Usefulness of low-cost simulation models to learning surgical techniques for placenta accreta spectrum: An observational educational study.

OBJECTIVE: To evaluate the utility of low-cost simulation models to teach surgical techniques for placenta accreta spectrum (PAS), included in a multimodal education workshop for PAS. METHODS: This was an observational, survey-based study. Participants were surveyed before and after the use of low-fidelity mannequins to simulate two surgical techniques for PAS (one-step conservative surgery [OSCS] and modified subtotal hysterectomy [MSTH]), within a multimodal educational workshop. The workshops included pre-course preparation, didactics, simulated practice of the techniques using low-cost models, and viewing live surgery. RESULTS: Six OSCS/MSTH training workshops occurred across six countries and a total of 270 participants were surveyed. The responses of 127 certified obstetricians and gynecologists (OB-GYNs) were analyzed. Participants expressed favorable impressions of all components of the simulated session. Perceived anatomical simulator fidelity, scenario realism, educational component effectiveness, and self-assessed performance improvement received ratings of 4-5 (positive end of the Likert scale) from over 90% of respondents. When asked about simulation's role in technique comprehension, comfort level in technique performance, and likelihood of recommending this workshop to others, more than 75% of participants rated these aspects with a score of 4-5 (positively) on the five-point scale. CONCLUSION: Low-cost simulation, within a multimodal education strategy, is a well-accepted intervention for teaching surgical techniques for PAS.

Automatic Auditing System for Endoscopic Exploration of the Stomach with Artificial Intelligence-Gastro UNAL: Gastroendoscopy UNit for Automatic Labeling / Sistema de auditoría automática para la exploración endoscópica del estómago con Inteligencia Artificial - Gastro UNAL: Gastroendoscopy UNit for Automatic Labeling

Abstract Introduction: Upper endoscopy is the standard method for diagnosing early-stage gastric cancer. However, according to estimates, up to 20% of tumors are not detected, and their accuracy may be affected by the variability in their performance. In Colombia, most diagnoses take place in advanced stages, which aggravates the problem. Protocols have been proposed to ensure the complete observation of areas prone to premalignant lesions to address variability. Objective: To build and validate an automatic audit system for endoscopies using artificial intelligence techniques. Methodology: In this study, 96 patients from a teaching hospital underwent video-documented endoscopies, spanning 22 stations rearranged to minimize overlaps and improve the identification of 13 key gastric regions. An advanced convolutional network was used to process the images, extracting visual characteristics, which facilitated the training of artificial intelligence in the classification of these areas. Results: the model, called Gastro UNAL, was trained and validated with images of 67 patients (70% of cases) and tested with 29 different patients (30% of cases), which reached an average sensitivity of 85,5% and a specificity of 98,8% in detecting the 13 gastric regions. Conclusions: The effectiveness of the model suggests its potential to ensure the quality and accuracy of endoscopies. This approach could confirm the regions evaluated, alerting less experienced or trained endoscopists about blind spots in the examinations, thus, increasing the quality of these procedures.

Resumen Introducción: La endoscopia digestiva alta es el método estándar para diagnosticar el cáncer gástrico en etapas tempranas. Sin embargo, su precisión puede verse afectada por la variabilidad en su realización, y se estiman hasta 20% de tumores no detectados. En Colombia, la mayoría de los diagnósticos se realizan en etapas avanzadas, lo que agrava el problema. Para abordar la variabilidad, se han propuesto protocolos con el fin de asegurar la observación completa de áreas propensas a lesiones premalignas. Objetivo: Construir y validar un sistema de auditoría automática para endoscopias usando técnicas de inteligencia artificial. Metodología: En este estudio, 96 pacientes de un hospital universitario se sometieron a endoscopias documentadas en video, abarcando 22 estaciones reorganizadas para minimizar solapamientos y mejorar la identificación de 13 regiones gástricas clave. Se utilizó una red convolucional avanzada para procesar las imágenes, extrayendo características visuales, lo que facilitó el entrenamiento de la inteligencia artificial en la clasificación de estas áreas. Resultados: El modelo, llamado Gastro UNAL, fue entrenado y validado con imágenes de 67 pacientes (70% de los casos) y probado con 29 pacientes distintos (30% de los casos), con lo que alcanzó una sensibilidad promedio del 85,5% y una especificidad del 98,8% en la detección de las 13 regiones gástricas. Conclusiones: La eficacia del modelo sugiere su potencial para asegurar la calidad y precisión de las endoscopias. Este enfoque podría confirmar las regiones evaluadas, alertando puntos ciegos en la exploración a los endoscopistas con menos experiencia o en entrenamiento, de tal forma que se aumente la calidad de estos procedimientos.

Leveraging a realistic synthetic database to learn Shape-from-Shading for estimating the colon depth in colonoscopy images.

Colonoscopy is the choice procedure to diagnose, screening, and treat the colon and rectum cancer, from early detection of small precancerous lesions (polyps), to confirmation of malign masses. However, the high variability of the organ appearance and the complex shape of both the colon wall and structures of interest make this exploration difficult. Learned visuospatial and perceptual abilities mitigate technical limitations in clinical practice by proper estimation of the intestinal depth. This work introduces a novel methodology to estimate colon depth maps in single frames from monocular colonoscopy videos. The generated depth map is inferred from the shading variation of the colon wall with respect to the light source, as learned from a realistic synthetic database. Briefly, a classic convolutional neural network architecture is trained from scratch to estimate the depth map, improving sharp depth estimations in haustral folds and polyps by a custom loss function that minimizes the estimation error in edges and curvatures. The network was trained by a custom synthetic colonoscopy database herein constructed and released, composed of 248400 frames (47 videos), with depth annotations at the level of pixels. This collection comprehends 5 subsets of videos with progressively higher levels of visual complexity. Evaluation of the depth estimation with the synthetic database reached a threshold accuracy of 95.65%, and a mean-RMSE of 0.451cm, while a qualitative assessment with a real database showed consistent depth estimations, visually evaluated by the expert gastroenterologist coauthoring this paper. Finally, the method achieved competitive performance with respect to another state-of-the-art method using a public synthetic database and comparable results in a set of images with other five state-of-the-art methods. Additionally, three-dimensional reconstructions demonstrated useful approximations of the gastrointestinal tract geometry. Code for reproducing the reported results and the dataset are available at https://github.com/Cimalab-unal/ColonDepthEstimation.

Removing non-nuclei information from histopathological images: A preprocessing step towards improving nuclei segmentation methods.

Disease interpretation by computer-aided diagnosis systems in digital pathology depends on reliable detection and segmentation of nuclei in hematoxylin and eosin (HE) images. These 2 tasks are challenging since appearance of both cell nuclei and background structures are very variable. This paper presents a method to improve nuclei detection and segmentation in HE images by removing tiles that only contain background information. The method divides each image into smaller patches and uses their projection to the noiselet space to capture different spatial features from non-nuclei background and nuclei structures. The noiselet features are clustered by a K-means algorithm and the resultant partition, defined by the cluster centroids, is herein named the noiselet code-book. A part of an image, a tile, is divided into patches and represented by the histogram of occurrences of the projected patches in the noiselet code-book. Finally, with these histograms, a classifier learns to differentiate between nuclei and non-nuclei tiles. By applying a conventional watershed-marked method to detect and segment nuclei, evaluation consisted in comparing pure watershed method against denoising-plus-watershed in an open database with 8 different types of tissues. The averaged F-score of nuclei detection improved from 0.830 to 0.86 and the dice score after segmentation increased from 0.701 to 0.723.

Intra-nucleus mosaic pattern (InMop) and whole-cell Haralick combined-descriptor for identifying and characterizing acute leukemia blasts on single cell peripheral blood images.

Acute leukemia is usually diagnosed when a test of peripheral blood shows at least 20% of abnormal immature cells (blasts), a figure even lower in case of recurrent cytogenetic abnormalities. Blast identification is crucial for white blood cell (WBC) counting, which depends on both identifying the cell type and characterizing the cellular morphology, processes susceptible of inter- and intraobserver variability. The present work introduces an image combined-descriptor to detect blasts and determine their probable lineage. This strategy uses an intra-nucleus mosaic pattern (InMop) descriptor that captures subtle nuclei differences within WBCs, and Haralick's statistics which quantify the local structure of both nucleus and cytoplasm. The InMop captures WBC inner-nucleus structure by applying a multiscale Shearlet decomposition over a repetitive pattern (mosaic) of automatically-segmented nuclei. As a complement, Haralick's statistics characterize the local structure of the whole cell from an intensity co-occurrence matrix representation. Both InMoP and Haralick-based descriptors are calculated using the b-channel from Lab color-space. The combined-descriptor is assessed by differentiating blasts from nonleukemic cells with support vector machine (SVM) classifiers and different transformation kernels, in two public and independent databases. The first database-D1 (n = 260) is composed of healthy and acute lymphoid leukemia (ALL) single cell images, and second database-D2 contains acute myeloid leukemia (AML) blasts (n = 3294) and nonblast (n = 15,071) cell images. In a first experiment, blasts versus nonblast differentiation is performed by training with a subset of D2 (n = 6588) and testing in D1 (n = 260), obtaining a training AUC of 0.991 ± 0.002 and AUC = 0.782 for the independent validation. A second experiment automatically differentiates AML blasts (260 images from D2) from ALL blasts (260 images from D1), with an AUC of 0.93. In a third experiment, state-of-the-art strategies, VGG16 and RESNEXT convolutional neural networks (CNN), separate blast from nonblast cells in both databases. The VGG16 showed an AUC of 0.673 and the RESNEXT of 0.75. Reported metrics for all the experiments are area under the ROC curve (AUC), accuracy and F1-score.

Investigating Tissue-Specific Abnormalities in Alzheimer's Disease with Multi-Shell Diffusion MRI.

BACKGROUND: Most studies using diffusion-weighted MRI (DW-MRI) in Alzheimer's disease (AD) have focused their analyses on white matter (WM) microstructural changes using the diffusion (kurtosis) tensor model. Although recent works have addressed some limitations of the tensor model, such as the representation of crossing fibers and partial volume effects with cerebrospinal fluid (CSF), the focus remains in modeling and analyzing the WM. OBJECTIVE: In this work, we present a brain analysis approach for DW-MRI that disentangles multiple tissue compartments as well as micro- and macroscopic effects to investigate differences between groups of subjects in the AD continuum and controls. METHODS: By means of the multi-tissue constrained spherical deconvolution of multi-shell DW-MRI, underlying brain tissue is modeled with a WM fiber orientation distribution function along with the contributions of gray matter (GM) and CSF to the diffusion signal. From this multi-tissue model, a set of measures capturing tissue diffusivity properties and morphology are extracted. Group differences were interrogated following fixel-, voxel-, and tensor-based morphometry approaches while including strong FWE control across multiple comparisons. RESULTS: Abnormalities related to AD stages were detected in WM tracts including the splenium, cingulum, longitudinal fasciculi, and corticospinal tract. Changes in tissue composition were identified, particularly in the medial temporal lobe and superior longitudinal fasciculus. CONCLUSION: This analysis framework constitutes a comprehensive approach allowing simultaneous macro and microscopic assessment of WM, GM, and CSF, from a single DW-MRI dataset.

Robust Descriptor of Pancreatic Tissue for Automatic Detection of Pancreatic Cancer in Endoscopic Ultrasonography.

Pancreatic cancer (PC) has a reported mortality of 98% and a 5-y survival rate of 6.7%. Experienced gastroenterologists detect 80% of those with early-stage PC by endoscopic ultrasonography (EUS). Here we propose an automatic second reader strategy to detect PC in an entire EUS procedure, rather than focusing on pre-selected frames, as the state-of-the-art methods do. The method unmasks echo tumoral patterns in frames with a high probability of tumor. First, speeded up robust features define a set of interest points with correlated heterogeneities among different filtering scales. Afterward, intensity gradients of each interest point are summarized by 64 features at certain locations and scales. A frame feature vector is built by concatenating statistics of each feature of the 15 groups of scales. Then, binary classification is performed by Support Vector Machine and Adaboost models. Evaluation was performed using a data set comprising 55 participants, 18 of PC class (16,585 frames) and 37 subjects of non-PC class (49,664 frames), randomly splitting 10 times. The proposed method reached an accuracy of 92.1%, sensitivity of 96.3% and specificity of 87.8.3%. The observed results are also stable in noisy experiments while deep learning approaches fail to maintain similar performance.

Diversidad y estructura arbórea en afloramientos de carbonato de calcio en Calakmul, México / Tree diversity and structure in carbonate calcium banks in Calakmul, Mexico

Resumen Introducción: La vegetación arbórea de selvas que se desarrolla en ambientes kársticos dominados por carbonato de calcio enfrenta la restricción de agua y nutrientes, lo que condiciona su desarrollo. Objetivo: Analizar la composición, diversidad y estructura de la vegetación arbórea que se desarrolla en afloramientos de calcio (yesales) y sus condiciones edáficas comparándolas con las presentes en vegetación secundaria (VS). Métodos: Se emplearon 17 parcelas de 1 000 m², 14 en yesales y 3 en VS. Se obtuvo una muestra compuesta de suelo por parcela y estimamos pH, conductividad eléctrica (CE-salinidad), % de carbonatos de calcio (CaCO3), materia orgánica (MO), fósforo (P) y nitrógeno (N). La diferencia en la composición de especies se evaluó mediante un análisis de similitud (ANOSIM). Empleamos métodos de rarefacción y extrapolación, estimando la diversidad mediante los números de Hill (q = 0, q = 1 y q = 2). Se utilizó un análisis de regresión linear para evaluar la influencia de las características edáficas en la diversidad, el diámetro y la altura promedios. Resultados: Los suelos en yesales presentaron concentraciones bajas de MO, P y N, valores altos de CE-salinidad y altos porcentajes de CaCO3. Se registraron 6 443 individuos de 54 especies en yesales y 594 individuos de 62 especies en la VS, siendo la composición significativamente diferente. La diversidad, los valores promedio de altura y diámetro fueron menores en yesales respecto de VS, estas diferencias estuvieron relacionadas con las condiciones edáficas. Conclusiones: La vegetación arbórea en yesales tiene una composición semejante a la de selvas subperennifolias de Calakmul. Las tallas pequeñas de los árboles están relacionadas con el alto porcentaje de CaCO3 y los altos valores de CE que condicionan la disponibilidad de MO, N y P. Este estudio apoya la idea de que precarias condiciones edáficas tienen una influencia negativa en la diversidad y la estructura horizontal y vertical de la vegetación arbórea.

Abstract Introduction: Tree vegetation of forests that develops in karst environments dominated by calcium carbonate faces the restriction of water and nutrients, which negatively affects its development. Objective: Analyze the composition, diversity, and structure of tree vegetation that develops in calcium outcrops (yesales) and their edaphic conditions compared to those present in the adjacent secondary vegetation (VS). Methods: Plots of 1 000 m² were used, 14 in yesales and 3 in VS. For soil sampling, we obtained a sample composed of each plot, and estimate pH, electrical conductivity (EC-salinity), % of calcium carbonates (CaCO3), organic matter (OM), phosphorus (P) and nitrogen content (N). The difference in species composition was estimated by similarity analysis (ANOSIM). We used rarefaction and extrapolation methods to standardize sample, and estimate diversity by Hill numbers (q = 0, q = 1 and q = 2). Linear regression was used to determine the relative influence of edaphic characteristics in diversity, average diameter, and height. Results: Soils in yesales presented low concentrations of OM, P, and N, with high values of EC-salinity and high percentages of CaCO3. In yesales, 6 443 individuals were recorded in 54 species and in the secondary vegetation 594 individuals and 62 species, the species composition being significantly different between both conditions. Diversity, average values of height, and diameter were significantly lower in yesales regarding the secondary vegetation, these differences were significantly related to edaphic conditions. Conclusions: Tree vegetation in yesales has a composition like the sub-evergreen forests of Calakmul. Small sizes in the arboreal individuals are related to the high percentage of CaCO3 and the high EC values, which partly condition the low availability of OM, N and P affecting the growth of the trees. This study supports the idea that precarious edaphic conditions have a negative influence on the diversity and horizontal and vertical structure of tree vegetation.

Factors associated with mortality in patients hospitalized with COVID-19: A prospective cohort in a Peruvian national referral hospital. / Factores asociados a mortalidad en pacientes hospitalizados con COVID-19: cohorte prospectiva en un hospital de referencia nacional de Perú.

OBJECTIVES: To describe and assess clinical characteristics and factors associated with mortality in adult patients with COVID-19 admitted to a national referral hospital in Peru. METHODS: We conducted a prospective cohort study that included hospitalized patients older than 18 years with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection diagnosis. Patients with a positive rapid serological test on admission but no respiratory symptoms nor compatible images were excluded. We collected the data from clinical records. RESULTS: A total of 813 adults were included, 544 (66.9%) with confirmed COVID-19. The mean age was 61.2 years (standard deviation: 15.0), and 575 (70.5%) were male. The most frequent comorbidities were hypertension (34.1%) and obesity (25.9%). On admission, the most frequent symptoms were dyspnea (82.2%) and cough (53.9%). A total of 114 (14.0%) patients received mechanical ventilation, 38 (4.7%) were admitted to the intensive care unit, and 377 (46.4%) died. The requirement for ventilatory support, greater lung involvement, and inflammatory markers were associated with higher mortality. It was found that for every 10-year age increase, the risk of dying increased 32% (relative risk: 1.32; 95% confidence interval: 1.25 to 1.38). Those who were admitted to the intensive care unit and and were placed on mechanical ventilation had 1.39 (95% confidence interval: 1.13 to 1.69) and 1.97 (95% confidence interval: 1.69 to 2.29) times the risk of dying compared to those who did not, respectively. CONCLUSION: We found a high mortality rate among hospitalized patients associated with older age, higher inflammatory markers, and greater lung involvement.

OBJETIVOS: Describir las características clínicas y evaluar los factores asociados con la mortalidad de los pacientes adultos con la nueva enfermedad causada por coronavirus 2019 (COVID-19) ingresados a un hospital de referencia nacional de Perú. MÉTODOS: Se realizó un estudio de cohorte prospectivo. Se incluyó a pacientes mayores de 18 años hospitalizados con el diagnóstico de infección por coronavirus 2 del síndrome respiratorio agudo severo (SARS-CoV-2). Se excluyó a quienes ingresaron con prueba rápida serológica positiva al ingreso, sin clínica sugestiva ni imágenes compatibles. Los datos se recolectaron a partir de la historia clínica. RESULTADOS: Se incluyó un total de 813 adultos, 544 (66,9%) tuvieron COVID-19 confirmado. La media de la edad fue de 61,2 años (desviación estándar: 15) y 575 (70,5%) fueron de sexo masculino. Las comorbilidades más frecuentes fueron hipertensión arterial (34,1%) y obesidad (25,9%). Los síntomas más frecuentes al ingreso fueron disnea (82,2%) y tos (53,9%). Un total de 114 (14%) pacientes recibieron ventilación mecánica, 38 (4,7%) ingresaron a unidad de cuidados intensivos y 377 (46,4%) fallecieron. Se asociaron a la mortalidad el requerimiento de soporte ventilatorio, el mayor compromiso pulmonar y los marcadores inflamatorios. Encontramos que por cada 10 años que aumentó la edad, el riesgo de morir se incrementó en 32% (riesgo relativo: 1,32; intervalo de confianza 95%: 1,25 a 1,38). Aquellos pacientes que requirieron ingreso a unidad de cuidados intensivos y ventilación mecánica tuvieron 1,39 (intervalo de confianza 95%: 1,13 a 1,69) y 1,97 (intervalo de confianza 95%: 1,69 a 2,29) veces el riesgo de morir, respectivamente. CONCLUSIÓN: La mortalidad encontrada en nuestro estudio fue alta y estuvo asociada a la edad, marcadores inflamatorios y compromiso respiratorio.

Factores asociados a mortalidad en pacientes hospitalizados con COVID-19: cohorte prospectiva en un hospital de referencia nacional de Perú / Factors associated with mortality in patients hospitalized with COVID-19: a prospective cohort in a Peruvian national referral hospital

Objetivos Describir las características clínicas y evaluar los factores asociados con la mortalidad de los pacientes adultos con la nueva enfermedad causada por coronavirus 2019 (COVID-19) ingresados a un hospital de referencia nacional de Perú. Métodos Se realizó un estudio de cohorte prospectivo. Se incluyó a pacientes mayores de 18 años hospitalizados con el diagnóstico de infección por coronavirus 2 del síndrome respiratorio agudo severo (SARS-CoV-2). Se excluyó a quienes ingresaron con prueba rápida serológica positiva al ingreso, sin clínica sugestiva ni imágenes compatibles. Los datos se recolectaron a partir de la historia clínica. Resultados Se incluyó un total de 813 adultos, 544 (66,9%) tuvieron COVID-19 confirmado. La media de la edad fue de 61,2 años (desviación estándar: 15) y 575 (70,5%) fueron de sexo masculino. Las comorbilidades más frecuentes fueron hipertensión arterial (34,1%) y obesidad (25,9%). Los síntomas más frecuentes al ingreso fueron disnea (82,2%) y tos (53,9%). Un total de 114 (14%) pacientes recibieron ventilación mecánica, 38 (4,7%) ingresaron a unidad de cuidados intensivos y 377 (46,4%) fallecieron. Se asociaron a la mortalidad el requerimiento de soporte ventilatorio, el mayor compromiso pulmonar y los marcadores inflamatorios. Encontramos que por cada 10 años que aumentó la edad, el riesgo de morir se incrementó en 32% (riesgo relativo: 1,32; intervalo de confianza 95%: 1,25 a 1,38). Aquellos pacientes que requirieron ingreso a unidad de cuidados intensivos y ventilación mecánica tuvieron 1,39 (intervalo de confianza 95%: 1,13 a 1,69) y 1,97 (intervalo de confianza 95%: 1,69 a 2,29) veces el riesgo de morir, respectivamente. Conclusión La mortalidad encontrada en nuestro estudio fue alta y estuvo asociada a la edad, marcadores inflamatorios y compromiso respiratorio.

Objectives To describe and assess clinical characteristics and factors associated with mortality in adult patients with COVID-19 admitted to a national referral hospital in Peru. Methods We conducted a prospective cohort study that included hospitalized patients older than 18 years with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection diagnosis. Patients with a positive rapid serological test on admission but no respiratory symptoms nor compatible images were excluded. We collected the data from clinical records. Results A total of 813 adults were included, 544 (66.9%) with confirmed COVID-19. The mean age was 61.2 years (standard deviation: 15.0), and 575 (70.5%) were male. The most frequent comorbidities were hypertension (34.1%) and obesity (25.9%). On admission, the most frequent symptoms were dyspnea (82.2%) and cough (53.9%). A total of 114 (14.0%) patients received mechanical ventilation, 38 (4.7%) were admitted to the intensive care unit, and 377 (46.4%) died. The requirement for ventilatory support, greater lung involvement, and inflammatory markers were associated with higher mortality. It was found that for every 10-year age increase, the risk of dying increased 32% (relative risk: 1.32; 95% confidence interval: 1.25 to 1.38). Those who were admitted to the intensive care unit and and were placed on mechanical ventilation had 1.39 (95% confidence interval: 1.13 to 1.69) and 1.97 (95% confidence interval: 1.69 to 2.29) times the risk of dying compared to those who did not, respectively. Conclusion We found a high mortality rate among hospitalized patients associated with older age, higher inflammatory markers, and greater lung involvement.

Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results.

(1) Background: Despite the complementarity between radiology and histopathology, both from a diagnostic and a prognostic perspective, quantitative analyses of these modalities are usually performed in disconnected silos. This work presents initial results for differentiating two major non-small cell lung cancer (NSCLC) subtypes by exploring cross-scale associations between Computed Tomography (CT) images and corresponding digitized pathology images. (2) Methods: The analysis comprised three phases, (i) a multi-resolution cell density quantification to identify discriminant pathomic patterns for differentiating adenocarcinoma (ADC) and squamous cell carcinoma (SCC), (ii) radiomic characterization of CT images by using Haralick descriptors to quantify tumor textural heterogeneity as represented by gray-level co-occurrences to discriminate the two pathological subtypes, and (iii) quantitative correlation analysis between the multi-modal features to identify potential associations between them. This analysis was carried out using two publicly available digitized pathology databases (117 cases from TCGA and 54 cases from CPTAC) and a public radiological collection of CT images (101 cases from NSCLC-R). (3) Results: The top-ranked cell density pathomic features from the histopathology analysis were correlation, contrast, homogeneity, sum of entropy and difference of variance; which yielded a cross-validated AUC of 0.72 ± 0.02 on the training set (CPTAC) and hold-out validation AUC of 0.77 on the testing set (TCGA). Top-ranked co-occurrence radiomic features within NSCLC-R were contrast, correlation and sum of entropy which yielded a cross-validated AUC of 0.72 ± 0.01. Preliminary but significant cross-scale associations were identified between cell density statistics and CT intensity values using matched specimens available in the TCGA cohort, which were used to significantly improve the overall discriminatory performance of radiomic features in differentiating NSCLC subtypes (AUC = 0.78 ± 0.01). (4) Conclusions: Initial results suggest that cross-scale associations may exist between digital pathology and CT imaging which can be used to identify relevant radiomic and histopathology features to accurately distinguish lung adenocarcinomas from squamous cell carcinomas.

Autism spectrum disorder characterization in children by capturing local-regional brain changes in MRI.

PURPOSE: To design a multiscale descriptor capable of capturing complex local-regional unfolding patterns to support quantitation and diagnosis of autism spectrum disorders (ASD) using T1-weighted structural magnetic resonance images (MRI) with voxel size of 1 × 1 × 1 mm. METHODS: The proposed image descriptor uses an adapted multiscale representation, the Curvelet transform, interpretable in terms of texture (local) and shape (regional) to characterize brain regions, and a Generalized Gaussian Distribution (GGD) to reduce feature dimensionality. In this approach, each MRI is first parcelled into 3D anatomical regions. Each resultant region is represented by a single 2D image where slices are placed next to each other. Each 2D image is characterized by mapping it to the Curvelet space and each of the different Curvelet sub-bands is described by the set of GGD parameters. To assess the discriminant power of the proposed descriptor, a classification model per brain region was built to differentiate ASD patients from control subjects. Models were constructed with support vector machines and evaluated using two samples from heterogeneous databases, namely Autism Brain Imaging Data Exchange - ABIDE I (34 ASD and 34 controls, mean age 11.46 ± 2.03 and 11.53 ± 1.79 yr, respectively, male population) and ABIDE II (42 ASD and 41 controls, mean age 10.09 ± 1.37 and 10.52 ± 1.27 yr, respectively, male population), for a total of 151 individuals. RESULTS: When the model was trained with ABIDE II sample and tested with ABIDE I on a hold-out validation, an area under receiver operator curve (AUC) of 0.69 was computed. When each sample was independently used under a cross-validation scheme, the estimated AUC was 0.75 ± 0.02 for ABIDE I and 0.77 ± 0.01 for ABIDE II. This analysis determined a set of discriminant regions widely reported in the literature as characteristic of ASD. CONCLUSIONS: The presented image descriptor demonstrated differences at local and regional level when high differences were observed in the Curvelet sub-bands. The method is simple in conceptual terms, robust to several sources of noise, and has a very low computational cost.

Two-photon absorption properties of BODIPY-like compounds based on BF2-naphthyridine complexes.

Boron dipyrromethene type molecules (BODIPYs) are versatile molecules which have been used for applications ranging from photodynamic therapy to solar cells (DSSC). However, these molecules usually do not present high two-photon absorption cross-sections, limiting their use in nonlinear optical applications. Herein, we study a series of BF2-naphthyridine based boron-complexes with electron-donating and withdrawing groups to increase their two-photon absorption. We have found two-photon absorption cross-sections up to approximately 270 GM, which corresponds to an increase of approximately five times in comparison to the average cross-section value reported for molecules with similar conjugation length, indicating such compounds as potential materials for nonlinear applications in both the visible and infrared spectral regions.

Increasing Incidence and Prevalence of Multiple Sclerosis in Puerto Rico (2013-2016).

BACKGROUND: The incidence of multiple sclerosis (MS) has been increasing worldwide over the past decades. However, this upward trend has not been examined at the country level in Latin America and the Caribbean (LAC). The aims of this study are to examine trends of MS incidence over 4 years and to provide age- and gender-standardized incidence rate estimates for a Caribbean island. METHODS: Data from the Puerto Rico (PR) MS Foundation's registry was used to identify all newly diagnosed MS cases between 2013 and 2016. MS patients were 18 years and older and met the 2010 revised McDonald criteria. Age- and gender-standardized incidence rates were estimated. RESULTS: A total of 583 new MS cases were diagnosed in PR from 2013 to 2016. The age- and gender-standardized MS incidence rate for PR increased from 6.1/100,000 in 2013 to 6.7/100,000 in 2016. The annual age-standardized MS incidence rates for females rose from 8.4/100,000 in 2013 to 9.8/100,000 in 2016 and were higher than males, which remained around 3.7/100,000. CONCLUSION: Incidence estimates for PR were higher than other LAC countries but consistent with MS increases in other world regions. Our findings tend to rule out several prior potential environmental explanations for high MS incidence rates.

Modelo para el análisis de la mortalidad en Colombia 2000-2012 / A model for mortality analysis in Colombia. 2000-2012

RESUMEN Objetivo Proponer y evaluar un modelo para el ajuste y predicción de la mortalidad en Colombia que permita analizar tendencias por edad, sexo, Departamento y causa. Metodología Los registros de defunciones no fetales fueron utilizados como fuente primaria de análisis. Estos datos se pre-procesaron recodificando las causas y redistribuyendo los códigos basura. El modelo de predicción se formuló como una aproximación lineal de un conjunto de variables de interés, en particular la población y el producto interno bruto departamental. Resultados Como caso particular de estudio se tomó la mortalidad de menores de 5 años, se observó una disminución sostenida a partir del año 2000 tanto a nivel nacional como departamental, con excepción de tres departamentos. La evaluación del poder predictivo de la metodología propuesta se realizó ajustando el modelo con los datos de 2000 a 2011, la predicción para el 2012 fue comparada con la tasa observada, estos resultados muestran que el modelo es suficientemente confiable para la mayor parte de las combinaciones departamento-causa. Conclusiones La metodología y modelo propuesto tienen el potencial de convertirse en un instrumento que permita orientar las prioridades del gasto en salud utilizando algún tipo de evidencia.(AU)

ABSTRACT Objective To propose and evaluate a model for fitting and forecasting the mortality rates in Colombia that allows analyzing the trends by age, sex, region and cause of death. Methodology The national death registries were used as primary source of analysis. The data was pre-processed recodifying the cause of death and redistributing the garbage codes. The forecast model was formulated as a linear approximation with a set of variables of interest, in particular the population and gross domestic product (GDP) by region. Results As study case we took the mortality under 5 years old, it decreased steadily since 2000 at the national level and at most of the regions. The predictive power of the proposed methodology was tested by fitting the model with the data from 2000 to 2011, the forecast for 2012 was compared with the actual rate, and these results show the model is reliable enough for most of the region-cause combinations. Conclusions The proposed methodology and model have the potential to become an instrument to guide health spending priorities using some kind of evidence.(AU)

Efecto del manejo sobre la diversidad de árboles en vegetación secundaria en la Reserva de la Biosfera de Calakmul, Campeche, México / Effect of management on tree diversity in secondary vegetation in the Biosphere Reserve of Calakmul, Campeche, Mexico

ResumenEl bosque tropical seco (BTS) de la Península de Yucatán ha sido manejado por siglos, pero la relación del efecto del manejo sobre la diversidad de árboles no ha sido completamente entendida. El objetivo de este estudio fue evaluar el efecto del manejo forestal (aclareos, aprovechamiento y enriquecimiento de especies) en la estructura de la vegetación secundaria derivada de bosques tropicales secos, en dos comunidades en Calakmul, Campeche, Sureste de México. Se analizaron cambios en la composición, riqueza de especies, diversidad de especies y estructura en vegetación secundaria sujetas a los siguientes tipos de manejo: 1) vegetación secundaria con manejo apícola (MA), 2) vegetación secundaria con manejo forestal (MF), 3) vegetación secundaria sin manejo (SM) y bosque tropical seco (BTS). La composición de especies fue similar entre vegetación secundaria manejada y no manejada. Por otro lado, entre vegetación secundaria manejada y el BTS hubo diferencias en la composición de especies. La riqueza de especies no fue diferente entre todas las condiciones. La MA mostró la más baja diversidad de especies y presentó la mayor densidad promedio (5 413 ± 770.26 ind./ha). La MF tuvo la menor densidad promedio (3 289 ± 1 183.60 ind./ ha). El BTS mostró la mayor área basal promedio (24.89 ± 1.56 m2/ha) respecto a las demás condiciones. Se concluye que es necesario mantener el monitoreo de las áreas manejadas, para detectar efectos del manejo que pueden ser adversos o favorables para la conservación de la diversidad florística de los BTS.

Abstract:The tropical dry forest (BTS) of Yucatan Peninsula has been managed for centuries, but the relationship between these management efforts and their effects on trees diversity has not been fully understood. The aim of this study was to evaluate the effect of forest management (thinning, harvesting and enrichment of species), in the structure of secondary vegetation derived from dry tropical forests, in two communities in Calakmul, Campeche, Southeast Mexico. We analyzed changes in the composition, species richness, species diversity, and structure in secondary vegetation subject to following types of management: (1) secondary vegetation with beekeeping management (MA), secondary vegetation with forest management (MF), natural secondary vegetation (SM) and tropical dry forest (BTS). The species composition was similar between secondary vegetation managed and unmanaged. On the other hand, between managed secondary vegetation and BTS there were differences in species composition. Species richness was not different between all conditions. MA showed the lowest species diversity and presented higher average density (5 413±770.26 ind.ha-1).MF had lowest average density (3 289 ± 1 183.60 ind.ha-1). BTS showed the highest average basal area (24.89 ± 1.56 m2.ha-1) regarding the other conditions. We concluded that is necessary to keep monitoring the managed areas to detect effects of management that may be adverse or favorable to conservation of floristic diversity of BTS. Rev. Biol. Trop. 65 (1): 41-53. Epub 2017 March 01.

A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers.

The treatment and management of early stage estrogen receptor positive (ER+) breast cancer is hindered by the difficulty in identifying patients who require adjuvant chemotherapy in contrast to those that will respond to hormonal therapy. To distinguish between the more and less aggressive breast tumors, which is a fundamental criterion for the selection of an appropriate treatment plan, Oncotype DX (ODX) and other gene expression tests are typically employed. While informative, these gene expression tests are expensive, tissue destructive, and require specialized facilities. Bloom-Richardson (BR) grade, the common scheme employed in breast cancer grading, has been shown to be correlated with the Oncotype DX risk score. Unfortunately, studies have also shown that the BR grade determined experiences notable inter-observer variability. One of the constituent categories in BR grading is the mitotic index. The goal of this study was to develop a deep learning (DL) classifier to identify mitotic figures from whole slides images of ER+ breast cancer, the hypothesis being that the number of mitoses identified by the DL classifier would correlate with the corresponding Oncotype DX risk categories. The mitosis detector yielded an average F-score of 0.556 in the AMIDA mitosis dataset using a 6-fold validation setup. For a cohort of 174 whole slide images with early stage ER+ breast cancer for which the corresponding Oncotype DX score was available, the distributions of the number of mitoses identified by the DL classifier was found to be significantly different between the high vs low Oncotype DX risk groups (P < 0.01). Comparisons of other risk groups, using both ODX score and histological grade, were also found to present significantly different automated mitoses distributions. Additionally, a support vector machine classifier trained to separate low/high Oncotype DX risk categories using the mitotic count determined by the DL classifier yielded a 83.19% classification accuracy. © 2017 International Society for Advancement of Cytometry.

A multiresolution prostate representation for automatic segmentation in magnetic resonance images.

PURPOSE: Accurate prostate delineation is necessary in radiotherapy processes for concentrating the dose onto the prostate and reducing side effects in neighboring organs. Currently, manual delineation is performed over magnetic resonance imaging (MRI) taking advantage of its high soft tissue contrast property. Nevertheless, as human intervention is a consuming task with high intra- and interobserver variability rates, (semi)-automatic organ delineation tools have emerged to cope with these challenges, reducing the time spent for these tasks. This work presents a multiresolution representation that defines a novel metric and allows to segment a new prostate by combining a set of most similar prostates in a dataset. METHODS: The proposed method starts by selecting the set of most similar prostates with respect to a new one using the proposed multiresolution representation. This representation characterizes the prostate through a set of salient points, extracted from a region of interest (ROI) that encloses the organ and refined using structural information, allowing to capture main relevant features of the organ boundary. Afterward, the new prostate is automatically segmented by combining the nonrigidly registered expert delineations associated to the previous selected similar prostates using a weighted patch-based strategy. Finally, the prostate contour is smoothed based on morphological operations. RESULTS: The proposed approach was evaluated with respect to the expert manual segmentation under a leave-one-out scheme using two public datasets, obtaining averaged Dice coefficients of 82% ± 0.07 and 83% ± 0.06, and demonstrating a competitive performance with respect to atlas-based state-of-the-art methods. CONCLUSIONS: The proposed multiresolution representation provides a feature space that follows a local salient point criteria and a global rule of the spatial configuration among these points to find out the most similar prostates. This strategy suggests an easy adaptation in the clinical routine, as supporting tool for annotation.

Unusual reversible elastomeric gels from Nostoc commune.

Nostoc commune cyanobacteria grow in extreme conditions of desiccation and nutrient-poor soils. Their colonies form spherical gelatinous bodies are composed of a variety of polysaccharides that allow them to store water and nutrients. In this paper, we study this type of biological gel that shows characteristics of both chemical and physical gels. The structure of this gel was assessed by means of scanning electron microscopy, plate-plate rheometry, Fourier transform infrared spectroscopy and absorption/desorption tests. The storage modulus of this gel was found to be frequency independent, as is usual for chemical gels. The stress sweeps showed a reversible stress softening behaviour that was explained in terms of the physical nature of the interactions of this network. The high density of physical crosslinks probably allows this physical network to behave as a highly elastomeric chemical network, limiting the relaxation of individual chains. On the other hand, reversibility is associated with the physical nature of its bonds.

A sparse Bayesian representation for super-resolution of cardiac MR images.

High-quality cardiac magnetic resonance (CMR) images can be hardly obtained when intrinsic noise sources are present, namely heart and breathing movements. Yet heart images may be acquired in real time, the image quality is really limited and most sequences use ECG gating to capture images at each stage of the cardiac cycle during several heart beats. This paper presents a novel super-resolution algorithm that improves the cardiac image quality using a sparse Bayesian approach. The high-resolution version of the cardiac image is constructed by combining the information of the low-resolution series -observations from different non-orthogonal series composed of anisotropic voxels - with a prior distribution of the high-resolution local coefficients that enforces sparsity. In addition, a global prior, extracted from the observed data, regularizes the solution. Quantitative and qualitative validations were performed in synthetic and real images w.r.t to a baseline, showing an average increment between 2.8 and 3.2 dB in the Peak Signal-to-Noise Ratio (PSNR), between 1.8% and 2.6% in the Structural Similarity Index (SSIM) and 2.% to 4% in quality assessment (IL-NIQE). The obtained results demonstrated that the proposed method is able to accurately reconstruct a cardiac image, recovering the original shape with less artifacts and low noise.