The aim of this study was to compare the changes in primary (heroin use-related) and secondary  outcome indicators of 3-month methadone maintenance treatment (MMT) between heroin users with and without HIV infection.A total of 242 intravenous heroin-dependent individuals (30 with and 212 without HIV infection) receiving MMT were recruited. Primary  and secondary (depressive symptoms and QOL) outcome indicators were determined before and after receiving 3-month MMT. Changes in primary and secondary outcome indicators between the two groups were compared using mixed-model analysis.Heroin users both with and without HIV infection showed significant improvement in three primary outcome indicators after 3-month MMT, and there was no difference in the changes of these primary outcome indicators between the two groups. However, improvements in depressive symptoms and the physical domain of QOL among HIV-infected heroin users were poorer than in those without HIV infection.The results of this study indicated that heroin users with HIV infection did improve in the primary but not the secondary outcomes after 3-month MMT. Heroin dependence is associated with poor physical and mental health, unemployment and criminal activity and has a chronic, relapsing course ,2. A recAn important issue that requires further study is whether the treatment outcomes of MMT are the same between heroin users with and without HIV infection. A cohort study showed that HIV-infected individuals have higher rates of depression and heroin use disorder than those not infected with HIV . In a stWhereas the severity of heroin dependence and harm caused by heroin use and the occurrence of heroin use are important outcome indicators of MMT, the levels of depressive symptoms and quality of life (QOL) are also important indicators. A 12-month follow-up study among heroin users showed that more severe depressive symptoms at baseline can predict more injection-related health problems and heroin use and less heroin abstinence 12 months later . TherefoQOL has been defined by the World Health Organization as a term of “individuals’ perceptions of their position in life in the context of the culture and value systems in which they live and in relation to their goals, standards, expectations and concerns” . ResearcThis study aimed to examine the changes in primary  and secondary (depressive symptoms and QOL) outcome indicators during 3-month MMT in heroin users with and without HIV infection. The group differences in the changes of these outcome indicators were also examined.This observational study recruited consecutive intravenous heroin users with heroin dependence according to the criteria of the DSM-IV-TR  who visiAt each interview, the instruments described below were used to collect data.[Ch] was used to evaluate participants’ severity of heroin use [[Ch] in this study was 0.78.The five-item SDSroin use ,26. EachParticipants reported whether they had used heroin before intake and during the last month of the study. Each participant had urine tests at the end of the study. Current heroin use at baseline and at the 3-month follow-up interview was determined by either self-reported use in the last month or by a positive heroin confirmation urine test at clinic visits.p < 0.05) to 0.742 (p < 0.001). Cronbach’s alpha of the Q-HOU in this study was 0.93. Higher total scores on the Q-HOU indicate that individuals are experiencing more severe harm owing to heroin use.This seven-item, four-point questionnaire assesses the subjective harmful experiences caused by heroin use during the past 3 months, including those related to physical and mental health, family relationships, interpersonal relationships, legal problems, academic or occupational performance, and economic hardship. For example, participants were asked, “Have you ever experienced physical problems due to heroin use during the past 3 months?” The 1-week test-retest reliability ranged from 0.394 (The Chinese version of the CES-D has been used to study depressive symptoms in Taiwan for many years . Possiblp < 0.01). The content validity coefficients were in the range of 0.53 to 0.78 for item-domain correlations and 0.51 to 0.64 for inter-domain correlations  [The Taiwan version of the WHOQOL-BREF includes 28 items, consisting of 26 standard items from the original WHOQOL-BREF and two Taiwanese national items [The attendance rate of MMT was determined by the days on which methadone was taken during the 3-month MMT.χ2 test and t-test. Changes in the severity of heroin dependence, harm caused by heroin use and depressive symptoms and the levels of QOL during the 3-month MMT were examined separately by the paired t-test in the two groups of participants. Change in status of current heroin use before and after the 3-month MMT was examined separately by the related-samples McNemar test in the two groups of participants. Meanwhile, we used generalized linear mixed-model analysis to examine group differences in the changes in heroin dependence, harm caused by heroin use, status of current heroin use, depressive symptoms and QOL during 3-month MMT between heroin users with and without HIV infection. Mixed-model analysis is useful in settings where unbalanced measurements are made of the same statistical units [Data analysis was performed using the Statistical Package for the Social Sciences (SPSS) 20.0 software. Baseline characteristics of the participants were compared between participants with and without HIV infection using the al units . In the Of the 30 participants with HIV infection, five 16.67%) were female and 25 (83.33%) were male. Of the 212 participants without HIV infection, 25 (11.79%) were female and 187 (88.21%) were male. There was no significant sex difference between the two groups. The mean age  was 37.20 years (7.48 years) and 37.73 years (7.29 years) in participants with and without HIV infection, respectively. The mean duration of education (SD) was 9.84 years (2.37 years) and 9.25 years (2.14 years) in participants with and without HIV infection, respectively. There were no significant differences in sex, age and duration of education between participants with and without HIV infection. The severities of heroin dependence, harm from heroin use and depressive symptoms, the levels of QOL, and the proportion rate of current heroin use at baseline in participants both with and without HIV infection are shown in Table 6.67% werBoth groups reported heroin use at the intake interview. Rates of self-reported heroin use during the last month of the study were 66.67% and 61.32% for HIV-positive and HIV-negative participants, respectively. The urine-positive rates for the HIV and non-HIV groups were 73.33% and 66.51%, respectively. Both the rate of self-reported heroin use and the urine-positive rate did not differ between the two groups. The difference in methadone dosages (SD) was not significant, with 52.33 (20.03) (mg/day) and 57.56 (30.12) (mg/day) for the HIV and non-HIV groups, respectively.Results of the comparison between treatment outcomes before and after MMT in heroin users with and without HIV infection are shown in Table Results of the comparison of changes in heroin dependence, harm caused by heroin use, current heroin use, depressive symptoms and QOL during the 3-month MMT between heroin users with and without HIV infection using mixed-model analysis are shown in Tables This study determined the changes in severity of heroin dependence, harm caused by heroin use, current heroin use, depressive level and QOL during 3-month MMT in heroin users with and without HIV infection. We observed that in heroin users both with and without HIV infection, three primary outcome indicators related to heroin use improved significantly after 3-month MMT, namely the severity of dependence and harm caused by heroin use and current heroin use. Two meta-analysis studies showed that MMT can reduce heroin use ,31. HoweNevertheless, we found that the effects of MMT on improving depressive symptoms and QOL are different between heroin users with and without HIV infection. In contrast to heroin users who are negative for HIV, the severity of depressive symptoms did not significantly improve after 3-month MMT in the HIV-infected heroin users. Comparison of the changes in depressive symptoms also showed that heroin users without HIV infection had greater improvement in depressive symptoms than HIV-infected users. Tsao et al. showed that people with HIV infection experience more pain and distress than the general population . Pain isWe found that heroin users without HIV infection also had greater improvement in the physical health domain of QOL than those with HIV infection after controlling for age, sex, education level, and attendance rate of MMT. Previous studies have found that HIV-positive individuals have low rates of coordinated and continuous health care use  but dispThere are some limitations to this study that should be addressed. First, imbalance in the sample sizes of the groups of heroin users with and without HIV infection may limit the inferences of our findings. However, we used mixed-model analysis, an effective method in unbalanced samples, to deal with this problem . Second,In conclusion, this study found that the primary outcomes regarding heroin use are improved during 3-month MMT in heroin users both with and without HIV infection, whereas poorer improvements in depressive symptoms and the physical domain of QOL were found in HIV-infected heroin users compared with those without HIV infection. Owing to the high prevalence rate of HIV-positive serostatus among heroin users, clinicians should take mood and QOL into consideration and incorporate multicomponent treatment programs when treating heroin users. Connock et al. showed that adjunctive psychosocial interventions may enhance the treatment outcomes of MMT . Archana


Rhodococcus erythropolis PR4. In the present study, we examined the effects of the introduction of groEL2 on the alkane tolerance of other Rhodococcus strains. The introduction of groEL2 into Rhodococcus strains led to increased alkane tolerance. The translocation of R. rhodochrous ATCC12674 cells to and survival in the n-octane (C8) phase in two phase culture were significantly enhanced by the introduction of groEL2 derived from strain PR4, suggesting that engineering cells to overexpress GroEL2 represents an effective strategy for enhancing organic solvent tolerance in Rhodococcus.We recently reported that the overexpression of GroEL2 played an important role in increasing the alkane tolerance of  The rhodococci are a group of Gram-positive bacteria that have useful industrial and/or ecological applications due to their diverse range of metabolic activities. Some rhodococci can degrade organic compounds, including xenobiotics such as PCBs, whereas others are capable of degrading various aliphatic and aromatic hydrocarbons –10, 13, n-octanol partition coefficient of organic solvents (log Po/w) is used as a toxicity index , n-dodecane (C12), n-hexadecane (C16), or pristane (C19) were used as representative alkanes of varying carbon-chain lengths. The respective log Po/w values of n-octane, n-dodecane, n-hexadecane, and pristane were 5.2, 6.1, 8.3, and 9.3. A visible increase in turbidity in the alkane phase and/or the presence of cells at the interface between the aqueous and alkane phases was observed in two phase cultures containing C12, C16, and C19, but not C8. In more than 85% of the strains examined, cells translocated to the alkane phase in the presence of C16 and C19. In two phase cultures with C12, approximately 60% of the strains translocated to the alkane phase. In contrast, no strains translocated to the alkane phase in the presence of C8 because all of the strains tested grew poorly in the presence of this alkane. These results revealed a close relationship between the log Po/w value  of an alkane, growth, and translocation frequency in Rhodococcus.To understand the overall relationship between alkane carbon length, growth, and cell localization, the localization of 46 Rhodococcus strains could grow in the alkane phase and were capable of translocation from one phase to another depending on the carbon-chain length of the alkane.We subsequently examined the alkane carbon-chain length threshold at which a shift in cell localization occurred. Although the alkane chain-length threshold at which cells transitioned from translocation to adhesion or could not be detected varied according to the strain tested, strains were more likely to transition from translocation to adhesion or to become undetectable under all conditions tested. These results suggested that many groEL2 was limited to strain PR4 only, we introduced the complete CDS of groEL2 into other Rhodococcus strains  of  strains . Plasmideriments , and fivR. rhodochrous R-1 and R-2) (R. rhodochrous ATCC12674 (pK4-EL2-1) was enhanced. These results suggested that the effects of introducing the complete CDS of groEL2 were not limited to PR4.The localization of 10 transformants was examined in two phase cultures with C8, C12, C16, or C19 using phase-contrast microscopy, and the results are summarized in R. rhodochrous ATCC12674 (pK4-EL2-1) was visible in the alkane phase in C8-containing two phase culture. Individual R. rhodochrous ATCC12674 (pK4-EL2-1) cells could be distinguished microscopically in the C8 phase (R. rhodochrous ATCC12674 (pK4-EL2-AT) were not observed in the C8 phase (R. rhodochrous ATCC12674 (pK4) or R. rhodochrous ATCC12674 (pK4-ΔEL2-1) cells were also not observed in the C8 phase (data not shown). These results indicated that R. rhodochrous ATCC12674 (pK4-EL2-1) was capable of translocating to and surviving in the C8 phase.Surprisingly, the turbidity of C8 phase . In contC8 phase . R. rhodR. rhodochrous ATCC12674 transformants to survive in C8-containing two phase culture. Cell suspensions (ca. 5 × 104 CFU mL−1 each) were prepared and resuspended in IB medium (R. rhodochrous ATCC12674 (pK4-EL2-1) was significantly higher than that of the other transformants  than in the three other strains examined. In addition, the survival rate of R. rhodochrous ATCC12674 (pK4-EL2-1) in the presence of C8 was significantly higher than those of the three other strains examined. These results suggested that the overexpression of GroEL2 due to the introduction of the complete CDS of groEL2 derived from PR4 enhanced the alkane tolerance of R. rhodochrous ATCC12674. In contrast, the relative abundance of GroEL2 and the survival rate in the presence of C8 were similar for R. rhodochrous ATCC12674 (pK4-EL2-AT) and R. rhodochrous ATCC12674 (pK4). Given that the amino acid sequence of GroEL2 was 99% identical between R. rhodochrous ATCC12674 and R. erythropolis PR4, groEL2 derived from R. rhodochrous ATCC12674 may have contributed to the increased alkane tolerance observed. The introduction of pK4-EL2-AT to R. erythropolis PR4 altered cell localization  from the n-hexadecane phase for R. rhodochrous ATCC12674 (pK4-EL2-1) grown in the presence of C8 was 0.0 ± 0.1 min−1 (R. rhodochrous ATCC12674 (pK4-EL2-1) and the three other transformants due to the insufficient growth of the three other strains, the R0 value suggested that the cell surface of R. rhodochrous ATCC12674 (pK4-EL2-1) was sufficiently lipophilic; its degree of lipophilicity was sufficient to promote immediate translocation to the alkane phase and the subsequent maintenance of translocation to the alkane phase.We previously reported that the overexpression/upregulation of GroEL2 could indirectly contribute to increased cell surface lipophilicity via protein refolding in olis PR4 . A kinetolis PR4  was used.1 min−1 . This va.1 min−1 . AlthouggroEL2 and subsequent overexpression of GroEL2 on the alkane tolerance of various Rhodococcus strains. The results obtained confirmed that the introduction of groEL2 and subsequent overexpression of GroEL2 led to an increase in alkane tolerance in some rhodococci. By utilizing the overexpression of GroEL2 via the introduction of a plasmid encoding groEL2 derived from PR4, we constructed a highly alkane-tolerant strain capable of translocating to and surviving in the presence of C8. It is generally believed that the shorter the carbon-chain length of an alkane, the more toxic it will be to bacteria in aqueous-alkane two phase culture (We herein examined the effects of the introduction of  culture . C8 is o


The use of antibiotics is the single most important driver in antibiotic resistance. Nevertheless, antibiotic overuse remains common. Decline in antibiotic prescribing in the United States coincided with the launch of national educational campaigns in the 1990s and other interventions, including the introduction of routine infant immunizations with the pneumococcal conjugate vaccine (PCV-7); however, it is unknown if these trends have been sustained through recent measurements.We performed an analysis of nationally representative data from the Medical Expenditure Panel Surveys from 2000 to 2010. Trends in population-based prescribing were examined for overall antibiotics, broad-spectrum antibiotics, antibiotics for acute respiratory tract infections (ARTIs) and antibiotics prescribed during ARTI visits. Rates were reported for three age groups: children and adolescents (<18 years), adults (18 to 64 years), and older adults (≥65 years).P <0.001) in 2010 than in 2000.An estimated 1.4 billion antibiotics were dispensed over the study period. Overall antibiotic prescribing decreased 18% (risk ratio (RR) 0.82, 95% confidence interval (95% CI) 0.72 to 0.94) among children and adolescents, remained unchanged for adults, and increased 30%  among older adults. Rates of broad-spectrum antibiotic prescriptions doubled from 2000 to 2010 . Proportions of broad-spectrum antibiotic prescribing increased across all age groups: 79%  for children and adolescents, 143%  for adults and 68%  for older adults. ARTI antibiotic prescribing decreased 57%  among children and adolescents and 38%  among adults; however, it remained unchanged among older adults. While the number of ARTI visits declined by 19%, patients with ARTI visits were more likely to receive an antibiotic (73% versus 64%; Antibiotic use has decreased among children and adolescents, but has increased for older adults. Broad-spectrum antibiotic prescribing continues to be on the rise. Public policy initiatives to promote the judicious use of antibiotics should continue and programs targeting older adults should be developed. Antibiotic resistance has been called one of the world’s most pressing public health problems . InfectiAntibiotics are one of the most commonly prescribed drugs in the US. The use of antibiotics is the main driver in creating selective pressure for the emergence of antibiotic-resistant bacteria. Nevertheless, antibiotic overuse remains common -8. DespiStreptococcus pneumoniae have declined. However, it is unknown if these national antibiotic prescribing patterns have continued in more recent years.To target this growing concern, in 1995, the US Centers for Disease Control and Prevention (CDC) launched its ‘Get Smart: Know When Antibiotics Work’ campaign, a broad-scale, national educational campaign to promote appropriate use of antibiotics, particularly for ARTIs . SubsequThe purpose of this study was to describe national trends in outpatient antibiotic prescribing, broad-spectrum antibiotic prescribing, and antibiotic prescribing for ARTIs from 2000 to 2010. We also aimed to examine national trends in ARTI visits and antibiotic prescribing trends during those visits.This study utilized data from the Medical Expenditure Panel Survey (MEPS) administered by the Agency for Healthcare Research and Quality . The MEPThe MEPS include three main components: the Household Component (HC), the Medical Provider Component (MPC) and the Insurance Component. The MEPS HC contains information on families and individuals regarding their demographics, health conditions, health status and expenditures. The MEPS MPC includes information provided by a sample of medical providers that supplements the information acquired during completion of the household interviews. Information in the MEPS MPC includes dates of visits, diagnosis and procedure codes, charges and payments. The Pharmacy Component, a sub-component of the MEPS HC, contains the ‘Prescribed Medicines’ files. Medications are obtained from survey respondents; additional details are imputed from data that are obtained from the patients’ pharmacies . Each record in the file represents a unique prescribed medicine event and purchase by the household respondent or member. Expenditures, medical conditions and household characteristics related to the prescribed medicine are also provided.International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) diagnoses codes. These conditions are recorded at the person-level in conjunction with medical events, including outpatient visits and antibiotic purchases. We assessed antibiotic prescribing trends in overall antibiotic use, broad-spectrum antibiotics, outpatient visits, and for common ARTIs that infrequently necessitate an antibiotic: acute nasopharyngitis and upper respiratory tract infection (ICD-9-CM diagnosis codes 460 and 465), bronchitis (466 and 490), influenza , pharyngitis , and sinusitis (461 and 473) [We performed a retrospective analysis of outpatient antibiotic prescribing from 2000 to 2010. In the MEPS, survey respondents are asked to report health conditions which are then coded by professional coders using the and 473) ,21,22. OTherapeutic drug classes were identified by using the National Drug Code directory, generic names, and the Multum Lexicon therapeutic classification database . ‘Broad-spectrum’ antibiotics were defined using the National Committee for Quality Assurance’s (NCQA) ‘Antibiotics of Concern List,’ which includes azithromycin, clarithromycin, fluoroquinolones, amoxicillin-clavulanate, and second- and third-generation cephalosporins . ‘Major Antibiotic prescriptions were analyzed at the level of a unique prescribed medicine event and prescription purchase. Prescription rates were defined as the annual number of antibiotic prescriptions divided by the overall US civilian non-institutionalized population. Population denominators were derived from the MEPS HC. The MEPS estimate US populations based on sampled persons in the target population  for the entire year. Beginning in 2001, MEPS transitioned to 2000 census-based population estimates for post-stratification and ranking; prior to 2001, 1990 census-based estimates were used. The sample design of the survey includes stratification, clustering, multiple stages of selection and disproportionate sampling. Furthermore, the MEPS use sampling weights to reflect adjustments for survey nonresponse and adjustments to population control totals from the Current Population Survey [We conducted analyses evaluating trends in both ARTI visits and visit-based prescribing. ARTI outpatient visit rates were defined as the annual number of ARTI outpatient visits divided by the corresponding population estimate. Rates in the first study year (2000) were compared with the final study year (2010) and reported per 1,000 persons per year. Risk ratios and 95% confidence intervals (95% CIs) were calculated.F test. All analyses were adjusted to account for the MEPS complex study design using weights, clustering and stratification to derive national estimates. SPSS 20.0®  was used for all statistical analyses. P-values of <0.05 indicated statistical significance.ARTI visit-based prescribing was determined as described in the study design and definitions. The only difference was that the data were limited to antibiotic events that occurred during an ARTI visit. Proportions were estimated accounting for the survey design and tested using the design-based During the study period (2000 to 2010), an estimated 1.4 billion outpatient antibiotics were dispensed in the US. The annual number of outpatient antibiotic prescriptions purchased ranged from 106 million in 2000 to 134 million in 2003.Annual antibiotic prescription rates are shown in Figure Table Table Table Broad-spectrum antibiotic prescriptions for ARTIs in 2000 versus 2010 are compared in Table Penicillin prescribing decreased for children and adolescents , adults  and older adults . Cephalosporin prescribing remained stable for children and adolescents , but decreased for adults  and older adults . In contrast, macrolide prescribing significantly increased for all age groups: children and adolescents , adults  and older adults . Finally, fluoroquinolone prescribing remained stable in adults  and older adults . There were too few events for fluoroquinolones to estimate use among children and adolescents.From 2000 to 2010, the survey estimated 3.1 billion office-based provider visits and hospital outpatient provider visits. The rate of outpatient visits did not change significantly from 2000 to 2010 for any of the age groups. In 2000, ARTI visits accounted for 28% of all outpatient visits; in 2010, ARTI visits accounted for approximately 21% of all outpatient visits.Table P <0.0001). Significant increases were observed in children and adolescents  and adults . Rates were similar among older adults .Although ARTI visits decreased over the study period, the proportion of antibiotic prescriptions associated with provider visits for ARTIs increased significantly from 2000 to 2010  of all antibiotics prescribed for ARTIs. This significant increase was consistent across all age groups: children and adolescents , adults  and older adults .There was also a substantial increase in broad-spectrum antibiotic prescribing during ARTI visits Figure . In 2000This study describes antibiotic prescribing in the US from 2000 to 2010. Overall, antibiotic use has remained stable; however, there were opposing trends among different age groups. While antibiotic use decreased among children and adolescents, there was no change among adults, and older adults experienced an increase.et al. demonstrated a 40% decrease in antibiotic prescribing from 1989–1990 through 1999–2000 among children <15 years of age. In a subsequent study, Finkelstein et al. observed a 24% decrease in antibiotic prescribing from 1996 to 2000 among children within nine US health plans [Earlier studies have documented declining antibiotic prescription rates among children and adolescents ,21,24. Mth plans . Our stuth plans ,27. In ath plans .et al. estimated that the rate of outpatient antibiotics among Medicare Part D beneficiaries (>65 years of age) in 2007 to 2009 was 1.10 person/year [Other age groups did not experience this decline. In the beginning of the study period, children and adolescents had the highest rate of antibiotic prescriptions compared to adults and older adults. However, older adults surpassed children and adolescents to attain the highest antibiotic prescribing rate in 2004; this was sustained through the end of the study period. A prior study demonstrated that, while overall antibiotic prescriptions remained stable among persons ≥5 years of age, there was a significant increase in antibiotic use among persons ≥50 years of age from 1995 to 2006 . Recentlson/year ; this rason/year . Our stuPerhaps the most alarming finding of this study is the substantial increase of broad-spectrum antibiotic prescribing. Previously, a cross-sectional study identified an increase in broad-spectrum antibiotic prescribing from 24% to 48% among adults and from 23% to 40% among children from 1991–1992 to 1998–1999 . Moreoveet al. previously demonstrated a decline in visit-related antibiotic prescribing rates between 1991–1992 and 1998–1999 for various respiratory tract infections [et al. found decreasing trends in visit-based ARTI antibiotic prescribing among children and adolescents from 1989 to 2000 [Changes in antibiotic prescribing rates might have resulted from changes in provider prescribing behavior and/or frequency of outpatient visits or encounters. Changes in outpatient visit rates for ARTIs might reflect changes in factors such as public behavior from education, insurance status, disease severity, and internet-based advice and prescribing . Therefofections . Similar to 2000 . This de to 2000 . However to 2000 ,31. WhenTaking into account reduced ARTI visit-rates, these trends suggest that, although patients may have been less likely to make a provider visit, clinicians had a higher propensity to treat these conditions with an antibiotic in 2010. Moreover, when antibiotics were prescribed during an ARTI visit, broad-spectrum agents were increasingly being selected. In 2010, broad-spectrum antibiotics comprised almost half of all antibiotics prescribed in US outpatient provider visits. Overall, this translates into nearly 32 million antibiotic prescriptions for respiratory tract infections, comprised largely of broad-spectrum agents.Finally, our study suggests that older adults experienced a significant increase in antibiotic prescribing and constituted the greatest proportion of patients receiving broad-spectrum antibiotics. This highlights the need for further studies to better understand antibiotic use among older adults. The older adult population is rapidly growing compared to other US age groups. As the ‘baby boomer’ generation approaches retirement age, they will comprise the largest US health care sector. Targeted quality initiatives for this population are needed. Currently, the NCQA has initiated quality improvement measures for antibiotic prescribing for children and adults; however, no such metrics exist for those 65 years of age and older.This study has limitations. First, the MEPS database does not provide information related to patient signs and symptoms, medication allergies, microbial etiology or severity of the condition; therefore, direct assessment of appropriate antibiotic prescribing is difficult. Because we relied on ICD-9-CM codes, diagnoses are subject to misclassification. Second, we were not able to distinguish between first and follow-up visits. The inclusion of follow-up visits may have resulted in underestimation of visit-based antibiotic prescriptions. Antibiotic data were based on antibiotic prescription purchases; thus, actual antibiotic prescribing might have been underestimated and may not necessarily reflect actual antibiotic consumption. Third, while this study was based on national population estimates, wide confidence intervals seen in point estimates of specific age groups might have been associated with smaller sample sizes, thereby resulting in possible unstable estimates of standard error. Fourth, while this study focused on ARTIs, varying trends for different indications that might affect total antibiotic use were not directly evaluated in this study. Lastly, the MEPS do not include federal institutions , thus our estimates may not be generalizable to those populations.In conclusion, overall antibiotic use has decreased among children and adolescents, but has increased among older adults. Broad-spectrum antibiotic prescribing has increased among all age ranges. ARTI visits have declined; however, patients who present for ARTI visits are more likely to receive an antibiotic than in the past. Our study highlights the persistent problem of antibiotic overuse. Public policy initiatives to promote the judicious use of antibiotics should continue and programs targeting older adults should be developed.CRF has received research grants and/or served as a scientific consultant/advisor for the NIH, AstraZeneca, Bristol-Myers Squibb, Elan, Forest, Ortho McNeil Janssen Pharmaceuticals, and Pfizer. GCL, KRR, RTA, KAL, IAM and JSL declare that they have no competing interests.All authors had full access to the data, which contain publicly accessible data sets available from the Agency of Health Care Research and Quality. Study concept: GCL and CRF; acquisition of data: GCL; analysis and interpretation of data: GCL, KAL, CRF; drafting of the manuscript: GCL; critical revisions of the manuscript for important intellectual content: GCL, KRR, RTA, KAL, IAM, JSL and CRF; statistical expertise: GCL, KAL, CRF; administrative, technical, or material support: GCL; study supervision: CRF. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1741-7015/12/96/prepub


C-means performs better than K-means algorithm.Understanding of the compressive strength of concrete is important for activities like construction arrangement, prestressing operations, and proportioning new mixtures and for the quality assurance. Regression techniques are most widely used for prediction tasks where relationship between the independent variables and dependent (prediction) variable is identified. The accuracy of the regression techniques for prediction can be improved if clustering can be used along with regression. Clustering along with regression will ensure the more accurate curve fitting between the dependent and independent variables. In this work cluster regression technique is applied for estimating the compressive strength of the concrete and a novel state of the art is proposed for predicting the concrete compressive strength. The objective of this work is to demonstrate that clustering along with regression ensures less prediction errors for estimating the concrete compressive strength. The proposed technique consists of two major stages: in the first stage, clustering is used to group the similar characteristics concrete data and then in the second stage regression techniques are applied over these clusters (groups) to predict the compressive strength from individual clusters. It is found from experiments that clustering along with regression techniques gives minimum errors for predicting compressive strength of concrete; also fuzzy clustering algorithm  Concrete is the most commonly used structural material and is composed of individual base materials . MateriaK-means and C-means are used for estimating the compressive strength of concrete in this work.A number of methods and techniques have been developed and proposed by many researchers to predict the properties of concrete. Determination of the compressive strength of concrete requires preparation, curing, and testing of special specimens . The earA number of works have been reported for estimation of compressive strength of the concrete and lots of recent computing techniques are used for better prediction of concrete compressive strengths. Artificial neural networks are used massively for estimating the concrete compressive strength apart from the other computing techniques. Some of the recent works related to estimating compressive strength of the concrete are discussed in this section.There are numerous applications of Artificial Neural Networks (ANN) in structural engineering. ANN is used for modeling the concrete strength in a number of works –11. ANNsFuzzy set theory has been applied in a wide range of scientific and construction research areas . A numbeAn extensive study of concrete curing process is performed by Abdel-Jawad (2006) , where iANNs and most of the soft computing based techniques are supervised learning techniques which require the efficient learning datasets for preparing the prediction models and always give low accuracies for prediction and other related tasks. To address this problem cluster regression based technique is proposed in this work to predict the compressive strength of concrete. The purpose of the proposed work is to demonstrate that regression technique along with clustering gives better prediction of compressive strength of the concrete. Rather than applying regression directly over the concrete dataset, it is applied over the groups (clusters) of concrete records in the proposed work. These clusters are formed on the basis of similarities between concrete records. Applying regression techniques over the clusters identifies more suitable estimations for each cluster  which ensures the minimum forecasting errors for estimating the compressive strength of concrete.The methodology of proposed technique of estimating (predicting) the concrete compressive strength is summarized in Clustering. In the first steps groups of similar concrete records from the concrete datasets are created. The number of groups can be decided by the user. In this study different numbers of groups are considered for the experiments. The groups are created using the clustering techniques K-means and fuzzy clustering technique C-means.Regression Analysis. After creating the group of similar concrete records, regression techniques are applied to identify the relationship between concrete compressive strength with other components of concrete. In this work three regression techniques SLR, LR, and LMS are used for estimating compressive strength of concrete.Performance Evaluation. Performance of prediction tasks performed in the previous steps is carried out with the help of predicting two popular error parameters MAE (Mean Absolute Error) and RMSE (Root Mean Square Error). These errors are calculated for each individual cluster and overall weighted average is also calculated for measuring the prediction errors.K-means and C-means algorithms for creating the groups of similar characteristics concrete data. Overviews for both of these algorithms are given in this section. Distance functions play an important role in clustering, which is also discussed here in brief.Clustering is performed using K number of disjoint partitions to create K number of clusters. These partitions are created using certain statistical measures like mean, median, and so forth. K-means [Clustering is a process of creating groups of similar objects. Clustering algorithms are categorized into five major categories, namely, partitioning techniques, hierarchical techniques, density based techniques, grid based techniques, and model based techniques. Partitioning techniques are the simplest techniques which create  K-means  is a claK-means algorithm [K, that is, the number of clusters to be formed, which partitions a set of n objects to generate the k clusters. The algorithm works in three steps. In the first step, K number of the objects are selected randomly, each of which represents the initial mean or center of the cluster. In the second step, the remaining objects are assigned to the cluster with minimum distance from cluster center or mean. In the third step, the new mean for each cluster is computed and the process iterates until the criterion function converges. The performance of K-means is measured using the square-error function defined in the following:E is the sum of the square error, p is the point in space representing a given object, and mi is the mean of cluster Ci. This criterion tries to make the resulting K clusters as compact and as separate as possible. The algorithm is consisting of five major steps which are summarizes as given in The lgorithm  is a parC-means (FCM) algorithm [c partitioning which is summarized as follows.Choose a number of clusters.Assign randomly to each point coefficients for being in the clusters.ε the given sensitivity threshold).Repeat until the algorithm has converged  of concrete records and Euclidian distance function is used to measure the distances between the pair of concrete records. Euclidian distance formula to measure the distance between two concrete records Ci and Cj with N features is presented in the following:Clustering algorithms creates the groups of similar records. In order to find the similar records distance functions are used. Distance functions are used to measure the distance between two objects (or records). If the distance between a pair of records is less (minimum) then the records are said to be similar. In this work two clustering algorithms N = 8) attributes (features) in this dataset, namely, Cement (C), Blast Furnace Slag (BFS), Fly Ash (F), Water (W), Superplasticizer (S), Coarse Aggregate (CA), Fine Aggregate (FA), and Age (A). The description of the dataset is presented in the In this work UCI machine learning repository  of concrRegression techniques are used to discover the relationship between a set of variables. These techniques are used for identifying the patterns (relations) of independent and dependent variables, the independent variables are technically termed as response variables, and dependent variable is termed as predictors. Regression techniques typically try to relate some statistical measures like mean or average between the set of variables to identify the relationship between them. In this paper three regression techniques, namely, Simple Linear Regression (SLR), Logistic Regression (LR), and Least Median of Squares Regression technique (LMS) are used to estimate the compressive strength of concrete.Y = c1X1 + c2X2 + ⋯+cNXN + b) is used to relate the predictor variables  and response to the predictor (Y). Simple linear regression is a statistical technique that fits a straight line to a set of  data pairs. The slope and intercept of the fitted line are chosen so as to minimize the sum of squared differences between observed response values and fitted response values. That is, a method of ordinary least squares is used to fit a straight line model to the data.Simple linear regression is the simplest regression analysis technique, where a line equation , and an odds ratio can be estimated. Logistic Regression is mostly used for prediction tasks involving multiple dependent variables and is also used for exploring the strong (dominating) dependent variables in prediction tasks. Logistic Regression is used for prediction of the probability of occurrence of an event by fitting data to a logistic curve. Logistic regression techniques are designed using logistic model which can be expressed as given in the following:p is the probability of a classification match, and x1, x2,…, xi are the explanatory, independent variables.Logistic Regression technique is the other regression technique, which is used to perform regression on multiple independent variables presented simultaneously to predict membership of one or other of the two dependent variable categories. If the dependent variable, Rousseeuw (1984)  introducK subsets each with a simpler distribution matching its guiding function. Each function is regressed on its own subset of data with a much smaller residue error. Both the regressions and the clustering optimize a common objective function.In regression clustering (RC) , regressThe performance of regression based prediction techniques is carried in terms of errors in regression. Two such common errors in regression based prediction are Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).The Mean Absolute Error (MAE) is the average of the absolute value of the residuals (error). The MAE is very similar to the RMSE but is less sensitive to large errors. The MAE is calculated using the following:The Root Mean Squared Error (RMSE) is the square root of the average squared distance of a data point from the fitted line. The RMSE is calculated using the following:K, which is the number of clusters, and D, a dataset containing n objects of Concrete samples. The algorithm is composed of five steps. In the first step the UCI machine repository concrete dataset is selected and given as input to the algorithm, and then in the second step, K numbers of clusters are created using K-means or C-means clustering techniques. In third step regression techniques are applied on each cluster created in step 2 to identify the relationship between the compressive strength and other concrete variables. In the fourth step, estimation of compressive strength of concrete is performed for individual clusters created in step 3 and prediction error MAE and RMSE are calculated for each cluster. Finally in the last step, weighted average (on the basis of number of concrete records belonging to various clusters) prediction errors MAE and RMSE are calculated for measuring overall errors in prediction of the compressive strength of concrete.Based on the methodology presented in previous section, the algorithm for predicting the compressive strength of concrete is now presented in this section . The algIn order to perform the experiments of proposed technique popular dataset of compressive concrete strength from UCI machine learning repository  is takenK-means clustering technique and regression techniques is taken from the WEKA. The implementation is performed in three major steps  with independent variables (other variables) for a cluster. In the third step, errors are calculated for the estimations. The model is thus prepared for predicting the compressive strength of concrete for the new concrete records (for which compressive strength is unknown). In case of a new concrete record, mapping of the record to a suitable cluster is carried out by measuring the belongingness to a cluster then using the equation of that cluster estimation of the compressive strength of concrete is performed.All the experiments are carried using Java programming language and Java based WEKA  tool . WEKA isK-means and C-means are applied to create hard and soft (fuzzy) clusters. The distribution of number of concrete instances for different number of clusters using K-means algorithm is shown in C-means algorithm is shown in C-means algorithm is fuzzy technique, one instance can belong to more than one cluster hence more number of instances is belonging to the clusters than the K-means algorithm.In order to estimate the compressive strength of concrete clustering is applied  and thus are more important in estimating compressive strength of concrete, components S and FA also affects slightly in SLR regression technique. Similarly, for LR regression technique for forecasting the compressive strength of concrete the components C, BFS, W, and FA have more weights than the other components. For the LMS regression technique also the weights of components C, BFS, W, and S are more in regression equations which indicate that these components are more important than the other components for estimating the compressive strength of concrete.K-means clustering and C-means fuzzy clustering techniques along with the three regression techniques, namely, simple linear regression (SLR), logistic regression (LR), and least median of squares (LMS), are used to predict (estimate) the compressive strength of the concrete. The purpose of the work is to demonstrate that if clustering can be combined along with the regression technique then prediction errors for estimating compressive strength can be minimized. It is demonstrated from the experiments that if the optimum number of clusters can be created on concrete data before applying the regression then prediction errors can be minimized in efficient manners. Clustering techniques K-means and C-means are generic in nature and are capable of creating the groups objects of any data type and any number of features (attributes); hence, the same proposed model can be used for estimating compressive strength of data for different concrete datasets. In this study it is found that for UCI machine learning repository concrete data, four to seven numbers of clusters along with the regression technique give minimum prediction errors for prediction the compressive strength of concrete. It is also found that fuzzy clustering algorithm C-means is more efficient than the K-means clustering algorithm for creating the clusters and gives minimum errors in predicting the compressive strength of the concrete. Partitioning based clustering algorithms K-means and C-means are used in this work; density based clustering algorithms can be explored for creating the groups of concrete data as a future scope of the work. The proposed model can also be validated by selecting other standard datasets and comparisons can be made between partitioning based and density based clustering techniques for predicting the compressive strength of concrete.In the presented work


Results of tensile strength versus strain rate are presented and compared with compressive strength and existing models at similar strain rates. Dynamic increase factor versus strain rate curves for tensile strength were also evaluated and discussed. The same tensile data are compared with strength data using a thermodynamic model. Results of the tests show a significant strain rate sensitive behavior, exhibiting dynamic tensile strength increasing with strain rate. In the quasistatic strain rate regime, the existing models often underestimate the experimental results. The thermodynamic theory for the splitting tensile strength of concrete satisfactorily describes the experimental findings of strength as effect of strain rates.The dynamic characterization of concrete is fundamental to understand the material behavior in case of heavy earthquakes and dynamic events. The implementation of material constitutive law is of capital importance for the numerical simulation of the dynamic processes as those caused by earthquakes. Splitting tensile concrete specimens were tested at strain rates of 10 It has long been known that concrete materials have a low tensile strength compared to their compressive strength. Since concrete is inherently weak in tension, it has been used as compressive member material in most concrete structures –5. HowevAt a time when a concrete structure comes under dynamic loading, two different modes of failure should be distinguished: local effects and the global effects on the structure. With the current computational facilities and the knowledge on computational modeling, force and stress distributions can be calculated in concrete structures under complex dynamic loading conditions. However, models for dynamic properties of materials are still in their infancy , 9. ConsThe compressive behavior of cement based materials under different strain rates has been studied rather extensively. Results of loading tests have confirmed an increase in compressive strength of concrete subjected to dynamic loading. This general result has been confirmed by many researchers in the course of many decades. Some comprehensive review papers of more recent works on this topic can be found in several surveys. For example, Abrams even in 1917 reported that an increase in the rate of loading was accompanied by an increase in the compressive strength of concrete. The same findings were announced by Wastein , and Atc−1) [−7 s−1 to 1 s−1) is rarely limited.From the literature review, it can be found that the dynamic tensile strength has not been studied extensively yet and also the data on the rate effect is mostly in the regime of high strain rates (above 1 s−1) –15. The −7 s−1 and 10−4 s−1 on an MTS material test machine. The primary objective of this study was to develop a method for estimating the tensile strength of concrete at both static and dynamic loading rates. A secondary objective was to gain some insight into failure mechanisms of concrete under different strain rates. Moreover, since tensile strength is an experimentally determined property, it is important to validate experimental results with acceptable numerical and analytical procedures.To investigate the effects of strain rate on the tensile strength of concrete, splitting tensile tests of plain concrete specimens were performed at strain rates between 10The mix proportion of concrete used is given in Following casting, the specimens were covered with a plastic membrane to prevent the moisture from evaporating. Specimens were demolded after 24 hours and moist-cured for 6 months. In this investigation, cubical (150 × 150 × 150 mm) specimens were used. Also, several companion 150 mm cubes were cast for obtaining static compressive and elastic modulus of concrete. The following value was obtained for the concrete at the age of 180 days: compressive strength = 51.8 MPa and elastic modulus = 30.3 GPa.The tensile strength of concrete can be determined from three types of tests: direct pull tests on briquettes and bobbins, modulus of rupture tests on beams, and splitting tensile tests. There are many technical difficulties in executing a true tensile strength test. A uniform stress distribution which makes it possible to calculate the true tensile strength is difficult to obtain. The method commonly used to determine tensile properties of concrete is the flexural beam test by third-point loading on a beam over a span. The flexural strength is calculated from the bending moment at failure, assuming a straight line stress distribution according to Hooke's law. This is not entirely true; however, the calculated flexural strength may be about twice as high as the true tensile strength. The beam test has the advantage that the end pieces of the broken beam can be used to determine the compressive strength of the concrete. These compressive strength results, however, probably differ more from the actual strength of the field concrete than from the compressive strength based on standard cylindrical specimens.Many attempts have been made to find a substitute for the beam test, and it is possible that the splitting tensile test of a cylindrical specimen may be the solution to the problem . The splResearchers have indicated that, among the three testing methods , the splitting tensile test gives the most accurate measurement of the true tensile strength of concrete-like materials in a wide strain rate . DifficuIn the splitting test, a concrete cylindrical or prismatic specimen is compressed along two diametrically opposed generators as shown schematically in σPmax, is the maximum tensile stress in the specimen when the applied load is P, D and B are the specimen depth and thickness, respectively   versus strain rate (or the logarithm of strain rate). The use of normalized DIF reduces the influence of the material strength on the DIF formulae , 34. ComThe experimental evidence shows also that concrete rate dependence is higher in tension than in compression . In the In authors' opinion, the presence of water in the capillary pores exerts an “external influence” on the material, which results in observed difference in material properties. As the capillary effects , under dTo correctly analyze the influence of the strain rate on the mechanical properties of concrete, the results in terms of failure were elaborated to obtain the DIF, thereby obtaining the curves in σts and σtd are the unconfined uniaxial tensile strength in quasistatic and dynamic loading conditions, respectively; γs = 10αs−1.33); σco = 10 MPa; σcs is the unconfined quasistatic uniaxial compressive strength (in MPa).The CEB model appears to property fit the available data. The DIF for the tensile strength is given byA series of dynamic splitting tests have been conducted by Tedesco et al. . for con−1, and for high strain rates a slope of 1/3 on a log (strain rate) versus log (DIF) was used, also following the CEB formulation. The proposed formulation then becomesγs = 10αs−2), and εs = 1 × 10−6 s−1.Malvar, and Ross . propose−5 and 8.3 × 10−5 N/mm2 mm2 per millisecond on specimens with different aggregate to cement ratio. They introduced strain rate into Drucker-Prager's equation for the tensile DIF expression for concrete as follows:Katayama et al.  studied Zhou and Hao  recommenBased on the testing results of rocks, Cadoni  suggesteBy plotting these relationships against the experimental results , the corTo treat concrete by thermodynamics means to consider it on an atomic level. Atoms are in a state of continuous motion; attracting and repulsing forces are acting on them. Each atom is situated on a certain energy level. Due to continuous motion there is always a chance that an atom overcomes the inherent energy barrier and moves to another place in the system. If external energy is added to a system of atoms, the energy barrier (activation energy) may be overcome more easily. Energy can be supplied by mechanical loading, heating up, or concentration gradients. The greater these external influences are, the more likely that place changes occur. Place changes of atoms can be detected in an average way by deformations, cracks, or chemical reactions.After presenting the experimental results, a fracture criterion is formulated to which the data is then compared. This has been done in essentially an empirical manner by combining terms derived from the Arrhenius rate equation to account for the temperature and strain rate effect which was shown to fit the experimental data over the entire range of parameters surprisingly well.U(σ) is assumed to be a function of the effective stress only. For the brittle concrete considered here, fracture is preceded by very little inelastic strain so that it is justified to neglect inelastic strain in the formulation. The other terms in , σ is the applied stress, and σ0 is a constant. (σ − σ0) is the effective stress relative to the thermal activation barrier. The need for inclusion of σ0 will become evident when examining the data. The linear form of (U(σ).The experimental data suggests that the stress dependence of the activation energy is linear and of the formU(σ)=U0−νU(σ)=U0−νσ yieldSubstituting  in 12)  12) and U0/ν + σ0 is the limiting stress when T = 0 K or when It will be noted that rding to , the appR = 1.986 Kcal/mol, T = 293 K, σ0 = 0, ν = 2500, and U0 = 8200 cal/mol. The constants in this equation are −7 to 10−4 s−1.The fractured surfaces of the specimens became more and more flattened with the increasing strain rate, and an increasing number of aggregates were broken along the fractured surface.With every order of magnitude increase in strain rate, the splitting tensile strength of concrete increases about 15 percent. This trend is nearly linear increase in strength with each order of magnitude increase in strain rate. This trend could be interpreted by combining the subcritical crack growth and fracture process zone: the concept of subcritical crack growth is applicable prior to the peak load; the concept of a larger process zone is applied in the postpeak load region, where the unstable crack propagation commences.The experimental evidence shows also that concrete rate dependence is higher in tension than in compression; this phenomenon could be explained by “Stefan's effect.” A comparison with existing empirical models was performed; the results show that, in the quasistatic and moderate strain rate regime, the existing models often underestimate the experimental results. Hence, more suitable expression for designing calculations should be considered.The thermodynamic theory for the splitting tensile strength of concrete satisfactorily describes the experimental findings of strength as effect of strain rates. The coefficient is not influenced by strain rate. Additional experiments to verify the thermodynamic model in compressive and flexural behavior of concrete are underway and will be reported in subsequent papers.The following conclusions are drawn from the splitting tensile test results and discussion presented in this paper. The concrete specimens were loaded at strain rates from 10