Crowdsourcing malaria parasite quantification: an online game for analyzing images of infected thick blood smears

Background: There are 600,000 new malaria cases daily worldwide. The gold standard for estimating the parasite burden and the corresponding severity of the disease consists in manually counting the number of parasites in blood smears through a microscope, a process that can take more than 20 minutes of an expert microscopist’s time. Objective: This research tests the feasibility of a crowdsourced approach to malaria image analysis. In particular, we investigated whether anonymous volunteers with no prior experience would be able to count malaria parasites in digitized images of thick blood smears by playing a Web-based game. Methods: The experimental system consisted of a Web-based game where online volunteers were tasked with detecting parasites in digitized blood sample images coupled with a decision algorithm that combined the analyses from several players to produce an improved collective detection outcome. Data were collected through the MalariaSpot website. Random images of thick blood films containing Plasmodium falciparum at medium to low parasitemias, acquired by conventional optical microscopy, were presented to players. In the game, players had to find and tag as many parasites as possible in 1 minute. In the event that players found all the parasites present in the image, they were presented with a new image. In order to combine the choices of different players into a single crowd decision, we implemented an image processing pipeline and a quorum algorithm that judged a parasite tagged when a group of players agreed on its position. Results: Over 1 month, anonymous players from 95 countries played more than 12,000 games and generated a database of more than 270,000 clicks on the test images. Results revealed that combining 22 games from nonexpert players achieved a parasite counting accuracy higher than 99%. This performance could be obtained also by combining 13 games from players trained for 1 minute. Exhaustive computations measured the parasite counting accuracy for all players as a function of the number of games considered and the experience of the players. In addition, we propose a mathematical equation that accurately models the collective parasite counting performance. Conclusions: This research validates the online gaming approach for crowdsourced counting of malaria parasites in images of thick blood films. The findings support the conclusion that nonexperts are able to rapidly learn how to identify the typical features of malaria parasites in digitized thick blood samples and that combining the analyses of several users provides similar parasite counting accuracy rates as those of expert microscopists. This experiment illustrates the potential of the crowdsourced gaming approach for performing routine malaria parasite quantification, and more generally for solving biomedical image analysis problems, with future potential for telediagnosis related to global health challenges

Crowdsourcing the corpasome

The suffix -ome conveys “comprehensiveness” in some way. The idea of the Corpasome started half-jokingly, acknowledging the efforts to sequence five members of my family. After the unexpected response from many scientists from around the world, it has become clear how useful this approach could be for understanding the genomic information contained in our personal genomics tests

Association between -T786C NOS3 polymorphism and resistant hypertension: a prospective cohort study

<p>Abstract</p> <p>Background</p> <p>It is estimated that 5% of the hypertensive patients are resistant to conventional antihypertensive therapy. Polymorphisms in the endothelial nitric oxide synthase (NOS3) gene have been associated with high blood pressure levels, but not with resistant hypertension. The aim of the present study was to investigate if the -786T>C and G894T (Glu298Asp) polymorphisms of the NOS3 gene were associated with resistant hypertension.</p> <p>Methods</p> <p>A prospective case-control observational study was performed. From a series of 950 consecutive patients followed up during 42 months, 48 patients with resistant hypertension were detected. 232 patients with controlled high blood pressure were also included.</p> <p>Results</p> <p>No differences were observed in the distribution of G894T (Glu298Asp) NOS3 genotypes between the resistant hypertension group and the controlled hypertension patients. However, genotype -786CC was more frequent in the group of patients with resistant hypertension (33.3%) than in the group of patients with controlled high blood pressure (17.7%) (p 0.03). Furthermore carriers of allele T (-786TC and -786TT) were more frequent in patients with controlled hypertension (82.3%) than those with resistant hypertension (66.7%) (Multivariate analysis; RR 2.09; 95% CI 1.03–4.24; p 0.004).</p> <p>Conclusion</p> <p>Our results indicate that genotype -786CC of the NOS3 gene increase the susceptibility to suffer resistant hypertension, which suggest that resistance to conventional therapy could be determined at the endothelial level.</p

Regular insulin added to total parenteral nutrition vs subcutaneous glargine in non-critically ill diabetic inpatients, a multicenter randomized clinical trial: INSUPAR trial

Background: There is no established insulin regimen in T2DM patients receiving parenteral nutrition. Aims: To compare the effectiveness (metabolic control) and safety of two insulin regimens in patients with diabetes receiving TPN. Design: Prospective, open-label, multicenter, clinical trial on adult inpatients with type 2 diabetes on a non-critical setting with indication for TPN. Patients were randomized on one of these two regimens: 100% of RI on TPN or 50% of Regular insulin added to TPN bag and 50% subcutaneous Gl. Data were analyzed according to intention-to-treat principle. Results: 81 patients were on RI and 80 on GI. No differences were observed in neither average total daily dose of insulin, programmed or correction, nor in capillary mean blood glucose during TPN infusion (165.3 +/- 35.4 in RI vs 172.5 +/- 43.6 mg/dL in GI; p = 0.25). Mean capillary glucose was significantly lower in the GI group within two days after TPN interruption (160.3 +/- 45.1 in RI vs 141.7 +/- 43.8 mg/dL in GI; p = 0.024). The percentage of capillary glucose above 180 mg/dL was similar in both groups. The rate of capillary glucose <= 70 mg/dL, the number of hypoglycemic episodes per 100 days of TPN, and the percentage of patients with non-severe hypoglycemia were significantly higher on GI group. No severe hypoglycemia was detected. No differences were observed in length of stay, infectious complications, or hospital mortality. Conclusion: Effectiveness of both regimens was similar. GI group achieved better metabolic control after TPN interruption but non-severe hypoglycemia rate was higher in the GI group. (C) 2019 The Author(s). Published by Elsevier Ltd

The RD-Connect Genome-Phenome Analysis Platform: Accelerating diagnosis, research, and gene discovery for rare diseases.

Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes

Identification of serum endoglin as a novel prognostic marker after acute myocardial infarction

Abstract Endoglin is a proliferation-associated and hypoxia-inducible protein expressed in endothelial cells. The levels of soluble circulating endoglin and their prognostic significance in patients with acute myocardial infarction (AMI) are not known. In this observational prospective study serum endoglin levels were measured by ELISA in 183 AMI patients upon admission to hospital and 48 hrs later and in 72 healthy controls. Endoglin levels in AMI patients on admission were significantly lower than in healthy controls (4.25 ± 0.99 ng/ml versus 4.59 ± 0.87 ng/ml; P= 0.013), and decreased further in the first 48 hours (3.65 ± 0.76 ng/ml, P < 0.001). Upon follow-up (median 319 days), patients who died had a significantly greater decrease in serum endoglin level over the first 48 hrs than those who survived (1.03 ± 0.91 versus 0.54 ± 0.55 ng/ml; P= 0.025). Endoglin decrease was an independent predictor of short-term (30 days) (hazard ratio 2.33;95% CI = 1.27–4.23; P= 0.006) cardiovascular mortality, and also predicts overall cardiovascular mortality during the follow-up (median 319 days) in AMI patients (hazard ratio 2.13;95% CI = 1.20–3.78; P= 0.01). In conclusion, early changes in serum endoglin may predict mortality after AMI

The RD-Connect Genome-Phenome Analysis Platform: Accelerating diagnosis, research, and gene discovery for rare diseases.

Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes