An exploratory study to evaluate visual function endpoints in non-advanced age-related macular degeneration.

BACKGROUND: To prevent irreversible vision loss in age-related macular degeneration (AMD), it is critical to detect retinal dysfunction before permanent structural loss occurs. In the current study we evaluated a series of visual function tests to identify potential endpoints to detect visual dysfunction in non-advanced AMD. METHODS: A series of visual function tests were performed on 23 non-advanced AMD subjects (AREDS grade 1-4 on simplified scale) and 34 age-matched normals (AREDS grade 0). Tests included some commonly used endpoints such as ETDRS visual acuity (VA), low luminance (LL) 2.0ND ETDRS VA, MNREAD as well as newly developed tests such as the Ora-VCF™ test, Ora-tablet reading test, color sensitivity etc. Differences between the two groups were compared for each test. Test-retest repeatability and reproducibility was assessed on a subset of subjects and percent agreement was calculated. RESULTS: There was no difference in standard ETDRS VA between non-advanced AMD (0.06 ± 0.02 logMAR) and normal groups (0.04 ± 0.02 logMAR) (p = 0.57). LL 2.0 ETDRS VA and MNREAD showed no difference between the groups (p > 0.05). Ora-VCF™ test was significantly worse in the non-advanced AMD group compared to normals (0.67 ± 0.07 in AMD; 0.45 ± 0.04 in normals, p = 0.005). Non-advanced AMD subjects also had significantly worse reading performance using the Ora-tablet with LL 2.0ND (114.55 ± 11.22 wpm in AMD; 145.17 ± 9.55 wpm in normals p = 0.049). No significant difference between the groups was noted using other tests. Repeatability was 82% for Ora-VCF™ test and 92% for Ora-tablet LL 2.0ND reading. Reproducibility was 89% for both Ora-VCF™ test and Ora-tablet LL 2.0ND reading. CONCLUSION: While there was no significant difference between non-advanced AMD and normal groups using some current common endpoints such as ETDRS VA, LL 2.0 ETDRS VA or MNREAD, Ora-VCF™ test and Ora-tablet LL 2.0ND reading tests were able to identify significant visual dysfunction in non-advanced AMD subjects. These tests show promise as endpoints for AMD studies.

Identification of Antibody Targets for Tuberculosis Serology using High-Density Nucleic Acid Programmable Protein Arrays.

Better and more diverse biomarkers for the development of simple point-of-care tests for active tuberculosis (TB), a clinically heterogeneous disease, are urgently needed. We generated a proteomic Mycobacterium tuberculosis (Mtb) High-Density Nucleic Acid Programmable Protein Array (HD-NAPPA) that used a novel multiplexed strategy for expedited high-throughput screening for antibody responses to the Mtb proteome. We screened sera from HIV uninfected and coinfected TB patients and controls (n = 120) from the US and South Africa (SA) using the multiplex HD-NAPPA for discovery, followed by deconvolution and validation through single protein HD-NAPPA with biologically independent samples (n = 124). We verified the top proteins with enzyme-linked immunosorbent assays (ELISA) using the original screening and validation samples (n = 244) and heretofore untested samples (n = 41). We identified 8 proteins with TB biomarker value; four (Rv0054, Rv0831c, Rv2031c and Rv0222) of these were previously identified in serology studies, and four (Rv0948c, Rv2853, Rv3405c, Rv3544c) were not known to elicit antibody responses. Using ELISA data, we created classifiers that could discriminate patients' TB status according to geography (US or SA) and HIV (HIV- or HIV+) status. With ROC curve analysis under cross validation, the classifiers performed with an AUC for US/HIV- at 0.807; US/HIV+ at 0.782; SA/HIV- at 0.868; and SA/HIV+ at 0.723. With this study we demonstrate a new platform for biomarker/antibody screening and delineate its utility to identify previously unknown immunoreactive proteins.

Tracking the Antibody Immunome in Type 1 Diabetes Using Protein Arrays.

We performed an unbiased proteome-scale profiling of humoral autoimmunity in recent-onset type 1 diabetes (T1D) patients and nondiabetic controls against ∼10 000 human proteins using a Nucleic Acid Programmable Protein Array (NAPPA) platform, complemented by a knowledge-based selection of proteins from genes enriched in human pancreas. Although the global response was similar between cases and controls, we identified and then validated six specific novel T1D-associated autoantibodies (AAbs) with sensitivities that ranged from 16 to 27% at 95% specificity. These included AAbs against PTPRN2, MLH1, MTIF3, PPIL2, NUP50 (from NAPPA screening), and QRFPR (by targeted ELISA). Immunohistochemistry demonstrated that NUP50 protein behaved differently in islet cells, where it stained both nucleus and cytoplasm, compared with only nuclear staining in exocrine pancreas. Conversely, PPIL2 staining was absent in islet cells, despite its presence in exocrine cells. The combination of anti-PTPRN2, -MLH1, -PPIL2, and -QRFPR had an AUC of 0.74 and 37.5% sensitivity at 95% specificity. These data indicate that these markers behave independently and support the use of unbiased screening to find biomarkers because the majority was not predicted based on predicted abundance. Our study enriches the knowledge of the "autoantibody-ome" in unprecedented breadth and width.

Automatic Identification and Quantification of Extra-Well Fluorescence in Microarray Images.

In recent studies involving NAPPA microarrays, extra-well fluorescence is used as a key measure for identifying disease biomarkers because there is evidence to support that it is better correlated with strong antibody responses than statistical analysis involving intraspot intensity. Because this feature is not well quantified by traditional image analysis software, identification and quantification of extra-well fluorescence is performed manually, which is both time-consuming and highly susceptible to variation between raters. A system that could automate this task efficiently and effectively would greatly improve the process of data acquisition in microarray studies, thereby accelerating the discovery of disease biomarkers. In this study, we experimented with different machine learning methods, as well as novel heuristics, for identifying spots exhibiting extra-well fluorescence (rings) in microarray images and assigning each ring a grade of 1-5 based on its intensity and morphology. The sensitivity of our final system for identifying rings was found to be 72% at 99% specificity and 98% at 92% specificity. Our system performs this task significantly faster than a human, while maintaining high performance, and therefore represents a valuable tool for microarray image analysis.

Autoantibody biomarkers for the detection of serous ovarian cancer.

Objective The purpose of this study was to identify a panel of novel serum tumor antigen-associated autoantibody (TAAb) biomarkers for the diagnosis of high-grade serous ovarian cancer. METHODS: To detect TAAb we probed high-density programmable protein microarrays (NAPPA) containing 10,247 antigens with sera from patients with serous ovarian cancer (n=30 cases/30 healthy controls) and measured bound IgG. We identified 735 promising tumor antigens and evaluated these with an independent set of serous ovarian cancer sera (n=30 cases/30 benign disease controls/30 healthy controls). Thirty-nine potential tumor autoantigens were identified and evaluated using an orthogonal programmable ELISA platform against a total of 153 sera samples (n=63 cases/30 benign disease controls/60 healthy controls). Sensitivities at 95% specificity were calculated and a classifier for the detection of high-grade serous ovarian cancer was constructed. RESULTS: We identified 11-TAAbs (ICAM3, CTAG2, p53, STYXL1, PVR, POMC, NUDT11, TRIM39, UHMK1, KSR1, and NXF3) that distinguished high-grade serous ovarian cancer cases from healthy controls with a combined 45% sensitivity at 98% specificity. CONCLUSION: These are potential circulating biomarkers for the detection of serous ovarian cancer, and warrant confirmation in larger clinical cohorts.

Autoantibody signature for the serologic detection of ovarian cancer.

Sera from patients with ovarian cancer contain autoantibodies (AAb) to tumor-derived proteins that are potential biomarkers for early detection. To detect AAb, we probed high-density programmable protein microarrays (NAPPA) expressing 5177 candidate tumor antigens with sera from patients with serous ovarian cancer (n = 34 cases/30 controls) and measured bound IgG. Of these, 741 antigens were selected and probed with an independent set of ovarian cancer sera (n = 60 cases/60 controls). Twelve potential autoantigens were identified with sensitivities ranging from 13 to 22% at >93% specificity. These were retested using a Luminex bead array using 60 cases and 60 controls, with sensitivities ranging from 0 to 31.7% at 95% specificity. Three AAb (p53, PTPRA, and PTGFR) had area under the curve (AUC) levels >60% (p < 0.01), with the partial AUC (SPAUC) over 5 times greater than for a nondiscriminating test (p < 0.01). Using a panel of the top three AAb (p53, PTPRA, and PTGFR), if at least two AAb were positive, then the sensitivity was 23.3% at 98.3% specificity. AAb to at least one of these top three antigens were also detected in 7/20 sera (35%) of patients with low CA 125 levels and 0/15 controls. AAb to p53, PTPRA, and PTGFR are potential biomarkers for the early detection of ovarian cancer.

Safety and patterns of survivorship in recurrent GBM following resection and surgically targeted radiation therapy: Results from a prospective trial.

BACKGROUND: Treatment of recurrent glioblastoma (GBM) remains problematic with survival after additional therapy typically less than 12 months. We prospectively evaluated whether outcomes might be improved with resection plus permanent implantation of a novel radiation device utilizing the gamma-emitting isotope Cs-131 embedded within bioresorbable collagen tiles. METHODS: Recurrent histologic GBM were treated in a single-arm trial. Following radiation, the surgical bed was lined with the tiles. Subsequent treatments were at the treating physician's discretion. RESULTS: 28 patients were treated (20 at first recurrence, range 1-3). Median age was 58 years, KPS was 80, female:male ratio was 10:18. Methylguanine methyltransferase (MGMT) was methylated in 11%, unmethylated in 18%, and unknown in 71%. Post implant, 17 patients (61%) received ≥1 course of systemic therapy. For all patients, Kaplan-Meier estimates of median time to local failure were 12.1 months, post-implant survival was 10.7 months for all patients and 15.1 months for patients who received systemic therapy; for all patients, median overall survival from diagnosis was 25.0 months (range 9.1-143.1). Sex, age, and number of prior progressions were not statistically significant. Local control was continuously maintained in 46% of patients. Two deaths within 30 days occurred, one from intracranial hemorrhage and one after persistent coma. Three symptomatic adverse events occurred: one wound infection requiring surgery and two late radiation brain injury, resolved non-surgically. CONCLUSION: This pre-commercial trial demonstrated acceptable safety and favorable post-treatment local control and survival. The device has received FDA clearance for use in newly diagnosed malignant and all recurrent intracranial neoplasms.

Protein microarray signature of autoantibody biomarkers for the early detection of breast cancer.

Cancer patients spontaneously generate autoantibodies (AAb) to tumor-derived proteins. To detect AAb, we have probed novel high-density custom protein microarrays (NAPPA) expressing 4988 candidate tumor antigens with sera from patients with early stage breast cancer (IBC), and bound IgG was measured. We used a three-phase serial screening approach. First, a prescreen was performed to eliminate uninformative antigens. Sera from stage I-III IBC (n = 53) and healthy women (n = 53) were screened for AAb to all 4988 protein antigens. Antigens were selected if the 95th percentile of signal of cases and controls were significantly different (p < 0.05) and if the number of cases with signals above the 95th percentile of controls was significant (p < 0.05). These 761 antigens were screened using an independent set of IBC sera (n = 51) and sera from women with benign breast disease (BBD) (n = 39). From these, 119 antigens had a partial area under the ROC curve (p < 0.05), with sensitivities ranging from 9-40% at >91% specificity. Twenty-eight of these antigens were confirmed using an independent serum cohort (n = 51 cases/38 controls, p < 0.05). Using all 28 AAb, a classifier was identified with a sensitivity of 80.8% and a specificity of 61.6% (AUC = 0.756). These are potential biomarkers for the early detection of breast cancer.

Early Ophthalmic Changes in Macula Does Not Correlate with Visual Function.

PURPOSE: Early detection and treatment of age-related macular degeneration require a clear understanding of the early progress of the disease. The purpose of this study was to investigate whether minimal macular ophthalmoscopic changes corresponded to changes in visual function. METHODS: Color macular photos from a group of older subjects who were classified as grade 0 on AREDS simplified grading were further evaluated by a retinal specialist using 5x magnification for possible minimal macular anomalies. Group 0-A (N = 15) were defined as subjects with no visible macular anomalies while Group 0-B (N = 19) comprised subjects for whom minimal macular mottling, pigment changes or very small drusen (< 63 µm) were observed in the study eye. All subjects had best VA of 20/25 or better and had no evidence of other retinal diseases in the study eye. All subjects underwent a series of visual function tests such as standard ETDRS VA, low luminance ETDRS VA, Pelli-Robson contrast sensitivity, variable contrast flicker (VCF) sensitivity, and reading speed (words per minute, wpm) using both MNRead and low luminance reading on a tablet. RESULTS: There was no significant difference between the mean age between the two groups (74.8 ± 5.2 years for 0-A vs 74.5 ± 4.4 for 0-B, p = 0.82). None of the visual function tests identified any significant difference between the two groups. Mean ETDRS VA was 0.0 ± 0.11 for 0-A subjects and 0.08 ± 0.12 for 0-B (p = 0.063). Mean Pelli-Robson log contrast sensitivity was 1.75 ± 0.29 for 0-A and 1.78 ± 0.17 for the 0-B group (p = 0.73). VCF threshold was 0.47 ± 0.25 for 0-A and 0.43 ± 0.22 for 0-B (p = 0.64). Reading speed using MNRead was 214 ± 47.4 wpm for 0-A and 210 ± 64.7 for 0-B (p = 0.85). Low luminance tablet reading speed was 137 ± 71.8 wpm for 0-A and 151 ± 39.4 (0-B) (p = 0.49). CONCLUSION: A panel of psychophysical tests did not demonstrate significant differences between subjects with and without minimal macular changes.

Resection and Surgically Targeted Radiation Therapy for the Treatment of Larger Recurrent or Newly Diagnosed Brain Metastasis: Results From a Prospective Trial.

Introduction Achieving durable local control (LC) for larger (e.g., >2-3 cm) brain metastasis whether newly diagnosed or recurrent remains problematic. Resection (R) alone is typically insufficient and adding radiation therapy (RT) still results in a 12-month recurrence rate of 20% or more in many series. Hypothesizing that R plus immediate radiation utilizing brachytherapy may improve outcomes for this cohort of patients, we designed and prospectively evaluated a permanently implanted surgically targeted radiation therapy (STaRT) device consisting of cesium-131 (Cs-131) seeds positioned within a collagen carrier (GammaTile, GT Medical Technologies, Tempe, AZ). The device was designed to prevent direct source-to-brain contact and maintain inter-source spacing after closure. Methods This was a subgroup analysis of a cohort of patients with either recurrent or previously untreated brain metastases enrolled in a prospective, multi-histology single-arm trial (ClinicalTrials.gov, NCT#03088579), conducted between February 2013 and February 2018, of resection and tumor bed brachytherapy with Cs-131 containing permanently implanted collagen tiles to deliver 60 Gray (Gy) at .5 cm depth. No additional local therapy was given without progression. Results A total of 16 metastases in 11 patients were treated; 12 tumors were recurrent and four were previously untreated. The median preoperative maximum diameter was 3.2 cm (range: 1.9-5.1 cm). Histology was seven breasts, six lungs, and three sarcomas. The median age was 60 years (range: 41-80 years); the Karnofsky Performance Status (KPS) was 70 (range: 70-90). The cohort consisted of seven females and four males. The mean time for implantation completion was five minutes. The median overall survival (OS) was 9.3 months. At a median radiographic follow-up of 9.5 months' treatment, site progression was found in 1/16 (6%) at 10.9 months, and the median treatment site time-to-progression (TTP) has not been reached [95% confidence interval (CI): >10.9 months]. At 12 months, the Kaplan-Meier (K-M) estimates for LC after R+STaRT for all tumors was 83%; for previously untreated tumors, LC at 12 months was 100% and for recurrent tumors, it was 80%. Two tumor beds (12.5%) experienced radiation brain changes: one had grade two and the other grade three. No surgical adverse events occurred. Conclusion In this single-arm precommercial study, R+STaRT demonstrated excellent safety and LC in this cohort. The device has recently received FDA clearance for use in newly diagnosed and recurrent brain metastasis, and randomized clinical trials vs. standard of care treatments in both settings are scheduled to open in 2020.

Bayesian prediction of an epidemic curve.

An epidemic curve is a graph in which the number of new cases of an outbreak disease is plotted against time. Epidemic curves are ordinarily constructed after the disease outbreak is over. However, a good estimate of the epidemic curve early in an outbreak would be invaluable to health care officials. Currently, techniques for predicting the severity of an outbreak are very limited. As far as predicting the number of future cases, ordinarily epidemiologists simply make an educated guess as to how many people might become affected. We develop a model for estimating an epidemic curve early in an outbreak, and we show results of experiments testing its accuracy.

Measuring the effect of commuting on the performance of the Bayesian Aerosol Release Detector.

BACKGROUND: Early detection of outdoor aerosol releases of anthrax is an important problem. The Bayesian Aerosol Release Detector (BARD) is a system for detecting releases of aerosolized anthrax and characterizing them in terms of location, time and quantity. Modelling a population's exposure to aerosolized anthrax poses a number of challenges. A major difficulty is to accurately estimate the exposure level--the number of inhaled anthrax spores--of each individual in the exposed region. Partly, this difficulty stems from the lack of fine-grained data about the population under surveillance. To cope with this challenge, nearly all anthrax biosurveillance systems, including BARD, ignore the mobility of the population and assume that exposure to anthrax would occur at one's home administrative unit--an assumption that limits the fidelity of the model. METHODS: We employed commuting data provided by the U.S. Census Bureau to parameterize a commuting model. Then, we developed methods for integrating commuting into BARD's simulation and detection algorithms and conducted two studies to measure the effect. The first study (simulation study) was designed to assess how BARD's detection and characterization performance are impacted by incorporation of commuting in BARD's outbreak-simulation algorithm. The second study (detection study) was designed to measure the effect of incorporating commuting in BARD's outbreak-detection algorithm. RESULTS: We found that failing to account for commuting in detection (when commuting is present in simulation) leads to a deterioration in BARD's detection and characterization performance that is both statistically and practically significant. We found that a simplified approach to accounting for commuting in detection--simplified to maintain tractability of inference--nearly fully restored both detection and characterization performance of BARD detector. CONCLUSION: We conclude that it is important to account for commuting (and mobility in general) in BARD's simulation algorithm. Further, the proposed method for incorporating commuting in BARD's detection algorithm can successfully perform the necessary correction in the detection algorithm, while preserving BARD's practicality. In our future work, we intend to further study the problem of the trade-off between running time and accuracy of the computation in BARD's version that includes commuting and ultimately find the best such trade-off.

Mining aggregates of over-the-counter products for syndromic surveillance.

There has recently been a surge of research efforts aimed at very early detection of disease outbreaks. An important strategy for improving the timeliness of outbreak detection is to identify signals that occur early in the epidemic process. We have developed a novel algorithm to identify aggregates of "similar" over-the-counter products that have strong association with a given disease. This paper discusses the proposed algorithm and reports the results of an evaluation experiment. The experimental results show that this algorithm holds promise for discovering product aggregates with outbreak detection performance that is superior to that of predefined categories. We also found that the products extracted by the proposed algorithm were more strongly correlated with the disease data than the standard predefined product categories, while also being more strongly correlated with each other than the products in any predefined category.

An Alternative Psychophysical Diagnostic Indicator of the Aging Eye.

PURPOSE: Impaired adaptation to changes in lighting levels as well as mesopic visual function is a common complaint in those over the age of 65. The use of photostress is a well-established method to test the adaption rate and the response of the visual cycle. In this study, we test visual function recovery to mesopic luminance stimuli following a long duration photostress in young and elderly subjects. If successful in strongly differentiating aging macular function, these methods may also be useful in the study of pathologies such as age-related macular degeneration. METHODS: A group of 12 older normal subjects (mean age 75.1 ± 4.79) and a control group of 5 younger normal subjects (mean age 26.2 ± 4.19) were subjected to macular photostress using the OraLux photostress system. The OraLux system provides a diffuse light source bleaching 84% of cone photopigment while maintaining an exposure safety factor of 200 times less than the maximum safe exposure. After each photostressing session, macular recovery was tracked using a foveal, variable contrast, flickering stimulus of mean luminance in the high mesopic range. Recovery was tracked for 300 seconds. The endpoint was time to recovery to each individual's baseline sensitivity as determined by two static sensitivity trials prior to photostress. RESULTS: Proportional hazards analysis of recovery time yielded a statistically significant difference between the older group and the young group (HR = 0.181; p=0.0289). The estimated hazard ratio of 0.181 indicates that older subjects return to baseline at less than one-fifth the rate of younger subjects. The hazards ratio remained statistically significant after adjusting for visual acuity (HR = 0.093; p=0.0424). CONCLUSION: Photostress recovery of flicker sensitivity under mesopic conditions is a strong differentiator of aging macular function. This agrees with subject-reported complaints in reduced luminance conditions after exposure to bright lights such as night driving. The qualitative similarity between the aging retina and changes in early AMD suggests that flicker recovery following photostress may be useful as a surrogate endpoint in AMD clinical trials.

Ventricular volume expansion in presymptomatic genetic frontotemporal dementia.

OBJECTIVE: To characterize the time course of ventricular volume expansion in genetic frontotemporal dementia (FTD) and identify the onset time and rates of ventricular expansion in presymptomatic FTD mutation carriers. METHODS: Participants included patients with a mutation in MAPT, PGRN, or C9orf72, or first-degree relatives of mutation carriers from the GENFI study with MRI scans at study baseline and at 1 year follow-up. Ventricular volumes were obtained from MRI scans using FreeSurfer, with manual editing of segmentation and comparison to fully automated segmentation to establish reliability. Linear mixed models were used to identify differences in ventricular volume and in expansion rates as a function of time to expected disease onset between presymptomatic carriers and noncarriers. RESULTS: A total of 123 participants met the inclusion criteria and were included in the analysis (18 symptomatic carriers, 46 presymptomatic mutation carriers, and 56 noncarriers). Ventricular volume differences were observed 4 years prior to symptom disease onset for presymptomatic carriers compared to noncarriers. Annualized rates of ventricular volume expansion were greater in presymptomatic carriers relative to noncarriers. Importantly, time-intensive manually edited and fully automated ventricular volume resulted in similar findings. CONCLUSIONS: Ventricular volume differences are detectable in presymptomatic genetic FTD. Concordance of results from time-intensive manual editing and fully automatic segmentation approaches support its value as a measure of disease onset and progression in future studies in both presymptomatic and symptomatic genetic FTD.

An Implementation of Bayesian Adaptive Regression Splines (BARS) in C with S and R Wrappers.

BARS (DiMatteo, Genovese, and Kass 2001) uses the powerful reversible-jump MCMC engine to perform spline-based generalized nonparametric regression. It has been shown to work well in terms of having small mean-squared error in many examples (smaller than known competitors), as well as producing visually-appealing fits that are smooth (filtering out high-frequency noise) while adapting to sudden changes (retaining high-frequency signal). However, BARS is computationally intensive. The original implementation in S was too slow to be practical in certain situations, and was found to handle some data sets incorrectly. We have implemented BARS in C for the normal and Poisson cases, the latter being important in neurophysiological and other point-process applications. The C implementation includes all needed subroutines for fitting Poisson regression, manipulating B-splines (using code created by Bates and Venables), and finding starting values for Poisson regression (using code for density estimation created by Kooperberg). The code utilizes only freely-available external libraries (LAPACK and BLAS) and is otherwise self-contained. We have also provided wrappers so that BARS can be used easily within S or R.

Cone photoreceptor macular function and recovery after photostress in early non-exudative age-related macular degeneration.

PURPOSE: To identify parameters from cone function and recovery after photostress that detect functional deficits in early non-exudative age-related macular degeneration (AMD) and to determine the repeatability of these parameters. METHODS: Cone-mediated visual function recovery after photostress was examined in three groups of subjects: young normal subjects (ages 20-29; N=8), older normal subjects (ages 50-90; N=9), and early non-exudative AMD subjects (ages 50-90; N=12). Eight AMD and four normal subjects were retested 1 year after the initial evaluation. Early Treatment Diabetic Retinopathy Study (ETDRS) visual acuity (VA) and parameters of cone function (baseline cone sensitivity and cone recovery half-life following photobleach) were measured and compared between AMD and normal subjects. Short-term repeatability was assessed for each subject's initial evaluation. Long-term repeatability was assessed by comparing outcomes from the initial evaluation and 1-year follow-up. RESULTS: The mean baseline cone threshold was significantly worse in subjects with early AMD compared to older normal subjects (-1.80±0.04 vs -1.57±0.06 log cd/m2p=0.0027). Moreover, the baseline cone threshold parameter exhibited good short-term (intraclass correlation coefficient [ICC]=0.88) and long-term (ICC=0.85) repeatability in all subjects. The cone intercept parameter and ETDRS VA were not significantly different between AMD and older normal subject groups. Cone recovery half-life was significantly different between older normal and AMD subject groups (p=0.041). Neither ETDRS VA nor cone function parameters were significantly different for any group at the 1-year follow-up. CONCLUSION: The baseline cone threshold shows potential as a novel parameter to assess visual dysfunction in early AMD. This outcome consistently detected deficits in AMD subjects, and differentiated them from age-matched controls with high test-retest repeatability.

Unsupervised clustering of over-the-counter healthcare products into product categories.

A general problem in biosurveillance is finding appropriate aggregates of elemental data to monitor for the detection of disease outbreaks. We developed an unsupervised clustering algorithm for aggregating over-the-counter healthcare (OTC) products into categories. This algorithm employs MCMC over hundreds of parameters in a Bayesian model to place products into clusters. Despite the high dimensionality, it still performs fast on hundreds of time series. The procedure was able to uncover a clinically significant distinction between OTC products intended for the treatment of allergy and OTC products intended for the treatment of cough, cold, and influenza symptoms.

Developing Human Radiation Biodosimetry Models: Testing Cross-Species Conversion Approaches Using an Ex Vivo Model System.

In the event of a large-scale radiation exposure, accurate and quick assessment of radiation dose received would be critical for triage and medical treatment of large numbers of potentially exposed individuals. Current methods of biodosimetry, such as the dicentric chromosome assay, are time consuming and require sophisticated equipment and highly trained personnel. Therefore, scalable biodosimetry approaches, including gene expression profiles in peripheral blood cells, are being investigated. Due to the limited availability of appropriate human samples, biodosimetry development has relied heavily on mouse models, which are not directly applicable to human response. Therefore, to explore the feasibility of using non-human primate (NHP) models to build and test a biodosimetry algorithm for use in humans, we irradiated ex vivo peripheral blood samples from both humans and rhesus macaques with doses of 0, 2, 5, 6 and 7 Gy, and compared the gene expression profiles 24 h later using Agilent human microarrays. Among the dose-responsive genes in human and using non-human primate, 52 genes showed highly correlated expression patterns between the species, and were enriched in p53/DNA damage response, apoptosis and cell cycle-related genes. When these interspecies-correlated genes were used to build biodosimetry models with using NHP data, the mean prediction accuracy on non-human primate samples was about 90% within 1 Gy of delivered dose in leave-one-out cross-validation. However, tests on human samples suggested that human gene expression values may need to be adjusted prior to application of the NHP model. A "multi-gene" approach utilizing all gene values for cross-species conversion and applying the converted values on the NHP biodosimetry models, gave a leave-one-out cross-validation prediction accuracy for human samples highly comparable (up to 94%) to that for non-human primates. Overall, this study demonstrates that a robust NHP biodosimetry model can be built using interspecies-correlated genes, and that, by using multiple regression-based cross-species conversion of expression values, absorbed dose in human samples can be accurately predicted by the NHP model.

Identification of Antibody Against SNRPB, Small Nuclear Ribonucleoprotein-Associated Proteins B and B', as an Autoantibody Marker in Crohn's Disease using an Immunoproteomics Approach.

BACKGROUND: Current non-invasive biomarkers for Crohn's disease are limited in their utility. Progress in identifying individual autoantigens and autoantibodies in Crohn's disease has been challenging due to limitations of available immunoassays. AIMS: Our aim was to identify autoantibodies associated with Crohn's disease that may be useful in diagnosis and management using an innovative protein array technology, namely nucleic acid programmable protein arrays [NAPPA]. METHODS: Serum samples of 96 patients with established Crohn's disease and 96 healthy controls were included and evenly split into discovery and validation sets randomly. Autoantibodies of both IgG and IgA classes were profiled against ~1900 human proteins in the discovery set on NAPPA. Autoantibodies discovered to be Crohn's disease-specific were further validated in the independent validation set by enzyme-linked immunosorbent assay. RESULTS: Overall, reactivity of IgG autoantibodies was stronger than that of IgA autoantibodies; however, IgA autoantibodies showed greater differential reactivity between cases and controls. Four IgA autoantibodies against SNRPB, PRPH, PTTG1 and SNAI1 were newly identified with sensitivities above 15% at 95% specificity, among which anti-SNRPB-IgA had the highest sensitivity of 24.0%. Autoantibodies associated with specific disease subtypes were also found. CONCLUSIONS: As one of the first studies to use immunoproteomics for the identification of autoantibodies in Crohn's disease, our results support the utility of NAPPA in implementing future expanded studies with better coverage of the human proteome and microbial proteomes relevant to Crohn's disease and identifying antibody markers that may have clinical impact in diagnosis and management.