A Fast and Accurate Method for Glaucoma Screening from Smartphone-Captured Fundus Images

The glaucoma is an eye disease that causes blindness when it progresses in an advanced stage. Early glaucoma diagnosis is essential to prevent the vision loss. However, early detection is not covered due to the lack of ophthalmologists and the limited accessibility to retinal image capture devices.In this paper, we present an automated method for glaucoma screening dedicated for Smartphone Captured Fundus Images (SCFIs). The implementation of the method into a smartphone associated to an optical lens for retina capturing leads to a mobile aided screening system for glaucoma. The challenge consists in insuring higher performance detection despite the moderate quality of SCFIs, with a reduced execution time to be adequate for the clinical use. The main idea consists in deducing glaucoma based on the vessel displacement inside the Optic Disk (OD), where the vessel tree remains sufficiently modeled on SCFIs. Within this objective, our major contribution consists in proposing: (1) a robust processing for locating vessel centroids in order to adequately model the vessel distribution, and (2) a feature vector that relevantly reflect two main glaucoma biomarkers in terms of vessel displacement. Furthermore, all processing steps are carefully chosen based on lower complexity, to be suitable for fast clinical screening.A first evaluation of our method is performed using the two public DRISHTI-DB and DRIONS-DB databases, where 99% and 95% accuracy, 96.77% and 97,5% specificity and 100% and 95% sensitivity are respectively achieved. Thereafter, the method is evaluated using two fundus image databases respectively captured through a smartphone and retinograph for the same persons. We achieve 100% accuracy using both databases which assesses the robustness of our method. In addition, the detection is performed on 0.027 and 0.029 second when executed respectively on the Samsung-M51 on the Samsung-A70 smartphone devices. Our proposed smartphone app provides a cost-effective and widely accessible mobile platform for early screening of glaucoma in remote clinics or areas with limited access to fundus cameras and ophthalmologists.     

Assessing performance of orthology detection strategies applied to eukaryotic genomes

Orthology detection is critically important for accurate functional annotation, and has been widely used to facilitate studies on comparative and evolutionary genomics. Although various methods are now available, there has been no comprehensive analysis of performance, due to the lack of a genomic-scale 'gold standard' orthology dataset. Even in the absence of such datasets, the comparison of results from alternative methodologies contains useful information, as agreement enhances confidence and disagreement indicates possible errors. Latent Class Analysis (LCA) is a statistical technique that can exploit this information to reasonably infer sensitivities and specificities, and is applied here to evaluate the performance of various orthology detection methods on a eukaryotic dataset. Overall, we observe a trade-off between sensitivity and specificity in orthology detection, with BLAST-based methods characterized by high sensitivity, and tree-based methods by high specificity. Two algorithms exhibit the best overall balance, with both sensitivity and specificity>80%: INPARANOID identifies orthologs across two species while OrthoMCL clusters orthologs from multiple species. Among methods that permit clustering of ortholog groups spanning multiple genomes, the (automated) OrthoMCL algorithm exhibits better within-group consistency with respect to protein function and domain architecture than the (manually curated) KOG database, and the homolog clustering algorithm TribeMCL as well. By way of using LCA, we are also able to comprehensively assess similarities and statistical dependence between various strategies, and evaluate the effects of parameter settings on performance. In summary, we present a comprehensive evaluation of orthology detection on a divergent set of eukaryotic genomes, thus providing insights and guides for method selection, tuning and development for different applications. Many biological questions have been addressed by multiple tests yielding binary (yes/no) outcomes but no clear definition of truth, making LCA an attractive approach for computational biology.     

猪瘟病毒抗体纳米荧光检测试纸条方法建立及性能评价

基于镧系元素Eu微球标记技术建立了一种猪瘟病毒抗体检测的免疫层析方法。通过对反应体系中包被浓度、复溶浓度以及反应时间等因素进行优化,确定最适的反应条件,建立检测方法,然后通过从敏感性、特异性、重复性、临床评价等方面对其进行性能评价。对反应体系进行优化,最终确定包被浓度为0.1 mg/mL,复溶浓度为6倍稀释,检测时间为15 min。通过对试纸条的性能评价可以得出,猪瘟病毒抗体荧光检测试纸条的敏感性为猪瘟阳性血清国家参考品倍比稀释至1∶128倍仍可以检测到;对常见的猪繁殖与呼吸综合征、猪I型疱疹病毒、猪口蹄疫、猪圆环病毒2型、猪流行性腹泻、牛病毒性腹泻病毒、羊边界病毒等抗体阳性血清无交叉反应;批内和批内变异系数均小于10%;经临床评价,与商品化的猪瘟病毒抗体ELISA检测试剂盒相比阳性符合率为90%,阴性符合率为100%,总符合率为97.7%;与荧光抗体中和试验(FVNT)的阴性符合率为100%,阳性符合率为93%,总符合率为98.5%。综合评定认为本研究建立的猪瘟病毒抗体纳米荧光检测方法,符合各项性能参数,可以快速、经济、方便地对猪个体及群体进行猪瘟病毒抗体检测评估,可广泛应用于猪场管理中。     

3种检测艰难梭菌方法的临床应用评估

本研究旨在对3种检测粪便样本中艰难梭菌的方法进行临床应用评估,为艰难梭菌的实验室检测提供参考。采用艰难梭菌毒素A/B(Clostridium difficile toxins A and B,CDAB)酶联免疫荧光检测法、环丝氨酸-头孢西丁-果糖琼脂(cycloserin-cefoxitin-fructose agar,CCFA)常规培养法和显色培养法(chromID~(TM))同步检测粪便样本中的艰难梭菌,并对培养所得菌株进行tcdB基因扩增以验证其产毒性。以艰难梭菌培养联合tcdB基因扩增为参考方法,分别计算上述3种方法的灵敏度、特异度、阳性预测值和阴性预测值等参数,分析其与参考方法的一致性。研究共收集临床粪便样本164份,参考方法检测结果为58份阳性,106份阴性。经统计分析,艰难梭菌毒素A/B法的灵敏度、特异度、阳性预测值和阴性预测值分别为51.7%、95.3%、85.7%和78.3%,CCFA常规培养法分别为72.4%、98.1%、95.5%和86.7%,而艰难梭菌显色培养法分别为94.8%、92.5%、87.3%和97.0%。3种方法中,艰难梭菌显色培养法与参考方法的一致性最高(Kappa=0.856)。采用该方法检测粪便样本中的艰难梭菌操作简便,成本经济,结果判断直观、准确,具有较好的临床应用价值。     

Automated Detection Framework of the Calcified Plaque with Acoustic Shadowing in IVUS Images

Intravascular Ultrasound (IVUS) is one ultrasonic imaging technology to acquire vascular cross-sectional images for the visualization of the inner vessel structure. This technique has been widely used for the diagnosis and treatment of coronary artery diseases. The detection of the calcified plaque with acoustic shadowing in IVUS images plays a vital role in the quantitative analysis of atheromatous plaques. The conventional method of the calcium detection is manual drawing by the doctors. However, it is very time-consuming, and with high inter-observer and intra-observer variability between different doctors. Therefore, the computer-aided detection of the calcified plaque is highly desired. In this paper, an automated method is proposed to detect the calcified plaque with acoustic shadowing in IVUS images by the Rayleigh mixture model, the Markov random field, the graph searching method and the prior knowledge about the calcified plaque. The performance of our method was evaluated over 996 in-vivo IVUS images acquired from eight patients, and the detected calcified plaques are compared with manually detected calcified plaques by one cardiology doctor. The experimental results are quantitatively analyzed separately by three evaluation methods, the test of the sensitivity and specificity, the linear regression and the Bland-Altman analysis. The first method is used to evaluate the ability to distinguish between IVUS images with and without the calcified plaque, and the latter two methods can respectively measure the correlation and the agreement between our results and manual drawing results for locating the calcified plaque in the IVUS image. High sensitivity (94.68%) and specificity (95.82%), good correlation and agreement (>96.82% results fall within the 95% confidence interval in the Student t-test) demonstrate the effectiveness of the proposed method in the detection of the calcified plaque with acoustic shadowing in IVUS images.     

Blind source separation,wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling

This paper proposes an automatic method for artefact removal and noise elimination from scalp electroencephalogram recordings (EEG). The method is based on blind source separation (BSS) and supervised classification and proposes a combination of classical and news features and classes to improve artefact elimination (ocular, high frequency muscle and ECG artefacts). The role of a supplementary step of wavelet denoising (WD) is explored and the interactions between BSS, denoising and classification are analyzed. The results are validated on simulated signals by quantitative evaluation criteria and on real EEG by medical expertise. The proposed methodology successfully rejected a good percentage of artefacts and noise, while preserving almost all the cerebral activity. The “denoised artefact-free” EEG presents a very good improvement compared with recorded raw EEG: 96% of the EEGs are easier to interpret.     

Automated Multi-Lesion Detection for Referable Diabetic Retinopathy in Indigenous Health Care

Diabetic Retinopathy (DR) is a complication of diabetes mellitus that affects more than one-quarter of the population with diabetes, and can lead to blindness if not discovered in time. An automated screening enables the identification of patients who need further medical attention. This study aimed to classify retinal images of Aboriginal and Torres Strait Islander peoples utilizing an automated computer-based multi-lesion eye screening program for diabetic retinopathy. The multi-lesion classifier was trained on 1,014 images from the São Paulo Eye Hospital and tested on retinal images containing no DR-related lesion, single lesions, or multiple types of lesions from the Inala Aboriginal and Torres Strait Islander health care centre. The automated multi-lesion classifier has the potential to enhance the efficiency of clinical practice delivering diabetic retinopathy screening. Our program does not necessitate image samples for training from any specific ethnic group or population being assessed and is independent of image pre- or post-processing to identify retinal lesions. In this Aboriginal and Torres Strait Islander population, the program achieved 100% sensitivity and 88.9% specificity in identifying bright lesions, while detection of red lesions achieved a sensitivity of 67% and specificity of 95%. When both bright and red lesions were present, 100% sensitivity with 88.9% specificity was obtained. All results obtained with this automated screening program meet WHO standards for diabetic retinopathy screening.     

Multicenter evaluation of the novel enzyme immunoassay based on P1-enriched protein for the detection of Mycoplasma pneumoniae infection

The aim of the study was to evaluate new Mycoplasma pneumoniae IgG, IgA and IgM EIA methods based on the enrichment of P1-protein (ThermoLabsystems, Helsinki, Finland) (L) for the detection of acute infection. This evaluation was performed in two independent routine clinical microbiology laboratories. The first laboratory used samples preselected by IgG and IgM Platelia enzyme immunoassay (P) and the second used samples preseleced by Serion ELISA Classic M. pneumoniae IgG, IgM (V). The L method was also compared to the FDA approved method of ImmunoWell M. pneumoniae IgG and IgM (G). When the agreement of two methods was applied as a serologic criteria for an acute infection, the following ratios of acute to nonacute infection were calculated 32/86 (totally 118) in the first and 20/72 (totally 92) in the second laboratory. In the first laboratory, the corresponding ratios by methods were 35/83 (sensitivity 100%, specificity 96.5%), 31/87 (sensitivity 97%, specificity 100%), and 55/63 (sensitivity 100%, specificity 79%) for the L, P and G methods, respectively. In the second laboratory, the ratios were 21/71 (sensitivity 100%, specificity 99%), 16/76 (sensitivity 83%, specificity 100%), and 53/39 (sensitivity 100, specificity 69%) for the L, V and G methods, respectively. Taking into account that the tested sample material was preselected by the P and V methods, which may have introduced some bias in their favor, the newly developed L method utilizing P1-enriched protein was found reliable for serodiagnosis of acute M. pneumoniae infection. The method G was the least specific in detection of acute infection.     

Detection of epileptogenic cortical malformations with surface-based MRI morphometry

Magnetic resonance imaging has revolutionized the detection of structural abnormalities in patients with epilepsy. However, many focal abnormalities remain undetected in routine visual inspection. Here we use an automated, surface-based method for quantifying morphometric features related to epileptogenic cortical malformations to detect abnormal cortical thickness and blurred gray-white matter boundaries. Using MRI morphometry at 3T with surface-based spherical averaging techniques that precisely align anatomical structures between individual brains, we compared single patients with known lesions to a large normal control group to detect clusters of abnormal cortical thickness, gray-white matter contrast, local gyrification, sulcal depth, jacobian distance and curvature. To assess the effects of threshold and smoothing on detection sensitivity and specificity, we systematically varied these parameters with different thresholds and smoothing levels. To test the effectiveness of the technique to detect lesions of epileptogenic character, we compared the detected structural abnormalities to expert-tracings, intracranial EEG, pathology and surgical outcome in a homogeneous patient sample. With optimal parameters and by combining thickness and GWC, the surface-based detection method identified 92% of cortical lesions (sensitivity) with few false positives (96% specificity), successfully discriminating patients from controls 94% of the time. The detected structural abnormalities were related to the seizure onset zones, abnormal histology and positive outcome in all surgical patients. However, the method failed to adequately describe lesion extent in most cases. Automated surface-based MRI morphometry, if used with optimized parameters, may be a valuable additional clinical tool to improve the detection of subtle or previously occult malformations and therefore could improve identification of patients with intractable focal epilepsy who may benefit from surgery.     

Performance assessment of new-generation Fitbit technology in deriving sleep parameters and stages

ABSTRACT

We compared performance in deriving sleep variables by both Fitbit Charge 2?, which couples body movement (accelerometry) and heart rate variability (HRV) in combination with its proprietary interpretative algorithm (IA), and standard actigraphy (Motionlogger® Micro Watch Actigraph: MMWA), which relies solely on accelerometry in combination with its best performing ‘Sadeh’ IA, to electroencephalography (EEG: Zmachine® Insight+ and its proprietary IA) used as reference. We conducted home sleep studies on 35 healthy adults, 33 of whom provided complete datasets of the three simultaneously assessed technologies. Relative to the Zmachine EEG method, Fitbit showed an overall Kappa agreement of 54% in distinguishing wake/sleep epochs and sensitivity of 95% and specificity of 57% in detecting sleep epochs. Fitbit, relative to EEG, underestimated sleep onset latency (SOL) by ~11 min and overestimated sleep efficiency (SE) by ~4%. There was no statistically significant difference between Fitbit and EEG methods in measuring wake after sleep onset (WASO) and total sleep time (TST). Fitbit showed substantial agreement with EEG in detecting rapid eye movement and deep sleep, but only moderate agreement in detecting light sleep. The MMWA method showed 51% overall Kappa agreement with the EEG one in detecting wake/sleep epochs, with sensitivity of 94% and specificity of 53% in detecting sleep epochs. MMWA, relative to EEG, underestimated SOL by ~10 min. There was no significant difference between Fitbit and MMWA methods in amount of bias in estimating SOL, WASO, TST, and SE; however, the minimum detectable change (MDC) per sleep variable with Fitbit was better (smaller) than with MMWA, respectively, by ~10 min, ~16 min, ~22 min, and ~8%. Overall, performance of Fitbit accelerometry and HRV technology in conjunction with its proprietary IA to detect sleep vs. wake episodes is slightly better than wrist actigraphy that relies solely on accelerometry and best performing Sadeh IA. Moreover, the smaller MDC of Fitbit technology in deriving sleep parameters in comparison to wrist actigraphy makes it a suitable option for assessing changes in sleep quality over time, longitudinally, and/or in response to interventions.     

A Systematic Review and Meta-Analysis of the Performance of Two Point of Care Typhoid Fever Tests,Tubex TF and Typhidot,in Endemic Countries

Background

In the absence of well-equipped laboratory infrastructure in many developing countries the accurate diagnosis of typhoid fever is challenging. Rapid diagnostic tests (RDT) with good performance indicators would be helpful to improve clinical management of suspected cases. We performed a systematic literature review and meta- analysis to determine the performance of TUBEX TF and Typhidot for the diagnosis of typhoid fever using PRISMA guidelines.

Methods

Titles and abstracts were reviewed for relevance. Articles were screened for language, reference method and completeness. Studies were categorized according to control groups used. Meta-analysis was performed only for categories where enough data was available to combine sensitivity and specificity estimates. Sub-analysis was performed for the Typhidot test to determine the influence of indeterminate results on test performance.

Results

A total of seven studies per test were included. The sensitivity of TUBEX TF ranged between 56% and 95%, Specificity between 72% and 95%. Meta-analysis showed an average sensitivity of 69% (95%CI: 45–85) and an average specificity of 88% (CI95%:83–91). A formal meta-analysis for Typhidot was not possible due to limited data available. Across the extracted studies, sensitivity and specificity estimates ranged from 56% to 84% and 31% to 97% respectively.

Conclusion

The observed performance does not support the use of either rapid diagnostic test exclusively as the basis for diagnosis and treatment. There is a need to develop an RDT for typhoid fever that has a performance level comparable to malaria RDTs.     

Detection of Seizure Event and Its Onset/Offset Using Orthonormal Triadic Wavelet Based Features

Background

Epileptic seizures are unpredictable in nature and its quick detection is important for immediate treatment of patients. In last few decades researchers have proposed different algorithms for onset and offset detection of seizure using Electroencephalogram (EEG) signals.

Development and validation of a computational method for assessment of missense variants in hypertrophic cardiomyopathy

Assessing the significance of novel genetic variants revealed by DNA sequencing is a major challenge to the integration of genomic techniques with medical practice. Many variants remain difficult to classify by traditional genetic methods. Computational methods have been developed that could contribute to classifying these variants, but they have not been properly validated and are generally not considered mature enough to be used effectively in a clinical setting. We developed a computational method for predicting the effects of missense variants detected in patients with hypertrophic cardiomyopathy (HCM). We used a curated clinical data set of 74 missense variants in six genes associated with HCM to train and validate an automated predictor. The predictor is based on support vector regression and uses phylogenetic and structural features specific to genes involved in HCM. Ten-fold cross validation estimated our predictor's sensitivity at 94% (95% confidence interval: 83%-98%) and specificity at 89% (95% confidence interval: 72%-100%). This corresponds to an odds ratio of 10 for a prediction of pathogenic (95% confidence interval: 4.0-infinity), or an odds ratio of 9.9 for a prediction of benign (95% confidence interval: 4.6-21). Coverage (proportion of variants for which a prediction was made) was 57% (95% confidence interval: 49%-64%). This performance exceeds that of existing methods that are not specifically designed for HCM. The accuracy of this predictor provides support for the clinical use of automated predictions alongside family segregation and population frequency data in the interpretation of new missense variants and suggests future development of similar tools for other diseases.     

An alternative sputum processing method using chitin for the isolation of Mycobacterium tuberculosis

An alternative bio-friendly sputum processing method is the need of the hour to augment the rate of detection of TB cases and to improve the sensitivity of rapid growth based diagnostic methods. Chitin, mucolytic in nature and present ubiquitously in animal kingdom, was found to have decontaminating activity when used for processing sputum specimens. The aim of the present study is to develop an alternative bio friendly sputum processing method using chitin. Smear microscopy was done on direct sputum samples and on the deposits obtained after processing with modified Petroff’s method as well as Chitin method. Two direct smears were made from each of the sputum samples and stained by Ziehl Neelsen and Auramine phenol (AP) method. The samples were divided in to two aliquots and processed by chitin and modified Petroff’s method. Smears were made from each of the deposits and stained by both methods. The deposits were inoculated on to two Lowenstein Jensen slopes. AP method showed a sensitivity of 95% in direct smear. Samples processed by chitin and the deposit smears stained by AP method showed a sensitivity of 80% and a specificity of 89% compared to that of modified Petroff’s method. The sensitivity of chitin culture is 87% and the specificity is 85%. Chitin–H₂So₄ solution took less time compared to 4% NaOH to homogenize the mucopurulent sputum specimens. Chitin–H₂So₄ can be used as an alternative method of sputum processing for the detection of M. tuberculosis.     

Detection of blob objects in microscopic zebrafish images based on gradient vector diffusion.

The zebrafish has become an important vertebrate animal model for the study of developmental biology, functional genomics, and disease mechanisms. It is also being used for drug discovery. Computerized detection of blob objects has been one of the important tasks in quantitative phenotyping of zebrafish. We present a new automated method that is able to detect blob objects, such as nuclei or cells in microscopic zebrafish images. This method is composed of three key steps. The first step is to produce a diffused gradient vector field by a physical elastic deformable model. In the second step, the flux image is computed on the diffused gradient vector field. The third step performs thresholding and nonmaximum suppression based on the flux image. We report the validation and experimental results of this method using zebrafish image datasets from three independent research labs. Both sensitivity and specificity of this method are over 90%. This method is able to differentiate closely juxtaposed or connected blob objects, with high sensitivity and specificity in different situations. It is characterized by a good, consistent performance in blob object detection.     

Bone Suppression Increases the Visibility of Invasive Pulmonary Aspergillosis in Chest Radiographs

Objective

Chest radiographs (CXR) are an important diagnostic tool for the detection of invasive pulmonary aspergillosis (IPA) in critically ill patients, but their diagnostic value is limited by a poor sensitivity. By using advanced image processing, the aim of this study was to increase the value of chest radiographs in the diagnostic work up of neutropenic patients who are suspected of IPA.

Methods

The frontal CXRs of 105 suspected cases of IPA were collected from four institutions. Radiographs could contain single or multiple sites of infection. CT was used as reference standard. Five radiologists and two residents participated in an observer study for the detection of IPA on CXRs with and without bone suppressed images (ClearRead BSI 3.2; Riverain Technologies). The evaluation was performed separately for the right and left lung, resulting in 78 diseased cases (or lungs) and 132 normal cases (or lungs). For each image, observers scored the likelihood of focal infectious lesions being present on a continuous scale (0–100). The area under the receiver operating characteristics curve (AUC) served as the performance measure. Sensitivity and specificity were calculated by considering only the lungs with a suspiciousness score of greater than 50 to be positive.

Results

The average AUC for only CXRs was 0.815. Performance significantly increased, to 0.853, when evaluation was aided with BSI (p = 0.01). Sensitivity increased from 49% to 66% with BSI, while specificity decreased from 95% to 90%.

Conclusion

The detection of IPA in CXRs can be improved when their evaluation is aided by bone suppressed images. BSI improved the sensitivity of the CXR examination, outweighing a small loss in specificity.     

血浆内皮素配合描记动态脑电图在儿童晕厥诊断中应用的临床意义

摘要 目的：探究血浆内皮素配合描记动态脑电图在儿童晕厥诊断中的应用意义。方法：选择2017年1月至2020年1月于我院接受治疗的83例存在晕厥风险儿童,采集其静脉血样进行血浆内皮素水平测定,并实施动态脑电图检测,而后以倾斜试验结果为金标准,分别分析单纯血浆内皮素、单纯动态脑电图以及血浆内皮素+动态脑电图对晕厥的诊断应用意义。结果：（1）83例入组儿童中阳性为68例,阴性为15例,血浆内皮素检测阳性51例,阴性32例,一致性为77.11 %,灵敏度为73.53 %,特异度为93.33 %,阳性预测值为98.04 %, 阴性预测值为43.75 %;（2）动态脑电图诊断一致性为78.31 %,灵敏度为80.88 %,特异度为66.67 %,阳性预测值为91.67 %,阴性预测值为43.48 %;（3）联合检测诊断一致性为93.98 %,灵敏度为94.12 %,特异度为93.33 %,阳性预测值为98.46 %,阴性预测值为77.78 %;（4）检测方式差异性比较发现,联合检测在一致性、灵敏度、阴性预测值方面明显优于血浆内皮素和动态脑电图检测,在特异度方面优于动态脑电图检测（P<0.05）。结论：血浆内皮素联合描记动态脑电图对儿童晕厥具有较好的诊断辅助价值,能够显著提高诊断的一致性、灵敏度和特异度。     

Interferon-gamma release assays for the diagnosis of extrapulmonary tuberculosis: a systematic review and meta-analysis

Interferon -gamma release assays (IGRAs) provide a new diagnostic method for Mycobacterium tuberculosis (TB) infection. However, the diagnostic value of IGRAs for extrapulmonary TB (EPTB) has not been clarified. We searched several databases and selected papers with strict inclusion criteria, evaluated the evidence of commercially available IGRAs (QuantiFERON(?) -TB Gold QFT-G or QFT-GIT and T-SPOT(?) .TB) on blood and the tuberculin skin test (TST) using random effects models. Twenty studies with 1711 patients were included. After excluding indeterminate results, pooled sensitivity for the diagnosis of EPTB was 72% [95% confidence interval (CI) 65-79%] for QFT-G or GIT and 90% (95% CI, 86-93%) for T-SPOT; in high-income countries the sensitivity of QFT-G or GIT (79%, 95% CI 72-86%) was much higher than that (29%, 95% CI 14-48%) in low/middle-income countries. Pooled specificity for EPTB was 82% (95% CI 78-87%) for QFT-G or GIT and 68% (95% CI 64-73%) for T-SPOT. Pooled sensitivity of TST from four studies in high-income countries was lower than that of IGRAs. T-SPOT was more sensitive in detecting EPTB than QFT-G or GIT and TST. However, both IGRAs and TST have similar specificity for EPTB. IGRAs have limited value as diagnostic tools to screen and rule out EPTB, especially in low/middle-income countries. The immune status of patients does not affect the diagnostic accuracy of IGRAs for EPTB.     

Auditory evoked potentials for the assessment of depth of anaesthesia: different configurations of artefact detection algorithms.

Monitoring the depth of anaesthesia has become an important research topic in the field of biosignal processing. Auditory evoked potentials (AEPs) have been shown to be a promising tool for this purpose. Signals recorded in the noisy environment of an operating theatre are often contaminated by artefacts. Thus, artefact detection and elimination in the underlying electroencephalogram (EEG) are mandatory before AEP extraction. Determination of a suitable artefact detection configuration based on EEG data from a clinical study is described. Artefact detection algorithms and an AEP extraction procedure encompassing the artefact detection results are presented. Different configurations of artefact detection algorithms are evaluated using an AEP verification procedure and support vector machines to determine a suitable configuration for the assessment of depth of anaesthesia using AEPs.     

一种检测kras基因突变的新型TB-ARMS qPCR方法的建立

在荧光定量PCR基础上建立一种简单有效并且高度灵敏的TB-ARMSkras基因突变检测方法,并对其检测性能进行评估,探讨其临床应用价值。针对kras基因8种常见的点突变类型,通过设计并优化突变特异性引物、野生型特异性封闭引物并综合应用突变富集扩增反应条件等多种手段,提高点突变检测的灵敏度和特异性,采用已知野生型基因组样品和构建的突变质粒作为标准品,进行方法学评价;通过对临床样本的检测及与现有商品化试剂盒的比较进行性能验证;通过对术前血浆和配对组织样品的对比检测,评估方法是否适用于血液样本的检测。建立了TB-ARMS kras突变检测的新方法,能检测的最低突变率可达到0.01%。通过综合采用野生型特异性封闭引物和突变富集扩增条件等方法证明了其0.01%的突变检测灵敏度。检测准确性优于现有商品化试剂盒,血浆DNA TB-ARMS qPCR检测结果与配对组织DNA测序结果相符合。因此,TB-ARMS kras基因突变检测方法具有广泛的临床应用价值,既适用于临床组织样品的检测,也可应用于液体活检。