Journal of Materials Science and Nanomaterials
Open Access

Nanomaterials; Nanotechnology; Chemicals toxicity

Introduction

It is unclear how well scientists, engineers, and regulators are equipped to manage the risks associated with nanomaterials and the products that are created from them because empirical understanding of nanoscale materials and their behaviors is developing slowly . These chemicals’ toxicity and exposure properties continue to be subject to significant scientific uncertainties. In the meanwhile, plans for the regulation of nanomaterials are unavoidably developing in light of expert, governmental, and legal counsel. However, it’s not entirely clear how effectively this is working [3]. Thus, from the perspective of significant experts, this article assesses the perceived level of regulatory and agency preparation.

In general, according to a recent expert survey, nano-experts are more concerned about the dangers of manufactured nanomaterials than are laypeople or the general public. According to other research, persons who create nanomaterials and products tend to be more optimistic than those who research or handle the risks associated with them. Finally, sector executives report not adhering to risk-avoidant health and safety policies, despite polls finding high levels of perceived uncertainty and risk [4].

Expert assessments of chemical risks, climate change detection and impacts, expert opinions on genetically modified organisms (GMOs), and expert perception of ecological risks are a few examples of studies that have examined expert opinion in earlier and frequently more contentious risk domains [5]. According to disciplinary domains and/ or institutional affiliation (for example, toxicologists in industry versus academia), differences of opinion have been shown to vary. The political views and ideals have also been demonstrated to strongly influence expert opinion. Researchers have discovered that when assessing risk in the face of significant ambiguity, scientists frequently apply standards or values [6]. For instance, Corley et al. discovered that economically conservative nanoscientists supported regulation less. Similar findings have been made regarding the relationship between trust (in scientists and/or the government) and risk perceptions, with higher levels of trust being associated with a reduction in perceived risk. Studies on the perceived risk of pesticides and GMOs have also provided prominent examples of this effect. In the context of nanotechnology, the impact of attributed stakeholder responsibility—that is, the level of accountability given to various stakeholders for risk mitigation or management—has gotten relatively little attention. However, a growing corpus of research in the field of public health reveals a connection between attributions of blame and support for legislative and regulatory initiatives [7].

More specifically, researchers in nanotechnology have shown that there are differences in the perceived need and support for the regulation of nanotechnologies. These differences have been explained by experts’ disciplinary background (i.e., chemistry, physics, materials science, engineering, biology, or other) only within the NSE (nano science and engineering) professional body [8]. The detected optimism bias of NSE researchers in comparison to NEHS (nano environment, health, and safety) scientists was another factor contributing to the disparity in viewpoints on danger. Powell also discovered that “upstream” and “downstream” researchers—experts active in developing nanotechnologies vs those assessing the potential effects of ENMs on human health and the environment—had very different perspectives on the novelty and dangers of nanomaterials. However, the novelty of nanomaterials is still a mostly unproven driver of expert perceptions and/or their views regarding risk and regulation.

However, no research has been done to categorise experts according to their specialised function in (1) producing materials, (2) researching their toxicological behaviour, or (3) evaluating and controlling their risks. Additionally, no studies have looked at these professional opinions as indicators of how well-prepared the regulatory framework or regulatory agencies are to supervise engineered nanomaterials across a range of technologies, uses, or contexts [9].

Methods

Setting: Nanotechnology

We concentrate on national policies that support nanoscale science, technology, and innovation to get insight into the significance of STI policy timing in the evolution of a fledgling technology. In order to generate new materials and technologies with innovative features and functions based on their small size, nanotechnology is the control and manipulation of atoms and molecules smaller than 100 nanometers. It’s crucial to highlight that the development of the scanning tunnelling microscope in 1981 made it possible to see and manipulate matter at the nanoscale, which is essential for the development of nanotechnology.

As a result, 1981 serves as a reliable beginning point for the analysis of how policies affect entrepreneurial and creative activities. Importantly, STI policies did not overtly support nanotechnology until the 1980s; as a result, we may observe the beginning of STI policies in this domain without being censored.

The fields of chemistry, physics, biology, robotics, and computer science are all intersected by nanotechnology [6,5] and it has affected a number of sectors, including textiles, biotech, cars, optics, medicines, printing, and dentistry. In fact, by the end of 2019, about 9000 commercial products across a variety of industries, including cosmetics, building materials, scientific instrumentation, and sporting goods, incorporated nanotechnology. Given that comparable policy encompasses science, technology, and innovation policies, this environment is very pertinent to our research question.

Data

The policies that are particularly created to encourage and promote a nation’s efforts in science, technology, and innovation are the focus of this study. From more than 20,000 pages of archived information from more than 1,200 publications and websites, we created a database of national-level STI policies and outcomes related to nanotechnology. Governments, associations and clubs, colleges, the media, market research companies, and nanotechnology companies are just a few examples of the many entities that serve as sources. The National Science Foundation of the United States, the Ministry of Education, Culture, Sports, Science and Technology of Japan, the Chinese Academy of Sciences, the Royal Society and the Department for Environment, Food and Rural Affairs of the United Kingdom, the European Commission, the National Institute for Nanotechnology of Canada, the Organization for Economic Co-operation and Development, the World Bank, and the United Nations Educational, Scientific & Cultural Organization are a few examples.

Dependent variables

The study of STI policy performance requires internationally comparable indicators, which can be difficult to obtain. We use multiple dependent variables to measure entrepreneurial activity and innovation, cultivated from several data sources. First, we looked at entrepreneurship using the number of nanotechnology firms started in each country as reported in the Nanowerk database. The most recent year of complete data for all countries is 2017, which was used for this study. To examine the relative level of nanotechnology specific entrepreneurship activity in a country, we calculated Nano Firms/All Firms (Country): the ratio of nanotechnology firms to the number of all new firms founded in a country according to the Worldbank Database. This ratio provides insight into how far nanotechnology entrepreneurship has diffused through a country. A more traditional measure of technology venturing such as the ratio of nanotechnology dedicated firms to all existing firms in a country was sought; however, countries differed in their reporting and definitions of companies, which made the total number of firms not comparable. The WorldBank reliably measures the number of new firms per country each year; thus, we relied upon these data. Next, we calculated Nano Firms Country/ World: the proportion of nanotechnology firms in the country compared to all nanotechnology firms in the world to construct a competitive measure of venturing [10].

Another way to measure innovation at the country level is through publications. We collected data on academic publications in nanotechnology-related areas from the Statnano database. To compare a country’s publication activity relative to other countries, we calculated Nano Publications: Country/World: the number of nanotechnology publications for each country was divided by the number of nanotechnology publications in the world, as indexed by ISI-Web of Science. Again, to use the latest available complete data and parallel the entrepreneurship measure, we collected data for the year 2017. As a comparison measure, we calculated Nano Publications per capita: by dividing the number of nanotechnology publication for each country by its population to determine a per capita innovation value for publishing activity. As a robustness check we analyzed the data using the absolute numbers instead of relative proportions. Unsurprisingly, these models provided similar, but amplified results [11].

Independent variables

To measure the timing of STI policies, we use the year of the country’s first implementation of national nanotechnology initiatives: Year of First National Nanotech Program. Second, we evaluate the amount of government nanotechnology R&D funding (one type of STI policy program) by each nation in 1990: National Nano R&D Funding in 1990. The first six countries to enact nanotechnology STI policies did so before 1990. There was a break in the pattern of policy adoption and no other countries passed related policies until 1993. Thus, 1990 is a natural break point for analysis and helps identify early enactors of related initiatives.

Control variables

While testing the goods of timing of public STI programs on nanotechnology advancement and entrepreneurship, we parse out implicit indispensable factors. Since the position of R&D backing in a country influences the position of invention, we control for the gross domestic expenditures on R&D as a chance of the country’s gross domestic product( GDP), indicated as ‘ R&D Intensity ’ as reported by UNESCO. We controlled for the countries ’ sizes using its population [12]. Two fresh variables ease of starting a business and transnational IP protection were included to control for a country’s relative structure for entrepreneurship and invention. The ‘ ease of starting a business ’ measure is an indicator generated by the World Bank that calculates a country’s regulation structure to support new business profitable exertion starting a business, permits, employment, property enrollment , levies, transnational trade, contract enforcement, investor protection, carrying electricity, carrying credit, and closing a business. The transnational IP protection measure is calculated by the Global Innovation Policy Center and accounts for five sets of pointers that indicate a country’s IP terrain [13]. Per capita models didn’t include the population control, as it was included in the dependent variables. All control variables were collected for 2014, the rearmost time of complete data for all countries and allows for a three- time pause. Eventually, we formalized the control variables as recommended by Aiken and West.

Results

Our logical strategy involves a country position retrogression on a fairly small sample. therefore, we used generalized direct models( GLM) in Stata 15 to dissect the relationship between public nanotechnology STI policy and applicable entrepreneurship and invention, which is an extension of the general direct models that allows for anon-normal distribution of dependent variables or residuals. Each dependent variable was modeled independently. To reduce the influence of heteroscedasticity, the models estimate robust standard crimes. As a robustness check, we also modeled the data using a general direct retrogression; still, this requires residuals to be distributed typically, which wasn’t widely the case. nonetheless, general direct models handed an r – squared of over0.55 indicating strong results for these models.

Discussion

This paper makes an original donation to the design, modeling and analysis of the nanomedicine sphere in terms of showing that one can automatically descry the applicability to a nano- related target from a CT summary inClinicalTrials.gov. We’ve created an annotated body of nanomedicine CTs, with training and testing sets that can be used to develop extended computational operations for supporting exploration in the nanomedicine field. To the stylish of our knowledge, there’s no similar intimately available reference dataset for clinical nanomedicine. Our approach has produced promising results given a subset of CTs uprooted fromClinicalTrials.gov, our system can be reliably used for automatically determining whether the CT involves the use of nanodrugs. We linked an algorithm suitable to deal with such a high- dimensional problem, both in terms of bracket performance and computational cost. Although the bracket results we attained in this study aren’t directly similar to those performing by other analogous state- of- the- art studies since the ultimate are concentrated on different disciplines and resort to different training and test sets, in general, our results( F = 0.955) outperform the results from other recent trials — that range in the interval(0.85,0.96) by F- measure — as reported away. To our knowledge, these results are the first operation of textbook mining to prize information about nanodrugs and nanodevices fromClinicalTrials.gov, banning the NanoSifter, which covers the dendrimer sphere alone. There are a number of reasons that justify performing such a categorization of CTs into the nanovs.non-nano orders. These include, for case, comparing heritage phrasings with nanotechnology- grounded phrasings — in terms of aspects similar as structure, function, toxicology, pharmacokinetics and pharmacodynamics( PK/ PD), clinical immunogenicity, safety and effectiveness —, which would give fresh information to experimenters in the nano sphere. This knowledge could lead to the exercise of being products that could be manufactured at the nanoscale and, thus,reclassified as nanotechnology once this is done. In utmost cases, current CTs on nanodrugs haven’t revealed unknown side goods due to the nanoparticle or any of its ingredients. Yet, before abandoned rectifiers agents that have now been reformulated as nanodrugs are presenting toxin and side goods due to the special physicochemical parcels acquired during the nanomanufacturing process that weren’t considered during the design of the original medicine. While safety and efficacity trials will, of course, still remain essential, our approach could vastly simplify and reduce the way involved with the need to pursue, as presently, assays and clinical trials, by rather reasoning clinical data and using modeling and simulation tools from the related previous trials.

Conclusion

With the volume of experimental and clinical data related to nanomedicine adding fleetly, homemade analysis and reflection of studies on nanodrugs has come slow and largely impracticable. In this environment, the development of automatic approaches targeted at differencing information from the nano andnon-nano disciplines becomes necessary. In this paper, we’ve presented two original benefactions to the nanoinformatics field. First, we’ve created a training and testing set for a double textual bracket problem targeted at relating preliminarily unseen CTs as being nano ornon-nano. Second, we’ve conducted a thorough review of the state of the art both on machine literacy- grounded ways for double document categorization and being depositories of medicines and registries of CTs. We named the bracket styles and algorithms reported in the literature as the stylish players for double textbook categorization problems and applied these styles to the training and test sets we created. We named the most effective system to classify CTs into the nano andnon-nano orders, therefore producing categorization models whose results outperform most stateof- the- art classifiers. We believe that such a classifier can help beget the exploration in translational nanomedicine, therefore enabling a wide range of operations that cannot be addressed well with a raw depository of unclassified CTs. The analysis of clinical trials related to nanomedicine, carried out by integrating reported results over all the different available databases worldwide, could affect in the birth of implicit correlations, and new patterns and trends in nanomedical data. The analysis of correlations between multiplepre-clinical and clinical studies may be of value in areas similar as nanotoxicity and targeted medicine remedy, where certain underpinning patterns and trends could support consequences that inform unborn exploration in nanomedicine. By way of an illustration, results could serve to compare new phrasings with being bones and determine fresh side goods that may arise due to the recently added factors and/ or the manufacturing process( i.e. to the operation of nanotechnology to the original medicine). This work could also grease experimenters in automatically discovering new knowledge from CTs similar as, for case, uncovering implicit toxin of new nanodrugs or retaining cases who are most likely to respond appreciatively to a certain nanoparticle intervention due to their participation in earlier CTs using analogous medicines.

Acknowledgement

None

Conflict of Interest

None

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