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AbstractIntroduction Hyperthermia (HT) induces various cellular biological processes, such as repair impairment and direct HT cell killing. In this context, in-silico biophysical models that translate deviations in the treatment conditions into clinical outcome variations may be used to study the extent of such processes and their influence on combined hyperthermia plus radiotherapy (HT + RT) treatments under varying conditions.Methods An extended linear-quadratic model calibrated for SiHa and HeLa cell lines (cervical cancer) was used to theoretically study the impact of varying HT treatment conditions on radiosensitization and direct HT cell killing effect. Simulated patients were generated to compute the Tumor Control Probability (TCP) under different HT conditions (number of HT sessions, temperature and time interval), which were randomly selected within margins based on reported patient data.Results Under the studied conditions, model-based simulations suggested a treatment improvement with a total CEM43 thermal dose of approximately 10 min. Additionally, for a given thermal dose, TCP increased with the number of HT sessions. Furthermore, in the simulations, we showed that the TCP dependence on the temperature/time interval is more correlated with the mean value than with the minimum/maximum value and that comparing the treatment outcome with the mean temperature can be an excellent strategy for studying the time interval effect.Conclusion The use of thermoradiobiological models allows us to theoretically study the impact of varying thermal conditions on HT + RT treatment outcomes. This approach can be used to optimize HT treatments, design clinical trials, and interpret patient data.

Introduction Cell repair dynamics are crucial in optimizing anti-cancer therapies. Various assays (eg, comet assay and γ-H2AX) assess post-radiation repair kinetics, but interpreting such data is challenging and model-based data analyses are required. However, ambiguities in parameter calibration remain an unsolved challenge. To address this, we propose combining survival dose-rate effects with computer simulations to gain knowledge about repair kinetics. Methods After a literature review, theoretical discriminators based on common fractionation/dose-rate-related effects were defined to discard unrealistic model dynamics. The Multi-Hit Repair (MHR) model was calibrated with canine osteosarcoma Abrams cell line data to study the discriminators’ efficacy in scenarios with limited survival data. Additionally, survival dose-rate-dependent data from the human SiHa cervical cancer cell line were used to illustrate the survival behavior at diverse dose-rates and the capability of the MHR to model these data. Results SiHa data confirmed the validity of the proposed discriminators. The discriminators filtered 99% of parameter sets, improving the calibration of Abrams cells data. Furthermore, results from both cell lines may hint universal aspects of cellular repair. Conclusions Dose-rate theoretical discrimination criteria are an effective method to understand repair kinetics and improve radiobiological model calibration. Moreover, this methodology may be used to analyze diverse biological data using dynamic models in-silico .

Abstract This experimental study investigates the friction and wear of three coatings commonly used in industrial applications, particularly in hydrodynamic bearings. The three materials under investigation were Babbitt, polyether ether ketone (PEEK) reinforced with 15% carbon fibers, and PEEK reinforced with 20% carbon fibers. The first polymer material was extruded, while the other was produced by fused deposition modelling (FDM). The materials were subjected to sliding tests in a pin-on-disc configuration, with a steel ball serving as the counter surface. The tests were conducted at room temperature, with a load of 10 N and under three different lubrication conditions: dry, grease, and oil. The linear speed was set at 0.3 m/s for the dry and semi-solid lubrication tests, while for the oil tests, the speed was set at 0.25 m/s. The greases used had consistency grades of NGLI 000 and NGLI 2. An ISO VG 68 circulation oil was used for the oil lubrication tests. Additionally, thermodynamic analyses were performed under the most severe conditions (i.e., dry) to investigate the steel-Babbitt and steel-PEEK contact.

Abstract Additive manufacturing (AM) is widely recognized as a versatile tool for achieving complex geometries and customized functionalities in designed materials. However, the challenge lies in selecting an appropriate AM method that simultaneously realizes desired microstructures and macroscopic geometrical designs in a single sample. This study presents a direct ink writing method for 3D printing intricate, high‐fidelity macroscopic cellulose aerogel forms. The resulting aerogels exhibit tunable anisotropic mechanical and thermal characteristics by incorporating fibers of different length scales into the hydrogel inks. The alignment of nanofibers significantly enhances mechanical strength and thermal resistance, leading to higher thermal conductivities in the longitudinal direction (65 mW m−1 K−1) compared to the transverse direction (24 mW m−1 K−1). Moreover, the rehydration of printed cellulose aerogels for biomedical applications preserves their high surface area (≈300 m2 g−1) while significantly improving mechanical properties in the transverse direction. These printed cellulose aerogels demonstrate excellent cellular viability (>90% for NIH/3T3 fibroblasts) and exhibit robust antibacterial activity through in situ‐grown silver nanoparticles.

Stenotrophomonas maltophilia, a non-fermentative, ubiquitous, gram-negative aerobic bacterium, is associated with high mortality rates, particularly in immunocompromised or debilitated patients. The prevalence rate of ICU-acquired pneumonia episodes caused by this microorganism has been found to be 2%. S. maltophilia has been identified as one of the top 10 microorganisms responsible for such infections in EU/EEA countries. This study describes an outbreak of S. maltophilia in an intensive care unit of a hospital in northern Italy. This includes an epidemiological investigation of the cases, the environmental microbiological controls carried out, a comparison of the strains by multilocus sequence typing (MLST), and the measures taken to prevent and control the outbreak. Among the seven clinical isolates of S. maltophilia analyzed herein, six demonstrated susceptibilities to trimethoprim–sulfamethoxazole. Conversely, one isolate of S. maltophilia exhibited resistance to first-line antibiotics. ST was found to be identical for six patients (ST 4), as well as in the environmental feedback on the trolley of Box 2. The analysis of the temporal and spatial progression of the outbreak has suggested that the transmission of S. maltophilia may have occurred through cross-transmission during care practices.

The increasing adoption of PEEK (polyetheretherketone) in many industrial applications has promoted intense research to optimize its lubrication along with the development of friction reducers (FRs), additives that help in reducing fuel consumption and, consequently, CO2 emissions. In this study, the effect of FRs in improving the lubrication of PEEK–steel couplings was evaluated and their mechanism studied using the Mini Traction Machine (MTM) tribometer. Different types of FRs (such as Molybdenum dithiocarbamate, glycerol monooleate, amine and polymeric derivatives) and coupling combinations (steel/steel, steel/PEEK and PEEK/steel) were considered. The oil samples were evaluated as fresh and after a rubbing time considering different operative conditions (from high to low T, fixed load and type of contact motion), and a measurement of the tribofilm was acquired. The experimental campaign showed a ranking among FRs friction-reducing behavior and, in some cases, a synergistic effect was noted between the tribofilm containing the friction modifier and the PEEK surface. Comparing the top performing FRs with reference oil showed a reduction in friction of 22%, 21% and 37%, respectively, in steel–steel, PEEK–steel and steel–PEEK couplings, while in the standard steel–steel coupling, two out of four FRs did not reduce the friction. After conditioning in the presence of PEEK, all friction-modifier additives reduced the friction effectively. This demonstrates the promising performance of PEEK, its compatibility with friction-reducing additives, and its applicability to sliding machine parts in order to improve efficiency and thus reduce CO2 emissions.

When thinking about colloidal particles, the fist image that comes into mind is that of tiny little polystyrene spheres with a narrow size distribution. While spherical polymer colloids are one of the workhorses of colloid science, scientists have been working on the development of progressively advanced strategies to move beyond particles with spherical shapes, and prepared polymer colloids with more complex morphologies. This short review aims at providing a summary of these developments, focusing primarily on methods applicable to submicron particles, with an eye towards their applications and some discussion about advantages and drawbacks of the various approaches.

The SARS-CoV-2 pandemic caused an increase in intensive care unit (ICU) hospitalizations with a rise in morbidity and mortality; nevertheless, there is still little evidence on the impact of the pandemic on antibiotic resistance in ICUs. This is a retrospective, monocentric epidemiological study. The aim of the study was to describe and analyze the impact of the SARS-CoV-2 pandemic on ICU bacterial resistance patterns. All bacteria isolated from all patients admitted to the E.O. Galliera ICU from January 2018 to December 2022 were included. Antibiotic resistance (AR) profiles were evaluated. A total of 1021 microorganisms were identified, of which 221 (12.47%) had a resistance pattern (resistant organisms; ROs). In this time, there were 1679 patients with a total of 12,030 hospitalization days. The majority of microorganisms were Gram-negative (79.66% in 2018, 77.29% in 2019, 61.83% in 2020, 62.56% in 2021, and 60.75% in 2022), but an increase in Gram-positive microorganisms was observed (20.34 to 39.25% between 2018 and 2022). The prevalence of AR was 19.44% in 2018, 11.54% in 2019, 38.04% in 2020, 34.15% in 2021, and 39.29% in 2022 for Gram-positive microorganisms and 19.86% in 2018, 13.56% in 2019, 18.12% in 2020, 12.41% in 2021, and 12.31% in 2012 for Gram-negative microorganisms. The incidence of ROs showed a COVID-19-related increase in 2020–2021, followed by a lowering trend since 2021, and a new increase in 2022. Possible explanations are antibiotic overtreatment and a decrease in containment measures. An interesting finding was the cumulative lowering trend of carbapenem-resistant K. pneumoniae and P. aeruginosa, probably due to different patient features.

Abstract Background Computed tomography (CT) enables quantification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, helping in outcome prediction. Methods From 1 to 22 March 2020, patients with pneumonia symptoms, positive lung CT scan, and confirmed SARS-CoV-2 on reverse transcription-polymerase chain reaction (RT-PCR) were consecutively enrolled. Clinical data was collected. Outcome was defined as favourable or adverse (i.e., need for mechanical ventilation or death) and registered over a period of 10 days following CT. Volume of disease (VoD) on CT was calculated semi-automatically. Multiple linear regression was used to predict VoD by clinical/laboratory data. To predict outcome, important features were selected using a priori analysis and subsequently used to train 4 different models. Results A total of 106 consecutive patients were enrolled (median age 63.5 years, range 26–95 years; 41/106 women, 38.7%). Median duration of symptoms and C-reactive protein (CRP) was 5 days (range 1–30) and 4.94 mg/L (range 0.1–28.3), respectively. Median VoD was 249.5 cm3 (range 9.9–1505) and was predicted by lymphocyte percentage (p = 0.008) and CRP (p < 0.001). Important variables for outcome prediction included CRP (area under the curve [AUC] 0.77), VoD (AUC 0.75), age (AUC 0.72), lymphocyte percentage (AUC 0.70), coronary calcification (AUC 0.68), and presence of comorbidities (AUC 0.66). Support vector machine had the best performance in outcome prediction, yielding an AUC of 0.92. Conclusions Measuring the VoD using a simple CT post-processing tool estimates SARS-CoV-2 burden. CT and clinical data together enable accurate prediction of short-term clinical outcome.

In the context of the global health issue caused by the growing occurrence of antimicrobial resistance (AMR), the need for novel antimicrobial agents is becoming alarming. Inorganic and organometallic complexes represent a relatively untapped source of antibiotics. Here, we report a computer-aided drug design (CADD) based on a ‘scaffold-hopping’ approach for the synthesis and antibacterial evaluation of fac-Re(I) tricarbonyl complexes bearing clotrimazole (ctz) as a monodentate ligand. The prepared molecules were selected following a pre-screening in silico analysis according to modification of the 2,2′-bipyridine (bpy) ligand in the coordination sphere of the complexes. CADD pointed to chiral 4,5-pinene and 5,6-pinene bipyridine derivatives as the most promising candidates. The corresponding complexes were synthesized, tested toward methicillin-sensitive and -resistant S. aureus strains, and the obtained results evaluated with regard to their binding affinity with a homology model of the S. aureus MurG enzyme. Overall, the title species revealed very similar minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values as those of the reference compound used as the scaffold in our approach. The obtained docking scores advocate the viability of ‘scaffold-hopping’ for de novo design, a potential strategy for more cost- and time-efficient discovery of new antibiotics.

This paper presents a dataset of images generated via 3D graphics rendering. The dataset is composed by pictures of the junction between the high-speed shaft and the external bracket of the power generator inside a wind turbine cabin, in presence and absence of oil leaks. Oil leak occurrence is an anomaly that can verify in a zone of interest of the junction. Since the wind turbines industry is becoming more and more important, turbines maintenance is growing in importance accordingly. In this context a dataset, as we propose, can be used, for example, to design machine learning algorithms for predictive maintenance. The renderings have been produced, from various framings and various leaks shapes and colors, using the rendering engine Keyshot9. Subsequent preprocessing has been performed with Matlab, including images grayscale conversion and image binarization. Finally, data augmentation has been implemented in Python, and it can be easily extended/customized for realizing any further processing. The Matlab and Python source codes are also provided. To the authors’ knowledge, there are no other public available datasets on this topic.

Antimicrobial resistance is one of the major human health threats, with significant impacts on the global economy. Antibiotics are becoming increasingly ineffective as drug-resistance spreads, imposing an urgent need for new and innovative antimicrobial agents. Metal complexes are an untapped source of antimicrobial potential. Rhenium complexes, amongst others, are particularly attractive due to their low in vivo toxicity and high antimicrobial activity, but little is known about their targets and mechanism of action. In this study, a series of rhenium di- and tricarbonyl diimine complexes were prepared and evaluated for their antimicrobial potential against eight different microorganisms comprising Gram-negative and -positive bacteria. Our data showed that none of the Re dicarbonyl or neutral tricarbonyl species have either bactericidal or bacteriostatic potential. In order to identify possible targets of the molecules, and thus possibly understand the observed differences in the antimicrobial efficacy of the molecules, we computationally evaluated the binding affinity of active and inactive complexes against structurally characterized membrane-bound S. aureus proteins. The computational analysis indicates two possible major targets for this class of compounds, namely lipoteichoic acids flippase (LtaA) and lipoprotein signal peptidase II (LspA). Our results, consistent with the published in vitro studies, will be useful for the future design of rhenium tricarbonyl diimine-based antibiotics.

Governments and institutions have the following sustainable development goals: the improvement of energy efficiency and the reduction of CO2 emissions, in a “green economy” approach, have currently become the fundamental drivers that push research and development activity toward the optimization of rotating machine components in the industrial sector, with a special focus on lubrication systems too. The activity is directed towards the optimization of tribological testing methods and equipment to better discriminate the performance of lubricants in operating conditions as predictive as possible of real applications. In this context, the present paper describes the results of an experimental campaign based on the use of a well-selected linear oscillation SRV * (Schwingung, Reibung, Verschleiss) tribometer procedure as a screening of a rig test, the FZG ** (ForschungsstellefürZahnräderundGetreibebau(German:ResearchCentreforGearsandGear;UniversityofMunich;Munich,Germany)) test, leading to concrete benefits such as saving time (time duration is 76% less without mentioning visual inspection and mounting/dismounting phase) and operative costs. Four cases for the determination of the failure load stage of SRV have been defined as links to seizure and microseizure phenomena. The procedure was tested for ten oils differing in scope (gas turbine oil, turbine oil, gear oil and circulating oil). The tests have been repeated three times and a procedure was defined for repeatability (± 1 stage difference between the minimum and maximum) for nine out of ten cases a failure stage could be defined. The same oils were also tested using the FZG scuffing test, and it can be seen that the results are very comforting as follows: a good correlation with the FZG rig test has been found for eight out of ten oils.

Abstract: Magnetic nanoparticles have been the subject of enormous investigations for their potential use as cancer treatment via hyperthermia. This is due to their ability to generate heat when exposed to an external magnetic field oscillating at sufficiently high frequency. There are many different parameters that need to be considered when designing the optimal nanoparticle formulation for hyperthermia. The effect of the formation of clusters of nanoparticles, which is either an unwanted side effect of poor colloidal stability of the particles, or a desired for- mulation strategy, has poorly understood consequences on the amount of heat generated by the nanoparticles. The objective of this work is to address this problem from the theoretical side by performing detailed simulations to investigate the effect of incorporation of magnetic nanoparticles in clusters on the amount of heat generated as a function of the particle size, cluster size and particles magnetic properties.

Abstract: While coupling mechanical and chemical processes is widespread in living organisms, the idea to harness the mechanically induced dissociation of weak covalent and non-covalent bonds to create artificial materials that respond to mechanical stimulation has only recently gained attention. Here we summarize our activities that mainly revolve around the exploitation of non-covalent interactions in (supramolecular) polymeric materials with the goal to translate mechanical stresses into useful, pre-defined events. Focusing on mechano- chromic polymers that alter their optical absorption or fluorescence properties, several new operating principles, mechanosensitive entities, and materials systems were developed. Such materials are expected to be useful for technical applications that range from the detection of very small forces in biological systems to the monitoring of degradation processes and damage in coatings and structural objects.

In this work, the effective mechanical reinforcement of polymeric nanocomposites containing spherical particle fillers is predicted based on a generalized analytical three-phase-series-parallel model, considering the concepts of percolation and the interfacial glassy region. While the concept of percolation is solely taken as a contribution of the filler-network, we herein show that the glassy interphase between filler and matrix, which is often in the nanometers range, is also to be considered while interpreting enhanced mechanical properties of particulate filled polymeric nanocomposites. To demonstrate the relevance of the proposed generalized equation, we have fitted several experimental results which show a good agreement with theoretical predictions. Thus, the approach presented here can be valuable to elucidate new possible conceptual routes for the creation of new materials with fundamental technological applications and can open a new research avenue for future studies.

Magnetite nanocrystal clusters are being investigated for their potential applications in catalysis, magnetic separation, and drug delivery. Controlling their size and size distribution is of paramount importance and often requires tedious trial-and-error experimentation to determine the optimal conditions necessary to synthesize clusters with the desired properties. In this work, magnetite nanocrystal clusters were prepared via a one-pot solvothermal reaction, starting from an available protocol. In order to optimize the experimental factors controlling their synthesis, response surface methodology (RSM) was used. The size of nanocrystal clusters can be varied by changing the amount of stabilizer (tribasic sodium citrate) and the solvent ratio (diethylene glycol/ethylene glycol). Tuning the experimental conditions during the optimization process is often limited to changing one factor at a time, while the experimental design allows for variation of the factors’ levels simultaneously. The efficiency of the design to achieve maximum refinement for the independent variables (stabilizer amount, diethylene glycol/ethylene glycol (DEG/EG) ratio) towards the best conditions for spherical magnetite nanocrystal clusters with desirable size (measured by scanning electron microscopy and dynamic light scattering) and narrow size distribution as responses were proven and tested. The optimization procedure based on the RSM was then used in reverse mode to determine the factors from the knowledge of the response to predict the optimal synthesis conditions required to obtain a good size and size distribution. The RSM model was validated using a plethora of statistical methods. The design can facilitate the optimization procedure by overcoming the trial-and-error process with a systematic model-guided approach.

Self-assembly of nanoparticles is one of the most promising methods for the preparation of novel materials and devices with exceptional properties. In order to control nanoparticles self-assembly, an understanding of their interactions is absolutely necessary. One convenient way to achieve a control on their interaction is through the use of external fields. Here we provide two different examples of how interparticle interactions are affected by interactions with external fields. In the first case, magnetic fields are used to induce dipolar interactions among concentrated suspension of superparamagnetic nanocolloids, which cause them to self-assemble into dense chain-like anisotropic structures, used as templates for the growth of porous materials with tunable properties. In the second case, it is shown how more commonly employed but less understood flow fields interact with clusters of particles, and lead to their restructuring or disassembly depending upon the shear stress applied.