Mauro Giacomini

Intra- and Interlaboratory Performances of Two Commercial Antimicrobial Susceptibility Testing Methods for Bifidobacteria and Nonenterococcal Lactic Acid Bacteria

ABSTRACT In a small-scale harmonization study involving nine laboratories in eight European countries, the intra- and interlaboratory performances of two commercially available systems, i.e., the VetMIC microplate system and Etest, for antimicrobial susceptibility testing of nonenterococcal lactic acid bacteria (NELAB) and bifidobacteria were analyzed. In addition, one laboratory also performed standard broth microdilution as a reference method. MICs of tetracycline, erythromycin, ampicillin, gentamicin, clindamycin, and streptomycin for the type strains of 25 species of NELAB and bifidobacteria and MICs of vancomycin for a selection of relevant taxa were determined. The previously described lactic acid bacterium susceptibility test medium (LSM) and related mixed-medium formulations, all including Iso-Sensitest broth as a basic component, were used as test media. The overall agreement of median MIC ranges ± 1 log2 dilution determined by the VetMIC and Etest methods with the median MICs determined by the reference method was very good for tetracycline, ampicillin, and streptomycin (92.3 to 100%) but low for erythromycin (19.5 to 30.7%) and clindamycin (50.0 to 80.8%). There was a consensus among the participating laboratories that VetMIC was preferred over Etest because of its lower cost, better growth support, and more uniform criteria for MIC end point reading. With the range for acceptable intralaboratory reproducibility being defined as the median MIC ± 1 log2 dilution, VetMIC results (with 69.2% of all data sets in the acceptable range) were shown to display greater reproducibility than Etest results (with 58.8% of all data sets in the acceptable range). Also at the interlaboratory level, the proportion of MIC values obtained with VetMIC that belonged to the complete agreement category (60.0%) was higher than the proportion of such values obtained with Etest (47.0%), which indicates a higher degree of interlaboratory reproducibility for the former method. Apart from some agent-specific effects, the majority of VetMIC and Etest replicate data sets were situated within a 1- to 2-log2 dilution range, suggesting that the two methods can be considered to be equivalent for recognizing resistance phenotypes. This multicenter study has further validated the standard use of LSM and related mixed-medium formulations with commercially available systems and formed the basis for the ongoing development of the ISO 10932/IDF 223 standard for susceptibility testing of NELAB and bifidobacteria.

Multicentre clinical trials’ data management: a hybrid solution to exploit the strengths of electronic data capture and electronic health records systems

Background: Clinical Trials (CTs) are indispensable instruments for evidence-based medicine and frequently necessitate the management, sharing and analysis of large amounts of data amongst partners in different locations. Methods: To effectively satisfy these requirements, the proposed solution combines a web platform and a Clinical Data Management System (CDMS) to exploit the strengths of Electronic Health Records (EHR) and Electronic Data Capture (EDC) systems. The core of the proposal is a relational database which has high data structuring characteristics and utilises biomedical controlled vocabularies (e.g. LOINC and ICD). In addition, units and normality ranges were collected for data comparison through the application of the Z-score transformation. Results: The obtained CDMS preserves the EDC’s flexibility and user autonomy and permits the creation of patient cohorts, as in the EHR. Accordingly, clinical information, after the initial recording, is available for different simultaneous multicentre CTs. Furthermore, interface runtime controls guarantee high data quality during data entering processes. Currently, the proposed system has been developed in the HIV and eye diseases fields in Italy. Conclusions: The proposed solution is flexible and suitable to perform multicentre research within a varying range of medical domains. In the future, the automatic importation of information from hospitals has been planned through an HL7 standard interface which would improve both data quantity and quality.

Intraspecific diversity of Edwardsiella ictaluri isolates from diseased freshwater catfish, Pangasianodon hypophthalmus (Sauvage), cultured in the Mekong Delta, Vietnam

A molecular epidemiology study was conducted on 90 Edwardsiella ictaluri isolates recovered from diseased farmed freshwater catfish, Pangasianodon hypophthalmus, cultured in the Mekong Delta, Vietnam. Thirteen isolates of E. ictaluri derived from diseased channel catfish, Ictalurus punctatus, cultured in the USA were included for comparison. All the E.ictaluri isolates tested were found to be biochemically indistinguishable. A repetitive (rep)-PCR using the single (GTG)(5) primer was shown to possess limited discriminatory power, yielding two similar DNA profiles categorized as (GTG)(5) -PCR group 1 or 2 among the Vietnam isolates and (GTG)(5) -PCR group 1 within the USA isolates. Macrorestriction analysis identified 14 and 22 unique pulsotypes by XbaI and SpeI, respectively, among a subset of 59 E. ictaluri isolates. Numerical analysis of the combined macrorestriction profiles revealed three main groups: a distinct cluster formed exclusively of the USA isolates, and a major and minor cluster with outliers contained the Vietnam isolates. Antibiotic susceptibility and plasmid profiling supported the existence of the three groups. The results indicate that macrorestriction analysis may be regarded as a suitable typing method among the E. ictaluri species of limited intraspecific diversity. Furthermore, the findings suggest that E. ictaluri originating from Vietnam may constitute a distinct genetic group.

Artificial neural network identification of heterotrophic marine bacteria based on their fatty-acid composition

The traditional approach to biochemical identification of marine fresh isolates requires considerably long culture preparation times and large quantities of expensive materials and reagents, and the results are not very reliable. On the other hand, taxonomy tests based on DNA composition, although sensitive and reliable, require long execution times and high costs, A method is presented for the classification of fatty-acid profiles, extracted from marine bacteria strains, at genus level based on supervised artificial neural networks. The proposed method allows the correct identification of all patterns belonging to the training set and almost all patterns belonging to the test set. Moreover, a quantitative measure of the importance of each fatty acid for bacterial classification is also achieved. This measure allows the determination of a cluster of fatty acids to be controlled with greater care. The results show that the proposed method is reproducible and rapid, so that it can be routinely used in the marine microbiology laboratory to identify fresh isolates.

The ligurian human immunodeficiency virus clinical network: a web tool to manage patients with human immunodeficiency virus in primary care and multicenter clinical trials.

Background In recent years, Highly-Active Anti-Retroviral Therapies (HAARTs) have modified the Human Immunodeficiency Virus (HIV) life-cycle and the disease is now considered chronic. Consequently, a longitudinal and complex follow-up is now required for HIV positive patients during their lifetime. Moreover, patients often encounter various complications due to comorbidities, related to the immunodeficiency state and HAARTs’ side effects. Thus, HIV positive patients are involved in multicenter clinical trials (MCTs) to improve treatments and discover a preventive vaccine. Therefore, physicians require proper instruments to access comprehensive patient data for managing patients during follow-ups, and tools for data collection and analysis in MCTs. Objective The Ligurian HIV Clinical Network aims to provide physicians with a Web-tool to administrate HIV positive patients’ data within primary-care and to reuse the collected clinical information to perform MCTs in Northern Italy. Methods The key aspect of the system is a relational database which allows the storage of various types of clinical information (eg, related to HIV, cardiovascular, or hepatic diseases) in multiple formats. The modular design of the database permits a rapid insertion of new parameters without requiring any changes in the database structure. Furthermore, codes from biomedical ontologies controlled vocabularies (“Logical Observation Identifier Names and Codes”, and “International Classification of Diseases 9”) and ontologies (“Systematized Nomenclature of Medicine Clinical Terms”), units and normality ranges used by all partners participating in the project were collected to achieve a complete semantic interoperability. Accordingly, data can be automatically normalized through the z score formula and physicians can extract and correctly compare information with external statistical tools. Moreover, to respect patients’ privacy and legal issues, a local identifier, determined through an HASH cryptography algorithm, is assigned to each patient during the registration process. The database is managed by a user-friendly Web-platform which allows quick access to information during medical examinations and the reusing of the collected data for present and future MCTs. Furthermore, a bidirectional middleware was created in order to import/export information through HL7 messaging. Hence, data can be manually entered by physicians or automatically collected within HL7-compliant Hospital Information systems. Results Presently, the direct storage of patients’ information from the San Paolo Hospital (Savona, Italy), and San Martino and Galliera hospitals in Genoa is in a test phase. Currently, 8 centers of Infectious Diseases (located in Liguria and Piedmont) are participating in the project and almost 400 HIV positive patients have been recorded in the system. Patient data has been used for primary care and research purposes. Currently, there are 4 on-going MCTs and preliminary results have already been presented at International HIV congresses. Conclusions The Web-platform allows effective management, sharing and reuse of information within primary care and clinical research. In the future it is planned to share the clinical information from this network with other HL7-compliant workgroups and to extend the platform to other infective diseases (eg, hepatitis).

Coordinated micro-generation and load management for energy saving policies

The paper describes a joint university-industry project aimed at defining hardware and software requirements for the development of a prototypical microgrid platform on existing sites able to manage generation resources and load. The paper describes an architecture developed for integrating existing software tools for on-line load monitoring and control and for management of in-site generation. The system acquires data from field and stores information on a server, from which an optimization tool gets data in order to perform its calculations and give the optimal set points to the programmable generation resources. Results are reported about experimentation at test sites with different generating units, such as cogeneration of heat and power (CHP) and/or renewable power generating units equipped with a real-time monitoring tool.

A qualitative approach to cell growth modeling and simulation for cancer chemotherapy

A qualitative model of cell growth that is based on qualitative process theory is presented. The model can be used to analyze the effects of the interaction of antiproliferative drugs on cells when the effects of each specific drug are known, which is useful when designing multidrug protocols for optimal cancer treatment. This model encompasses both structural and behavioral aspects. This makes it suitable for drawing conclusions about differences among different types of cell growth and about system behavior under different situations. adding some significant options with respect to closed-form cell cycle models. Moreover, qualitative modeling, unlike closed-form modeling, allows causal explanations of events: in this respect, the qualitative simulation presented-based on reasoning in terms of processes, individual views. and history limits-makes causes of specific behaviors even clearer than qualitative simulation based on constraints. This model is able to adapt to the amount of information supplied by the user: if this is scarce (only relating to the cell cycle phase on which each drug acts), the model will produce a simulation in which only cell cycle phase information for the combination is present; if the information supplied is more detailed, the simulation output will be more detailed as well.<<ETX>>

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach

BackgroundTo investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) “black-box” approaches, for automated diagnosis of Age-related Macular Degeneration (AMD).MethodsData from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients’ attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance.ResultsStudy population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians’ decision pathways to diagnose AMD.ConclusionsBoth black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support.

Immunogenicity of GX301 cancer vaccine: Four (telomerase peptides) are better than one

Peptide540–548, peptide611–626, peptide672–686 and peptide766–780, which are derived from human telomerase, constitute the immunogenic component of the GX301 cancer vaccine. The relative immunogenicity of these peptides is unknown, thus it is unsure whether their combined use offers real advantages over single peptide stimulation. Hence, this study compared the number of specific immune responses and responders to each peptide, as well as to their mixture (meaning the co-presence of the 4 peptides in the same culture well), achieved after ex vivo stimulation of PBMC from 21, HLA-A2+ (n.11) or HLA-A2- (n.10), healthy donors. The study was performed on freshly collected PBMC (T0) and on PBMC stimulated for 10 d with single peptides or their mixture (T1). Peptide-specific immune responses were analyzed by Elispot and cytokine intracellular staining by flow cytometry. The results showed that each peptide induced specific immune responses in some subjects, with different panels of responders among the peptides. Moreover, the numbers of responses and responders to the single peptides or their mixture were comparable. Importantly, the overall number of responders to the 4 peptides was higher than to each single peptide, or to their mixture, both at T0 and T1. These data demonstrate the immunogenicity of each of the 4 GX301 telomerase peptides. Moreover, they show the advantage of multi-peptide over single peptide stimulation, providing a clear support to their combined administration in vaccination protocols. However, the data pose a warning against peptide administration as a mixture due to possible interference phenomena during antigen presentation processes.

A qualitative process theory based model of the HIV-1 virus-cell interaction.

We present a qualitative model of the interaction between the HIV-1 virus and the human cell, based on qualitative process theory. This model takes into account a previous qualitative model of the cell growth. The model presented here can be regarded as a first step for setting up a comprehensive model of the HIV-1 virus-cell interaction in which the possible points where a drug can attack the virus are evident. The first simulation trials indicate that the presented model reproduces (even though in a simplified way) the features of the real behaviour that have been considered in the modelling phase. Although our simulation is limited in the knowledge it expresses, it still gives a stimulating opportunity for the evaluation of the criteria chosen as discriminant in the interaction between virus and cell.