Urinary Metabolic Distinction of Niemann-Pick Class 1 Disease through the Use of Subgroup Discovery.

In this investigation, we outline the applications of a data mining technique known as Subgroup Discovery (SD) to the analysis of a sample size-limited metabolomics-based dataset. The SD technique utilized a supervised learning strategy, which lies midway between classificational and descriptive criteria, in which given the descriptive property of a dataset (i.e., the response target variable of interest), the primary objective was to discover subgroups with behaviours that are distinguishable from those of the complete set (albeit with a differential statistical distribution). These approaches have, for the first time, been successfully employed for the analysis of aromatic metabolite patterns within an NMR-based urinary dataset collected from a small cohort of patients with the lysosomal storage disorder Niemann-Pick class 1 (NPC1) disease (n = 12) and utilized to distinguish these from a larger number of heterozygous (parental) control participants. These subgroup discovery strategies discovered two different NPC1 disease-specific metabolically sequential rules which permitted the reliable identification of NPC1 patients; the first of these involved 'normal' (intermediate) urinary concentrations of xanthurenate, 4-aminobenzoate, hippurate and quinaldate, and disease-downregulated levels of nicotinate and trigonelline, whereas the second comprised 'normal' 4-aminobenzoate, indoxyl sulphate, hippurate, 3-methylhistidine and quinaldate concentrations, and again downregulated nicotinate and trigonelline levels. Correspondingly, a series of five subgroup rules were generated for the heterozygous carrier control group, and 'biomarkers' featured in these included low histidine, 1-methylnicotinamide and 4-aminobenzoate concentrations, together with 'normal' levels of hippurate, hypoxanthine, quinolinate and hypoxanthine. These significant disease group-specific rules were consistent with imbalances in the combined tryptophan-nicotinamide, tryptophan, kynurenine and tyrosine metabolic pathways, along with dysregulations in those featuring histidine, 3-methylhistidine and 4-hydroxybenzoate. In principle, the novel subgroup discovery approach employed here should also be readily applicable to solving metabolomics-type problems of this nature which feature rare disease classification groupings with only limited patient participant and sample sizes available.

Application of data augmentation techniques towards metabolomics.

Niemann-Pick Class 1 (NPC1) disease is a rare and debilitating neurodegenerative lysosomal storage disease (LSD). Metabolomics datasets of NPC1 patients available to perform this type of analysis are often limited in the number of samples and severely unbalanced. In order to improve the predictive capability and identify new biomarkers in an NPC1 disease urinary dataset, data augmentation (DA) techniques based on computational intelligence have been employed to create synthetic samples, i.e. the addition of noise, oversampling techniques and conditional generative adversarial networks. These techniques have been used to evaluate their predictive capacities on a set of urine samples donated by 13 untreated NPC1 disease and 47 heterozygous (parental) carrier control participants. Results on the prediction have also been obtained using different machine learning classification models and the partial least squares techniques. These results provide strong evidence for the ability of DA techniques to generate good quality synthetic data. Results acquired show increases in sensitivity of 20%-50%, an F1 score of 6%-30%, and a predictive capacity of 0.3 (out of 1). Additionally, more conventional forms of multivariate data analysis have been employed. These have allowed the detection of unusual urinary metabolite profiles, and the identification of biomarkers through the use of synthetically augmented datasets. Results indicate that urinary branched-chain amino acids such as valine, 3-aminoisobutyrate and quinolinate, may be employable as valuable biomarkers for the diagnosis and prognostic monitoring of NPC1 disease.

Dealing with distribution mismatch in semi-supervised deep learning for COVID-19 detection using chest X-ray images: A novel approach using feature densities.

In the context of the global coronavirus pandemic, different deep learning solutions for infected subject detection using chest X-ray images have been proposed. However, deep learning models usually need large labelled datasets to be effective. Semi-supervised deep learning is an attractive alternative, where unlabelled data is leveraged to improve the overall model's accuracy. However, in real-world usage settings, an unlabelled dataset might present a different distribution than the labelled dataset (i.e. the labelled dataset was sampled from a target clinic and the unlabelled dataset from a source clinic). This results in a distribution mismatch between the unlabelled and labelled datasets. In this work, we assess the impact of the distribution mismatch between the labelled and the unlabelled datasets, for a semi-supervised model trained with chest X-ray images, for COVID-19 detection. Under strong distribution mismatch conditions, we found an accuracy hit of almost 30%, suggesting that the unlabelled dataset distribution has a strong influence in the behaviour of the model. Therefore, we propose a straightforward approach to diminish the impact of such distribution mismatch. Our proposed method uses a density approximation of the feature space. It is built upon the target dataset to filter out the observations in the source unlabelled dataset that might harm the accuracy of the semi-supervised model. It assumes that a small labelled source dataset is available together with a larger source unlabelled dataset. Our proposed method does not require any model training, it is simple and computationally cheap. We compare our proposed method against two popular state of the art out-of-distribution data detectors, which are also cheap and simple to implement. In our tests, our method yielded accuracy gains of up to 32%, when compared to the previous state of the art methods. The good results yielded by our method leads us to argue in favour for a more data-centric approach to improve model's accuracy. Furthermore, the developed method can be used to measure data effectiveness for semi-supervised deep learning model training.

A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica.

The implementation of deep learning-based computer-aided diagnosis systems for the classification of mammogram images can help in improving the accuracy, reliability, and cost of diagnosing patients. However, training a deep learning model requires a considerable amount of labelled images, which can be expensive to obtain as time and effort from clinical practitioners are required. To address this, a number of publicly available datasets have been built with data from different hospitals and clinics, which can be used to pre-train the model. However, using models trained on these datasets for later transfer learning and model fine-tuning with images sampled from a different hospital or clinic might result in lower performance. This is due to the distribution mismatch of the datasets, which include different patient populations and image acquisition protocols. In this work, a real-world scenario is evaluated where a novel target dataset sampled from a private Costa Rican clinic is used, with few labels and heavily imbalanced data. The use of two popular and publicly available datasets (INbreast and CBIS-DDSM) as source data, to train and test the models on the novel target dataset, is evaluated. A common approach to further improve the model's performance under such small labelled target dataset setting is data augmentation. However, often cheaper unlabelled data is available from the target clinic. Therefore, semi-supervised deep learning, which leverages both labelled and unlabelled data, can be used in such conditions. In this work, we evaluate the semi-supervised deep learning approach known as MixMatch, to take advantage of unlabelled data from the target dataset, for whole mammogram image classification. We compare the usage of semi-supervised learning on its own, and combined with transfer learning (from a source mammogram dataset) with data augmentation, as also against regular supervised learning with transfer learning and data augmentation from source datasets. It is shown that the use of a semi-supervised deep learning combined with transfer learning and data augmentation can provide a meaningful advantage when using scarce labelled observations. Also, we found a strong influence of the source dataset, which suggests a more data-centric approach needed to tackle the challenge of scarcely labelled data. We used several different metrics to assess the performance gain of using semi-supervised learning, when dealing with very imbalanced test datasets (such as the G-mean and the F2-score), as mammogram datasets are often very imbalanced. Graphical Abstract Description of the test-bed implemented in this work. Two different source data distributions were used to fine-tune the different models tested in this work. The target dataset is the in-house CR-Chavarria-2020 dataset.

Improving Uncertainty Estimation With Semi-Supervised Deep Learning for COVID-19 Detection Using Chest X-Ray Images.

In this work we implement a COVID-19 infection detection system based on chest X-ray images with uncertainty estimation. Uncertainty estimation is vital for safe usage of computer aided diagnosis tools in medical applications. Model estimations with high uncertainty should be carefully analyzed by a trained radiologist. We aim to improve uncertainty estimations using unlabelled data through the MixMatch semi-supervised framework. We test popular uncertainty estimation approaches, comprising Softmax scores, Monte-Carlo dropout and deterministic uncertainty quantification. To compare the reliability of the uncertainty estimates, we propose the usage of the Jensen-Shannon distance between the uncertainty distributions of correct and incorrect estimations. This metric is statistically relevant, unlike most previously used metrics, which often ignore the distribution of the uncertainty estimations. Our test results show a significant improvement in uncertainty estimates when using unlabelled data. The best results are obtained with the use of the Monte Carlo dropout method.

Correcting data imbalance for semi-supervised COVID-19 detection using X-ray chest images.

A key factor in the fight against viral diseases such as the coronavirus (COVID-19) is the identification of virus carriers as early and quickly as possible, in a cheap and efficient manner. The application of deep learning for image classification of chest X-ray images of COVID-19 patients could become a useful pre-diagnostic detection methodology. However, deep learning architectures require large labelled datasets. This is often a limitation when the subject of research is relatively new as in the case of the virus outbreak, where dealing with small labelled datasets is a challenge. Moreover, in such context, the datasets are also highly imbalanced, with few observations from positive cases of the new disease. In this work we evaluate the performance of the semi-supervised deep learning architecture known as MixMatch with a very limited number of labelled observations and highly imbalanced labelled datasets. We demonstrate the critical impact of data imbalance to the model's accuracy. Therefore, we propose a simple approach for correcting data imbalance, by re-weighting each observation in the loss function, giving a higher weight to the observations corresponding to the under-represented class. For unlabelled observations, we use the pseudo and augmented labels calculated by MixMatch to choose the appropriate weight. The proposed method improved classification accuracy by up to 18%, with respect to the non balanced MixMatch algorithm. We tested our proposed approach with several available datasets using 10, 15 and 20 labelled observations, for binary classification (COVID-19 positive and normal cases). For multi-class classification (COVID-19 positive, pneumonia and normal cases), we tested 30, 50, 70 and 90 labelled observations. Additionally, a new dataset is included among the tested datasets, composed of chest X-ray images of Costa Rican adult patients.

Dynamic changes of the intraocular pressure and the pressure of cerebrospinal fluid in nonglaucomatous neurological patients.

PURPOSE: To describe the dynamic changes of the intraocular pressure (IOP) and intracranial pressure (ICP) with normal or pathological values (intracranial hypertension) in nonglaucomatous neurological patients during lumbar punction (LP). METHODS: Case-control study, prospective measurement of tonometry in both groups referred for LP. Intraocular pressure, ICP and translaminar pressure difference (TPD) were compared pre- and post-LP. RESULTS: Thirty-six patients (72 eyes) with mean age of 38.5 (16-64) years and BMI of 26.81 kg/m2 were analysed. The initial mean ICP was 12.81 (± 6.6) mmHg. The mean TPD before and after the LP was 1.48 mmHg and 0.65 mmHg, respectively. The mean IOP of both eyes decreased to 0.8 mmHg post-LP in patients with pathological ICP (p = 0.0193) and normal ICP (p = 0.006). CONCLUSIONS: We found a statistically significant decrease of the IOP post-LP compared to the pre-LP in both groups, being higher in patients with pathological ICP. There were no significant differences of the IOP in patients with normal versus pathological ICP pre-LP/post-LP; neither was found a correlation between ICP and IOP.

Analysis and test of efficient methods for building recursive deterministic perceptron neural networks.

The Recursive Deterministic Perceptron (RDP) feed-forward multilayer neural network is a generalisation of the single layer perceptron topology. This model is capable of solving any two-class classification problem as opposed to the single layer perceptron which can only solve classification problems dealing with linearly separable sets. For all classification problems, the construction of an RDP is done automatically and convergence is always guaranteed. Three methods for constructing RDP neural networks exist: Batch, Incremental, and Modular. The Batch method has been extensively tested and it has been shown to produce results comparable with those obtained with other neural network methods such as Back Propagation, Cascade Correlation, Rulex, and Ruleneg. However, no testing has been done before on the Incremental and Modular methods. Contrary to the Batch method, the complexity of these two methods is not NP-Complete. For the first time, a study on the three methods is presented. This study will allow the highlighting of the main advantages and disadvantages of each of these methods by comparing the results obtained while building RDP neural networks with the three methods in terms of the convergence time, the level of generalisation, and the topology size. The networks were trained and tested using the following standard benchmark classification datasets: IRIS, SOYBEAN, and Wisconsin Breast Cancer. The results obtained show the effectiveness of the Incremental and the Modular methods which are as good as that of the NP-Complete Batch method but with a much lower complexity level. The results obtained with the RDP are comparable to those obtained with the backpropagation and the Cascade Correlation algorithms.

Glioma del quiasma óptico en una paciente con neurofibromatosis tipo 1 morbilidad visual y cambios a largo plazo demostrados por TC y RM / Neurofibromatosis type 1 and chiasma glioma

Objetivo: reportar los hallazgos clínicos y radiológicos de una paciente de 27 años con neurofibromatosis tipo 1 y glioma del quiasma seguida durante 19 años, sin tratamiento, la progresión de la disminución de la agudeza visual y del campo, así como los cambios del tumor mediante neuroimagen. Material y método: revisión clínica de la paciente, del expediente y de su archivo radiológico. Se efectuaron resonancia magnética, tomografía, agudeza visual, campos visuales y visión al color. Resultados: los estudios de TC mostraron hiperintensidad del tumor en la fase simple que disminuyó con el tiempo y no hubo cambios en el tamaño del tumor durante los 19 años de seguimiento. La visión de cada ojo se deterioró dos líneas, el OD de 20/400 a cuenta dedos a 1 m y la del OI de 20/30 a 20/50. Sólo el ojo izquierdo que tenía el mejor campo visual mostró reducción, el ojo derecho mostró cambios mínimos. Conclusiones. muy lento deterioro de la visión y el campo ocurrieron en ambos ojos de esta paciente. Por neuroimagen no se comprobó crecimiento tumoral sólo hiperintensidad de la fase simple en TC. Esto apoya otras observaciones de la baja agresividad tumoral del glioma del quiasma que aparece en la infancia, sin involucro del hipotálamo y que pudo ser observado sin tratamiento durante 19 años.

Hemianopsias homónimas: relación topográfica, etiológica y evolución clínica / Homonymus hemianopsias: etiology, topography and clinical entity

Las hemianopsias homónimas son el resultado de lesiones retroquiasmáticas de la vía visual, y son producidas por una gran variedad de lesiones y en diferentes topografías. El presente estudio se realizó para determinar la etiología, topografía y evolución de esta entidad clínica. Se realizarón un estudio prospectivo en el Instituto Nacional de Neurología y Neurocirugía Manuel Velasco Suárez (INNN), donde se reclutaron 123 pacientes con diagnóstico de hemianopsia homónima. La edad promedio de los pacientes del estudio fue de 48 años, la mayoría de las hemianopsias fueron izquierdas (63 por ciento) y la localización principal fue occipital (55 por ciento). La etiología más frecuente fue vascular (76.5 por ciento). En la mayoría de los pacientes hubo una recuperación, aunque no total, del déficit visual, siendo mejor el pronostico en los pacientes que tuvieron hemorragia intracerebral de etiología hipertensiva como causa de la hemianopsia. Esta serie de pacientes es la más grande reportada hasta el momento en la literatura y es la primera que analiza el pronóstico de los pacientes en relación a la patología que provoco la hemianopsia.

Amplificación en oftalmoscopia indirecta binocular / Magnification in binocular indirect ophthalmoscopy

Propósito. Amplificar la imagen estereoscópica del oftalmoscopio indirecto binocular. Método. Se intercala una lente positiva inmediatamente por delante del sistema de observación e iluminación del oftalmoscopio indirecto binocular, usando a su vez una lente de condensación de bajo poder (+14 D) y de 50 mm de diámetro. Resultado. Permite observar papila óptica, área macular y retina periférica con una nitidez, magnificación y estereopsis de imagen comparable a la obtenida con biomicroscopia. Conclusiones. El procedimiento contrarresta las desventajas del oftalmoscopio indirecto binocular con relación al oftalmoscopio directo para ver finos detalles en fondo de ojo.

Síndrome de Wyburn-Mason o Síndrome de Bonnet-Blanc-Dechaume (Angiomatosis encéfalo-retino-facial). Reporte de un caso clínico y evaluación neurorradiológica / Wyburn-Mason syndrome or Bonnet-Blanc-Dechaume Syndrome (encephalo-retino-facial angiomaatosis)

Las malformaciones arteriovenosas retinianas (MAV) (angioma racemoso) generalmente se describen como malformaciones vasculares congénitas que no cambian.Los autores describen los hallazgos clínicos y radiológicos de una mujer con una malformación vascular de fondo de ojo a quien se realizó tomografía, angiotomografía y angiografía carotidea para establecer el diagnóstico de Síndrome de Wyburn-Mason.La malformación afectaba la circulación de la hemicara derecha, la retina, órbita y un nido malformativo intracraneal del mismo lado.La paciente sufrió oclusión de los vasos retinianos de la MAV, lo que ocasionó severa baja de agudeza visual del OD. Los estudios neurorradiológicos fueron clave para establecer el diagnóstico de Síndrome de Wyburn-Mason el cual es una de las facomatosis más raras. El seguimiento durante 25 años de esta paciente mostró que pueden ocurrir oclusiones de la MAV retiniana espontáneamente.

Resonancia magnética en el síndrome de Vogt-Koyanagi-Harada (uveo-meningoencefalitis) / Magnetic resonance in the Not-Koyanagi-Harada syndrome (uveo-meningoencefalitis)

Propósito. Investigar posibles cambios en el síndrome VKH con los patrones de neuroimagen que pudiera identificarse y contribuir al diagnóstico, al determinar posibles alteraciones oculares y cerebrales en las diferentes fases del padecimiento. Método. Se estudiaron 11 pacientes, 5 con resonancia magnética en fase oftálmica aguda, 6 casos fase de convalecencia y 10 pacientes adultos sanos ocular y neurológicamente como grupo control para establecer parámetros comparativos en medidas de esclerótica, coriorretina y nervio óptico. Resultado. Los estudios de RM aportan información sobre el desprendimiento de retina, engrosamiento del tracto uveal en general, pero no permite diferenciar las señales precisas entre estas estructuras. La esclerótica en fase de convalecencia está más engrosada en OD 17 por ciento y OI 23 por ciento. La coriorretina engrosada en fase aguda OD 67 por ciento y OI 91 por ciento más en convalecencia más engrosada el 28 por ciento ambos ojos y los nervios ópticos menos gruesos en convalecencias el 13 por ciento OD y 16 por ciento en OI. Conclusión. Las medidas aportadas por la RM en mm del nervio óptico, esclerótica y coriorretina difieren de las anatómicas conocidas. La coriorretina se mostró engrosada tanto en fase aguda como de convalencencia y la esclerótica engrosada sólo en la convalecencia. El nervio óptico disminuido de grosor en fase de convalecencia, no se demostró alteración alguna en meninges ni estructuras vecinas excepto un caso

Cromatografía del thiner. Manifestaciones oculares en inhaladores crónicos / Cromatography of thiner. Ocular manifestations in chronic inhalators

Objetivo: Evaluar y analizar la composición del thiner del DF y compararlo con una muestra de thiner extranjero (USA) como parte de una evaluación química y toxicológica, y reportar las manifestaciones oftalmológicas de inhaladores crónicos de thiner Método: Para el análisis del thiner se utilizó un cromatógrafo de gases marca Perkin-Elmer, modelo sigma 300 equipado con detector de ionización de flama, y los potenciales visuales evocados con equipo Nicolet Path finder II. Con. método prospectivo de 1983-1996 se hizo estudio oftalmológico completo de 40 inhaladores crónicos de thiner. Resultados: En las muestras de thiner de México, se identificaron 20 elementos logrando una separación cuantitativa del 93.96 por ciento al 100 por ciento, predominando el tolueno con el 79.48 por ciento, y en el thiner de USA se identificaron 9 elementos con mayor concentración de metilpropilcetona 21.44 por ciento (cuadro 1). Tiempo de inhalar thiner de 3-15 años, edad 18-38 años, promedio 26 años, razón hombre/mujer 10:1, agudeza visual final de 20/200 o menor en el 84 por ciento, discromatopsia total en el 84 por ciento, con alteraciones campimétricas de sólo islotes de visión periférica en el 87 por ciento, midriasis en el 84 por ciento, con arreflexia a la luz y conservación del punto próximo de convergencia. Oftalmoscópicamente en la fase aguda neurorretinitis generalizada, y en la fase crónica atrófia óptica, degeneración retiniana difusa con envainamiento arteriolar y vasos fantasmas. Conclusiones: El thiner es una mezcla variada de solventes tóxicos, su inhalación crónica afecta a largo plazo nervio óptico y retina, con un daño gradual, bilateral e irreversible de predominio arteriolar con envenenamiento y vasos fantasmas. Las alteraciones oftalmoscópicas y la midriasis con la disociación pupilar "Luz-Acomodación-Convergencia" podría ser una orientación para el clínico en la sospecha de esta toxicomanía

The recursive deterministic perceptron neural network.

We introduce a feedforward multilayer neural network which is a generalization of the single layer perceptron topology (SLPT), called recursive deterministic perceptron (RDP). This new model is capable of solving any two-class classification problem, as opposed to the single layer perceptron which can only solve classification problems dealing with linearly separable sets (two subsets X and Y of R(d) are said to be linearly separable if there exists a hyperplane such that the elements of X and Y lie on the two opposite sides of R(d) delimited by this hyperplane). We propose several growing methods for constructing a RDP. These growing methods build a RDP by successively adding intermediate neurons (IN) to the topology (an IN corresponds to a SLPT). Thus, as a result, we obtain a multilayer perceptron topology, which together with the weights, are determined automatically by the constructing algorithms. Each IN augments the affine dimension of the set of input vectors. This augmentation is done by adding the output of each of these INs, as a new component, to every input vector. The construction of a new IN is made by selecting a subset from the set of augmented input vectors which is LS from the rest of this set. This process ends with LS classes in almost n-1 steps where n is the number of input vectors. For this construction, if we assume that the selected LS subsets are of maximum cardinality, the problem is proven to be NP-complete. We also introduce a generalization of the RDP model for classification of m classes (m>2) allowing to always separate m classes. This generalization is based on a new notion of linear separability for m classes, and it follows naturally from the RDP. This new model can be used to compute functions with a finite domain, and thus, to approximate continuous functions. We have also compared - over several classification problems - the percentage of test data correctly classified, or the topology of the 2 and m classes RDPs with that of the backpropagation (BP), cascade correlation (CC), and two other growing methods.

Panoftalmoscopía amplificada / Amplified panophthalmoscopy

Se presenta un nuevo dispositivo óptico para pan-oftalmoscopía amplificada que emplea un principio óptico semejante al de la oftalmoscopía indirecta al del amplificador de Coddington; el sistema óptico puede variarse de tamaño, dependiendo de la magnificación deseada. La imagen es invertida y revertidad, mostrando la totalidad de la retina posterios al ecuador, en una sola imagen para ser visualizada en estereopsis sin requerir visor binocular alguno, puede ser captada fotográficamente o tomada en video. La observación del fondo también puede llevarse a cabo utilizando un microscopio quirúrgico con luz coaxial o colateral permitiendo la visualización panorámica o bien fina de pequeás áreas del fondo de ojo. Este dispositivo pudiera aportar un elemento más de ayuda al clínico para la observación panorámica oftalmoscópica rutinaria