Efficient automated error detection in medical data using deep-learning and label-clustering.

Medical datasets inherently contain errors from subjective or inaccurate test results, or from confounding biological complexities. It is difficult for medical experts to detect these elusive errors manually, due to lack of contextual information, limiting data privacy regulations, and the sheer scale of data to be reviewed. Current methods for training robust artificial intelligence (AI) models on data containing mislabeled examples generally fall into one of several categories-attempting to improve the robustness of the model architecture, the regularization techniques used, the loss function used during training, or selecting a subset of data that contains cleaner labels. This last category requires the ability to efficiently detect errors either prior to or during training, either relabeling them or removing them completely. More recent progress in error detection has focused on using multi-network learning to minimize deleterious effects of errors on training, however, using many neural networks to reach a consensus on which data should be removed can be computationally intensive and inefficient. In this work, a deep-learning based algorithm was used in conjunction with a label-clustering approach to automate error detection. For dataset with synthetic label flips added, these errors were identified with an accuracy of up to 85%, while requiring up to 93% less computing resources to complete compared to a previous model consensus approach developed previously. The resulting trained AI models exhibited greater training stability and up to a 45% improvement in accuracy, from 69 to over 99% compared to the consensus approach, at least 10% improvement on using noise-robust loss functions in a binary classification problem, and a 51% improvement for multi-class classification. These results indicate that practical, automated a priori detection of errors in medical data is possible, without human oversight.

Development of an artificial intelligence model for predicting the likelihood of human embryo euploidy based on blastocyst images from multiple imaging systems during IVF.

STUDY QUESTION: Can an artificial intelligence (AI) model predict human embryo ploidy status using static images captured by optical light microscopy? SUMMARY ANSWER: Results demonstrated predictive accuracy for embryo euploidy and showed a significant correlation between AI score and euploidy rate, based on assessment of images of blastocysts at Day 5 after IVF. WHAT IS KNOWN ALREADY: Euploid embryos displaying the normal human chromosomal complement of 46 chromosomes are preferentially selected for transfer over aneuploid embryos (abnormal complement), as they are associated with improved clinical outcomes. Currently, evaluation of embryo genetic status is most commonly performed by preimplantation genetic testing for aneuploidy (PGT-A), which involves embryo biopsy and genetic testing. The potential for embryo damage during biopsy, and the non-uniform nature of aneuploid cells in mosaic embryos, has prompted investigation of additional, non-invasive, whole embryo methods for evaluation of embryo genetic status. STUDY DESIGN, SIZE, DURATION: A total of 15 192 blastocyst-stage embryo images with associated clinical outcomes were provided by 10 different IVF clinics in the USA, India, Spain and Malaysia. The majority of data were retrospective, with two additional prospectively collected blind datasets provided by IVF clinics using the genetics AI model in clinical practice. Of these images, a total of 5050 images of embryos on Day 5 of in vitro culture were used for the development of the AI model. These Day 5 images were provided for 2438 consecutively treated women who had undergone IVF procedures in the USA between 2011 and 2020. The remaining images were used for evaluation of performance in different settings, or otherwise excluded for not matching the inclusion criteria. PARTICIPANTS/MATERIALS, SETTING, METHODS: The genetics AI model was trained using static 2-dimensional optical light microscope images of Day 5 blastocysts with linked genetic metadata obtained from PGT-A. The endpoint was ploidy status (euploid or aneuploid) based on PGT-A results. Predictive accuracy was determined by evaluating sensitivity (correct prediction of euploid), specificity (correct prediction of aneuploid) and overall accuracy. The Matthew correlation coefficient and receiver-operating characteristic curves and precision-recall curves (including AUC values), were also determined. Performance was also evaluated using correlation analyses and simulated cohort studies to evaluate ranking ability for euploid enrichment. MAIN RESULTS AND THE ROLE OF CHANCE: Overall accuracy for the prediction of euploidy on a blind test dataset was 65.3%, with a sensitivity of 74.6%. When the blind test dataset was cleansed of poor quality and mislabeled images, overall accuracy increased to 77.4%. This performance may be relevant to clinical situations where confounding factors, such as variability in PGT-A testing, have been accounted for. There was a significant positive correlation between AI score and the proportion of euploid embryos, with very high scoring embryos (9.0-10.0) twice as likely to be euploid than the lowest-scoring embryos (0.0-2.4). When using the genetics AI model to rank embryos in a cohort, the probability of the top-ranked embryo being euploid was 82.4%, which was 26.4% more effective than using random ranking, and ∼13-19% more effective than using the Gardner score. The probability increased to 97.0% when considering the likelihood of one of the top two ranked embryos being euploid, and the probability of both top two ranked embryos being euploid was 66.4%. Additional analyses showed that the AI model generalized well to different patient demographics and could also be used for the evaluation of Day 6 embryos and for images taken using multiple time-lapse systems. Results suggested that the AI model could potentially be used to differentiate mosaic embryos based on the level of mosaicism. LIMITATIONS, REASONS FOR CAUTION: While the current investigation was performed using both retrospectively and prospectively collected data, it will be important to continue to evaluate real-world use of the genetics AI model. The endpoint described was euploidy based on the clinical outcome of PGT-A results only, so predictive accuracy for genetic status in utero or at birth was not evaluated. Rebiopsy studies of embryos using a range of PGT-A methods indicated a degree of variability in PGT-A results, which must be considered when interpreting the performance of the AI model. WIDER IMPLICATIONS OF THE FINDINGS: These findings collectively support the use of this genetics AI model for the evaluation of embryo ploidy status in a clinical setting. Results can be used to aid in prioritizing and enriching for embryos that are likely to be euploid for multiple clinical purposes, including selection for transfer in the absence of alternative genetic testing methods, selection for cryopreservation for future use or selection for further confirmatory PGT-A testing, as required. STUDY FUNDING/COMPETING INTEREST(S): Life Whisperer Diagnostics is a wholly owned subsidiary of the parent company, Presagen Holdings Pty Ltd. Funding for the study was provided by Presagen with grant funding received from the South Australian Government: Research, Commercialisation, and Startup Fund (RCSF). 'In kind' support and embryology expertise to guide algorithm development were provided by Ovation Fertility. 'In kind' support in terms of computational resources provided through the Amazon Web Services (AWS) Activate Program. J.M.M.H., D.P. and M.P. are co-owners of Life Whisperer and Presagen. S.M.D., M.A.D. and T.V.N. are employees or former employees of Life Whisperer. S.M.D, J.M.M.H, M.A.D, T.V.N., D.P. and M.P. are listed as inventors of patents relating to this work, and also have stock options in the parent company Presagen. M.V. sits on the advisory board for the global distributor of the technology described in this study and also received support for attending meetings. TRIAL REGISTRATION NUMBER: N/A.

A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data.

Training on multiple diverse data sources is critical to ensure unbiased and generalizable AI. In healthcare, data privacy laws prohibit data from being moved outside the country of origin, preventing global medical datasets being centralized for AI training. Data-centric, cross-silo federated learning represents a pathway forward for training on distributed medical datasets. Existing approaches typically require updates to a training model to be transferred to a central server, potentially breaching data privacy laws unless the updates are sufficiently disguised or abstracted to prevent reconstruction of the dataset. Here we present a completely decentralized federated learning approach, using knowledge distillation, ensuring data privacy and protection. Each node operates independently without needing to access external data. AI accuracy using this approach is found to be comparable to centralized training, and when nodes comprise poor-quality data, which is common in healthcare, AI accuracy can exceed the performance of traditional centralized training.

Automated detection of poor-quality data: case studies in healthcare.

The detection and removal of poor-quality data in a training set is crucial to achieve high-performing AI models. In healthcare, data can be inherently poor-quality due to uncertainty or subjectivity, but as is often the case, the requirement for data privacy restricts AI practitioners from accessing raw training data, meaning manual visual verification of private patient data is not possible. Here we describe a novel method for automated identification of poor-quality data, called Untrainable Data Cleansing. This method is shown to have numerous benefits including protection of private patient data; improvement in AI generalizability; reduction in time, cost, and data needed for training; all while offering a truer reporting of AI performance itself. Additionally, results show that Untrainable Data Cleansing could be useful as a triage tool to identify difficult clinical cases that may warrant in-depth evaluation or additional testing to support a diagnosis.

Quantitative analysis of oxysterols in zebrafish embryos by HPLC-MS/MS.

Zebrafish is an in vivo model used in toxicology to estimate the effects of xenobiotics and their teratogenic consequences. The knowledge of the oxysterols profile in zebrafish, during early embryonic stages, provides important information on the role and biological function of these molecules. This work reports the development and validation of a LC-MS/MS method for the determination of 7 different oxysterols in zebrafish embryos. Sample was treated with a combination of liquid/liquid extraction (LLE) followed by micro solid phase extraction (µSPE) clean-up in order to remove matrix interference and obtain a suitable enrichment factor of the analytes. The method was validated on 2 different embryos growing stages, 3-4 and 24 h post fertilization (hpf), as slight differences in terms of recovery and matrix effect were shown. The validation results provided good accuracy (bias ≤17%; 20% at LOQ) and repeatability (≤15%; ≤19% at LOQ), with low LOQs in the range 22 and 65 pg on 100 embryos sample, without any analyte derivatization, demonstrating the suitability of this analytical method as a useful tool to understand the correlation between oxysterols profile and developmental abnormalities induced by xenobiotic exposure.

Toxicological assessment and developmental abnormalities induced by butylparaben and ethylparaben exposure in zebrafish early-life stages.

Toxicological effects of butylparaben (BuP) and ethylparaben (EtP) on zebrafish (Danio rerio) early-life stages are not well established. The present study evaluated, using zebrafish embryos and larvae, the toxicity of BuP and EtP through benchmark dose (BMD) approach. BuP was more toxic than EtP to zebrafish larvae. In fact, Lethal Concentration 50 (LC50) values at 96 h post-fertilization (hpf) for BuP and EtP were 2.34 mg/L and 20.86 mg/L, respectively. Indeed, BMD confidence interval (lower bound (BMDL) - upper bound (BMDU) was 0.91-1.92 mg/L for BuP and 10.8-17.4 mg/L for EtP. Zebrafish embryos exposed to 1 mg/L, 2.5 mg/L of BuP and 5 mg/L, 10 mg/L, 20 mg/L, 30 mg/L of EtP showed several developmental abnormalities and teratological effects compared to negative control. Exposed zebrafish developed reduced heartbeat, reduction in blood circulation, blood stasis, pericardial edema, deformed notochord and misshaped yolk sac. Embryos exposed to the highest concentrations of the chemicals (2.5 mg/L of BuP, 10 mg/L, 20 mg/L and 30 mg/L of EtP) showed the developmental abnormalities at 48 hpf while those treated with 1 mg/L of BuP and 10 mg/L of EtP reported behavioral changes at 72 hpf, including trembling of head, pectoral fins and spinal cord. This research identified the lethal and sublethal effects of BuP and EtP in zebrafish early-life stages and could be helpful to elucidate the developmental pathways of toxicity of parabens.

Embryotoxicity of methylparaben to zebrafish (Danio rerio) early-life stages.

Methylparaben (MeP) is widely used as preservative in personal care products, food commodities and pharmaceuticals due to its antimicrobial properties. Its widespread use resulted in the contamination of aquatic environment and raised concerns about the potential adverse effects on human health, especially in the developing organisms. The aim of the present study was to evaluate the embryotoxicity of MeP in zebrafish early-life stages applying the benchmark-dose (BMD) methodology to Fish embryo acute toxicity (FET) tests-OECD guideline 236. Toxic effects were studied by daily evaluation of lethal endpoints, hatching rate and sublethal alterations. Zebrafish fertilized eggs were exposed until 96 h post fertilization (hpf) to five concentrations of MeP: 1 mg/L, 10 mg/L, 30 mg/L, 60 mg/L and 80 mg/L. The lethal concentration 50 (LC 50) was 72.67 mg/L. Indeed, BMD confidence interval (lower bound, BMDL-upper bound, BMDU) was 40.8-57.4 mg/L for lethal endpoints and 16-26.5 mg/L for toxicity index, that includes both lethal and sublethal alterations. Zebrafish embryos exposed to MeP developed sublethal alterations including pericardial edema, yolk edema, blood stasis, reduction in blood circulation, reduced heartbeat and notochord curvature. The number of embryos exposed to the highest concentrations of MeP that reported sublethal alterations increased between 24hpf and 48 hpf-72 hpf-96 hpf. Only zebrafish larvae treated with 30 mg/L of MeP showed behavioural changes. This study highlighted the detrimental effects of MeP on zebrafish early-life stages with attention to its developmental toxicity.

Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF.

STUDY QUESTION: Can an artificial intelligence (AI)-based model predict human embryo viability using images captured by optical light microscopy? SUMMARY ANSWER: We have combined computer vision image processing methods and deep learning techniques to create the non-invasive Life Whisperer AI model for robust prediction of embryo viability, as measured by clinical pregnancy outcome, using single static images of Day 5 blastocysts obtained from standard optical light microscope systems. WHAT IS KNOWN ALREADY: Embryo selection following IVF is a critical factor in determining the success of ensuing pregnancy. Traditional morphokinetic grading by trained embryologists can be subjective and variable, and other complementary techniques, such as time-lapse imaging, require costly equipment and have not reliably demonstrated predictive ability for the endpoint of clinical pregnancy. AI methods are being investigated as a promising means for improving embryo selection and predicting implantation and pregnancy outcomes. STUDY DESIGN, SIZE, DURATION: These studies involved analysis of retrospectively collected data including standard optical light microscope images and clinical outcomes of 8886 embryos from 11 different IVF clinics, across three different countries, between 2011 and 2018. PARTICIPANTS/MATERIALS, SETTING, METHODS: The AI-based model was trained using static two-dimensional optical light microscope images with known clinical pregnancy outcome as measured by fetal heartbeat to provide a confidence score for prediction of pregnancy. Predictive accuracy was determined by evaluating sensitivity, specificity and overall weighted accuracy, and was visualized using histograms of the distributions of predictions. Comparison to embryologists' predictive accuracy was performed using a binary classification approach and a 5-band ranking comparison. MAIN RESULTS AND THE ROLE OF CHANCE: The Life Whisperer AI model showed a sensitivity of 70.1% for viable embryos while maintaining a specificity of 60.5% for non-viable embryos across three independent blind test sets from different clinics. The weighted overall accuracy in each blind test set was >63%, with a combined accuracy of 64.3% across both viable and non-viable embryos, demonstrating model robustness and generalizability beyond the result expected from chance. Distributions of predictions showed clear separation of correctly and incorrectly classified embryos. Binary comparison of viable/non-viable embryo classification demonstrated an improvement of 24.7% over embryologists' accuracy (P = 0.047, n = 2, Student's t test), and 5-band ranking comparison demonstrated an improvement of 42.0% over embryologists (P = 0.028, n = 2, Student's t test). LIMITATIONS, REASONS FOR CAUTION: The AI model developed here is limited to analysis of Day 5 embryos; therefore, further evaluation or modification of the model is needed to incorporate information from different time points. The endpoint described is clinical pregnancy as measured by fetal heartbeat, and this does not indicate the probability of live birth. The current investigation was performed with retrospectively collected data, and hence it will be of importance to collect data prospectively to assess real-world use of the AI model. WIDER IMPLICATIONS OF THE FINDINGS: These studies demonstrated an improved predictive ability for evaluation of embryo viability when compared with embryologists' traditional morphokinetic grading methods. The superior accuracy of the Life Whisperer AI model could lead to improved pregnancy success rates in IVF when used in a clinical setting. It could also potentially assist in standardization of embryo selection methods across multiple clinical environments, while eliminating the need for complex time-lapse imaging equipment. Finally, the cloud-based software application used to apply the Life Whisperer AI model in clinical practice makes it broadly applicable and globally scalable to IVF clinics worldwide. STUDY FUNDING/COMPETING INTEREST(S): Life Whisperer Diagnostics, Pty Ltd is a wholly owned subsidiary of the parent company, Presagen Pty Ltd. Funding for the study was provided by Presagen with grant funding received from the South Australian Government: Research, Commercialisation and Startup Fund (RCSF). 'In kind' support and embryology expertise to guide algorithm development were provided by Ovation Fertility. J.M.M.H., D.P. and M.P. are co-owners of Life Whisperer and Presagen. Presagen has filed a provisional patent for the technology described in this manuscript (52985P pending). A.P.M. owns stock in Life Whisperer, and S.M.D., A.J., T.N. and A.P.M. are employees of Life Whisperer.

A four-gene LincRNA expression signature predicts risk in multiple cohorts of acute myeloid leukemia patients.

Prognostic gene expression signatures have been proposed as clinical tools to clarify therapeutic options in acute myeloid leukemia (AML). However, these signatures rely on measuring large numbers of genes and often perform poorly when applied to independent cohorts or those with older patients. Long intergenic non-coding RNAs (lincRNAs) are emerging as important regulators of cell identity and oncogenesis, but knowledge of their utility as prognostic markers in AML is limited. Here we analyze transcriptomic data from multiple cohorts of clinically annotated AML patients and report that (i) microarrays designed for coding gene expression can be repurposed to yield robust lincRNA expression data, (ii) some lincRNA genes are located in close proximity to hematopoietic coding genes and show strong expression correlations in AML, (iii) lincRNA gene expression patterns distinguish cytogenetic and molecular subtypes of AML, (iv) lincRNA signatures composed of three or four genes are independent predictors of clinical outcome and further dichotomize survival in European Leukemia Net (ELN) risk groups and (v) an analytical tool based on logistic regression analysis of quantitative PCR measurement of four lincRNA genes (LINC4) can be used to determine risk in AML.

Structural and functional characterization of TesB from Yersinia pestis reveals a unique octameric arrangement of hotdog domains.

Acyl-CoA thioesterases catalyse the hydrolysis of the thioester bonds present within a wide range of acyl-CoA substrates, releasing free CoASH and the corresponding fatty-acyl conjugate. The TesB-type thioesterases are members of the TE4 thioesterase family, one of 25 thioesterase enzyme families characterized to date, and contain two fused hotdog domains in both prokaryote and eukaryote homologues. Only two structures have been elucidated within this enzyme family, and much of the current understanding of the TesB thioesterases has been based on the Escherichia coli structure. Yersinia pestis, a highly virulent bacterium, encodes only one TesB-type thioesterase in its genome; here, the structural and functional characterization of this enzyme are reported, revealing unique elements both within the protomer and quaternary arrangements of the hotdog domains which have not been reported previously in any thioesterase family. The quaternary structure, confirmed using a range of structural and biophysical techniques including crystallography, small-angle X-ray scattering, analytical ultracentrifugation and size-exclusion chromatography, exhibits a unique octameric arrangement of hotdog domains. Interestingly, the same biological unit appears to be present in both TesB structures solved to date, and is likely to be a conserved and distinguishing feature of TesB-type thioesterases. Analysis of the Y. pestis TesB thioesterase activity revealed a strong preference for octanoyl-CoA and this is supported by structural analysis of the active site. Overall, the results provide novel insights into the structure of TesB thioesterases which are likely to be conserved and distinguishing features of the TE4 thioesterase family.

Dual epigenetic targeting with panobinostat and azacitidine in acute myeloid leukemia and high-risk myelodysplastic syndrome.

Therapeutic options are limited for elderly patients with acute myeloid leukemia (AML). A phase Ib/II study was undertaken to evaluate the maximum-tolerated dose (MTD) and preliminary efficacy of the pan-histone deacetylase inhibitor panobinostat (LBH589) in combination with azacitidine in patients with AML or high-risk myelodysplastic syndrome (MDS) naïve to intensive chemotherapy. Thirty-nine patients (AML=29, MDS=10) received azacitidine 75 mg/m(2) subcutaneously (days 1-5) and oral panobinostat (starting on day 5, thrice weekly for seven doses) in 28-day cycles until toxicity or disease progression. Dose-limiting toxicities during the phase Ib stage were observed in 0/4 patients receiving 10 mg panobinostat, in 1/7 patients (fatigue) receiving 20 mg, in 1/6 patients (fatigue) receiving 30 mg and in 4/5 patients (fatigue, syncope, hyponatremia and somnolence) receiving 40 mg. In phase II, an additional 17 patients received panobinostat at a MTD of 30 mg. The overall response rate (ORR=CR+CRi+PR) in patients with AML was 31% (9/29) and that in patients with MDS was 50% (5/10). After a median follow-up of 13 months, the median overall survival was 8 and 16 months in patients with AML and MDS, respectively. Increased histone H3 and H4 acetylation was a useful early biomarker of clinical response. Combining panobinostat with azacitidine was tolerable and clinically active in high-risk MDS/AML patients, warranting further exploration.

Structural and dynamic requirements for optimal activity of the essential bacterial enzyme dihydrodipicolinate synthase.

Dihydrodipicolinate synthase (DHDPS) is an essential enzyme involved in the lysine biosynthesis pathway. DHDPS from E. coli is a homotetramer consisting of a 'dimer of dimers', with the catalytic residues found at the tight-dimer interface. Crystallographic and biophysical evidence suggest that the dimers associate to stabilise the active site configuration, and mutation of a central dimer-dimer interface residue destabilises the tetramer, thus increasing the flexibility and reducing catalytic efficiency and substrate specificity. This has led to the hypothesis that the tetramer evolved to optimise the dynamics within the tight-dimer. In order to gain insights into DHDPS flexibility and its relationship to quaternary structure and function, we performed comparative Molecular Dynamics simulation studies of native tetrameric and dimeric forms of DHDPS from E. coli and also the native dimeric form from methicillin-resistant Staphylococcus aureus (MRSA). These reveal a striking contrast between the dynamics of tetrameric and dimeric forms. Whereas the E. coli DHDPS tetramer is relatively rigid, both the E. coli and MRSA DHDPS dimers display high flexibility, resulting in monomer reorientation within the dimer and increased flexibility at the tight-dimer interface. The mutant E. coli DHDPS dimer exhibits disorder within its active site with deformation of critical catalytic residues and removal of key hydrogen bonds that render it inactive, whereas the similarly flexible MRSA DHDPS dimer maintains its catalytic geometry and is thus fully functional. Our data support the hypothesis that in both bacterial species optimal activity is achieved by fine tuning protein dynamics in different ways: E. coli DHDPS buttresses together two dimers, whereas MRSA dampens the motion using an extended tight-dimer interface.

Impact of the reduction of environmental and equipment contamination on vancomycin-resistant enterococcus rates.

More than 1,500 perirectal swab cultures and 552 environmental and equipment cultures were collected during the study period. Enterococcus faecium was the most frequent species isolated, being responsible for 71% of the positive cultures. Fifty infections were documented, with bloodstream infections (18, 36%) being the most frequent, followed by urinary tract infection (15, 30%). An educational intervention was given to 136 healthcare workers (HCWs), and a questionnaire regarding vancomycin-resistant enterococcus (VRE) transmission was also performed pre- and post-intervention. Overall, 858 opportunities of patient care were evaluated. The compliance with contact precautions did not improve; however, in general, the proportion of correct answers regarding VRE increased significantly when comparing pre- and post-intervention periods (p < 0.05). On the other hand, the proportion of environmental and equipment contaminated by VRE decreased significantly from pre- (23.2%) to post-intervention (8.2%) (p < 0.001) and was associated with a significant decrease in VRE infection from 7.7 to 1.9 when comparing the pre- and post-intervention periods. The use of vancomycin (defined daily dose [DDD]) did not change significantly over the study period (p = 0.970), and the use of teicoplanin increased (p < 0.001). Seventy-six percent of E. faecium belong to type and subtype A by pulsed-field gel electrophoresis (PFGE). This predominant type was found in the environment and caused colonization and infection. In conclusion, the present study showed that reduction of the proportion of environmental and equipment contamination was associated with a decrease of colonization and infection due to VRE, and that the strategy to control VRE dissemination should be based on local problems.

Vancomycin-dependent Enterococcus faecium vanA: characterization of the first case isolated in a university hospital in Brazil

In this study, we report the characterization of a strain of Enterococcus faecium vanA, which grows only in the presence of vancomycin (VDEfm-UEL). The bacterium was isolated from the feces of a female patient who had undergone surgical treatment of Reinke’s edema and was receiving intravenous vancomycin therapy for infection with methicillin/oxacillin-resistant Staphylococcus aureus, a postoperative complication. Antimicrobial dependence was further confirmed by the vancomycin E-test. VDEfm-UEL was also shown to be resistant to ampicillin, ciprofloxacin, chloramphenicol, erythromycin, levofloxacin, penicillin, rifampicin, and teicoplanin. The putative virulence genes efaA, gelE and esp were detected by PCR. The ddl gene from VDEfm-UEL was cloned and sequenced. Vancomycin dependence seems to be associated with the insertion of a nucleotide in that sequence, which results in a frame-shift mutation, introducing a premature stop codon. This is the first report of vancomycin-dependent E. faecium isolation in a university hospital in Brazil.

Vancomycin-dependent Enterococcus faecium vanA: characterization of the first case isolated in a university hospital in Brazil.

In this study, we report the characterization of a strain of Enterococcus faecium vanA, which grows only in the presence of vancomycin (VDEfm-UEL). The bacterium was isolated from the feces of a female patient who had undergone surgical treatment of Reinke's edema and was receiving intravenous vancomycin therapy for infection with methicillin/oxacillin-resistant Staphylococcus aureus, a postoperative complication. Antimicrobial dependence was further confirmed by the vancomycin E-test. VDEfm-UEL was also shown to be resistant to ampicillin, ciprofloxacin, chloramphenicol, erythromycin, levofloxacin, penicillin, rifampicin, and teicoplanin. The putative virulence genes efaA, gelE and esp were detected by PCR. The ddl gene from VDEfm-UEL was cloned and sequenced. Vancomycin dependence seems to be associated with the insertion of a nucleotide in that sequence, which results in a frame-shift mutation, introducing a premature stop codon. This is the first report of vancomycin-dependent E. faecium isolation in a university hospital in Brazil.

Characterisation of dihydrodipicolinate synthase (DHDPS) from Bacillus anthracis.

Bacillus anthracis is a Gram-positive spore-forming bacterium that is the causative agent of anthrax disease. The use of anthrax as a bioweapon has increased pressure for the development of an effective treatment. Dihydrodipicolinate synthase (DHDPS) catalyses the first committed step in the biosynthetic pathway yielding two essential bacterial metabolites, meso-diaminopimelate (DAP) and (S)-lysine. DHDPS is therefore a potential antibiotic target, as microbes require either lysine or DAP as a component of the cell wall. This paper is the first biochemical description of DHDPS from B. anthracis. Enzyme kinetic analyses, isothermal titration calorimetry (ITC), mass spectrometry and differential scanning fluorimetry (DSF) were used to characterise B. anthracis DHDPS and compare it with the well characterised Escherichia coli enzyme. B. anthracis DHDPS exhibited different kinetic behaviour compared with E. coli DHDPS, in particular, substrate inhibition by (S)-aspartate semi-aldehyde was observed for the B. anthracis enzyme (K(si(ASA))=5.4+/-0.5 mM), but not for the E. coli enzyme. As predicted from a comparison of the X-ray crystal structures, the B. anthracis enzyme was not inhibited by lysine. The B. anthracis enzyme was thermally stabilised by the first substrate, pyruvate, to a greater extent than its E. coli counterpart, but has a weaker affinity for pyruvate based on enzyme kinetics and ITC studies. This characterisation will provide useful information for the design of inhibitors as new antibiotics targeting B. anthracis.