Comparative quantification of umbilical cord blood CD34+ and CD34+ bright cells using the ProCount™-BD and ISHAGE protocols

The total number of CD34+ cells is the most relevant clinical parameter when selecting human umbilical cord blood (HUCB) for transplantation. The objective of the present study was to compare the two most commonly used CD34+ cell quantification methods (ISHAGE protocol and ProCount™ - BD) and analyze the CD34+ bright cells whose 7-amino actinomycin D (7AAD) analysis suggests are apoptotic or dead cells. Twenty-six HUCB samples obtained at the Placental Blood Program of New York Blood Center were evaluated. The absolute numbers of CD34+ cells evaluated by the ISHAGE (with exclusion of 7AAD+ cells) and ProCount™ (with exclusion of CD34+ bright cells) were determined. Using the ISHAGE protocol we found 35.6 ± 19.4 CD34+ cells/µL and with the ProCount™ method we found 36.6 ± 23.2 CD34+ cells/µL. With the ProCount™ method, CD34+ bright cell counts were 9.3 ± 8.2 cells/µL. CD34+ bright and regular cells were individually analyzed by the ISHAGE protocol. Only about 1.8% of the bright CD34+ cells are alive, whereas a small part (19.0%) is undergoing apoptosis and most of them (79.2%) are dead cells. Our study showed that the two methods produced similar results and that 7AAD is important to exclude CD34 bright cells. These results will be of value to assist in the correct counting of CD34+ cells and to choose the best HUCB unit for transplantation, i.e., the unit with the greatest number of potentially viable stem cells for the reconstitution of bone marrow. This increases the likelihood of success of the transplant and, therefore, the survival of the patient.

Texture analysis of computed tomography images of acute ischemic stroke patients

Computed tomography (CT) images are routinely used to assess ischemic brain stroke in the acute phase. They can provide important clues about whether to treat the patient by thrombolysis with tissue plasminogen activator. However, in the acute phase, the lesions may be difficult to detect in the images using standard visual analysis. The objective of the present study was to determine if texture analysis techniques applied to CT images of stroke patients could differentiate between normal tissue and affected areas that usually go unperceived under visual analysis. We performed a pilot study in which texture analysis, based on the gray level co-occurrence matrix, was applied to the CT brain images of 5 patients and of 5 control subjects and the results were compared by discriminant analysis. Thirteen regions of interest, regarding areas that may be potentially affected by ischemic stroke, were selected for calculation of texture parameters. All regions of interest for all subjects were classified as lesional or non-lesional tissue by an expert neuroradiologist. Visual assessment of the discriminant analysis graphs showed differences in the values of texture parameters between patients and controls, and also between texture parameters for lesional and non-lesional tissue of the patients. This suggests that texture analysis can indeed be a useful tool to help neurologists in the early assessment of ischemic stroke and quantification of the extent of the affected areas.

Manual and semi-automatic quantification of in vivo ¹H-MRS data for the classification of human primary brain tumors

In vivo proton magnetic resonance spectroscopy (¹H-MRS) is a technique capable of assessing biochemical content and pathways in normal and pathological tissue. In the brain, ¹H-MRS complements the information given by magnetic resonance images. The main goal of the present study was to assess the accuracy of ¹H-MRS for the classification of brain tumors in a pilot study comparing results obtained by manual and semi-automatic quantification of metabolites. In vivo single-voxel ¹H-MRS was performed in 24 control subjects and 26 patients with brain neoplasms that included meningiomas, high-grade neuroglial tumors and pilocytic astrocytomas. Seven metabolite groups (lactate, lipids, N-acetyl-aspartate, glutamate and glutamine group, total creatine, total choline, myo-inositol) were evaluated in all spectra by two methods: a manual one consisting of integration of manually defined peak areas, and the advanced method for accurate, robust and efficient spectral fitting (AMARES), a semi-automatic quantification method implemented in the jMRUI software. Statistical methods included discriminant analysis and the leave-one-out cross-validation method. Both manual and semi-automatic analyses detected differences in metabolite content between tumor groups and controls (P < 0.005). The classification accuracy obtained with the manual method was 75% for high-grade neuroglial tumors, 55% for meningiomas and 56% for pilocytic astrocytomas, while for the semi-automatic method it was 78, 70, and 98%, respectively. Both methods classified all control subjects correctly. The study demonstrated that ¹H-MRS accurately differentiated normal from tumoral brain tissue and confirmed the superiority of the semi-automatic quantification method.

Determining aflatoxins B1, B2, G1 and G2 in maize using florisil clean up with thin layer chromatography and visual and densitometric quantification

A method for determining aflatoxins B1 (AFB1), B2 (AFB2),G1 (AFG1) andG2 (AFG2) in maize with florisil clean up was optimised aiming at one-dimensional thin layer chromatography (TLC) analysis with visual and densitometric quantification. Aflatoxins were extracted with chloroform: water (30:1, v/v), purified through florisil cartridges, separated on TLC plate, detected and quantified by visual and densitometric analysis. The in-house method performance characteristics were determined by using spiked, naturally contaminated maize samples, and certified reference material. The mean recoveries for aflatoxins were 94.2, 81.9, 93.5 and 97.3% in the range of 1.0 to 242 µg/kg for AFB1, 0.3 to 85mg/kg for AFB2, 0.6 to 148mg/kg for AFG1 and 0.6 to 140mg/kg for AFG2, respectively. The correlation values between visual and densitometric analysis for spiked samples were higher than 0.99 for AFB1, AFB2, AFG1 and 0.98 for AFG2. The mean relative standard deviations (RSD) for spiked samples were 16.2, 20.6, 12.8 and 16.9% for AFB1, AFB2, AFG1 and AFG2, respectively. The RSD of the method for naturally contaminated sample (n = 5) was 16.8% for AFB1 and 27.2% for AFB2. The limits of detection of the method (LD) were 0.2, 0.1, 0.1 and 0.1mg/kg and the limits of quantification (LQ) were 1.0, 0.3, 0.6 and 0.6mg/kg for AFB1, AFB2, AFG1 and AFG2, respectively.

Detection and quantification of Roundup Ready® soybean residues in sausage samples by conventional and real-time PCR

The increasing presence of products derived from genetically modified (GM) plants in human and animal diets has led to the development of detection methods to distinguish biotechnology-derived foods from conventional ones. The conventional and real-time PCR have been used, respectively, to detect and quantify GM residues in highly processed foods. DNA extraction is a critical step during the analysis process. Some factors such as DNA degradation, matrix effects, and the presence of PCR inhibitors imply that a detection or quantification limit, established for a given method, is restricted to a matrix used during validation and cannot be projected to any other matrix outside the scope of the method. In Brazil, sausage samples were the main class of processed products in which Roundup Ready® (RR) soybean residues were detected. Thus, the validation of methodologies for the detection and quantification of those residues is absolutely necessary. Sausage samples were submitted to two different methods of DNA extraction: modified Wizard and the CTAB method. The yield and quality were compared for both methods. DNA samples were analyzed by conventional and real-time PCR for the detection and quantification of Roundup Ready® soybean in the samples. At least 200 ng of total sausage DNA was necessary for a reliable quantification. Reactions containing DNA amounts below this value led to large variations on the expected GM percentage value. In conventional PCR, the detection limit varied from 1.0 to 500 ng, depending on the GM soybean content in the sample. The precision, performance, and linearity were relatively high indicating that the method used for analysis was satisfactory.

Development and validation of a method for detection and quantification of ochratoxin A in green coffee using liquid chromatography coupled to mass spectrometry

A method using Liquid Chromatography Tanden Mass Spectrometry (LC-MS/MS) with matrix-matched calibration curve was developed and validated for determining ochratoxin A (OTA) in green coffee. Linearity was found between 3.0 and 23.0 ng.g-1. Mean recoveries ranged between 90.45% and 108.81%; the relative standard deviation under repeatability and intermediate precision conditions ranged from 5.39% to 9.94% and from 2.20% to 14.34%, respectively. The limits of detection and quantification were 1.2 ng.g-1 and 3.0 ng.g-¹, respectively. The method developed was suitable and contributed to the field of mycotoxin analysis, and it will be used for future production of the Certified Reference Material (CRM) for OTA in coffee.

Optimization of image analysis techniques for quality assessment of whole-wheat breads made with fat replacer

The cellular structure of healthy food products, with added dietary fiber and low in calories, is an important factor that contributes to the assessment of quality, which can be quantified by image analysis of visual texture. This study seeks to compare image analysis techniques (binarization using Otsu’s method and the default ImageJ algorithm, a variation of the iterative intermeans method) for quantification of differences in the crumb structure of breads made with different percentages of whole-wheat flour and fat replacer, and discuss the behavior of the parameters number of cells, mean cell area, cell density, and circularity using response surface methodology. Comparative analysis of the results achieved with the Otsu and default ImageJ algorithms showed a significant difference between the studied parameters. The Otsu method demonstrated the crumb structure of the analyzed breads more reliably than the default ImageJ algorithm, and is thus the most suitable in terms of structural representation of the crumb texture.