Detection Of Different Subtypes Research Articles

Learning models for classifying Raman spectra of genomic DNA from tumor subtypes

An early and accurate detection of different subtypes of tumors is crucial for an effective guidance to personalized therapy and in predicting the ability of tumor to metastasize. Here we exploit the Surface Enhanced Raman Scattering (SERS) platform, based on disordered silver coated silicon nanowires (Ag/SiNWs), to efficiently discriminate genomic DNA of different subtypes of melanoma and colon tumors. The diagnostic information is obtained by performing label free Raman maps of the dried drops of DNA solutions onto the Ag/NWs mat and leveraging the classification ability of learning models to reveal the specific and distinct physico-chemical interaction of tumor DNA molecules with the Ag/NW, here supposed to be partly caused by a different DNA methylation degree.

Proteomics: Potential techniques for discovering the pathogenesis of connective tissue diseases-interstitial lung disease.

Interstitial lung disease (ILD) is one of the most serious lung complications of connective tissue disease (CTD). The application of proteomics in the past decade has revealed that various proteins are involved in the pathogenesis of each subtype of CTD-ILD through different pathways, providing novel ideas to study pathological mechanisms and clinical biomarkers. On this basis, a multidimensional diagnosis or prediction model is established. This paper reviews the results of proteomic detection of different subtypes of CTD-ILD and discusses the role of some differentially expressed proteins in the development of pulmonary fibrosis and their potential clinical applications.

An optimal deep learning framework for multi-type hemorrhagic lesions detection and quantification in head CT images for traumatic brain injury.

Traumatic Brain Injury (TBI) could lead to intracranial hemorrhage (ICH), which has now been identified as a major cause of death after trauma if it is not adequately diagnosed and properly treated within the first 24 hours. CT examination is widely preferred for urgent ICH diagnosis, which enables the fast identification and detection of ICH regions. However, the use of it requires the clinical interpretation by experts to identify the subtypes of ICH. Besides, it is unable to provide the details needed to conduct quantitative assessment, such as the volume and thickness of hemorrhagic lesions, which may have prognostic importance to the decision-making on emergency treatment. In this paper, an optimal deep learning framework is proposed to assist the quantitative assessment for ICH diagnosis and the accurate detection of different subtypes of ICH through head CT scan. Firstly, the format of raw input data is converted from 3D DICOM to NIfTI. Secondly, a pre-trained multi-class semantic segmentation model is applied to each slice of CT images, so as to obtain a precise 3D mask of the whole ICH region. Thirdly, a fine-tuned classification neural network is employed to extract the key features from the raw input data and identify the subtypes of ICH. Finally, a quantitative assessment algorithm is adopted to automatically measure both thickness and volume via the 3D shape mask combined with the output probabilities of the classification network. The results of our extensive experiments demonstrate the effectiveness of the proposed framework where the average accuracy of 96.21 percent is achieved for three types of hemorrhage. The capability of our optimal classification model to distinguish between different types of lesion plays a significant role in reducing the false-positive rate in the existing work. Furthermore, the results suggest that our automatic quantitative assessment algorithm is effective in providing clinically relevant quantification in terms of volume and thickness. It is more important than the qualitative assessment conducted through visual inspection to the decision-making on emergency surgical treatment.

Viral antigen nanoparticles for discriminated and quantitative detection of different subtypes of anti-virus immunoglobulins.

The aim of this study is to develop a novel method for the accurate diagnosis of the infection status of viral diseases, which requires discriminated and quantitative detection of different anti-virus immunoglubulin subtypes. Considering hepatitis A as a representative model disease, viral antigen nanoparticles (vAgNPs) were designed and synthesized by genetically presenting hepatitis A virus (HAV) antigens on the surface of human heavy chain ferritin (hFTH) nanoparticles to detect anti-HAV antibodies with discriminating immunoglobulin subtypes M and G (IgM and IgG, respectively). The vAgNPs also display multi-copies of hexa-histidine peptide (H6) on their surface to chemisorb gold ions (Au3+), which is vital for the autonomous generation of quantitatively meaningful detection signals. The quantitative level of anti-HAV IgM or IgG in 30 patient sera was successfully analyzed using the vAgNPs of HAV, which was performed through label-free one-step-immunoassay based on the self-enhancement of optical signals from gold nanoparticles clustered on the viral antigen nanoparticles. The diagnostic performance was compared with that of enzyme-linked immunosorbent assay (ELISA), which did not enable accurate quantitative assay due to the poor linearity between the antibody concentration and detection signal. Furthermore, these vAgNP-based immunoassays did not produce any false negative/positive signals, indicating 100% sensitivity and 100% specificity.

Influenza A virus subtypes in wild birds in North-Eastern Spain (Catalonia)

Since the spread of H5N1 highly pathogenic avian influenza virus in 2005, many surveillance programmes have been initiated in poultry and wild birds worldwide. This study describes for the first time the detection of different subtypes of avian influenza viruses (AIV) in wild birds in the West Mediterranean area (Catalonia, North-Eastern Spain). During a 3-year period (from mid-2006 to mid-2009), 1374 birds from 16 different families were examined, and a total of 62 AIV were detected by means of a real-time reverse transcriptase PCR assay. AIV were more frequently detected in Anatidae, Phoenicopteridae, Rallidae and Laridae families. Of the 62 positive samples, 28 AIV could be isolated in embryonated eggs. All isolates were subtyped by haemagglutinin and neuraminidase inhibition techniques and 10 different haemagglutinins (HA) and 7 neuraminidases (NA) were found in 13 different subtype combinations. The most common combinations were H4N6 (22.2%) and H1N1 (18.5%). The HA and NA gene sequences of different AIV subtypes were compared and aligned with those available AIV strains from genome databases. Our studies on AIV phylogenetic analysis revealed that all AIV genes sequenced from wild birds in North-Eastern Spain clustered within Eurasian avian clades, including the sequences of H8, N4 and N5 genes analyzed for the first time in Europe. The results contribute to the understanding of AIV in the Mediterranean area and in Europe.

Rapid haemagglutinin subtyping and pathotyping of avian influenza viruses by a DNA microarray

Rapid and reliable methods are fundamental for the comprehensive characterization of emerging and evolving avian influenza viruses. Although microarrays provide new possibilities with their parallel approach, their use in diagnostic laboratories is still limited due to economical and practical factors. An easy-to-use, low-cost microarray-based assay for haemagglutinin subtyping and pathotyping of avian influenza viruses and specific detection of highly pathogenic H5N1/Asia clade 2.2 is described as a novel diagnostic tool. The ArrayTube™ platform is user-friendly, inexpensive and allows processing of many samples. The sensitivity of the assay developed was comparable to real-time RT-PCR, and the simultaneous detection of different subtypes was possible. Validation with 90 influenza A virus isolates representing all 16 haemagglutinin subtypes and 44 field samples (cloacal swabs from wild and domestic birds) demonstrated the feasibility of the system for sensitive and specific characterization of AIV. Facilitating haemagglutinin subtyping and pathotyping for the majority of influenza A-positive cloacal swabs within 24 h, the new assay enables detailed AIV diagnosis even in less well-equipped laboratories.

A sensitive one-step real-time PCR for detection of avian influenza viruses using a MGB probe and an internal positive control

BackgroundAvian influenza viruses (AIVs) are endemic in wild birds and their introduction and conversion to highly pathogenic avian influenza virus in domestic poultry is a cause of serious economic losses as well as a risk for potential transmission to humans. The ability to rapidly recognise AIVs in biological specimens is critical for limiting further spread of the disease in poultry. The advent of molecular methods such as real time polymerase chain reaction has allowed improvement of detection methods currently used in laboratories, although not all of these methods include an Internal Positive Control (IPC) to monitor for false negative results.Therefore we developed a one-step reverse transcription real time PCR (RRT-PCR) with a Minor Groove Binder (MGB) probe for the detection of different subtypes of AIVs. This technique also includes an IPC.MethodsRRT-PCR was developed using an improved TaqMan technology with a MGB probe to detect AI from reference viruses. Primers and probe were designed based on the matrix gene sequences from most animal and human A influenza virus subtypes. The specificity of RRT-PCR was assessed by detecting influenza A virus isolates belonging to subtypes from H1–H13 isolated in avian, human, swine and equine hosts. The analytical sensitivity of the RRT-PCR assay was determined using serial dilutions of in vitro transcribed matrix gene RNA. The use of a rodent RNA as an IPC in order not to reduce the efficiency of the assay was adopted.ResultsThe RRT-PCR assay is capable to detect all tested influenza A viruses. The detection limit of the assay was shown to be between 5 and 50 RNA copies per reaction and the standard curve demonstrated a linear range from 5 to 5 × 108 copies as well as excellent reproducibility. The analytical sensitivity of the assay is 10–100 times higher than conventional RT-PCR.ConclusionThe high sensitivity, rapidity, reproducibility and specificity of the AIV RRT-PCR with the use of IPC to monitor for false negative results can make this method suitable for diagnosis and for the evaluation of viral load in field specimens.

Hydrophilic polymeric microspheres : Their use in immunological methods

Hydrophilic polymeric microspheres were prepared by y-radiation initiated copolymerization of 2-hydroxyethyl methacrylate, ethylene dimethacrylate, methylenebisacrylamide and methacrylic acid in aqueous solution with possible addition of special monomers bearing fluorescent or other specific ligand groups. The microspheres were used in detection of different subtypes of immunoglobulin Fc-receptors and complement components receptors as well as in the studies of the process of receptor-mediated phagocytosis. A simple assay for testing the phagocytic activity of peripheral blood leukocytes is proposed for clinical applications.

Localization of concanavalin A binding sites in human pituitary adenoma cells as revealed by HRP-labelling method.

Intracellular lectin (Con A)-binding sites of human pituitary adenoma were examined by electron microscopy using the horseradish-peroxidase (HRP)-labelling technique. In this study were used 16 cases of human pituitary adenomas operated on in our clinics between 1977 and 1981: they concluded 4 each of PRL-, GH-, ACTH-producing and hormonal non-functioning adenomas. In parallel with the detection of lectin-binding sites, basal levels of their secreting hormones were determined by the radioimmunoassay technique, and their producing hormones were characterized light microscopically by the immunocytochemical HRP-labelling technique. In the present study, for hormonal functioning adenoma cells, mature or large granules of each specific type of adenoma cells had no definite Con A-binding sites. On the other hand, immature or small secretory granules of RPL- or GH-producing adenoma cells showed a positive reaction with Con A. ACTH-producing adenomas so far examined revealed no definite binding sites. Some variable results were obtained concerning non-functioning adenomas. Lectins have been and will be very useful in the detection of different subtypes of adenomas in each specific group.