Quantification of Mitral Regurgitation with Multiple Jets: In Vitro Comparison of Two-dimensional PISA techniques

Purpose: Traditionally, the proximal isovelocity surface area (PISA) is based on the assumption of a single hemisphere (hemispheric PISA), but this technique has not been validated for the quantification of mitral regurgitation (MR) with multiple jets. Methods: The left heart simulator was actuated by a pulsatile pump at various stroke amplitudes. The regurgitant volume (Rvol) passing through the mitral valve phantoms with single and double regurgitant orifices of varying size and interspace was quantified by a flowmeter as reference technique. Color Doppler 3-D full-volumes were obtained, and Rvol were derived from 2-D PISA surfaces on the basis of hemispheric and hemicylindric assumption with one base (partial hemicylindric PISA) or 2 bases (total hemicylindric PISA). Results: 72 regurgitant volumes (Rvol range: 8 to 76 ml/beat) were obtained. Hemispheric PISA Rvol correlated well with reference Rvol by one orifice (R²=0.97; bias -2.7±3.2ml), but less by ≥ one orifice (R²=0.89). When a fusion of two PISAs occured, addition of two hemispheric PISA overestimated Rvol (bias 9.1±12.2ml, fig.1), and single hemispheric PISA underestimated Rvol (bias -12.4±4.9ml). If an integrated approach was used (hemispheric in single orifice, total hemicylindric in two non-fused PISAs and partial hemicylindric in two fused PISAs), the correlation was R²=0.95, bias -1.6±5.6ml (fig.2). In the ROC analysis, the cutoff to detect ≥ moderate-to-severe Rvol (≥45ml) was 42ml (AUC 0.99, sens. 100%, spec. 93%). Conclusions: In MR with two regurgitant jets, the 2-D hemicylindric assumption of the PISA offers a better quantification of Rvol than the hemispheric assumption. Quantification of MR using 2-D PISA requires an integrated approach that considers number of regurgitant orifices and fusion of the PISAs.

Diagnostic validity of clinical signs associated with a large exophoria at near

Purpose. To analyze the diagnostic validity of accommodative and binocular tests in a sample of patients with a large near exophoria with moderate to severe symptoms. Methods. Two groups of patients between 19 and 35 years were recruited from a university clinic: 33 subjects with large exophoria at near vision and moderate or high visual discomfort and 33 patients with normal heterophoria and low visual discomfort. Visual discomfort was defined using the Conlon survey. A refractive exam and an exhaustive evaluation of accommodation and vergence were assessed. Diagnostic validity by means of receiver operator characteristic (ROC) curves, sensitivity (S), specificity (Sp), and positive and negative likelihood ratios (LR+, LR−) were assessed. This analysis was also carried out considering multiple tests as serial testing strategy. Results. ROC analysis showed the best diagnostic accuracy for receded near point of convergence (NPC) recovery (area = 0.929) and binocular accommodative facility (BAF) (area = 0.886). Using the cut-offs obtained with ROC analysis, the best diagnostic validity was obtained for the combination of NPC recovery and BAF (S  =  0.77, Sp = 1, LR+ = value tending to infinity, LR− = 0.23) and the combination of NPC break and recovery with BAF (S  =  0.73, Sp = 1, LR+ = tending to infinity, LR− = 0.27). Conclusions. NPC and BAF tests were the tests with the best diagnostic accuracy for subjects with large near exophoria and moderate to severe symptoms.

Use of Extended Characteristics of Locomotion and Feeding Behavior for Automated Identification of Lame Dairy Cows.

This study was carried out to detect differences in locomotion and feeding behavior in lame (group L; n = 41; gait score ≥ 2.5) and non-lame (group C; n = 12; gait score ≤ 2) multiparous Holstein cows in a cross-sectional study design. A model for automatic lameness detection was created, using data from accelerometers attached to the hind limbs and noseband sensors attached to the head. Each cow's gait was videotaped and scored on a 5-point scale before and after a period of 3 consecutive days of behavioral data recording. The mean value of 3 independent experienced observers was taken as a definite gait score and considered to be the gold standard. For statistical analysis, data from the noseband sensor and one of two accelerometers per cow (randomly selected) of 2 out of 3 randomly selected days was used. For comparison between group L and group C, the T-test, the Aspin-Welch Test and the Wilcoxon Test were used. The sensitivity and specificity for lameness detection was determined with logistic regression and ROC-analysis. Group L compared to group C had significantly lower eating and ruminating time, fewer eating chews, ruminating chews and ruminating boluses, longer lying time and lying bout duration, lower standing time, fewer standing and walking bouts, fewer, slower and shorter strides and a lower walking speed. The model considering the number of standing bouts and walking speed was the best predictor of cows being lame with a sensitivity of 90.2% and specificity of 91.7%. Sensitivity and specificity of the lameness detection model were considered to be very high, even without the use of halter data. It was concluded that under the conditions of the study farm, accelerometer data were suitable for accurately distinguishing between lame and non-lame dairy cows, even in cases of slight lameness with a gait score of 2.5.

Free mycophenolic acid should be monitored in renal transplant recipients with hypoalbuminemia

The current approach for therapeutic drug monitoring in renal transplant recipients receiving mycophenolate mofetil (MMF) is measurement of total mycophenolic acid (MPA) concentration. Because MPA is highly bound, during hypoalbuminemia the total concentration no longer reflects the free (pharmacologically active) concentration. The authors investigated what degree of hypoalbuminemia causes a significant change in protein binding and thus percentage free MPA. Forty-two renal transplant recipients were recruited for the study. Free and total concentrations of MPA (predose, and 1, 3, and 6 hours post-MMF dose samples) and plasma albumin concentrations were determined on day 5 posttransplantation. Six-hour area under the concentration-time curve (AUC(0-6)) values were calculated for free and total MPA, and percentage free MPA was determined for each patient. The authors found a significant relationship between low albumin concentrations and increased percentage free MPA (Spearman correlation = -0.54, P < 0.0001). Receiver operating characteristic (ROC) curve analysis was performed on the albumin versus percentage free MPA data. The cutoff value of albumin determined from the ROC analysis that differentiated normal from elevated percentage free MPA (defined as greater than or equal to3%) in this patient population was 31 g/L. At this cutoff value albumin was found to be a good predictor of altered free MPA percentage, with a sensitivity and specificity of 0.75 and 0.80, respectively, and an area under the ROC curve of 0.79. To rationalize MMF dosing regimens in hypoalbuminemic patients (plasma albumin less than or equal to 31 g/L), clinicians should consider monitoring the free MPA concentration.

Determining the fetal scalp lactate level that indicates the need for intervention in labour

Background: Fetal scalp lactate testing has been shown to be as useful as pH with added benefits. One remaining question is What level of lactate should trigger intervention in the first stage of labour?' Aims: This study aimed to establish the lactate level in the first stage of labour that indicates the need for intervention to ensure satisfactory outcomes for both babies and mothers. Methods: A prospective study at Mater Mothers' Hospital, Brisbane, Australia, a tertiary referral centre. One hundred and forty women in labour, with non-reassuring fetal heart rate traces, were tested using fetal blood scalp sampling of 5 mu L of capillary blood tested on an Accusport (Boeringer, Mannheim, East Sussex, UK) lactate meter. Decision to intervene in labour was based on clinical assessment plus a predetermined cut off. Main outcome measures were APGAR scores, cord arterial pH, meconium stained liquor and Intensive Care Nursery admission. Results: Two-graph receiver operating characteristic (TG-ROC) analysis showed optimal specificity, and sensitivity for predicting adverse neonatal outcomes was a scalp lactate level above 4.2 mmol/L. Conclusions: Fetal blood sampling remains the standard for further investigating-non-reassuring cardiotocograph (CTG) traces. Even so, it is a poor predictor of fetal outcomes. Scalp lactate has been shown to be at least as good a predictor as scalp pH, with the advantages of being easier, cheaper and with a lower rate of technical failure. Our study, found that a cut off fetal scalp lactate level of 4.2 mmol/L, in combination with an assessment of the entire clinical picture, is a useful tool in identifying those women who need intervention.

Prediction of protein B-factor profiles

The polypeptide backbones and side chains of proteins are constantly moving due to thermal motion and the kinetic energy of the atoms. The B-factors of protein crystal structures reflect the fluctuation of atoms about their average positions and provide important information about protein dynamics. Computational approaches to predict thermal motion are useful for analyzing the dynamic properties of proteins with unknown structures. In this article, we utilize a novel support vector regression (SVR) approach to predict the B-factor distribution (B-factor profile) of a protein from its sequence. We explore schemes for encoding sequences and various settings for the parameters used in SVR. Based on a large dataset of high-resolution proteins, our method predicts the B-factor distribution with a Pearson correlation coefficient (CC) of 0.53. In addition, our method predicts the B-factor profile with a CC of at least 0.56 for more than half of the proteins. Our method also performs well for classifying residues (rigid vs. flexible). For almost all predicted B-factor thresholds, prediction accuracies (percent of correctly predicted residues) are greater than 70%. These results exceed the best results of other sequence-based prediction methods. (C) 2005 Wiley-Liss, Inc.

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

A research platform for artificial neural networks with applications in pediatric epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and nonepileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that (1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and (2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).

Use of Hemoglobin A1C to Detect Haitian-Americans with Undiagnosed Type 2 Diabetes

OBJECTIVE: To evaluate the validity of hemoglobin A1C (A1C) as a diagnostic tool for type 2 diabetes and to determine the most appropriate A1C cutoff point for diagnosis in a sample of Haitian-Americans. SUBJECTS AND METHODS: Subjects (n = 128) were recruited from Miami-Dade and Broward counties, FL. Receiver operating characteristics (ROC) analysis was run in order to measure sensitivity and specificity of A1C for detecting diabetes at different cutoff points. RESULTS: The area under the ROC curve was 0.86 using fasting plasma glucose ≥ 7.0 mmol/L as the gold standard. An A1C cutoff point of 6.26% had sensitivity of 80% and specificity of 74%, whereas an A1C cutoff point of 6.50% (recommended by the American Diabetes Association – ADA) had sensitivity of 73% and specificity of 89%. CONCLUSIONS: A1C is a reliable alternative to fasting plasma glucose in detecting diabetes in this sample of Haitian-Americans. A cutoff point of 6.26% was the optimum value to detect type 2 diabetes.

A Research Platform for Artificial Neural Networks with Applications in Pediatric Epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and non-epileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that 1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and 2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).

Virtual Clinical Trials in PET Imaging for Improved Diagnosis of and Evaluation of Therapies for Cancer

Thesis (Ph.D.)--University of Washington, 2016-08

Clinically-relevant threshold of preformed donor-specific anti-HLA antibodies in kidney transplantation.

BACKGROUND: Pretransplant anti-HLA donor-specific antibodies (DSA) are recognized as a risk factor for acute antibody-mediated rejection (AMR) in kidney transplantation. The predictive value of C4d-fixing capability by DSA or of IgG DSA subclasses for acute AMR in the pretransplant setting has been recently studied. In addition DSA strength assessed by mean fluorescence intensity (MFI) may improve risk stratification. We aimed to analyze the relevance of preformed DSA and of DSA MFI values. METHODS: 280 consecutive patients with negative complement-dependent cytotoxicity crossmatches received a kidney transplant between 01/2008 and 03/2014. Sera were screened for the presence of DSA with a solid-phase assays on a Luminex flow analyzer, and the results were correlated with biopsy-proven acute AMR in the first year and survival. RESULTS: Pretransplant anti-HLA antibodies were present in 72 patients (25.7%) and 24 (8.6%) had DSA. There were 46 (16.4%) acute rejection episodes, 32 (11.4%) being cellular and 14 (5.0%) AMR. The incidence of acute AMR was higher in patients with pretransplant DSA (41.7%) than in those without (1.6%) (p<0.001). The median cumulative MFI (cMFI) of the group DSA+/AMR+ was 5680 vs 2208 in DSA+/AMR- (p=0.058). With univariate logistic regression a threshold value of 5280 cMFI was predictive for acute AMR. DSA cMFI's ability to predict AMR was also explored by ROC analysis. AUC was 0.728 and the best threshold was a cMFI of 4340. Importantly pretransplant DSA>5280 cMFI had a detrimental effect on 5-year graft survival. CONCLUSIONS: Preformed DSA cMFI values were clinically-relevant for the prediction of acute AMR and graft survival in kidney transplantation. A threshold of 4300-5300 cMFI was a significant outcome predictor.

Do simple ventilation and gas exchange measurements predict early successful weaning from respiratory support in unselected general intensive care patients?

Background. The value of respiratory variables as weaning predictors in the intensive care unit (ICU) is controversial. We evaluated the ability of tidal volume (Vtexp), respiratory rate ( f ), minute volume (MVexp), rapid shallow breathing index ( f/Vt), inspired–expired oxygen concentration difference [(I–E)O2], and end-tidal carbon dioxide concentration (PE′CO2) at the end of a weaning trial to predict early weaning outcomes. Methods. Seventy-three patients who required .24 h of mechanical ventilation were studied. A controlled pressure support weaning trial was undertaken until 5 cm H2O continuous positive airway pressure or predefined criteria were reached. The ability of data from the last 5 min of the trial to predict whether a predefined endpoint indicating discontinuation of ventilator support within the next 24 h was evaluated. Results. Pre-test probability for achieving the outcome was 44% in the cohort (n¼32). Non-achievers were older, had higher APACHE II and organ failure scores before the trial, and higher baseline arterial H+ concentrations. The Vt, MV, f, and f/Vt had no predictive power using a range of cut-off values or from receiver operating characteristic (ROC) analysis. The [I–E]O2 and PE′CO2 had weak discriminatory power [areaunder the ROC curve: [I–E]O2 0.64 (P¼0.03); PE′CO2 0.63 (P¼0.05)]. Using best cut-off values for [I–E]O2 of 5.6% and PE′CO2 of 5.1 kPa, positive and negative likelihood ratios were 2 and 0.5, respectively, which only changed the pre- to post-test probability by about 20%. Conclusions. In unselected ICU patients, respiratory variables predict early weaning from mechanical ventilation poorly.

Endoluminal calprotectin measurement in assessment of pouchitis and a new index of disease activity: a pilot study

Pouchitis is the most common complication following proctocolectomy with ileal pouch-anal anastomosis for ulcerative colitis (UC). To provide a standardized definition of pouchitis clinical, endoscopic and histological markers were grouped and weighted in the pouch disease activity index (PDAI). However, the delay in the assessment of the final score due to the time requested for histological analysis remains the main obstacle to the index implementation in clinical practice so that the use of modified-PDAI (mPDAI) with exclusion of histologic subscore has been proposed. We tested the ability of calprotectin measurement in the pouch endoluminal content to mimic the histologic score as defined in the PDAI, the index that we adopted as gold standard for pouchitis diagnosis. Calprotectin was measured by ELISA in the pouch endoluminal content collected during endoscopy in 40 consecutive patients with J-pouch. In each patient PDAI and mPDAI were calculated and 15% of patients were erroneously classified by mPDAI. ROC analysis of calprotectin values vs. acute histological subscore ≥ 3 identified different calprotectin cut-off values with corresponding sensitivity and specificity allowing the definition and scoring of different range of calprotectin subscores. We incorporated the calprotectin score in the mPDAI obtaining a new score that shows the same specificity as PDAI for diagnosis of pouchitis and higher sensitivity when compared with mPDAI. The use of the proposed new score, once validated in a larger series of patients, might be useful in the early management of patients with symptoms of pouchitis.

Mapping tissue inhomogeneity in acute myocarditis: a novel analytical approach to quantitative myocardial edema imaging by T2-mapping

BACKGROUND: The purpose of the present study was to investigate the diagnostic value of T2-mapping in acute myocarditis (ACM) and to define cut-off values for edema detection. METHODS: Cardiovascular magnetic resonance (CMR) data of 31 patients with ACM were retrospectively analyzed. 30 healthy volunteers (HV) served as a control. Additionally to the routine CMR protocol, T2-mapping data were acquired at 1.5 T using a breathhold Gradient-Spin-Echo T2-mapping sequence in six short axis slices. T2-maps were segmented according to the 16-segments AHA-model and segmental T2 values as well as the segmental pixel-standard deviation (SD) were analyzed. RESULTS: Mean differences of global myocardial T2 or pixel-SD between HV and ACM patients were only small, lying in the normal range of HV. In contrast, variation of segmental T2 values and pixel-SD was much larger in ACM patients compared to HV. In random forests and multiple logistic regression analyses, the combination of the highest segmental T2 value within each patient (maxT2) and the mean absolute deviation (MAD) of log-transformed pixel-SD (madSD) over all 16 segments within each patient proved to be the best discriminators between HV and ACM patients with an AUC of 0.85 in ROC-analysis. In classification trees, a combined cut-off of 0.22 for madSD and of 68 ms for maxT2 resulted in 83% specificity and 81% sensitivity for detection of ACM. CONCLUSIONS: The proposed cut-off values for maxT2 and madSD in the setting of ACM allow edema detection with high sensitivity and specificity and therefore have the potential to overcome the hurdles of T2-mapping for its integration into clinical routine.