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In Vivo Testing of the Protective Effect of Gloves
Published in Robert N. Phalen, Howard I. Maibach, Protective Gloves for Occupational Use, 2023
N. Hamnerius, C. Svedman, M. Bruze, O. Bergendorff
In vivo testing can be performed in animals or humans. It is important to know if a relationship exists between in vitro and in vivo testing, as well as the nature of the relationship, to reduce in vivo testing, which is time-consuming and must consider ethical aspects. Often, however, because of the different factors that influence the use of gloves in real life, in vivo testing is to be preferred.
Non-Photocatalytic and Photocatalytic Inactivation of Viruses Using Antiviral Assays and Antiviral Nanomaterials
Published in Devarajan Thangadurai, Saher Islam, Charles Oluwaseun Adetunji, Viral and Antiviral Nanomaterials, 2022
Suman Tahir, Noor Tahir, Tajamal Hussain, Zubera Naseem, Muhammad Zahid, Ghulam Mustafa
In vivo analysis states to test the impact of numerous biological facts on entire living cells or beings, involving humans, plants, and animals. The study is meant to determine the influence of drugs or chemicals on the biological system. In the antiviral investigation, the goal was to shield lives through chemicals and vaccines, so an emphasis was placed on safety of chemical drugs and vaccines for humans, plants, and animals. Pardi and Hogan coworkers established that the single less-dosage intradermal immunisation with the lipid-NP-encapsulated nucleoside-functionalized mRNA (mRNA-LNP) coding pre-membrane and enveloped glycoproteins form Zika virus (ZIKV) strain (Pardi et al. 2017). By vaccinating a living rat, a single dosage of 50 μg was realised to be sufficient for shielding nonhuman primates contrary to the task of five weeks afterward vaccination. The effort displays that the nucleoside-functionalized mRNA-LNP might be a probable nominee as an anti-ZIKV vaccine. This in vivo mice data established that animal vaccine analysis is an obligatory and valuable process for studying antiviral action of NPs.
Bias, Conflict of Interest, Ignorance, and Uncertainty
Published in Ted W. Simon, Environmental Risk Assessment, 2019
In summary, both the diversity and complexity of the prediction models used with ToxCast™ data are increasing. This is hardly surprising given the relative “newness” of the data and prediction models. The happy consequence of this diversity of approaches is that the field of in vitro-to-in vivo prediction models is rapidly maturing.
New frontier radioiodinated probe based on in silico resveratrol repositioning for microtubules dynamic targeting
Published in International Journal of Radiation Biology, 2023
Ashgan F. Mahmoud, Mohamed H. Aboumanei, Walaa Hamada Abd-Allah, Mohamed M. Swidan, Tamer M. Sakr
Drug repositioning is considered a strategy for repurposing the therapeutic aim of Food and Drug Administration (FDA)-approved drug based on the phenomenon of polypharmacology; the ability of one drug to target multiple proteins/receptors in the cell (Peters 2012; Winum et al. 2012; Pantziarka et al. 2014; Pompili et al. 2016). This strategy provides pivotal merits over ‘de novo’ drug discovery approach where the safety and pharmacokinetic profiles of the estimated drug had been already stated (Brown and Patel 2017). Furthermore, it is significantly cost-effective and less time consuming (Pantziarka 2017). The polypharmacological repositioning strategy is successfully achieved though the utilization of the scientific data compromised from the variant drug discovery which integrated with some mechanistic arrays (Nowak-Sliwinska et al. 2019). These encompass computational (in silico) approaches, synthetic chemistry, in vitro studies, in vivo preliminary in animal models and indeed clinical studies (Pillaiyar et al. 2020; Yang et al. 2021). Recently, the drug repositioning era toward the natural compounds and their derivatives has gained much focus in cancer management (Hopkins 2009; Newman and Cragg 2012; Sakr et al. 2018; Nowak-Sliwinska et al. 2019).
Re-envisioning the design of nanomedicines: harnessing automation and artificial intelligence
Published in Expert Opinion on Drug Delivery, 2023
Jonathan Zaslavsky, Pauric Bannigan, Christine Allen
In terms of research, introducing some elements of automation and ML in the current approach to nanomedicine development is necessary to incite more widespread adoption of these tools. However, full automation or relying solely on ML-based predictions is not necessarily the end goal. Inevitably, certain processes will be too hard to automate, and may be more trouble than they are worth. As such, it will be necessary to evaluate the most salient and amenable processes for automation. For example, small-scale formulation screening is well suited for automation by increasing the throughput and improves long-term sustainability by reducing the amount of material otherwise needed if it was carried out by hand. By contrast, in vivo studies or procuring clinical data are not feasible via high-throughput approaches. As such, early-stage formulation development would experience the greatest returns from automation and ML, allowing for faster progression to preclinical and clinical studies. Humans will continue to play a role and have the space to be creative, by spending more time in experimental design and planning. In any case, there are options to explore, such as the wide range of commercially available robots (e.g. liquid handlers, microfluidic systems) and expansive set of open-source ML frameworks (e.g. PyTorch, Tensorflow, etc.) which can be used to modulate the level of automation and ways that AI/ML is applied.
Circulating cell-free mitochondrial DNA in brain health and disease: A systematic review and meta-analysis
Published in The World Journal of Biological Psychiatry, 2022
Sarah Sohyun Park, Hyunjin Jeong, Ana C. Andreazza
Two investigators (SP and HJ) independently screened the titles and abstracts of all studies from the search. The following criteria was used to screen all articles from the initial search: (i) a cross-sectional, case–control or longitudinal study in humans with measurements of ccf-mtDNA; (ii) the study included both disease (cases) and non-disease healthy (controls) participants; and (iii) the study reported ccf-mtDNA concentrations using mean and standard deviation (SD), the sample size, and p-values. Studies were excluded if they met the following criteria: (i) no measurement of ccf-mtDNA in humans; (ii) no healthy control (HC) group; (iii) a review paper, book chapter or comments; (iv) an in vivo or in vitro study; or (v) the author failed to reply when requested for additional information.