Evaluation of a Fully Automated Antinuclear Antibody Indirect Immunofluorescence Assay in Routine Use.

Indirect immunofluorescence assay (IFA) using HEp-2 cells as a substrate is the gold standard for detecting antinuclear antibodies (ANA) in patient serum. However, the ANA IFA has labor-intensive nature of the procedure and lacks adequate standardization. To overcome these drawbacks, the automation has been developed and implemented to the clinical laboratory. The purposes of this study were to evaluate the analytical performance of a fully automated Helios ANA IFA analyzer in a real-life laboratory setting, and to compare the time and the cost of ANA IFA testing before and after adopting the Helios system. A total of 3,276 consecutive serum samples were analyzed for ANA using the Helios system from May to August 2019. The positive/negative results, staining patterns, and endpoint titers were compared between Helios and visual readings. Furthermore, the turnaround time and the number of wells used were compared before and after the introduction of Helios system. Of the 3,276 samples tested, 748 were positive and 2,528 were negative based on visual readings. Using visual reading as the reference standard, the overall relative sensitivity, relative specificity, and concordance of Helios reading were 73.3, 99.4, and 93.4% (κ = 0.80), respectively. For pattern recognition, the overall agreement was 70.1% (298/425) for single patterns, and 72.4% (89/123) for mixed patterns. For titration, there was an agreement of 75.9% (211/278) between automated and classical endpoint titers by regarding within ± one titer difference as acceptable. Helios significantly shortened the median turnaround time from 100.6 to 55.7 h (P < 0.0001). Furthermore, routine use of the system reduced the average number of wells used per test from 4 to 1.5. Helios shows good agreement in distinguishing between positive and negative results. However, it still has limitations in positive/negative discrimination, pattern recognition, and endpoint titer prediction, requiring additional validation of results by human observers. Helios provides significant advantages in routine laboratory ANA IFA work in terms of labor, time, and cost savings. We hope that upgrading and developing softwares with more reliable capabilities will allow automated ANA IFA analyzers to be fully integrated into the routine operations of the clinical laboratory.

Pattern-based recognition for the rapid determination of identity, concentration, and enantiomeric excess of subtly different threo diols.

A pattern-based recognition approach for the rapid determination of the identity, concentration, and enantiomeric excess of chiral vicinal diols, specifically threo diols, has been developed. A diverse enantioselective sensor array was generated using three chiral boronic acid receptors and three pH indicators. The optical response produced by the sensor array was analyzed by two pattern-recognition algorithms: principal component analysis and artificial neural networks. Principal component analysis demonstrated good chemoselective and enantioselective separation of the analytes, and an artificial neural network was used to accurately determine the concentrations and enantiomeric excesses of five unknown samples with an average absolute error of +/-0.08 mM in concentration and 3.6% in enantiomeric excess. The speed of the analysis was enhanced by using a 96-well plate format, portending applications in high-throughput screening for asymmetric-catalyst discovery. X-ray crystallography and (11)B NMR spectroscopy was utilized to study the enantioselective nature of the boronic acid host 2.

Automatic ham classification method based on support vector machine model increases accuracy and benefits compared to manual classification.

The thickness of the subcutaneous fat (SFT) is a very important parameter in the ham, since determines the process the ham will be submitted. This study compares two methods to predict the SFT in slaughter line: an automatic system using an SVM model (Support Vector Machine) and a manual measurement of the fat carried out by an experienced operator, in terms of accuracy and economic benefit. These two methods were compared to the golden standard obtained by measuring SFT with a ruler in a sample of 400 hams equally distributed within each SFT class. The results show that the SFT prediction made by the SVM model achieves an accuracy of 75.3%, which represents an improvement of 5.5% compared to the manual measurement. Regarding economic benefits, SVM model can increase them between 12 and 17%. It can be concluded that the classification using SVM is more accurate than the one performed manually with an increase of the economic benefit for sorting.

A fully automated Drosophila olfactory classical conditioning and testing system for behavioral learning and memory assessment.

BACKGROUND: Aversive olfactory classical conditioning has been the standard method to assess Drosophila learning and memory behavior for decades, yet training and testing are conducted manually under exceedingly labor-intensive conditions. To overcome this severe limitation, a fully automated, inexpensive system has been developed, which allows accurate and efficient Pavlovian associative learning/memory analyses for high-throughput pharmacological and genetic studies. NEW METHOD: The automated system employs a linear actuator coupled to an odorant T-maze with airflow-mediated transfer of animals between training and testing stages. Odorant, airflow and electrical shock delivery are automatically administered and monitored during training trials. Control software allows operator-input variables to define parameters of Drosophila learning, short-term memory and long-term memory assays. RESULTS: The approach allows accurate learning/memory determinations with operational fail-safes. Automated learning indices (immediately post-training) and memory indices (after 24h) are comparable to traditional manual experiments, while minimizing experimenter involvement. COMPARISON WITH EXISTING METHODS: The automated system provides vast improvements over labor-intensive manual approaches with no experimenter involvement required during either training or testing phases. It provides quality control tracking of airflow rates, odorant delivery and electrical shock treatments, and an expanded platform for high-throughput studies of combinational drug tests and genetic screens. The design uses inexpensive hardware and software for a total cost of ∼$500US, making it affordable to a wide range of investigators. CONCLUSIONS: This study demonstrates the design, construction and testing of a fully automated Drosophila olfactory classical association apparatus to provide low-labor, high-fidelity, quality-monitored, high-throughput and inexpensive learning and memory behavioral assays.

Automated chest-radiography as a triage for Xpert testing in resource-constrained settings: a prospective study of diagnostic accuracy and costs.

Molecular tests hold great potential for tuberculosis (TB) diagnosis, but are costly, time consuming, and HIV-infected patients are often sputum scarce. Therefore, alternative approaches are needed. We evaluated automated digital chest radiography (ACR) as a rapid and cheap pre-screen test prior to Xpert MTB/RIF (Xpert). 388 suspected TB subjects underwent chest radiography, Xpert and sputum culture testing. Radiographs were analysed by computer software (CAD4TB) and specialist readers, and abnormality scores were allocated. A triage algorithm was simulated in which subjects with a score above a threshold underwent Xpert. We computed sensitivity, specificity, cost per screened subject (CSS), cost per notified TB case (CNTBC) and throughput for different diagnostic thresholds. 18.3% of subjects had culture positive TB. For Xpert alone, sensitivity was 78.9%, specificity 98.1%, CSS $13.09 and CNTBC $90.70. In a pre-screening setting where 40% of subjects would undergo Xpert, CSS decreased to $6.72 and CNTBC to $54.34, with eight TB cases missed and throughput increased from 45 to 113 patients/day. Specialists, on average, read 57% of radiographs as abnormal, reducing CSS ($8.95) and CNTBC ($64.84). ACR pre-screening could substantially reduce costs, and increase daily throughput with few TB cases missed. These data inform public health policy in resource-constrained settings.

Learning a cost function for microscope image segmentation.

Quantitative analysis of microscopy images is increasingly important in clinical researchers' efforts to unravel the cellular and molecular determinants of disease, and for pathological analysis of tissue samples. Yet, manual segmentation and measurement of cells or other features in images remains the norm in many fields. We report on a new system that aims for robust and accurate semi-automated analysis of microscope images. A user interactively outlines one or more examples of a target object in a training image. We then learn a cost function for detecting more objects of the same type, either in the same or different images. The cost function is incorporated into an active contour model, which can efficiently determine optimal boundaries by dynamic programming. We validate our approach and compare it to some standard alternatives on three different types of microscopic images: light microscopy of blood cells, light microscopy of muscle tissue sections, and electron microscopy cross-sections of axons and their myelin sheaths.

Differentiation of tea varieties using UV-Vis spectra and pattern recognition techniques.

Tea, one of the most consumed beverages all over the world, is of great importance in the economies of a number of countries. Several methods have been developed to classify tea varieties or origins based in pattern recognition techniques applied to chemical data, such as metal profile, amino acids, catechins and volatile compounds. Some of these analytical methods become tedious and expensive to be applied in routine works. The use of UV-Vis spectral data as discriminant variables, highly influenced by the chemical composition, can be an alternative to these methods. UV-Vis spectra of methanol-water extracts of tea have been obtained in the interval 250-800 nm. Absorbances have been used as input variables. Principal component analysis was used to reduce the number of variables and several pattern recognition methods, such as linear discriminant analysis, support vector machines and artificial neural networks, have been applied in order to differentiate the most common tea varieties. A successful classification model was built by combining principal component analysis and multilayer perceptron artificial neural networks, allowing the differentiation between tea varieties. This rapid and simple methodology can be applied to solve classification problems in food industry saving economic resources.

Multiscale peak alignment for chromatographic datasets.

Chromatography has been extensively applied in many fields, such as metabolomics and quality control of herbal medicines. Preprocessing, especially peak alignment, is a time-consuming task prior to the extraction of useful information from the datasets by chemometrics and statistics. To accurately and rapidly align shift peaks among one-dimensional chromatograms, multiscale peak alignment (MSPA) is presented in this research. Peaks of each chromatogram were detected based on continuous wavelet transform (CWT) and aligned against a reference chromatogram from large to small scale gradually, and the aligning procedure is accelerated by fast Fourier transform cross correlation. The presented method was compared with two widely used alignment methods on chromatographic dataset, which demonstrates that MSPA can preserve the shapes of peaks and has an excellent speed during alignment. Furthermore, MSPA method is robust and not sensitive to noise and baseline. MSPA was implemented and is available at http://code.google.com/p/mspa.

A cost-sensitive extension of AdaBoost with markov random field priors for automated segmentation of breast tumors in ultrasonic images.

PURPOSE: A cost-sensitive extension of AdaBoost based on Markov random field (MRF) priors was developed to train an ensemble segmentation process which can avoid irregular shape, isolated points and holes, leading to lower error rate. The method was applied to breast tumor segmentation in ultrasonic images. METHODS: A cost function was introduced into the AdaBoost algorithm that penalizes dissimilar adjacent labels in MRF regularization. The extended AdaBoost algorithm generates a series of weak segmentation processes by sequentially selecting a process whose error rate weighted by the cost is minimum. The method was tested by generation of an ensemble segmentation process for breast tumors in ultrasonic images. This was followed by a active contour to refine the extracted tumor boundary. RESULTS: The segmentation performance was evaluated by tenfold cross validation test, where 300 carcinomas, 50 fibroadenomas, and 50 cysts were used. The experimental results revealed that the error rate of the proposed ensemble segmentation was two-thirds the error rate of the segmentation trained by AdaBoost without MRF. By combining the ensemble segmentation with a geodesic active contour, the average Jaccard index between the extracted tumors and the manually segmented true regions was 93.41%, significantly higher than the conventional segmentation process. CONCLUSION: A cost-sensitive extension of AdaBoost based on MRF priors provides an efficient and accurate means for the segmentation of tumors in breast ultrasound images.

Efficient computation and model selection for the support vector regression.

In this letter, we derive an algorithm that computes the entire solution path of the support vector regression (SVR). We also propose an unbiased estimate for the degrees of freedom of the SVR model, which allows convenient selection of the regularization parameter.