A new method for non-labeling attomolar detection of diseases based on an individual gold nanorod immunosensor

Herein, we present the use of a single gold nanorod sensor for detection of diseases on an antibody-functionalized surface, based on antibody–antigen interaction and the localized surface plasmon resonance (LSPR) ?max shifts of the resonant Rayleigh light scattering spectra. By replacing the cetyltrimethylammonium bromide (CTAB), a tightly packed self-assembled monolayer of HS(CH2)11(OCH2CH2)6OCH2COOH(OEG6) has been successfully formed on the gold nanorod surface prior to the LSPR sensing, leading to the successful fabrication of individual gold nanorod immunosensors. Using prostate specific antigen (PSA) as a protein biomarker, the lowest concentration experimentally detected was as low as 111 aM, corresponding to a 2.79 nm LSPR ?max shift. These results indicate that the detection platform is very sensitive and outperforms detection limits of commercial tests for PSA so far. Correlatively, its detection limit can be equally compared to the assays based on DNA biobarcodes. This study shows that a gold nanorod has been used as a single nanobiosensor to detect antigens for the first time; and the detection method based on the resonant Rayleigh scattering spectrum of individual gold nanorods enables a simple, label-free detection with ultrahigh sensitivity.

An Excess of Risk-Increasing Low-Frequency Variants Can Be a Signal of Polygenic Inheritance in Complex Diseases

In most complex diseases, much of the heritability remains unaccounted for by common variants. It has been postulated that lower-frequency variants contribute to the remaining heritability. Here, we describe a method to test for polygenic inheritance from lower-frequency variants by using GWAS summary association statistics. We explored scenarios with many causal low-frequency variants and showed that there is more power to detect risk variants than to detect protective variants, resulting in an increase in the ratio of detected risk to protective variants (R/P ratio). Such an excess can also occur if risk variants are present and kept at lower frequencies because of negative selection. The R/P ratio can be falsely elevated because of reasons unrelated to polygenic inheritance, such as uneven sample sizes or asymmetric population stratification, so precautions to correct for these confounders are essential. We tested our method on published GWAS results and observed a strong signal in some diseases (schizophrenia and type 2 diabetes) but not others. We also explored the shared genetic component in overlapping phenotypes related to inflammatory bowel disease (Crohn disease [CD] and ulcerative colitis [UC]) and diabetic nephropathy (macroalbuminuria and end-stage renal disease [ESRD]). Although the signal was still present when both CD and UC were jointly analyzed, the signal was lost when macroalbuminuria and ESRD were jointly analyzed, suggesting that these phenotypes should best be studied separately. Thus, our method may also help guide the design of future genetic studies of various traits and diseases.

Periodontitis and systemic diseases:A record of discussions of working group 4 of the Joint EFP/AAP Workshop on Periodontitis and Systemic Diseases

Background There has been an explosion in research into possible associations between periodontitis and various systemic diseases and conditions. Aim To review the evidence for associations between periodontitis and various systemic diseases and conditions, including chronic obstructive pulmonary disease (COPD), pneumonia, chronic kidney disease, rheumatoid arthritis, cognitive impairment, obesity, metabolic syndrome and cancer, and to document headline discussions of the state of each field. Periodontal associations with diabetes, cardiovascular disease and adverse pregnancy outcomes were not discussed by working group 4. Results Working group 4 recognized that the studies performed to date were largely cross-sectional or case-control with few prospective cohort studies and no randomized clinical trials. The best current evidence suggests that periodontitis is characterized by both infection and pro-inflammatory events, which variously manifest within the systemic diseases and disorders discussed. Diseases with at least minimal evidence of an association with periodontitis include COPD, pneumonia, chronic kidney disease, rheumatoid arthritis, cognitive impairment, obesity, metabolic syndrome and cancer. The working group agreed that there is insufficient evidence to date to infer causal relationships with the exception that organisms originating in the oral microbiome can cause lung infections. Conclusions The group was unanimous in their opinion that the reported associations do not imply causality, and establishment of causality will require new studies that fulfil the Bradford Hill or equivalent criteria. Precise and community-agreed case definitions of periodontal disease states must be implemented systematically to enable consistent and clearer interpretations of studies of the relationship to systemic diseases. The members of the working group were unanimous in their opinion that to develop data that best inform clinicians, investigators and the public, studies should focus on robust disease outcomes and avoid surrogate endpoints. It was concluded that because of the relative immaturity of the body of evidence for each of the purported relationships, the field is wide open and the gaps in knowledge are large. © 2013 European Federation of Periodontology and American Academy of Periodontology.

Toward Unconstrained Ear Recognition From Two-Dimensional Images

Ear recognition, as a biometric, has several advantages. In particular, ears can be measured remotely and are also relatively static in size and structure for each individual. Unfortunately, at present, good recognition rates require controlled conditions. For commercial use, these systems need to be much more robust. In particular, ears have to be recognized from different angles ( poses), under different lighting conditions, and with different cameras. It must also be possible to distinguish ears from background clutter and identify them when partly occluded by hair, hats, or other objects. The purpose of this paper is to suggest how progress toward such robustness might be achieved through a technique that improves ear registration. The approach focuses on 2-D images, treating the ear as a planar surface that is registered to a gallery using a homography transform calculated from scale-invariant feature-transform feature matches. The feature matches reduce the gallery size and enable a precise ranking using a simple 2-D distance algorithm. Analysis on a range of data sets demonstrates the technique to be robust to background clutter, viewing angles up to +/- 13 degrees, and up to 18% occlusion. In addition, recognition remains accurate with masked ear images as small as 20 x 35 pixels.

3D Morphable Model Construction for Robust Ear and Face Recognition

Recent work suggests that the human ear varies significantly between different subjects and can be used for identification. In principle, therefore, using ears in addition to the face within a recognition system could improve accuracy and robustness, particularly for non-frontal views. The paper describes work that investigates this hypothesis using an approach based on the construction of a 3D morphable model of the head and ear. One issue with creating a model that includes the ear is that existing training datasets contain noise and partial occlusion. Rather than exclude these regions manually, a classifier has been developed which automates this process. When combined with a robust registration algorithm the resulting system enables full head morphable models to be constructed efficiently using less constrained datasets. The algorithm has been evaluated using registration consistency, model coverage and minimalism metrics, which together demonstrate the accuracy of the approach. To make it easier to build on this work, the source code has been made available online.

Robust 2D Ear Registration and Recognition Based on SIFT Point Matching

Significant recent progress has shown ear recognition to be a viable biometric. Good recognition rates have been demonstrated under controlled conditions, using manual registration or with specialised equipment. This paper describes a new technique which improves the robustness of ear registration and recognition, addressing issues of pose variation, background clutter and occlusion. By treating the ear as a planar surface and creating a homography transform using SIFT feature matches, ears can be registered accurately. The feature matches reduce the gallery size and enable a precise ranking using a simple 2D distance algorithm. When applied to the XM2VTS database it gives results comparable to PCA with manual registration. Further analysis on more challenging datasets demonstrates the technique to be robust to background clutter, viewing angles up to +/- 13 degrees and with over 20% occlusion.