E. coli strains were incubated, for only two hours, in the presence of different bactericidal antibiotics  in a range of concentrations that included the clinical breakpoints used as references in microbial diagnostic. Spectra were acquired and processed to isolate spectral modifications associated with the antibiotic effect. We evidenced an “antibiotic effect signature” which is expressed with specific Raman peaks and the coexistence of three spectral populations in the presence of antibiotic. We devised an algorithm and a test procedure that overcome single-cell heterogeneities to estimate the MIC and determinate the susceptibility phenotype of the tested bacteria using only a few single-cell spectra in four hours only if including the preculture step.Raman spectrometry appears to be an opportunity to perform rapid tests in microbiological diagnostics as it provides phenotype-related information from single bacterial cells thus holding the promise of direct analysis of clinical specimens without any time-consuming growth phase. Here, we demonstrate the feasibility of a rapid antibiotic-susceptibility determination based on the use of Raman spectra acquired on single bacterial cells. After a two-hour preculture step, one susceptible and two resistant  Standard methods routinely used for AST require several preparatory steps such as growth and isolation on solid agar culture medium for 18 to 24 hours before assays such as the gold standard broth microdilution assay can be performed. Typically, the test results in the determination of the Minimum Inhibitory Concentration (MIC) of the considered strain for the given antibiotic. The comparison of this MIC to reference breakpoints defined by institutions such as the Clinical and Laboratory Standards Institute 3 and the European Committee on Antimicrobial Susceptibility Testing 4 allows to qualify a strain as resistant (R), intermediate (I) or susceptible (S) to the antibiotic. Even in automated formats such as Vitek-2  or Phoenix , AST from bacterial colonies still takes 6–10 hours after isolation of colonies, leading to actionable results being delivered 30 to 72 hours after sampling. As a consequence, test results are not used to tailor the initial therapeutic decision and to reduce broad spectrum antibiotic usage. MALDI-TOF has helped to speed up the microbial identification – although it still requires bacterial growth – and it is now key to speed up the AST5 in order to provide actionable information sooner to clinicians. Nowadays, one of the fastest approaches for resistance detection consists in molecular biology – mostly PCR-based – assays. Such assays only require small numbers of micro-organisms and can be performed directly from a clinical specimen in just hours without requiring any time-consuming bacterial culture. Nevertheless, such tests are based on amplification of known antibiotic resistance genes and inherently fail to detect unknown or unexpected phenotypes7. Alternatively, several phenotypic approaches, based on optical methods, enable direct analysis of single microorganisms present in a clinical sample. Fluorescence microscopy and phase-contrast imaging have recently been used to test the direct effect of antibiotics on cell morphology and early division cycles9. Although they may reach single-cell sensitivity and provide excellent performance in some cases, such methods are still limited by the growth speed and structural change characteristic of the microbial species. Probing the initial metabolic response of single micro-organisms to the presence of antibiotics, independently of any morphological changes and division, would further accelerate testing. Among optical methods adapted to the characterization of individual cells, Raman micro-spectroscopy has been shown to accurately provide identification information from single bacterial cells14 and has also been shown to detect metabolic changes at the single bacterial cell level when they are incubated in presence of antibiotics17. More advanced methods using Surface Enhanced Raman Spectroscopy have also been demonstrated18 that could provide several advantages regarding the acquisition time19 or the selectivity20 (if required) at the cost of some additional reagents or protocol complexity. However, most of the related studies were restricted to very high antibiotic concentrations15. Other studies have demonstrated that resistance phenotypes can be detected with antibiotic concentrations in the scope of AST but when averaging the response of a number of cells24. Demonstrating that Raman microspectroscopy is able to perform single-cell AST tests would provide a technique that is both able to provide identification and AST. It would be a major improvement of the overall achievable time to phenotypic results. In the present study, we performed Raman micro-spectrometry at the level of single bacterial cells incubated in the presence of antibiotic concentrations close to clinical breakpoints. We observed that in such conditions, Raman spectra are modified in a unique manner in the presence of antibiotic, defining what could be called an “antibiotic effect signature”. We then studied the occurrence of this “signature” inside a population of bacterial cells as a function of the antibiotic concentration. Interestingly, we show that the antibiotic effect signature is unequally spread among the bacterial cells population and that this variability reflects the coexistence of different spectral populations which relative abundance varies depending on antibiotic concentration. Furthermore, we demonstrate, by analyzing these differences through a Support Vector Machine (SVM) with a radial basis kernel, that this differential spectral behavior can be used to determinate the susceptibility phenotype and estimate the MIC of the considered strain, and that this prediction can be done using only a very small number of cells of the considered bacterial population.Today, the initial antibiotherapies are usually empiric and suboptimal since antibiotic susceptibility data are not available on the collection day of the sample. Consequently, developing new rapid microbiological diagnosis and more specifically Antibiotic Susceptibility Testing (AST) that would give a response in a few hours instead of a few days is a key challenge not only to promote optimal patient outcomes but also to attenuate or stop the development of new antibiotic-resistant organismsThe standard broth microdilution test used to determine the MIC of an antibiotic relies on a series of overnight bacterial cultures in presence of the antibiotic at increasing concentrations. The concentration range used corresponds to a series of doubling concentrations including the low and high breakpoints as they are defined by the CLSI and by the EUCAST . This procedure was designed to remove the main part of the strain-specific information and the metabolic variability originating from the bacteria at different dates and evidences spectral changes that are reproducible for different dates and strains.σ range around the mean shown as a light-blue band in Fig. By doing so, we could clearly detect the antibiotic effect on the susceptible strain. The results, presented in Fig. 1, 1240 cm−1, 1483 cm−1 and 1574 cm−1, are attributed to nucleic acid15 whereas peaks at 1125 cm−1 and 1443 cm−1, with negative weights, are generally attributed to proteins15.In the PCA plots, the main separation between the spectra from exposed susceptible bacteria and those from non-exposed or resistant bacteria was observed parallel to the axis of the first “principal component” (PC1) for gentamicin and ciprofloxacin Fig. . For amoIn order to determine whether this spectral signature of the antibiotic effect and the associated score could be directly used to determine the MIC of the susceptible strain, we studied the variations (and distributions) of the PC1 score of single EC1 bacterial cells incubated with increasing concentrations of antibiotic, from well under (including 0) to well above the MIC. The results obtained for the three antibiotics see Fig.  showed t−1, 1240 cm−1, 1483 cm−1 and 1574 cm−1 and negative peaks at 1125 cm−1 and 1443 cm−1. The third population, with high negative scores, was called “ATB effect −” group. This last group has a mean difference spectrum with about the same peaks and opposite intensities . The main discriminating axis for the antibiotic effect was found parallel to PC1 again, with about no discrimination along the PC2 axis see Fig. . The loaNote that the coexistence of these two or three groups at a given antibiotic concentration, with possibly comparable relative sizes, largely explains the high variance observed among spectra of exposed susceptible bacteria described above. This also explains why this variance is mainly concentrated on the 6 peak positions listed above. In addition, we noticed that, for each antibiotic, the “no effect” group decreased below 50% while the two other groups became a majority at concentrations around the MIC . All spectra from the resistant strains were excluded from this learning database.26 (software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm) to deal with the non-linear nature of the observed effect, especially the coexistence of the opposite “ATB effect+” and “ATB effect−” populations among the difference spectra of the exposed susceptible bacteria. Note that, although we used the full difference spectra for the classification, we observed that a linear SVM had a limited performance for this task. This suggests that the full data were not or poorly linearly separable due to the aforementioned opposite populations, as evidenced in two dimensions by the PCA  kernelPCA Fig. , hence tDue to the high intercellular spectral variability, including the presence of exposed bacteria without observable spectral changes, it was necessary to repeat the test on a small number of difference spectra to reliably decide whether the bacterial cells were susceptible or not to the antibiotic at a given concentration. In view of validating the test in realistic conditions, as close as possible to a direct sample clinical application, we restricted the mean spectrum of non-exposed bacteria used in the calculation of the difference spectra to the same small number of non-exposed bacteria. We could reach satisfying performance with only 5 difference spectra for gentamicin and ciprofloxacin (from 5 exposed and 5 non-exposed cells) and 11 difference spectra for amoxicillin (from 11 exposed and 11 non-exposed cells). Each of the 5 (or 11) tested difference spectra was labeled by the classifier and a majority vote was applied to assign a class to the tested set. The detailed performances are represented as confusion matrices in Fig. E. coli23 or vancomycin susceptibility phenotypes in Enterococci23 but Dekter et al. (2016) made the first demonstration of an AST based on an antibiotic-concentration range using Raman spectrometry22. The demonstration was performed on a large bacterial biomass previously incubated for five hours in presence of different antibiotic concentrations relevant for a clinical AST test. Here, we addressed this question at the single cell level. We demonstrated that the AST can be performed using Raman spectra acquired on single cells after a two-hour incubation in presence of antibiotic and that only few cells (from 2 × 5 to 2 × 11) were required to perform the test reliably despite an observed intrinsic variability. Thus opening the way to direct sample no-growth analysis, even in pauci-bacterial samples.In the present work, we addressed the question of performing an AST using Raman spectrometry on single bacterial cells. This technology appeared to be an opportunity to perform a rapid test that would rely on bacterial metabolism analysis instead of cell division. Previous works had shown the ability of Raman spectrometry to discriminate between mechanisms of actions of several antibiotics in 27 or in a bacterial population that was incubated in the presence of antibiotic17. Different publications have also described bacterial phenotypic heterogeneity inside a genetically homogeneous population31. We excluded that all these cells with non-modified spectra may be “persister cells” such as previously described34, because the typical occurrence of the “persisters” is in the 10−4 to 10−6 range, a much lower frequency than the ones we observed. The spectral signature that we evidenced exhibited peaks at 783 cm−1, 1240 cm−1, 1483 cm−1 and 1574 cm−1 (with positive weights) and at 1125 cm−1 and 1443 cm−1 (with negative weights) which were respectively attributed to nucleic acid and proteins by Moritz et al.15.For these authors, the increase in the intensity of the DNA/RNA peaks was observed during the exponential growth phase, which is also what we observed in a previous work on bacterial micro-colonies grown on agar25. Based on our observations, we propose to link the DNA/RNA peaks modification to an apoptosis process. Our hypothesis is inspired from observations on eukaryotic cells, for which the authors described that the 782 cm−1 peak increase or decrease corresponded to DNA condensation or DNA fragmentation, respectively37. Dwyer et al.38 strengthened this hypothesis by having shown that antibiotic-incubated bacteria exhibit the same hallmarks of DNA/RNA condensation and fragmentation and also related the process to apoptotic process using genetic and proteomic data. Bernatova et al. (2013) reported the decrease of the 785 cm−1 peak in bacteria incubated with ciprofloxacin and suggested that it could be associated with DNA fragmentation39. As a consequence, our hypothesis is that the observed groups with modified spectra – namely the “ATB effect + ” group and the “ATB effect −” group – inside an initially homogeneous susceptible population correspond to groups of cells undergoing DNA condensation and DNA segmentation, respectively. We hence demonstrate that those spectral regions which were previously reported as non-significant due to this high variability by Munchberg et al.16 in fact contribute on the contrary to the Raman signature of the antibiotic effect and completely explains the associated variability. We could thus evidence the modifications that occurred at the metabolic level of single bacterial cells when they are exposed to antibiotic during two hours. These observations obtained in Raman spectrometry are consistent with other studies realised on larger bacterial biomasses using other methods, showing that antibiotic rapidly affects viability, morphology, some metabolic mechanisms like respiration and related oxidative stress42, or cell wall synthesis43.Our demonstration first relies on Raman spectra acquisition on single cells and the use of unsupervised classification based on PCA. This first step allowed us to evidence a Raman spectral modification linked to the presence of the antibiotic at inhibitory concentrations. When the bacteria from the susceptible strains were incubated in presence of antibiotic, this signature was expressed with either positive or negative scores leading to two different cell populations (“ATB effect+ ” and “ATB effect−”) co-existing with a third population of cells exhibiting no spectral modifications. When antibiotic concentrations increased, the ratio of these two first “ATB effect+ ” and “ATB effect−” populations increased leading to an overall increased spectral variability of the global population. At antibiotic concentrations above the MIC, modified spectral populations became majority. Spectral heterogeneity has already been described in bacterial coloniesAfter evidencing the Raman spectral signature of the antibiotic effect and the spectral cells heterogeneity, we demonstrated that the AST could be performed despite the intrinsic variability of the acquired spectra and the existence of distinct spectral populations. For that purpose, we were able to train a classifier that allowed us to detect the presence or absence of  antibiotic effect on a single bacterial cell at a given antibiotic concentration and assign it to one of the classes “ATB effect” and “No effect”. Repeating the test on a small number of exposed bacterial cells (5 to 11 depending on the antibiotic) associated with the same number of non-exposed bacterial cells  and making a vote, we could infer the phenotypic antibiotic effect at the scale of the whole bacteria population. Instead of using spectra averaged over a few cells as commonly proposed in the literature, which would result in the destruction of the signal of interest in concentrations close to the MIC, we hence propose to truly analyze bacteria at the single cell level. We leveraged on their heterogeneity for analysis and the proposed vote scheme for the final attribution of the measured phenotype accounts for the observed heterogeneity in bacterial cells state. Using this classification scheme for different antibiotic concentrations, we observed a sharp transition from the “No effect” labelling to the “ATB effect” one at antibiotic concentrations very close to the considered strain reference MIC. We show that the phenotypic reference MIC is also around the concentration, within a standard 2-fold dilution range, at which we could observe a transition of the majority of the cell population from a “no effect” to an “ATB effect+/−” Raman spectrum for susceptible strains. This strongly suggests that we do observe a feature directly linked to the cell capabilities to divide or survive in the presence of antibiotics and that is closely correlated to the observed cell division in a broth micro-dilution test.If further confirmed on a larger antibiotic and strain panel, in particular concerning the correlation between the observed Raman transition and the reference broth micro-dilution MIC, our method would not only allow one to determine the Susceptible or Resistant phenotype of a given strain but also to estimate its MIC in a very short time since only a few bacterial cells are required in each tested condition to perform the analysis. This would open the way to achieving AST directly from a clinical sample and drastically reduce the time to result. Such strategies would change the paradigm of clinical decision-making from probabilistic initial decision to real-time medicine where initial therapeutic decisions could be based on the results of very rapid phenotype determination.Escherichia coli strains were obtained from the American Type Culture Collection  and from bioMérieux Culture Collection . Culture medium Trypcase Soja (TSA Ref. 43011) and TSB (Ref. 42100) were provided by bioMérieux. Phosphate buffer saline  was purchased from Applichem . Gentamicin (Ref. 00768065) was provided by bioMérieux. Ciprofloxacin (Ref. 17850) and amoxicillin (Ref. A8523) were purchased from Fluka-Sigma-Aldrich . Microcon® centrifugal filter (Ref. 42413) device, Microcon® in the text, was from Millipore . Swabs (Ref. 149–0266) were purchased from VWR . Coverslips (Ref. 0107052) were from Marienfeld . Geneframes (Ref. AB-0577) were purchased from Thermo Scientific . Minimal Inhibiting Concentration (MIC) was controlled either with Vitek® 2 or with e-test®. Bacteria suspension concentrations were measured using a KONTRON UVIKON 940 spectrophotometer. Correspondence between measured optical density at 550 nm and bacteria concentration were calibrated for the tested strains using petri dishes numerations.6 CFU/mL in 5 mL of TSB and followed by a 2-h culture at 37 °C to get bacteria in early stage of growth with less variations between them. The culture was then centrifuged at 1200 × g for 8 min. The pellet was suspended in water and was adjusted by dilution to a final concentration of 5·107 CFU/mL. 150 µL of the suspension were diluted in a final volume of 250 µL of PBS containing 10% TSB with varying concentrations of antibiotic and incubated for 2 h at 37 °C. A final 10-fold dilution was selected to express a weaker fluorescence and less pronounced culture medium Raman features on individual bacterial spectra. Bacteria were also prepared without antibiotic using the same protocol. After incubation, samples were then centrifuged at 1200 × g for 8 min in the Microcon® device (Millipore), washed with 300 µL of water and centrifuged at 1200 × g for 10 min. Bacteria located on the Microcon® device filter were transferred manually from the filter to a coverslip with a swab. Three samples with different antibiotic concentrations and one sample without antibiotic were transferred onto the coverslip. The coverslip was then set upside down onto a geneframe previously sticked on a microscope glass slide.Efforts were made to minimize variations in sample handling: the two-hour growth and bacterial concentrations were standardized. Strains were stored at −80 °C in broth containing glycerol. Before experiments, a first overnight culture was performed on TSA culture medium at 37 °C. This first culture was stored at 4 °C and constituted a “stock culture” which was used as a single source during the experimental campaign (3 weeks of storage at most). The sample preparation consisted in picking up one colony from this stock culture, diluting in TSB and culturing for 15 h at 37 °C to revitalize the bacteria. For culture, TSB was selected as the preferred liquid culture medium as it is a generic culture medium. The overnight culture was diluted at a final concentration of 5·10The choice of the tested antibiotic concentrations resulted from a compromise between concentrations used in the reference broth microdilution, the MIC of the tested strains and clinical breakpoints. The objective was to allow the observation of interesting biological phenomena, around the MIC of the tested strain, and to confirm the usability of the method for a Susceptible, Intermediate or Resistant categorization. High concentrations (≥8 MIC) were tested for ciprofloxacin and gentamicin but were not with amoxicillin. Indeed, amoxicillin concentrations superior to 16 µg/ml, caused major filamentations and lysis. These extensive damages to susceptible cells resulted in the impossibility to measure single-cell spectra in such conditions.Immediately after incubation, an aliquot of each sample was serially diluted in water. 100 µL of dilution were plated onto a TSA culture medium without antibiotic and incubated for 24 h at 37 °C. Bacterial colonies were enumerated to evaluate the concentration of bacteria alive in the different samples. Results are presented in supplementary Fig. Raman spectra were acquired using a LabRam ARAMIS (HORIBA JOBIN-YVON) equipped with a 600 lines/mm grating, a VENTUS (LASER QUANTUM) 532 nm CW laser and a ZEISS 100x oil immersion objective with a 1.3 numerical aperture. The confocal pinhole was set to 400 µm which results in a measurement volume corresponding to a single cell in this setup. Typical laser power on the sample was measured to be 9 mW and spectra were integrated during 15 s for each single cell. Resulting scattered light was collected on a 1024 × 256 Synapse CCD (HORIBA JOBIN YVON) cooled at −70 °C. To gather spectra, the geneframe holding the coverslip and the samples was loaded on the Raman spectrometer stage. Each spectrum was acquired on a different single cell. A procedure was adopted to decorrelate measures from various potentially confounding factors such as the time delay between sample transfer and spectrum measurement, variation of the substrates or other unplanned experimental condition variations. Five single bacteria spectra were acquired followed by one glass spectrum for a given tested condition. After acquiring those 6 spectra, a new batch of 6 spectra was immediately acquired for a different condition. This process was repeated until all tested conditions of an experiment reached at least 50 gathered spectra.−1. Signal measured in spectra mainly originated from fluorescence, in the form of a slowly-varying and intense background with poor specificity, and from Raman scattering of bacteria and glass substrate. As the antibiotic effect of interest was presumably weak, we removed background and glass contribution, with the effect of significantly reducing the impact of non-biological and non-specific spectra variability. Four preprocessing steps were thus used in this study: suppression of “cosmic spikes”, correction of a possible wavenumber shift in the spectra, removal of background and glass contribution, and normalization of spectral intensities by their mean value in the region of interest (ROI) [650–1750] cm−1. A more detailed description of the first two and the last steps can be found in a previous publication25. Background and glass-contribution removal was done in two steps: we first removed glass contribution  and then removed the slowly-varying background, mainly due to fluorescence and estimated by peak-clipping using the SNIP algorithm44. The resulting spectrum was called the “normalized net spectrum”. Variance values were computed at each wavenumber for a given spectra dataset using the standard definition as the expectation of the squared difference between the intensities and their mean value over the studied dataset, at that given wavenumber. The spectrum obtained by plotting the variance of a dataset for each Raman Shift was called the “variance spectrum” for this dataset, as shown in Fig. 45.Each spectrum was composed of 1024 channels corresponding to a Raman shift ranging from 395 to 3075 cmThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.Supplementary Figures


In conclusion, the infectious disease meeting via telemedicine has been an effective tool for economic and professional development and multidisciplinary management of complex patients. The appropriate use of antibiotics reduced the multi-drug resistant bacteria selection, thus improving patient safety.Implementation of antimicrobial stewardship program is a pivotal practice element for healthcare institution. We developed a remote infectious disease consultancy program via telemedicine in a high-specialized pediatric cardiac hospital. A consultation for antibiotic strategy for each patient was available via telemedicine in addition to biweekly discussion of all clinical cases. Aim of this study was to evaluate the impact of the remote stewardship program in terms of a) appropriateness of antibiotic prescription; b) incidence of multi-resistant infection; and c) cost. A ‘before - after’ study was performed comparing the period immediately before starting the program and one year after. There was a trend in the reduction of nosocomial infectious disease rate (9.5 vs 6.5 per 1000 person days), with a reduction in the overall antibiotic cost  and in the average antibiotics packages used per admission (9 vs 6.7 packages). A significant reduction in the multi-drug resistant isolation rate was observed (104 vs 79 per 1000 person days,  CCPM), was founded in Sicily following an agreement between the public health agency of Sicily region, and The Bambino Gesù Children’s Hospital (OPBG), the largest pediatric research hospital within the Italian Healthcare System. The CCPM, which is part of Taormina hospital (220 beds), includes an intensive care unit, an ordinary ward, a cardiac outpatient center, and an operating room with a Hybrid Cath lab. The center performs complex treatments like sternotomies, interventions with extracorporeal circulation, cardiac catheterizations, ExtraCorporeal Membrane Oxygenation (ECMO) procedures, mechanical ventilation, on children often in critical conditions and low birth-weight requiring hospital-wide support for long periods. Despite an active surveillance system of nosocomial infections in ICU (Intensive Care Unit) by the CCPM health personnel, nor an infectious disease expert was available onsite, neither molecular biology tools were available in the laboratory. In January 2015, after a cluster of pneumonia patients admitted in ICU, OPBG decided to start a remote antimicrobial stewardship program for CCPM with a multidisciplinary team using telemedicine.Improving the use of antibiotics is a key strategy to address antibiotic resistance and an important issue for patient safety and public health . AntibioThe aim of this study is to evaluate the impact of the remote stewardship program compared to the previous period in terms of: a) appropriateness of antibiotic prescription; b) incidence of multi-resistant infections; and c) costs.The remote stewardship program consisted of a biweekly discussion of all clinical cases admitted in CCPM and a review of antibiotic strategies for each patient. Telepresence techniques were used with commercial tools  integrated with a high-definition camera, microphones, real-time sharing of files, desktop sharing and sharing of the cardiac radiology information system. During the online meetings, cardiologists, anesthesiologists, and surgeons from CCPM met infectious diseases and microbiology specialists from OPBG. Clinical records, diagnostic images, and other relevant reports were shared and reviewed. Antibiotic strategies were agreed and evaluated according to the discussion. Extra meetings were held in case of need. A teleconsultation tool, allowing consultation between a provider and specialist at distance using either store and forward telemedicine or real time videoconferencing, has been used only in selected cases.In order to measure the intervention impact, we performed a ‘before - after’ study comparing a period immediately before the intervention (1 January 2014–1 March 2015) to a post-intervention period (1 March 2015–1 March 2016).We assessed the following measures: a) incidence of nosocomial infections; b) incidence of isolation of multi-drug resistant bacteria; c) direct and indirect costs including those associated with antibiotic treatments and length of stay. The personnel involved was interviewed concerning their satisfaction in using teleconsultation for antibiotic stewardship.p < 0.05. Analyses were performed through STATA software.Continuous variables are reported as the mean ± SD and data ranges or median and 1st-3rd quartile, as appropriate. Differences between groups were analyzed with Student’s t test for normally distributed variables or with the Wilcoxon test. Rates were compared through the mid-p test. Statistical significance was considered for In the pre-intervention period 683 patients were included versus 531 patients in the post-intervention, as shown by the average weight Diagnostic Related Group (DRG)'s, populations are comparable and characterized by high complexity care (Table The main topics discussed during the meetings were monitoring and managing of Multi-Drug Resistant (MDR) Enterobacteriaceae and the antibiotic prophylaxis protocols in surgery. Furthermore, management of diagnostic specimens submitting to the microbiology laboratory was modified. To improve accuracy of microbiological diagnosis, CCPM clinicians ensured that diagnostic specimens were properly obtained and promptly submitted to the microbiology laboratory, preferably before the institution of antimicrobial therapy. In fact, before ASP meetings, a major issue was the timing of initial  therapy that should be guided by the urgency of the situation and the clinical presentation. The meetings trained clinicians about: 1) understanding the difference between empirical and definitive therapy; 2) identifying opportunities to switch to narrow-spectrum, more effective agents for the shortest duration necessary; 3) understanding drug characteristics that are peculiar to antimicrobial agents (such as pharmacodynamics and efficacy at the site of infection) and 4) understanding antimicrobial susceptibility testing.p = 0.92).The average stay in pediatric ICU was not significantly different between the pre and post ASP meetings .The hospital infections in ICU rate per 1000/person days decreased from 9.5 in the pre-intervention period to 6.1 for 1000/person days in the post intervention, though the difference was not statistically significant (p = 0.01). We are aware that other variables as seasonal variation or surgical case load during that time have not been considered in this analysis, but we assumed that the reduction in the antibiotics consumption is the first factor associated to the reduction of the MDR rate.The rate of MDR isolation decreased from 104 to 79 per 1000 person days with a significant reduction of 25%  was 6126 packs in 2013; 5296 in 2014; and 3779 in 2015 (the difference between 2013 and 2015 is 2347 drug packages), with a mean packages consumption of 9 vs 6.7 in 2014 and 2015, respectively.The medical doctors interviewed were satisfied by the intervention and reported that they had improved their knowledge about the antibiotics prescription and the protocols adopted.In this scenario where the health service has to promote cost reduction, network organization and clinical specialization, the information technology (ITC) offers economic sustainable tools to support professional training and patient safety. Telemedicine technologies are rapidly being integrated into infectious diseases programs with the aim of increasing access to infectious diseases specialty care and reducing costs . In FranThe infectious diseases meeting at the CCPM was an effective tool for economic and professional development, and multidisciplinary management of complex patients. It allowed the sharing of protocols and best practices, promoted the prudent use of antibiotics  and it wThe appropriate use of antibiotics reduces the selection of MDR bacteria, and the risk for patients and health workers , althoug


Antibiotic resistance (ABR) has become a major public health challenge in most parts of the world including Ghana and is a major threat to gain in bacterial disease control. The role of prescribers in the control of antibiotics is identified as crucial in developing interventions to control ABR. To guide policy recommendations on ABR, a study was carried out among prescribers to identify gaps in their knowledge of ABR and to document their prescription practices.A cross-sectional survey was conducted among prescribers from both public and private facilities in the Brong Ahafo Region of Ghana using both quantitative and qualitative methods in 2014.Three hundred and seventy nine prescribers participated in the quantitative study and a subset of 33 participated in in-depth interviews. Majority (50.0%) of the prescribers interviewed were nurses. Most (51.0%) of the prescribers were located in hospitals. Knowledge of ABR was high among all the prescribers. About 80.0% percent of all prescribers agreed that the antibiotics that are currently used could lose its efficacy in future. There is no singular formal source of information on antibiotic resistance. The prescribers held a strong perception that antibiotic resistance is imminent though their knowledge on various resistant bacterial strains was limited. Prescribers attributed ABR burden to factors such as poor prescription practices and limited ABR control measures. The prescription practices of the prescribers vary but were mostly inappropriate among the lower cadre.The knowledge of ABR is high among prescribers. There is however a gap in the knowledge and perception of optimal antibiotic prescription practices among prescribers. There is the need for a formal source of information on ABR to support prescriber’s antibiotic prescription practices.The online version of this article (doi:10.1186/s12913-017-2365-2) contains supplementary material, which is available to authorized users. Antibiotic resistance (ABR) has become a major public health challenge in most parts of the sub-Saharan Africa including Ghana , 2. SeveThe high burden of ABR has been an existing concern , 11 thatABR can be associated with misuse of antibiotics as a result of several factors including inappropriate prescription by prescribers and misuse of antibiotics by patients , 12, 21.Ghana recognizes the increasing burden of ABR and is in the process of establishing policies to prevent and manage it. The role of prescribers is identified as crucial in developing interventions to control ABR . The knoA cross-sectional survey was conducted among prescribers from both public and private facilities in the Brong Ahafo Region of Ghana using both quantitative and qualitative methods from January to March 2014.2. Majority of the population are Akans. Christianity is the most common religion (70.8%) among the population. Illiteracy rate is high: 48.5% of persons aged 15 years and above cannot read or write in any language and about 42.0% of children are not in school  the information is not straight forward. If we are able to get a main source of information about antibiotics it will be well” There were no differences in the sources of information among the different health professionals and there was no specific channel for obtaining information on ABR as alluded to below.Barriers to prescribers access to antibiotic related information is limited by lack of internet facility, lack of a uniform source of information and lack of local data on patterns of ABR.Multidrug Resistant Mycobacterium tuberculosis control and to a less extent about the control of other infections such as Methicillin Resistant Staphylococcus aureus. The prescribers perceived ABR could have serious consequences on mortality trends and health expenditure as demonstrated below:“That is the most dangerous thing that can happen to mankind. We may move to the last limit where we cannot get any antibiotics to treat certain diseases. Talking about antibiotics, if you look at treatment of TB, which is a bacterium. We are strict because it is a combination course. If you joke and one of them develops a resistance, the other three cannot do anything. And to get another drug that will work like the former one doesn’t take 1 day. It takes a period. And the cost of it is also another thing…. So we need to be careful with antibiotics especially if mankind wants to live long on this earth” Generally, the prescribers perceived ABR as a very crucial issue that needs to be addressed. Their perception of ABR burden was mainly informed by its impact on “...Most of the hospitals also don’t have machines for culture sensitivity to actually isolate the real bacteria that is causing the disease. So just prescribing any form of antibiotic may increase the resistance. “…antibiotic resistance is possible because in Ghana .., you can go to any drug store and buy any antibiotics without any prescription or these days we don’t know the source of medication that we are getting whether they are the correct or the fake ones. The efficacy level is questionable.” Prescribers perception of causes of ABR were several. Their perceptions include antibiotic over prescription, irrational prescription of antibiotics, patient non-compliance to medications, unregulated dispensing of antibiotics leading to over the counter purchases. During the in-depth interviews the prescribers shared their perceptions regarding ABR from different perspectives.However, health facility preventive measures such as hand washing, use of cleaning solutions, and sterilization were rarely mentioned by the prescribers.To learn about prescription practices, Upper Respiratory Tract Infections (URTI) was presented as case scenario to prescribers.“Most of the URTIs practically are viral infections and not bacteria. However, if I establish that the cause of the URTI is a bacteria then I give antibiotics. As first line treatment I give amoxicillin for the typical ones or azithromycin for the atypical ones. But if the patient have already gone for the first line treatment drug, then I give the second line treatment drugs like amoxiclav and other higher antibiotics. But for kids and toddlers I give flucloxacillin for the start because the cause is usually gram positive, staph [Staphyloccocus] and strep.[Streptococcus] infection” Most of prescribers indicated that they diagnose URTI using the patient’s complaints and physical examination and to a less extent laboratory confirmation of the causative agents. URTIs are often treated presumptively with antibiotics of varying types depending on the disease progression or severity at presentation. In some cases, second and third generation antibiotics are primarily used. Not all prescribers adhere to the Ghana National Treatment Guidelines in their choice of antibiotics for treating URTI. This is illustrated by the following quotes:“The number one reason is the insurance. You always want to prescribe what the patients will get in your facility; the second factor is affordability of the patient, if the patient can afford some of the high ones you can prescribe for them and the third factor will be previous drug history, what has the patient taken before coming to you. You don’t want to continue on the same line. The other factor will be patient choice, sometimes you have to agree with the patient” Prescribers rationale for their choice of antibiotics in treating patients is also influenced other factors such as patient’s ability to pay for the antibiotic, antibiotic availability patient drug history, allowable restrictions of antibiotic at the level of the facility, allowable antibiotics on insurance protocols, feedback on recovery due to treatment, patient age, medical history of the patient, and clinician wanting to impress patient. A prescriber had this to say:The prescribers shared different experiences on patient’s demand for antibiotics. Prescribers do not mostly consider patients expectations in the prescription of antibiotics. However, patients who are able demonstrate why they should be prescribed a particular antibiotic may receive what they ask for. Also, patients who may have had a bad experience with a particular antibiotic may not accept the same the antibiotic if it is prescribed for them. Patients who are educated demand specific antibiotics but the less educated ones accept whatever is prescribed for them.“…. No! As a clinician no! If the patient comes to me with an expectation, then it is my job to explain to the patient… in certain cases it maybe what you wanted to prescribe then it makes your work easier but if it is not, I will take my time and educate the patient on what I think is best for the patient” “You will have that problem of being influenced by patients who are educated and also the health staff themselves on the UTI (urinary tract infections), they will request specifically for some type of antibiotic but for the general population, they rely on what we prescribe for them. ” Prescribers also hold the perception that they know the best drugs for the patients’ illness.p < 0.01).Knowledge on the availability of an infection control committee was assessed among the prescribers. About 47.0% (178/379) of prescribers indicated non -availability of infection control committees in their facilities. Only 16.4% of prescribers reported of functional Drug Therapeutics Committee (DTC) in their facility. There was a significant difference in prescribers knowledge of a functional DTC in their facility  African region.  It is imMethicilin Resistant Staploccous aureus (MRSA) [Multidrug Resistant Mycobacterium Tuberculosis was limited. Specifically, the knowledge of extended-spectrum beta-lactamases producing Enterobacteriaceae was limited though there was an outbreak in a major hospitals in Ghana [The role of hospital infection prevention protocols are important to prevention of bacterial resistant strains such as s (MRSA) . In thisin Ghana  and repoin Ghana , 42. In in Ghana . This isin Ghana . There iThe knowledge of ABR is high among prescribers. There is however a gap in the knowledge and perception of optimal antibiotic prescription practices among health professionals. There is the need for a formal source of information of ABR to support prescriber’s antibiotic prescription practices.


Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a highly aggressive hematological malignancy with extremely poor outcome. The median overall survival for adult patients is 9–13 months. Pediatric patients are exceedingly rare with an unclear clinical course. Currently, no standardized therapy has been established, although an acute lymphoblastic leukemia type of treatment appears to be more effective in those patients who are able to tolerate aggressive chemotherapy. SL-401 is a targeted therapy directed to CD123, a protein ubiquitously expressed at high level on the surface of BPDCN blasts. In adult phase 2 trials, it has demonstrated efficacy with 90% overall response rate. No pediatric patients with BPDCN using SL-401 have been reported.Here, we report the first pediatric experience of three children with BPDCN treated with SL-401 at our institution. All patients tolerated SL-401 without significant toxicities. One patient with multiply relapsed and refractory disease had no response. The other two cases had significant and rapid clinical improvement after the two courses of treatment. However, the response was transient, and growth of soft tissue mass was observed in-between cycles in both patients with large tumor burden.This is the first report of SL-401 in pediatric patients with BPDCN. Sl-401 was well tolerated and can produce a promising response. Further testing this agent in children is warranted. Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a highly aggressive hematological malignancy that primarily involves bone marrow (BM) and/or skin; secondary sites include lymph nodes, soft tissue, and central nervous system , 2. BPDCCD123, interleukin 3 (IL-3) receptor α chain, is ubiquitously expressed at high level on the surface of BPDCN blasts, making it an attractive therapeutic target . After bUp to date, no pediatric patients with BPDCN using SL-401 have been reported. Here, we report the first experience of three children with BPDCN treated with SL-401 at City of Hope (COH)  relapse at 22 months and achieved a second CR after first salvage therapy. Due to invasive fungal infection, she subsequently received 4 cycles of decitabine and was found to have second BM and CNS relapse 5 months later. She received second salvage therapy with fludarabine and cytarabine, then transferred care to COH. Upon arrival, her BM was in CR, and minimal residual disease (MRD) was positive at 0.37%, with a strong signal of CD123. CSF was negative. However, a new small scalp mass was noted.She received SL-401 treatment as a bridging therapy while an unrelated donor search was ongoing. During course 1, she developed facial flushing and fever that resulted in a 24-h delay of the third dose. At the end of course 1, she had stable scalp lesions and 0.33% BM MRD. During course 2, she developed tachycardia, hypoxia, and urgency to evacuate bowl shortly after the second dose. The symptoms improved quickly after oxygen and diphenhydramine. She tolerated subsequent doses with premedication. However, the scalp lesion enlarged and was confirmed positive for BPDCN. End of course 2 BM showed 80% blast. Blasts from both scalp lesion and BM remained to be CD123 positive.SL-401 was discontinued. She did not respond to two additional salvage chemotherapies with SMILE  and CEC . The patient died 5 months later from progressive disease.A 12-year-old girl presented with localized, progressive soft tissue mass of the posterior medial left ankle and no BM involvement. The family initially refused chemotherapy but agreed to SL-401. She received five courses of SL-401 without any AEs. She was in school full time during the treatment. There was a visual decrease in the size of the mass. At the end of course 2, the mass had decreased from the size of a large grapefruit to approximately 2 × 3 cm  [Pretreatment disease evaluation revealed 43% circulating blasts, chest and abdominal lymphadenopathy, and extensive lung involvement Fig. d, g. DurSL-401 was discontinued, and the patient planned to receive total marrow and lymphoid irradiation followed by haploidentical SCT . After 2BPDCN is a highly aggressive hematological malignancy. In the past, despite intensive combination chemotherapy, most adult patients die within 2 years of diagnosis . The pubSL-401, a targeted therapy directed to CD123, has demonstrated a tolerable safety profile and robust activity in adult patients with BPDCN. Here, we report the first pediatric experience with SL-401. Similar to adults, we observed mild, transient infusion-related AEs that were easily manageable. One patient with large tumor burden development hypoalbuminemia and weight gain, suggesting possible development of CLS. Despite that, this patient tolerated more frequent dosing. Another patient was able to maintain an excellent quality of life and attended school full time. Overall, the therapy is very well tolerated.Two of the three patients responded to SL-401. None of them achieved CR. Unlike in adults where single-agent SL-401 activity is quite robust and durable; the response was transient, and the growth of soft tissue mass was observed in-between cycles in both responding patients who had large tumor burden. It is not clear why the response was less durable in our patients when compared to what was observed in adults. We speculate that there might be underlying biological differences between children and adults, and additional studies with more patients are needed. Our experience suggests that in children, SL-401 might be considered for combination therapy; albeit this study represents very few patients to draw definitive conclusions on single agent versus combination.Of note, tumors from patient #1 and #3 remained to be CD123 positive despite clinical evidence of disease progression. This was consistent with what was observed in the adult study, suggesting that the mechanism of resistance was not due to the loss or decreased expression of CD123  RecentlyIn summary, this is the first report of SL-401 in pediatric patients with BPDCN. SL-401 was well tolerated and can produce a promising response. Further testing this agent in children is warranted.


Furthermore, we show the rate of convergence of these operators (univariate case) to certain functions with the help of the illustrations using Maple algorithms and in the bivariate case, the rate of convergence of these operators is compared with the associated GBS operators by illustrative graphics.Agrawal  In the following, let For Forand allwe haveSince mentclass2pt{minimIfhas continuous partial derivativesandthen the inequalitywhereare the positive constants such thatandFrom the mean value theorem we have In our next result, we study the degree of approximation for the bivariate operators by means of the Lipschitz class.For LetThen, for allwe haveBy our hypothesis, we can write Let Forandwe haveLet Now, operating by mentclass2pt{minimx and y are given by For Clearly, both moduli of continuity satisfy the properties of the usual modulus of continuity.Ifandthen we haveUsing the definition of partial moduli of continuity, Lemma f belong to K-functional of the function Let M in the above inequality is independent of δ and f and The constant For the functionwe have the following inequality: whereandand the constantM (>0), is independent offandWe define the auxiliary operators as follows: Let nd using , we are sidering , 3.6), , \documend using  \documenLetThen for everyuniformly inf, we have By Taylor’s formula for entclass1pt{minimaApplying the Cauchy-Schwarz inequality entclass1pt{minimaentclass1pt{minimaf by the operator In the following example, the rate of convergence of the bivariate operators given by  to a cerLet given by  to \docuB-continuous and B-differentiable functions. Later, Dobrescu and Matei ,,8], Bögend Matei  discussed Cottin  establisd Cottin  studied h et al.  proposednd Ispir  introducnd Ispir .X and Y be compact subsets of B-continuous (Bögel continuous) function at B-bounded on B-continuous function is a B-bounded function on Let B-bounded functions on B-continuous functions on Throughout this paper, Cb(X×Y) .B-differentiable (Bögel differentiable) function at A function B-differential of f at the point B-differentiable functions is denoted by et al. in [The limit is said to be the t al. in  and 16]B-differeWe define the GBS operator of the operator given by , for anyq-Bernstein-Schurer-Kantorovich type is Hence for any For everyat each pointthe operator , , 4.1), aForwithand eachwe haveBy our hypothesis, using the relations entclass1pt{minimaNow, let Now, we illustrate the rate of convergence of the GBS operators  to certaLet Lastly, we compare the convergence of the operators given by  and its For


The increase in antibiotic resistant bacteria poses a threat to the continued use of antibiotics to treat bacterial infections. The overuse and misuse of antibiotics has been identified as a significant driver in the emergence of resistance. Finding optimal treatment regimens is therefore critical in ensuring the prolonged effectiveness of these antibiotics. This study uses mathematical modelling to analyse the effect traditional treatment regimens have on the dynamics of a bacterial infection. Using a novel approach, a genetic algorithm, the study then identifies improved treatment regimens. Using a single antibiotic the genetic algorithm identifies regimens which minimise the amount of antibiotic used while maximising bacterial eradication. Although exact treatments are highly dependent on parameter values and initial bacterial load, a significant common trend is identified throughout the results. A treatment regimen consisting of a high initial dose followed by an extended tapering of doses is found to optimise the use of antibiotics. This consistently improves the success of eradicating infections, uses less antibiotic than traditional regimens and reduces the time to eradication. The use of genetic algorithms to optimise treatment regimens enables an extensive search of possible regimens, with previous regimens directing the search into regions of better performance. The discovery of penicillin in 1928 dramatically changed human and animal health and well being. Since then, the discovery of additional antibiotics has further increased survival rates in areas such as surgery and during cancer chemotherapy. However, a lack of new antibiotics and an increase in resistance means these advances are under threat346The apparent ease at which antibiotic resistance spreads is due to the ability of bacteria to acquire additional genes. Genes can be acquired via either mutations or horizontal gene transfer (HGT). While mutations are undoubtedly a source of resistance, HGT is responsible for increased propagation of resistance through bacterial populations10Effective antibiotic treatment regimens consist primarily of two variables: the dose and the duration of treatment. For most antibiotics, the manufacturer identifies a traditional treatment regimen which is implemented by doctors and veterinary surgeons when prescribing these antibiotics. These traditional treatment regimens usually consist of a fixed dose administered for a specified duration. Drug efficiency studies are used to determine the dose and duration for these treatment regimens. However, one limitation of this approach is that it only provides information for the regimen being analysed and offers no indication for other potential regimens. AliAbadi and LeesAs the threat of antibiotic resistance spreads the need to optimise antibiotic dosage regimens becomes essential. Mathematical modelling is increasingly being used to investigate optimal treatment regimens for antibiotic therapy13141516a, is associated with carrying the genes which infer resistance to antibioticsβ, resulting in the loss of susceptible bacteria and the addition of resistant bacteria. There are 3 main mechanisms of HGT: transformation, transduction and conjugation. This study does not distinguish between the differing modes of HGT. Both susceptible and resistant bacteria die at a natural death rate, θ, and through exposure to antibiotics, Ai(C).In keeping with previous studies91418C(t) = C(t) + Dn, where D is a vector of doses D = . Traditional treatment regimens assume that D1 = D2 = … = D10, however this study relaxes this constraint. Treatment regimens within this study are limited to a maximum of 10 doses but this could be increased indefinitely. The full model can therefore be written as Antibiotics are added to the system in daily doses. When D = D1, D, …, D10.Deterministic modelling contains no randomness and as a result produces the same outcome each time it is run. Provided the population densities are not too small, deterministic models produce good approximations of the system dynamics. However hosts treated with the same treatment regimen will not all respond in exactly the same way. The small population size of resistant bacteria mean that stochastic events may lead to the emergence or extinction of a resistant strain. A stochastic framework was therefore produced for Numerical simulations were run to analyse the effect different treatment regimens have on the population size of bacteria within an infection. The success rate and time to eradication of the infection were analysed. Treatment regimens are obtained from traditional regimens and from solutions derived using a GA. The results presented were performed with an initial resistant population of 10% of the total bacterial population. When analysed with an initial resistant population of 1% of the total bacterial population the results follow a similar pattern see .μg/ml , which would maximise the success rate of eradicating the infection by minimising the fitness (objective) function  was used to identify effective dosage vectors, function .iDi, exposes the environment to less antibiotic reducing the likelihood of resistance developing. However, using less antibiotic increases the total bacterial burden on the host over the length of the infection, N = S + R. The increased bacterial load not only compromises the health of the host but also offers more opportunity for mutations to arise increasing the risk of further resistance developing. A trade-off exists between the total amount of antibiotic used and the total bacterial load over the course of the infection. Weights w1 and w2 allow for more emphasis to be placed on minimising one term over the other. To ensure a trade-off exists, w1 = 0, hence the objective is solely to maximise treatment success.) Due to the difference in the magnitude of the values of each term, correcting factors α1 and α2 were used to transform the terms between 0 and 1.Minimising the total amount of antibiotic used, ∑μg/ml within the GA. This is in keeping with the maximum concentration from the traditional treatment regimen . The GA was run for varying maximum daily dosages of 60, 50 and 40 μg/ml per day. The successful dosage vectors were then run through a stochastic model to generate a success rate of eradicating the infection.Due to the toxic nature of antibiotics the total antibiotic concentration within the system at any point in time was constrained to a maximum of 60 D1 and D3). However, these treatment regimens have a lower success rate, 91.2%  and 92.3% , than the traditional regimen, 96.4% . For all three maximum daily doses, the longer duration regimens  are more efficient at treating the infection than the shorter durations with success rates of 94.3% , 94.4%  and 95%  respectively. The lack of noise within the deterministic model allows the GA to be very effective in minimising the total antibiotic used. When the shorter dosage vectors from the GA using the deterministic model are analysed using the stochastic model there is too little antibiotic administered over too short a duration leading to the emergence of resistant bacteria.The dosage vectors from the GA begin with an increased dose which tapers off as the treatment progresses . ResultsD2, D5 and D8 use less antibiotic in total over a shorter duration than the traditional regimen. Regimen D2 uses 30% less antibiotic over 5 days instead of 8. Regimen D5 produces a dosage vector which uses 23% less antibiotic than the traditional regimen and delivers it over 6 days instead of 8. The dosage vector from D8 uses 15% less antibiotic and is shorter by 1 day in duration.The total concentration of antibiotic in the traditional regimen  increaseAll the regimens identified by the GA see a reduction in the time to eradication for the infection. The median time to eradication for the 8 day traditional treatment was 7.13 days . By distributing the antibiotic in a high initial dose with tapering smaller doses the median time to eradication for all the the regimens identified by the GA is between 4 and 5.5 days.The GA was run using a stochastic model to maximise the probability of eradication and explore the effectiveness of a longer treatment duration. For the GA using the stochastic model the second term, minimising the bacterial load, in F  was replS2 has the greatest success rate, 98.4% , an increase on the traditional 8 day treatment, 96.4% . The GA was able to identify alternative treatment regimens using less antibiotic with a success rate of eradication equal to or better than the traditional treatment. The alternative regimens also successfully treat the infection over a shorter duration than the traditional regimen, around 4 to 5 days, vs. 7 to 7.5 days respectively.The dosage vectors from the stochastic model are noisy due to the randomness in the model. Despite this, the dosage vectors begin to converge to a similar pattern identified using the GA with the deterministic model. A large initial dose followed by an extended period of tapering lower doses is observed. The median time to eradication for the stochastic results are comparable to the deterministic results. However, by using more antibiotic over the longer treatment duration the stochastic regimens have a greater success rate. Despite the increase in total antibiotic these dosage vectors use between 11 and 19% less antibiotic than the traditional regimen with a similar or increased success rate. Dosage regimen μg/ml of antibiotics be distributed to maximise the probability of eradication? (i.e. set w1 = 0 in S4) as the optimal distribution of the antibiotics. This regimen resulted in a success rate of 99.7%  compared to 96.4%  obtained from the traditional treatment , transmission rate (β) and cost of resistance (a) were examined. Further sensitivity analysis was performed for parameters concerning the effectiveness of the antibioitcs: degradation rate (g), MIC of susceptible (micS) and resistant bacteria (micR) and the shape of the antibiotic death function (k). Changes in parameters r, a, g and micR show the greatest change and can be found in T3) and GA generated treatment regimens (S2 and S4).Due to the difficulty in obtaining exact parameter values for an infection, the effect changes in parameter values have on the success rate of different treatment regimens was analysed. Parameter values relating to the virulence of the bacteria; replication rate  whereas the tapered regimens remain significantly higher at 79.3%  and 92.4%. Across all the parameter values analysed regimen S4 consistently maintains a success rate above 90%. Whereas when the same amount of antibiotic is distributed in a traditional manner the success rate can drop to below 50%. Despite regimen S2 using less antibiotic it also consistently performs better than the traditional regimen.As While the previous tapered regimens perform well when the parameter values are altered, they are not necessarily the optimal dosage vectors for these new parameter sets. To examine whether the tapered effect was a consequence of the parameter values chosen the GA was used to generate optimal dosage vectors for the varied parameter values found in Current antibiotic treatment regimens consist of a fixed daily dose administered for a set duration. While these traditional regimens may be easier to administer due to the constant dose, there is little evidence that this is the optimal way of administering antibiotics. Despite the continued increase in antibiotic resistance these traditional treatment regimens remain largely unchanged. More research must be dedicated to ensuring we are using antibiotics in an optimal way.μg/ml of antibiotic per day for 10 days. While this regimen successfully eradicated the infection in 99.8% of cases, the daily dose of antibiotic falls between the MIC of the susceptible and resistant bacteria initially facilitating the emergence of resistance. This is due to the time it takes for the total concentration of antibiotic to increase above the MIC of the resistant strain. While between the two MIC points the susceptible bacteria are eradicated allowing the resistant population to increase with little competition. Provided treatment is continued, the concentration of antibiotic will eventually increase above the MIC of the resistant strain.This study considered a traditional treatment regimen of 23 The GA, using the deterministic and stochastic models, identified that optimal dosage vectors contain an initial high dose followed by tapering lower doses. Initially increasing the concentration of antibiotic above the MIC of the resistant bacteria eradicates the selective advantage observed in the traditional treatment regimen. Smaller doses of antibiotic are then administered to maintain the concentration above the MIC of the resistant strain. In the example shown the tapered regimens reduce the amount of antibiotic required to successfully treat the infection by as much as 23%. In some regimens produced by the GA, the maximum concentration of antibiotic within the system was lower than that observed with the traditional treatment regimen  despite μg/ml, after 3 days.If traditional treatment regimens are adapted to deliver doses above the MIC of the resistant strain then the initial facilitation of resistant bacteria will disappear. However, the total antibiotic concentration within the system will considerably increase. In this scenario increasing the daily dose to above that of the MIC of the resistant strain would increase the total antibiotic concentration to beyond the level determined as toxic, 60 μg/ml of antibiotic per day for 8 days was therefore taken as a baseline treatment.The set duration of treatment is often subjective with increased length being used as a precaution. Studies have looked to find the optimal length of therapy2223μg/ml of antibiotics and achieved a success rate of 96.9% with a time to eradication of 7.13 days. The alternative treatment regimen identified by the GA applied the 184 μg/ml of antibiotic in a high dose tapered regimen to achieve a success rate of 99.7% with a time to eradication of 3.94 days. This success rate is comparable with the success rate for the 10 day traditional treatment but the GA generated regimen uses 20% less antibiotic over fewer days to achieve it. By redistributing the antibiotic in a high dose, tapered pattern the time to eradication of the infection reduces considerably, allowing shorter treatment regimens to be just as effective.The GA can be used to redistribute the antibiotic within the traditional regimen to produce a more efficient treatment regimen. The 8 day traditional treatment used 184 Clostridium difficile2830Studies have shown that shorter treatment regimens can be effective in treating bacterial infections25Using a GA to search for an effective treatment regimen allowed for a constrained search of all possible dosage vectors. The lack of noise within the deterministic model allows the GA to converge to a specific minimum antibiotic concentration. However, when this is analysed using the stochastic model, random events mean these treatments are not as efficient. The stochastic model therefore identifies slightly longer treatments with more antibiotic than the deterministic model, increasing the success rate. The increased computation time of the GA using the stochastic model makes it inefficient. The GA using the deterministic model is much less computationally expensive and produces the same loading dose but with a shorter tapered duration than the results using the stochastic model. The results from the deterministic model still have value and provide a suitable starting point from which to base potential future treatment regimens.The use of the GA suggests that, in order to optimise antibiotic treatment regimens, the idea of constant doses needs to be addressed. Research indicates that the use of combination or sequential treatments are more effective in preventing resistance1315g. The half-life of the antibiotic is therefore given by Experimental data33et al.To model the relationship between antibiotic concentration and antibiotic induced death rate the extension of the Emax model of antibiotic treatment by Regoes The parameter values  were choThis study uses the well-established Gillespie algorithmw1 and w2, respectively). Varying the weights had no significant effect on the results  compared to using the deterministic model.An alternative approach is to use the stochastic model as part of the fitness function evaluation within the GA. However, the computational time increases substantially .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


The promyelocytic leukemia zinc finger (PLZF), also known as ZBTB16 (Zinc Finger And BTB Domain Containing 16), is a transcription factor involved in the regulation of diverse biological processes, including cell proliferation, differentiation, organ development, stem cell maintenance and innate immune cell development. A number of recent studies have now implicated PLZF in cancer progression as a tumor suppressor. However, in certain cancer types, PLZF may function as an oncoprotein. Here, we summarize our current knowledge on the role of PLZF in various cancer types, in particular prostate cancer, including its deregulation, genomic alterations and potential functions in prostate cancer progression. The promyelocytic leukemia zinc finger protein (PLZF), also known as ZBTB16  Domain Containing 16) or ZFP145 (Zinc Finger Protein 145), is a member of the Krüppel C2H2-type zinc-finger protein family and encodes a zinc finger transcription factor. PLZF was first identified in an acute promyelocytic leukemia (APL) patient with a t reciprocal chromosomal translocation which resulted in an in-frame fusion with the retinoic acid receptor alpha (RARA) gene. The PLZF-RAR fusion protein suppresses normal functions of PLZF and RARA, and is thus implicated in APL development. Since then significant new findings established PLZF as a multifaceted protein that regulates diverse biological processes, including cell proliferation, apoptosis, cell cycle, differentiation, and development  [PLZF has a wide tissue distribution. Early studies focused on PLZF primarily in stem cells and early progenitor cells , but it as.org/) . PLZF isas.org/) . In the as.org/) , 6.−/− mouse has major musculoskeletal-limb defects with homeotic transformations of vertebral segments, deformed cartilage and skeletal patterning, and alterations in digit formation. The abnormal skeletal phenotype resembles the general features of luxoid mouse, which was first described in the 1950s [−/− mice [Previous studies showed that under normal physiological conditions PLZF is essential for normal differentiation and development. In 2000, a Plzf knockout mouse model was developed that displayed abnormal patterning of all limb skeletal structures . Plzf−/−he 1950s , 9. Geno−/− mice , indicat−/− mice .PLZF has also been associated with stem and progenitor cell maintenance in hematopoietic stem cells (HSC) and neural progenitors. HSCs from luxoid mice exhibit an amplified aging phenotype associated with loss of stemness , while fPLZF mediates its effects mainly by modulating transcription, acting either as an activator or a repressor. PLZF consists of a BTB/POZ domain, a second repression domain (RD2), and a zinc finger domain Figure . The BTBThe mechanisms of PLZF-mediated repression of gene expression and the genes affected were recently reviewed in detail . BrieflyIn early studies, PLZF was primarily linked to haematological malignancies, but it is now also implicated in various solid tumors Table , includiin vitro and in vivo [PLZF expression is high in epidermal melanocytes from the foreskin, but is decreased in melanoma cell lines  and its  in vivo  which ap in vivo .In human hepatocellular carcinoma (HCC), PLZF expression is significantly decreased at both mRNA and protein levels compared with paired adjacent normal tissue . In addiPLZF expression is reduced in pancreatic cancer relative to normal tissue, and is lost in 38 of 247 (15.4%) cases . Loss ofPLZF expression is elevated in various colorectal cancer cell lines compared with isolated normal colonocytes . It is iThere is moderate to strong expression of PLZF in luminal cells of benign respiratory bronchus and well-differentiated lung adenocarcinoma, but lower levels in the basal layer . FurtherPLZF is expressed in the normal thyroid and various thyroid lesions. Interestingly, whereas in the normal thyroid, adenomatous lesions, and follicular adenoma PLZF is mainly in the nucleus, in papillary thyroid carcinoma and anaplastic thyroid carcinoma it is primarily expressed in the cytosol 33). Furthermore, increased cytosolic PLZF expression was correlated with capsular invasion and lymph node metastasis in papillary thyroid carcinoma [. FurtherThe studies summarized above suggest that the full-length nuclear PLZF may function as a tumor suppressor protein. On the other hand, there is evidence suggesting that in some cases PLZF may also serve as an oncoprotein. For example, IHC analysis showed a significant increase in the percentage of nuclear PLZF-positive cells in clear cell renal cell carcinoma, glioblastoma, and testicular seminoma tissues, compared with the corresponding normal tissues . In addiin vitro, in particular while studying specific molecular interactions. Since the structural organization of living tissue composed of many cell types interacting with one another may affect these processes, further studies should focus on novel in vitro models, such as organoids and explant systems, but also in vivo models, e.g. utilizing the Plzf-/- mice in the genetic background relevant to the cancer of interest.It should be noted that many of the studies on PLZF so far utilized individual cell culture models Androgen signaling is one of the most central pathways in PCa . This isPLZF expression is also regulated by PTEN/AKT/FOXO3 signaling, another important pathway in PCa cells . There aIHC analysis of multiple PCa cohorts indicated no significant change in PLZF expression in benign versus malignant prostate; furthermore, there was no correlation to Gleason score or T-stage . AnotherSeveral studies have assessed the possible role of PLZF in PCa. For example, ectopic expression of PLZF inhibits proliferation of PCa cell lines LNCaP , DU145 , and PC3The data published so far suggest that PLZF acts as a tumor suppressor primarily via its ability to inhibit transcription. For example, the PLZF-RARα fusion protein that arises in APL potently silences retinoic acid-regulated gene expression. Similarly, engineered PLZF fusion with estrogen receptor-α (ERα) or AR leads to inhibition of their target gene expression , 45. In In LNCaP cells, PLZF knockdown significantly increases expression of AR target genes, whereas ectopic expression of PLZF suppresses androgen-regulated gene expression and inhibits AR transcriptional activity . This apAs summarized above, PLZF expression in PCa cells is rapidly induced by androgens, while PLZF acts as a negative feedback regulator of AR. Interestingly, this feedback loop is inhibited by another androgen induced protein, Kallikrein Related Peptidase 4 (KLK4), whose expression is highly prostate enriched . KLK4 exAnother pathway that is critical for proliferation and survival of PCa cells is PI3K-AKT-mTOR signaling , 48. A nGlobal gene expression analysis in PCa cells revealed that PLZF-repressed genes are significantly enriched in the MAPK signaling pathway, including five genes with PLZF-binding sites in their regulatory regions: RRAS, MKNK2, DDIT3, JUND, and JUN (31). In keeping with these data, PLZF knockdown induces phospho-ERK1/2 expression upon EGF stimulation in LNCaP cells  (Figure in vitro and in vivo data. PLZF loss may also be involved in progression to CRPC. Therefore, reinstating PLZF expression/reactivation may be a novel strategy for PCa therapy. This could potentially be achieved by gene therapy of wild type PLZF, for example by adenovirus or oncolytic virus delivery, or by small molecules that interfere with inhibitory interactions, similar to that achieved for p53 [PLZF is implicated in various cancer types as a tumor suppressor protein. In PCa, there is diminished or loss of PLZF expression in high grade tumors, especially in CRPC. PLZF inhibits PCa cell growth through inhibitory effects on AR, mTOR, and MAPK signaling Figure . Thus, l for p53 . In addi


Research productivity is an important component of the CanMEDS Scholar role and is an accreditation requirement of Canadian Otolaryngology training programs. Our objective was to determine if an association exists between publication rates before and during Otolaryngology residency.We obtained the names for all certified Canadian Otolaryngologists who graduated between 1998 and 2013 inclusive, and conducted a Medline search for all of their publications. Otolaryngologists were subgrouped based on year of residency graduation and the number of articles published pre-residency and during residency (0 or ≥1). Chi-squared analyses were used to evaluate whether publications pre-residency and year of graduation were associated with publications during residency.R2 = 0.594 and R2 = 0.759, respectively), whereas publication rates after residency graduation has stagnated (R2 = 0.023). The odds of publishing during residency was 5.85 times higher (95% CI 2.69-12.71) if a resident published prior to residency (p < 0.0001). The Spearman correlation coefficient between publications before and during residency is 0.472 (p < 0.0001).We obtained data for 312 Canadian Otolaryngologists. Of those 312 graduates, 46 (14.7%) had no identifiable publications on PubMed and were excluded from the final data analysis. Otolaryngology residents had a mean 0.65 (95% CI 0.50-0.80) publications before residency and 3.35 (95% CI 2.90-3.80) publications during residency. Between 1998 and 2013, mean publication rates before and during residency both increased significantly (Residents who publish at least one paper before residency are nearly six times as likely to publish during residency than those who did not publish before residency. These findings may help guide Otolaryngology program selection committees in ranking the best CaRMS candidates. Medical students compete each year for approximately 30 Otolaryngology - Head and Neck Surgery (OTOHNS) residency positions country-wide through the Canadian Residency Matching Service (CaRMS). In 2015, 60 Canadian medical graduates applied for the 29 available residency positions, making OTOHNS the third most competitive surgical discipline [There is previous literature that can help program selection committees predict which applicants will be most successful during residency , 3. In 2Most medical schools and residency programs now support and expect completion of a scholarly project as part of postgraduate training. A recent study by Chen and colleagues showed a significant increase in the number of resident publications in the last few years . While iWe theorized that research productivity in medical school was associated with research productivity in OTOHNS residency. A recent study by Gupta and colleagues found that publishing prior to residency is significantly associated with publishing during a Pediatrics residency . While pThis study targeted all practicing otolaryngologists who were certified by the Royal College of Physicians and Surgeons of Canada (RCPSC) between 1998 and 2013, inclusive. We created a database from information contained in the publicly available Royal College Specialist Directory  which inEach otolaryngologist was searched by full name in PubMED and any publications matching to that author were recorded. To increase the sensitivity of our search, multiple permutations were used for each search query Table . In an aOtolaryngology – Head and Neck Surgery (OTOHNS) certification in Canada is completed in the summer of each year on June 30, following the final RCPSC examinations. The expected during residency and pre-residency time periods were classified based on the certification date. OTOHNS residency in Canada is five years in duration, thus the five years preceding certification were designated as residency, and prior to this was considered pre-residency. Each period was extended by one year, to capture articles that were likely completed within the pre or during residency period, but not published until afterwards see Fig. . ArticleR2). Chi-squared analyses were used to evaluate whether publications pre-residency and year of graduation were associated with publications during residency. A Mantel-Haenszel calculation was used to measure the combined odds-ratio for the study population. A Spearman correlation coefficient was calculated for the relationship between the number of publications before residency compared to the number during residency.Each otolaryngologist was categorized as having either 0, 1, 2 or ≥ 3 publications for both of the time periods. Descriptive statistics were performed, including the mean rates of publication in each group, with 95% confidence intervals (95% CI). The data was separated by certification year in order to calculate trends in average publication rates over time using linear regression . This CV was subsequently cross-referenced against the list of publications that had been identified in our search. Any publications meeting the following exclusion criteria outlined in Table Papers meeting the inclusion criteria were then manually cross-referenced against the publications identified in our search. True positives, false positives and false negatives were tracked and used to determine sensitivity and positive predictive value.As all data was publically available on PubMed and the Royal College website, research ethics board approval was not necessary for this study.Using the Royal College database and Medline/PubMed, 3441 publications for 312 residents were identified between 1998 and 2013. 46 (14.7%) of these had no publications during their career. There was an average of 0.65 (95% CI 0.50-0.80) publications before residency and 3.35 (95% CI 2.90-3.80) publications during residency. The number of residents with no publications was 216 (63%) prior to residency compared to 83 (26.6%) during residency. Only 7% (23/312) had ≥3 publications before residency, while 42% (131/312) had ≥3 publications during residency.p < 0.0001). There was a linear correlation between research publications prior to and during residency . Table Residents who had at least 1 publication prior to residency were nearly six times more likely to publish at least once during residency . Individuals completing residency in 1998 published an average of 1.7 publications during residency, compared to 5.5 publications for those finishing in 2013 (R2 = 0.758). This strong trend of increasing research productivity over time did not persist when looking at publication rates after completion of residency. Following graduation from Otolaryngology residency, individuals published an average 0.6 publications/year in 1998 compared to 0.3 publications/year in 2013 (R2 = 0.023). Figures Between 1998 and 2013, publication rates before and during residency both increased significantly, whereas publication rates after residency graduation stagnated. Residents who graduated from residency in 1998 had an average of 0.3 publications prior to residency, compared to 1.2 publications for graduates from 2013 (Thirty-one authors (10.3% response rate) provided us with either a copy of their CV or a separate up-to-date list of their publications. Each of the 874 publications that met the inclusion criteria were cross-referenced against the list of publications identified in our initial study. We identified 35 missed publications , resulting in a search sensitivity of 96.1%. Based on the validation data, we identified an average of 1.1 missed publications per author.The validation process initially identified 15 articles  as false positives as they were not found on the CV provided by the authors. These 5 authors were contacted to verify the accuracy of their CV. Upon further investigation, each of these 15 publications were in fact accurately attributed to the author in question .Compared to residents who did not publish prior to residency, we found that Otolaryngology residents who published at least once prior to residency were nearly six times as likely to publish in postgraduate training. Our results suggest a moderately correlated linear relationship of the number of papers published before residency with the number during residency. These findings indicate that research conducted prior to residency increases the likelihood the resident will publish scholarly work after residency begins.Despite the demonstrated association between pre-residency publication and publications during residency, other variables are likely also at play and further work is required to identify and account for these confounding factors. Not all residency candidates have research experience, and publishing as a medical student or resident has not been shown to make an individual a better clinician. As previously stated, nearly 15% of Otolaryngologists in our study did not have any identifiable publications. Further, pre-residency publication status is not a definite predictor of a resident’s publication potential, as 65% (141/216) of individuals without pre-residency publications did proceed to publish as a resident.The results from this study could be used to help guide the research curriculum within Otolaryngology residency programs. For example, residents without prior research experience may benefit from early mentorship and formal training in research skills, and residents with prior experience may benefit from an amount of protected research time proportional to their research interests.Although a strength of our dataset is that it is a recent comprehensive national population study not affected by response rates, the correct author and publication time period could not be confirmed on an individual basis. Individuals whose residency periods were extended to greater than five years  may not be accurately identified in our analysis. This could result in misclassification of publications into the wrong time period (pre vs. during residency) and could skew our results either towards or away from the null hypothesis (that there is no correlation between pre or during residency publications).Further, PubMed was exclusively used for data collection and any publications not listed on PubMed were not captured in the dataset, potentially underestimating the true number of a researcher’s publications. We exclusively used this search method because of the basic scientific rigor associated with the PubMed’s abstract listing criteria .Future research may include attempting to further stratify candidates based on various personal, program and publication variables.h-index, an objective and easily calculable measure to evaluate both the number, as well as the relative importance of an author’s scientific contributions. By looking not only at the number of publications but also the number of times each paper has been cited in the literature, the h-index is considered to be a more accurate marker of publication quality and academic success [Personal variables include: an individual’s previous graduate degrees held, medical school, and completion of a fellowship. Another interesting personal variable is the individual’s  success .Future studies could also investigate the effect that the research environment  has on the resident’s research productivity.Publication variables that could be examined include type of publication  and journal impact factor. Furthermore, future studies could also investigate the authorship trends of research in Otolaryngology. Single authorship is becoming increasingly less common in our current academic world, and inappropriate assignment of authorship credit is an increasingly well known phenomenon . For exaWhile our study found that publication rates after residency graduation have not been rising over time, our data was not detailed enough to allow us to separate academic otolaryngologists from community otolaryngologists. A future study could collect this data and perform a subgroup analysis to compare the trends in research productivity over time between these two groups.In this nation-wide sample of Canadian certified otolaryngologists, we demonstrated that pre-residency publication is significantly associated with subsequent publication during residency. Over the past 16 years, publication rates have steadily increased both before and during residency. During that same timespan, publication rates among practicing otolaryngologists has remained relatively stagnant. Our analysis did not take into account several potential confounding variables.Pre-residency research productivity is a predictor for research productivity in residency and can act as a helpful marker for residency program selection committees in ranking candidates for the CaRMS match.