Trust and Expectations of Researchers and Public Health Departments for the Use of HIV Molecular Epidemiology.

Background: Molecular epidemiology (ME) is a technique used to study the dynamics of pathogen transmission through a population. When used to study HIV infections, ME generates powerful information about how HIV is transmitted, including epidemiologic patterns of linkage and, potentially, transmission direction. Thus, ME raises challenging questions about the most responsible way to protect individual privacy while acquiring and using these data to advance public health and inform HIV intervention strategies. Here, we report on stakeholders' expectations for how researchers and public health agencies might use HIV ME. Methods: We conducted in-depth semistructured interviews with 40 key stakeholders to find out how these individuals respond to the proposed risks and benefits of HIV ME. Transcripts were coded and analyzed using Atlas.ti. Expectations were assessed through analysis of responses to hypothetical scenarios designed to help interviewees think through the implications of this emerging technique in the contexts of research and public health. Results: Our analysis reveals a wide range of imagined responsibilities, capabilities, and trustworthiness of researchers and public health agencies. Specifically, many respondents expect researchers and public health agencies to use HIV ME carefully and maintain transparency about how data will be used. Informed consent was discussed as an important opportunity for notification of privacy risks. Furthermore, some respondents wished that public health agencies were held to the same form of oversight and accountability represented by informed consent in research. Conclusions: To prevent HIV ME from becoming a barrier to testing or a source of public mistrust, the sense of vulnerability expressed by some respondents must be addressed. In research, informed consent is an obvious opportunity for this. Without giving specimen donors a similar opportunity to opt out, public health agencies may find it difficult to adopt HIV ME without deterring testing and treatment.

An open access medical knowledge base for community driven diagnostic decision support system development.

INTRODUCTION: While early diagnostic decision support systems were built around knowledge bases, more recent systems employ machine learning to consume large amounts of health data. We argue curated knowledge bases will remain an important component of future diagnostic decision support systems by providing ground truth and facilitating explainable human-computer interaction, but that prototype development is hampered by the lack of freely available computable knowledge bases. METHODS: We constructed an open access knowledge base and evaluated its potential in the context of a prototype decision support system. We developed a modified set-covering algorithm to benchmark the performance of our knowledge base compared to existing platforms. Testing was based on case reports from selected literature and medical student preparatory material. RESULTS: The knowledge base contains over 2000 ICD-10 coded diseases and 450 RX-Norm coded medications, with over 8000 unique observations encoded as SNOMED or LOINC semantic terms. Using 117 medical cases, we found the accuracy of the knowledge base and test algorithm to be comparable to established diagnostic tools such as Isabel and DXplain. Our prototype, as well as DXplain, showed the correct answer as "best suggestion" in 33% of the cases. While we identified shortcomings during development and evaluation, we found the knowledge base to be a promising platform for decision support systems. CONCLUSION: We built and successfully evaluated an open access knowledge base to facilitate the development of new medical diagnostic assistants. This knowledge base can be expanded and curated by users and serve as a starting point to facilitate new technology development and system improvement in many contexts.

Coronary artery spasms due to tyrosine kinase inhibitors used in chronic myeloid leukemia.

Tyrosine kinase inhibitors (TKIs) have changed the landscape of treatment for patients with chronic myeloid leukemia (CML) leading to a life expectancy comparable to the general population. Side effects commonly encountered during TKI treatment are pleural effusion due to use of dasatinib and vascular side effects due to nilotinib and ponatinib. Coronary artery spasm (CAS), although encountered during treatment with other chemotherapeutic drugs, have to our knowledge never been reported during TKI treatment. Here, we describe two cases of coronary artery spasms which are likely due to TKIs.

Perceptions of molecular epidemiology studies of HIV among stakeholders.

Background: Advances in viral sequence analysis make it possible to track the spread of infectious pathogens, such as HIV, within a population. When used to study HIV, these analyses (i.e., molecular epidemiology) potentially allow inference of the identity of individual research subjects. Current privacy standards are likely insufficient for this type of public health research. To address this challenge, it will be important to understand how stakeholders feel about the benefits and risks of such research. Design and Methods: To better understand perceived benefits and risks of these research methods, in-depth qualitative interviews were conducted with HIV-infected individuals, individuals at high-risk for contracting HIV, and professionals in HIV care and prevention. To gather additional perspectives, attendees to a public lecture on molecular epidemiology were asked to complete an informal questionnaire. Results: Among those interviewed and polled, there was near unanimous support for using molecular epidemiology to study HIV. Questionnaires showed strong agreement about benefits of molecular epidemiology, but diverse attitudes regarding risks. Interviewees acknowledged several risks, including privacy breaches and provocation of anti-gay sentiment. The interviews also demonstrated a possibility that misunderstandings about molecular epidemiology may affect how risks and benefits are evaluated. Conclusions: While nearly all study participants agree that the benefits of HIV molecular epidemiology outweigh the risks, concerns about privacy must be addressed to ensure continued trust in research institutions and willingness to participate in research.

Diagnostic Delay of Endometriosis in the Netherlands.

BACKGROUND/AIMS: Endometriosis has a long diagnostic delay that is influenced by varying socio-economic and healthcare factors. In the Dutch situation, these factors are not yet identified. The aim of this study is to determine the length of the diagnostic delay of endometriosis in the Netherlands and to identify which variables affect this delay. METHODS: A retrospective study among 139 patients diagnosed with endometriosis in a secondary care hospital with a specialized multidisciplinary endometriosis team. The diagnostic process was evaluated using a questionnaire-guided telephonic interview. RESULTS: The median time interval from the onset of symptoms to diagnosis was 89 months or 7.4 years, divided in 7 months patient delay, 35 months general practitioner (GP) delay and 5 months gynecologist delay. Determinants for a longer diagnostic delay were young age at onset of symptoms, use of oral contraceptives or analgesics prescribed by GP, alternative diagnoses considered by the GP, and cyclic symptoms. Subfertility as presenting symptom resulted in faster diagnosis. CONCLUSION: This study shows that the time interval to the diagnosis of endometriosis is long and mainly consists of the period of time the woman consults her first line medical professional.

Ensuring privacy in the study of pathogen genetics.

Rapid growth in the genetic sequencing of pathogens in recent years has led to the creation of large sequence databases. This aggregated sequence data can be very useful for tracking and predicting epidemics of infectious diseases. However, the balance between the potential public health benefit and the risk to personal privacy for individuals whose genetic data (personal or pathogen) are included in such work has been difficult to delineate, because neither the true benefit nor the actual risk to participants has been adequately defined. Existing approaches to minimise the risk of privacy loss to participants are based on de-identification of data by removal of a predefined set of identifiers. These approaches neither guarantee privacy nor protect the usefulness of the data. We propose a new approach to privacy protection that will quantify the risk to participants, while still maximising the usefulness of the data to researchers. This emerging standard in privacy protection and disclosure control, which is known as differential privacy, uses a process-driven rather than data-centred approach to protecting privacy.

Protecting count queries in study design.

OBJECTIVE: Today's clinical research institutions provide tools for researchers to query their data warehouses for counts of patients. To protect patient privacy, counts are perturbed before reporting; this compromises their utility for increased privacy. The goal of this study is to extend current query answer systems to guarantee a quantifiable level of privacy and allow users to tailor perturbations to maximize the usefulness according to their needs. METHODS: A perturbation mechanism was designed in which users are given options with respect to scale and direction of the perturbation. The mechanism translates the true count, user preferences, and a privacy level within administrator-specified bounds into a probability distribution from which the perturbed count is drawn. RESULTS: Users can significantly impact the scale and direction of the count perturbation and can receive more accurate final cohort estimates. Strong and semantically meaningful differential privacy is guaranteed, providing for a unified privacy accounting system that can support role-based trust levels. This study provides an open source web-enabled tool to investigate visually and numerically the interaction between system parameters, including required privacy level and user preference settings. CONCLUSIONS: Quantifying privacy allows system administrators to provide users with a privacy budget and to monitor its expenditure, enabling users to control the inevitable loss of utility. While current measures of privacy are conservative, this system can take advantage of future advances in privacy measurement. The system provides new ways of trading off privacy and utility that are not provided in current study design systems.

iDASH: integrating data for analysis, anonymization, and sharing.

iDASH (integrating data for analysis, anonymization, and sharing) is the newest National Center for Biomedical Computing funded by the NIH. It focuses on algorithms and tools for sharing data in a privacy-preserving manner. Foundational privacy technology research performed within iDASH is coupled with innovative engineering for collaborative tool development and data-sharing capabilities in a private Health Insurance Portability and Accountability Act (HIPAA)-certified cloud. Driving Biological Projects, which span different biological levels (from molecules to individuals to populations) and focus on various health conditions, help guide research and development within this Center. Furthermore, training and dissemination efforts connect the Center with its stakeholders and educate data owners and data consumers on how to share and use clinical and biological data. Through these various mechanisms, iDASH implements its goal of providing biomedical and behavioral researchers with access to data, software, and a high-performance computing environment, thus enabling them to generate and test new hypotheses.

Anomaly and signature filtering improve classifier performance for detection of suspicious access to EHRs.

Our objective is to facilitate semi-automated detection of suspicious access to EHRs. Previously we have shown that a machine learning method can play a role in identifying potentially inappropriate access to EHRs. However, the problem of sampling informative instances to build a classifier still remained. We developed an integrated filtering method leveraging both anomaly detection based on symbolic clustering and signature detection, a rule-based technique. We applied the integrated filtering to 25.5 million access records in an intervention arm, and compared this with 8.6 million access records in a control arm where no filtering was applied. On the training set with cross-validation, the AUC was 0.960 in the control arm and 0.998 in the intervention arm. The difference in false negative rates on the independent test set was significant, P=1.6×10(-6). Our study suggests that utilization of integrated filtering strategies to facilitate the construction of classifiers can be helpful.

Spectral Anonymization of Data.

The goal of data anonymization is to allow the release of scientifically useful data in a form that protects the privacy of its subjects. This requires more than simply removing personal identifiers from the data, because an attacker can still use auxiliary information to infer sensitive individual information. Additional perturbation is necessary to prevent these inferences, and the challenge is to perturb the data in a way that preserves its analytic utility.No existing anonymization algorithm provides both perfect privacy protection and perfect analytic utility. We make the new observation that anonymization algorithms are not required to operate in the original vector-space basis of the data, and many algorithms can be improved by operating in a judiciously chosen alternate basis. A spectral basis derived from the data's eigenvectors is one that can provide substantial improvement. We introduce the term spectral anonymization to refer to an algorithm that uses a spectral basis for anonymization, and we give two illustrative examples.We also propose new measures of privacy protection that are more general and more informative than existing measures, and a principled reference standard with which to define adequate privacy protection.

[Activity of tigecycline against pathogen bacteria isolated in respiratory infectious disease in Europe. TEST study 2004-2007]. / Activité in vitro de la tigécycline vis-à-vis des germes pathogènes isolés d'infections respiratoires en Europe. Le Programme TEST 2004-2007.

Tigecycline (TGC), a semisynthetic glycylcycline, has a documented activity on Gram+ and Gram- pathogens including oxacillin-resistant (MRSA) and an extended-spectrum beta-lactamase (ESBL)-producing Enterobacteriaceae. Tigecycline Evaluation and Surveillance Trial (TEST) is an international surveillance study designed to assess the in vitro activity of TGC and 11 comparators against a range of important clinical pathogens from both the community and the hospital. The aim of this study was to assess efficacy of TGC, using this database, against pathogens implicated in community or hospital pneumonia and sinusitis. A total of 4163 isolates were consecutively collected in 21 European countries during three years (2004-2007). In all center, minimum inhibitory concentration (MIC) were determinated with the same Microscan panel (Dade-Behring). Tigecycline exhibited a good activity against respiratory pathogens, with the exception of Pseudomonas aeruginosa. Hundred percent of cocci Gram+ (Streptococcus pneumoniae, Staphylococcus aureus, Enterococcus sp.) and 100% of Haemophilus sp. are inhibited with 0.5 mg/L, without effect of an associated beta-lactam resistance mechanism. TGC is active in vitro on 89% of Enterobacteriaceae, with MIC 90 less or equal to 2mg/L. Eighty-nine percent of Enterobacter sp. and 77% of Serratia sp. are susceptible with range of MIC 90 from 2 to 4 mg/L. These interesting results obtained in vitro are to be strengthened by clinical studies.

[Meaning of the Quantiferon TB Gold tube test in tuberculosis screening among the hospital staff in case of very old or recent positive skin tests]. / Signification de Quantiféron TB Gold en tube en dépistage de la tuberculose parmi le personnel hospitalier en cas d'intradermoréactions positives très anciennes ou récentes.

UNLABELLED: The tuberculin skin test or PPD performed on health professionals evaluates delayed hypersensitivity to a Mycobacterium tuberculosis (Mt) antigen mixture. A very positive or increased area of induration indicates latent tuberculosis. Yet, prescribing a treatment is often difficult because of the test's poor specificity. OBJECTIVE: To determine if the T-lymphocytes of the proband exposed to specific Mt antigens to secrete interferon-gamma (IFN-gamma) can be measured; observe an immunizing response to cellular mediation improves T it this specificity? PATIENTS AND METHODS: The Department of Occupational Health at CHR of Metz performed a Quantiferon TB Gold (tube method, Cellestis) test on three groups of employees distributed according to the anteriority of a very positive IDR (>15mm). The test measures by method ELISA, the quantity of IFN-gamma secreted by the lymphocytes T collected in 1ml of total blood, after in vitro stimulation by antigens ESAT-6, CFP-10 and TB7.7. Group I is composed of 53 people (46 women [W] and 7 men [M]) and related to a recent discovery of October 2006 and June 2007, whereas group II, includes 28 of them (25W and 3M), is made of subjects with known very positive PPD for eight to 27 years. The age (41 years on average) and the sex-ratio are identical for these two groups. Group III is made of employees having made a tuberculosis there is more than 30 years. In the group I, the rate of IFN was supervised at the end of the treatment among patients who received it. RESULTS: The test Quantiferon TB Gold was positive at 15 subjects of group I (13W and 2M), that is to say 28.3%, and unspecified in one case (1.9%); positive at nine subjects of group II (9W), that is to say 32.1%. These rates of positivity are not significantly different. According to the experts consulted in group I, an antituberculosis treatment was proposed 11 times with 10 effective treatments (one refusal). A proportioning after treatment was carried out among six patients. The rate of IFN remained positive at five of the six supervised patients. This test made it possible to avoid the treatment of the 37 employees with test Quantferon TB negative Gold found over the nine months period of recruitment of group I. The choice not to treat was facilitated. CONCLUSION: The test Quantiferon indeed allows to eliminate the false positive of the skin test (1.6) so avoiding useless, expensive treatments and unwanted effects of antituberculosis medicines . On the other hand, the persistent positivity of this test 30 years after one firstly infection or after a contact does not allow to use it at present as certain control of a latent infection, unless having a negative value known about the hiring pa.

SMART--an integrated wireless system for monitoring unattended patients.

Monitoring vital signs and locations of certain classes of ambulatory patients can be useful in overcrowded emergency departments and at disaster scenes, both on-site and during transportation. To be useful, such monitoring needs to be portable and low cost, and have minimal adverse impact on emergency personnel, e.g., by not raising an excessive number of alarms. The SMART (Scalable Medical Alert Response Technology) system integrates wireless patient monitoring (ECG, SpO(2)), geo-positioning, signal processing, targeted alerting, and a wireless interface for caregivers. A prototype implementation of SMART was piloted in the waiting area of an emergency department and evaluated with 145 post-triage patients. System deployment aspects were also evaluated during a small-scale disaster-drill exercise.

Approximation properties of haplotype tagging.

BACKGROUND: Single nucleotide polymorphisms (SNPs) are locations at which the genomic sequences of population members differ. Since these differences are known to follow patterns, disease association studies are facilitated by identifying SNPs that allow the unique identification of such patterns. This process, known as haplotype tagging, is formulated as a combinatorial optimization problem and analyzed in terms of complexity and approximation properties. RESULTS: It is shown that the tagging problem is NP-hard but approximable within 1 + ln((n2 - n)/2) for n haplotypes but not approximable within (1-epsilon) ln(n/2) for any epsilon > 0 unless NP subset DTIME(n(log log n)). A simple, very easily implementable algorithm that exhibits the above upper bound on solution quality is presented. This algorithm has running time O(np/2(2m-p+1)) < or = O(m(n2-n)/2) where p < or = min(n, m) for n haplotypes of size m. As we show that the approximation bound is asymptotically tight, the algorithm presented is optimal with respect to this asymptotic bound. CONCLUSION: The haplotype tagging problem is hard, but approachable with a fast, practical, and surprisingly simple algorithm that cannot be significantly improved upon on a single processor machine. Hence, significant improvement in computational efforts expended can only be expected if the computational effort is distributed and done in parallel.

Small, fuzzy and interpretable gene expression based classifiers.

MOTIVATION: Interpretation of classification models derived from gene-expression data is usually not simple, yet it is an important aspect in the analytical process. We investigate the performance of small rule-based classifiers based on fuzzy logic in five datasets that are different in size, laboratory origin and biomedical domain. RESULTS: The classifiers resulted in rules that can be readily examined by biomedical researchers. The fuzzy-logic-based classifiers compare favorably with logistic regression in all datasets. AVAILABILITY: Prototype available upon request.

A primer on gene expression and microarrays for machine learning researchers.

Data originating from biomedical experiments has provided machine learning researchers with an important source of motivation for developing and evaluating new algorithms. A new wave of algorithmic development has been initiated with the publication of gene expression data derived from microarrays. Microarray data analysis is particularly challenging given the large number of measurements (typically in the order of thousands) that are reported for relatively few samples (typically in the order of dozens). Many data sets are now available on the web. It is important that machine learning researchers understand how data are obtained and which assumptions are necessary in the analysis. Microarray data have the potential to cause significant impact in machine learning research, not just as a rich and realistic source of cases for testing new algorithms, as has been the UCI machine learning repository in the past decades, but also as a main motivation for their development. In this article, we briefly review the biology underlying microarrays, the process of obtaining gene expression measurements, and the rationale behind the common types of analyses involved in a microarray experiment. We outline the main challenges and reiterate critical considerations regarding the construction of supervised learning models that use this type of data. The goal of this article is to familiarize machine learning researchers with data originated from gene expression microarrays.

Myelomeningocele and female sexuality: an issue?

During the last 40 years more and more myelomeningocele (MMC) patients have survived to adulthood and since the 1980s there is growing interest in sexual functioning of these patients. However, most of what is known about the impact of MMC on sexual functioning pertains to males. We have reviewed the literature pertaining to female sexuality and MMC with regard to sexual development, activity, dysfunction, sexual knowledge and education, pregnancy and delivery, as well as future trends.

An Epicurean learning approach to gene-expression data classification.

We investigate the use of perceptrons for classification of microarray data where we use two datasets that were published in [Nat. Med. 7 (6) (2001) 673] and [Science 286 (1999) 531]. The classification problem studied by Khan et al. is related to the diagnosis of small round blue cell tumours (SRBCT) of childhood which are difficult to classify both clinically and via routine histology. Golub et al. study acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). We used a simulated annealing-based method in learning a system of perceptrons, each obtained by resampling of the training set. Our results are comparable to those of Khan et al. and Golub et al., indicating that there is a role for perceptrons in the classification of tumours based on gene-expression data. We also show that it is critical to perform feature selection in this type of models, i.e. we propose a method for identifying genes that might be significant for the particular tumour types. For SRBCTs, zero error on test data has been obtained for only 13 out of 2308 genes; for the ALL/AML problem, we have zero error for 9 out of 7129 genes that are used for the classification procedure. Furthermore, we provide evidence that Epicurean-style learning and simulated annealing-based search are both essential for obtaining the best classification results.

[The treatment of carpal tunnel syndrome]. / De behandeling van het carpale-tunnelsyndroom.

The invasive treatment of carpal tunnel syndrome is only indicated if the symptoms persistently interfere with the normal lifestyle (for a period of at least several months), and if the diagnosis has been established beyond reasonable doubt. The standard treatment still consists of an open surgical cleavage of the transverse carpal ligament (with an incision distal to the transverse crease of the wrist). Between 75 and 90% of patients obtain long-term relief as a result of the operation. Endoscopic release may result in a more rapid recovery. However, it is more often associated with complications than the conventional operation, and it requires new equipment and extensive training. The injection of corticosteroids near the carpal tunnel is reasonably safe and also effective, albeit mostly just in the short term; in most patients the symptoms recur within a few months.

Molecular characterization of celtix-1, a bromodomain protein interacting with the transcription factor interferon regulatory factor 2.

Transcriptional control at the G1/S-phase transition of the cell cycle requires functional interactions of multimeric promoter regulatory complexes that contain DNA binding proteins, transcriptional cofactors, and/or chromatin modifying enzymes. Transcriptional regulation of the human histone H4/n gene (FO108) is mediated by Interferon Regulatory Factor-2 (IRF-2), as well as other histone gene promoter factors. To identify proteins that interact with cell-cycle regulatory factors, we performed yeast two-hybrid analysis with IRF-2 and identified a novel human protein termed Celtix-1 which binds to IRF-2. Celtix-1 contains several phylogenetically conserved domains, including a bromodomain, which is found in a number of transcriptional cofactors. Using a panel of IRF-2 deletion mutants in yeast two-hybrid assays, we established that Celtix-1 contacts the C-terminus of IRF-2. Celtix-1 directly interacts with IRF-2 based on binding studies with glutathione S-transferase (GST)/IRF-2 fusion proteins, and immunofluorescence studies suggest that Celtix-1 and IRF-2 associate in situ. Celtix-1 is distributed throughout the nucleus in a heterodisperse pattern. A subset of Celtix-1 colocalizes with the hyperacetylated forms of histones H3 and H4, as well as with the hyperphosphorylated, transcriptionally active form of RNA polymerase II. We conclude that the bromodomain protein Celtix-1 is a novel IRF-2 interacting protein that associates with transcriptionally active chromatin in situ.