Acute gastrointestinal infection (AGI) represents a significant public health concern. To control and treat AGI, it is critical to quickly and accurately identify its causes. The use of novel ...multiplex molecular assays for pathogen detection and identification provides a unique opportunity to improve pathogen detection, and better understand risk factors and burden associated with AGI in the community. In this study, de-identified results from BioFire® FilmArray® Gastrointestinal (GI) Panel were obtained from January 01, 2016 to October 31, 2018 through BioFire® Syndromic Trends (Trend), a cloud database. Data was analyzed to describe the occurrence of pathogens causing AGI across United States sites and the relative rankings of pathogens monitored by FoodNet, a CDC surveillance system were compared. During the period of the study, the number of tests performed increased 10-fold and overall, 42.6% were positive for one or more pathogens. Seventy percent of the detections were bacteria, 25% viruses, and 4% parasites. Clostridium difficile, enteropathogenic Escherichia coli (EPEC) and norovirus were the most frequently detected pathogens. Seasonality was observed for several pathogens including astrovirus, rotavirus, and norovirus, EPEC, and Campylobacter. The co-detection rate was 10.2%. Enterotoxigenic E. coli (ETEC), Plesiomonas shigelloides, enteroaggregative E. coli (EAEC), and Entamoeba histolytica were detected with another pathogen over 60% of the time, while less than 30% of C. difficile and Cyclospora cayetanensis were detected with another pathogen. Positive correlations among co-detections were found between Shigella/Enteroinvasive E. coli with E. histolytica, and ETEC with EAEC. Overall, the relative ranking of detections for the eight GI pathogens monitored by FoodNet and BioFire Trend were similar for five of them. AGI data from BioFire Trend is available in near real-time and represents a rich data source for the study of disease burden and GI pathogen circulation in the community, especially for those pathogens not often targeted by surveillance.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Host gene expression signatures discriminate bacterial and viral infection but have not been translated to a clinical test platform. This study enrolled an independent cohort of patients to describe ...and validate a first-in-class host response bacterial/viral test.
Subjects were recruited from 2006 to 2016. Enrollment blood samples were collected in an RNA preservative and banked for later testing. The reference standard was an expert panel clinical adjudication, which was blinded to gene expression and procalcitonin results.
Four U.S. emergency departments.
Six-hundred twenty-three subjects with acute respiratory illness or suspected sepsis.
Forty-five-transcript signature measured on the BioFire FilmArray System (BioFire Diagnostics, Salt Lake City, UT) in ~45 minutes.
Host response bacterial/viral test performance characteristics were evaluated in 623 participants (mean age 46 yr; 45% male) with bacterial infection, viral infection, coinfection, or noninfectious illness. Performance of the host response bacterial/viral test was compared with procalcitonin. The test provided independent probabilities of bacterial and viral infection in ~45 minutes. In the 213-subject training cohort, the host response bacterial/viral test had an area under the curve for bacterial infection of 0.90 (95% CI, 0.84-0.94) and 0.92 (95% CI, 0.87-0.95) for viral infection. Independent validation in 209 subjects revealed similar performance with an area under the curve of 0.85 (95% CI, 0.78-0.90) for bacterial infection and 0.91 (95% CI, 0.85-0.94) for viral infection. The test had 80.1% (95% CI, 73.7-85.4%) average weighted accuracy for bacterial infection and 86.8% (95% CI, 81.8-90.8%) for viral infection in this validation cohort. This was significantly better than 68.7% (95% CI, 62.4-75.4%) observed for procalcitonin (p < 0.001). An additional cohort of 201 subjects with indeterminate phenotypes (coinfection or microbiology-negative infections) revealed similar performance.
The host response bacterial/viral measured using the BioFire System rapidly and accurately discriminated bacterial and viral infection better than procalcitonin, which can help support more appropriate antibiotic use.
The purpose of this study was to assess baseline variability in histogram and texture features derived from apparent diffusion coefficient (ADC) maps from diffusion-weighted MRI (DW-MRI) examinations ...and to identify early treatment-induced changes to these features in patients with head and neck squamous cell carcinoma (HNSCC) undergoing definitive chemoradiation. Patients with American Joint Committee on Cancer Stage III–IV (7
th
edition) HNSCC were prospectively enrolled on an IRB-approved study to undergo two pre-treatment baseline DW-MRI examinations, performed 1 week apart, and a third early intra-treatment DW-MRI examination during the second week of chemoradiation. Forty texture and six histogram features were derived from ADC maps. Repeatability of the features from the baseline ADC maps was assessed with the intra-class correlation coefficient (ICC). A Wilcoxon signed-rank test compared average baseline and early treatment feature changes. Data from nine patients were used for this study. Comparison of the two baseline ADC maps yielded 11 features with an ICC ≥ 0.80, indicating that these features had excellent repeatability: Run Gray-Level Non-Uniformity, Coarseness, Long Zone High Gray-Level, Variance (Histogram Feature), Cluster Shade, Long Zone, Variance (Texture Feature), Run Length Non-Uniformity, Correlation, Cluster Tendency, and ADC Median. The Wilcoxon signed-rank test resulted in four features with significantly different early treatment-induced changes compared to the baseline values: Run Gray-Level Non-Uniformity (p = 0.005), Run Length Non-Uniformity (p = 0.005), Coarseness (p = 0.006), and Variance (Histogram) (p = 0.006). The feasibility of histogram and texture analysis as a potential biomarker is dependent on the baseline variability of each metric, which disqualifies many features.
Lung cancer screening via annual low-dose computed tomography (LDCT) has poor adoption. We conducted a prospective case-control study among 958 individuals eligible for lung cancer screening to ...develop a blood-based lung cancer detection test that when positive is followed by an LDCT. Changes in genome-wide cell-free DNA (cfDNA) fragmentation profiles (fragmentomes) in peripheral blood reflected genomic and chromatin characteristics of lung cancer. We applied machine learning to fragmentome features to identify individuals who were more or less likely to have lung cancer. We trained the classifier using 576 cases and controls from study samples, and then validated it in a held-out group of 382 cases and controls. The validation demonstrated high sensitivity for lung cancer, and consistency across demographic groups and comorbid conditions. Applying test performance to the screening eligible population in a five-year model with modest utilization assumptions suggested the potential to prevent thousands of lung cancer deaths.
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•An algorithm to predict the presence enterovirus D68 among a commercial respiratory disease diagnostic test was developed.•The algorithm was used with test results exported to an ...epidemiology network for real-time monitoring and historical outbreak prediction.•Historical outbreak predictions coincide with known periods of high EV-D68 circulation in 2014 and 2016.•The algorithm alerted clinical laboratories of the potential circulation of EV-D68 in 2018, prompting clinical testing for EV-D68 at one site.
In 2014, enterovirus D68 (EV-D68) was responsible for an outbreak of severe respiratory illness in children, with 1,153 EV-D68 cases reported across 49 states. Despite this, there is no commercial assay for its detection in routine clinical care. BioFire® Syndromic Trends (Trend) is an epidemiological network that collects, in near real-time, deidentified. BioFire test results worldwide, including data from the BioFire® Respiratory Panel (RP).
Using the RP version 1.7 (which was not explicitly designed to differentiate EV-D68 from other picornaviruses), we formulate a model, Pathogen Extended Resolution (PER), to distinguish EV-D68 from other human rhinoviruses/enteroviruses (RV/EV) tested for in the panel. Using PER in conjunction with Trend, we survey for historical evidence of EVD68 positivity and demonstrate a method for prospective real-time outbreak monitoring within the network.
PER incorporates real-time polymerase chain reaction metrics from the RPRV/EV assays. Six institutions in the United States and Europe contributed to the model creation, providing data from 1,619 samples spanning two years, confirmed by EV-D68 gold-standard molecular methods. We estimate outbreak periods by applying PER to over 600,000 historical Trend RP tests since 2014. Additionally, we used PER as a prospective monitoring tool during the 2018 outbreak.
The final PER algorithm demonstrated an overall sensitivity and specificity of 87.1% and 86.1%, respectively, among the gold-standard dataset. During the 2018 outbreak monitoring period, PER alerted the research network of EV-D68 emergence in July. One of the first sites to experience a significant increase, Nationwide Children's Hospital, confirmed the outbreak and implemented EV-D68 testing at the institution in response. Applying PER to the historical Trend dataset to determine rates among RP tests, we find three potential outbreaks with predicted regional EV-D68 rates as high as 37% in 2014, 16% in 2016, and 29% in 2018.
Using PER within the Trend network was shown to both accurately predict outbreaks of EV-D68 and to provide timely notifications of its circulation to participating clinical laboratories
In radiation oncology, 18F-FDG Positron Emission Tomography (PET) is used for determining metabolic activity of cancers as well as delineating gross tumor volumes (GTV) for treatment planning. More ...recently, PET is being utilized for adaptive therapies for gynecological malignancies in which tumor response may be estimated and treatments adjusted during the course of radiation. In addition to treatment assessment, 18F-FDG PET has become a tool in the prediction of tumor response because of the derived Standard Uptake Value (SUV), a measure of the metabolic activity of a tumor. In this study, we seek to establish texture analysis as complimentary to SUV for predicting tumor response as well as understanding temporal changes during treatment in gynecological cancers. An additional experiment was performed studying the variability of texture features from baseline and intra-treatment PET scans due to reconstruction parameters in order to identify features that show statistically significant changes during treatment and that are independent of reconstruction parameters. In this IRB approved clinical research study, 29 women with node positive gynecological malignancies visible on PET including cervical, endometrial, vulvar, and vaginal cancers are treated with radiation therapy. Prescribed dose varied between 45-50.4Gy, with a 55-70Gy boost to the PET positive nodes. A baseline, intra-treatment (between 30-36Gy), and post-treatment PET-CT were obtained with tumor response determined by a physician according to post-treatment RECIST. All volumes were re-contoured on the intra-treatment PET-CT. Primary GTVs were segmented both with the 40% SUVmax threshold method and a validated gradient-based contouring tool, PET Edge (MIM Software Inc., Cleveland, OH). A MATLAB Graphical User Interface (GUI) called Duke FIRE (Functional Imaging Research Environment) was developed for this study in order to calculate four mathematical algorithms representing the spatial distribution of pixels in an image: gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), gray level size zone matrix (GLSZM), and the neighborhood gray level difference matrix (NGLDM). Features representing characteristics of the image are derived from these texture matrices: 12 local features from the GLCM, 11 regional features from the GLRLM, 11 regional features from the GLSZM, and 5 local features from the NGLDM. Additionally, 6 global SUV histogram features including SUVmean, SUVmedian, SUVmax, skewness, kurtosis, and variance as well as metabolic volume (MV) and total lesion glycolysis (TLG) are extracted. The prognostic power of each baseline feature derived from both gradient-based and threshold segmentation methods was determined using the Wilcoxon rank-sum test. Receiver operating characteristic (ROC) curves were calculated to understand the sensitivity and specificity of baseline texture features compared to SUV metrics. Changes in features from baseline to intra-treatment PET-CT were determined using the Wilcoxon signed-rank test. A subset of 7 patient baseline and intra-treatment raw PET data was reconstructed 6 times using a TrueX+TOF algorithm on a Siemens Biograph mCT with varying iterations and Gaussian filter widths. Texture features were derived from the GTV as before. Texture features per patient were normalized to the respective clinical baseline value in order to limit variability to reconstruction parameters. Mean percent ranges of each feature at baseline and intra-treatment were determined and the change in features was compared using the Wilcoxon signed-rank test. Of the 29 patients, there were 16 complete responders, 7 partial responders, and 6 non-responders. Comparing CR/PR vs. NR for the gradient-based GTVs, 7 texture values had a p < 0.05. The threshold GTVs yielded 4 texture features with p < 0.05. ROC and logistic regression was performed and texture features from both PET Edge and thresholding yielding a higher area under the curve (AUC) than SUV metrics. Features derived from PET Edge GTVs also showed higher AUCs than the threshold GTVs. From baseline to intra-treatment, 16 texture features changed with p < 0.05. Texture analysis of PET imaged gynecological tumors is considerably more powerful than SUV in early prognosis of tumor response, especially when using a gradient based method. We then took the 16 texture features showing significant changes (p < 0.05) between baseline and intra-treatment PET scans in 29 patients and tested these against the subset of reconstructed features to determine if these changes were dependent upon the method in which the scans were reconstructed. A total of 13 features (including entropy, zone non-uniformity, and complexity) were found to be consistently different even when subjected to different means of reconstruction, however 3 of the 16 (inverse variance, run percentage, and zone percentage) were found to be dependent upon these reconstruction parameters. Texture features such as entropy, zone non-uniformity, and complexity are excellent candidates for future investigations of changes in texture analysis during radiation therapy of gynecological cancers. Caution should be taken with inverse variance, run percentage, and zone percentage due to their dependence upon reconstruction parameters. This comprehensive work characterizes gynecological cancers using texture analysis in order to identify texture features that may be used for predicting tumor response as well as reflecting changes during treatment. It is the first study to our knowledge that utilizes all 4 texture matrices (GLCM, GLRLM, GLSZM, and NGLDM) and found 7 statistically significant features classifying responding and non-responding gynecological tumors: energy, entropy, max probability, zone gray level non uniformity, zone size non uniformity, contrast (NGLDM), and complexity. A novel method was implemented extending the NGLDM and its respective features to 3D space for this study. It is also the first study concluding that a semi-automatic gradient-based segmentation method results in more, stronger predictors than using a 40% SUVmax threshold method. Finally, this is the first study to examine variability of texture features with reconstruction parameters and to identify texture features as reliable and independent of reconstruction. In conclusion, texture analysis is a promising method of characterizing tumors visible on PET and should be considered for future studies.
Twenty-six adults were given a chair massage and 24 control group adults were asked to relax in the massage chair for 15 minutes, two times per week for five weeks. On the first and last days of the ...study they were monitored for EEG, before, during and after the sessions. In addition, before and after the sessions they performed math computations, they completed POMS Depression and State Anxiety Scales and they provided a saliva sample for Cortisol. At the beginning of the sessions they completed Life Events, Job Stress and Chronic POMS Depression Scales. Group by repeated measures and post hoc analyses revealed the following: 1) frontal delta power increased for both groups, suggesting relaxation; 2) the massage group showed decreased frontal alpha and beta power (suggesting enhanced alertness); while the control group showed increased alpha and beta power; 3) the massage group showed increased speed and accuracy on math computations while the control group did not change; 4) anxiety levels were lower following the massage but not the control sessions, although mood state was less depressed following both the massage and control sessions; 5) salivary Cortisol levels were lower following the massage but not the control sessions but only on the first day; and 6) at the end of the 5 week period depression scores were lower for both groups but job stress scores were lower only for the massage group.
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