Impending aortic aneurysm rupture - a case report and review of the warning signs.

Abdominal aortic aneurysm (AAA) may present with subtle clinical findings. Recognition of the imaging features of an impending rupture is key for timely diagnosis. This report reviews the classic computed tomography findings of impending AAA rupture and presents a recent case which illustrates the key features.

Targeting of radiolabeled J591 antibody to PSMA-expressing tumors: optimization of imaging and therapy based on non-linear compartmental modeling.

BACKGROUND: We applied a non-linear immunokinetic model to quantitatively compare absolute antibody uptake and turnover in subcutaneous LNCaP human prostate cancer (PCa) xenografts of two radiolabeled forms of the humanized anti-prostate-specific membrane antigen (PSMA) monoclonal antibody J591 ((124)I-J591 and (89)Zr-J591). Using the model, we examined the impact of dose on the tumor and plasma positron emission tomography (PET)-derived time-activity curves. We also sought to predict the optimal targeting index (ratio of integrated-tumor-to-integrated-plasma activity concentrations) for radioimmunotherapy. METHODS: The equilibrium rates of antibody internalization and turnover in the tumors were derived from PET images up to 96 h post-injection using compartmental modeling with a non-linear transfer rate. In addition, we serially imaged groups of LNCaP tumor-bearing mice injected with (89)Zr-J591 antibody doses ranging from antigen subsaturating to saturating to examine the suitability of using a non-linear approach and derived the time-integrated concentration (in µM∙hours) of administered tracer in tumor as a function of the administered dose of antibody. RESULTS: The comparison of (124)I-J591 and (89)Zr-J591 yielded similar model-derived values of the total antigen concentration and internalization rate. The association equilibrium constant (k a) was twofold higher for (124)I, but there was a ~tenfold greater tumoral efflux rate of (124)I from tumor compared to that of (89)Zr. Plots of surface-bound and internalized radiotracers indicate similar behavior up to 24 h p.i. for both (124)I-J591 and (89)Zr-J591, with the effect of differential clearance rates becoming apparent after about 35 h p.i. Estimates of J591/PSMA complex turnover were 3.9-90.5 × 10(12) (for doses from 60 to 240 µg) molecules per hour per gram of tumor (20 % of receptors internalized per hour). CONCLUSIONS: Using quantitative compartmental model methods, surface binding and internalization rates were shown to be similar for both (124)I-J591 and (89)Zr-J591 forms, as expected. The large difference in clearance rates of the radioactivity from the tumor is likely due to differential trapping of residualizing zirconium versus non-residualizing iodine. Our non-linear model was found to be superior to a conventional linear model. This finding and the calculated activity persistence time in tumor have important implications for radioimmunotherapy and other antibody-based therapies in patients.

Utility of FDG-PET in clinical neuroendocrine prostate cancer.

BACKGROUND: Fluorodeoxyglucose (FDG) positron emission tomography (PET) has well-characterized limitations in prostate adenocarcinoma (PCA). However, data assessing the utility of PET in neuroendocrine prostate cancer (NEPC) is limited to isolated case reports. Herein, we describe the first case series to assess the utility of FDG-PET in NEPC. METHODS: Inclusion criteria consisted of clinically progressive metastatic PCA in the setting of a chromogranin-A levels >1.5× the upper limit of normal, and ≥1 FDG-PET scan after the diagnosis of NEPC, which yielded 23 patients. All metastatic lesions on CT, PET, and bone scan were read by two independent physicians. RESULTS: Five hundred ninety two unique lesions were identified across all imaging modalities, 510 were bone metastases, and 82 were soft tissue metastases. Of bone lesions, 22.2%, 92.7%, and 77.6% were detected by PET, CT, and bone scan, respectively. Of soft tissue lesions, 95.1% and 97.5% were detected by PET and CT, respectively. Stratified by the median survival from NEPC diagnosis, patients who survived <2.2 versus ≥2.2 years had more PET avid bone (8 vs. 2, P = 0.06) and soft tissue lesions (7 vs. 1, P = 0.01), and higher average SUVmax of bone (5.49 vs. 3.40, P = 0.04) and soft tissue lesions (8.02 vs. 3.90, P = 0.0002). CONCLUSIONS: In patients with clinical NEPC, we demonstrate that FDG-PET has clinical utility in the detection of metastatic disease. In addition to detection, PET allows for treatment response to determine tumor viability. With novel therapies on the horizon to treat NEPC, consideration to investigate the use of FDG-PET to monitor response is warranted.

Correction of faulty sensors in phased array radars using symmetrical sensor failure technique and cultural algorithm with differential evolution.

Three issues regarding sensor failure at any position in the antenna array are discussed. We assume that sensor position is known. The issues include raise in sidelobe levels, displacement of nulls from their original positions, and diminishing of null depth. The required null depth is achieved by making the weight of symmetrical complement sensor passive. A hybrid method based on memetic computing algorithm is proposed. The hybrid method combines the cultural algorithm with differential evolution (CADE) which is used for the reduction of sidelobe levels and placement of nulls at their original positions. Fitness function is used to minimize the error between the desired and estimated beam patterns along with null constraints. Simulation results for various scenarios have been given to exhibit the validity and performance of the proposed algorithm.

A prospective pilot study of (89)Zr-J591/prostate specific membrane antigen positron emission tomography in men with localized prostate cancer undergoing radical prostatectomy.

PURPOSE: In this pilot study we explored the feasibility of (89)Zr labeled J591 monoclonal antibody positron emission tomography of localized prostate cancer. MATERIALS AND METHODS: Before scheduled radical prostatectomy 11 patients were injected intravenously with (89)Zr-J591, followed 6 days later by whole body positron emission tomography. Patients underwent surgery the day after imaging. Specimens were imaged by ex vivo micro positron emission tomography and a custom 3 Tesla magnetic resonance scanner coil. Positron emission tomography images and histopathology were correlated. RESULTS: Median patient age was 61 years (range 47 to 68), median prostate specific antigen was 5.2 ng/ml (range 3.5 to 12.0) and median biopsy Gleason score of the 11 index lesions was 7 (range 7 to 9). On histopathology 22 lesions were identified. Median lesion size was 5.5 mm (range 2 to 21) and median Gleason score after radical prostatectomy was 7 (range 6 to 9). Eight of 11 index lesions (72.7%) were identified by in vivo positron emission tomography. Lesion identification improved with increasing lesion size for in vivo and ex vivo positron emission tomography (each p <0.0001), and increasing Gleason score (p = 0.14 and 0.01, respectively). Standardized uptake values appeared to correlate with increased Gleason score but not significantly (p = 0.19). CONCLUSIONS: To our knowledge this is the first report of (89)Zr-J591/prostate specific membrane antigen positron emission tomography in localized prostate cancer cases. In this setting (89)Zr-J591 bound to tumor foci in situ and positron emission tomography identified primarily Gleason score 7 or greater and larger tumors, likely corresponding to clinically significant disease warranting definitive therapy. A future, larger clinical validation trial is planned to better define the usefulness of (89)Zr-J591 positron emission tomography for localized prostate cancer.

PSMA-targeted dendrimers: a patent evaluation (WO2012078534).

Prostate-specific membrane antigen (PSMA) is a cell surface protein expressed primarily in prostate cancer and solid tumor neovasculature. PSMA has been a target for both imaging and therapeutic investigations due to its increased expression in advanced disease and in prostate cancer patients receiving hormonal therapy. The recent approval of new androgen receptor pathway targeted drugs for prostate cancer has sharply increased this interest in creating novel probes to measure this disease-specific biomarker. Monoclonal antibodies and small molecules targeting PSMA, however, have been investigated for diagnostic and therapeutic purposes, with only limited success. In fact, only one agent (monoclonal antibody agent - capromab pendetide) has been FDA-approved for human use. PSMA-targeting dendrimers may provide a targeting method that might maintain the specificity of monoclonal antibodies, achieve high levels of tumor concentration (similar to small molecule antagonist ligands) and avoid accumulation in non-target sites of PSMA expression (such as renal tubules). The current patent application provides theoretical evidence for the advantage of PSMA-targeting dendrimers with in vitro data for PSMA binding, but lacks sufficient preclinical animal binding, dosimetry and toxicology data.

DLocalMotif: a discriminative approach for discovering local motifs in protein sequences.

MOTIVATION: Local motifs are patterns of DNA or protein sequences that occur within a sequence interval relative to a biologically defined anchor or landmark. Current protein motif discovery methods do not adequately consider such constraints to identify biologically significant motifs that are only weakly over-represented but spatially confined. Using negatives, i.e. sequences known to not contain a local motif, can further increase the specificity of their discovery. RESULTS: This article introduces the method DLocalMotif that makes use of positional information and negative data for local motif discovery in protein sequences. DLocalMotif combines three scoring functions, measuring degrees of motif over-representation, entropy and spatial confinement, specifically designed to discriminatively exploit the availability of negative data. The method is shown to outperform current methods that use only a subset of these motif characteristics. We apply the method to several biological datasets. The analysis of peroxisomal targeting signals uncovers several novel motifs that occur immediately upstream of the dominant peroxisomal targeting signal-1 signal. The analysis of proline-tyrosine nuclear localization signals uncovers multiple novel motifs that overlap with C2H2 zinc finger domains. We also evaluate the method on classical nuclear localization signals and endoplasmic reticulum retention signals and find that DLocalMotif successfully recovers biologically relevant sequence properties. AVAILABILITY: http://bioinf.scmb.uq.edu.au/dlocalmotif/

A probabilistic model of nuclear import of proteins.

MOTIVATION: Nucleo-cytoplasmic trafficking of proteins is a core regulatory process that sustains the integrity of the nuclear space of eukaryotic cells via an interplay between numerous factors. Despite progress on experimentally characterizing a number of nuclear localization signals, their presence alone remains an unreliable indicator of actual translocation. RESULTS: This article introduces a probabilistic model that explicitly recognizes a variety of nuclear localization signals, and integrates relevant amino acid sequence and interaction data for any candidate nuclear protein. In particular, we develop and incorporate scoring functions based on distinct classes of classical nuclear localization signals. Our empirical results show that the model accurately predicts whether a protein is imported into the nucleus, surpassing the classification accuracy of similar predictors when evaluated on the mouse and yeast proteomes (area under the receiver operator characteristic curve of 0.84 and 0.80, respectively). The model also predicts the sequence position of a nuclear localization signal and whether it interacts with importin-α. AVAILABILITY: http://pprowler.itee.uq.edu.au/NucImport

Automatic, context-specific generation of Gene Ontology slims.

BACKGROUND: The use of ontologies to control vocabulary and structure annotation has added value to genome-scale data, and contributed to the capture and re-use of knowledge across research domains. Gene Ontology (GO) is widely used to capture detailed expert knowledge in genomic-scale datasets and as a consequence has grown to contain many terms, making it unwieldy for many applications. To increase its ease of manipulation and efficiency of use, subsets called GO slims are often created by collapsing terms upward into more general, high-level terms relevant to a particular context. Creation of a GO slim currently requires manipulation and editing of GO by an expert (or community) familiar with both the ontology and the biological context. Decisions about which terms to include are necessarily subjective, and the creation process itself and subsequent curation are time-consuming and largely manual. RESULTS: Here we present an objective framework for generating customised ontology slims for specific annotated datasets, exploiting information latent in the structure of the ontology graph and in the annotation data. This framework combines ontology engineering approaches, and a data-driven algorithm that draws on graph and information theory. We illustrate this method by application to GO, generating GO slims at different information thresholds, characterising their depth of semantics and demonstrating the resulting gains in statistical power. CONCLUSIONS: Our GO slim creation pipeline is available for use in conjunction with any GO-annotated dataset, and creates dataset-specific, objectively defined slims. This method is fast and scalable for application to other biomedical ontologies.

How to improve postgenomic knowledge discovery using imputation.

While microarrays make it feasible to rapidly investigate many complex biological problems, their multistep fabrication has the proclivity for error at every stage. The standard tactic has been to either ignore or regard erroneous gene readings as missing values, though this assumption can exert a major influence upon postgenomic knowledge discovery methods like gene selection and gene regulatory network (GRN) reconstruction. This has been the catalyst for a raft of new flexible imputation algorithms including local least square impute and the recent heuristic collateral missing value imputation, which exploit the biological transactional behaviour of functionally correlated genes to afford accurate missing value estimation. This paper examines the influence of missing value imputation techniques upon postgenomic knowledge inference methods with results for various algorithms consistently corroborating that instead of ignoring missing values, recycling microarray data by flexible and robust imputation can provide substantial performance benefits for subsequent downstream procedures.

Ameliorative missing value imputation for robust biological knowledge inference.

Gene expression data is widely used in various post genomic analyses. The data is often probed using microarrays due to their ability to simultaneously measure the expressions of thousands of genes. The expression data, however, contains significant numbers of missing values, which can impact on subsequent biological analysis. To minimize the impact of these missing values, several imputation algorithms including Collateral Missing Value Estimation (CMVE), Bayesian Principal Component Analysis (BPCA), Least Square Impute (LSImpute), Local Least Square Impute (LLSImpute), and K-Nearest Neighbour (KNN) have been proposed. These algorithms, however, exploit either only the global or local correlation structure of the data, which normally can lead to higher estimation errors. This paper presents an Ameliorative Missing Value Imputation (AMVI) technique which has ability to exploit global/local and positive/negative correlations in a given dataset by automatic selection of the optimal number of predictor genes k using a wrapper non-parametric method based on Monte Carlo simulations. The AMVI technique has CMVE strategy at its core because CMVE has demonstrated improved performance compared to both low variance methods like BPCA, LLSImpute, and high variance methods such as KNN and ZeroImpute, as CMVE exploits positive/negative correlations. The performance of AMVI is compared with CMVE, BPCA, LLSImpute, and KNN by randomly removing between 1% and 15% missing values in eight different ovarian, breast cancer and yeast datasets. Together with the standard NRMS error metric, the True Positive (TP) rate of the significant genes selection, biological significance of the selected genes and the statistical significance test results are presented to investigate the impact of missing values on subsequent biological analysis. The enhanced performance of AMVI was demonstrated by its lower NRMS error, improved TP rate, bio significance of the selected genes and statistical significance test results, when compared with the aforementioned imputation methods across all the datasets. The results show that AMVI adapted to the latent correlation structure of the data and proved to be an effective and robust approach compared with the trial and error methodology for selecting k. The results confirmed that AMVI can be successfully applied to accurately impute missing values prior to any microarray data analysis.

Collateral missing value imputation: a new robust missing value estimation algorithm for microarray data.

MOTIVATION: Microarray data are used in a range of application areas in biology, although often it contains considerable numbers of missing values. These missing values can significantly affect subsequent statistical analysis and machine learning algorithms so there is a strong motivation to estimate these values as accurately as possible before using these algorithms. While many imputation algorithms have been proposed, more robust techniques need to be developed so that further analysis of biological data can be accurately undertaken. In this paper, an innovative missing value imputation algorithm called collateral missing value estimation (CMVE) is presented which uses multiple covariance-based imputation matrices for the final prediction of missing values. The matrices are computed and optimized using least square regression and linear programming methods. RESULTS: The new CMVE algorithm has been compared with existing estimation techniques including Bayesian principal component analysis imputation (BPCA), least square impute (LSImpute) and K-nearest neighbour (KNN). All these methods were rigorously tested to estimate missing values in three separate non-time series (ovarian cancer based) and one time series (yeast sporulation) dataset. Each method was quantitatively analyzed using the normalized root mean square (NRMS) error measure, covering a wide range of randomly introduced missing value probabilities from 0.01 to 0.2. Experiments were also undertaken on the yeast dataset, which comprised 1.7% actual missing values, to test the hypothesis that CMVE performed better not only for randomly occurring but also for a real distribution of missing values. The results confirmed that CMVE consistently demonstrated superior and robust estimation capability of missing values compared with other methods for both series types of data, for the same order of computational complexity. A concise theoretical framework has also been formulated to validate the improved performance of the CMVE algorithm. AVAILABILITY: The CMVE software is available upon request from the authors.

Human adjuvant disease: presentation as a multiple sclerosis-like syndrome.

Twenty-six women had a systemic disease with central nervous system (CNS) involvement at a mean age of 39.2 years (range, 23 to 64 years) after receiving silicone breast implants (n = 25) or silicone fluid injections into breasts (n = 1). The median latency period between breast surgery and onset of symptoms was 5.71 years (range, 3 months to 15 years). All patients had evidence of disseminated CNS lesions; 20 patients also had evidence of peripheral neuropathy. Additional problems included myalgia (n = 24), joint stiffness (n = 23), arthralgia (n = 22), sicca complex (dry eyes and dry mouth) (n = 19), headache (n = 16), skin rash (n = 15), joint swelling (n = 14), Raynaud's phenomena (n = 14), fever (n = 13), hair loss (n = 12), allergies (n = 11), sensitivity to sunlight (n = 10), and lymphadenopathy (n = 9). Magnetic resonance imaging brain scans were abnormal in 22 of 26 patients (21, white matter lesions; 1, ischemic lesions; 4, cerebral atrophy). Spinal tap revealed oligoclonal bands in 18 of 23 patients. Visual evoked responses were delayed in 14 of 23 patients, and autodirected antibodies were detected in 16 of 26. Sural nerve biopsy results showed loss of myelinated fibers in 15 of 15. Seventeen of 24 patients (71%) who had implant removal were found to have grossly ruptured implants. We believe our patients had a new syndrome triggered by the foreign material in their body. This syndrome appears as a systemic inflammatory autoimmune disease with central nervous system involvement resembling multiple sclerosis.

Human papillomaviruses (HPV) are associated with multiple myeloma.

Human papillomaviruses (HPV) are small DNA tumor viruses, and are associated with epithelial neoplasias. Although HPV are believed to be exclusively permissive in terminally differentiated squamous cells, we have previously identified HPV sequences in lymphoid tissues of five patients. Because this result suggested that the range of the host virus cells could include cells of lymphoid origin, we used PCR and in situ hybridization to analyze nonepithelial tissues of patients with multiple myeloma from two institutions. A statistically significant association was established between HPV and multiple myeloma (p<0.001). This study supports the hypothesis that HPV can infect lymphoid cells.

Atypical chest pain syndrome in patients with breast implants.

Eleven patients, aged 36 to 55 years, with silicone breast implants had episodes of severe chest pain similar to heart attacks 6 weeks to 7 years after breast implantation; one patient had a severe attack 1 month after explantation. The chest pain, which was not related to physical exertion, lasted from 15 minutes to 4 days, and descriptions of it varied from a "pressing" type of pain to "stabbing" pain with radiation to the shoulders, left arm, and jaw. The associated symptoms were diaphoresis, nausea, vomiting, dyspnea, and palpitations. All of the patients had a normal electrocardiogram (ECG) with the exception of one, whose ECG showed nonspecific ST changes. Ten had cardiac evaluations, all of which yielded normal results. All had implant removal, and five were found to have at least one ruptured implant. Nine had an implant capsule biopsy; all had chronic inflammatory rinds, and five had free silicone in tissue whether or not the implants were ruptured. All eight who had a pectoralis major muscle biopsy had abnormal results: (neurogenic atrophy [six], fasciitis [three], myositis [one], chronic inflammation [one], free silicone [one], and neuroma [one]). We concluded that silicone breast implants may cause an atypical chest pain syndrome, probably due to local inflammatory reactions and neuroma formation.

Adjuvant breast disease: an evaluation of 100 symptomatic women with breast implants or silicone fluid injections.

We evaluated 100 referred women with breast implants (n = 97) or silicone fluid injections (n = 3) into breasts who developed various symptoms. All reported symptoms occurred at a median latency period of 6 years (range 0-24 years) after implantation or injection of silicone. Commonest symptoms were weakness (95%), fatigability (95%), myalgia (90%), morning stiffness (89%), arthralgia (81%), memory loss (81%), sensory loss (77%), headache (73%) and dry eyes and dry mouth (72%). Laboratory results revealed abnormal levels of serum immunoglobulins or complement in 57% and autoantibodies in 78%. Sural nerve biopsy was abnormal in 80% with the major finding of loss of myelinated fibers in 79%. Biceps muscle biopsy was abnormal in 58% with the major finding of neurogenic atrophy in 27%. Ninety-six patients underwent implant removal; 60% of the patients were found to have one or both implants ruptured with silicone spilled into tissue. At time of removal, a pectoralis major muscle biopsy was taken which was abnormal in 89% with the major finding of neurogenic atrophy in 55%. Biopsy of implant capsule was abnormal in 94% showing foreign body giant cells containing refractile material consistent with silicone in 69% whether or not the elastomer shell was ruptured. Silicone can cause a systemic autoimmune disease with a variety of symptoms probably due to a global activation of the immune system. Since our patients had objective laboratory and histologic findings together with a high rate of mechanical implant failure, further investigations are necessary.

IgG subclass deficiency in amyotrophic lateral sclerosis.

In order to get to clues about T-cell independent versus T-cell dependent immune mechanism in ALS, we measured IgG subclasses in 25 ALS-patients: 16 patients had deficiency of T-cell dependent expressed IgG1 or IgG3 or both with essentially normal levels of T-cell independent expressed IgG2 and IgG4. Ten of these patients had no prior treatment and five of these 10 had normal total IgG. Six patients had some immunosuppressive treatment before measurements of subclasses were done and all of them had deficiency of total IgG. Eight of 14 patients who underwent a d-xylose breath test, had evidence of small bowel overgrowth, which was confirmed by cultures of duodenal aspirate. IgG1 and IgG3 are T-cell dependent antibodies against protein antigens with close linkage on chromosome 14. The findings suggest a defect in the IgG subclass expression in ALS.