Pancreatic function in infants identified as having cystic fibrosis in a neonatal screening program

The use of the dried-blood immunoreactive-trypsin assay for the detection of cystic fibrosis in newborns has been questioned on the grounds that it may fail to identify patients with enough pancreatic function to have normal fat absorption. To investigate this possibility, we assessed pancreatic function in 78 patients identified in a neonatal screening program as having cystic fibrosis. The diagnosis of cystic fibrosis was confirmed by abnormal results on a sweat chloride test. The results of measurements of fecal fat excretion, pancreatic-stimulation tests, and estimations of the serum level of pancreatic isoamylase indicated that 29 of the 78 children (37 percent) had substantial preservation of pancreatic function. These children (median age, four years) had growth that was close to normal and comparable to growth in children with severe pancreatic insufficiency who received oral enzyme therapy. Pancreatic insufficiency subsequently developed in 6 of the 29 patients, at 3 to 36 months of age. We conclude that the serum immunoreactive-trypsin assay used in neonatal screening programs identifies patients with cystic fibrosis who have sufficient pancreatic function to have normal fat absorption and that a substantial proportion of infants identified as having cystic fibrosis are in this category.

Serum bile acid concentrations during pregnancy and their relationship to obstetric cholestasis

We have prospectively studied changes in serum postprandial cholylglycine (CG) concentration during 297 pregnancies. We found an increase in CG concentration from 0.3 mumol/L at 15 weeks' pregnancy to 0.6 mumol/L at 40 weeks' pregnancy. Although this increase was statistically significant (p less than 0.005), median concentrations of CG remained well within the normal range (0-1.5 mumol/L). However, 10% of the group showed markedly elevated serum CG concentrations at 30 weeks' pregnancy, and the CG level in this group continued to rise during the third trimester. Pruritus was significantly more common in the group with elevated CG concentrations (48%) than in the group with normal CG levels (20%) (p less than 0.005). Serum CG was a much more sensitive predictor of pruritus during pregnancy than other biochemical liver tests. Elevated CG levels were found more commonly in Mediterranean and Asian patients than patients of other ethnic origins (p less than 0.025). No statistically significant associations were found between elevated CG concentrations and maternal age, number of previous pregnancies, pruritus during previous pregnancies, contraceptive-induced cholestasis, and fetal maturity. We conclude that obstetric cholestasis is probably much more common than previously suspected and that consideration should be given to the measurement of serum bile acids in all pregnant individuals with unexplained pruritus.

Maternal hyperphenylalaninemia fetal effects

Thirty-four children of 11 mothers with untreated hyperphenylalaninemia had a pattern of malformation consisting of prenatal and postnatal growth retardation, microcephaly and central nervous system dysfunction, increased incidence of malformations, and a peculiar facial appearance. Maternal hyperphenylalaninemia appears to be teratogenic, with a variability related to the blood phenylalanine concentration.

Quasi-multistatic MIST beamforming for the early detection of breast cancer

Microwave imaging via space-time (MIST) beamforming has been shown to be one of the most promising imaging modalities for detecting small malignant breast tumors. This paper outlines two modifications to the MIST system developed by Hagness for the early detection of breast cancer, resulting in a quasi-multistatic MIST beamformer (multi-MIST). Multistatic MIST beamforming involves illuminating the breast with an ultrawideband (UWB) signal from one antenna while collecting the reflections at an array of antennas, as opposed to traditional monostatic MIST beamforming where only the transmitting antenna records the reflections from the breast. In order to process the multistatic data, traditional data-adaptive artifact removal algorithms have to be modified to accommodate signals from all antennas. Also, the MIST beamforming algorithm, which spatially focuses the signal and compensates for frequency-dependent propagation effects, has to be modified. The algorithms are tested on a 2-D anatomically accurate finite-difference time-domain model of the breast. The multi-MIST beamformer described here is shown to offer an improved signal to clutter ratio when compared to the traditional monostatic MIST beamformer.

Evaluation of Image Reconstruction Algorithms for Confocal Microwave Imaging: Application to Patient Data

Confocal Microwave Imaging (CMI) for the early detection of breast cancer has been under development for over two decades and is currently going through early-phase clinical evaluation. The image reconstruction algorithm is a key signal processing component of any CMI-based breast imaging system and impacts the efficacy of CMI in detecting breast cancer. Several image reconstruction algorithms for CMI have been developed since its inception. These image reconstruction algorithms have been previously evaluated and compared, using both numerical and physical breast models, and healthy volunteer data. However, no study has been performed to evaluate the performance of image reconstruction algorithms using clinical patient data. In this study, a variety of imaging algorithms, including both data-independent and data-adaptive algorithms, were evaluated using data obtained from a small-scale patient study conducted at the University of Calgary. Six imaging algorithms were applied to reconstruct 3D images of five clinical patients. Reconstructed images for each algorithm and each patient were compared to the available clinical reports, in terms of abnormality detection and localisation. The imaging quality of each algorithm was evaluated using appropriate quality metrics. The results of the conventional Delay-and-Sum algorithm and the Delay-Multiply-and-Sum (DMAS) algorithm were found to be consistent with the clinical information, with DMAS producing better quality images compared to all other algorithms.

Lipid Lowering Therapy, Low-Density Lipoprotein Level and Risk of Intracerebral Hemorrhage - A Meta-Analysis

BACKGROUND

The association of lipid lowering therapy and intracerebral hemorrhage risk is controversial.

METHODS

We performed a cumulative meta-analysis of lipid lowering trials that reported intracerebral hemorrhage. Statin, fibrate, ezetimibe, PCSK9, and CETP trials were included. We explored whether the association of lipid lowering therapy and risk of intracerebral hemorrhage may vary by baseline low-density lipoprotein (LDL) level, mean change in LDL or baseline cardiovascular risk of population.

RESULTS

Among 39 trials (287,651 participants), lipid lowering therapy was not associated with a statistically significant increased risk of intracerebral hemorrhage (ICH) in primary and secondary prevention trials combined (odds ratio [OR], 1.12; 95% confidence interval [CI], .98-1.28). Lipid lowering was associated with an increased risk of ICH in secondary prevention trials (OR, 1.18; 95% CI, 1.00-1.38), but not in primary prevention trials (OR, 1.01; 95% CI, .78-1.30), but the test for interaction was not significant (P for interaction = .31). Meta-regression of baseline LDL or difference in LDL reduction between active and control did not explain significant heterogeneity between studies for ICH risk. Of 1000 individuals treated for 1 year for secondary prevention, we estimated 9.17 (95% CI, 5.78-12.66) fewer ischemic strokes and .48 (95% CI, .06-1.02) more ICH, and a net reduction of 8.69 in all stroke per 1000 person-years.

CONCLUSIONS

The benefits of lipid lowering therapy in prevention of ischemic stroke greatly exceed the risk of ICH. Concern about ICH should not discourage stroke clinicians from prescribing lipid lowering therapy for secondary prevention of ischemic stroke.

Assessment of pancreatic function in screened infants with cystic fibrosis

Previously we have reported that 37% of infants with cystic fibrosis diagnosed by neonatal screening with the dried blood spot immunoreactive trypsin assay have pancreatic sufficiency. However, 34 of the 78 infants had pancreatic function tests an average 2.3 years after diagnosis, thus it was possible that the percentage with neonatal pancreatic sufficiency was underestimated, due to the loss of pancreatic function with time in some infants. To assess this hypothesis we have assessed pancreatic function at the time of diagnosis in a further 20 infants since the completion of the previous study. Results of fecal fat determinations and/or pancreatic stimulation tests indicate that 10 (50%) of these infants have pancreatic sufficiency. Combining these results with those of the previous study, 31 of 64 patients (48%) have pancreatic sufficiency at this early age. We have also monitored the progression of pancreatic disease in the 39 patients with pancreatic sufficiency recognized to date. Eleven have developed pancreatic insufficiency and require enzyme replacement therapy. Five others have shown further improvement of colipase secretion with age. We conclude that the dried blood immunoreactive trypsin screening program for cystic fibrosis does recognize patients with pancreatic sufficiency, and at diagnosis nearly half the patients are in this category. To date, 28% of patients with pancreatic sufficiency have demonstrated a variable decline in pancreatic function with age.

Effect of Logarithmic and Linear Frequency Scales on Parametric Modelling of Tissue Dielectric Data

The dielectric properties of biological tissues have been studied widely over the past half-century. These properties are used in a vast array of applications, from determining the safety of wireless telecommunication devices to the design and optimisation of medical devices. The frequency-dependent dielectric properties are represented in closed-form parametric models, such as the Cole-Cole model, for use in numerical simulations which examine the interaction of electromagnetic (EM) fields with the human body. In general, the accuracy of EM simulations depends upon the accuracy of the tissue dielectric models. Typically, dielectric properties are measured using a linear frequency scale; however, use of the logarithmic scale has been suggested historically to be more biologically descriptive. Thus, the aim of this paper is to quantitatively compare the Cole-Cole fitting of broadband tissue dielectric measurements collected with both linear and logarithmic frequency scales. In this way, we can determine if appropriate choice of scale can minimise the fit error and thus reduce the overall error in simulations. Using a well-established fundamental statistical framework, the results of the fitting for both scales are quantified. It is found that commonly used performance metrics, such as the average fractional error, are unable to examine the effect of frequency scale on the fitting results due to the averaging effect that obscures large localised errors. This work demonstrates that the broadband fit for these tissues is quantitatively improved when the given data is measured with a logarithmic frequency scale rather than a linear scale, underscoring the importance of frequency scale selection in accurate wideband dielectric modelling of human tissues.

Prospective study of liver function in children following multiple halothane anaesthetics at short intervals

In the 4 years January 1981 to December 1984, 186 children received 1362 halothane anaesthetics. Twenty-five patients were anaesthetized with halothane at least 10 times in a year. No patient developed postoperative jaundice. Sixty-nine children were exposed to two halothane anaesthetics within 28 days on 149 occasions and serial estimations of serum liver enzyme concentrations were obtained. Minor increases in the concentrations of serum AST and ALT were recorded in 10.6% and 4.7% of patient entries, respectively. Postoperative GGT and SAP concentrations were increased in 2.7% of patient entries. Patients in whom enzyme values were increased before the first anaesthetic were no more likely than other subjects to develop further changes.

Diagnosing Breast Cancer with Microwave Technology: remaining challenges and potential solutions with machine learning

Currently, breast cancer often requires invasive biopsies for diagnosis, motivating researchers to design and develop non-invasive and automated diagnosis systems. Recent microwave breast imaging studies have shown how backscattered signals carry relevant information about the shape of a tumour, and tumour shape is often used with current imaging modalities to assess malignancy. This paper presents a comprehensive analysis of microwave breast diagnosis systems which use machine learning to learn characteristics of benign and malignant tumours. The state-of-the-art, the main challenges still to overcome and potential solutions are outlined. Specifically, this work investigates the benefit of signal pre-processing on diagnostic performance, and proposes a new set of extracted features that capture the tumour shape information embedded in a signal. This work also investigates if a relationship exists between the antenna topology in a microwave system and diagnostic performance. Finally, a careful machine learning validation methodology is implemented to guarantee the robustness of the results and the accuracy of performance evaluation.

Parameter Search Algorithms for Microwave Radar-Based Breast Imaging: Focal Quality Metrics as Fitness Functions

Inaccurate estimation of average dielectric properties can have a tangible impact on microwave radar-based breast images. Despite this, recent patient imaging studies have used a fixed estimate although this is known to vary from patient to patient. Parameter search algorithms are a promising technique for estimating the average dielectric properties from the reconstructed microwave images themselves without additional hardware. In this work, qualities of accurately reconstructed images are identified from point spread functions. As the qualities of accurately reconstructed microwave images are similar to the qualities of focused microscopic and photographic images, this work proposes the use of focal quality metrics for average dielectric property estimation. The robustness of the parameter search is evaluated using experimental dielectrically heterogeneous phantoms on the three-dimensional volumetric image. Based on a very broad initial estimate of the average dielectric properties, this paper shows how these metrics can be used as suitable fitness functions in parameter search algorithms to reconstruct clear and focused microwave radar images.

Multi-frequency symmetry difference electrical impedance tomography with machine learning for human stroke diagnosis

OBJECTIVE

Multi-frequency symmetry difference electrical impedance tomography (MFSD-EIT) can robustly detect and identify unilateral perturbations in symmetric scenes. Here, an investigation is performed to assess if the algorithm can be successfully applied to identify the aetiology of stroke with the aid of machine learning.

METHODS

Anatomically realistic four-layer finite element method models of the head based on stroke patient images are developed and used to generate EIT data over a 5 Hz-100 Hz frequency range with and without bleed and clot lesions present. Reconstruction generates conductivity maps of each head at each frequency. Application of a quantitative metric assessing changes in symmetry across the sagittal plane of the reconstructed image and over the frequency range allows lesion detection and identification. The algorithm is applied to both simulated and human (n = 34 subjects) data. A classification algorithm is applied to the metric value in order to differentiate between normal, haemorrhage and clot values.

MAIN RESULTS

An average accuracy of 85% is achieved when MFSD-EIT with support vector machines (SVM) classification is used to identify and differentiate bleed from clot in human data, with 77% accuracy when differentiating normal from stroke in human data.

CONCLUSION

Applying a classification algorithm to metrics derived from MFSD-EIT images is a novel and promising technique for detection and identification of perturbations in static scenes.

SIGNIFICANCE

The MFSD-EIT algorithm used with machine learning gives promising results of lesion detection and identification in challenging conditions like stroke. The results imply feasible translation to human patients.

Nitroprusside in children after cardiopulmonary bypass: a study of thiocyanate toxicity

Thiocyanate levels, an indicator of nitroprusside toxicity, were studied in 22 children after repair of structural heart disease during cardiopulmonary bypass. At the total dose (2.6 +/- 2.3 mg/kg) and time (34.4 +/- 19 h) ranges of this study, no evidence of toxicity was detected, despite this total dose exceeding recommended maximum in some patients. Nitroprusside infusion, as described, in children with normal hepatic and renal function is safe and may not warrant routine assessment of thiocyanate levels.

Image-based classification of bladder state using electrical impedance tomography

OBJECTIVE

In this study, we examine the potential of using machine learning classification to determine the bladder state ('not full', 'full') with electrical impedance tomography (EIT) images of the pelvic region. Accurate classification of these states would enable urinary incontinence (UI) monitoring to alert the patient, before involuntary voiding occurs, in a low-cost and discrete manner.

APPROACH

Using both numerical and experimental data, we form datasets that contain diverse observations with varying clinical parameters such as bladder volume, urine conductivity, and the reference used for time-difference imaging. We then classify the bladder state using both pixel-wise and feature extraction-based classification techniques. We employ principal component analysis, wavelets, and image segmentation to help create features.

MAIN RESULTS

The performance was compared across several classifier algorithms. The minimum accuracy was 77.50%. The highest accuracy observed was 100%, and was found by combining principal component analysis and the Gaussian radial based function kernel support vector machine. This combination also offered the best trade-off between classification performance and the costs of training time and memory space. The biggest challenge in bladder state classification is classifying volumes near the separation volume of not full and full, in which choosing the most suitable classifier combination can minimize this error.

SIGNIFICANCE

We performed the first machine learning classification of bladder EIT images, achieving high classification accuracies with both numerical and experimental data. This work highlights the potential of using image-based machine learning with an EIT device to support bladder monitoring for those suffering from UI.

Establishing electroporation thresholds for targeted cell specific cardiac ablation in a 2D culture model

BACKGROUND

Irreversible electroporation has emerged as a new modality to overcome issues associated with other energy sources for cardiac ablation. Strong evidence on the optimal, effective, and selective voltage threshold is lacking for both in-vitro and pre-clinical in-vivo studies. The aim of this study is to examine the optimal threshold for selective cell ablation on cardiac associated cell types.

METHODS

Conventional monophasic and biphasic pulses of different field strength were delivered in a monolayer culture system of cardiomyocytes, neurons and adipocytes. The dynamics of cell death mechanisms were examined at different time points.

RESULTS

Neurons exhibit higher susceptibility to electroporation and cell death at higher field strength of 1250 V/cm in comparison to cardiomyocytes. Cardiac adipocytes showed lower susceptibility to electroporation in comparison to other cell types. A significant proportion of cardiomyocytes recovered after 24 hours post-electroporation, while neuronal cell death remained consistent but with a significant delayed cell death at a higher voltage threshold. Caspase 3/7 activity was observed in both cardiomyocytes and neurons, with a higher level of activity in cardiomyocytes in response to electroporation. Biphasic and monophasic pulses showed no significant difference in both cell types, and significantly lower cell death in neurons when inter pulse interval was reduced.

CONCLUSIONS

This study presents important findings on the differences in the susceptibility of neurons and cardiomyocytes to IRE. Cell type alone yielded selective and different dynamics in terms of the evolution and signaling mechanism of cell death in response to electroporation. This article is protected by copyright. All rights reserved.

Classification of breast tumor models with a prototype microwave imaging system

PURPOSE

The assessment of the size and shape of breast tumors is of utter importance to the correct diagnosis and staging of breast cancer. In this paper, we classify breast tumor models of varying sizes and shapes using signals collected with a monostatic ultra-wideband radar microwave imaging prototype system with machine learning algorithms specifically tailored to the collected data.

METHODS

A database comprising 13 benign and 13 malignant tumor models with sizes between 13 and 40 mm was created using dielectrically representative tissue mimicking materials. These tumor models were placed inside two breast phantoms: a homogeneous breast phantom and a breast phantom with clusters of fibroglandular mimicking tissue, accounting for breast heterogeneity. The breast phantoms with tumors were imaged with a monostatic microwave imaging prototype system, over a 1-6 GHz frequency range. The classification of benign and malignant tumors embedded in the two breast phantoms was completed, and tumor classification was evaluated with Principal Component Analysis as a feature extraction method, and tuned Naïve Bayes (NB), decision trees (DT), and k-nearest neighbours (kNN) as classifiers. We further study which antenna positions are better placed to classify tumors, discuss the feature extraction method and optimize classification algorithms, by tuning their hyperparameters, to improve sensitivity, specificity and the receiver operating characteristic curve, while ensuring maximum generalization and avoiding overfitting and data contamination. We also added a realistic synthetic skin response to the collected signals and examined its global effect on classification of benign vs malignant tumors.

RESULTS

In terms of global classification performance, kNN outperformed DT and NB machine learning classifiers, achieving a classification accuracy of 96.2% when classifying between benign and malignant tumor phantoms in a homogeneous breast phantom (both when the skin artifact is and is not considered).

CONCLUSIONS

We experimentally classified tumor models as benign or malignant with a microwave imaging system, and we showed a methodology that can potentially assess the shape of breast tumors, which will give further insight into the correct diagnosis and staging of breast cancer.

Dielectric characterization of diseased human trabecular bones at microwave frequency

The objective of this study is to determine whether in vitro dielectric properties of human trabecular bones, can distinguish between osteoporotic and osteoarthritis patients' bone samples. Specifically this study enlightens intra-patient variation of trabecular bone microarchitecture and dielectric properties, inter-disease comparison of bone dielectric properties, and finally establishes the correlation to traditional bone histomorphometry parameter (bone volume fraction) for diseased bone tissue. Bone cores were obtained from osteoporotic and osteoarthritis patients (n = 12). These were scanned using microCT to examine bone volume fraction. An open-ended coaxial probe measurement technique was employed to measure dielectric properties over the 0.5 - 8.5 GHz frequency range. The dielectric properties of osteoarthritis patients are significantly higher than osteoporotic patients; with an increase of 41% and 45% for relative permittivity and conductivity respectively. The dielectric properties within each patient vary significantly, variation in relative permittivity and conductivity was found to be greater than 25% and 1.4% respectively. A weak correlation (r  =  0.5) is observed between relative permittivity and bone volume fraction. Osteoporotic and osteoarthritis bones can be differentiated based on difference of dielectric properties. Although these do not correlate strongly to bone volume fraction, it should be noted that bone volume fraction is a poor predictor of fracture risk. The dielectric properties of bones are found to be influenced by mineralization levels of bones. Therefore, dielectric properties of bones may have potential as a diagnostic measure of osteoporosis.