Characterization of cation exchanger stationary phases applied for the separations of therapeutic monoclonal antibodies.

Cation exchange chromatography (CEX) is a well established strategy for the characterization of monoclonal antibodies (mAbs). The optimization of mobile phase conditions is well described in the literature, but there is a lack of information about CEX stationary phases for the analysis of therapeutic proteins. The aim of this study was to compare five state-of-the-art CEX stationary phases based on the retention, selectivity and resolving power achieved in pH- and salt-gradient modes, with various therapeutic mAbs and their variants. The Sepax Antibodix WCX-NP3, Thermo MAbPac SCX-10 RS, YMC BioPro SP-F, Waters Protein-Pak Hi Res SP and Agilent Bio mAb NP1.7 SS were considered in this study. In terms of retention, the YMC Bio Pro SP-F material was the less retentive one, while the Agilent Bio mAb NP1.7 SS provides the highest retention. Regarding the selectivity achieved between the main mAbs isoforms and their variants, the Thermo MabPac SCX column generally gave the highest selectivity. Finally, it was hard to rank columns in term of kinetic performance since their performance is strongly solute (mAb) and elution mode (pH or salt gradient) dependent. However, the highest resolution--in most cases--was observed on the strong cation exchanger YMC Bio Pro SP-F material.

The impact of storage conditions upon gentamicin coated antimicrobial implants

A systematic approach was developed to investigate the stability of gentamicin sulfate (GS) and GS/poly (lactic-co-glycolic acid) (PLGA) coatings on hydroxyapatite surfaces. The influence of environmental factors (light, humidity, oxidation and heat) upon degradation of the drug in the coatings was investigated using liquid chromatography with evaporative light scattering detection and mass spectrometry. GS coated rods were found to be stable across the range of environments assessed, with only an oxidizing atmosphere resulting in significant changes to the gentamicin composition. In contrast, rods coated with GS/PLGA were more sensitive to storage conditions with compositional changes being detected after storage at 60 °C, 75% relative humidity or exposure to light. The effect of γ-irradiation on the coated rods was also investigated and found to have no significant effect. Finally, liquid chromatography–mass spectrometry analysis revealed that known gentamines C1, C1a and C2 were the major degradants formed. Forced degradation of gentamicin coatings did not produce any unexpected degradants or impurities.

Time series morphological analysis applied to biomedical signals events detection

Dissertation submitted in the fufillment of the requirements for the Degree of Master in Biomedical Engineering

Direct analysis of dried blood spots coupled with mass spectrometry: concepts and biomedical applications.

Because of the emergence of dried blood spots (DBS) as an attractive alternative to conventional venous plasma sampling in many pharmaceutical companies and clinical laboratories, different analytical approaches have been developed to enable automated handling of DBS samples without any pretreatment. Associated with selective and sensitive MS-MS detection, these procedures give good results in the rapid identification and quantification of drugs (generally less than 3 min total run time), which is desirable because of the high throughput requirements of analytical laboratories. The objective of this review is to describe the analytical concepts of current direct DBS techniques and to present their advantages and disadvantages, with particular focus on automation capacity and commercial availability. Finally, an overview of the different biomedical applications in which these concepts could be of major interest will be presented.

Biomedical Ti-Mo Alloys with Surface Machined and Modified by Laser Beam: Biomechanical, Histological, and Histometric Analysis in Rabbits

Purpose: In vivo bone response was assessed by removal torque, hystological and histometrical analysis on a recently developed biomedical Ti-15Mo alloy, after surface modification by laser beam irradiation, installed in the tibia of rabbits. Materials and Methods: A total of 32 wide cylindrical Ti-15Mo dental implants were obtained (10mm × 3.75mm). The implants were divided into two groups: 1) control samples (Machined surface - MS) and 2) implants with their surface modified by Laser beam-irradiation (Test samples - LS). Six implants of each surface were used for removal torque test and 10 of each surface for histological and histometrical analysis. The implants were placed in the tibial metaphyses of rabbits. Results: Average removal torque was 51.5Ncm to MS and >90Ncm to LS. Bone-to-implant-contact percentage was significantly higher for LS implants both in the cortical and marrow regions. Conclusions: The present study demonstrated that laser treated Ti-15Mo alloys are promising materials for biomedical application. © 2011 Wiley Periodicals, Inc.

Statistical methods for biomedical signal analysis and processing

Statistical modelling and statistical learning theory are two powerful analytical frameworks for analyzing signals and developing efficient processing and classification algorithms. In this thesis, these frameworks are applied for modelling and processing biomedical signals in two different contexts: ultrasound medical imaging systems and primate neural activity analysis and modelling. In the context of ultrasound medical imaging, two main applications are explored: deconvolution of signals measured from a ultrasonic transducer and automatic image segmentation and classification of prostate ultrasound scans. In the former application a stochastic model of the radio frequency signal measured from a ultrasonic transducer is derived. This model is then employed for developing in a statistical framework a regularized deconvolution procedure, for enhancing signal resolution. In the latter application, different statistical models are used to characterize images of prostate tissues, extracting different features. These features are then uses to segment the images in region of interests by means of an automatic procedure based on a statistical model of the extracted features. Finally, machine learning techniques are used for automatic classification of the different region of interests. In the context of neural activity signals, an example of bio-inspired dynamical network was developed to help in studies of motor-related processes in the brain of primate monkeys. The presented model aims to mimic the abstract functionality of a cell population in 7a parietal region of primate monkeys, during the execution of learned behavioural tasks.

On Time Series Analysis of Public Health and Biomedical Data

A time series is a sequence of observations made over time. Examples in public health include daily ozone concentrations, weekly admissions to an emergency department or annual expenditures on health care in the United States. Time series models are used to describe the dependence of the response at each time on predictor variables including covariates and possibly previous values in the series. Time series methods are necessary to account for the correlation among repeated responses over time. This paper gives an overview of time series ideas and methods used in public health research.

International comparison of resource allocation in health sciences : an analysis of expenditures on biomedical research in 19 industrialized countries /

Item 507

Content-aware approach for improving biomedical image analysis: an interdisciplinary study series

Biomedicine is a highly interdisciplinary research area at the interface of sciences, anatomy, physiology, and medicine. In the last decade, biomedical studies have been greatly enhanced by the introduction of new technologies and techniques for automated quantitative imaging, thus considerably advancing the possibility to investigate biological phenomena through image analysis. However, the effectiveness of this interdisciplinary approach is bounded by the limited knowledge that a biologist and a computer scientist, by professional training, have of each other’s fields. The possible solution to make up for both these lacks lies in training biologists to make them interdisciplinary researchers able to develop dedicated image processing and analysis tools by exploiting a content-aware approach. The aim of this Thesis is to show the effectiveness of a content-aware approach to automated quantitative imaging, by its application to different biomedical studies, with the secondary desirable purpose of motivating researchers to invest in interdisciplinarity. Such content-aware approach has been applied firstly to the phenomization of tumour cell response to stress by confocal fluorescent imaging, and secondly, to the texture analysis of trabecular bone microarchitecture in micro-CT scans. Third, this approach served the characterization of new 3-D multicellular spheroids of human stem cells, and the investigation of the role of the Nogo-A protein in tooth innervation. Finally, the content-aware approach also prompted to the development of two novel methods for local image analysis and colocalization quantification. In conclusion, the content-aware approach has proved its benefit through building new approaches that have improved the quality of image analysis, strengthening the statistical significance to allow unveiling biological phenomena. Hopefully, this Thesis will contribute to inspire researchers to striving hard for pursuing interdisciplinarity.

Introducing CHIMeRA: from the development of a comprehensive biomedical database to the analysis of its sub-components.

In this thesis we discuss the expansion of an existing project, called CHIMeRA, which is a comprehensive biomedical network, and the analysis of its sub-components by using graph theory. We describe how it is structured internally, what are the existing databases from which it retrieves information and what machine learning techniques are used in order to produce new knowledge. We also introduce a new technique for graph exploration that is aimed to speed-up the network cover time under the condition that the analyzed graph is stellar; if this condition is satisfied, the improvement in the performance compared to the conventional exploration technique is extremely appealing. We show that the stellar structure is highly recurrent for sub-networks in CHIMeRA generated by queries, which made this technique even more interesting. Finally, we describe the convenience in using the CHIMeRA network for research purposes and what it could become in a very near future.

Gait kinematic analysis in patients with a mild form of central cord syndrome

Background: Central cord syndrome (CCS) is considered the most common incomplete spinal cord injury (SCI). Independent ambulation was achieved in 87-97% in young patients with CCS but no gait analysis studies have been reported before in such pathology. The aim of this study was to analyze the gait characteristics of subjects with CCS and to compare the findings with a healthy age, sex and anthropomorphically matched control group (CG), walking both at a self-selected speed and at the same speed. Methods: Twelve CCS patients and a CG of twenty subjects were analyzed. Kinematic data were obtained using a three-dimensional motion analysis system with two scanner units. The CG were asked to walk at two different speeds, at a self-selected speed and at a slower one, similar to the mean gait speed previously registered in the CCS patient group. Temporal, spatial variables and kinematic variables (maximum and minimum lower limb joint angles throughout the gait cycle in each plane, along with the gait cycle instants of occurrence and the joint range of motion ROM) were compared between the two groups walking at similar speeds. Results: The kinematic parameters were compared when both groups walked at a similar speed, given that there was a significant difference in the self-selected speeds (p < 0.05). Hip abduction and knee flexion at initial contact, as well as minimal knee flexion at stance, were larger in the CCS group (p < 0.05). However, the range of knee and ankle motion in the sagittal plane was greater in the CG group (p < 0.05). The maximal ankle plantar-flexion values in stance phase and at toe off were larger in the CG (p < 0.05). Conclusions: The gait pattern of CCS patients showed a decrease of knee and ankle sagittal ROM during level walking and an increase in hip abduction to increase base of support. The findings of this study help to improve the understanding how CCS affects gait changes in the lower limbs.

GPIomics: global analysis of glycosylphosphatidylinositol-anchored molecules of Trypanosoma cruzi

Glycosylphosphatidylinositol (GPI) anchoring is a common, relevant posttranslational modification of eukaryotic surface proteins. Here, we developed a fast, simple, and highly sensitive (high attomole-low femtomole range) method that uses liquid chromatography-tandem mass spectrometry (LC-MS(n)) for the first large-scale analysis of GPI-anchored molecules (i.e., the GPIome) of a eukaryote, Trypanosoma cruzi, the etiologic agent of Chagas disease. Our genome-wise prediction analysis revealed that approximately 12% of T. cruzi genes possibly encode GPI-anchored proteins. By analyzing the GPIome of T. cruzi insect-dwelling epimastigote stage using LC-MS(n), we identified 90 GPI species, of which 79 were novel. Moreover, we determined that mucins coded by the T. cruzi small mucin-like gene (TcSMUG S) family are the major GPI-anchored proteins expressed on the epimastigote cell surface. TcSMUG S mucin mature sequences are short (56-85 amino acids) and highly O-glycosylated, and contain few proteolytic sites, therefore, less likely susceptible to proteases of the midgut of the insect vector. We propose that our approach could be used for the high throughput GPIomic analysis of other lower and higher eukaryotes. Molecular Systems Biology 7 April 2009; doi:10.1038/msb.2009.13

Predictability Analysis of Voice Signals Analyzing Healthy and Pathologic Samples

Research Foundation of the State of Sao Paulo (FAPESP)