A comparison of Covid-19 early detection between convolutional neural networks and radiologists

[EN] Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.Project Chest screening for patients with COVID 19 (COV2000750 Special COVID19 resolution) funded by Instituto de Salud Carlos III. Project DIRAC (INNVA1/2020/42) funded by the Agencia Valenciana de la Innovacion, Generalitat Valenciana.Albiol Colomer, A.; Albiol, F.; Paredes Palacios, R.; Plasencia-Martínez, JM.; Blanco Barrio, A.; García Santos, JM.; Tortajada, S.... (2022). A comparison of Covid-19 early detection between convolutional neural networks and radiologists. Insights into Imaging. 13(1):1-12. https://doi.org/10.1186/s13244-022-01250-311213

Management Implications of the Biodiversity and Socio-Economic Impacts of Shrimp Trawler By-Catch in Bahía de Kino, Sonora, México

The shrimp fishery is the most economically important fishery in Mexico. The trawler-based portion of this fishery results in high rates of by-catch. This study quantifies and describes the biodiversity of by-catch associated with trawling in the Bahía de Kino region of Sonora, Mexico. Data were collected from 55 trawls, on six boats, over 14 nights, during November of 2003, 2004, 2006–2009. By-catch rates within trawl samples averaged 85.9% measured by weight. A total of 183 by-catch species were identified during the course of this study, including 97 species of bony fish from 43 families, 19 species of elasmobranchs from 12 families, 66 species of invertebrates from eight phyla, and one species of marine turtle; seven of the documented by-catch species are listed on the IUCN Red List, CITES, or the Mexican NOM-059-ECOL-2010; 35 species documented in the by-catch are also targeted by local artisanal fishers. Some of the species frequently captured as juveniles in the by-catch are economically important to small-scale fishers in the region, and are particularly sensitive to overexploitation due to their life histories. This study highlights the need for further research quantifying the impacts of high levels of by-catch upon small-scale fishing economies in the region and presents strong ecological and economic rationale for by-catch management within the shrimp fishery of the Gulf of California. Site-specific by-catch management plans should be piloted in the Bahía de Kino region to address the growing momentum in national and international fisheries policy regimes toward the reduction of by-catch in shrimp fisheries

A comparison of Covid-19 early detection between convolutional neural networks and radiologists

Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.Project Chest screening for patients with COVID 19 (COV2000750 Special COVID19 resolution) funded by Instituto de Salud Carlos III. Project DIRAC (INNVA1/2020/42) funded by the Agencia Valenciana de la Innovación, Generalitat Valenciana.Peer reviewe

Identification of new risk loci and pathways involved in GCA pathogenesis by a genome-wide.

Congresos y conferencias: Comunicación de Congreso - oral

Potassium and Sodium Transport in Yeast

[EN] As the proper maintenance of intracellular potassium and sodium concentrations is vital for cell growth, all living organisms have developed a cohort of strategies to maintain proper monovalent cation homeostasis. In the model yeast Saccharomyces cerevisiae, potassium is accumulated to relatively high concentrations and is required for many aspects of cellular function, whereas high intracellular sodium/potassium ratios are detrimental to cell growth and survival. The fact that S. cerevisiae cells can grow in the presence of a broad range of concentrations of external potassium (10 M–2.5 M) and sodium (up to 1.5 M) indicates the existence of robust mechanisms that have evolved to maintain intracellular concentrations of these cations within appropriate limits. In this review, current knowledge regarding potassium and sodium transporters and their regulation will be summarized. 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PLCG2 protective variant p.P522R modulates tau pathology and disease progression in patients with mild cognitive impairment

Abstract A rare coding variant (rs72824905, p.P522R) conferring protection against Alzheimer’s disease (AD) was identified in the gene encoding the enzyme phospholipase-C-γ2 (PLCG2) that is highly expressed in microglia. To explore the protective nature of this variant, we employed latent process linear mixed models to examine the association of p.P522R with longitudinal cognitive decline in 3595 MCI patients, and in 10,097 individuals from population-based studies. Furthermore, association with CSF levels of pTau181, total tau, and Aβ1-42 was assessed in 1261 MCI patients. We found that MCI patients who carried the p.P522R variant showed a slower rate of cognitive decline compared to non-carriers and that this effect was mediated by lower pTau181 levels in CSF. The effect size of the association of p.P522R with the cognitive decline and pTau181 was similar to that of APOE-ε4, the strongest genetic risk factor for AD. Interestingly, the protective effect of p.P522R was more pronounced in MCI patients with low Aβ1-42 levels suggesting a role of PLCG2 in the response to amyloid pathology. In line with this hypothesis, we observed no protective effect of the PLCG2 variant on the cognitive decline in population-based studies probably due to the lower prevalence of amyloid positivity in these samples compared to MCI patients. Concerning the potential biological underpinnings, we identified a network of co-expressed proteins connecting PLCG2 to APOE and TREM2 using unsupervised co-regulatory network analysis. The network was highly enriched for the complement cascade and genes differentially expressed in disease-associated microglia. Our data show that p.P522R in PLCG2 reduces AD disease progression by mitigating tau pathology in the presence of amyloid pathology and, as a consequence, maintains cognitive function. Targeting the enzyme PLCG2 might provide a new therapeutic approach for treating AD

Macro-mineral concentrations in soil and forage in three grassland sites at Zacatecas

Mineral concentration in forage is an important factor for extensive livestock production. Therefore, a study was performed in order to evaluate the soil mineral contents and their relationships with forage mineral concentrations taking into account three grassland sites located at Zacatecas state, México. Soil organic matter (OM) content and pH as well as soil and forage contents of Ca, P, Mg, Na and K were estimated. Soil OM contents were not different (P>0.05) among sites averaging 2.99 %. Soil pH of site 2 was higher (P£0.05) than those of sites 1 and 3. Soil of site 2 had higher P, Ca and Mg concentrations than the minimum contents used as references. Soil contents of Na and K were lower than the reference contents suggesting deficiencies in all three sites. Considering requirements for growing cattle, P, Ca and Na were at insufficient levels in forage from all three sites. Significant correlations (r Pearson) suggest a positive effect of soil P content on forage P and Mg concentrations. Soil P content could affect forage Ca concentration and Ca:P ratio. Other correlations suggest soil Ca negative effects on forage Ca concentration and Ca:P ratio.

The rho exchange factors vav2 and vav3 control a lung metastasis-specific transcriptional program in breast cancer cells

The guanosine triphosphatases of the Rho and Rac subfamilies regulate protumorigenic pathways and are activated by guanine nucleotide exchange factors (Rho GEFs), which could be potential targets for anticancer therapies. We report that two Rho GEFs, Vav2 and Vav3, play synergistic roles in breast cancer by sustaining tumor growth, neoangiogenesis, andmany of the steps involved in lung-specific metastasis. The involvement of Vav proteins in these processes did not correlate with Rac1 and RhoA activity or cell migration, implying the presence of additional biological programs. Microarray analyses revealed that Vav2 and Vav3 controlled a vast transcriptional program in breast cancer cells through mechanisms that were shared between the two proteins, isoform-specific or synergistic. Furthermore, the abundance of Vavregulated transcripts wasmodulated by Rac1-dependent and Rac1-independent pathways. This transcriptome encoded therapeutically targetable proteins that played nonredundant roles in primary tumorigenesis and lung-specific metastasis, such as integrin-linked kinase (Ilk), the transforming growth factor-β family ligand inhibin βA, cyclooxygenase-2, and the epithelial cell adhesionmolecule Tacstd2. It also contained gene signatures that predicted disease outcome in breast cancer patients. These results identify possible targets for treating breast cancer and lung metastases and provide a potential diagnostic tool for clinical use.X.R.B.’s work was supported by the Spanish Ministry of Economy and Competitiveness (SAF2009-07172 and RD06/0020/0001), the Castilla-León Autonomous Government (GR97), the 7th Framework European Union Program (FP7-HEALTH-2007-A-201862), and the Spanish Association Against Cancer. A portion of X.R.B.’s funding comes from the European Regional Development Fund. Work in J.M.P.’s laboratory is partially supported by grants from the Spanish Ministry of Economy and Competitiveness (SAF2006-00121 and RD06/0020/0029) and the Madrid Autonomous Government (S2006/BIO-0232). C.C.’s and M.M.-M.’s salaries were partially supported by the JAE-Doc program, the Spanish National Research Council, the RD06/0020/0001 grant, and the European Regional Development Fund.Peer Reviewe