How Does the Study MD of pH-Dependent Exposure of Nanoparticles Affect Cellular Uptake of Anticancer Drugs?

The lack of knowledge about the uptake of NPs by biological cells poses a significant problem for drug delivery. For this reason, designing an appropriate model is the main challenge for modelers. To address this problem, molecular modeling studies that can describe the mechanism of cellular uptake of drug-loaded nanoparticles have been conducted in recent decades. In this context, we developed three different models for the amphipathic nature of drug-loaded nanoparticles (MTX-SS-γ-PGA), whose cellular uptake mechanism was predicted by molecular dynamics studies. Many factors affect nanoparticle uptake, including nanoparticle physicochemical properties, protein-particle interactions, and subsequent agglomeration, diffusion, and sedimentation. Therefore, the scientific community needs to understand how these factors can be controlled and the NP uptake of nanoparticles. Based on these considerations, in this study, we investigated for the first time the effects of the selected physicochemical properties of the anticancer drug methotrexate (MTX) grafted with hydrophilic-γ-polyglutamic acid (MTX-SS-γ-PGA) on its cellular uptake at different pH values. To answer this question, we developed three theoretical models describing drug-loaded nanoparticles (MTX-SS-γ-PGA) at three different pH values, such as (1) pH 7.0 (the so-called neutral pH model), (2) pH 6.4 (the so-called tumor pH model), and (3) pH 2.0 (the so-called stomach pH model). Exceptionally, the electron density profile shows that the tumor model interacts more strongly with the head groups of the lipid bilayer than the other models due to charge fluctuations. Hydrogen bonding and RDF analyses provide information about the solution of the NPs with water and their interaction with the lipid bilayer. Finally, dipole moment and HOMO-LUMO analysis showed the free energy of the solution in the water phase and chemical reactivity, which are particularly useful for determining the cellular uptake of the NPs. The proposed study provides fundamental insights into molecular dynamics (MD) that will allow researchers to determine the influence of pH, structure, charge, and energetics of NPs on the cellular uptake of anticancer drugs. We believe that our current study will be useful in developing a new model for drug delivery to cancer cells with a much more efficient and less time-consuming model.

One Step before Synthesis: Structure-Property-Condition Relationship Models to Sustainable Design of Efficient TiO2-Based Multicomponent Nanomaterials.

To control the photocatalytic activity, it is essential to consider several parameters affecting the structure of ordered multicomponent TiO2-based photocatalytic nanotubes. The lack of systematic knowledge about the relationship between structure, property, and preparation parameters may be provided by applying a machine learning (ML) methodology and predictive models based on the quantitative structure-property-condition relationship (QSPCR). In the present study, for the first time, the quantitative mapping of preparation parameters, morphology, and photocatalytic activity of 136 TiO2 NTs doped with metal and non-metal nanoparticles synthesized with the one-step anodization method has been investigated via linear and nonlinear ML methods. Moreover, the developed QSPCR model, for the first time, provides systematic knowledge supporting the design of effective TiO2-based nanotubes by proper structure manipulation. The proposed computer-aided methodology reduces cost and speeds up the process (optimize) of efficient photocatalysts' design at the earliest possible stage (before synthesis) in line with the sustainability-by-design strategy.

Trends in premature cerebrovascular disease mortality in the Polish population aged 25-64 years, 2000-2016.

Background: Many scientific reports have shown a decrease in total cerebrovascular disease (CeVD) mortality over the past few decades, but too little attention has been paid to premature mortality. CeVD accounted for 22.5% and 17.8% of premature cardiovascular disease deaths in Poland, in 2000 and 2016, respectively. Objective: The aim of the study was to analyse premature CeVD mortality in the Polish population in the recent years, the dynamics of its changes and the potential factors that may have contributed to the decline in mortality. The main goal of the study was to overview the levels and trends in premature CeVD mortality with an emphasis on haemorrhagic, ischaemic and unspecified (not specified as haemorrhagic or ischaemic) stroke. Material and methods: The analysis was based on a database of the Central Statistical Office of Poland and included data from 2000-2016 on premature cerebrovascular deaths occurring between 25 and 64 years of age (N=104,786). CeVD and haemorrhagic, ischaemic or unspecified stroke were coded with ICD-10 codes I60-I69, I61-I62, I63 and I64, respectively. The analysis included assessment of CeVD deaths distribution and evaluation of age-specific mortality rates in 10-year age groups and age-standardised mortality rates (SMR) in the age group 25-64 years, separately for men and women. Trends in SMRs have been studied in the period 2000-2016. Results: The number of CeVD deaths decreased by 32.8% in men and 48.8% in women. There was a two-fold decline in CeVD mortality: from 59 to 29 male and from 30 to 12 female per 100,000. In addition, a 2-year increase in the median age of CeVD death was observed (Men: 56.4 to 58.4 years, Women: 56.4 to 58.7 years, p<0.001). A statistically significant decline in mortality (per 100,000) was also noticed for haemorrhagic stroke (Men: 18.7 to 10.4; Women: 9.6 to 3.8), ischaemic stroke (Men: 11.8 to 8.4; Women: 4.7 to 3.0) and unspecified stroke (Men: 19.7 to 3.5; Women: 9.1 to 1.3). Conclusions: A substantial decline in premature CeVD mortality was observed in the period 2000-2016. Additionally, the number of deaths that could not be classified as haemorrhagic or ischaemic stroke death decreased significantly. The increasingly widespread use of new post-stroke therapies and their availability make it possible to expect a further decrease in CeVD mortality. However, the necessary actions should be taken to compensate for the disparities in CeVD mortality between men and women.

Development of a novel in silico model of zeta potential for metal oxide nanoparticles: a nano-QSPR approach.

Once released into the aquatic environment, nanoparticles (NPs) are expected to interact (e.g. dissolve, agglomerate/aggregate, settle), with important consequences for NP fate and toxicity. A clear understanding of how internal and environmental factors influence the NP toxicity and fate in the environment is still in its infancy. In this study, a quantitative structure-property relationship (QSPR) approach was employed to systematically explore factors that affect surface charge (zeta potential) under environmentally realistic conditions. The nano-QSPR model developed with multiple linear regression (MLR) was characterized by high robustness [Formula: see text] and external predictivity [Formula: see text] The results clearly showed that zeta potential values varied markedly as functions of the ionic radius of the metal atom in the metal oxides, confirming that agglomeration and the extent of release of free MexOy largely depend on their intrinsic properties. A developed nano-QSPR model was successfully applied to predict zeta potential in an ionized solution of NPs for which experimentally determined values of response have been unavailable. Hence, the application of our model is possible when the values of zeta potential in the ionized solution for metal oxide nanoparticles are undetermined, without the necessity of performing more time consuming and expensive experiments. We believe that our studies will be helpful in predicting the conditions under which MexOy is likely to become problematic for the environment and human health.

Toward the Integration of Machine Learning and Molecular Modeling for Designing Drug Delivery Nanocarriers.

The pioneering work on liposomes in the 1960s and subsequent research in controlled drug release systems significantly advances the development of nanocarriers (NCs) for drug delivery. This field is evolved to include a diverse array of nanocarriers such as liposomes, polymeric nanoparticles, dendrimers, and more, each tailored to specific therapeutic applications. Despite significant achievements, the clinical translation of nanocarriers is limited, primarily due to the low efficiency of drug delivery and an incomplete understanding of nanocarrier interactions with biological systems. Addressing these challenges requires interdisciplinary collaboration and a deep understanding of the nano-bio interface. To enhance nanocarrier design, scientists employ both physics-based and data-driven models. Physics-based models provide detailed insights into chemical reactions and interactions at atomic and molecular scales, while data-driven models leverage machine learning to analyze large datasets and uncover hidden mechanisms. The integration of these models presents challenges such as harmonizing different modeling approaches and ensuring model validation and generalization across biological systems. However, this integration is crucial for developing effective and targeted nanocarrier systems. By integrating these approaches with enhanced data infrastructure, explainable AI, computational advances, and machine learning potentials, researchers can develop innovative nanomedicine solutions, ultimately improving therapeutic outcomes.

Quantitative Structure-Permittivity Relationship Study of a Series of Polymers.

Dielectric constant is an important property which is widely utilized in many scientific fields and characterizes the degree of polarization of substances under the external electric field. In this work, a structure-property relationship of the dielectric constants (ε) for a diverse set of polymers was investigated. A transparent mechanistic model was developed with the application of a machine learning approach that combines genetic algorithm and multiple linear regression analysis, to obtain a mechanistically explainable and transparent model. Based on the evaluation conducted using various validation criteria, four- and eight-variable models were proposed. The best model showed a high predictive performance for training and test sets, with R2 values of 0.905 and 0.812, respectively. Obtained statistical performance results and selected descriptors in the best models were analyzed and discussed. With the validation procedures applied, the models were proven to have a good predictive ability and robustness for further applications in polymer permittivity prediction.

Longevity of Polish top-class athletes compared to other social elites and well-known people in the years 2001-2021.

OBJECTIVES: The aim of this work is to initiate or revive a scientific discussion on the impact of professional life on the parameters of human lifespan. MATERIAL AND METHODS: Presented analysis is based on 8578 Polish elite or well-known person who died in 2001-2021. RESULTS: The results of the conducted analysis indicate that in the case of men the highest values of the median age at death were characteristic of freelancers (median [Me] ± quartile deviation [QD] 85.5±8.5 years), followed by scientists and academic teachers of the biological and medical specialty (Me±QD 84.0±7.5 years) and officers of power structures (Me±QD 83.5±8.5 years). Subsequently, the highest value of the median age at death was recorded for social activists (Me±QD 83.0±9.5 years), clergy (Me±QD 82.0±7.5 years) and scientists and academic teachers of specialties other than biological and medical (Me±QD 82.0±8.0 years). Significantly, at the very end of this list are athletes (Me±QD 77.0±9.0 years). Nevertheless, the results of the analysis confirm that professional athletes are characterized by higher median age at death compared to the general population. Analysis made only within athletes group demonstrated that the parameters of lifespan of athletes of endurance disciplines (Me±QD 78.0±8.0 years) are the most favorable compared to athletes of other disciplines, in particular in compare to team sports athletes (Me±QD 75.0±10.0 years) or combat sports athletes (Me±QD 75.0±7.1 years). CONCLUSIONS: What is new and innovative in this paper is comparing the lifespan characteristics of athletes in comparison to widely represented group of other professions with higher socio-economic status. Unexpectedly, the lifespan of athletes occurred to be lower than for fast all other analyzed occupational groups, except mainly of entertainment musicians. Finally, the results presented in this paper emphasize the need to analyze the lifespan characteristics of athletes in a broader scope than only in relation to the general population. Int J Occup Med Environ Health 2024;37(3):335-50.

Core, Coating, or Corona? The Importance of Considering Protein Coronas in nano-QSPR Modeling of Zeta Potential.

To control stability in a biological medium, several factors affecting the zeta potential (ζ) of nanoparticles (NPs) must be considered, including complex interactions between the nanostructure and the composition of the protein corona (PC). Effective in silico methods (based on machine learning and quantitative structure-property relationship (QSPR) models) could help predict and characterize the relationship between the physicochemical properties of NP and the formation of PC and biological outcomes in the medium at an early stage of the experiment. However, the models currently developed are limited to simple descriptors that do not represent the complex interactions between the core, the coating, and their PC fingerprints. To be useful, the models developed should be described as a function of both the structural properties determined by the core and coating of the NPs and the biological medium determined by the formation of the protein corona. We have developed a set of complex descriptors that describe the quantitative relationship between the value of the zeta potential (ζ), core, the coating of NPs, and their PC fingerprints (the so-called nano-QSPR model). The nano-QSPR model was developed based on a genetic algorithm using a partial least-squares regression method (GA-PLS), which is characterized by high external predictive power (Q2EXT = 0.89). The GA-PLS model was developed using descriptors that describe (i) the core structure (determined by 7 different types of polymer-based NMs in the range of 20 different sizes), (ii) the coating structure with 7 different functional groups, and (iii) 80 different types of protein compositions adsorbed on the surface of the NPs. The presented study answers the question of how complex interactions between the corona and NP determine the zeta potential (ζ) of NP in a given medium. Moreover, our current study is a proof-of-concept that the zeta potential of NPs modeled on the original structure depends not only on the NPs themselves but also on the structure and properties determined by the NP core and coating, as well as the biological medium determined by the formation of the protein corona. On the basis of these results, our studies will be useful in determining the stability and mechanism of cell uptake, toxicity, and ability to predict the zeta potential of compounds not yet tested.

A bibliometric analysis of the recent achievements in pulmonary safety of nanoparticles.

Assessing research activity is an important step for planning future initiatives oriented toward filling the remaining gaps in a field. Therefore, the objective of the current study was to review recently published research on pulmonary toxicity caused by nanomaterials. However, here, instead of reviewing possible toxic effects and discussing their mode of action, the goal was to establish trends considering for example examined so far nanomaterials or used testing strategies. A total of 2316 related articles retrieved from the three most cited databases (PubMed Scopus, Web of Science), selected based on the title and abstract requirements, were used as the source of the review. Based on the bibliometric analysis, the nano-meter metal oxides, and carbon-based nanotubes were identified as the most frequently studied nanomaterials, while quantum dots, which might induce possible harmful effects, were not considered so far. The majority of testing of pulmonary safety is based on in vitro studies with observed growth of the contribution of novel testing strategies, such as 3D lung model, air-liquid interface system, or omic analysis.

Disparities in knowledge of cardiovascular risk factors and prevention methods related to cardiovascular status and functional health literacy in Poland, 2020-2021.

BACKGROUND: Numerous studies have reported a significant role of health literacy (HL) in the prevention or treatment of various diseases. However, in Poland, there was no scientific research involving simultaneously the status of cardiovascular disease (CVD) and HL in assessment of health knowledge; therefore, it became the objective of our study. AIMS: We aimed to evaluate the level of CVD knowledge depending on CVD status and functional HL in the Polish population. METHODS: The study population consisted of 2827 participants from the WOBASZ II Survey aged 20-89 years: 2266 were CVD-free (non-CVD), 361 were hospitalized for CVD (CVDH[+]), and 200 were diagnosed with CVD but not hospitalized (CVDH[-]). The Newest Vital Sign test (NVS) was applied to determine functional HL. Self-reported knowledge of CVD risk factors (RFs) and prevention methods (PMs) in participants with different CVD status depending on HL was estimated. Multivariable ordinal and binary logistic regression analyses were performed to find predictors of RFs and PMs knowledge. RESULTS: The knowledge of CVD RFs and/or PMs was strictly related to HL and CVD status. Inadequate HL decreased the satisfactory (≥5 RFs/PMs) knowledge of RFs (odds ratio [OR], 0.50; 95% confidence interval [CI], 0.40-0.62) and PMs (OR, 0.56; 95% CI, 0.45-0.71). CVDH(-) participants were more likely to have satisfactory PMs knowledge (OR, 1.49; 95% CI, 1.02-2.16), while CVDH(+) participants satisfactory RFs knowledge (OR, 1.85; 95% CI, 1.35-2.53). CONCLUSIONS: HL and CVD status are the key determinants of CDV RFs/PMs knowledge. Functional HL significantly affects health knowledge; therefore, HL screening should be recommended in primary care to increase the effectiveness of primary CVD prevention.

Identifying associations between the social network index, its components, and the prevalence of cardiovascular diseases in Polish adults. Results of the cross-sectional WOBASZ II study.

BACKGROUND: Psychosocial risk factors are important determinants of cardiovascular diseases (CVDs): people involved in positive relationships live longer than those with low social support (SS). AIMS: Our study aimed to evaluate the association between SS, components of the social network, and CVDs. METHODS: A cross-sectional population-based survey WOBASZ II conducted in the years 2013-2014 included a sample of 6043 individuals, aged 20 and over, who completed the Berkman-Syme questionnaire to assess SS using the social network index (SNI). RESULTS: Higher percentage of low SS was observed in women (52.15%) compared to men (45.4%) (P <0.001). People with a low SNI had a worse CVD risk factor profile. None of the analyzed social contacts (with children, relatives, or friends), regardless of how satisfactory they were, was associated with CVDs in men. In women, satisfying contact with children or relatives appeared to be associated with better cardiovascular health. Furthermore, active participation in organized social activity increased the chance of arrhythmia in both sexes: 1.50 (1.04-2.15); P = 0.029 in men; 1.47 (1.11-1.95); P = 0.007 in women. Although a low SNI was associated with analyzed CVDs in the univariate analysis, it was not confirmed in the fully adjusted model. CONCLUSIONS: More women had low SS compared to men. People with low SS had a worse CVD risk factor profile. There was a significant independent relationship between different components of the SNI, such as social contacts and CVDs in women and active participation in organized social activity and arrhythmia in both sexes.

NanoMixHamster: a web-based tool for predicting cytotoxicity of TiO2-based multicomponent nanomaterials toward Chinese hamster ovary (CHO-K1) cells.

Nano-QSAR models can be effectively used for prediction of the biological activity of nanomaterials that have not been experimentally tested before. However, their use is associated with the need to have appropriate knowledge and skills in chemoinformatics. Thus, they are mainly aimed at specialists in the field. This significantly limits the potential group of recipients of the developed solutions. In this perspective, the purpose of the presented research was to develop an easily accessible and user-friendly web-based application that could enable the prediction of TiO2-based multicomponent nanomaterials cytotoxicity toward Chinese Hamster Ovary (CHO-K1) cells. The graphical user interface is clear and intuitive and the only information required from the user is the type and concentration of the metals which will be modifying TiO2-based nanomaterial. Thanks to this, the application will be easy to use not only by cheminformatics but also by specialists in the field of nanotechnology or toxicology, who will be able to quickly predict cytotoxicity of desired nanoclusters. We have performed case studies to demonstrate the features and utilities of developed application. The NanoMixHamster application is freely available at https://nanomixhamster.cloud.nanosolveit.eu/.

Integrated Approach to Interaction Studies of Pyrene Derivatives with Bovine Serum Albumin: Insights from Theory and Experiment.

This work aimed to investigate the interaction of bovine serum albumin with newly synthesized potent new pyrene derivatives (PS1 and PS2), which might prove useful to have a better antibacterial character as found for similar compounds in the previous report [Low et al. Bioconjugate Chemistry 2014, 12, 2269-2284]. However, to date, binding studies with plasma protein are still unknown. Steady-state fluorescence spectroscopy and lifetime fluorescence studies show that the static interaction binding mode and binding constants of PS1 and PS2 are 7.39 and 7.81 [Kb × 105 (M-1)], respectively. The experimental results suggest that hydrophobic forces play a crucial role in interacting pyrene derivatives with BSA protein. To verify this, molecular docking and molecular dynamics simulations were performed to predict the nature of the interaction and the dynamic behavior of the two compounds in the BSA complex, PS1 and PS2, under physiological conditions of pH = 7.1. In addition, the free energies of binding for the BSA-PS1 and BSA-PS2 complexes were estimated at 300 K based on the molecular mechanics of the Poisson-Boltzmann surface (MMPBSA) with the Gromacs package. PS2 was found to have a higher binding affinity than PS1. To determine the behavior of the orbital transitions in the ground state geometry, we found that both compounds have similar orbital transitions from HOMO-LUMO via π → π* and HOMO-1-LUMO+1 via n → π*, which was included in the FMO analysis. A cytotoxicity study was performed to determine the toxicity of the compounds. Based on the MD study, the stability of the compounds with BSA and the dynamic binding modes were further revealed, as well as the nature of the binding force components involved and the important residues involved in the binding process. From the binding energy analysis, it can be assumed that PS2 may be more active than PS1.

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.

Dietary Plant Sterols and Phytosterol-Enriched Margarines and Their Relationship with Cardiovascular Disease among Polish Men and Women: The WOBASZ II Cross-Sectional Study.

Dietary cholesterol has been suggested to increase the risk of cardiovascular disease (CVD). Phytosterols, present in food or phytosterol-enriched products, can reduce cholesterol available for absorption. The present study aimed to investigate the association between habitual intake of total and individual plant sterols (ß-sitosterol, campesterol, and stigmasterol) or a diet combined with phytosterol-enriched products and CVD in a cross-section of Polish adults, participants of the Multicenter National Health Survey II (WOBASZ II). Among men (n = 2554), median intakes of plant sterols in terciles ranged between 183−456 mg/d and among women (n = 3136), 146−350 mg/d in terciles. The intake of phytosterols, when consumed with food containing phytosterols, including margarine, ranged between 184−459 mg/d for men and 147−352 mg/d for women. Among both men and women, beta-sitosterol intake predominated. Plant sterol intake was lower among both men and women with CVD (p = 0.016) compared to those without CVD. Diet quality, as measured by the Healthy Diet Index (HDI), was significantly higher in the third tercile of plant sterol intake for both men and women and the entire study group (p < 0.0001). This study suggests that habitual dietary intake of plant sterols may be associated with a lower chance of developing CVD, particularly in men.

Dietary Total Antioxidant Capacity-A New Indicator of Healthy Diet Quality in Cardiovascular Diseases: A Polish Cross-Sectional Study.

This study aimed to assess the relationship between the dietary total antioxidant capacity (DTAC) and the occurrence of cardiovascular diseases (CVDs), as well as healthy diet quality, in a representative sample (n = 5690) of the whole Polish adult population (WOBASZ II study). Daily food consumption was estimated by the single 24 h dietary recall method. Antioxidant vitamins (C, E, and ß-carotene) and minerals (Zn, Fe, Mn, and Cu) from the diet and supplements were calculated using 5D Diet software, and dietary total polyphenol intake (DTPI) was determined using the Phenol-Explorer database and our database. Total diet quality was measured by the Healthy Diet Indicator (HDI) based on World Health Organization (WHO) recommendations for the prevention of CVD. DTAC was calculated using the data on food consumption and the antioxidant potential of foods measured by the FRAP (ferric ion reducing antioxidant potential) method. It was shown that higher DTAC was associated with a higher intake of polyphenols, antioxidant vitamins, and minerals. Moreover, a higher quartile of DTAC was associated with a reduced odds ratio for cardiovascular diseases in a Polish population, as well as with a higher HDI. Therefore, dietary recommendations for the prevention and therapy of CVDs should take into account a high DTAC. DTAC, measured by the FRAP method, can be considered an indicator of healthy diet quality.

Implantable cardiac defibrillator events in patients with arrhythmogenic right ventricular cardiomyopathy.

OBJECTIVE: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is associated with a risk of sudden cardiac death. Optimal risk stratification is still under debate. The main purpose of this long-term, single-centre observation was to analyse predictors of appropriate and inappropriate implantable cardioverter-defibrillator (ICD) interventions in the population of patients with ARVC with a high risk of life-threatening arrhythmias. METHODS: The study comprised 65 adult patients (median age 40 years, 48 men) with a definite diagnosis of ARVC who received ICD over a time span of 20 years in primary (40%) or secondary (60%) prevention of sudden cardiac death. The study endpoints were first appropriate and inappropriate ICD interventions (shock or antitachycardia pacing) after device implantation. RESULTS: During a median follow-up of 7.75 years after ICD implantation, nine patients died and six individuals underwent heart transplantation. Appropriate ICD interventions occurred in 43 patients (66.2%) and inappropriate ICD interventions in 18 patients (27.7%). Multivariable analysis using cause-specific hazard model identified three predictors of appropriate ICD interventions: right ventricle dysfunction (cause-specific HR 2.85, 95% CI 1.56 to 5.21, p<0.001), age <40 years at ICD implantation (cause-specific HR 2.37, 95% CI 1.13 to 4.94, p=0.022) and a history of sustained ventricular tachycardia (cause-specific HR 2.55, 95% CI 1.16 to 5.63, p=0.020). Predictors of inappropriate ICD therapy were not found. Complications related to ICD implantation occurred in 12 patients. CONCLUSIONS: Right ventricle dysfunction, age <40 years and a history of sustained ventricular tachycardia were predictors of appropriate ICD interventions in patients with ARVC. The results may be used to improve risk stratification before ICD implantation.

Representing and describing nanomaterials in predictive nanoinformatics.

Engineered nanomaterials (ENMs) enable new and enhanced products and devices in which matter can be controlled at a near-atomic scale (in the range of 1 to 100 nm). However, the unique nanoscale properties that make ENMs attractive may result in as yet poorly known risks to human health and the environment. Thus, new ENMs should be designed in line with the idea of safe-and-sustainable-by-design (SSbD). The biological activity of ENMs is closely related to their physicochemical characteristics, changes in these characteristics may therefore cause changes in the ENMs activity. In this sense, a set of physicochemical characteristics (for example, chemical composition, crystal structure, size, shape, surface structure) creates a unique 'representation' of a given ENM. The usability of these characteristics or nanomaterial descriptors (nanodescriptors) in nanoinformatics methods such as quantitative structure-activity/property relationship (QSAR/QSPR) models, provides exciting opportunities to optimize ENMs at the design stage by improving their functionality and minimizing unforeseen health/environmental hazards. A computational screening of possible versions of novel ENMs would return optimal nanostructures and manage ('design out') hazardous features at the earliest possible manufacturing step. Safe adoption of ENMs on a vast scale will depend on the successful integration of the entire bulk of nanodescriptors extracted experimentally with data from theoretical and computational models. This Review discusses directions for developing appropriate nanomaterial representations and related nanodescriptors to enhance the reliability of computational modelling utilized in designing safer and more sustainable ENMs.

Character of abuse towards the elderly through history.

Abuse, neglect and other forms of discrimination towards the elderly have always been present in history. There has been a model of "old person" in every culture, according to which older people are treated. The more idealized the model is, the less tolerant and more demanding society is towards the elderly. Presented results lead to the conclusion that significant number of older people feel mistreated because of their age in many aspects of daily life. Various forms of abuse may be difficult to thoroughly analyze, for they are so persistent in our collective consciousness. Unfortunately, material civilization progress is not sufficient to eliminate abuse. Thus, monitoring and relevant assistance to people exposed to abuse and violence, particularly those with compromised defense abilities, is crucial.

Zeta potentials (ζ) of metal oxide nanoparticles: A meta-analysis of experimental data and a predictive neural networks modeling.

Zeta potential is usually measured to estimate the surface charge and the stability of nanomaterials, as changes in these characteristics directly influence the biological activity of a given nanoparticle. Nowadays, theoretical methods are commonly used for a pre-screening safety assessments of nanomaterials. At the same time, the consistency of data on zeta potential measurements in the context of environmental impact is an important challenge. The inconsistency of data measurements leads to inaccuracies in predictive modeling. In this article, we report a new curated dataset of zeta potentials measured for 208 silica- and metal oxide nanoparticles in different media. We discuss the data curation framework for zeta potentials designed to assess the quality and usefulness of the literature data for further computational modeling. We also provide an analysis of specific trends for the datapoints harvested from different literature sources. In addition to that, we present for the first time a structure-property relationship model for nanoparticles (nano-SPR) that predicts values of zeta potential values measured in different environmental conditions (i.e., biological media and pH).