A multitask deep representation for Gleason score classification to support grade annotations.

The Gleason grade system is the main standard to quantify the aggressiveness and progression of prostate cancer. Currently, exists a high disagreement among experts in the diagnosis and stratification of this disease. Deep learning models have emerged as an alternative to classify and support experts automatically. However, these models are limited to learn a rigid stratification rule that can be biased during training to a specific observer. Therefore, this work introduces an embedding representation that integrates an auxiliary task learning to deal with the high inter and intra appearance of the Gleason system. The proposed strategy implements as a main task a triplet loss scheme that builds a feature embedding space with respect to batches of positive and negative histological training patches. As an auxiliary task is added a cross-entropy that helps with inter-class variability of samples while adding robust representations to the main task. The proposed approach shows promising results achieving an average accuracy of 66% and 64%, for two experts without statistical difference. Additionally, reach and average accuracy of 73% in patches where both pathologists are agree, showing the robustness patterns learning from the approach.

Learning a Triplet Embedding Distance to Represent Gleason Patterns.

Gleason grade stratification is the main histological standard to determine the severity and progression of prostate cancer. Nonetheless, there is a high variability on disease diagnosis among expert pathologists (kappa lower than 0.44). End-to-end deep representations have recently deal with the automatic classification of Gleason grades, where each grade is limited to namely code high-visual-variability sharing patterns among classes. Such limitation on models may be attributed to the relatively few labels to train the representation, as well as, to the natural imbalanced sets, available in clinical scenarios. To overcome such limitation, this work introduces a new embedding representation that learns intra and inter-Gleason relationships from more challenging class samples (grades tree and fourth). The proposed strategy implements a triplet loss scheme building a hidden embedding space that correctly differentiates close Gleason levels. The proposed approach shows promising results achieving an average accuracy of 74% to differentiate between degrees three and four. For classification of all degrees, the proposed approach achieves an average accuracy of 62%.

La educación como determinante social de la salud y el impacto de la COVID-19: avances y desafíos / Education as a social determinant of health and the impact of COVID-19: progress and challenges

Webinar N° 10 del ORAS-CONHU, realizado el 09 de julio de 2020, se realizó un webinar con el objetivo de analizar los avances y desafío que tiene la región andina en cuanto a las acciones y medidas multi e intersectoriales tomadas para garantizar educación para todos en tiempos de covid-19. Al reconocer que la educación es uno de los determinantes sociales de la salud, nos vemos en la necesidad de analizar tanto la dimensión global de esta relación, como cada una de las interacciones que se producen con otras variables del desarrollo humano, para dar respuesta a la Agenda Mundial en materia de salud y educación AL 2030. Conferencistas del webinar: Fernando Berríos, Coordinador de Unesco Perú, Luis Jorge Hernández Flores, Profesor Universidad de los Andes de Colombia, Fabián León Tamariz, Decano de la Facultad de Ciencias Químicas de la Universidad de Cuenca, Ecuador y José Ferrer Vicerrector de la Universidad de las Ciencias de la Salud de Venezuela.

Genome sequence variation in the constricta strain dramatically alters the protein interaction and localization map of Potato yellow dwarf virus.

The genome sequence of the constricta strain of Potato yellow dwarf virus (CYDV) was determined to be 12 792 nt long and organized into seven ORFs with the gene order 3'-N-X-P-Y-M-G-L-5', which encodes the nucleocapsid, phospho, movement, matrix, glyco, and RNA-dependent RNA polymerase proteins, respectively, except for X, which is of unknown function. Cloned ORFs for each gene, except L, were used to construct a protein interaction and localization map (PILM) for this virus, which shares greater than 80 % amino acid similarity in all ORFs except X and P with the sanguinolenta strain of this species (SYDV). Protein localization patterns and interactions unique to each viral strain were identified, resulting in strain-specific PILMs. Localization of CYDV and SYDV proteins in virus-infected cells mapped subcellular loci likely to be sites of replication, morphogenesis and movement.

Closed-Loop Control of Postprandial Glycemia Using an Insulin-on-Board Limitation Through Continuous Action on Glucose Target.

BACKGROUND: Postprandial (PP) control remains a challenge for closed-loop (CL) systems. Few studies with inconsistent results have systematically investigated the PP period. OBJECTIVE: To compare a new CL algorithm with current pump therapy (open loop [OL]) in the PP glucose control in type 1 diabetes (T1D) subjects. METHODS: A crossover randomized study was performed in two centers. Twenty T1D subjects (F/M 13/7, age 40.7 ± 10.4 years, disease duration 22.6 ± 9.9 years, and A1c 7.8% ± 0.7%) underwent an 8-h mixed meal test on four occasions. In two (CL1/CL2), after meal announcement, a bolus was given followed by an algorithm-driven basal infusion based on continuous glucose monitoring (CGM). Alternatively, in OL1/OL2 conventional pump therapy was used. Main outcome measures were as follows: glucose variability, estimated with the coefficient of variation (CV) of the area under the curve (AUC) of plasma glucose (PG) and CGM values, and from the analysis of the glucose time series; mean, maximum (Cmax), and time to Cmax glucose concentrations and time in range (<70, 70-180, >180 mg/dL). RESULTS: CVs of the glucose AUCs were low and similar in all studies (around 10%). However, CL achieved greater reproducibility and better PG control in the PP period: CL1 = CL2 0.05) nor the need for oral glucose was significantly different (CL 40.0% vs. OL 22.5% of meals; P = 0.054). CONCLUSIONS: This novel CL algorithm effectively and consistently controls PP glucose excursions without increasing hypoglycemia. Study registered at ClinicalTrials.gov : study number NCT02100488.

Isolation and identification of molecular species of phosphatidylcholine and lysophosphatidylcholine from jojoba seed meal (Simmondsia chinensis).

A mixture of lysophosphatidylcholine (LPC) and phosphatidylcholine (PC) has been isolated by column chromatography from a jojoba meal (Simmondsia chinensis) extract. The molecular species of both classes could be separated and isolated by C18 reversed phase HPLC. The two major compounds were identified by 1D and 2D (1)H and (13)C NMR, by MS, and by GC-MS as 1-oleoyl-3-lysophosphatidylcholine and 1,2-dioleoyl-3-phosphatidylcholine. Eight other molecular species of LPC and four other molecular species of PC could be assigned by comparison of the mass spectra of the isolated compounds with the spectra of the two major compounds. Complete characterization of the individual molecular species was achieved by GC and GC-MS analysis of the fatty acyl composition from the isolated compounds. The PC/LPC proportion in the phospholipid mixture from three different samples is 1.6 +/- 0.1. LPC is considered to be an important bioactive compound; the results of this study suggest further research for the evaluation of potential health benefits of jojoba meal phospholipids.