Search results for: needle defoliation

Authors: Inês Amorim Leitão, Loes van Shaick, António Dinis Ferreira, Violette Geissen

Abstract:

Plastic pollution is a growing concern worldwide: plastics are commercialized in large quantities and it takes a long time for them to degrade. When in the environment, plastic is fragmented into microplastics (<5mm), which have been found in all environmental compartments at different locations. Microplastics contribute to the environmental pollution in water, air and soil and are linked to human health problems. The progressive increase of population living in cities led to the aggravation of the pollution problem worldwide, especially in urban environments. Urban areas represent a strong source of pollution, through the roads, industrial production, wastewater, landfills, etc. It is expected that pollutants such as microplastics are transported diffusely from the sources through different pathways such as wind and rain. Therefore, it is very complex to quantify, control and treat these pollutants, designated current problematic issues by the European Commission. Green areas are pointed out by experts as natural filters for contaminants in cities, through their capacity of retention by vegetation. These spaces have thus the capacity to control the load of pollutants transported. This study investigates the spatial distribution of microplastics in urban soils of different land uses, their transport through atmospheric deposition, wind erosion, runoff and streams, as well as their deposition in vegetation like grass and tree leaves in urban environment. Coimbra, a medium large city located in the central Portugal, is the case-study. All the soil, sediments, water and vegetation samples were collected in Coimbra and were later analyzed in the Wageningen University & Research laboratory. Microplastics were extracted through the density separation using Sodium Phosphate as solution (~1.4 g cm−3) and filtration methods, visualized under a stereo microscope and identified using the u-FTIR method. Microplastic particles were found in all the different samples. In terms of soils, higher concentrations of microplastics were found in green parks, followed by landfills and industrial places, and the lowest concentrations in forests and pasture land-uses. Atmospheric deposition and streams after rainfall events seems to represent the strongest pathways of microplastics. Tree leaves can retain microplastics on their surfaces. Small leaves such as needle leaves seem to present higher amounts of microplastics per leaf area than bigger leaves. Rainfall episodes seem to reduce the concentration of microplastics on leaves surface, which suggests the wash of microplastics down to lower levels of the tree or to the soil. When in soil, different types of microplastics could be transported to the atmosphere through wind erosion. Grass seems to present high concentrations of microplastics, and the enlargement of the grass cover leads to a reduction of the amount of microplastics in soil, but also of the microplastics moved from the ground to the atmosphere by wind erosion. This study proof that vegetation can help to control the transport and dispersion of microplastics. In order to control the entry and the concentration of microplastics in the environment, especially in cities, it is essential to defining and evaluating nature-based land-use scenarios, considering the role of green urban areas in filtering small particles.

Keywords: microplastics, cities, sources, pathways, vegetation

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Authors: Meenakshi Barsaul Chauhan, Aastha Chauhan, Shilpa Hurmade, Rajeev Sen, Jyotsna Sen, Monika Dalal

Abstract:

Introduction: Ovarian masses are common forms of neoplasm in women and represent 2/3rd of gynaecological malignancies. A pre-operative suggestion of malignancy can guide the gynecologist to refer women with suspected pelvic mass to a gynecological oncologist for appropriate therapy and optimized treatment, which can improve survival. In the younger age group preoperative differentiation into benign or malignant pathology can decide for conservative or radical surgery. Imaging modalities have a definite role in establishing the diagnosis. By using International Ovarian Tumor Analysis (IOTA) classification with sonography, costly radiological methods like Magnetic Resonance Imaging (MRI) / computed tomography (CT) scan can be reduced, especially in developing countries like India. Thus, this study is being undertaken to evaluate the role of clinical methods and sonography for diagnosis of the nature of the ovarian tumor. Material And Methods: This prospective observational study was conducted on 40 patients presenting with ovarian masses, in the Department of Obstetrics and Gynaecology, at a tertiary care center in northern India. Functional cysts were excluded. Ultrasonography and color Doppler were performed on all the cases.IOTA rules were applied, which take into account locularity, size, presence of solid components, acoustic shadow, dopper flow etc . Magnetic Resonance Imaging (MRI) / computed tomography (CT) scans abdomen and pelvis were done in cases where sonography was inconclusive. In inoperable cases, Fine needle aspiration cytology (FNAC) was done. The histopathology report after surgery and cytology report after FNAC was correlated statistically with the pre-operative diagnosis made clinically and sonographically using IOTA rules. Statistical Analysis: Descriptive measures were analyzed by using mean and standard deviation and the Student t-test was applied and the proportion was analyzed by applying the chi-square test. Inferential measures were analyzed by sensitivity, specificity, negative predictive value, and positive predictive value. Results: Provisional diagnosis of the benign tumor was made in 16(42.5%) and of the malignant tumor was made in 24(57.5%) patients on the basis of clinical findings. With IOTA simple rules on sonography, 15(37.5%) were found to be benign, while 23 (57.5%) were found to be malignant and findings were inconclusive in 2 patients (5%). FNAC/Histopathology reported that benign ovarian tumors were 14 (35%) and 26(65%) were malignant, which was taken as the gold standard. The clinical finding alone was found to have a sensitivity of 66.6% and a specificity of 90.9%. USG alone had a sensitivity of 86% and a specificity of 80%. When clinical findings and IOTA simple rules of sonography were combined (excluding inconclusive masses), the sensitivity and specificity were 83.3% and 92.3%, respectively. While including inconclusive masses, sensitivity came out to be 91.6% and specificity was 89.2. Conclusion: IOTA's simple sonography rules are highly sensitive and specific in the prediction of ovarian malignancy and also easy to use and easily reproducible. Thus, combining clinical examination with USG will help in the better management of patients in terms of time, cost and better prognosis. This will also avoid the need for costlier modalities like CT, and MRI.

Keywords: benign, international ovarian tumor analysis classification, malignant, ovarian tumours, sonography

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Authors: Adriana Haulica

Abstract:

Powered by Machine Learning, Precise medicine is tailored by now to use genetic and molecular profiling, with the aim of optimizing the therapeutic benefits for cohorts of patients. As the majority of Machine Language algorithms come from heuristics, the outputs have contextual validity. This is not very restrictive in the sense that medicine itself is not an exact science. Meanwhile, the progress made in Molecular Biology, Bioinformatics, Computational Biology, and Precise Medicine, correlated with the huge amount of human biology data and the increase in computational power, opens new healthcare challenges. A more accurate diagnosis is needed along with real-time treatments by processing as much as possible from the available information. The purpose of this paper is to present a deeper vision for the future of Artificial Intelligence in Precise medicine. In fact, actual Machine Learning algorithms use standard mathematical knowledge, mostly Euclidian metrics and standard computation rules. The loss of information arising from the classical methods prevents obtaining 100% evidence on the diagnosis process. To overcome these problems, we introduce MEDICOMPILLS, a new architectural concept tool of information processing in Precise medicine that delivers diagnosis and therapy advice. This tool processes poly-field digital resources: global knowledge related to biomedicine in a direct or indirect manner but also technical databases, Natural Language Processing algorithms, and strong class optimization functions. As the name suggests, the heart of this tool is a compiler. The approach is completely new, tailored for omics and clinical data. Firstly, the intrinsic biological intuition is different from the well-known “a needle in a haystack” approach usually used when Machine Learning algorithms have to process differential genomic or molecular data to find biomarkers. Also, even if the input is seized from various types of data, the working engine inside the MEDICOMPILLS does not search for patterns as an integrative tool. This approach deciphers the biological meaning of input data up to the metabolic and physiologic mechanisms, based on a compiler with grammars issued from bio-algebra-inspired mathematics. It translates input data into bio-semantic units with the help of contextual information iteratively until Bio-Logical operations can be performed on the base of the “common denominator “rule. The rigorousness of MEDICOMPILLS comes from the structure of the contextual information on functions, built to be analogous to mathematical “proofs”. The major impact of this architecture is expressed by the high accuracy of the diagnosis. Detected as a multiple conditions diagnostic, constituted by some main diseases along with unhealthy biological states, this format is highly suitable for therapy proposal and disease prevention. The use of MEDICOMPILLS architecture is highly beneficial for the healthcare industry. The expectation is to generate a strategic trend in Precise medicine, making medicine more like an exact science and reducing the considerable risk of errors in diagnostics and therapies. The tool can be used by pharmaceutical laboratories for the discovery of new cures. It will also contribute to better design of clinical trials and speed them up.

Keywords: bio-semantic units, multiple conditions diagnosis, NLP, omics

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