Pain relativity in motor control.

Motivational theories of pain highlight its role in people's choices of actions that avoid bodily damage. By contrast, little is known regarding how pain influences action implementation. To explore this less-understood area, we conducted a study in which participants had to rapidly point to a target area to win money while avoiding an overlapping penalty area that would cause pain in their contralateral hand. We found that pain intensity and target-penalty proximity repelled participants' movement away from pain and that motor execution was influenced not by absolute pain magnitudes but by relative pain differences. Our results indicate that the magnitude and probability of pain have a precise role in guiding motor control and that representations of pain that guide action are, at least in part, relative rather than absolute. Additionally, our study shows that the implicit monetary valuation of pain, like many explicit valuations (e.g., patients' use of rating scales in medical contexts), is unstable, a finding that has implications for pain treatment in clinical contexts.

Sensors based on SAW and FBAR technologies

Over the last few years a number of sensing platforms are being investigated for their use in drug development, microanalysis or medical diagnosis. Lab-on-a-chip (LOC) are devices integrating more than one laboratory functions on a single device chip of a very small size, and typically consist of two main components: microfluidic handling systems and sensors. The physical mechanisms that are generally used for microfluidics and sensors are different, hence making the integration of these components difficult and costly. In this work we present a lab-on-a-chip system based on surface acoustic waves (for fluid manipulation) and film bulk acoustic resonators (for sensing). Coupling surface acoustic waves into liquids induces acoustic streaming and motion of micro-droplets, whilst it is well-known that bulk acoustic waves can be used to fabricate microgravimetric sensors. Both technologies offer exceptional sensitivity and can be fabricated from piezoelectric thin films deposited on Si substrates, reducing the fabrication time/cost of the LOC devices. © 2013 SPIE.

Using Structural and Functional Information in Diagnostic Design

We wish to design a diagnostic for a device from knowledge of its structure and function. the diagnostic should achieve both coverage of the faults that can occur in the device, and should strive to achieve specificity in its diagnosis when it detects a fault. A system is described that uses a simple model of hardware structure and function, representing the device in terms of its internal primitive functions and connections. The system designs a diagnostic in three steps. First, an extension of path sensitization is used to design a test for each of the connections in teh device. Next, the resulting tests are improved by increasing their specificity. Finally the tests are ordered so that each relies on the fewest possible connections. We describe an implementation of this system and show examples of the results for some simple devices.

Low-level laser therapy and light-emitting diode effects in the secretion of neuropeptides SP and CGRP in rat skin

The phototherapy effects in the skin are related to biomodulation, usually to accelerate wound healing. However, there is no direct proof of the interrelation between the effects of low-level laser therapy (LLLT) and light-emitting diode (LED) in neuropeptide secretion, these substances being prematurely involved in the neurogenic inflammation phase of wound healing. This study therefore focused on investigating LLLT and LED in Calcitonin gene-related peptide (CGRP) and substance P (SP) secretion in healthy rat skin. Forty rats were randomly distributed into five groups with eight rats each: Control Group, Blue LED Group (470 nm, 350 mW power), Red LED Group (660 nm, 350 mW power), Red Laser Group (660 nm, 100 mW power), and Infrared Laser Group (808 nm, 100 mW power) (DMCA (R) Equipamentos Ltda., So Carlos, So Paulo, Brazil). the skin of the animals in the experimental groups was irradiated using the punctual contact technique, with a total energy of 40 J, single dose, standardized at one point in the dorsal region. After 14 min of irradiation, the skin samples were collected for CGRP and SP quantification using western blot analysis. SP was released in Infrared Laser Group (p = 0.01); there was no difference in the CGRP secretion among groups. Infrared (808 nm) LLLT enhances neuropeptide SP secretion in healthy rat skin.

Synteza i charakterystyka wybranych nanocząstek o potencjalnych zastosowaniach w medycynie

Zakład Biofizyki Molekularnej, Centrum NanoBioMedyczne UAM

Sinusite odontogénica

Monografia apresentada à Universidade Fernando Pessoa para obtenção do grau de Licenciada em Medicina Dentária.

Estudo do comportamento no âmbito da radioprotecção de uma população trabalhadora na área da medicina dentária

Monografia apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Licenciada em Medicina Dentária

Manifestações orais em crianças em tratamento oncológico

Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Medicina Dentária

Digital signal processing and artificial intelligence for the automated classification of food allergy

As a by-product of the ‘information revolution’ which is currently unfolding, lifetimes of man (and indeed computer) hours are being allocated for the automated and intelligent interpretation of data. This is particularly true in medical and clinical settings, where research into machine-assisted diagnosis of physiological conditions gains momentum daily. Of the conditions which have been addressed, however, automated classification of allergy has not been investigated, even though the numbers of allergic persons are rising, and undiagnosed allergies are most likely to elicit fatal consequences. On the basis of the observations of allergists who conduct oral food challenges (OFCs), activity-based analyses of allergy tests were performed. Algorithms were investigated and validated by a pilot study which verified that accelerometer-based inquiry of human movements is particularly well-suited for objective appraisal of activity. However, when these analyses were applied to OFCs, accelerometer-based investigations were found to provide very poor separation between allergic and non-allergic persons, and it was concluded that the avenues explored in this thesis are inadequate for the classification of allergy. Heart rate variability (HRV) analysis is known to provide very significant diagnostic information for many conditions. Owing to this, electrocardiograms (ECGs) were recorded during OFCs for the purpose of assessing the effect that allergy induces on HRV features. It was found that with appropriate analysis, excellent separation between allergic and nonallergic subjects can be obtained. These results were, however, obtained with manual QRS annotations, and these are not a viable methodology for real-time diagnostic applications. Even so, this was the first work which has categorically correlated changes in HRV features to the onset of allergic events, and manual annotations yield undeniable affirmation of this. Fostered by the successful results which were obtained with manual classifications, automatic QRS detection algorithms were investigated to facilitate the fully automated classification of allergy. The results which were obtained by this process are very promising. Most importantly, the work that is presented in this thesis did not obtain any false positive classifications. This is a most desirable result for OFC classification, as it allows complete confidence to be attributed to classifications of allergy. Furthermore, these results could be particularly advantageous in clinical settings, as machine-based classification can detect the onset of allergy which can allow for early termination of OFCs. Consequently, machine-based monitoring of OFCs has in this work been shown to possess the capacity to significantly and safely advance the current state of clinical art of allergy diagnosis

Artefact detection and removal algorithms for EEG diagnostic systems

The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer.

Comparison of ELISA for tissue transglutaminase autoantibodies with antiendomysium antibodies in pediatric and adult patients with celiac disease.

BACKGROUND: Tissue transglutaminase (t-TG) is the main autoantigen recognized by the endomysium antibodies (EMA) observed in patients with celiac disease (CD). The aim of the study was to assess an ELISA method for t-TG antibodies (t-TGA) with respect to EMA IF assay in pediatric and adult patients. METHODS: t-TGA were analyzed by ELISA in 220 sera samples: 82 patients with biopsy-proven untreated CD (23 adults and 59 children), 14 CD children on gluten-free diet, 18 asymptomatic relatives of CD patients, and 106 age-matched control patients with gluten-unrelated gastrointestinal diseases (58 adults and 48 children). Serum IgA EMA were tested on umbilical cord sections in all patients. RESULTS: The great majority (92.7%) of untreated CD patients (both adults and children) were t-TGA positive (values ranging from 20.1 to > 300 AU). None of the child control patients and only two out of 58 (3.4%) of the adults with unrelated gastrointestinal diseases had serum t-TGA positivity; two out of 18 first-degree relatives with biopsy-proved silent CD were t-TGA (as well as EMA) positive. Finally, two out of 14 CD children, assuming a gluten-free diet, had serum t-TGA (as well as EMA). A highly significant correlation (P < 0.001) was observed between t-TGA concentrations and EMA. t-TGA showed a sensitivity of 87% and 95%, a specificity of 97% and 100% for adults and children, respectively. CONCLUSION: The method is highly sensitive and specific in the diagnosis of CD and is promising as a tool for routine diagnostic use and population screening, especially in children.

Medical education as moral formation: an Aristotelian account of medical professionalsim.

The medical professionalism movement, bolstered by many influential medical organizations and institutions, has in the last decade produced a number of conceptual definitions of professionalism and a number of concrete proposals for its measurement and teaching. These projects, however laudable, are misguided when they treat professionalism as a unitary descriptive concept rather than as a contested and therefore primarily evaluative one; when they conceive professionalism as a domain of medical practice separable in principle from other domains; and when they treat professionalism as, in principle, a specifiable goal or product of sufficiently well designed educational curricula. The logic of professionalism-as-product corresponds to the logic of techne (art or practical skill) in Aristotle's Nicomachean Ethics. Aristotle provides a cogent argument, however, that the moral excellences denoted by "professionalism" cannot be "produced" or even prespecified in the concrete; rather, they must be acquired through long practice under the careful concrete guidance of teachers who themselves embody these moral excellences. Phronesis (practical wisdom) rather than techne must therefore be the guiding logic of educational initiatives in medical professional formation, with particular emphasis on close mentorship and on the moral character both of students and of those who teach them.

Changing epidemiology of serious bacterial infections in febrile infants without localizing signs.

OBJECTIVE: Historically, management of infants with fever without localizing signs (FWLS) has generated much controversy, with attempts to risk stratify based on several criteria. Advances in medical practice may have altered the epidemiology of serious bacterial infections (SBIs) in this population. We conducted this study to test the hypothesis that the rate of SBIs in this patient population has changed over time. PATIENTS AND METHODS: We performed a retrospective review of all infants meeting FWLS criteria at our institution from 1997-2006. We examined all clinical and outcome data and performed statistical analysis of SBI rates and ampicillin resistance rates. RESULTS: 668 infants met criteria for FWLS. The overall rate of SBIs was 10.8%, with a significant increase from 2002-2006 (52/361, 14.4%) compared to 1997-2001 (20/307, 6.5%) (p = 0.001). This increase was driven by an increase in E. coli urinary tract infections (UTI), particularly in older infants (31-90 days). CONCLUSIONS: We observed a significant increase in E. coli UTI among FWLS infants with high rates of ampicillin resistance. The reasons are likely to be multifactorial, but the results themselves emphasize the need to examine urine in all febrile infants <90 days and consider local resistance patterns when choosing empiric antibiotics.

Involuntary Memories and Dissociative Amnesia: Assessing Key Assumptions in PTSD Research.

Autobiographical memories of trauma victims are often described as disturbed in two ways. First, the trauma is frequently re-experienced in the form of involuntary, intrusive recollections. Second, the trauma is difficult to recall voluntarily (strategically); important parts may be totally or partially inaccessible-a feature known as dissociative amnesia. These characteristics are often mentioned by PTSD researchers and are included as PTSD symptoms in the DSM-IV-TR (American Psychiatric Association, 2000). In contrast, we show that both involuntary and voluntary recall are enhanced by emotional stress during encoding. We also show that the PTSD symptom in the diagnosis addressing dissociative amnesia, trouble remembering important aspects of the trauma is less well correlated with the remaining PTSD symptoms than the conceptual reversal of having trouble forgetting important aspects of the trauma. Our findings contradict key assumptions that have shaped PTSD research over the last 40 years.

Micro-Anatomical Quantitative Imaging Towards Enabling Automated Diagnosis of Thick Tissues at the Point of Care

Histopathology is the clinical standard for tissue diagnosis. However, histopathology has several limitations including that it requires tissue processing, which can take 30 minutes or more, and requires a highly trained pathologist to diagnose the tissue. Additionally, the diagnosis is qualitative, and the lack of quantitation leads to possible observer-specific diagnosis. Taken together, it is difficult to diagnose tissue at the point of care using histopathology.

Several clinical situations could benefit from more rapid and automated histological processing, which could reduce the time and the number of steps required between obtaining a fresh tissue specimen and rendering a diagnosis. For example, there is need for rapid detection of residual cancer on the surface of tumor resection specimens during excisional surgeries, which is known as intraoperative tumor margin assessment. Additionally, rapid assessment of biopsy specimens at the point-of-care could enable clinicians to confirm that a suspicious lesion is successfully sampled, thus preventing an unnecessary repeat biopsy procedure. Rapid and low cost histological processing could also be potentially useful in settings lacking the human resources and equipment necessary to perform standard histologic assessment. Lastly, automated interpretation of tissue samples could potentially reduce inter-observer error, particularly in the diagnosis of borderline lesions.

To address these needs, high quality microscopic images of the tissue must be obtained in rapid timeframes, in order for a pathologic assessment to be useful for guiding the intervention. Optical microscopy is a powerful technique to obtain high-resolution images of tissue morphology in real-time at the point of care, without the need for tissue processing. In particular, a number of groups have combined fluorescence microscopy with vital fluorescent stains to visualize micro-anatomical features of thick (i.e. unsectioned or unprocessed) tissue. However, robust methods for segmentation and quantitative analysis of heterogeneous images are essential to enable automated diagnosis. Thus, the goal of this work was to obtain high resolution imaging of tissue morphology through employing fluorescence microscopy and vital fluorescent stains and to develop a quantitative strategy to segment and quantify tissue features in heterogeneous images, such as nuclei and the surrounding stroma, which will enable automated diagnosis of thick tissues.

To achieve these goals, three specific aims were proposed. The first aim was to develop an image processing method that can differentiate nuclei from background tissue heterogeneity and enable automated diagnosis of thick tissue at the point of care. A computational technique called sparse component analysis (SCA) was adapted to isolate features of interest, such as nuclei, from the background. SCA has been used previously in the image processing community for image compression, enhancement, and restoration, but has never been applied to separate distinct tissue types in a heterogeneous image. In combination with a high resolution fluorescence microendoscope (HRME) and a contrast agent acriflavine, the utility of this technique was demonstrated through imaging preclinical sarcoma tumor margins. Acriflavine localizes to the nuclei of cells where it reversibly associates with RNA and DNA. Additionally, acriflavine shows some affinity for collagen and muscle. SCA was adapted to isolate acriflavine positive features or APFs (which correspond to RNA and DNA) from background tissue heterogeneity. The circle transform (CT) was applied to the SCA output to quantify the size and density of overlapping APFs. The sensitivity of the SCA+CT approach to variations in APF size, density and background heterogeneity was demonstrated through simulations. Specifically, SCA+CT achieved the lowest errors for higher contrast ratios and larger APF sizes. When applied to tissue images of excised sarcoma margins, SCA+CT correctly isolated APFs and showed consistently increased density in tumor and tumor + muscle images compared to images containing muscle. Next, variables were quantified from images of resected primary sarcomas and used to optimize a multivariate model. The sensitivity and specificity for differentiating positive from negative ex vivo resected tumor margins was 82% and 75%. The utility of this approach was further tested by imaging the in vivo tumor cavities from 34 mice after resection of a sarcoma with local recurrence as a bench mark. When applied prospectively to images from the tumor cavity, the sensitivity and specificity for differentiating local recurrence was 78% and 82%. The results indicate that SCA+CT can accurately delineate APFs in heterogeneous tissue, which is essential to enable automated and rapid surveillance of tissue pathology.

Two primary challenges were identified in the work in aim 1. First, while SCA can be used to isolate features, such as APFs, from heterogeneous images, its performance is limited by the contrast between APFs and the background. Second, while it is feasible to create mosaics by scanning a sarcoma tumor bed in a mouse, which is on the order of 3-7 mm in any one dimension, it is not feasible to evaluate an entire human surgical margin. Thus, improvements to the microscopic imaging system were made to (1) improve image contrast through rejecting out-of-focus background fluorescence and to (2) increase the field of view (FOV) while maintaining the sub-cellular resolution needed for delineation of nuclei. To address these challenges, a technique called structured illumination microscopy (SIM) was employed in which the entire FOV is illuminated with a defined spatial pattern rather than scanning a focal spot, such as in confocal microscopy.

Thus, the second aim was to improve image contrast and increase the FOV through employing wide-field, non-contact structured illumination microscopy and optimize the segmentation algorithm for new imaging modality. Both image contrast and FOV were increased through the development of a wide-field fluorescence SIM system. Clear improvement in image contrast was seen in structured illumination images compared to uniform illumination images. Additionally, the FOV is over 13X larger than the fluorescence microendoscope used in aim 1. Initial segmentation results of SIM images revealed that SCA is unable to segment large numbers of APFs in the tumor images. Because the FOV of the SIM system is over 13X larger than the FOV of the fluorescence microendoscope, dense collections of APFs commonly seen in tumor images could no longer be sparsely represented, and the fundamental sparsity assumption associated with SCA was no longer met. Thus, an algorithm called maximally stable extremal regions (MSER) was investigated as an alternative approach for APF segmentation in SIM images. MSER was able to accurately segment large numbers of APFs in SIM images of tumor tissue. In addition to optimizing MSER for SIM image segmentation, an optimal frequency of the illumination pattern used in SIM was carefully selected because the image signal to noise ratio (SNR) is dependent on the grid frequency. A grid frequency of 31.7 mm-1 led to the highest SNR and lowest percent error associated with MSER segmentation.

Once MSER was optimized for SIM image segmentation and the optimal grid frequency was selected, a quantitative model was developed to diagnose mouse sarcoma tumor margins that were imaged ex vivo with SIM. Tumor margins were stained with acridine orange (AO) in aim 2 because AO was found to stain the sarcoma tissue more brightly than acriflavine. Both acriflavine and AO are intravital dyes, which have been shown to stain nuclei, skeletal muscle, and collagenous stroma. A tissue-type classification model was developed to differentiate localized regions (75x75 µm) of tumor from skeletal muscle and adipose tissue based on the MSER segmentation output. Specifically, a logistic regression model was used to classify each localized region. The logistic regression model yielded an output in terms of probability (0-100%) that tumor was located within each 75x75 µm region. The model performance was tested using a receiver operator characteristic (ROC) curve analysis that revealed 77% sensitivity and 81% specificity. For margin classification, the whole margin image was divided into localized regions and this tissue-type classification model was applied. In a subset of 6 margins (3 negative, 3 positive), it was shown that with a tumor probability threshold of 50%, 8% of all regions from negative margins exceeded this threshold, while over 17% of all regions exceeded the threshold in the positive margins. Thus, 8% of regions in negative margins were considered false positives. These false positive regions are likely due to the high density of APFs present in normal tissues, which clearly demonstrates a challenge in implementing this automatic algorithm based on AO staining alone.

Thus, the third aim was to improve the specificity of the diagnostic model through leveraging other sources of contrast. Modifications were made to the SIM system to enable fluorescence imaging at a variety of wavelengths. Specifically, the SIM system was modified to enabling imaging of red fluorescent protein (RFP) expressing sarcomas, which were used to delineate the location of tumor cells within each image. Initial analysis of AO stained panels confirmed that there was room for improvement in tumor detection, particularly in regards to false positive regions that were negative for RFP. One approach for improving the specificity of the diagnostic model was to investigate using a fluorophore that was more specific to staining tumor. Specifically, tetracycline was selected because it appeared to specifically stain freshly excised tumor tissue in a matter of minutes, and was non-toxic and stable in solution. Results indicated that tetracycline staining has promise for increasing the specificity of tumor detection in SIM images of a preclinical sarcoma model and further investigation is warranted.

In conclusion, this work presents the development of a combination of tools that is capable of automated segmentation and quantification of micro-anatomical images of thick tissue. When compared to the fluorescence microendoscope, wide-field multispectral fluorescence SIM imaging provided improved image contrast, a larger FOV with comparable resolution, and the ability to image a variety of fluorophores. MSER was an appropriate and rapid approach to segment dense collections of APFs from wide-field SIM images. Variables that reflect the morphology of the tissue, such as the density, size, and shape of nuclei and nucleoli, can be used to automatically diagnose SIM images. The clinical utility of SIM imaging and MSER segmentation to detect microscopic residual disease has been demonstrated by imaging excised preclinical sarcoma margins. Ultimately, this work demonstrates that fluorescence imaging of tissue micro-anatomy combined with a specialized algorithm for delineation and quantification of features is a means for rapid, non-destructive and automated detection of microscopic disease, which could improve cancer management in a variety of clinical scenarios.