Genetic analysis of bacteriophages from clinical and environmental samples

Bacteriophages, viruses infecting bacteria, are uniformly present in any location where there are high numbers of bacteria, both in the external environment and the human body. Knowledge of their diversity is limited by the difficulty to culture the host species and by the lack of the universal marker gene present in all viruses. Metagenomics is a powerful tool that can be used to analyse viral communities in their natural environments. The aim of this study was to investigate diverse populations of uncultured viruses from clinical (a sputum of patient with cystic fibrosis, CF) and environmental samples (a sludge from a dairy food wastewater treatment plant) containing rich bacterial populations using genetic and metagenomic analyses. Metagenomic sequencing of viruses obtained from these samples revealed that the majority of the metagenomic reads (97-99%) were novel when compared to the NCBI protein database using BLAST. A large proportion of assembled contigs were assignable as novel phages or uncharacterised prophages, the next largest assignable group being single-stranded eukaryotic virus genomes. Sputum from a cystic fibrosis patient contained DNA typical of phages of bacteria that are traditionally involved in CF lung infections and other bacteria that are part of the normal oral flora. The only eukaryotic virus detected in the CF sputum was Torque Teno virus (TTV). A substantial number of assigned sequences from dairy wastewater could be affiliated with phages of bacteria that are typically found in the soil and aquatic environments, including wastewater. Eukaryotic viral sequences were dominated by plant pathogens from the Geminiviridae and Nanoviridae families, and animal pathogens from the Circoviridae family. Antibiotic resistance genes were detected in both metagenomes suggesting phages could be a source for transmissible antimicrobial resistance. Overall, diversity of viruses in the CF sputum was low, with 89 distinct viral genotypes predicted, and higher (409 genotypes) in the wastewater. Function-based screening of a metagenomic library constructed from DNA extracted from dairy food wastewater viruses revealed candidate promoter sequences that have ability to drive expression of GFP in a promoter-trap vector in Escherichia coli. The majority of the cloned DNA sequences selected by the assay were related to ssDNA circular eukaryotic viruses and phages which formed a minority of the metagenome assembly, and many lacked any significant homology to known database sequences. Natural diversity of bacteriophages in wastewater samples was also examined by PCR amplification of the major capsid protein sequences, conserved within T4-type bacteriophages from Myoviridae family. Phylogenetic analysis of capsid sequences revealed that dairy wastewater contained mainly diverse and uncharacterized phages, while some showed a high level of similarity with phages from geographically distant environments.

Clinical outcome of breast cancer patients with liver metastases alone in the anthracycline-taxane era: a retrospective analysis of two prospective, randomised metastatic breast cancer trials.

Liver metastases have long been known to indicate an unfavourable disease course in breast cancer (BC). However, a small subset of patients with liver metastases alone who were treated with pre-taxane chemotherapy regimens was reported to have longer survival compared with patients with liver and metastases at other sites. In the present study, we examined the clinical outcome of breast cancer patients with liver metastases alone in the context of two phase III European Organisation for Research and Treatment of Cancer (EORTC) trials which compared the efficacy of doxorubicin (A) versus paclitaxel (T) (trial 10923) and of AC (cyclophosphamide) versus AT (trial 10961), given as first-line chemotherapy in metastatic BC patients. The median follow-up for the patients with liver metastases was 90.5 months in trial 10923 and 56.6 months in trial 10961. Patients with liver metastases alone comprised 18% of all patients with liver metastases, in both the 10923 and 10961 trials. The median survival of patients with liver metastases alone and liver plus other sites of metastases were 22.7 and 14.2 months (log rank test, P=0.002) in trial 10923 and 27.1 and 16.8 months (log rank test, P=0.19) in trial 10961. The median TTP (time to progression) for patients with liver metastases alone was also longer compared with the liver plus other sites of metastases group in both trials: 10.2 versus 8.8 months (log rank test, P=0.02) in trial 10923 and 8.3 versus 6.7 months (log rank test, P=0.37) in trial 10961. Most patients with liver metastases alone have progression of their disease in their liver again (96 and 60% of patients in trials 10923 and 10961, respectively). Given the high prevalence of breast cancer, improved detection of liver metastases, encouraging survival achieved with currently available cytotoxic agents and the fact that a significant portion of patients with liver metastases alone have progression of their tumour in the liver again, a more aggressive multimodality treatment approach through prospective clinical trials seems worth exploring in this specific subset of women.

Qualitative analysis of the interdisciplinary interaction between data analysis specialists and novice clinical researchers.

BACKGROUND: The inherent complexity of statistical methods and clinical phenomena compel researchers with diverse domains of expertise to work in interdisciplinary teams, where none of them have a complete knowledge in their counterpart's field. As a result, knowledge exchange may often be characterized by miscommunication leading to misinterpretation, ultimately resulting in errors in research and even clinical practice. Though communication has a central role in interdisciplinary collaboration and since miscommunication can have a negative impact on research processes, to the best of our knowledge, no study has yet explored how data analysis specialists and clinical researchers communicate over time. METHODS/PRINCIPAL FINDINGS: We conducted qualitative analysis of encounters between clinical researchers and data analysis specialists (epidemiologist, clinical epidemiologist, and data mining specialist). These encounters were recorded and systematically analyzed using a grounded theory methodology for extraction of emerging themes, followed by data triangulation and analysis of negative cases for validation. A policy analysis was then performed using a system dynamics methodology looking for potential interventions to improve this process. Four major emerging themes were found. Definitions using lay language were frequently employed as a way to bridge the language gap between the specialties. Thought experiments presented a series of "what if" situations that helped clarify how the method or information from the other field would behave, if exposed to alternative situations, ultimately aiding in explaining their main objective. Metaphors and analogies were used to translate concepts across fields, from the unfamiliar to the familiar. Prolepsis was used to anticipate study outcomes, thus helping specialists understand the current context based on an understanding of their final goal. CONCLUSION/SIGNIFICANCE: The communication between clinical researchers and data analysis specialists presents multiple challenges that can lead to errors.

What leads Indians to participate in clinical trials? A meta-analysis of qualitative studies.

BACKGROUND: With the globalization of clinical trials, large developing nations have substantially increased their participation in multi-site studies. This participation has raised ethical concerns, among them the fear that local customs, habits and culture are not respected while asking potential participants to take part in study. This knowledge gap is particularly noticeable among Indian subjects, since despite the large number of participants, little is known regarding what factors affect their willingness to participate in clinical trials. METHODS: We conducted a meta-analysis of all studies evaluating the factors and barriers, from the perspective of potential Indian participants, contributing to their participation in clinical trials. We searched both international as well as Indian-specific bibliographic databases, including Pubmed, Cochrane, Openjgate, MedInd, Scirus and Medknow, also performing hand searches and communicating with authors to obtain additional references. We enrolled studies dealing exclusively with the participation of Indians in clinical trials. Data extraction was conducted by three researchers, with disagreement being resolved by consensus. RESULTS: Six qualitative studies and one survey were found evaluating the main themes affecting the participation of Indian subjects. Themes included Personal health benefits, Altruism, Trust in physicians, Source of extra income, Detailed knowledge, Methods for motivating participants as factors favoring, while Mistrust on trial organizations, Concerns about efficacy and safety of trials, Psychological reasons, Trial burden, Loss of confidentiality, Dependency issues, Language as the barriers. CONCLUSION: We identified factors that facilitated and barriers that have negative implications on trial participation decisions in Indian subjects. Due consideration and weightage should be assigned to these factors while planning future trials in India.

So different, yet so similar: meta-analysis and policy modeling of willingness to participate in clinical trials among Brazilians and Indians.

BACKGROUND: With the global expansion of clinical trials and the expectations of the rise of the emerging economies known as BRICs (Brazil, Russia, India and China), the understanding of factors that affect the willingness to participate in clinical trials of patients from those countries assumes a central role in the future of health research. METHODS: We conducted a systematic review and meta-analysis (SRMA) of willingness to participate in clinical trials among Brazilian patients and then we compared it with Indian patients (with results of another SRMA previously conducted by our group) through a system dynamics model. RESULTS: Five studies were included in the SRMA of Brazilian patients. Our main findings are 1) the major motivation for Brazilian patients to participate in clinical trials is altruism, 2) monetary reimbursement is the least important factor motivating Brazilian patients, 3) the major barrier for Brazilian patients to not participate in clinical trials is the fear of side effects, and 4) Brazilian patients are more likely willing to participate in clinical trials than Indians. CONCLUSION: Our study provides important insights for investigators and sponsors for planning trials in Brazil (and India) in the future. Ignoring these results may lead to unnecessary fund/time spending. More studies are needed to validate our results and for better understanding of this poorly studied theme.

Structured Illumination Microscopy and a Quantitative Image Analysis for the Detection of Positive Margins in a Pre-Clinical Genetically Engineered Mouse Model of Sarcoma.

Intraoperative assessment of surgical margins is critical to ensuring residual tumor does not remain in a patient. Previously, we developed a fluorescence structured illumination microscope (SIM) system with a single-shot field of view (FOV) of 2.1 × 1.6 mm (3.4 mm2) and sub-cellular resolution (4.4 μm). The goal of this study was to test the utility of this technology for the detection of residual disease in a genetically engineered mouse model of sarcoma. Primary soft tissue sarcomas were generated in the hindlimb and after the tumor was surgically removed, the relevant margin was stained with acridine orange (AO), a vital stain that brightly stains cell nuclei and fibrous tissues. The tissues were imaged with the SIM system with the primary goal of visualizing fluorescent features from tumor nuclei. Given the heterogeneity of the background tissue (presence of adipose tissue and muscle), an algorithm known as maximally stable extremal regions (MSER) was optimized and applied to the images to specifically segment nuclear features. A logistic regression model was used to classify a tissue site as positive or negative by calculating area fraction and shape of the segmented features that were present and the resulting receiver operator curve (ROC) was generated by varying the probability threshold. Based on the ROC curves, the model was able to classify tumor and normal tissue with 77% sensitivity and 81% specificity (Youden's index). For an unbiased measure of the model performance, it was applied to a separate validation dataset that resulted in 73% sensitivity and 80% specificity. When this approach was applied to representative whole margins, for a tumor probability threshold of 50%, only 1.2% of all regions from the negative margin exceeded this threshold, while over 14.8% of all regions from the positive margin exceeded this threshold.

Decision making in childhood asthma exacerbation – a clinical judgement analysis

Background: Clinical decisions which impact directly on patient safety and quality of care are made during acute asthma attacks by individual doctors on the basis of their knowledge and experience. These include administration of systemic corticosteroids (CS), oral antibiotics, and admission to hospital. Clinical judgement analysis provides a methodology for comparing decisions between practitioners with different training and experience, and improving decision making. Methods: Stepwise linear regression was used to select clinical cues based on visual analogue scale assessments of the propensity of 62 clinicians to prescribe a short course of oral CS (decision 1), a course of antibiotics (decision 2), and/or admit to hospital (decision 3) for 60 â??paperâ?? patients. Results:When compared by specialty, paediatriciansâ?? models for decision 1 were more likely to include as a cue level of alertness (54% v. 16%); for decision 2 presence of crepitations (49% v. 16%), and less likely to include inhaled CS (8% v. 40%), respiratory rate (0% v. 24%), and air entry (70% v. 100%). When compared to other grades, the models derived for decision 3 by consultants/general practitioners were more likely to include wheeze severity as a cue (39% v. 6%). Conclusions: Clinicians differed in their use of individual cues and the number included in their models. Patient safety and quality of care will benefit from clarification of decision making strategies as general learning points during medical training, in the development of guidelines and care pathways, and by clinicians developing self-awareness of their own preferences.

Optimising the use of virtual and conventional simulation: a clinical and economic analysis

Background and purpose: Currently, optimal use of virtual simulation for all treatment sites is not entirely clear. This study presents data to identify specific patient groups for whom conventional simulation may be completely eliminated and replaced by virtual simulation. Sampling and method: Two hundred and sixty patients were recruited from four treatment sites (head and neck, breast, pelvis, and thorax). Patients were randomly assigned to be treated using the usual treatment process involving conventional simulation, or a treatment process differing only in the replacement of conventional plan verification with virtual verification. Data were collected on set-up accuracy at verification, and the number of unsatisfactory verifications requiring a return to the conventional simulator. A micro-economic costing analysis was also undertaken, whereby data for each treatment process episode were also collected: number and grade of staff present, and the time for each treatment episode. Results: The study shows no statistically significant difference in the number of returns to the conventional simulator for each site and study arm. Image registration data show similar quality of verification for each study arm. The micro-costing data show no statistical difference between the virtual and conventional simulation processes. Conclusions: At our institution, virtual simulation including virtual verification for the sites investigated presents no disadvantage compared to conventional simulation.

Feasibility of a Clinical Chemical Analysis Approach to Predict Misuse of Growth Promoting Hormones in Cattle

A study was performed to determine if targeted metabolic profiling of cattle sera could be used to establish a predictive tool for identifying hormone misuse in cattle. Metabolites were assayed in heifers (n ) 5) treated with nortestosterone decanoate (0.85 mg/kg body weight), untreated heifers (n ) 5), steers (n ) 5) treated with oestradiol benzoate (0.15 mg/kg body weight) and untreated steers (n ) 5). Treatments were administered on days 0, 14, and 28 throughout a 42 day study period. Two support vector machines (SVMs) were trained, respectively, from heifer and steer data to identify hormonetreated animals. Performance of both SVM classifiers were evaluated by sensitivity and specificity of treatment prediction. The SVM trained on steer data achieved 97.33% sensitivity and 93.85% specificity while the one on heifer data achieved 94.67% sensitivity and 87.69% specificity. Solutions of SVM classifiers were further exploited to determine those days when classification accuracy of the SVM was most reliable. For heifers and steers, days 17-35 were determined to be the most selective. In summary, bioinformatics applied to targeted metabolic profiles generated from standard clinical chemistry analyses, has yielded an accurate, inexpensive, high-throughput test for predicting steroid abuse in cattle.

Performing meta-analysis with incomplete statistical information in clinical trials

Background: Results from clinical trials are usually summarized in the form of sampling distributions. When full information (mean, SEM) about these distributions is given, performing meta-analysis is straightforward. However, when some of the sampling distributions only have mean values, a challenging issue is to decide how to use such distributions in meta-analysis. Currently, the most common approaches are either ignoring such trials or for each trial with a missing SEM, finding a similar trial and taking its SEM value as the missing SEM. Both approaches have drawbacks. As an alternative, this paper develops and tests two new methods, the first being the prognostic method and the second being the interval method, to estimate any missing SEMs from a set of sampling distributions with full information. A merging method is also proposed to handle clinical trials with partial information to simulate meta-analysis.