Predicting electrophoretic mobility of TiO2, ZnO, and CeO2 nanoparticles in natural waters: The importance of environment descriptors in nanoinformatics models.

Natural and engineered nanoparticles (NPs) entering the environment are influenced by many physicochemical processes and show various behavior in different systems (e.g., natural waters showing different characteristics). Determining the physicochemical characteristics and predicting the behavior of nanoparticles ending up in the natural aquatic environment are key aspects of their risk assessment. Here, we show that the quantitative structure-property relationship modeling method used in nanoinformatics (nano-QSPR) can be successfully applied to predict environmental fate-relevant properties (electrophoretic mobility) of TiO2, ZnO, and CeO2 nanoparticles. However, in contrast to the previous works, we postulate to use, in parallel: (i) the nanoparticles' structure descriptors (S-descriptors) and (ii) the environment descriptors (E-descriptors) as the input variables. Thus, the method should be abbreviated more precisely as nano-QSEPR ("E" stands for the "environment"). As a proof-of-the-concept, we have developed a group of models (including MLR, GA-PLS, PCR, and Meta-Consensus models) with high predictive capabilities (QEXT2 = 0.931 for the GA-PLS model), where the S-descriptors are represented by the core-shell model descriptor and the E-descriptors - by different ambient water features (including ions concentration and the ionic strength). The newly proposed nano-QSEPR modeling scheme can be efficiently used to design safe and sustainable nanomaterials.

Human papillomavirus self-sampling for long-term non-attenders in cervical cancer screening: A randomised feasibility study in Estonia.

OBJECTIVE: Organised cervical cancer screening was started in Estonia in 2006, but participation is still low. Human papillomavirus (HPV) self-sampling has proved to increase screening uptake. This study addressed the feasibility of HPV self-sampling and the acceptance of this method among long-term screening non-attenders. METHODS: A randomised intervention study was conducted in Estonia in 2020. Women born in 1958-1983 without a Pap smear in 2013-2019 were identified in the Estonian Health Insurance Fund database. From them, 12,000 women were randomly allocated to three equal-sized study groups. The opt-out group received a questionnaire and a Qvintip® sampling device by regular mail. Two opt-in groups received a questionnaire and an e-mail invitation to order a self-sampler online; one received Qvintip and the other Evalyn® Brush. Participants background characteristics were obtained from the Population Register. The effect of covariates on participation rate was estimated with multivariate Poisson regression. Acceptance of self-sampling was analysed according to agreement with statements in the questionnaire. RESULTS: The overall participation rate was 16% with significant differences between opt-out (26%) and opt-in (11%) groups. Compared to the opt-out Qvintip group, adjusted relative risks for the Qvintip and Evalyn Brush opt-in groups were 0.41 (95% confidence interval (CI) 0.37-0.45) and 0.44 (95% CI 0.40-0.49), respectively. Participation was associated with living place, citizenship, and education. Self-sampling was well accepted: 98% agreed that it was easy to use, 88% preferred it as a screening method in future. CONCLUSIONS: The results show the feasibility and good acceptance of HPV self-sampling among long-term screening non-attenders in Estonia.

Machine learning and materials modelling interpretation of in vivo toxicological response to TiO2 nanoparticles library (UV and non-UV exposure).

Assessing the risks of nanomaterials/nanoparticles (NMs/NPs) under various environmental conditions requires a more systematic approach, including the comparison of effects across many NMs with identified different but related characters/descriptors. Hence, there is an urgent need to provide coherent (eco)toxicological datasets containing comprehensive toxicity information relating to a diverse spectra of NPs characters. These datasets are test benches for developing holistic methodologies with broader applicability. In the present study we assessed the effects of a custom design Fe-doped TiO2 NPs library, using the soil invertebrate Enchytraeus crypticus (Oligochaeta), via a 5-day pulse via aqueous exposure followed by a 21-days recovery period in soil (survival, reproduction assessment). Obviously, when testing TiO2, realistic conditions should include UV exposure. The 11 Fe-TiO2 library contains NPs of size range between 5-27 nm with varying %Fe (enabling the photoactivation of TiO2 at energy wavelengths in the visible-light range). The NPs were each described by 122 descriptors, being a mixture of measured and atomistic model descriptors. The data were explored using single and univariate statistical methods, combined with machine learning and multiscale modelling techniques. An iterative pruning process was adopted for identifying automatically the most significant descriptors. TiO2 NPs toxicity decreased when combined with UV. Notably, the short-term water exposure induced lasting biological responses even after longer-term recovery in clean exposure. The correspondence with Fe-content correlated with the band-gap hence the reduction of UV oxidative stress. The inclusion of both measured and modelled materials data benefitted the explanation of the results, when combined with machine learning.

Predicting Cytotoxicity of Metal Oxide Nanoparticles using Isalos Analytics Platform.

A literature curated dataset containing 24 distinct metal oxide (MexOy) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of MexOy NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by MexOy NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the MexOy conduction band (EC), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⟂ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project's Integrated Approach to Testing and Assessment (IATA).

NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment.

Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational 'safe-by-design' approaches to facilitate NM commercialization.

Internet recruitment for sexually transmitted infection screening among men who have sex with men in Eastern Europe.

Men who have sex with men (MSM) face negative health outcomes such as sexually transmitted infections (STIs) at disproportionate rates. Nonetheless, infections may be underestimated due to limited uptake in testing. To increase testing, screening interventions have been utilized in the past; however, some have resulted in limitations such as poor recruitment. To increase recruitment for screening of MSM in Estonia, two different recruitment strategies were examined. Recruitment was separated into two promotional periods: passive and active. Passive consisted of banners on gay-related sites, while active also placed banners on websites to the general public such as Facebook linked to specific thematic pages and users self-identifying as men. More men were recruited during the active period of five weeks (n = 134) than the passive period of 46 weeks (n = 126). Active promotion was so successful in that the number of home sampling kit orders far exceeded what was projected, forcing promotion to the general public to be closed after 13 days. Recruiting MSM through a combination of general public and gay-related websites and applications has the ability to quickly recruit for testing interventions. This method can recruit a large number in a short amount of time; therefore, a budget must be planned accordingly to support testing for all that participate.

Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks.

An investigation of cell-penetrating peptides (CPPs) by using combination of Artificial Neural Networks (ANN) and Principle Component Analysis (PCA) revealed that the penetration capability (penetrating/non-penetrating) of 101 examined peptides can be predicted with accuracy of 80%-100%. The inputs of the ANN are the main characteristics classifying the penetration. These molecular characteristics (descriptors) were calculated for each peptide and they provide bio-chemical insights for the criteria of penetration. Deeper analysis of the PCA results also showed clear clusterization of the peptides according to their molecular features.

Characterization of a Novel Cytotoxic Cell-penetrating Peptide Derived From p14ARF Protein.

The tumor suppressor p14ARF is widely deregulated in many types of cancers and is believed to function as a failsafe mechanism, inhibiting proliferation and inducing apoptosis as cellular response to a high oncogene load. We have found that a 22-amino-acid-long peptide derived from the N-terminal part of p14ARF, denoted ARF(1-22), which has previously been shown to mimic the function of p14ARF, has cell-penetrating properties. This peptide is internalized to the same extent as the cell-penetrating peptide (CPP) TP10 and dose-dependently decreases proliferation in MCF-7 and MDA MB 231 cells. Uptake of the ARF(1-22) peptide is associated with low membrane disturbance, measured by deoxyglucose and lactate dehydrogenase (LDH) leakage, as compared to its scrambled peptide. Also, flow cytometric analysis of annexin V/propidium iodide (PI) binding and Hoechst staining of nuclei suggest that ARF(1-22) induces apoptosis, whereas scrambled or inverted peptide sequences have no effect. The ARF(1-22) peptide mainly translocates cells through endocytosis, and is found intact inside cells for at least 3 hours. To our knowledge, this is the first time a CPP having pro-apoptopic activity has been designed from a protein.

Characterization of a novel cytotoxic cell-penetrating peptide derived from p14ARF protein.

The tumor suppressor p14ARF is widely deregulated in many types of cancers and is believed to function as a failsafe mechanism, inhibiting proliferation and inducing apoptosis as cellular response to a high oncogene load. We have found that a 22-amino-acid-long peptide derived from the N-terminal part of p14ARF, denoted ARF(1-22), which has previously been shown to mimic the function of p14ARF, has cell-penetrating properties. This peptide is internalized to the same extent as the cell-penetrating peptide (CPP) TP10 and dose-dependently decreases proliferation in MCF-7 and MDA MB 231 cells. Uptake of the ARF(1-22) peptide is associated with low membrane disturbance, measured by deoxyglucose and lactate dehydrogenase (LDH) leakage, as compared to its scrambled peptide. Also, flow cytometric analysis of annexin V/propidium iodide (PI) binding and Hoechst staining of nuclei suggest that ARF(1-22) induces apoptosis, whereas scrambled or inverted peptide sequences have no effect. The ARF(1-22) peptide mainly translocates cells through endocytosis, and is found intact inside cells for at least 3 hours. To our knowledge, this is the first time a CPP having pro-apoptopic activity has been designed from a protein.