Carla Duarte

Lab-on-Chip Cytometry Based on Magnetoresistive Sensors for Bacteria Detection in Milk

Flow cytometers have been optimized for use in portable platforms, where cell separation, identification and counting can be achieved in a compact and modular format. This feature can be combined with magnetic detection, where magnetoresistive sensors can be integrated within microfluidic channels to detect magnetically labelled cells. This work describes a platform for in-flow detection of magnetically labelled cells with a magneto-resistive based cell cytometer. In particular, we present an example for the validation of the platform as a magnetic counter that identifies and quantifies Streptococcus agalactiae in milk.

Performance enhanced UV/vis spectroscopic microfluidic sensor for ascorbic acid quantification in human blood.

Quantitative analysis of antioxidants in a fast, simple and accurate manner is of great importance in the view of real-time monitoring the health of individuals. Recently, we have developed a UV/vis spectroscopic microfluidic sensor to specifically quantify ascorbic acid based on the immobilization of ascorbate oxidase, a relatively unstable enzyme. In this work, three different strategies for the immobilization of the unstable enzyme, including alumina sol-gel encapsulation, physisorption to PDMS channels with, and without alumina xerogel modification, were compared to build a microsensor. We found that the loading amount of the enzyme is not the determinative factor for the performance of the microfluidic biosensor but the retained activity of the enzyme and diffusion in the microfluidic channel. Taking into account of the two factors, the protocol of adsorbing enzymes to alumina (Al2O3) xerogel modified PDMS surface was demonstrated to be the best for preparing the microfluidic sensor among the utilized protocols. The microsensor prepared under the optimized protocol was further used to quantify ascorbic acid in human blood, where only dozens of microliters of blood (few drops) was required, demonstrating its potential application in clinical diagnosis. The developed strategy is featured with optimized enzymatic activity, simple process of microfluidic platform, low sample consumption, and straightforward spectrophotometry based detection.

Semi-Quantitative Method for Streptococci Magnetic Detection in Raw Milk

Bovine mastitis is the most costly disease for dairy farmers and the most frequent reason for the use of antibiotics in dairy cattle; thus, control measures to detect and prevent mastitis are crucial for dairy farm sustainability. The aim of this study was to develop and validate a sensitive method to magnetically detect Streptococcus agalactiae (a Group B streptococci) and Streptococcus uberis in raw milk samples. Mastitic milk samples were collected aseptically from 44 cows with subclinical mastitis, from 11 Portuguese dairy farms. Forty-six quarter milk samples were selected based on bacterial identification by conventional microbiology. All samples were submitted to PCR analysis. In parallel, these milk samples were mixed with a solution combining specific antibodies and magnetic nanoparticles, to be analyzed using a lab-on-a-chip magnetoresistive cytometer, with microfluidic sample handling. This paper describes a point of care methodology used for detection of bacteria, including analysis of false positive/negative results. This immunological recognition was able to detect bacterial presence in samples spiked above 100 cfu/mL, independently of antibody and targeted bacteria used in this work. Using PCR as a reference, this method correctly identified 73% of positive samples for streptococci species with an anti-S. agalactiae antibody, and 41% of positive samples for an anti-GB streptococci antibody.

Enhancing analog IC design optimization kernels with simple fuzzy models

This paper addresses the analog integrated circuit design automation by proposing an innovative circuit-level optimization kernel. The proposed approach, first, models analog design knowledge using soft computing techniques, than, enhances a stochastic optimization kernel by embedding the design knowledge model. The proposed approach uses common IC design environments and is validated for well known design examples.

Magnetic Counter for Group B Streptococci Detection in Milk

Identification of bovine mastitis pathogens is necessary to control the disease, reduce the risk of chronic infections, and target the antimicrobial therapy to be prescribed. Development prospects for new bovine mastitis diagnosis methodologies go also through rapid and efficient devices that can offer a “cow-side” use, meaning that raw milk collected for analysis should have limited pretreatment. This paper aims at developing a magnetic counter that identifies the presence of Streptococcus agalactiae (a Group B Streptococci) in raw milk. The detection is done with an integrated microfluidic platform, where 50 nm magnetic beads attached to Streptococcus agalactiae are dynamically detected by magnetoresistive sensors. This device allows the analysis of raw milk without bridging the microfluidic channels, making this integrated platform very attractive for fast bacteriological contamination screening.

Semi-quantitative method for Staphylococci magnetic detection in raw milk

Bovine mastitis is the most costly disease for dairy farmers, hence, control measures to prevent it are crucial for dairy farm sustainability. Staphylococcus aureus is considered a major mastitis pathogen because of its impact on milk quality and low cure rates. Prevention of S. aureus mastitis includes segregation of infected animals, whilst treatment of such animals should be performed for a longer time to improve cure rates. This makes identification of S. aureus infected quarters and animals of significant importance. The experiments reported in this research paper aimed to develop and validate a sensitive method for magnetic detection of S. aureus and of the Staphylococcus genus in raw milk samples. Mastitic milk samples were collected aseptically from 47 cows with subclinical mastitis, from 12 Portuguese dairy farms. Forty nine quarter milk samples were selected based on bacteriological results. All samples were submitted to PCR analysis. In parallel, these milk samples were mixed with a solution combining specific antibodies and magnetic nanoparticles, to be analysed using a lab-on-a-chip magnetoresistive cytometer, with microfluidic sample handling. The antibodies used in this work were a rabbit polyclonal IgG anti-S. aureus ScpA protein and a mouse monoclonal IgM anti-S. aureus ATCC 29740. This paper describes the methodology used for magnetic detection of bacteria, including analysis of false positive/negative results. This immunological recognition was able to detect bacterial presence above 100 cfu/ml, independently of antibody and targeted bacteria used in this work. Comparison with PCR results showed sensitivities of 57·1 and 79·3%, specificity values of 75 and 50%, and PPV values of 40 and 95·8% for magnetic identification of Staphylococci species with an anti-S. aureus antibody and an anti-Staphylococcus spp. antibody, respectively. Some constraints are described as well as the methods limitations in bacterial quantification. Sensitivities and specificities require to be improved, nevertheless, the methodology described may form the basis for a means of identifying S. aureus infected cows at the point of care.

Optimal OpAmp sizing based on a fuzzy-genetic kernel

In this paper an innovative fuzzy-genetic approach is proposed to address the problem of analog circuit sizing. The proposed approach introduces a fuzzy mutation operator which models expert design knowledge and this way not only avoids local minima but also reduces the search dynamically the space. The proposed approach is compared against a state-of-the art genetic approach, for the optimal operational amplifier sizing, and presents a faster convergence rate.

FUGA: a fuzzy-genetic analog circuit optimization kernel

This paper describes an innovative analog circuit design optimization kernel. The new approach generates fuzzy models for qualitative reasoning based on a DOE approach. The models are then used within a standard genetic algorithm implementation enhancing the search by incorporating design knowledge represented by the fuzzy models. The achieved performance is discussed for a set of well known analog circuit structures.