Detection and genotyping of human papillomavirus by real-time PCR assay

BackgroundDiagnosis of human papillomavirus (HPV) disease remains a challenge due to several factors related to the cost, the workload of available commercial assays to detect and genotype HPV, and to the low prevalence of infected patients.ObjectiveOur study aimed to develop a real-time PCR, based on SPF10 primers, in order to combine HPV-DNA detection and genotype identification avoiding the negative samples.Study designValidation of SYBR-green based SPF10 real-time PCR on HPV-DNA plasmids followed by the investigation of the viral status in 92 samples from oropharyngeal (94%) cutaneous biopsies (3%) and anal smears (3%) which had previously been HPV-genotyped by LiPA hybridization. In-house HPV viral loads were performed to evaluate the SPF10 real-time PCR sensitivity.ResultsData showed that 100% of HPV plasmids, assessable by LiPA hybridization, were detected and genotyped appropriately after SPF10 real-time PCR assays. These results defined a range of melting temperature peaks for HPV positivity by real-time PCR. The efficient determination of the presence of HPV-DNA by SPF10 real-time PCR was validated for 98% of clinical samples compared to commercial method. Discordant results were due to a low HPV-DNA amount and to a supplementary HPV genotype identified. The SPF10 real-time PCR sensitivity was evaluated between 1 and 10 copies/10³ cells using in-house HPV (6, 11 and 16) viral load assays.ConclusionThe real-time PCR method was efficient in combining screening and genotyping of HPV-DNA. Cost and workload reduction by SPF10 real-time PCR approach may facilitate earlier diagnosis and clinical management of HPV infected patients.     

Multi-scale graph-based grading for Alzheimer’s disease prediction

The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer’s disease (AD) is clinically relevant, and may above all have a significant impact on accelerating the development of new treatments. In this paper, we present a new MRI-based biomarker that enables us to accurately predict conversion of MCI subjects to AD. In order to better capture the AD signature, we introduce two main contributions. First, we present a new graph-based grading framework to combine inter-subject similarity features and intra-subject variability features. This framework involves patch-based grading of anatomical structures and graph-based modeling of structure alteration relationships. Second, we propose an innovative multiscale brain analysis to capture alterations caused by AD at different anatomical levels. Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time. During our experiments using the ADNI-1 dataset, the proposed multiscale graph-based grading method obtained an area under the curve (AUC) of 81% to predict conversion of MCI subjects to AD within three years. Moreover, when combined with cognitive scores, the proposed method obtained 85% of AUC. These results are competitive in comparison to state-of-the-art methods evaluated on the same dataset.     

Brain multigraph prediction using topology-aware adversarial graph neural network

Brain graphs (i.e, connectomes) constructed from medical scans such as magnetic resonance imaging (MRI) have become increasingly important tools to characterize the abnormal changes in the human brain. Due to the high acquisition cost and processing time of multimodal MRI, existing deep learning frameworks based on Generative Adversarial Network (GAN) focused on predicting the missing multimodal medical images from a few existing modalities. While brain graphs help better understand how a particular disorder can change the connectional facets of the brain, synthesizing a target brain multigraph (i.e, multiple brain graphs) from a single source brain graph is strikingly lacking. Additionally, existing graph generation works mainly learn one model for each target domain which limits their scalability in jointly predicting multiple target domains. Besides, while they consider the global topological scale of a graph (i.e., graph connectivity structure), they overlook the local topology at the node scale (e.g., how central a node is in the graph). To address these limitations, we introduce topology-aware graph GAN architecture (topoGAN), which jointly predicts multiple brain graphs from a single brain graph while preserving the topological structure of each target graph. Its three key innovations are: (i) designing a novel graph adversarial auto-encoder for predicting multiple brain graphs from a single one, (ii) clustering the encoded source graphs in order to handle the mode collapse issue of GAN and proposing a cluster-specific decoder, (iii) introducing a topological loss to force the prediction of topologically sound target brain graphs. The experimental results using five target domains demonstrated the outperformance of our method in brain multigraph prediction from a single graph in comparison with baseline approaches.     

基于表面肌电图的人体运动意图识别研究进展

分析基于表面肌电图人体运动意图识别的研究方法和识别效果。     

Belastungsabhängige Segmentstabilität im Funktions-MRT bei Nachwuchsathleten mit Spondylolisthesis mit und ohne Rückenschmerzen

BackgroundSegmental spinal instability is assumed to be a source of low back pain in adolescent athletes (AA). It is unclear, if imaging techniques allow for differentiation of load-dependent segment stability in AA with spondylolisthesis in dependence of pain.Material and MethodsTwenty-two AA (14.1±1.6 years, 164±9 cm, 59±13 kg) with spondylolisthesis L5/S1 were included. Following an orthopedic examination with history of pain (in everyday life [PE]; during sports [PS]) functional MRI was performed (1. Upright-loaded; 2. Supine-unloaded). Anterior translation [mm], lordosis angle [°] and „lumbar disc degeneration-Index (LDDG [1–5]) were determined within MRI. Absolute values and differences (loaded - unloaded condition) are shown by the factor pain (α=0.05).ResultsEight AA reported PE, 10 PS and 10 AA were pain-free (PF). Lordosis angle and anterior translation were higher during loaded condition (60±10° to 46±11; 7.2±2.0mm° to 5.8±1.9 mm). AA with pain showed loaded/unloaded smaller differences of lordosis angles (PE/NP: 13.8±6.3°/14.9±4.6; PS/PF: 13.3±5.1/15.1±5.1; p>0.05) and higher differences of anterior translation (PE: 1.7±1.3 mm/1.2±1.3; PS: 2.2±1.3/0.7±0.8; p=0.002) compared to PF. LDDG was higher for AA with PS than in PF (PS/PF: Median 2 (1.25;3)/ 3.5 (2;4)).ConclusionsAA with spondylolisthesis and PS show lower lordosis and higher segmental anterior translation as well as disc degeneration. Low back pain during sports in AA with spondylolisthesis might be seen as a predictor for higher segmental spinal instability.Level of evidence2 (Cohort study)     

Acceleration of dynamic fluorescence molecular tomography with principal component analysis

Dynamic fluorescence molecular tomography (FMT) is an attractive imaging technique for three-dimensionally resolving the metabolic process of fluorescent biomarkers in small animal. When combined with compartmental modeling, dynamic FMT can be used to obtain parametric images which can provide quantitative pharmacokinetic information for drug development and metabolic research. However, the computational burden of dynamic FMT is extremely huge due to its large data sets arising from the long measurement process and the densely sampling device. In this work, we propose to accelerate the reconstruction process of dynamic FMT based on principal component analysis (PCA). Taking advantage of the compression property of PCA, the dimension of the sub weight matrix used for solving the inverse problem is reduced by retaining only a few principal components which can retain most of the effective information of the sub weight matrix. Therefore, the reconstruction process of dynamic FMT can be accelerated by solving the smaller scale inverse problem. Numerical simulation and mouse experiment are performed to validate the performance of the proposed method. Results show that the proposed method can greatly accelerate the reconstruction of parametric images in dynamic FMT almost without degradation in image quality.OCIS codes: (170.3010) Image reconstruction techniques, (170.3880) Medical and biological imaging, (100.3190) Inverse problems, (170.6960) Tomography     

Genome-wide pathway-based association study implicates complement system in the development of Kashin-Beck disease in Han Chinese

Kashin-Beck disease (KBD) is a chronic osteochondropathy. The pathogenesis of KBD remains unknown. To identify relevant biological pathways for KBD, we conducted a genome-wide pathway-based association study (GWPAS) following by replication analysis, totally using 2743 Chinese Han adults. A modified gene set enrichment algorithm was used to detect association between KBD and 963 biological pathways. Cartilage gene expression analysis and serum complement measurement were performed to evaluate the functional relevance of identified pathway with KBD. We found that the Complement and Coagulation Cascades (CACC) pathway was significantly associated with KBD (P value = 3.09 × 10^− 5, false-discovery rate = 0.042). Within the CACC pathway, the most significant association was observed at rs1656966 (P value = 1.97 × 10^− 4) of KNG1 gene. Further replication study observed that rs1656966 (P value = 0.037) was significantly associated with KBD in an independent validation sample of 1026 subjects. Gene expression analysis observed that CFD (ratio = 3.39 ± 2.68), A2M (ratio = 3.67 ± 5.63), C5 (ratio = 2.65 ± 2.52) and CD46 (ratio = 2.29 ± 137) genes of the CACC pathway were up-regulated in KBD articular cartilage compared to healthy articular cartilage. The serum level of complement C5 in KBD patients were significantly higher than that in healthy controls (P value = 0.038). Our study is the first to suggest that complement system-related CACC pathway contributed to the development of KBD.     

Detecting rare events using extreme value statistics applied to epileptic convulsions in children

ObjectiveNocturnal home monitoring of epileptic children is often not feasible due to the cumbersome manner of seizure detection with the standard method of video electroencephalography monitoring. The goal of this paper is to propose a method for hypermotor seizure detection based on accelerometers that are attached to the extremities.MethodsSupervised methods that are commonly used in literature need annotation of data and hence require expert (neurologist) interaction resulting in a substantial cost. In this paper an unsupervised method is proposed that uses extreme value statistics and seizure detection based on a model of normal behavior that is estimated using all recorded and unlabeled data. In this way the expensive interaction can be avoided.ResultsWhen applying this method to a labeled dataset, acquired from 7 patients, all hypermotor seizures are detected in 5 of the 7 patients with an average positive predictive value (PPV) of 53%. For evaluating the performance on an unlabeled dataset, seizure events are presented to the system as normal movement events. Since hypermotor seizures are rare compared to normal movements, the very few abnormal events have a negligible effect on the quality of the model. In this way, it was possible to evaluate the system for 3 of the 7 patients when 3% of the training set was composed of seizure events. This resulted in sensitivity scores of 80%, 22% and 90% and a PPV of 89%, 21% and 44% respectively. These scores are comparable with a state-of-the-art supervised machine learning based approach which requires a labeled dataset.ConclusionsA person-dependent epileptic seizure detection method has been designed that requires little human interaction. In contrast to traditional machine learning approaches, the imbalance of the dataset does not cause substantial difficulties.