Facial artery musculomucosal flap for reconstruction of skull base defects

Facial Artery Musculomucosal Flap in Skull Base Reconstruction Xie L. MD, Lavigne F. MD, Rahal A. MD, Moubayed SP MD, Ayad T. MD Introduction: Failure in skull base defects reconstruction can have serious consequences such as meningitis and pneumocephalus. The nasoseptal flap is usually the first choice but alternatives are necessary when this flap is not available. The facial artery musculomucosal (FAMM) flap has proven to be successful in head and neck reconstruction but it has never been reported in skull base reconstruction. Objective: To show that the FAMM flap can reach some key areas of the skull base and be considered as a new alternative in skull base defects reconstruction. Methods: We conducted a cadaveric study with harvest of modified FAMM flaps, endoscopic skull base dissection and maxillectomies in 13 specimens. Measures were taken for each harvested FAMM flap. Results: The approximate mean area for reconstruction from the combination of the distal FAMM and the extension flaps is 15.90 cm2. The flaps successfully covered the simulated defects of the frontal sinus, the ethmoid areas, the planum sphenoidale, and the sella turcica. Conclusion: The FAMM flap can be considered as a new alternative in the reconstruction of skull base defects. Modifications add extra length to the traditional FAMM flap and can contribute to a tighter seal of the defect as opposed to the FAMM flap alone.

Long-term facial artery catheter implantation for serial arterial blood sampling and invasive arterial blood pressure measurement in horses

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

An anatomical and analytical study of the Modiolus: Enlightening its relevance to plastic surgery

Background: This study was designed to evaluate the structures, muscles, and fasciae of which the modiolus is composed. It can aid in the understanding and, therefore, the utilization of plastic surgery for the aesthetic or reconstructive treatment of that region, especially the angle of the mouth. Methods: Dissections of the midface were done on five different cadavers. They were of different races (3 males, 2 females). The anatomy of the modiolus was studied in detail. New anatomical observations were classified as type I through type VI. Results: The perifacial artery fascia contributed to the modiolus in four (80%) specimens and was not part of it in 1 (20%) specimen. The facial artery was anterior to it in one (20%) specimen, lateral in four (80%) specimens, and never medial to it. No significant relationship was observed between the perifacial artery fascia contribution to the modiolus and gender or race. Also, the location of the facial artery lateral or anterior to the modiolus was not significantly related to gender or race. In addition, the deep and superficial fasciae of the face converged not anterior to the masseter muscle but actually at the modiolus, which was different from observations made by others. Conclusion: The modiolus is of critical importance in aesthetic and reconstructive plastic surgery of the face. © 2008 Springer Science+Business Media, LLC and International Society of Aesthetic Plastic Surgery.

Anatomic dissection of the arterial supply of the lips:an anatomical and analytical approach

Previous investigators have not described some of the new anatomic variations or provided quantitative and analytical data of the arterial anatomy of the lips in as much depth as in this study. Dissections of 14 different facial sides of cadavers were done. Through investigating the arterial supply of the upper and lower lips, measurements were performed and statistically analyzed. The main arterial supply of the upper lip was from the superior labial artery (SLA, mean external diameter, 1.8 mm [SD, 0.74 mm]); in addition, the subalar and septal branches contributed to its vascularization. The origin of the SLA was above the labial commissure in 78.6%. The subalar branch was not found but replaced by the alar artery that arose from the infraorbital artery in 1 specimen. The main arterial supply of the lower lip was derived from 3 branches of the facial artery, the inferior labial artery (mean external diameters, 1.4 mm [SD, 0.31 mm]) and the horizontal and vertical labiomental arteries. The inferior labial artery originated mostly below the labial commissure in 42.9% and formed a common trunk with the SLA in 28.6%. The horizontal labiomental artery was present in all, but vertical labiomental artery was absent in 21.4% of specimens. Overall, observed anatomic variations were classified into types I to VIII. Significant relations between the demographic variables and measured parameters were reported including the correlation coefficient among evaluated parameters. In conclusion, this study provides various information that aids in creating new flaps and supports the vascular base for clinical procedures in reconstructive surgery of the lip.

Metabolic acidosis in healthy mules under general anaesthesia with halothane

Objective To report the severe metabolic acidosis identified in a group of 11 healthy mules anaesthetized with halothane for castration.Study design Data generated from a prospective study.Animals Eleven mules aged 2.5-8 years, weighing 230-315 kg and 11 horses aged 1.5-3.5 years, weighing 315-480 kg.Methods Animals were anaesthetized for castration as part of an electroencephalographic study. Preanaesthetic medication was acepromazine (0.03 mg kg(-1)) administered through a preplaced jugular venous catheter. Anaesthesia was induced 30-90 minutes later with intravenous thiopental (10 mg kg(-1)). After orotracheal intubation, anaesthesia was maintained with halothane vaporised in oxygen. The animals' lungs were ventilated to maintain the end-tidal CO(2) concentration between 3.9 and 4.5 kPa (29-34 mmHg). Anaesthetic monitoring included invasive blood pressure measurement via the auricular artery (mules) and submandibular branch of the facial artery (horses). Arterial blood gas samples were drawn from these catheters at three time points during surgery and pH, PaCO(2), base excess (ecf) and HCO(3)(-) were measured. Values were compared between groups using a Mann-Whitney test. p was taken as <0.05. Results are reported as median (range).Results PaCO(2) did not differ between groups but pH was significantly lower in mules [7.178 (7.00-7.29)] compared to horses [7.367 (7.24-7.43)] (p = 0.0002). HCO(3)(-) values were significantly lower in the mules [16.6 (13.0-22.3) mM] compared to horses [23.7 (20.9-23.7) mM] (p = 0.0001), whilst base excess (ecf) was significantly more negative in the mules [-11.4 (-1.27 to -16) mM] compared to horses [-1.3 (-5.8 to +2.4) mM] (p = 0.0004).Conclusion and clinical relevance This study demonstrated severe metabolic acidosis in healthy mules, which may have prompted intervention with drug therapies in a clinical arena. It is probable that the acidosis existed prior to anaesthesia and caused by diet, but other possible causes are considered.

Avaliação hemodinâmica de equinos com oclusão jugular por trombose induzida submetidos a exercício físico e teste da terapia com estreptoquinase

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Avaliação endoscópica das vias aéreas, perfil de gases sanguíneos, eletrólitos e do equilíbrio ácido-base em equinos submetidos ao treinamento de três tambores: João Pedro Borges Barbosa. -

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Horner-s Syndrome following common carotid artery translocation in a horse: case report

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

An application of fractal image-set coding in facial recognition

Faces are complex patterns that often differ in only subtle ways. Face recognition algorithms have difficulty in coping with differences in lighting, cameras, pose, expression, etc. We propose a novel approach for facial recognition based on a new feature extraction method called fractal image-set encoding. This feature extraction method is a specialized fractal image coding technique that makes fractal codes more suitable for object and face recognition. A fractal code of a gray-scale image can be divided in two parts – geometrical parameters and luminance parameters. We show that fractal codes for an image are not unique and that we can change the set of fractal parameters without significant change in the quality of the reconstructed image. Fractal image-set coding keeps geometrical parameters the same for all images in the database. Differences between images are captured in the non-geometrical or luminance parameters – which are faster to compute. Results on a subset of the XM2VTS database are presented.

Nurse-determined assessment of cardiac output. Comparing a non-invasive cardiac output device and pulmonary artery catheter: a prospective observational study.

Background The accurate measurement of Cardiac output (CO) is vital in guiding the treatment of critically ill patients. Invasive or minimally invasive measurement of CO is not without inherent risks to the patient. Skilled Intensive Care Unit (ICU) nursing staff are in an ideal position to assess changes in CO following therapeutic measures. The USCOM (Ultrasonic Cardiac Output Monitor) device is a non-invasive CO monitor whose clinical utility and ease of use requires testing. Objectives To compare cardiac output measurement using a non-invasive ultrasonic device (USCOM) operated by a non-echocardiograhically trained ICU Registered Nurse (RN), with the conventional pulmonary artery catheter (PAC) using both thermodilution and Fick methods. Design Prospective observational study. Setting and participants Between April 2006 and March 2007, we evaluated 30 spontaneously breathing patients requiring PAC for assessment of heart failure and/or pulmonary hypertension at a tertiary level cardiothoracic hospital. Methods SCOM CO was compared with thermodilution measurements via PAC and CO estimated using a modified Fick equation. This catheter was inserted by a medical officer, and all USCOM measurements by a senior ICU nurse. Mean values, bias and precision, and mean percentage difference between measures were determined to compare methods. The Intra-Class Correlation statistic was also used to assess agreement. The USCOM time to measure was recorded to assess the learning curve for USCOM use performed by an ICU RN and a line of best fit demonstrated to describe the operator learning curve. Results In 24 of 30 (80%) patients studied, CO measures were obtained. In 6 of 30 (20%) patients, an adequate USCOM signal was not achieved. The mean difference (±standard deviation) between USCOM and PAC, USCOM and Fick, and Fick and PAC CO were small, −0.34 ± 0.52 L/min, −0.33 ± 0.90 L/min and −0.25 ± 0.63 L/min respectively across a range of outputs from 2.6 L/min to 7.2 L/min. The percent limits of agreement (LOA) for all measures were −34.6% to 17.8% for USCOM and PAC, −49.8% to 34.1% for USCOM and Fick and −36.4% to 23.7% for PAC and Fick. Signal acquisition time reduced on average by 0.6 min per measure to less than 10 min at the end of the study. Conclusions In 80% of our cohort, USCOM, PAC and Fick measures of CO all showed clinically acceptable agreement and the learning curve for operation of the non-invasive USCOM device by an ICU RN was found to be satisfactorily short. Further work is required in patients receiving positive pressure ventilation.

Selecting, optimizing and fusing ‘salient’ Gabor features for facial expression recognition

This paper describes a novel framework for facial expression recognition from still images by selecting, optimizing and fusing ‘salient’ Gabor feature layers to recognize six universal facial expressions using the K nearest neighbor classifier. The recognition comparisons with all layer approach using JAFFE and Cohn-Kanade (CK) databases confirm that using ‘salient’ Gabor feature layers with optimized sizes can achieve better recognition performance and dramatically reduce computational time. Moreover, comparisons with the state of the art performances demonstrate the effectiveness of our approach.

Facial feature detection for in-car environment

Acoustically, vehicles are extremely noisy environments and as a consequence audio-only in-car voice recognition systems perform very poorly. Seeing that the visual modality is immune to acoustic noise, using the visual lip information from the driver is seen as a viable strategy in circumventing this problem. However, implementing such an approach requires a system being able to accurately locate and track the driver’s face and facial features in real-time. In this paper we present such an approach using the Viola-Jones algorithm. Using this system, we present our results which show that using the Viola-Jones approach is a suitable method of locating and tracking the driver’s lips despite the visual variability of illumination and head pose.

Exploring visual features through Gabor representations for facial expression detection

Gabor representations have been widely used in facial analysis (face recognition, face detection and facial expression detection) due to their biological relevance and computational properties. Two popular Gabor representations used in literature are: 1) Log-Gabor and 2) Gabor energy filters. Even though these representations are somewhat similar, they also have distinct differences as the Log-Gabor filters mimic the simple cells in the visual cortex while the Gabor energy filters emulate the complex cells, which causes subtle differences in the responses. In this paper, we analyze the difference between these two Gabor representations and quantify these differences on the task of facial action unit (AU) detection. In our experiments conducted on the Cohn-Kanade dataset, we report an average area underneath the ROC curve (A`) of 92.60% across 17 AUs for the Gabor energy filters, while the Log-Gabor representation achieved an average A` of 96.11%. This result suggests that small spatial differences that the Log-Gabor filters pick up on are more useful for AU detection than the differences in contours and edges that the Gabor energy filters extract.

Temporal-based support vector machines for facial expression recognition

When classifying a signal, ideally we want our classifier to trigger a large response when it encounters a positive example and have little to no response for all other examples. Unfortunately in practice this does not occur with responses fluctuating, often causing false alarms. There exists a myriad of reasons why this is the case, most notably not incorporating the dynamics of the signal into the classification. In facial expression recognition, this has been highlighted as one major research question. In this paper we present a novel technique which incorporates the dynamics of the signal which can produce a strong response when the peak expression is found and essentially suppresses all other responses as much as possible. We conducted preliminary experiments on the extended Cohn-Kanade (CK+) database which shows its benefits. The ability to automatically and accurately recognize facial expressions of drivers is highly relevant to the automobile. For example, the early recognition of “surprise” could indicate that an accident is about to occur; and various safeguards could immediately be deployed to avoid or minimize injury and damage. In this paper, we conducted initial experiments on the extended Cohn-Kanade (CK+) database which shows its benefits.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.