Behavioural immune landscapes of inflammation.

Transcriptional and proteomic profiling of individual cells have revolutionized interpretation of biological phenomena by providing cellular landscapes of healthy and diseased tissues1,2. These approaches, however, do not describe dynamic scenarios in which cells continuously change their biochemical properties and downstream 'behavioural' outputs3-5. Here we used 4D live imaging to record tens to hundreds of morpho-kinetic parameters describing the dynamics of individual leukocytes at sites of active inflammation. By analysing more than 100,000 reconstructions of cell shapes and tracks over time, we obtained behavioural descriptors of individual cells and used these high-dimensional datasets to build behavioural landscapes. These landscapes recognized leukocyte identities in the inflamed skin and trachea, and uncovered a continuum of neutrophil states inside blood vessels, including a large, sessile state that was embraced by the underlying endothelium and associated with pathogenic inflammation. Behavioural screening in 24 mouse mutants identified the kinase Fgr as a driver of this pathogenic state, and interference with Fgr protected mice from inflammatory injury. Thus, behavioural landscapes report distinct properties of dynamic environments at high cellular resolution.

A self-supervised embedding of cell migration features for behavior discovery over cell populations.

BACKGROUND AND OBJECTIVE: Recent studies point out that the dynamics and interaction of cell populations within their environment are related to several biological processes in immunology. Hence, single-cell analysis in immunology now relies on spatial omics. Moreover, recent literature suggests that immunology scenarios are hierarchically organized, including unknown cell behaviors appearing in different proportions across some observable control and therapy groups. These dynamic behaviors play a crucial role in identifying the causes of processes such as inflammation, aging, and fighting off pathogens or cancerous cells. In this work, we use a self-supervised learning approach to discover these behaviors associated with cell dynamics in an immunology scenario. MATERIALS AND METHODS: Specifically, we study the different responses of control group and therapy groups in a scenario involving inflammation due to infarct, with a focus on neutrophil migration within blood vessels. Starting from a set of hand-crafted spatio-temporal features, we use a recurrent neural network to generate embeddings that properly describe the dynamics of the migration processes. The network is trained using a novel multi-task contrastive loss that, on the one hand, models the hierarchical structure of our scenario (groups-behaviors-samples) and, on the other, ensures temporal consistency within the embedding, enforcing that subsequent temporal samples obtained from a given cell stay close in the latent space. RESULTS: Our experimental results demonstrate that the resulting embeddings improve the separability of cell behaviors and log-likelihood of the therapies, when compared to the hand-crafted feature extraction and recent methods from the state of the art, even with dimensionality reduction (16 vs. 21 hand-crafted features). CONCLUSIONS: Our approach enables single-cell analyses at a population level, being able to automatically discover shared behaviors among different groups. This, in turn, enables the prediction of the therapy effectiveness based on their proportions within a study group.

Asymmetric multi-task learning for interpretable gaze-driven grasping action forecasting.

This work tackles the problem of automatically predicting the grasping intention of humans observing their environment, with eye-tracker glasses and video cameras recording the scene view. Our target application is the assistance to people with motor disabilities and potential cognitive impairments, using assistive robotics. Our proposal leverages the analysis of human attention captured in the form of gaze fixations recorded by an eye-tracker on the first person video, as the anticipation of prehension actions is a well studied and well known phenomenon. We propose a multi-task system that simultaneously addresses the prediction of human attention in the near future, and the anticipation of grasping actions. In our model, visual attention is modeled as a competitive process between a discrete set of states, each one associated to a well-known gaze movement pattern from visual psychology. We additionally consider an asymmetric multitask problem, where attention modeling is an auxiliary task that helps to regularize the learning process of the main action prediction task, and propose a constrained multi-task loss that naturally deals with this asymmetry. Our model shows superior performance than other losses for dynamic multi-task learning, current dominant deep architectures for general action forecasting and particularly-tailored models for predicting grasping intention. In particular, it provides state-of-the-art performance in three datasets for egocentric action anticipation, with an average precision of 0.569 and 0.524 in GITW and Sharon datasets, respectively, and an accuracy of 89.2% and a success rate of 51.7% in Invisible dataset.

URI-CADS: A Fully Automated Computer-Aided Diagnosis System for Ultrasound Renal Imaging.

Ultrasound is a widespread imaging modality, with special application in medical fields such as nephrology. However, automated approaches for ultrasound renal interpretation still pose some challenges: (1) the need for manual supervision by experts at various stages of the system, which prevents its adoption in primary healthcare, and (2) their limited considered taxonomy (e.g., reduced number of pathologies), which makes them unsuitable for training practitioners and providing support to experts. This paper proposes a fully automated computer-aided diagnosis system for ultrasound renal imaging addressing both of these challenges. Our system is based in a multi-task architecture, which is implemented by a three-branched convolutional neural network and is capable of segmenting the kidney and detecting global and local pathologies with no need of human interaction during diagnosis. The integration of different image perspectives at distinct granularities enhanced the proposed diagnosis. We employ a large (1985 images) and demanding ultrasound renal imaging database, publicly released with the system and annotated on the basis of an exhaustive taxonomy of two global and nine local pathologies (including cysts, lithiasis, hydronephrosis, angiomyolipoma), establishing a benchmark for ultrasound renal interpretation. Experiments show that our proposed method outperforms several state-of-the-art methods in both segmentation and diagnosis tasks and leverages the combination of global and local image information to improve the diagnosis. Our results, with a 87.41% of AUC in healthy-pathological diagnosis and 81.90% in multi-pathological diagnosis, support the use of our system as a helpful tool in the healthcare system.

An interpretable CNN-based CAD system for skin lesion diagnosis.

Recently, convolutional neural networks have greatly outperformed previous systems based on handcrafted features once the size of public databases has increased. However, these algorithms learn feature representations that are difficult to interpret and analyse. On the other hand, experts require automatic systems to explain their decisions according to clinical criteria which, in the field of melanoma diagnosis, are related to the analysis of dermoscopic features found in the lesions. In recent years, the interpretability of deep networks has been explored using methods that obtain visual features highlighted by neurones or analyse activations to extract more useful information. Following the latter approach, this study proposes a system for melanoma diagnosis that explicitly incorporates dermoscopic feature segmentations into a diagnosis network through a channel modulation scheme. Modulation weights control the influence of the detected visual patterns based on the lesion content. As shown in the experimental section, our design not only improves the system performance on the ISIC 2016 (average AUC of 86.6% vs. 85.8%) and 2017 (average AUC of 94.0% vs. 93.8%) datasets, but also notably enhances the interpretability of the diagnosis, providing useful and intuitive cues to clinicians.

ACME: Automatic feature extraction for cell migration examination through intravital microscopy imaging.

Cell detection and tracking applied to in vivo fluorescence microscopy has become an essential tool in biomedicine to characterize 4D (3D space plus time) biological processes at the cellular level. Traditional approaches to cell motion analysis by microscopy imaging, although based on automatic frameworks, still require manual supervision at some points of the system. Hence, when dealing with a large amount of data, the analysis becomes incredibly time-consuming and typically yields poor biological information. In this paper, we propose a fully-automated system for segmentation, tracking and feature extraction of migrating cells within blood vessels in 4D microscopy imaging. Our system consists of a robust 3D convolutional neural network (CNN) for joint blood vessel and cell segmentation, a 3D tracking module with collision handling, and a novel method for feature extraction, which takes into account the particular geometry in the cell-vessel arrangement. Experiments on a large 4D intravital microscopy dataset show that the proposed system achieves a significantly better performance than the state-of-the-art tools for cell segmentation and tracking. Furthermore, we have designed an analytical method of cell behaviors based on the automatically extracted features, which supports the hypotheses related to leukocyte migration posed by expert biologists. This is the first time that such a comprehensive automatic analysis of immune cell migration has been performed, where the total population under study reaches hundreds of neutrophils and thousands of time instances.

The Homologous Recombination Deficiency Scar in Advanced Cancer: Agnostic Targeting of Damaged DNA Repair.

BRCA1 and BRCA2 are the most recognized tumor-suppressor genes involved in double-strand DNA break repair through the homologous recombination (HR) system. Widely known for its role in hereditary cancer, HR deficiency (HRD) has turned out to be critical beyond breast and ovarian cancer: for prostate and pancreatic cancer also. The relevance for the identification of these patients exceeds diagnostic purposes, since results published from clinical trials with poly-ADP ribose polymerase (PARP) inhibitors (PARPi) have shown how this type of targeted therapy can modify the long-term evolution of patients with HRD. Somatic aberrations in other HRD pathway genes, but also indirect genomic instability as a sign of this DNA repair impairment (known as HRD scar), have been reported to be relevant events that lead to more frequently than expected HR loss of function in several tumor types, and should therefore be included in the current diagnostic and therapeutic algorithm. However, the optimal strategy to identify HRD and potential PARPi responders in cancer remains undefined. In this review, we summarize the role and prevalence of HRD across tumor types and the current treatment landscape to guide the agnostic targeting of damaged DNA repair. We also discuss the challenge of testing patients and provide a special insight for new strategies to select patients who benefit from PARPi due to HRD scarring.

DermaKNet: Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for Skin Lesion Diagnosis.

Traditional approaches to automatic diagnosis of skin lesions consisted of classifiers working on sets of hand-crafted features, some of which modeled lesion aspects of special importance for dermatologists. Recently, the broad adoption of convolutional neural networks (CNNs) in most computer vision tasks has brought about a great leap forward in terms of performance. Nevertheless, with this performance leap, the CNN-based computer-aided diagnosis (CAD) systems have also brought a notable reduction of the useful insights provided by hand-crafted features. This paper presents DermaKNet, a CAD system based on CNNs that incorporates specific subsystems modeling properties of skin lesions that are of special interest to dermatologists aiming to improve the interpretability of its diagnosis. Our results prove that the incorporation of these subsystems not only improves the performance, but also enhances the diagnosis by providing more interpretable outputs.

Epidemiologia descriptiva de hendiduras labiopalatinas en la Clínica de Labio y Paladar Hendidos de Morelia, Michoacán, México (1989-2012) y su comparación con algunas poblaciones internacionales / Descriptive epidemiology of lip-palate fissures at the Lip and Palate Cleft Clinic in Morelia, Michoacan, Mexico (1989-2012) and a comparison with some international populations

Introducción y Objetivo. Las hendiduras labiopalatinas son malformaciones congénitas que afectan a la región maxilofacial. Su etiología es multifactorial, con factores genéticos y ambientales. Para la raza caucásica se estima una incidencia de 1:700 nacimientos, cifra que disminuye en otras poblaciones. El sexo masculino está afectado con mayor frecuencia, y solo las hendiduras palatinas aisladas presentan predominio femenino. Debido al desarrollo embriológico, el lado de mayor afectación es el izquierdo. Nuestro estudio tiene como objetivo conocer la distribución y frecuencia de las malformaciones atendidas en una institución de salud pública mexicana y su comparación con otras poblaciones internacionales. Material y Método. Llevamos a cabo un estudio observacional, descriptivo, transversal y retrospectivo, evaluando el total de expedientes clínicos de pacientes atendidos en el Servicio de Cirugía Reconstructiva Pediátrica de Morelia, Michoacan, México, durante el periodo 1989-2012. Realizamos la investigación bibliográfica en las bases de datos MEDLINE, LILACS y SciELO. Resultados. Obtuvimos un total de 800 expedientes clínicos, 460 (57.5%) de pacientes masculinos y 340 (42.5%) femeninos. La afectación de mayor frecuencia fue la hendidura labiopalatina, presente en 448 casos (56%). Esta situación se asemeja a estudios previos sobre el tema hechos en Bolivia, Sudan y México (54%). No fue posible determinar una incidencia poblacional total, ya que la institución donde realizamos el estudio atiende solo a población pediátrica. Conclusiones. La recolección de datos en nuestra institución mostro un predominio de presencia de hendidura labiopalatina en el sexo masculino, con presentación labiopalatina unilateral y del lado izquierdo. En base a ello podemos afirmar que la distribución epidemiológica de las hendiduras labiopalatinas encontrada en la Clínica de Labio y Paladar Hendidos de Morelia, Michoacan, México, se coloca en una posición intermedia al compararla con otras poblaciones internacionales (AU)

Background and Objective. The lip and palate clefts are congenital malformation that affects the maxillofacial region. Their etiology is multifactorial with such as genetics as environmental factors. For the caucuses race it´s estimated a incidence of 1:700 per births, number that decreases in other populations. The masculine sex is affected more frequently, only in the case of isolated palate cleft there’s a predominance of the feminine sex. Because of the embryologic development, the side that is more affected is the left side. The present study has as objective the knowledge of distribution and frequency of malformations treated in a Mexican public hospital and to compare with other international populations. Methods. We conduct an observational, descriptive, transverse and retrospective study, where we evaluated the total of clinic records of patients attended in the Pediatric Reconstructive Surgery Service in Morelia, Michoacan, Mexico, between 1989-2012. The bibliographic investigation was made at MEDLINE, LILACS and SciELO. Results. We got a total recruit of 800 clinical records, 460 (57.5%) male patients and 340 (42.5%) female. The most frequent affection was the combination of lip and palate with 448 (56%) cases. Situation that was similar to previous studies of Bolivia, Sudan and Mexico. It was not possible to determine the incidence because our hospital is only a pediatric institute. Conclusions. The data recollection at our center showed predominance in male patients, the most frequent malformation was unilateral lip and palate cleft and left side. We conclude that the epidemiologic distribution of the lip and palate clefts found at the Lip and Palate Cleft Clinic at Morelia, Michoacan, Mexico, is at a medium position compared with other international populations (AU)

Gestación satisfactoria tras metroplastia de Strassman en mujer con útero bicorne / Satisfactory pregnancy after Strassman’s metroplasty in a woman with bicorn uterus

La incidencia de anomalías uterinas congénitas es difícil de determinar debido a que muchas de esas mujeres no son diagnosticadas, especialmente si están asintomáticas. Del 2 al 4% de las mujeres en edad fértil con resultados reproductivos normales presentan anomalías uterinas. Las pacientes con malformaciones uterinas tienen un ayor riesgo de sufrir complicaciones obstétricas. Paul Strassman en 1907 reportó el primer caso de corrección de útero bicorne por colpotomía anterior con éxito. Presentamos un caso de una mujer con el antecedente de cirugía de metroplastia de Strassman para corrección de un útero bicorne bicollis, con una gestación que llegó a término, mediante cesárea, satisfactoriamente (AU)

The incidence of congenital uterine anomalies is difficult to determine because many of these women, who have anomalies, are not diagnosed, especially if they are asymptomatic. From 2 to 4% of women in childbearing age with normal reproductive outcomes have uterine abnormalities. Patients with uterine malformations have more risk of obstetric complications. Paul Strassman in 1907 reported the first case of bicornuate uterus correction by anterior colpotomy successfully. We report a case of a woman with a bicornuate uterus, who underwent Strassman metroplasty and had a term pregnancy by successfully cesarean (AU)