BackgroundOver recent decades, a dramatic increase in infections caused by multidrug-resistant pathogens has been observed worldwide. The aim of the present study was to investigate the relationship between local resistance bacterial patterns and antibiotic consumption in an intensive care unit in a Romanian university hospital.MethodsA prospective study was conducted between 1st January 2012 and 31st December 2013. Data covering the consumption of antibacterial drugs and the incidence density for the main resistance phenotypes was collected on a monthly basis, and this data was aggregated quarterly. The relationship between the antibiotic consumption and resistance was investigated using cross-correlation, and four regression models were constructed, using the SPSS version 20.0 (IBM, Chicago, IL) and the R version 3.2.3 packages.ResultsDuring the period studied, the incidence of combined-resistant and carbapenem-resistant P. aeruginosa strains increased significantly [(gradient = 0.78, R2 = 0.707, p = 0.009) (gradient = 0.74, R2 = 0.666, p = 0.013) respectively], mirroring the increase in consumption of β-lactam antibiotics with β-lactamase inhibitors (piperacillin/tazobactam) and carbapenems (meropenem) [(gradient = 10.91, R2 = 0.698, p = 0.010) and (gradient = 14.63, R2 = 0.753, p = 0.005) respectively]. The highest cross-correlation coefficients for zero time lags were found between combined-resistant vs. penicillins consumption and carbapenem-resistant P. aeruginosa strains vs. carbapenems consumption (0.876 and 0.928, respectively). The best model describing the relation between combined-resistant P. aeruginosa strains and penicillins consumption during a given quarter incorporates both the consumption and the incidence of combined-resistant strains in the hospital department during the previous quarter (multiple R2 = 0.953, p = 0.017). The best model for explaining the carbapenem resistance of P. aeruginosa strains based on meropenem consumption during a given quarter proved to be the adjusted model which takes into consideration both previous consumption and incidence density of strains during the previous quarter (Multiple R2 = 0.921, p = 0.037).ConclusionsThe cross-correlation coefficients and the fitted regression models provide additional evidence that resistance during the a given quarter depends not only on the consumption of antibacterial chemotherapeutic drugs in both that quarter and the previous one, but also on the incidence of resistant strains circulating during the previous quarter.Electronic supplementary materialThe online version of this article (10.1186/s12941-017-0251-8) contains supplementary material, which is available to authorized users.

BackgroundDue to the vulnerable nature of its patients, the wide use of invasive devices and broad-spectrum antimicrobials used, the intensive care unit (ICU) is often called the epicentre of infections. In the present study, we quantified the burden of hospital acquired pathology in a Romanian university hospital ICU, represented by antimicrobial agents consumption, costs and local resistance patterns, in order to identify multimodal interventional strategies.MethodsBetween 1st January 2012 and 31st December 2013, a prospective study was conducted in the largest ICU of Western Romania. The study group was divided into four sub-samples: patients who only received prophylactic antibiotherapy, those with community-acquired infections, patients who developed hospital acquired infections and patients with community acquired infections complicated by hospital-acquired infections. The statistical analysis was performed using the EpiInfo version 3.5.4 and SPSS version 20.ResultsA total of 1596 subjects were enrolled in the study and the recorded consumption of antimicrobial agents was 1172.40 DDD/ 1000 patient-days.The presence of hospital acquired infections doubled the length of stay (6.70 days for patients with community-acquired infections versus 16.06/14.08 days for those with hospital-acquired infections), the number of antimicrobial treatment days (5.47 in sub-sample II versus 11.18/12.13 in sub-samples III/IV) and they increased by 4 times compared to uninfected patients. The perioperative prophylactic antibiotic treatment had an average length duration of 2.78 while the empirical antimicrobial therapy was 3.96 days in sample II and 4.75/4.85 days for the patients with hospital-acquired infections. The incidence density of resistant strains was 8.27/1000 patient-days for methicilin resistant Staphylococcus aureus, 7.88 for extended spectrum β-lactamase producing Klebsiella pneumoniae and 4.68/1000 patient-days for multidrug resistant Acinetobacter baumannii. ConclusionsSome of the most important circumstances collectively contributing to increasing the consumption of antimicrobials and high incidence densities of multidrug-resistant bacteria in the studied ICU, are represented by prolonged chemoprophylaxis and empirical treatment and also by not applying the definitive antimicrobial therapy, especially in patients with favourable evolution under empirical antibiotic treatment. The present data should represent convincing evidence for policy changes in the antibiotic therapy.

Aims: Long noncoding RNAs (lncRNAs) play key roles in the pathophysiology of DKD involving actions of microRNAs (miRNAs). The aims of the study were to establish the involvement of selected lncRNAs in the epigenetic mechanisms of podocyte damage and tubular injury in DKD of type 2 diabetes mellitus (DM) patients in relation to a particular miRNAs profile. Methods: A total of 136 patients with type 2 DM and 25 healthy subjects were assessed in a cross-sectional study concerning urinary albumin: creatinine ratio (UACR), eGFR, biomarkers of podocyte damage (synaptopodin, podocalyxin) and of proximal tubule (PT) dysfunction (Kidney injury molecule-1-KIM-1, N-acetyl-D-glucosaminidase-NAG), urinary lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), nuclear-enriched abundant transcript 1 (NEAT1), myocardial infarction-associated transcript (MIAT), taurine-upregulated gene 1 (TUG1), urinary miRNA21, 124, 93, 29a. Results: Multivariable regression analysis showed that urinary lncMALAT1 correlated directly with urinary synaptopodin, podocalyxin, KIM-1, NAG, miRNA21, 124, UACR, and negatively with eGFR, miRNA93, 29a (p<0.0001; R 2 =0.727); urinary lncNEAT1 correlated directly with synaptopodin, KIM-1, NAG, miRNA21, 124, and negatively with eGFR, miRNA93, 29a (p<0.0001; R 2 =0.702); urinary lncMIAT correlated directly with miRNA93 and 29a, eGFR (p<0.0001; R 2 =0.671) and negatively with synaptopodin, KIM-1, NAG, UACR, miRNA21, 124 (p<0.0001; R 2 =0.654); urinary lncTUG1 correlated directly with eGFR, miRNA93, 29a, and negatively with synaptopodin, podocalyxin, NAG, miRNA21, 124 (p<0.0001; R 2 =0.748). Conclusions: In patients with type 2 DM lncRNAs exert either deleterious or protective functions within glomeruli and PT. LncRNAs may contribute to DKD through modulating miRNAs expression and activities. This observation holds true independently of albuminuria and DKD stage.

Maintaining an upright posture while talking or texting on the phone is a frequent dual-task demand. Using a within-subjects design, the aim of the present study was to assess the impact of a smartphone conversation or message texting on standing plantar pressure and postural balance performance in healthy young adults. Thirty-five subjects (mean age 21.37 ± 1.11 years) were included in this study. Simultaneous foot plantar pressure and stabilometric analysis were performed using the PoData system, under three conditions: no phone (control), talking on a smartphone (talk) and texting and sending a text message via a smartphone (text). Stabilometric parameters (center of pressure (CoP) path length, 90% confidence area and maximum CoP speed) were significantly affected by the use of different smartphone functions (p < 0.0001). The CoP path length and maximum CoP speed were significantly higher under the talk and text conditions when compared to the control. CoP path length, 90% confidence area and maximum CoP speed were significantly increased in talk compared to text and control. Talking on the phone also influenced the weight distribution on the left foot first metatarsal head and heel as compared with message texting. Postural stability in healthy young adults was significantly affected by talking and texting on a smartphone. Talking on the phone proved to be more challenging.