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Materials at the nanoscale exhibit specific physicochemical interactions with their environment. Therefore, evaluating their toxic potential is a primary requirement for regulatory purposes and for the safer development of nanomedicines. In this review, to aid the understanding of nano–bio interactions from environmental and health and safety perspectives, the potential, reality, challenges, and future advances that artificial intelligence (AI) and machine learning (ML) present are described. Herein, AI and ML algorithms that assist in the reporting of the minimum information required for biomaterial characterization and aid in the development and establishment of standard operating procedures are focused. ML tools and ab initio simulations adopted to improve the reproducibility of data for robust quantitative comparisons and to facilitate in silico modeling and meta‐analyses leading to a substantial contribution to safe‐by‐design development in nanotoxicology/nanomedicine are mainly focused. In addition, future opportunities and challenges in the application of ML in nanoinformatics, which is particularly well‐suited for the clinical translation of nanotherapeutics, are highlighted. This comprehensive review is believed that it will promote an unprecedented involvement of AI research in improvements in the field of nanotoxicology and nanomedicine. Machine learning (ML) tools in computational nanotoxicology are adopted to improve the reproducibility of the data for robust quantitative comparisons and to facilitate in silico modeling and meta‐analyses leading to a substantial contribution in nanotoxicology/nanomedicine. Herein, the potential, reality, challenges, and future advances that artificial intelligence (AI) and ML present in advanced material design and toxicity predictions are described.

Purpose The study evaluates the effects on sero-immunity, health status and quality of life of children and adolescents after the upsurge of the Omicron variant in Germany. Methods This multicenter cross-sectional study (IMMUNEBRIDGE Kids) was conducted within the German Network University Medicine (NUM) from July to October 2022. SARS-CoV-2- antibodies were measured and data on SARS-CoV-2 infections, vaccinations, health and socioeconomic factors as well as caregiver-reported evaluation on their children’s health and psychological status were assessed. Results 497 children aged 2–17 years were included. Three groups were analyzed: 183 pre-schoolchildren aged 2–4 years, 176 schoolchildren aged 5–11 years and 138 adolescents aged 12–18 years. Positive antibodies against the S- or N-antigen of SARS-CoV-2 were detected in 86.5% of all participants (70.0% [128/183] of pre-schoolchildren, 94.3% of schoolchildren [166/176] and 98.6% of adolescents [136/138]). Among all children, 40.4% (201/497) were vaccinated against COVID-19 (pre-schoolchildren 4.4% [8/183], schoolchildren 44.3% [78/176] and adolescents 83.3% [115/138]). SARS-CoV-2 seroprevalence was lowest in pre-school. Health status and quality of life reported by the parents were very positive at the time of the survey (Summer 2022). Conclusion Age-related differences on SARS-CoV-2 sero-immunity could mainly be explained by differences in vaccination rates based on the official German vaccination recommendations as well as differences in SARS-CoV-2 infection rates in the different age groups. Health status and quality of life of almost all children were very good independent of SARS-CoV-2 infection and/or vaccination. Trial registration German Registry for Clinical Trials Identifier Würzburg: DRKS00025546 (registration: 11.09.2021), Bochum: DRKS00022434 (registration:07.08.2020), Dresden: DRKS 00022455 (registration: 23.07.2020).

A broad range of inorganic nanoparticles (NPs) and their dissolved ions possess a possible toxicological risk for human health and the environment. Reliable and robust measurements of dissolution effects may be influenced by the sample matrix, which challenges the analytical method of choice. In this study, CuO NPs were investigated in several dissolution experiments. Two analytical techniques (dynamic light scattering (DLS) and inductively-coupled plasma mass spectrometry (ICP-MS)) were used to characterize NPs (size distribution curves) time-dependently in different complex matrices (e.g., artificial lung lining fluids and cell culture media). The advantages and challenges of each analytical approach are evaluated and discussed. Additionally, a direct-injection single particle (DI sp)ICP-MS technique for assessing the size distribution curve of the dissolved particles was developed and evaluated. The DI technique provides a sensitive response even at low concentrations without any dilution of the complex sample matrix. These experiments were further enhanced with an automated data evaluation procedure to objectively distinguish between ionic and NP events. With this approach, a fast and reproducible determination of inorganic NPs and ionic backgrounds can be achieved. This study can serve as guidance when choosing the optimal analytical method for NP characterization and for the determination of the origin of an adverse effect in NP toxicity.

The knowledge about a potential in vivo uptake and subsequent toxicological effects of aluminum (Al), especially in the nanoparticulate form, is still limited. This paper focuses on a three day oral gavage study with three different Al species in Sprague Dawley rats. The Al amount was investigated in major organs in order to determine the oral bioavailability and distribution. Al-containing nanoparticles (NMs composed of Al 0 and aluminum oxide (Al 2 O 3 )) were administered at three different concentrations and soluble aluminum chloride (AlCl 3 ·6H 2 O) was used as a reference control at one concentration. A microwave assisted acid digestion approach followed by inductively coupled plasma mass spectrometry (ICP-MS) analysis was developed to analyse the Al burden of individual organs. Special attention was paid on how the sample matrix affected the calibration procedure. After 3 days exposure, AlCl 3 ·6H 2 O treated animals showed high Al levels in liver and intestine, while upon treatment with Al 0 NMs significant amounts of Al were detected only in the latter. In contrast, following Al 2 O 3 NMs treatment, Al was detected in all investigated organs with particular high concentrations in the spleen. A rapid absorption and systemic distribution of all three Al forms tested were found after 3-day oral exposure. The identified differences between Al 0 and Al 2 O 3 NMs point out that both, particle shape and surface composition could be key factors for Al biodistribution and accumulation.

In dieser Arbeit wurden Strategien für den Nachweis von Aluminium-Nanopartikeln (AlNP) in biologisch relevanten Matrices mittels induktiv gekoppelte Plasma Massenspektrometrie (ICP-MS) entwickelt. Da nicht für jeden Analyten geeignete Referenznanopartikel verfügbar sind, wurde eine Quantifizierungsstrategie basierend auf der Analytempfindlichkeit eines dualen Eintragssystems (Mikrotropfengenerator und konventioneller Zerstäuber mit Sprühkammer) entwickelt, um die Transporteffizienz materialspezifisch und unabhängig von der Verwendung von ReferenzNanopartikeln bestimmen zu können. Aufgrund der hohen Flexibilität des entwickelten Injektionssystems konnten Gold-, Silber- und Cerdioxid-Nanopartikel mit bis zu drei zusätzlichen Quantifizierungsstrategien in einem Analysenlauf charakterisiert werden. Später wurde dieser Ansatz erfolgreich auf die Bestimmung des Aluminium- und Eisengehaltes in Einzelzellen angewendet. Neben der Bestimmung der materialspezifischen Transporteffizienz wurde auch die Größenabhängigkeit der Transporteffizienz untersucht, denn die verwendeten AlNP hatten eine Größe von 18 nm und waren damit deutlich kleiner als die in der DIN/ISO TS 19590:2019 empfohlenen Referenznanopartikel. Zu diesem Zweck wurde das theoretische Konzept der nanopartikelspezifischen Poisson-Statistik zur Vorhersage der Anzahl der Partikelereignisse an eine Trenntechnik und einen im dualen Eintragssystem verwendeten Mikrotropfengenerator angepasst, welches zusätzlich die Qualitätskriterien der DIN/ISO TS 19590:2019 für Einzelpartikel-ICP-MS-Messungen beinhaltet. Durch Hinzufügen der verwendeten instrumentellen und experimentellen Einstellungen zur Poisson-Statistik kann so die Anfangskonzentration der zu messenden Partikellösung für monodisperse Partikel berechnet und die erwartete Unsicherheit der Partikelanzahlkonzentration vorhergesagt werden.m zweiten Teil dieser Arbeit wurde ein experimenteller Versuchsplan (DoE, engl. Design of Experiment) verwendet, um die optimale Ionisierung von Aluminium in Matrices mit zunehmender Komplexität im Plasma sowie die Abtrennung von polyatomaren Interferenzen in der Kollisionszelle zu untersuchen. Dazu wurde die standardisierte Auswertung eines DoE an das theoretische Konzept der Ionisation des Analyten im Plasma und der Reduktion polyatomarer Interferenzen in der Kollisionszelle eines ICP-MSGerätes angepasst. Mit Hilfe einer Simulation der kinetischen Energiediskriminierung konnten die experimentell ermittelten Werte für die kinetische Energie von Aluminium im Massenspektrometer bestätigt werden. Die DoE-Ergebnisse lieferten dabei erstmals ein vollständiges Bild über die Wechselwirkung von Kollisionszell-Bias (CCT, engl. Collision Cell Technology) und Pole-Bias, die erforderlich ist, um eine optimale Energiebarriere für die Trennung der polyatomaren Störungen vom Analyten nach dem Kontakt mit dem Kollisionsgas zu bilden. Auf der Grundlage der DoE-Ergebnisse wurde ein spezifischer Matrix-Tune etabliert, um eine maximale Empfindlichkeit für Aluminium und später auch für Eisen in einer humanen Zellmatrix zu erreichen, indem ausgewählte instrumentelle Parameter automatisch optimiert wurden. Abschließend wurde der Matrixtune für ionisches Aluminium in menschlichen Zellen (HUH-7) validiert, einschließlich der Charakterisierung von AlNP, und für die Untersuchung der AlNP-Aufnahme durch HUH-7-Zellen angewendet. Eisen wurde verwendet, um die einzelnen Zellen zu identifizieren, die in das ICP-MS eingetragen wurden.

Nano-carrier systems such as liposomes have promising biomedical applications. Nevertheless, characterization of these complex samples is a challenging analytical task. In this study a coupled hydrodynamic chromatography-single particle-inductively coupled plasma mass spectrometry (HDC-spICP-MS) approach was validated based on the technical specification (TS) 19590:2017 of the international organization for standardization (ISO). The TS has been adapted to the hyphenated setup. The quality criteria (QC), e.g., linearity of the calibration, transport efficiency, were investigated. Furthermore, a cross calibration of the particle size was performed with values from dynamic light scattering (DLS) and transmission electron microscopy (TEM). Due to an additional Y-piece, an online-calibration routine was implemented. This approach allows the calibration of the ICP-MS during the dead time of the chromatography run, to reduce the required time and enhance the robustness of the results. The optimized method was tested with different gold nanoparticle (Au-NP) mixtures to investigate the characterization properties of HDC separations for samples with increasing complexity. Additionally, the technique was successfully applied to simultaneously determine both the hydrodynamic radius and the Au-NP content in liposomes. With the established hyphenated setup, it was possible to distinguish between different subpopulations with various NP loads and different hydrodynamic diameters inside the liposome carriers.

Computational Nanotoxicology Machine learning tools are making great strides in advancing computational nanotoxicology via in‐silico modeling and ab‐initio simulations to understand the nano‐bio interactions from environmental and health safety perspectives. In article number 2000084, Ajay Vikram Singh and co‐workers describe the potential, reality, challenges, and future advances that artifi cial intelligence (AI) and machine learning (ML) present in advanced material design and toxicity predictions.

Background Next generation sequencing is a key technique in small RNA biology research that has led to the discovery of functionally different classes of small non-coding RNAs in the past years. However, reliable annotation of the extensive amounts of small non-coding RNA data produced by high-throughput sequencing is time-consuming and requires robust bioinformatics expertise. Moreover, existing tools have a number of shortcomings including a lack of sensitivity under certain conditions, limited number of supported species or detectable sub-classes of small RNAs. Results Here we introduce unitas, an out-of-the-box ready software for complete annotation of small RNA sequence datasets, supporting the wide range of species for which non-coding RNA reference sequences are available in the Ensembl databases (currently more than 800). unitas combines high quality annotation and numerous analysis features in a user-friendly manner. A complete annotation can be started with one simple shell command, making unitas particularly useful for researchers not having access to a bioinformatics facility. Noteworthy, the algorithms implemented in unitas are on par or even outperform comparable existing tools for small RNA annotation that map to publicly available ncRNA databases. Conclusions unitas brings together annotation and analysis features that hitherto required the installation of numerous different bioinformatics tools which can pose a challenge for the non-expert user. With this, unitas overcomes the problem of read normalization. Moreover, the high quality of sequence annotation and analysis, paired with the ease of use, make unitas a valuable tool for researchers in all fields connected to small RNA biology.

Cardiopulmonary exercise testing is a well-known, valuable tool in the clinical evaluation of patients with different causes of exercise limitation and unexplained dyspnea. A wealth of data is generated by each individual test. This may be challenging regarding a comprehensive and reliable interpretation of an exercise study in a timely manner. An optimized graphical display of exercise data may substantially help to improve the efficacy and reliability of the interpretation process. However, there are limited and heterogeneous recommendations on standardized graphical display in current exercise testing guidelines. To date, a widely used three-by-three array of specifically arranged graphical panels known as the “nine-panel plot” is probably the most common method of plotting exercise gas exchange data in a standardized way. Furthermore, optimized scaling of the plots, the use of colors and style elements, as well as suitable averaging methods have to be considered to achieve a high level of quality and reproducibility of the results. Specific plots of key parameters may allow a fast and reliable visual determination of important diagnostic and prognostic markers in cardiac and pulmonary diseases.

Mindfulness-based stress reduction (MBSR) can improve symptoms of chronic inflammation; in asthma, improving asthma control and reducing airway inflammation. Understanding the neural mechanisms underlying these salubrious outcomes could help identify neuroimmune phenotypes and personalize interventions. Adults with asthma were randomized to 8 weeks of MBSR ( n  = 38) or a wait-list group ( n  = 34). Clinically relevant asthma-related and psychological outcomes were measured, and task-based fMRI data were acquired during exposure to emotional cues at baseline, post-intervention, and 6mo follow-up. Whole-brain group x time interactions and voxelwise regressions were used to evaluate changes in neural responses to emotion cues from baseline and their relationship to psychological and biological outcomes. Post-intervention, MBSR participants showed decreased lateral prefrontal/orbitofrontal cortex responses to aversive cues relative to controls, which was associated with increased mindfulness. Across participants, decreased salience network reactivity at post-intervention was associated with reduced psychological distress and airway inflammation. At 6 months, some relationships persisted while others did not. Results suggest that mindfulness training reduced effortful regulation of cognitive and affective responses to emotional cues, instead promoting more efficient processing strategies and reduced affective reactivity. Our findings clarify neural mechanisms underlying MBSR’s clinical benefits for asthma, underscoring mind-brain-immune relationships as a critical target for asthma treatment.