Medical Geology: An Interdisciplinary Approach Intended to Unfold the Issues of Natural Environment on Public Health

Prashanth, Manthena; Verma, Omkar

doi:10.56153/g19088-021-0060-5

Cited by 3 publications

(1 citation statement)

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“…So, the strict collaboration of geologists, geoscientists, and mineralogists, considered in this case as "medical geologists", with other experts in different disciplines (e.g. : health sciences, pathology, epidemiology) might be useful to reach the goal aimed at eradicating or reducing the effects of the diseases and understanding the pathways to find long-term solutions [50].…”

Section: Introductionmentioning

confidence: 99%

Asbestos Bodies in Human Lung: Localization of Iron and Carbon in the Coating

Croce,

Gatti,

Calisi

et al. 2024

Geosciences

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Asbestos is a term that includes six fibrous mineral phases related to different lung diseases, including asbestosis, lung cancer, and Malignant Pleural Mesothelioma (MPM). Since the last century, these minerals have been widely studied under their mineralogical/chemical and physical aspects with in vivo and in vitro studies to understand the mechanisms of their carcinogenicity. There are several techniques described in the literature, as optical and electron microscopies, for the identification of coated (asbestos bodies, ABs) and uncoated fibers, but only micro-Raman spectroscopy permits a sure characterization of these minerals—and of the related phases—directly in the histological sections of pulmonary parenchyma without any manipulation. In this case, the risk of the loss of associated inorganic phases from asbestos bodies (ABs) and fibers (e.g.: iron or carbonaceous micro-particles) is avoided. Asbestos bodies are produced by the activity of alveolar macrophages with degradation/inactivation of asbestos fibers. Inside the alveolar macrophages, organic and inorganic material settles on the foreign fibers forming an iron-rich proteic and carbonaceous coating. In this study, Variable Pressure Scanning Electron Microscopy with annexed Electron Dispersive Spectroscopy (VP-SEM/EDS) and micro-Raman spectroscopy were applied to the characterization of the phases in the ABs. Characterization of carbonaceous materials (CMs), observed in pristine asbestos phases in previous works, was therefore performed, addressing the micro-Raman laser beam on different points of the asbestos bodies, and Raman mappings on ABs were carried out for the first time. Coupling the data obtained by VP-SEM/EDS and micro-Raman spectroscopy, it was possible to collect information about the iron and carbonaceous phases adhered to the fibers, probably lost during the classical tissue digestion procedures. Information about both mineral and carbonaceous components might be useful to understand the whole structure of “asbestos bodies” and the inflammogenic and carcinogenic effects of the asbestos phases coupled to CMs, that might derive from cigarette smoke or from environmental pollution; this study might be useful to deepen also the possible detrimental role of ABs in the tissues.

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Section: Introductionmentioning

confidence: 99%

Asbestos Bodies in Human Lung: Localization of Iron and Carbon in the Coating

Croce,

Gatti,

Calisi

et al. 2024

Geosciences

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Geological education scenario in India and role of open educational resources in the light of COVID-19 pandemic

et al. 2022

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Geological education plays a major role in the social, economic, and cultural growth and development of any country. India is a vast diversified country with wide-ranging geological features, consisting of rocks of all geological ages with well-developed physiographic divisions. The Indian lithospheric plate is a storehouse of vast georesources and also serves as a natural field laboratory for developing and testing numerous geological principles. The exploration of Indian georesources began in the past; with the organized exploration began way back in the 1830s. The number of institutions offering geological education is, however, comparatively low. Thus, Geology as a science discipline is less popular in comparison with other science subjects such as Physics, Chemistry, Zoology, Botany, and Environmental science, based on enrolment data of the learners enrolled in academic sessions from 2011-2012 to 2018-2019 of the Master's degree programs. Analysis indicates that teaching-learning resources of foreign origin are dominantly being adopted as learning resources in the teaching-learning pedagogy of degree programs in Geology, and are over-riding those of Indian origin. The COVID-19 pandemic is affecting geological education in two ways namely, the disruption of conventional (face-to-face) teaching together with not allowing learners of Geology programs to complete their compulsory out-door geological fieldwork assignments. The analysis of data gathered by an online questionnaire survey shows that the use of indigenous open educational resources in Geology appears as a plausible solution to overcome the learning barriers created by the COVID-19 pandemic, and also to enrich teaching-learning resources of Indian origin. This article provides an up-to-date detailed account of the necessity and evolution of geological education, the current popularity of Geology as a science discipline, the nature of available geological teaching-learning resources, the impact of the COVID-19 on geological education, and the role of open educational resources in providing quality and equitable geological education, whilst removing educational barriers created by COVID-19 in India.

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Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image

et al. 2023

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Machine learning (ML) and artificial intelligence (AI) are widely applied in many disciplines including medicine. Pattern recognition or automatization has been successfully implemented in various field studies. Similarly, multiple efforts have been made in medicine to implement AI/ML technology to solve medical problems, for example, for automating osteoporosis detection. In general, the success of AI/ML technology is highly dependent on the amount of available data, especially during the training stage. Feature generation is a common technique that allows the manipulation of available data for the training stages. This paper aims to study the feasibility of adopting signal-processing techniques for feature generation in medical image processing. Signal attributes from signal processing workflow were adopted and applied to image processing of CT and DEXA scanning data to differentiate between normal and osteoporotic bone. Five attributes, namely amplitude, frequency, instantaneous phase, roughness, and first derivative or contrast attributes, have been tested. An attribute index number is formulated to indicate the attribute’s strength at the selected region of interest (ROI). A case study applying these attributes to the CNN model is presented. More than five hundred CT scan images of normal and osteoporosis bone were used during the training stage to test classification performance with and without developed attributes as an input. From the ten selected CT scan images used to test the CNN model, 90% were well predicted in the scenario only utilizing the grayscale as input. However, when including the developed attributes, the CNN can predict all the images well (100% were well predicted). In conclusion, the technique adopted from the signal-processing technique has the potential to enhance feature generation in image processing, whereby the results can be used for the early application of AI/ML in osteoporosis identification. Further research testing this proposed method in different image modalities needs to be conducted to verify the robustness of the proposed method.

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Medical Geology: An Interdisciplinary Approach Intended to Unfold the Issues of Natural Environment on Public Health

Cited by 3 publications

References 0 publications

Asbestos Bodies in Human Lung: Localization of Iron and Carbon in the Coating

Asbestos Bodies in Human Lung: Localization of Iron and Carbon in the Coating

Geological education scenario in India and role of open educational resources in the light of COVID-19 pandemic

Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image

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