Dataset Information

Bioplasmonic paper as a platform for detection of kidney cancer biomarkers.

ABSTRACT: We demonstrate that a common laboratory filter paper uniformly adsorbed with biofunctionalized plasmonic nanostructures can serve as a highly sensitive transduction platform for rapid detection of trace bioanalytes in physiological fluids. In particular, we demonstrate that bioplasmonic paper enables rapid urinalysis for the detection of kidney cancer biomarkers in artificial urine down to a concentration of 10 ng/mL. Compared to conventional rigid substrates, bioplasmonic paper offers numerous advantages such as high specific surface area (resulting in large dynamic range), excellent wicking properties (naturally microfluidic), mechanical flexibility, compatibility with conventional printing approaches (enabling multiplexed detection and multimarker biochips), and significant cost reduction.

SUBMITTER: Tian L

PROVIDER: S-EPMC3514911 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Bioplasmonic paper as a platform for detection of kidney cancer biomarkers.

Tian Limei L Morrissey Jeremiah J JJ Kattumenu Ramesh R Gandra Naveen N Kharasch Evan D ED Singamaneni Srikanth S

Analytical chemistry 20121102 22

We demonstrate that a common laboratory filter paper uniformly adsorbed with biofunctionalized plasmonic nanostructures can serve as a highly sensitive transduction platform for rapid detection of trace bioanalytes in physiological fluids. In particular, we demonstrate that bioplasmonic paper enables rapid urinalysis for the detection of kidney cancer biomarkers in artificial urine down to a concentration of 10 ng/mL. Compared to conventional rigid substrates, bioplasmonic paper offers numerous ...[more]

PMID: 23098272

Similar Datasets

Project description:Viral detection presents a host of challenges for even the most sensitive analytical techniques, and the complexity of common detection platforms typically preclude portability. With these considerations in mind, we designed a paper microzone plate-based virus detection system for the detection of viral genetic material that can be performed with simple instruments. The sensing system can detect viral cDNA reverse-transcribed from total RNA extraction by utilizing a biotinylated capture probe and an Alexa Fluor® 647-labeled reporter probe. The biotinylated capture probe was linked to the paper surface via NeutrAvidin® that was physically adsorbed on the paper. After addition of reverse-transcribed sample and reporter probe in sequence, the reverse-transcribed target captured the reporter probe and tethered it to the capture probe in a bridged format. Fluorescence intensity was imaged using a Western blot imaging system, and higher target concentration was visible by the increased emission intensity from Alexa Fluor® 647. By utilizing paper, this detection setup could also serve as a sample concentration method via evaporation, which could remarkably lower the detection limit if needed. This detection platform used Epstein-Barr virus (EBV) RNA as a proof-of-concept by sensing cDNA resulting from reverse transcription and can be further expanded as a general method for other pathogens. EBV is a well-known human tumor virus, which has also recently been linked to the development of cervical cancer. The assay was accomplished within two hours including the room-temperature RNA extraction and reverse transcription steps. Also, this paper microzone plate-based platform can potentially be applicable for the development of point-of-care (POC) detection kits or devices due to its robust design, convenient interface, and easy portability. The experiment could be stopped after each step, and continued at a later time. The shelf-life of the modified paper plate setup was at least 3 months without a discernible change in signal, and the result from day 1 could be read at 3 months - both of which are important criteria for POC analytical testing tools, especially in resource-poor settings. All of the required assay steps could potentially be performed without any significant equipment using inexpensive paper microzone plates, which will be ideal for further development of POC testing devices. Although, this platform is not at the stage where it can be directly used in a point-of-care setting, it does have fundamental characteristics such as a stable platform, a simple detection method, and relatively common reagents that align closely with a POC system.

Project description:Being able to identify patients in whom immunological tolerance has been established or is developing would allow an individually tailored approach to post-transplant management of kidney allograft recipients. Ex vivo immunological monitoring was performed on samples from five groups of European renal transplant recipients (“IOT samples”): ten drug-free tolerant recipients who were functionally stable despite remaining immunosuppression-free for more than one year (Tol-DF); also functionally stable patients on minimal immunosuppression (<10 mg/day prednisone, s-LP); stable patients maintained with calcineurin inhibitors (s-CNI); stable patients maintained on CNI-free immunosuppression regimen (s-nCNI); patients showing signs of chronic rejection (CR) and healthy controls (HC). Among the investigation of other biomarkers and bioassays, gene expression profiles were generated on custom Agilent 8x15K 60mer oligonucleotide microarrays (“RISET 2.0”) on the IOT cohort (training set) and on an independent cohort of patients from the ITN (USA) that contained similar groups of patients and included 23 tolerant recipients (“ITN samples”, test set). Set of genes were identified, whose expression on whole blood allowed the identification of 100% of the tolerant recipients in the training set and 84% in the test set. Keywords: classification of clinical samples, tolerance prediction IOT samples: 13 samples from 10 Tol-DF patients, 16 samples from 11 s-LP patients, 8 samples from 8 s-nCNI patients, 40 samples from 28 s-CNI patients, 10 samples from 9 CR patients, and 8 samples from 8 HC donors were analysed on a custom Agilent 8x15K 60mer oligonucleotide microarray encomprising 5069 probes in triplicates. The microarray is dedicated to transplantation research and was designed based on current literature and published and unpublished data provided by the RISET consortium. For the probe selection procedure we specially focused on the detection of multiple transcript variants of a gene, on optimized hybridization properties of the probes, and on the avoidance of crosshybridization. The sampling dates of replicate samples from the same patient range from one day to 1.5 years. ITN samples: 31 samples from 23 Tol-DF patients, 14 samples from 11 s-LP patients, 52 samples from 34 s-CNI patients, 25 samples from 18 CR patients, and 20 samples from 20 HC donors were analysed on the same microarray platform and served as test set. The samples of the two cohorts differ in the protocol of RNA preparation.

Project description:ObjectiveA mini-invasive and good-compliance program is critical to broaden colorectal cancer (CRC) screening and reduce CRC-related mortality. Blood testing combined with imaging examination has been proved to be feasible on screen for multicancer and guide intervention. The study aims to construct a machine learning-assisted detection platform with available multi-targets for CRC and colorectal adenoma (CRA) screening.MethodsThis was a retrospective study that the blood test data from 204 CRCs, 384 CRAs, and 229 healthy controls was extracted. The classified models were constructed with 4 machine learning (ML) algorithms including support vector machine (SVM), random forest (RF), decision tree (DT), and eXtreme Gradient Boosting (XGB) based on the candidate biomarkers. The importance index was used by SHapely Adaptive exPlanations (SHAP) analysis to identify the dominant characteristics. The performance of classified models was evaluated. The most dominating features from the proposed panel were developed by logistic regression (LR) for identification CRC from control.ResultsThe candidate biomarkers consisted of 26 multi-targets panel including CEA, AFP, and so on. Among the 4 models, the SVM classifier for CRA yields the best predictive performance (the area under the receiver operating curve, AUC: .925, sensitivity: .904, and specificity: .771). As for CRC classification, the RF model with 26 candidate biomarkers provided the best predictive parameters (AUC: .941, sensitivity: .902, and specificity: .912). Compared with CEA and CA199, the predictive performance was significantly improved. The streamlined model with 6 biomarkers for CRC also obtained a good performance (AUC: .946, sensitivity: .885, and specificity: .913).ConclusionsThe predictive models consisting of 26 multi-targets panel would be used as a non-invasive, economical, and effective risk stratification platform, which was expected to be applied for auxiliary screening of CRA and CRC in clinical practice.

Dataset Information

Bioplasmonic paper as a platform for detection of kidney cancer biomarkers.

Publications

Bioplasmonic paper as a platform for detection of kidney cancer biomarkers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets