained reductions in total antibiotic use when interventions are conducted early in the course of therapy and target all antibiotics. Shifting to such a model may help strengthen the effectiveness of ASPs in non-ICU settings. Antibiotic overuse has led to increasing rates of antibiotic resistant bacterial infections and antimicrobial-related adverse events, resulting in increased patient morbidity and mortality . In respA recent systemic review and meta-analysis demonstrated that ASPs lead to reductions in total antibiotic use within hospitals as well as antibiotic acquisition costs. Although the strategies utilized were varied, most successful ASP programs incorporated prospective audit and feedback (PAAF) as a critical component . The magAlthough existing literature highlights the importance of PAAF, its impact is likely dependent on how frequently it is completed, how quickly after antibiotic prescription it is initiated, and which antibiotics are targeted ,8. EveryWe evaluated the impact of extending prospective audit and feedback to target all systemic antibiotics on all workdays through sequential, staggered time series analysis on three non-ICU clinical wards of a community teaching hospital.The study was conducted at Michael Garron Hospital (MGH), a 490-bed urban community teaching hospital in Toronto, Ontario, Canada. The study took place on three inpatient services: surgery , respiratory medicine, and general internal medicine. Patients were admitted to respective wards based on admission diagnosis. Most patients were admitted from the emergency department or electively post-surgical. Transfer between units was not restricted yet occurred infrequently. Prior to ASP implementation on the study wards, the hospital had an existing ASP utilizing PAAF in the Intensive Care unit, which began in April 2010. However, physicians on the three intervention wards had not received prior exposure to PAAF. The intervention was implemented in a staggered manner across time among the three services. On the surgical wards, the baseline period spanned from July 1, 2009 through June 30, 2010, and the intervention period from July 1, 2010 through September 30, 2014. On the respiratory ward, the baseline period lasted from October 1, 2009 through September 30, 2010, and the intervention period from October 1, 2010 through September 30, 2014. On the medical wards, the baseline period took place from January 1, 2010 through March 31, 2012, and the intervention period from April 1, 2012 through September 30, 2014. In order to compare the patient population before and after intervention implementation, patient characteristics such as age, sex, complexity, and discharge diagnoses, were obtained from the hospital’s decision support system. Patient complexity was measured using the Ontario Ministry’s Health Based Allocation Model Inpatient Grouping (HIG) weights. Patients are assigned HIG weights based on several factors, including age category, number of intervention events, and intervention type .Education was provided to clinicians about the importance and goals of antimicrobial stewardship prior to intervention implementation. This was completed through informal and formal presentations provided by an ASP physician during pre-planned department or division meetings. During these meetings, consultation was sought to determine preferred feedback mechanisms. The ASP team consisted of one of two infectious diseases physicians and two ASP pharmacists. The two ASP pharmacists had prior formal training in antimicrobial stewardship provision. The infectious diseases physicians involved with the ASP started working at the hospital providing infectious disease consultations in 2006 and 2011.Every weekday at 06:00, automated reports were generated for inpatients on intervention wards who were receiving any systemic antibiotic at that time. These reports were available for the pharmacists in hardcopy in the inpatient pharmacy. The ASP pharmacists were encouraged to be physically present on the ward to review cases with an aim to optimize antimicrobial use according to local published antimicrobial guidelines. The pharmacists were free to discuss changes to antimicrobial management with the clinical team caring for the patient before review with the infectious disease physician. After initial pharmacist evaluation, all pharmacist recommendations for optimization and challenging cases were reviewed with an infectious diseases physician daily. Following this discussion, a note was placed in the patient’s electronic medical record providing specific recommendations regarding optimizing antibiotic therapy. If the suggestions were of a more urgent or challenging manner, an ASP pharmacist or physician would provide the recommendations by phone or in person with the patient’s most responsible physician. The decision to change an antibiotic was left to the discretion of the clinical team caring for the patient.Our primary outcome was rates of total systemic antibiotic use  among patients admitted to surgical, respiratory, and medical wards at MGH in the baseline (before the introduction of PAAF) and intervention (after the introduction of PAAF) periods. In order to account for variable antimicrobial practices in different clinical areas, each ward acted as its own control. For the period between July 1, 2009 and November 30, 2009, paper-based charts were manually reviewed to determine antimicrobial use and calculate days on therapy (DOTs) for systemic antibiotics. On December 1, 2009, the hospital implemented an electronic medical record system, and for the remainder of the study period, the pharmacy’s computerized database was used to determine antibiotic use and calculate DOTs. Total antibiotic use was calculated as the sum of days on therapy for all systemic antibiotics. Antibiotic use was then standardized by patient days .C. difficile infection (HA-CDI). Antibiotic cost data was determined through use of financial charge data (in Canadian dollars) and standardized by patient days. HA-CDI cases were defined using the Ontario Ministry of Health and Long-Term Care case definitions and attributed to wards by the Infection Prevention and Control team based on review of epidemiological links, antibiotic exposure, and time spent in specific locations. HA-CDI rates were standardized per 1,000 patient days.Secondary outcomes included direct antibiotic costs as well as cases of hospital-acquired Process measures included the type and frequency of ASP recommendations over the intervention period. These recommendations were classified according to the Infectious Diseases Society of America guidelines , and recIn order to ensure no unanticipated patient harm as a result of intervention implementation, all-cause mortality, seven-day readmission (pertaining to an emergency department visit or admission to an inpatient unit at MGH) and mean length of stay were measured during the study period. All outcomes were standardized by patient days  on a particular ward per month.0 estimates the baseline level of total systemic antibiotic use prior to PAAF, β1 estimates the slope prior to PAAF, β2 estimates the change in the level of antibiotic use immediately after PAAF, and β3 estimates the change in the slope after PAAF. β1 and β3 are summed to obtain the post-PAAF slope [An interrupted time series analysis was performed using segmented linear regression models with auto-correlated error structures that were fit to each of the three ward-specific time series. We assessed changes in the mean level of total systemic antibiotic use pre-post intervention, and in the rate of change of total systemic antibiotic use over time, using a first-order autoregressive model. Each time series was plotted to visually identify patterns of interest. Thereafter, linear regression models with fitted ARIMA errors were used in our respective time series to account for the correlation between repeated observations. The residuals of the fitted models were assessed for autocorrelation using the Durbin-Watson and Ljung-Box tests, and by examining plots of the autocorrelation and partial autocorrelation functions. The model equation is:AF slope . P-value2 test for categorical variables, and two-tailed Student t-test for continuous variables. For all analyses, p-values ≤0.05 were considered significant. Statistical analyses were performed using R version 3.1.1 and were determined a priori.Changes in total antibiotic costs, rates of HA-CDI, all-cause mortality, length of stay, and seven-day readmission in each study ward before and after PAAF implementation were compared using the Wilcoxon rank-sum test. Patient characteristics in the baseline and intervention periods were compared using the χApproval for this study was obtained from the MGH Research Ethics Board. The need for individual patient consent was not required for this quality improvement project.Patient characteristics across the study wards during the baseline and intervention periods are presented in In each study ward, gender and patient complexity were similar in the baseline and intervention periods. On the respiratory and medical wards, the mean age of patients significantly increased in the intervention period. The majority of discharge diagnoses remained similar after ASP implementation. On the medical wards, however, the proportion of patients with diagnoses categorized as “other” significantly increased, predominately due to an increase in patients diagnosed with mental illnesses . Also, the proportion of patients diagnosed with multi-systemic or unspecified site infections increased significantly from 2% to 4% in the intervention period on the medical wards.On the surgical wards, a total of 1,176 antibiotic recommendations were made over the 51 month intervention period (mean 23.0 recommendations/month). Data on acceptance or rejection were available for 1,112 (94.5%) recommendations, of which 93.0% were accepted. After prospective audit and feedback implementation on the respiratory ward, 1,529 antibiotic recommendations were made (mean 31.8 recommendations/month). Data on acceptance or rejection were available for 1,495 (97.8%) recommendations, of which 97.5% were accepted. On the medical wards, 1,794 recommendations were made over the 30 month intervention period (mean 59.8 recommendations/month). Data on acceptance or rejection were available for 1,720 (95.9%) recommendations; of these, 97.3% were accepted. The most common recommendations made over the study period included optimizing the duration of antibiotic therapy (33.1%), recommendations to discontinue antibiotic therapy (22.4%), and changes in the route of administration (16.2%). The types and frequency of antibiotic recommendations by ward are presented in On the surgical wards, total antibiotic use decreased from 765 DOTs per 1,000 patient days in the baseline period to 572 DOTs per 1,000 patient days after PAAF initiation. The results of the interrupted time series model  demonstrOn the respiratory ward, total antibiotic use decreased from 946 DOTs per 1,000 patient days in the baseline period to 678 DOTs per 1,000 patient days after the introduction of PAAF. Time series modelling showed that there was a non-significant declining trend in total systemic antibiotic use prior to the introduction of the intervention (p = 0.061). Immediately after the implementation of PAAF, total systemic antibiotic use was significantly decreased by 100 DOTs per 1,000 patient days (p = 0.029), a 10% reduction. In the intervention period, the trend in total systemic antibiotic use on the respiratory ward demonstrated a non-significant decline (p = 0.234) .On the medical wards, total antibiotic use decreased from 430 DOTs per 1,000 patient days in the baseline period to 293 DOTs per 1,000 patient days after the introduction of PAAF. Time series modelling showed that there was a non-significant decline in total systemic antibiotic use during the baseline period (p = 0.139). Immediately following the introduction of the stewardship program, total antibiotic use was significantly decreased by 91 DOTs per 1,000 patient days (p = 0.006), representing a 20% reduction. In the months following the introduction of PAAF, the trend in total systemic antibiotic use on the medical wards stabilized (p = 0.537) .On the surgical wards, daily antibiotic costs were reduced by 35% after the introduction of antimicrobial stewardship, from $5.64 to $3.66 per patient day (p<0.001). Costs decreased from $9.40 to $5.51 per patient day following PAAF on the respiratory ward, representing a 41% reduction (p<0.001). After PAAF was implemented on the medical wards, antibiotic costs declined from $3.35 to $2.17 per patient day, indicating a 35% decrease (p<0.001).The incidence of HA-CDI demonstrated a non-significant declining trend after the implementation of PAAF on the surgical wards, from 0.82 to 0.39 cases per 1,000 patient days (p = 0.174), on the respiratory ward from 2.37 to 0.82 cases per 1,000 patient days (p = 0.060), and on the medical wards from 0.79 to 0.44 cases per 1,000 patient days (p = 0.079).Changes in clinical outcomes across the study periods are presented in C. difficile infection. There were no significant changes to patient outcomes on the surgical and respiratory wards following intervention initiation. On the medical wards, however, readmission increased, while mortality decreased.We evaluated the impact of prospective audit and feedback targeting all systemic antibiotics in three non-ICU services of a large community hospital. The intervention was associated with statistically significant, immediate reductions in total antibiotic use across the three locations, despite differences in patient populations and the timing of intervention initiation. After PAAF was introduced on the surgical, respiratory, and medical wards, total systemic antibiotic use was reduced by 12%, 10%, and 20%, respectively and these reductions were sustained for up to 50 months following the implementation of the intervention. PAAF was also associated with substantial reductions in antibiotic costs and a non-significant declining trend in the incidence of nosocomial Although other studies have demonstrated reductions in total antibiotic utilization after the implementation of antimicrobial stewardship, our study adds substantially to the existing literature. We staggered intervention implementation across the study wards, allowing us to evaluate the independent impact of our intervention on wards with different patient characteristics. There is limited literature evaluating the differential impact of PAAF in different clinical settings outside of the ICU, yet in our study we demonstrated that our intervention is generalizable to general surgical wards, as well as general medical and respiratory wards. Current literature evaluating the impact of comprehensive ASPs on surgical wards is limited and have shown variable success –15. Our Determining which antibiotics to target and how frequently to provide PAAF is often an arbitrary decision at the initiation of an ASP with little evidence to guide these program decisions. Many published ASP interventions used weekly rounds but provided little direction to clinicians outside of guidelines or education between rounds . As suchA major factor predicting the success of an ASP is physician engagement. The success of PAAF demonstrated in the literature is likely related to the combined impact of the intervention to optimize antimicrobial use as well as the additional benefit of providing the opportunity for case-specific, real-time education with antibiotic prescribers. Each episode of PAAF is a learning opportunity to improve prescribing behaviors moving forward . By condC. difficile infection following the introduction of stewardship. The literature has shown mixed results concerning the impact of stewardship on nosocomial C. difficile infection; some studies have shown a beneficial effect [C. difficile after ASP implementation. This finding was, however, only based on three studies, and publication bias in this area was found to be significant [C. difficile infections, such that there may be insufficient power to detect changes. Although reductions in hospital-acquired C. difficile infection rates did not reach statistical significance, the consistent decrease in C. difficile infection on all study wards after intervention implementation suggests that further study in this area may be warranted.In our study, we observed a non-significant declining trend in the incidence of hospital-acquired l effect –21, whill effect . A recennificant . In our On the surgical and respiratory wards, patient outcomes did not change significantly following the introduction of antimicrobial stewardship. On the medical wards, however, readmission significantly increased in the intervention period, while mortality demonstrated a significant decrease. It is worthy to note, however, that we did not attribute mortality, readmission, or length of stay specifically to diagnoses related to infectious diseases, and thus, changes in these metrics could be due to factors other than stewardship. The increased readmission rate on the medical wards could be related to the increase in the proportion of patients with a mental health diagnosis during the intervention period, as the readmission rate for patients with a mental health diagnosis has been noted to be greater . While tC. difficile infection across wards. In our study, the medical wards had a longer baseline relative to the surgical and respiratory wards, which may have improved our ability to detect significant changes in antibiotic use on this ward. Electronic antibiotic use data became available in January 2010, and thus we used all available data to conduct analyses in the medical wards. In contrast, it was not realistic to use this baseline for the surgical and respiratory wards, as antimicrobial utilization data would need to be manually extracted from paper-based charts. Despite the longer baseline period, the results in the medical wards were similar to those found in the surgical and respiratory wards. Sensitivity analyses restricting the baseline period to the same length as the surgical and respiratory wards did not impact the significance of our findings. In addition, by restricting our analysis to total antibiotic use in specific wards, rather than total antibiotic use throughout the hospital, we may have disregarded antibiotic use that patients had on non-study wards. Lastly, we did not perform a detailed cost analysis to evaluate whether targeting all systemic antibiotics provided cost advantages over targeting only expensive or broad-spectrum antibiotics. Although cost avoidance is an important factor to assist in the financial justification for dedicated resources to provide stewardship, the goal of antimicrobial stewardship is to optimize antimicrobial use and resultant patient outcomes, not to save money.There are several limitations to our study. As our study was not a randomized trial, there were some changes in the characteristics of the study populations between the baseline and intervention periods that could have potentially contributed to changes in antibiotic use patterns. Patient complexity, however, remained similar, and the magnitude of the changes in discharge diagnoses were small. Furthermore, this study was not a prospectively designed randomized controlled trial, and therefore issues related to selection bias or co-interventions could impact the validity of the findings. The potential for bias was limited, however, by the implementation of our intervention at different times in each inpatient service. The lack of potential confounding is demonstrated by the similarity in direction and magnitude of changes in antibiotic use, costs, and nosocomial C. difficile. However, the impact of antimicrobial stewardship programs on clinical outcomes needs further study. These results illustrate that antibiotic stewardship can be successfully expanded outside of the critical care setting, and that the focus should shift from targeting only expensive and/or broad-spectrum agents to optimizing the use of all systemic antibiotics on all days. Each episode of PAAF acts as an opportunity to educate clinicians about the importance of antimicrobial optimization and assists with prescriber engagement. At a time when concerns about antibiotic misuse and resistance are rising, it is important that funding agencies allocate sufficient resources to stewardship programs to facilitate expanding the role of PAAF.Our study demonstrated the sustainable impact of an antimicrobial stewardship program using prospective audit and feedback, targeting all systemic antibiotics among patients on any duration of antibiotic therapy in three non-ICU settings of a large community hospital. There were substantial reductions in antibiotic utilization and costs, and a non-significant declining trend in the incidence of hospital-acquired S1 Dataset(XLSX)Click here for additional data file.


E. coli was detected in 107 (76%) out of the 141 positive urine samples. Antibiotic resistance rates of E. coli isolates for ampicillin, co-trimoxazole, ciprofloxacin, amoxicillin and nitrofurantoin were 59.8%, 31.8%, 23.4%, 1.9% and 0.9% respectively. E. coli isolates were sensitive to nitrofurantoin (98.1%) followed by amoxicillin (78.5%). The overall physician antibiotic prescription rate was 82.2%. Amoxicillin (39.6%) and nitrofurantoin (28.6%) were the most common prescribed antibiotics. Meanwhile, whilst physicians in public primary care prescribed more amoxicillin  and nitrofurantoin , physicians in private clinics prescribed more cefuroxime and ciprofloxacin (P<0.05). Matching of antibiotic prescription and antibiotic sensitivity of E. coli isolates occurred in public than private primary care prescriptions  and for other uropathogens . Mismatching differences of antibiotic prescription and resistance were not evident. In conclusion, nitrofurantoin and amoxicillin should be used as first line antibiotic treatment for uncomplicated UTI. There were significant differences in antibiotic prescription patterns between public and private primary care. Public primary care practitioners were more likely to prescribe first line antibiotic treatment which match antibiotic sensitivity of E. coli isolates and other uropathogens. Further exploration of physician prescribing behaviour and educational interventions, particularly in private primary care may helpful. Meanwhile, development and dissemination of guidelines for primary care management of uncomplicated UTI as well as continued surveillance of antibiotic resistance and physician antibiotic prescription is recommended.Uncomplicated urinary tract infections (UTI) are common in primary care. Whilst primary care physicians are called to be antimicrobial stewards, there is limited primary care antibiotic resistance surveillance and physician antibiotic prescription data available in southern Chinese primary care. The study aimed to investigate the antibiotic resistance rate and antibiotic prescription patterns in female patients with uncomplicated UTI. Factors associated with antibiotic resistance and prescription was explored. A prospective cohort study was conducted in 12 primary care group clinics in Hong Kong of patients presenting with symptoms of uncomplicated UTI from January 2012 to December 2013. Patients’ characteristics such as age, comorbidity, presenting symptoms and prior antibiotic use were recorded by physicians, as well as any empirical antibiotic prescription given at presentation. Urine samples were collected to test for antibiotic resistance of uropathogens. Univariate analysis was conducted to identify factors associated with antibiotic resistance and prescription. A total of 298 patients were included in the study.  Escherichia coli (E. coli) is the most common pathogen isolated in around 75% of uncomplicated UTI . Written formal consent was obtained from all participants.Participants were given verbal instructions on the steps of collecting a clean catch mid-stream urine (MSU) sample. The sample was collected in a sterile container refrigerated and delivered within the same day to the centralized accredited University Pathology laboratory for processing. Only one sample per patient was obtained.3, 103−4, 104−5, and >105 cfu/ml as no growth, insignificant, scanty, moderate and heavy growth respectively. Only urine cultures with heavy growth of a single bacterial type (>105 cfu/ml) were included in the analysis.In order to identify the major pathogen of UTI, the collected MSU specimens were processed for urine microscopy and cultures. Urine microscopy was performed by standard methods according to laboratory operating procedures . The urine sample was mixed well and a known quantity was examined under the inverted microscope at 20X power field in a flat-bottomed microtitre tray. The number of red and white cells were enumerated and reported as negative, moderate , large number  of cells. Specimens were inoculated by the paper strip method (MAST) onto chromogenic medium  and incubated aerobically for 18–24 h at 35°C. The number of colonies were enumerated and reported with 0, <10To identify antibiotic resistance patterns in the urinary isolates, the samples were further tested for antibiotic susceptibility to amoxicillin, ampicillin, ciprofloxacin, co-trimoxazole, gentamicin and nitrofurantoin and to detect ESBL positivity. Antibiotic susceptibilities were performed and interpreted as according to Clinical and Laboratory Standards Institute (CLSI) . Specimens which were not processed immediately were stored at -4°C.Descriptive statistics  were used to describe the frequency of bacterial pathogens, antibiotic resistance and prescription pattern.E. coli antibiotic resistance was stratified with age group and chronic medical conditions and analysed by chi-square test. Odds ratios (OR) with 95% confidence intervals (95% CI) were calculated to identify factors associated with E. coli isolates resistance to antibiotics and for antibiotic prescription. A two tailed P< 0.05 was considered significant and all statistical analyses were performed by using SPSS v 21.0 Window .Chi-squared test and univariate analysis were used for statistical analysis. Of the invited group clinics, 5 public and 12 private group clinics participated in the study and 306 female patient presentations were recruited into the study. Of these, eight clinical presentations were from previously recruited patients and only their first presentation were included in the study. Thus a total of 298 patients participated in the study of 145 (48.7%) and 153 (51.3%) were recruited from public and private clinics respectively . The meaOf the samples received, two were discarded as there was leakage on transit and could not be processed. A positive urine culture was found in 141 (47.3%) of the collected samples  whilst 7E. coli (75.9%). Other uropathogens included Klebsiella (9.2%), Enterococcus (3.5%), Proteus Mirabilis (3.5%) and Citrobacter (2.8%).Among the 141 positive urine isolates, the most common uncomplicated UTI pathogen was E. coli infection occurred mostly in women who aged 51 above (63.5%) and most likely to present with two or more symptoms (79.5%) of which dysuria, frequency and urgency were common symptoms experienced , amoxicillin (78.5%) and ciprofloxacin (76.6%) (Of the 107  (76.6%) .E. coli isolates with resistance to one drug can also be resistance to other drugs. Multidrug resistance  occurred in 12.1% of E. coli isolates (n = 13).E. coli isolates. 85.7% (n = 12) of ESBL producing isolates were found amongst older women who were aged 51 years and above. All ESBL producing isolates were resistant to ampicillin, whilst almost half were resistant to ciprofloxacin  and co-trimoxazole .ESBL producers (n = 14) were found in 9.9% of the positive isolates (n = 141) and almost all ESBL producers (n = 13) were from P = 0.011) for having resistance with ampicillin. In addition, The OR of patients who aged >50 were 8.14  for having multidrug resistance. Meanwhile, although antibiotic resistance appear higher in those with more than two chronic medical conditions for most antibiotic types, this was not statistically significant   .P = 0.029) for resistance to ciprofloxacin than those without antibiotics treatment. In addition, the OR of those exposed to antibiotics in the past year were 3.29  to have multidrug resistance than those without previous antibiotics treatment  to have ciprofloxacin resistance  were prescribed antibiotics. Amoxicillin (39.6%) was most commonly prescribed, followed by nitrofurantoin (28.6%) and ciprofloxacin (10.2%). The mean duration of antibiotic prescription was 6.86±0.54 days .P = 0.147). Meanwhile, the OR of public primary care physicians to private primary care physicians in prescribing amoxicillin were 2.84 (95% CI: 1.67 to 4.85) and 2.01 (95% CI: 1.14 to 3.55) for nitrofurantoin. The results were statistically significant (P<0.001 and 0.015)  .P = 0.003) and ciprofloxacin   .P = 0.370). However, patients aged 65 years or above received more amoxicillin as empirical antibiotic treatment (P = 0.047), whilst patients aged between 17 and 50 years old received more cefuroxime (P<0.001) and ofloxacin (P = 0.005).There was no association between the rate of antibiotic prescription with age (P = 0.001).The OR of patients with co-existing medical conditions was 3.08 (95% CI: 1.60 to 5.94) to be prescribed amoxicillin than those without chronic medical conditions and this was significantly significant (P = 0.122) and co-trimoxazole was 2.58  for patients with previous history of a UTI in the past 6 months than patients without a recent UTI. However the results were not statistically significant.The OR of prescribing ciprofloxacin was 2.17  and urgency . Meanwhile, The OR of patients presenting with more than two urinary symptoms were 0.33  time than those with one or two presenting symptoms to have empirical antibiotic treatment  for E. coli isolates and 6.19  for other uropathogens between public primary care and private primary care physicians , ciprofloxacin (76.6% versus 76.3–77.2%), co-trimoxamole (68.2% versus 64.7–65%) were similar. In addition, a lower antibiotic sensitivity to amoxicillin (Sensitivity rates of 81.3%-83.6% vs 78.5%) was observed, although resistance rates were low and comparable .Reduced susceptibility to amoxicillin in our study may indicate patients presenting to primary care which may have less severe symptoms and likely to present earlier or may reflect changes in antibiotic susceptibilities due to physicians’ prescribing behaviour. The Centre of Health Protection in Hong Kong publishes sentinel surveillance information of outpatient urinary samples every month . MeanwhiE. coli isolates resistance to ciprofloxacin. A study in Europe suggests that the prior use of antibiotics may be a contributory factor for antibiotic resistance; Sotto et al [E. coli antibiotic resistance to ciprofloxacin.Previous studies have suggested an association between antibiotic resistance and increasing age  and whilto et al  found thThe results of our study suggest that nitrofurantoin is the most appropriate first line antibiotic of choice which had the lowest rate of resistance and the highest sensitivity. Second line treatment would be amoxicillin. Our result is similar with current guidelines on antibiotic prescription for UTI, such as Infectious Diseases Society of America guidelines ,18 and iIn our study, the most commonly prescribed antibiotic amongst primary care physicians is amoxicillin followed by nitrofurantoin. Selection of appropriate antibiotics should be based on differing resistance patterns in the locality , as wellP = 0.005), while public primary care physicians prescribed more amoxicillin (P<0.001) and nitrofurantoin (P = 0.015). This may be attributed to guidelines, drug formulary and the cost of treatment. The public primary care clinics are governed by the Hospital Authority which has guidance for public primary care physicians on their clinical management system. Private group practices have their own clinical management systems and are not restricted by guidelines and have a wider drug formulary. Meanwhile, private group practices are able to absorb higher drug costs. In Hong Kong, a 3-day course of nitrofurantoin and ciprofloxacin costs 2.38 and 25.8 HKD respectively (1 USD = 7.8 HKD) [Meanwhile, our result shows that private primary care physicians prescribed more ciprofloxacin (7.8 HKD) . The pre7.8 HKD)  and clinInternational recommendations for treating uncomplicated UTI in primary care is a 3 day course of antibiotics , howeverThis is the first study to investigate both antibiotic resistance and prescription rates of patients with uncomplicated UTI in Chinese primary care settings. Previous antibiotic resistance data was conducted in outpatients departments and public primary care clinics. Meanwhile, there have been no studies documenting physician prescribing habits in the region. The strength of this study has been to show the use of both antibiotic susceptibility data and antibiotic prescribing behaviour in identifying highlighting gaps associated with antibiotic appropriateness.3 and 104 cfu/ml may further enhance the study, particularly in detailing threshold for treatment and symptom resolution.This study has several limitations. The participation of group practices in selected districts may limit the representativeness of patients across the Hong Kong S.A.R region. Due to the uncommon and sporadic presentation for UTI across a number of sites, the recruitment period was long and we were unable to document the number of patients who were approached but declined to participate in the study which may attribute to selection bias of the study sample. In addition, primary care physicians may also be more likely to prescribe an appropriate antibiotic as they were aware of being observed. Recall bias may also occur in patients recounting prior UTI episodes and prior antibiotic use. However these limitations are likely to be underestimating our study findings. The study was designed to reflect the pragmatic approach of UTI diagnosis and empirical antibiotic prescription in primary care. The use of urine dipstick in the diagnostic criteria, the follow up of presenting symptoms and analysis of urine culture yields of 10Our study has highlighted a gap between physician current prescribing habits and antibiotic resistance. There is a need for increased efforts for education and dissemination to improve appropriate antibiotic prescription. It may also be advantageous for the surveillance data to show intermediate susceptibility to particular antibiotics as well as resistance data to help guide primary care physicians on antibiotic choice. The prospective cohort and multicentre approach in this study yields a more comprehensive picture of the complexities in the patient presentation and doctors’ prescription of antibiotics not addressed in previous regional studies. Additional information on patient’s clinical response and recovery and physician’s decision on antibiotic prescription and choice can be considered in further studies.This study identified antibiotic resistance amongst uropathogens and physician antibiotic prescription in primary care settings. There is scope to encourage nitrofurantoin as first line treatment in primary care settings particularly in private primary care and to explore its use and barriers for use. Enhancing antibiotic guidance for UTI and continued surveillance of antibiotic resistance and physician antibiotic prescription may improve appropriate antibiotic prescription for UTI in Chinese primary care.S1 TableaStatistically significant at P<0.05.(PDF)Click here for additional data file.S2 TableaStatistically significant at P<0.05.(PDF)Click here for additional data file.


Advances in magnetic resonance imaging and the introduction of clinical decision support systems has underlined the need for an analysis tool to extract and analyze relevant information from magnetic resonance imaging data to aid decision making, prevent errors, and enhance health care.The aim of this study was to design and develop a modular medical image region of interest analysis tool and repository (MIROR) for automatic processing, classification, evaluation, and representation of advanced magnetic resonance imaging data.The clinical decision support system was developed and evaluated for diffusion-weighted imaging of body tumors in children . Mevislab software and Python have been used for the development of MIROR. Regions of interests were drawn around benign and malignant body tumors on different diffusion parametric maps, and extracted information was used to discriminate the malignant tumors from benign tumors.Using MIROR, the various histogram parameters derived for each tumor case when compared with the information in the repository provided additional information for tumor characterization and facilitated the discrimination between benign and malignant tumors. Clinical decision support system cross-validation showed high sensitivity and specificity in discriminating between these tumor groups using histogram parameters.MIROR, as a diagnostic tool and repository, allowed the interpretation and analysis of magnetic resonance imaging images to be more accessible and comprehensive for clinicians. It aims to increase clinicians’ skillset by introducing newer techniques and up-to-date findings to their repertoire and make information from previous cases available to aid decision making. The modular-based format of the tool allows integration of analyses that are not readily available clinically and streamlines the future developments. Magnetic resonance imaging (MRI) is a fast-growing clinical imaging modality and has become the modality of choice for the evaluation of disease and treatment management across multiple therapeutic areas. It has increasingly been used in oncology, central nervous system diseases, musculoskeletal disorders, and cardiovascular disease due to its superior soft-tissue imaging capabilities, lack of ionizing radiation, and noninvasive nature -4.MRI technology constantly advances with new magnetic resonance applications being pioneered, investigated, mainstreamed, and added to clinical applications and capabilities. Nevertheless, clinical interpretation remains largely by qualitative expert review. In addition, new advanced and computationally intensive medical quantitative image analysis techniques are constantly being developed and validated. These techniques have allowed the discovery of specific biomarkers of both disease and treatment response and have exposed clinicians to new information in a computable format -8. HowevIn the past decade, clinical decision support (CDS) systems have increasingly gained attention, and the routine uptake of these intelligent systems is becoming more common -17. IntrAvailability of a user-friendly and flexible MRI CAD that encompasses a variety of medical image analysis techniques and postprocessing methods and can act as a CDS could facilitate the uptake of new advanced magnetic resonance techniques in the real-time clinical setting; it could also allow health care investigators to interrogate their data in a scientifically informative and convenient manner to determine a robust and efficient diagnosis. The aim of this study was to design, develop, and evaluate a medical image region of interest analysis tool and repository (MIROR) platform for conventional magnetic resonance data aimed at improving clinical performance through the provision of real-time diagnostic support for clinicians.To the best of our knowledge, there is only the International Network for Pattern Recognition of Tumours Using Magnetic Resonance Decision Support System validated and available for the analysis of magnetic resonance spectroscopy (MRS) data . HoweverFeatures available in the presented version of the MIROR are (1) a clinician-friendly graphical user interface; (2) measurement of morphologic properties such as size, shape, volume, length dimensions, and center-of-mass location of the region of interest (ROI); (3) an integrated magnetic resonance diffusion-weighted imaging (DWI) analysis application based on intravoxel incoherent motion (IVIM) model; (4) statistical data analysis of the ROI overlaid on standard MRI images (such as T1-weighted and T2-weighted scans or the advanced quantitative maps) to provide decision support in forms of comparison with other differential diagnosis and several different image volumes to aid diagnosis and determination of prognosis; and (5) a self-archiving repository of the extracted data and features. Availability of the latter 2 options in combination with the first 3 will move the designed tool from a CAD toward becoming a CDS for MRI data. MIROR also allows investigators to further grow, advance, and combine different analysis techniques and types of imaging sequences to extend the tool to a more sophisticated decision support, dependent on their individual center’s needs to better inform diagnosis. MIROR’s self-archiving, evolving repository is the core of its decision support. This unique feature of the MIROR distinguishes it from pervious CADs and CDSs. First, the repository’s continuous development allows for improvement in the predication accuracy for the available biomarkers and disease in the database; second, it permits provision of a decision support system compatible to additional disease types by means of importing and appending the repository.We used a modular and open architecture design  in the dBased on the recommendations of the American medical informatics association , an evidMIROR can import all file formats supported by the National Library of Medicine Insight Segmentation and Registration Toolkit, such as digital imaging and communications in medicine (DICOM) files, Neuroimaging Informatics Technology Initiative files, JPEG as well as text files, and comma spreadsheets. Built-in Mevislab modules and Python were used to create this functionality. Using the module raw images, postprocessed quantitative maps and data files can be imported from the clinical data warehouses, such as hospital picture archiving and communication system (PACS), the local servers, or MIROR for future analysis and visualization .Comparing the MIROR architecture with previously developed CDSs with a clinical data base and domain expert knowledge base, MIROR does not connect to hospital electronic health record (EHR) system or any Internet-based database or medical knowledge representations or guidelines ,35,36. HOne of the main strengths of MIROR is its ability to allow for integration of new advanced and computationally intensive quantitative analyses that are not readily available to be used in routine clinical practice under the Advance Quantitative analysis module . In thisD), pseudo-diffusion coefficient (IVIM-D*), and perfusion fraction (IVIM-f) [This analysis allows the computation of tissue diffusion coefficient (IVIM-(IVIM-f) . HoweverThe MIROR-embedded statistical analysis module provides specialists with instruments for (1) analyzing and interpreting individual patient MRI data and (2) comparing it with results of previous cases directly from the MIROR-evolving repository via powerful statistical techniques to future inform the investigation . MIROR pNote that MIROR is a nonregion-specific MRI CDS, and its novel quantitative image analysis and statistical analysis modules are designed to analyze any region of the body and aid in resolving different demanding diagnostic problems.The frontend of the MIROR is a clinician’s user-friendly graphical interface that displays MRI images as well as quantified parametric maps and allows clinicians to define their ROI. It also provides real-time morphological and statistical results for compassion with the repository data so as to aid clinical evaluation of the disease.MIROR is currently being developed, evaluated, and used at Birmingham Children’s Hospital to determine its role in facilitating noninvasive diagnosis in children presenting with solid body tumors in clinical practice.Solid masses in children represent a diagnostic dilemma, as neoplastic and non-neoplastic lesions can appear similar on conventional imaging. Although in some cases the clinical history and physical examination findings indicate a likely diagnosis, the majority of cases require further evaluation with MRI to assess the extent of the lesion and make a specific diagnosis. It is often difficult to determine whether a lesion is benign or malignant or identify specific tumor type based on conventional MRI alone.This study evaluated the impact of information provided by MIROR in aiding clinicians to distinguish between benign and malignant solid body pediatric tumor types using DWI. Accuracy testing involved examination of MIROR for a cohort of real patient cases with recent visits to Birmingham Children’s Hospital and comparison of the MIROR outcome with the radiologist’s initial opinion and final diagnosis derived based on the opinion of the clinical multidisciplinary team of experts together with pathology.The body tumor patient cohort studied consisted of children (aged 0-16 years) with solid tumors, undergoing diagnostic MRI with multi b-value DWI at Birmingham Children’s Hospital from 2012 to September 2016. A total of 48 children were enrolled, of whom 37 had malignant tumors and the rest had benign tumors. Details of the malignant and benign body tumors along with patients’ demographics are presented in 2, matrix size 122 to 192 × 128 to 192, slice thickness of 5 mm, and in-plane resolution of 1.56 × 1.56 mm2. For all subjects, 6 b-values: 0, 50, 100, 150, 600, and 1000 s/mm2 were acquired in 3 orthogonal directions with TR/TE=3200 to 9900/92 ms and number of averages=3. The signal to noise of the MRI dataset was approximately 30 (SD 10) for b1000 and 60 (SD 10) for b0 images.We performed the MRI on a Siemens Avanto 1.5 T MRI scanner . The diffusion-weighted MRI protocol used an echo-planar imaging sequence in an axial acquisition plane with a field-of-view (FOV) 221 to 350 × 172 to 317 mm0 and b1000), IVIM-D, IVIM-D*, and IVIM-f maps. These values are a quantitative measure of diffusion related to tissue cellularity [D, IVIM-D*, and IVIM-f) and means for direct and real-time statistical analysis of these variables are unavailable clinically, despite the growing body of evidence for their potential value in noninvasive diagnosis.Depending on their ability to cooperate, children were awake, sedated, or under general anesthesia. MRI acquired with different diffusion weightings  was used to compute ADC , it was assigned to that particular group. However, if the value of the parameter under study fell within both tumor group regions, we made use of second-adjusted classification based on distance from the mean of the statistical parameter of the groups in the training set. To further evaluate the significance of the statistical parameters and information provided by MIROR, k-nearest neighbor (KNN) and support vector machine (SVM) pattern recognition techniques, followed by leave-one-out cross-validation, were utilized to test the accuracy of derived data in distinguishing between tumor types, using all parameters as classification features. KNN was chosen for its simplicity and performance on basic recognition problems; it has been a ubiquitous classification method with good scalability. SVM outperforms conventional pattern recognition methods, especially when the number of training data is small and number of input variables is large .To account for the data skewness and imbalanced distribution of the 2 groups, synthetic minority oversampling technique has been used to allow for building a larger decision region that contains nearby instances of the minority class  when KNNFeature selection was performed before classification by means of calculating the significance level of the histogram derived parameters between the tumor groups. Then, benign and malignant tumors’ histogram permanents  were compared using the nonparametric Mann-Whitney U test. The authors used the Bonferroni correction method. Parameters showing a significant difference between the 2 groups were used for classification. All statistical analyses were performed using SPSS Statistics  software.All patients were consented for research to the UK Children’s Cancer and Leukemia Group, Functional Imaging Group database, a UK National Health Service Research Ethics committee-approved study . Informed participation and publication consent was given by parents/guardians.MIROR is aimed at improving clinical practice through the provision of real-time diagnostic support. To ensure achievement of the latter, each individual application and module has been tested in such an environment by allowing clinicians to interrogate it about the most important clinical questions and provide feedback. We used an iterative process of design, testing, and revision of the MIROR by a diverse team, including medical informatics experts, clinical content experts, and end users to ensure reliable translation of the tool to clinical practice. Experts in MRI and medical imaging, including PhD researchers specializing in MRI and data processing, a physician, and a senior consultant radiologist pilot tested MIROR iteratively during the development and refinement of the tool.MRI datasets and ADC produced by the scanner were imported to MIROR from a local PACS. An ROI was drawn around the entire solid tumor for each case on a high-resolution image by one clinician, which was then checked by another (KM and KF) before being transferred to a matched parametric map . The entire tumor volume, including cystic and necrotic areas, was included in the ROI to determine representative data for heterogeneous tumors . A histoP=.12), 90th (P=.22), 95th (P=.82), or 98th centile (P=.41) ADC values between benign and malignant tumors (P=.03), median (P=.005), 2nd (P=.04), 5th (P=.02), 10th (P=.01), 15th (P=.005), 25th (P=.004), and 75th centile (P=.03) ADC values, higher kurtosis (P<.01), more positively skewed histograms (P<.001), and higher entropy (P=.03). These results are in agreement with similar studies published for adults [To establish differences between the malignant and benign lesions histogram parameters, the authors compared the histogram-derived parameters in the repository. Analysis showed apparent differences between malignant and benign tumors, with lower ADC values and higher skewness and kurtosis in malignant lesions. There was no significant difference between 85th  in discriminating between benign and malignant tumors. Note that the threshold classification was performed based on a single feature input and a 2-step classification process, with a less strict rule in second layer to reclassify the cases in the ambiguous group. Using all of the above extracted features, more advanced pattern recognition techniques, and 10-fold cross-validation, an accuracy of 0.89  and 0.93  was obtained by SVM and KNN, respectively. To further evaluate MIROR in terms of its added clinical value, radiologist initial diagnosis from the first MRI scans was compared with the final diagnosis obtained from histopathology, and the outcome of the 3 statistical analyses based on MIROR provided information for classifying the tumor types for this cohort of patients . For theA higher amount of uncertainty was observed in the initial diagnosis of the benign group. The benign group diagnostic uncertainty rate decreased when we used the information provided by MIROR. Moreover, the false diagnosis rate for both the malignant and benign groups was reduced compared with the radiologist’s initial report with all 3 analysis methods. Additional statistical information provided to clinicians by MIROR can allow for a better and more informed noninvasive discrimination of benign and malignant body tumors in children.Net reclassification improvement (NRI)  for KNN,Continuous developments of magnetic resonance systems have transformed this domain from a pure imaging system to a sophisticated precise metric system that generates a substantial amount of information and data. The complex structure of the clinical data generated often does not ease the difficulty in discriminating between different diagnoses and promotes adoption of intelligent CDSs ,56. CDSsRadiologists are moving toward quantitative imaging techniques that are difficult to apply and complex to interpret ,57. MIROMIROR as a diagnostic tool allows its users to extract specific region morphological features, request specific quantified metrics and features (as a biomarker), and compare with relevant findings available in its repository to gain maximal statistical power with regard to outcome prediction for the input case into the support system. MIROR can direct users to refine their search patterns looking for particular diagnoses, even if they themselves are not immediately aware of the significance of these findings.Use of modular programming in the development of MIROR enforces logical boundaries between magnetic resonance analysis applications, thereby improving maintainability ,59. ModuIn terms of its added clinical value and its impact on providing clinical evidence, MIROR will assist clinicians to better understand the pathophysiological difference between the different tumor types and provide information that could help them to better understand the mechanisms of diseases to improve the diagnosis and prognosis of tumors. MRI biomarkers provide information on both the tumor and its interaction with its environment and can potentially provide new information, which is not available from histology or tumor genetics. Analysis of cancer imaging big data will allow uncovering the relation and structure of cancer disease from an angle that has not previously been viewed.f is related to tumor vascularity, which is again an important pathophysiological property of the tumor. Making these advanced MRI techniques available to clinicians in their multidisciplinary team meetings, where imaging and histopathology are evaluated together for individual cases, is an important goal and will allow an improved understanding of pathophysiology for these tumors in vivo and ex vivo.Although we concentrate on developing these advanced MRI methods as a noninvasive diagnostic aid, they provide important information on tissue properties. Apparent diffusion coefficient shows a strong inverse correlation to cellularity—a key feature of tumors and tumor aggressiveness. Likewise, there is an increasing understanding that IVIM-As a part of future research, we plan to work on functionality and intelligent scaling of quantitative applications of MIROR by further enhancing its statistical capabilities and extension to more embedded advanced quantitative analysis modules such as magnetic resonance spectroscopy and integration of real-time interactive machine learning to optimize the use of available data in MIROR based on the guidelines .It should be noted that a major limitation of the study was that MIROR was trained and tested with a rather limited set of data analyzed retrospectively. A major focus of future work will be the validation of the tool based on a prospective dataset in real time and in a multicenter clinical setting to reinforce the credibility, usability, and efficiency of the proposed CDSs applications.The proposed CDS is a diagnostic tool and repository that allows the interpretation and analysis of magnetic resonance images to be more accessible and comprehensive for clinicians. The process and experiences described here provide a model for development of the other CDSs attempting to perform a nonregion-specific quantitative analysis of MRI data. MIROR aims to increase clinician’s skillset by introducing newer techniques and up-to-date findings to their repertoire and make information from previous cases available to aid decision making. The modular-based format of the tool allowed integration of analyses that are not readily available clinically, and streamlines future developments. Pipelines for new analysis applications are available or already in development and will be shortly available under the MIROR platform.


Rhabdomyosarcoma (RMS) is a predominantly pediatric soft-tissue cancer where the tumor cells exhibit characteristics of the developing skeletal muscle, and the two most common sub-types are embryonal and alveolar RMS. Elevated activation of the receptor tyrosine kinase (RTK) MET is frequent in RMS and is thought to cause increased tumor metastasis and lack of differentiation. However, the reasons underlying dysregulated MET expression and activation in RMS are not well understood. Therefore, we explored the role of Sprouty 2 (SPRY2), a modulator of RTK signaling, in regulating MET. We identify SPRY2 as a novel MET interactor that colocalizes with and binds MET in both embryonal and alveolar RMS. We find that depletion of SPRY2 leads to MET degradation, resulting in reduced migratory and clonogenic potential, and induction of differentiation in both embryonal and alveolar RMS, outcomes that are identical to depletion of MET. Activation of the ERK/MAPK pathway, known to be crucial for regulating cell migration and whose inhibition is required for myogenic differentiation, was downregulated upon depletion of MET or SPRY2. This provides a direct connection to the decreased migration and induction of differentiation upon depletion of MET or SPRY2. Thus, these data indicate that SPRY2 interacts with MET and stabilizes it in order to maintain signaling downstream of MET, which keeps the ERK/MAPK pathway active, resulting in metastatic potential and inhibition of differentiation in RMS. Our results identify a novel mechanism by which MET signaling is stabilized in RMS, and is a potential target for therapeutic intervention in RMS. It is a relatively rare , but aggressive malignancy4. The most common variants are embryonal  and alveolar rhabdomyosarcoma , which exhibit distinct clinical and molecular features6. Histopathologically, ERMS tumors are characterized by zones of hypo and hyper-cellularity, whereas loose nests of rounded cells interspersed by fibro-vascular septa are characteristic of ARMS7. ARMS is highly aggressive, frequently characterized by the chromosomal translocations t involving PAX3-FKHR, or t involving PAX7-FKHR fusion. ERMS has a relatively more favorable prognosis, and is associated with loss of heterozygosity of 11p15.5, p53 pathway disruption and RAS activation8.Rhabdomyosarcoma (RMS) is the most common pediatric soft-tissue sarcoma, accounting for about 3% of childhood cancers12. Thus, RMS tumor cells recapitulate the embryonic myogenic program, although unlike embryonic myogenesis where cells exit the proliferative cycle upon terminal differentiation, the tumor cells persist in an undifferentiated state. Despite their resemblance to myogenic cells, the cell type of origin in RMS is debated. RMS have been proposed to arise from skeletal muscle stem cells (satellite cells), de-differentiation of terminally differentiated myogenic cells, or mesenchymal stem cells committing to the skeletal muscle lineage15.RMS tumors show morphological similarities to developing muscle cells and express muscle differentiation markers such as MyoD, myogenin, and myosin heavy chain (MHC)19. MET was identified as a fusion oncogene in osteosarcoma, and is known to control cell proliferation, survival, and migration, in response to binding by its ligand hepatocyte growth factor (HGF) during developmental morphogenesis and in multiple cancer types21. During mammalian development, MET expression in myogenic precursors is required for their migration to target organs such as limbs17. During adult regenerative myogenesis, MET activates and regulates satellite cell migration, and controls myocyte fusion24. Interestingly, MET is overexpressed, aberrantly activated, essential for metastasis and inhibition of differentiation in RMS, and is a potential candidate for therapeutic targeting27. Thus, identification of MET regulators will be critical to understanding RMS pathology, and attenuating MET signaling by targeting MET or its regulators, could serve as intervention points in RMS patients.Another common thread between mammalian myogenesis and RMS tumors is the expression of a receptor tyrosine kinase (RTK)–MET, by the myogenic progenitors and RMS cells28. The Sprouty (SPRY) family of proteins are important modulators of RTK signaling and SPRY2, a member of the family, functions as a bimodal regulator30. Versatility of SPRY2 in modulating RTK-mediated signaling is cell type, and RTK context dependent, which can result in opposing effects, potentiating or dampening signals transduced from RTKs31. While SPRY2 inhibits fibroblast growth factor (FGF)-mediated extracellular-signal-regulated kinase (ERK) signaling by preventing RAF activation, it augments epidermal growth factor receptor (EGFR)-induced ERK signaling, by inhibiting EGFR endocytosis and degradation33. SPRY2 also exhibits contrasting tumor suppressive or oncogenic roles in different cancer contexts36. For example, overexpression of SPRY2 negatively regulated HGF-mediated ERK and AKT signaling in human leiomyosarcoma, whereas SPRY2 overexpression increased MET activation resulting in enhanced cell migration and invasion in colonic adenocarcinomas36.Regulation of RTK signaling cascades is essential for physiological homeostasisAssociation of MET activity with enhanced metastatic potential and inhibition of differentiation underscores the importance of understanding MET regulation in RMS. Since regulation of MET in RMS is largely unexplored and reports indicate that SPRY2 can alter MET signaling in cancers, we carefully analyzed MET, SPRY2 and their role in RMS, using representative RMS cell lines. Loss of SPRY2 function led to a significant reduction in MET protein levels in RMS cells, mediated primarily by the proteasomal pathway in ERMS and lysosomal pathway in ARMS. We uncovered that MET and SPRY2 interact physically and colocalize with each other in RMS cells. Notably, knockdown of SPRY2 or MET lead to similar functional outcomes in RMS cells, mediated by reduced ERK activation resulting in decreased migratory potential, and inducing differentiation. Thus, our study shows that SPRY2 is a key interactor and regulator of MET in RMS, functioning to stabilize the MET receptor to sustain downstream signaling, which is essential for maintenance of migratory, metastatic and clonogenic capabilities, as well as to inhibit differentiation in RMS.19, and SPRY2 has been reported to regulate RTK signaling33. No reports exist on SPRY2 as a regulator of MET signaling in RMS. Therefore, to ascertain whether SPRY2 has any role in regulating MET in RMS, we characterized SPRY2 and MET expression in embryonal  and alveolar  RMS cell lines. We found that MET transcript  in RD and SJRH30 cells , where miR-27a binds and downregulates MET and SPRY2 transcripts52. Intriguingly, downregulation of MET protein required concomitant overexpression of miR-27a and silencing of SPRY2, indicating that while miR-27a controls MET transcript levels, SPRY2 is critical in stabilizing MET protein in NSCLC52. SPRY2 is known to positively regulate MET levels in colon carcinoma, although it is unclear whether SPRY2 induces MET transcriptional activation or reduces MET degradation36. Thus, SPRY2 functions to maintain MET levels in multiple tumor types, although the precise mechanism is unclear. We observed that MET protein was significantly downregulated in SPRY2 depleted RMS cells, without altering MET transcript levels. This suggests that at least in RMS, SPRY2 does not regulate MET expression transcriptionally, but presence of SPRY2 inhibits MET protein degradation resulting in its stabilization. SPRY2 knockdown resulted in MET degradation preferentially via the proteasomal pathway in ERMS and lysosome-mediated pathway in ARMS, indicating that multiple degradatory mechanisms regulate MET, as reported previously39. Since we found that MET and SPRY2 interact with each other, it is likely that this interaction results in MET stabilization. Interestingly, this interaction also stabilizes SPRY2, since MET depletion led to a reduction in SPRY2 protein levels without affecting transcript levels in ARMS. One possibility is that the MET-SPRY2 interaction renders motifs on both proteins that target them for degradation, such as ubiquitination, inaccessible.SPRY2 modulates MET signaling in cancer types such as leiomyosarcomas, hepatocarcinomas, and colonic adenocarcinomas12. Previous studies indicate that inhibition of MET reduces the metastatic potential and overrides the differentiation block in RMS cells27. Our finding that SPRY2 stabilizes MET, and depletion of SPRY2 leads to MET downregulation indicates that loss of SPRY2 should lead to decreased metastatic potential and permit differentiation of RMS cells. We found this to be the case where SPRY2 depletion led to decreased migration, and induction of differentiation. Also, SPRY2 and MET conferred clonogenic potential on RMS cells and silencing either significantly reduced colony forming ability of these cells. Whether SPRY2 plays MET independent roles in regulating RMS migration, differentiation or clonogenic potential is unknown, and could be investigated by overexpressing SPRY2 in MET depleted RMS cells.Although there are differences between embryonal and alveolar RMS, both tumor sub-types share similar features such as tumor cells exhibiting muscle cell characteristics and inhibition of differentiation27. We observed a significant increase in cell death in ERMS cells upon MET knockdown, but not in ARMS, compared to a study which reported increased cell death in both ERMS and ARMS27. This could be due to differences in experimental set up, where we analyzed cells six days PsiRT, whereas the previous study used a doxycycline-inducible conditional lentiviral shRNA construct targeting MET over five days to study proliferation and cell death27. We found that downregulation of SPRY2 caused significantly reduced proliferation in ERMS and significantly decreased cell death in ARMS, indicating that SPRY2 might have MET independent functions in RMS. The varying trends of cell death and proliferation seen in MET or SPRY2 silenced RD and SJRH30 cells in this study reflect the intrinsic molecular differences between the two RMS sub-types.We did not observe any effect of MET silencing on cellular proliferation in RMS cells. A previous study reported that RMS proliferation rate decreased upon downregulation of MET20. While the ERK (p44/42)/MAPK pathway regulates cell migration downstream of MET receptor, both MAPK pathways are essential for myogenic differentiation49. Inhibition of ERK/MAPK signal is required for the myogenic program to proceed normally, whereas p38 activation is required at early stages but needs to be suppressed at later stages for myogenic differentiation49. Our results suggest that ERK/MAPK pathway is the direct target of MET and SPRY2 in RMS, and the decreased metastatic potential and induction of differentiation observed upon MET or SPRY2 depletion is mediated by this pathway. Interestingly, we also observed a significant decrease in p38/MAPK activation in ARMS but not ERMS cells upon MET or SPRY2 depletion, which might be because ARMS tumor cells resemble the developing fetal muscles compared to ERMS which exhibit characteristics of embryonic muscles11. At later stages of myogenesis, p38/MAPK is suppressed, which may not be taking place in ARMS cells, contributing to the lack of differentiation49. ERMS cells migrated at a slower rate (42-h) compared to ARMS cells (24-h) in wound-healing assays, agreeing with reports that ARMS is relatively more metastatic and aggressive compared to ERMS38.Two pathways reported to function downstream of MET are the ERK (p44/42)/MAPK and p38/MAPK pathwaysWe find that in addition to MET, SPRY2, a bimodal regulator of RTK signaling also exhibits elevated levels of expression in RMS. However, SPRY2 has never been shown to directly interact with the RTK itself to regulate it or downstream signaling. Here, we show that SPRY2 colocalizes and interacts with MET, stabilizing and preventing MET degradation, which leads to elevated ERK/MAPK signaling Fig. . All of 2 and 95% humidity.Human RMS cells of embryonal  and alveolar  sub-type were purchased from American Type Cell Culture (ATCC) . ARMS cell lines - RH4, RH28 and RH41 were kindly provided by Dr. Peter J. Houghton . These cell lines were grown and maintained as recommended, in Dulbecco Modified Eagle Medium (DMEM)  supplemented with 10% (v/v) fetal bovine serum  and 1% penicillin-streptomycin  at 37 °C, 5% COFor expression analyses at transcript and protein levels, ~60,000 and 3,00,000 cells were plated in each well of 24-well  and 6-well plates , respectively and allowed to grow to ~80% confluence. Cells from 4 wells of a 24-well plate and one 6-well plate were harvested per replicate, to isolate RNA and prepare protein lysates, respectively. These experiments were carried out in triplicates. For knockdown experiments, reverse transfection was performed in RD and SJRH30 cells. Briefly, ~60,000 cells were seeded in each well of a 24-well plate that was pre-layered with transfection mix comprising 100 µl Opti-MEM , 10 nM of MET or SPRY2 or control siRNAS  and 2 µl Lipofectamine RNAiMAX . At 48 and 144-h post siRNA transfection (PsiRT), 4 wells of a 24-well plate and one 24-well plate were harvested per replicate to isolate RNA and prepare protein extracts, respectively. ON-TARGETplus SMARTpool siRNAs targeting human MET (10 nM) and SPRY2 (50 nM)  were used to validate the knockdown observed using the Silencer Select siRNAs (Ambion). These experiments were performed a minimum of three times. All experiments requiring knockdown were subsequently performed using Ambion Silencer Select siRNAs. Where specified, RMS cells were incubated in the presence of lysosomal inhibitor Bafilomycin A1 , proteasomal inhibitor MG132 , or vehicle control, 96-h PsiRT. For immunofluorescence studies, RMS cells were seeded onto gelatin-coated coverslips  in 24-well plates . Approximately 30,000 cells/well were plated for immunofluorescence based expression and colocalization experiments, whereas ~50,000 cells/well were reverse transfected and seeded, as described above, for analysis of knockdown induced differentiation of RMS cells.MET and SPRY2 transcripts were quantified by quantitative real time polymerase chain reaction (qPCR) using SYBR Green with ROX as internal reference  using the ABI 7500 Fast Real Time PCR system (Applied Biosystems). The primers used for gene expression profiling in different RMS cell lines are listed in Supplementary Table GAPDH and HPRT were used as endogenous controls or reference genes for normalizing expression of target genes (that is MET and SPRY2)54. RNA from mesenchymal stem cells, closest appropriate control for RMS cells that are thought to originate from cells of the mesenchymal origin14, was used as a control or calibrator. All the qPCR assays were performed in a final reaction volume of 20 μl using 100 nM forward and reverse primers of the respective genes, as recommended by ABI. Each reaction was performed in triplicates. The expression of target genes in the siRNA transfected RMS cells was normalized to the levels in control siRNA transfected cells55. Three replicate cDNA samples were used for quantifying endogenous expression of target genes in different cell lines. Three matched sets of MET or SPRY2 and control siRNA transfected samples were used as biological replicates for performing expression analysis and the error bar represents±standard error of the mean (SEM).RNA was isolated from RMS cells harvested as described in the cell culture section above, using RNeasy mini kit . cDNA was synthesized from 1 µg of RNA using SuperScript III reverse transcriptase  and oligo (dT) . RMS cells were lysed in ice-cold lysis buffer  supplemented with 1× protease inhibitor . Debris was removed by centrifugation at 6000 rpm for 5 min at 4 °C and the lysates obtained were quantified using BCA Protein Assay kit  as per manufacturer’s protocol. Approximately 500 µg protein was incubated with Protein A Sepharose 4 Fast flow beads  for 1-h at 4 °C on a tube mixer, followed by centrifugation to remove the beads and the proteins that were bound non-specifically. Subsequently, the pre-cleared lysates were used to immunoprecipitate MET-conjugated proteins by incubation with MET antibody overnight at 4 °C on a tube mixer. IgG raised in rabbit was used as a control antibody. The antibody-bound proteins were then incubated with Protein A Sepharose 4 Fast flow beads for 6-h at 4 °C on a tube mixer to capture the immunocomplexes. Immunoprecipitates were washed thrice using ice-cold lysis buffer and eluted by boiling in 2× Laemmli sample buffer at 95 °C for 3 min. The immunoprecipitates were subjected to western analysis as described below. Antibodies and the concentrations used for immunoprecipitation are listed in Supplementary Table Protein lysates were prepared from RMS cells that were plated, cultured, and harvested at required time points as explained above in the cell culture section. Briefly, the cells were harvested by aspirating the medium from the wells, trypsinizing and centrifuging at 6000 rpm/4 °C/6 min. Subsequently, cells were washed with ice-cold 1 × DPBS  twice, followed by centrifugation. Cells were lysed in ice-cold radioimmunoprecipitation assay (RIPA) buffer  containing protease inhibitor  and phosphatase inhibitor , each at 1:100 dilution. The protein samples were quantified using BCA Protein Assay kit  as per manufacturer’s protocol. Equal concentrations of protein extracts were separated by 10% or 12% SDS-PAGE, depending on the molecular weight of the protein to be detected, followed by transfer to PVDF membrane  at 4 °C overnight. Subsequently, the membranes were blocked in 5% skimmed milk for 5-h, incubated overnight in primary antibody at 4 °C, and 2-h in appropriate HRP-conjugated secondary antibody at room temperature. Signals were detected using the Luminata Forte HRP substrate . Blots were imaged using ImageQuant LAS 4000 and densitometric analyses were performed using and ImageQuant TL v7.0 software . For densitometric analyses, reference genes used as loading controls for normalization were β-Actin, GAPDH, or β-Tubulin. Data from a minimum of three independent replicates were plotted as an average, with the error bars representing ± SEM. Antibodies and the concentrations used for western blotting are listed in Supplementary Table 5 cells/well was used so that the cells attain about 90% confluence by around 96-h post siRNA transfection. Subsequently, the media containing the transfection mix was aspirated out of each well, preserved in labeled tubes and the cell monolayers were scratched carefully with a sterile 200 μl pipette tip. The wells were washed gently with 1×DPBS  thrice to get rid of the scraped floating cells and the preserved transfection mix was added back to the respective wells. The scratch was imaged immediately for the zero-time point (0) using an inverted microscope . Subsequently, the cells were incubated at 37 °C, 5% CO2 and 95% humidity, and the scratch monitored and documented over time at the same positions. A mark was made on the plastic plate as a reference point to ensure that the same area was imaged every time over the time course of the experiment41. Imaging was performed until the time point (t) when the wound closed in the control siRNA transfected RMS cells. The images were used to measure the scratched area at zero-time point (A0) and at the time point of closure (At), using a macro (freely available at http://rsb.info.nih.gov/ij/) written at Montpellier RIO Imaging56 for ImageJ software58. Percentage wound closure was calculated using the formula ((A0 - At)/A0)×100. Wound closure assays were also performed on SJRH30 cells in the presence and absence of MEK/ERK inhibitor U0126 . The wound healing assays were performed independently a minimum of five times for each transfection.RMS cells were seeded in 6-well plates and reverse transfected with MET, SPRY2, or control siRNAs respectively, as described above in the cell culture section. A seeding cell density of ~2.5 × 10Transwell migration assay was performed to evaluate the effect of MET and SPRY2 knock-down on metastatic potential of RMS cells. Briefly, 96-h post transfection with MET, SPRY2 or control siRNA, RMS cells were trypsinized and re-suspended in serum free medium. Approximately 80,000 RD and 60,000 SJRH30 cells were seeded in transwell inserts , and placed into 24-well cell culture plates containing DMEM with 10% FBS. After incubation at 37 °C for 24-h, non-migrating cells on the upper surface of the membrane were removed with cotton swabs and migrated cells at the base of the inserts were fixed in 4% PFA (~5 min), permeabilized with methanol (20 min) and stained with 2% crystal violet dye (15 min). Cells were counted manually and imaged using a stereomicroscope . Percentage cell migration was calculated relative to control siRNA transfected RMS cells. The transwell migration assays were performed independently a minimum of three times for each transfection. Graphical data for the wound closure and transwell migration assays are represented as mean ± SEM.Anchorage-independent colony forming ability of MET or SPRY2 silenced RMS cells was examined by soft agar assay. A base layer was prepared by pipetting 2 ml 0.7% agar  in 10%FBS/1%Pen-Strep/DMEM, in 6-well plates. This base layer was overlaid with 0.35% agar with 6000 siRNA transfected RMS cells (96-h PsiRT) suspended in it. The cells were then cultured for about 2-weeks. 1 ml culture medium was supplemented to each well every 5 days to prevent desiccation of agar. Colonies were fixed and stained using 0.05% crystal violet in neutral buffered formalin for 1-h. Colonies were counted manually and imaged using a stereomicroscope (Leica S8 APO).For anchorage-dependent colony formation assay MET, SPRY2, or control siRNA transfected RMS cells were plated 96-h PsiRT in 6 well plates at 3000 cells/well and cultured for 8 days. Colonies were washed twice with 1×PBS, fixed in ice cold methanol (20 min) and stained with 2% crystal violet (~30 min). The wells were imaged using a digital camera and representative individual colonies form different transfections were imaged using a stereomicroscope (Leica S8 APO). Colony forming units or colonies comprising >30 cells were counted manually under the stereomicroscope.Both the clonogenic assays were performed independently a minimum of three times for each transfection and graphical data is represented as mean ± SEM.RMS cells for immunofluorescence were grown as detailed in the cell culture section above. The cells were fixed in 4% paraformaldehyde (PFA) for 20 min and washed with PBS following which they were blocked with 5% goat serum  in PBS containing 0.1% Triton-X-100 for 1-h at room temperature (RT). The cells were incubated overnight at 4 °C with appropriate concentrations of respective primary antibodies listed in Supplementary Table 40.The colocalization between SPRY2 and MET was quantified using the colocalization tool in the Leica Application Suite X (LAS X) software, with the threshold and background parameters for both channels set to 60% and 20%, respectively. Analysis was performed on 17 individual cells, in three different images, showing SPRY2-MET colocalization and the mean Pearson’s correlation coefficient was calculated from these regions of interest (ROIs)p-value is indicated on the graph along with asterisks and p-value ≤ 0.05 is considered significant. The level of significance is indicated as *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.Experimental data was plotted in GraphPad Prism 5  and analyzed using the parametric, paired t-test using the SigmaPlot12.5 . All graphical data is represented as mean ± SEM, and each experiment was performed at least in triplicates. The Supplementary Figure 1Supplementary Figure 2Supplementary Table 1Supplementary Table 2


Workability is regarded as one of the important parameters of high-performance concrete and monitoring it is essential in concrete quality management at construction sites. The conventional workability test methods are basically based on length and time measured by a ruler and a stopwatch and, as such, inevitably involves human error. In this paper, we propose a 4D slump test method based on digital measurement and data processing as a novel concrete workability test. After acquiring the dynamically changing 3D surface of fresh concrete using a 3D depth sensor during the slump flow test, the stream images are processed with the proposed 4D slump processing algorithm and the results are compressed into a single 4D slump image. This image basically represents the dynamically spreading cross-section of fresh concrete along the time axis. From the 4D slump image, it is possible to determine the slump flow diameter, slump flow time, and slump height at any location simultaneously. The proposed 4D slump test will be able to activate research related to concrete flow simulation and concrete rheology by providing spatiotemporal measurement data of concrete flow. Monitoring the workability of freshly mixed concrete is necessary to ensure that the concrete is properly placed during construction and that adequately hardened strength is achieved after construction . Both arIn the field of concrete materials, highly flowable self-consolidating concrete (SCC) is attracting attention for its ability to fill congested rebar spaces in a formwork under its own weight without using a vibrator. The characteristics of SCC are filling ability, passing ability, and stability . The useAs a standard test method to assess the filling ability of flowable concrete, the slump flow test shown in With the development of SCC and high-performance concrete, instead of the conventional test devices, a rheometer has been introduced to measure the rheological parameters of concrete . For exaIn this study, a new framework for qualitatively evaluating concrete workability using a depth sensor is proposed. Kinect, a low-cost depth sensor, is utilized to measure the dynamically changing 3D concrete surface during a slump flow test, as shown in This paper is organized as follows. The Kinect is used to acquire spatial information of the dynamically changing surface of fresh concrete during the concrete slump flow test. The raw data cannot be directly utilized to analyze useful information on the concrete workability; several stages of data processing are needed to produce slump flow diameter, slump flow time, and slump height. The proposed algorithm for data processing consists of the following procedures: (1) representation of 3D spatial information at the camera frame {C} from the depth image; (2) determination of a ground plane equation; (3) calculation of a transformation matrix between the camera frame {C} and the slump frame {S} where the slump cone is initially located; (4) reconstruction of the 3D surface for concrete slump; and (5) cross-section extraction and construction of a 4D slump image. Each procedure is described in the following sections.d is represented at the pixel location . P described in the camera coordinates is projected to the point p on the image plane, which is apart from the camera frame by a focal length f. Here, the coordinate system of  is referenced to the camera frame {C}. Using the proportionality of two similar triangles OPQ and Opc, the following equations for coordinates X and Y are obtained:f is focal length and xc and yc are optical center pixels in the IR camera. The focal length and optical center pixels can be determined by a standard calibration of the camera.While projecting a known speckle pattern of near-infrared light, the Kinect sensor acquires disparity images at the IR camera in real time , where tZ is expressed as a nonlinear function of disparity d [d is derived from the geometric relationship in Z0 is the distance to the reference plane, b is the baseline between the IR projector and camera. Since the output value by the sensor is the normalized disparity d’ in practice, which ranges from 0 to 211 − 1, d should be replaced with md’ + n [Depth parity d . The mat md’ + n ,28 and tZ0, b, f, m, and n but depth Z can be determined by two parameters, C0 and C1. These parameters should be estimated by depth calibration. In the calibration experiment, several sets of disparity information and the measured depth are collected. Since the inverse of depth has a linear relationship with the disparity in Equation (3), the calibration parameters C0 and C1 can be calculated in terms of pseudo-inverse form by the least square method. By calibrating the parameters, the systematic error due to Z0, b, and f can be eliminated and depth accuracy can be improved [Equation (3) includes five parameters such as improved .X, Y, Z for the given  are calculated as follows:To sum up, the final coordinates of Using the above equations with the calibration parameters, it is possible to reconstruct the points in 3D coordinates again from the disparity image.X,Y,Z) in the camera frame {C}, the expression of CP is used in this paper. Here, the leading superscript indicates the coordinate system to which the points P are referenced. For the data processing of the measured point cloud, it is convenient to use a description of the points in frame {S}, that is, SP. In order to change the description from frame {C} to {S}, the coordinate transformation can be adopted as a mathematical tool. The theory of coordinate transformation is well established in the field of robot kinematics to express the position and orientation of a robot’s end effector, which is serially connected by several links and joint angles [During the slump flow test, the surface of the concrete is detected by a Kinect depth sensor, as shown in t angles . As the xy plane is the ground plane and whose Z-axis is the opposite direction of gravity. In order to transform CP to SP, the relative rotation matrix SPSORG are required. In order to calculate the rotation matrix In a, b, c, and d) of the following equation from a set of selected data on the ground plane around the slump.In this paper, the RANSAC (RANdom SAmple Consensus) algorithm  is utiliZ-axis in frame {S} is the orthonormal vector of the ground plane. It is defined as v in this paper and is expressed as the parameters of the ground plane equation as follows:The unit vector of the Z-axis of the frame {S}, v is used to calculate the relative orientation between the camera frame {C} and the slump frame {S} in the next section.It is reasonable to use the RANSAC algorithm since it detects outlier data of Kinect and does not use them in fitting the plane equation. That is, unlike the least square method, it is possible to remove the effects of the extreme values resulting from erroneous measurement or environmental conditions such as light intensity or reflectivity. CPSORG. CPSORG is the vector from the camera frame {C} to the slump frame {S}.In order to describe the measured points of fresh concrete in slump frame {S}, the transformation matrix between {C} and {S} should be obtained. The relationship between {S} and {C} is characterized by the rotation matrix, Z-Y-X Euler angles [α is determined when the vector β when rotation β about When an orthonormal vector to the ground plane is represented as r angles . Any oriα and β can be easily expressed in terms of the components of the orthonormal vector v in frame {C}, as illustrated in X-axis, and β refers to the angle between Z-axis. They can be expressed as follows:The angles SP to CP is represented by the orientation and position information as follows [The homogeneous transformation matrix  follows :(9)TSC= CPSORG represents the position vector of the origin of {S} in frame {C}. The description of points with respect to the slump frame {S} is calculated by the inverse of The columns of ix, iy) in the plane, that is, TIN itself does not have equal intervals in the X- and Y-axes. For ease of data analysis, such as cross-section extraction, it is desirable to construct a network composed of regular grids, as shown in z = ax + by + c, and any point located at a specific grid  within this triangle can be linearly interpolated. In this grid coordinate, it is easy to extract the slump height at a specific location of the ground plane.Triangulated irregular network (TIN) is the digital data structure generally used in geographic information systems (GIS) to represent the topographical surface. TIN is composed of contiguous, non-overlapping triangles. It can be used to convert the measured scattered data points into a model of the 3D surface. The surface of slump flow generated by TIN is shown in Through the above-mentioned data processing algorithm, the shape information obtained from the depth sensor camera can be expressed in a 3D surface where the height data are available at any grid position of the slump frame {S}. The dynamically changing shapes of the concrete slump over time are the four-dimensional spatiotemporal data, as shown in By selectively extracting the information from each slump in As a low-cost 3D depth sensor, Microsoft Kinect was utilized to acquire data during the slump test. Kinect consists of an IR laser pattern projector, an IR camera, and an RGB camera. When the IR projector projects a laser speckle pattern onto an object, an image of the pattern on the object is captured by the IR camera and then processed to reconstruct a 3D map of the object . The KinThe Kinect was assembled with an in-house mounting frame, as shown in As a flowable concrete, an SSC mixture with a water-to-binder ratio of 39% was used in the experiment. The proportions of concrete mix are shown in The slump flow test was performed based on ASTM C1611 . The conz at a specific location  of i, the slump diameter at any time, and the height of slump at any time and location. In The depth images recorded at 24 Hz sampling frequency during the slump flow test were processed by the 4D slump processing algorithm described in In the 4D slump image, it is possible to detect the boundary of the slump flow, as presented in In this paper, a novel workability test method using a 3D depth sensor was proposed and the dynamically spreading diameter of concrete slump flow was successfully visualized through a 4D slump processing algorithm. In order to represent the surface of fresh concrete in the slump frame, we transformed the depth data in the camera frame {C} acquired by Kinect into the data in slump frame {S} with the calculation of the relative orientation and position between the {C} and {S} frames. The scattered data in the slump frame were then interpolated in the regularly spaced grid and the cross-sections of the slump flow at each time frame were collected. Through the experiment, it was confirmed that the information on the spreading slump flow diameter, slump height, and slump flow time could be obtained by a single 4D slump image using the proposed 4D slump processing algorithm. In the conventional concrete workability test device, it is impossible to obtain these kinds of images and graphs.The proposed 4D slump test method offers several advantages. First, instead of human measurements using a ruler and a stopwatch, which can introduce human error, it can digitize the workability data and quantitatively evaluate the workability at low cost. Second, it affords non-contact, portable digital measurement and provides additional information besides the basic information while performing the existing workability test. Third, measurement of concrete flow will be helpful in the quality monitoring of concrete by providing a digital record that cannot be easily modified. In addition, it will accelerate the development of flowable concrete based on numerical simulations by providing digital data for concrete flow experiments. Beside systematic error, random error, and resolution discussed in this paper, the depth measurement quality is influenced by the light condition and target reflectivity. At a high light intensity, the IR camera cannot detect laser speckles on the object since the relative contrast of the speckles is decreased. That is to say, the signal-to-noise ratio of the reflected pattern is low under sunlight. If the water content of fresh concrete is high or the water in the fresh concrete tends to rise to the surface, speckle patterns projected by an IR projector are not reflected back from the concrete surface. Consequently, the Kinect will create gaps and outliers ,36. In t