Demographic, clinical, and functional determinants of antithrombotic treatment in patients with nonvalvular atrial fibrillation

Background: This study assessed the sociodemographic, functional, and clinical determinants of antithrombotic treatment in patients with nonvalvular atrial fibrillation (NVAF) attended in the internal medicine setting. Methods: A multicenter, cross-sectional study was conducted in NVAF patients who attended internal medicine departments for either a routine visit (outpatients) or hospitalization (inpatients). Results: A total of 961 patients were evaluated. Their antithrombotic management included: no treatment (4.7%), vitamin K antagonists (VKAs) (59.6%), direct oral anticoagulants (DOACs) (21.6%), antiplatelets (6.6%), and antiplatelets plus anticoagulants (7.5%). Permanent NVAF and congestive heart failure were associated with preferential use of oral anticoagulation over antiplatelets, while intermediate-to high-mortality risk according to the PROFUND index was associated with a higher likelihood of using antiplatelet therapy instead of oral anticoagulation. Longer disease duration and institutionalization were identified as determinants of VKA use over DOACs. Female gender, higher education, and having suffered a stroke determined a preferential use of DOACs. Conclusions: This real-world study showed that most elderly NVAF patients received oral anticoagulation, mainly VKAs, while DOACs remained underused. Antiplatelets were still offered to a proportion of patients. Longer duration of NVAF and institutionalization were identified as determinants of VKA use over DOACs. A poor prognosis according to the PROFUND index was identified as a factor preventing the use of oral anticoagulation

Drone RGB Images as a Reliable Information Source to Determine Legumes Establishment Success

[EN] The use of drones in agriculture is becoming a valuable tool for crop monitoring. There are some critical moments for crop success; the establishment is one of those. In this paper, we present an initial approximation of a methodology that uses RGB images gathered from drones to evaluate the establishment success in legumes based on matrixes operations. Our aim is to provide a method that can be implemented in low-cost nodes with relatively low computational capacity. An index (B1/B2) is used for estimating the percentage of green biomass to evaluate the establishment success. In the study, we include three zones with different establishment success (high, regular, and low) and two species (chickpea and lentils). We evaluate data usability after applying aggregation techniques, which reduces the picture's size to improve long-term storage. We test cell sizes from 1 to 10 pixels. This technique is tested with images gathered in production fields with intercropping at 4, 8, and 12 m relative height to find the optimal aggregation for each flying height. Our results indicate that images captured at 4 m with a cell size of 5, at 8 m with a cell size of 3, and 12 m without aggregation can be used to determine the establishment success. Comparing the storage requirements, the combination that minimises the data size while maintaining its usability is the image at 8 m with a cell size of 3. Finally, we show the use of generated information with an artificial neural network to classify the data. The dataset was split into a training dataset and a verification dataset. The classification of the verification dataset offered 83% of the cases as well classified. The proposed tool can be used in the future to compare the establishment success of different legume varieties or species.This research and the contract of S.Y. were funded by project PDR18-XEROCESPED, under the PDR-CM 2014-2020, by the EU (European Agricultural Fund for Rural Development, EAFRD), Spanish Ministry of Agriculture, Fisheries and Food (MAPA) and Comunidad de Madrid regional government through IMIDRA and the contract of L.P. was funded by Conselleria de Educacion, Cultura y Deporte with the Subvenciones para la contratacion de personal investigador en fase postdoctoral, APOSTD/2019/04.Parra-Boronat, L.; Mostaza-Colado, D.; Yousfi, S.; Marin, JF.; Mauri, PV.; Lloret, J. (2021). Drone RGB Images as a Reliable Information Source to Determine Legumes Establishment Success. Drones. 5(3):1-18. https://doi.org/10.3390/drones5030079S1185

Toward a new clinical classification of patients with familial hypercholesterolemia: One perspective from Spain

The introduction of singular therapies, such as proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9i), to lower high cholesterol levels requires better classification of patients eligible for intensive lipid lowering therapy. According to the European Medicines Administration, PCSK9i are recommended in primary prevention in familial hypercholesterolemia (FH) patients. Therefore, an FH diagnosis is not simply an academic issue, because it has many clinical implications. The bases of a diagnosis of FH are not entirely clear. The availability of genetic testing, including large genome-wide association analyses and whole genome studies, has shown that some patients with a clinical diagnosis of definite FH have no mutations in the genes associated with the disease. This fact does not exclude the very high cardiovascular risk of these patients, and an early and intensive lipid lowering therapy is recommended in all FH patients. Because an FH diagnosis is a cornerstone for decisions about therapies, a precise definition of FH is urgently required. This is an expert consensus document from the Spanish Atherosclerosis Society. We propose the following classification: familial hypercholesterolemia syndrome integrated by (1) heterozygous familial hypercholesterolemia: patients with clinically definite FH and a functional mutation in one allele of the LDLR, ApoB:100, and PCSK9 genes; (2) homozygous familial hypercholesterolemia: mutations affect both alleles; (3) polygenic familial hypercholesterolemia: patients with clinically definite FH but no mutations associated with FH are found (to be distinguished from non-familial, multifactorial hypercholesterolemia); (4) familial hypercholesterolemia combined with hypertriglyceridemia: a subgroup of familial combined hyperlipidaemia patients fulfilling clinically definite FH with associated hypertriglyceridemia

Evaluation of contemporary treatment of high- and very high-risk patients for the prevention of cardiovascular events in Europe – Methodology and rationale for the multinational observational SANTORINI study

Publisher Copyright: © 2021 The AuthorsBackground and aims: Clinical practice before 2019 suggests a substantial proportion of high and very high CV risk patients taking lipid-lowering therapy (LLT) would not achieve the new LDL-C goals recommended in the 2019 ESC/EAS guidelines (<70 and < 55 mg/dL, respectively). To what extent practice has changed since the last ESC/EAS guideline update is uncertain, and quantification of remaining implementation gaps may inform health policy. Methods: The SANTORINI study is a multinational, multicentre, prospective, observational, non-interventional study documenting patient data at baseline (enrolment) and at 12-month follow-up. The study recruited 9606 patients ≥18 years of age with high and very high CV risk (as assigned by the investigators) requiring LLT, with no formal patient or comparator groups. The primary objective is to document, in the real-world setting, the effectiveness of current treatment modalities in managing plasma levels of LDL-C in high- and very high-risk patients requiring LLT. Key secondary effectiveness objectives include documenting the relationship between LLT and levels of other plasma lipids, high-sensitivity C-reactive protein (hsCRP) and overall predicted CV risk over one year. Health economics and patient-relevant parameters will also be assessed. Conclusions: The SANTORINI study, which commenced after the 2019 ESC/EAS guidelines were published, is ideally placed to provide important contemporary insights into the evolving management of LLT in Europe and highlight factors contributing to the low levels of LDL-C goal achievement among high and very high CV risk patients. It is hoped the findings will help enhance patient management and reduce the burden of ASCVD in Europe.Peer reviewe

Methodology to Differentiate Legume Species in Intercropping Agroecosystems Based on UAV with RGB Camera

Mixed crops are one of the fundamental pillars of agroecological practices. Row intercropping is one of the mixed cropping options based on the combination of two or more species to reduce their impacts. Nonetheless, from a monitoring perspective, the coexistence of different species with different characteristics complicates some processes, requiring a series of adaptations. This article presents the initial development of a procedure that differentiates between chickpea, lentil, and ervil in an intercropping agroecosystem. The images have been taken with a drone at the height of 12 and 16 m and include the three crops in the same photograph. The Vegetation Index and Soil Index are used and combined. After generating the index, aggregation techniques are used to minimize false positives and false negatives. Our results indicate that it is possible to differentiate between the three crops, with the difference between the chickpea and the other two legume species clearer than that between the lentil and the ervil in images gathered at 16 m. The accuracy of the proposed methodology is 95% for chickpea recognition, 86% for lentils, and 60% for ervil. This methodology can be adapted to be applied in other crop combinations to improve the detection of abnormal plant vigour in intercropping agroecosystems

Methodology to Differentiate Legume Species in Intercropping Agroecosystems Based on UAV with RGB Camera

Mixed crops are one of the fundamental pillars of agroecological practices. Row intercropping is one of the mixed cropping options based on the combination of two or more species to reduce their impacts. Nonetheless, from a monitoring perspective, the coexistence of different species with different characteristics complicates some processes, requiring a series of adaptations. This article presents the initial development of a procedure that differentiates between chickpea, lentil, and ervil in an intercropping agroecosystem. The images have been taken with a drone at the height of 12 and 16 m and include the three crops in the same photograph. The Vegetation Index and Soil Index are used and combined. After generating the index, aggregation techniques are used to minimize false positives and false negatives. Our results indicate that it is possible to differentiate between the three crops, with the difference between the chickpea and the other two legume species clearer than that between the lentil and the ervil in images gathered at 16 m. The accuracy of the proposed methodology is 95% for chickpea recognition, 86% for lentils, and 60% for ervil. This methodology can be adapted to be applied in other crop combinations to improve the detection of abnormal plant vigour in intercropping agroecosystems

The Combined Use of Remote Sensing and Wireless Sensor Network to Estimate Soil Moisture in Golf Course

In gardening, particularly in golf courses, soil moisture management is critical for maximizing water efficiency. Remote sensing has been used to estimate soil moisture in recent years with relatively low accuracies. In this paper, we aim to use remote sensing and wireless sensor networks to generate soil moisture indexes for a golf course. In the golf course, we identified three types of soil, and data was gathered for three months. Mathematical models were obtained using data from Sentinel-2, bands with a resolution of 10 and 20 m, and sensed soil moisture. Models with acceptable accuracy were obtained only for one out of three soil types, the natural soil in which natural vegetation is grown. Two multiple regression models are presented with an R2 of 0.46 for bands at 10 m and 0.70 for bands at 20 m. Their mean absolute error was lower than 3% in both cases. For the modified soils, the greens, and the golf course fairway, it was not feasible to obtain regression models due to the temporal uniformity of the grass and the range of variation of soil moisture. The developed moisture indexes were compared with existing options. The attained accuracies improve the current models. The verification indicates that the model generated with band 4 and band 12 is the one with better accuracy