Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases

Computational models offer a unique setting to test strategies to mitigate the spread of infectious diseases, providing useful insights to applied public health. To be actionable, models need to be informed by data, which can be available at different levels of detail. While high-resolution data describing contacts between individuals are increasingly available, data gathering remains challenging, especially during a health emergency. Many models thus use synthetic data or coarse information to evaluate intervention protocols. Here, we evaluate how the representation of contact data might affect the impact of various strategies in models, in the realm of COVID-19 transmission in educational and work contexts. Starting from high-resolution contact data, we use detailed to coarse data representations to inform a model of SARS-CoV-2 transmission and simulate different mitigation strategies. We find that coarse data representations estimate a lower risk of superspreading events. However, the rankings of protocols according to their efficiency or cost remain coherent across representations, ensuring the consistency of model findings to inform public health advice. Caution should be taken, however, on the quantitative estimations of those benefits and costs triggering the adoption of protocols, as these may depend on data representation.     

Ag/ZnO nanomaterials as high performance sensors for flammable and toxic gases

Ag/ZnO nanocomposites supported on polycrystalline Al2O3 were synthesized by an unprecedented approach combining plasma enhanced chemical vapor deposition (PE-CVD) of ZnO matrices and the subsequent deposition of Ag nanoparticles (NPs) by radio frequency (RF) sputtering. The system structure, composition and morphology were investigated by glancing incidence x-ray diffraction (GIXRD), secondary ion mass spectrometry (SIMS), field emission scanning electron microscopy (FE-SEM) and energy dispersive x-ray spectroscopy (EDXS). A tailored dispersion and distribution of silver particles could be obtained under mild conditions by the sole variation of the sputtering time. Gas sensing properties toward flammable and toxic gases, both reducing (CH3CH2OH, CH3COCH3) and oxidizing (O3), were investigated in the temperature range 100-400?°C. Beside the high sensitivity, the developed sensors exhibited a response proportional to Ag content, thanks to catalytic and electronic effects promoted by silver NPs. In addition, discrimination between oxidizing and reducing analytes was enabled by a suitable choice of the adopted working temperature.     

Identification and measurement of illicit drugs and their metabolites in urban wastewater by liquid chromatography-tandem mass spectrometry

Residues of illicit drugs and their metabolites that are excreted by humans may flow into and through wastewater treatment plants. The aim of this study was to develop a method for the determination of cocaine, amphetamines, morphine, cannabinoids, methadone, and some of their metabolites in wastewater. Composite 24-h samples from urban treatment plants were enriched with deuterated internal standards before solid-phase extraction. High-pressure liquid chromatography tandem mass spectrometry with multiple reaction monitoring was used for quantitation. Recoveries were generally higher than 80%, and limits of quantifications were in the low nanograms-per-liter range for untreated and treated wastewater. The overall variability of the method was lower than 10% for untreated and 5% for treated wastewater. The method was applied to wastewater samples coming from two treatment plants in Italy and Switzerland. Quantification ranges were found to be 0.2-1 microg/L for cocaine and its metabolite benzoylecgonine, 80-200 ng/L for morphine, 10 ng/L for 6-acetylmorphine, 60-90 ng/L for 11-nor-9-carboxy-Delta9-tetrahydrocannabinol, 10-90 ng/L for methadone and its main metabolite 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine, and lower than 20 ng/L for amphetamines. As previously reported for cocaine, this method could be useful to estimate and monitor drug consumption in the population in real time, helping social scientists and authorities to combat drug abuse.     

Stereodivergent Biocatalytic Formal Reduction of α-Angelica Lactone to (R)- and (S)-γ-Valerolactone in a One-Pot Cascade

The formal asymmetric and stereodivergent enzymatic reduction of α-angelica lactone to both enantiomers of γ-valerolactone was achieved in a one-pot cascade by uniting the promiscuous stereoselective isomerization activity of Old Yellow Enzymes with their native reductase activity. In addition to running the cascade with one enzyme for each catalytic step, a bifunctional isomerase-reductase biocatalyst was designed by fusing two Old Yellow Enzymes, thereby generating an unprecedented case of an artificial enzyme catalyzing the reduction of nonactivated C=C bonds to access (R)-valerolactone in overall 41 % conversion and up to 91 % ee. The enzyme BfOYE4 could be used as single biocatalyst for both steps and delivered (S)-valerolactone in up to 84 % ee and 41 % overall conversion. The reducing equivalents were provided by a nicotinamide recycling system based on formate and formate dehydrogenase, added in a second step. This enzymatic system provides an asymmetric route to valuable chiral building blocks from an abundant bio-based chemical.     

Overcoming Multidrug Resistance (MDR): Design,Biological Evaluation and Molecular Modelling Studies of 2,4-Substituted Quinazoline Derivatives

Some 2,4-disubstituted quinazolines were synthesized and studied as multidrug resistance (MDR) reversers. The new derivatives carried the quinazoline-4-amine scaffold found in modulators of the ABC transporters involved in MDR, as the TKIs gefitinib and erlotinib. Their behaviour on the three ABC transporters, P-gp, MRP1 and BCRP, was investigated. Almost all compounds inhibited the P-gp activity in MDCK-MDR1 cells overexpressing P-gp, showing EC₅₀ values in the nanomolar range ( 1 d , 1 e , 2 a , 2 c , 2 e ). Some compounds were active also towards MRP1 and/or BCRP. Docking results obtained by in silico studies on the P-gp crystal structure highlighted common features for the most potent compounds. The P-gp selective compound 1 e was able to increase the doxorubicin uptake in HT29/DX cells and to restore its antineoplastic activity in resistant cancer cells in the same extent of sensitive cells. Compound 2 a displayed a dual inhibitory effect showing good activities towards both P-gp and BCRP.     

A continuous flow reactor for the flexible production of different formulations: CFD-aided design

The increasing need to improve the sustainability of industrial processes requires more flexible and intensified solutions. For this purpose, nowadays lots of efforts are made to switch from batch to continuous processes, the latter being able to ensure the same processing history to all fluid elements, with a consequent better control of the operating conditions and product quality. The present work aims at developing a continuous flow reactor for the production of several fine chemicals, including medical-surgical aids, but also other substances for specific industrial sectors. The plant is basically an inline reactor equipped with various static mixers and side inlets, and it is conceived to ensure on-site production. This is an important feature also in light of the recent COVID-19 pandemic, which asked for flexible and distributed production of chemicals. Numerical simulations based on computational fluid dynamics are employed to study the performance, in terms of pressure drops and degree of mixing, of different static mixers, that is, the Lightnin Inliner Series 50 and Ross low pressure drop (LPD), combining various elements of mixing and injections in different operating conditions in both laminar and turbulent regimes. The results highlighted how numerical simulations may represent a valid tool for supporting the detailed design of such flow reactors by allowing the evaluation of the optimal design solutions.     

Vapor-Phase Synthesis of Molecularly Imprinted Polymers on Nanostructured Materials at Room-Temperature

Molecularly imprinted polymers (MIPs) have recently emerged as robust and versatile artificial receptors. MIP synthesis is carried out in liquid phase and optimized on planar surfaces. Application of MIPs to nanostructured materials is challenging due to diffusion-limited transport of monomers within the nanomaterial recesses, especially when the aspect ratio is >10. Here, the room temperature vapor-phase synthesis of MIPs in nanostructured materials is reported. The vapor phase synthesis leverages a >1000-fold increase in the diffusion coefficient of monomers in vapor phase, compared to liquid phase, to relax diffusion-limited transport and enable the controlled synthesis of MIPs also in nanostructures with high aspect ratio. As proof-of-concept application, pyrrole is used as the functional monomer thanks to its large exploitation in MIP preparation; nanostructured porous silicon oxide (PSiO₂) is chosen to assess the vapor-phase deposition of PPy-based MIP in nanostructures with aspect ratio >100; human hemoglobin (HHb) is selected as the target molecule for the preparation of a MIP-based PSiO₂ optical sensor. High sensitivity and selectivity, low detection limit, high stability and reusability are achieved in label-free optical detection of HHb, also in human plasma and artificial serum. The proposed vapor-phase synthesis of MIPs is immediately transferable to other nanomaterials, transducers, and proteins.     

“A Study in Yellow”: Investigations in the Stereoselectivity of Ene-Reductases

Ene-reductases from the Old Yellow Enzyme (OYE) superfamily are a well-known and efficient biocatalytic alternative for the asymmetric reduction of C=C bonds. Considering the broad variety of substituents that can be tolerated, and the excellent stereoselectivities achieved, it is apparent why these enzymes are so appealing for preparative and industrial applications. Different classes of C=C bonds activated by at least one electron-withdrawing group have been shown to be accepted by these versatile biocatalysts in the last decades, affording a vast range of chiral intermediates employed in the synthesis of pharmaceuticals, agrochemicals, flavours, fragrances and fine chemicals. In order to access both enantiomers of reduced products, stereodivergent pairs of OYEs are desirable, but their natural occurrence is limited. The detailed knowledge of the stereochemical course of the reaction can uncover alternative strategies to orient the selectivity via mutagenesis, evolution, and substrate engineering. An overview of the ongoing studies on OYE-mediated bioreductions will be provided, with particular focus on stereochemical investigations by deuterium labelling.     

Flow regime identification in aerated stirred vessel using passive acoustic emission and machine learning

Smart at- or online process sensors, which employ machine learning (ML) to process data, have been the subject of extensive research in recent years, due to their potential for real-time process control. In this paper, a passive acoustic emission process sensor has been used to detect gas–liquid regimes within a stirred, aerated vessel using novel ML approaches. Pressure fluctuations (acoustic emissions) in an air-water system were recorded using a piezoelectric sensor installed on the external wall of three identical cylindrical tanks of diameter, T = 160 mm, filled to a volume of 5 L (height, H = 1.5 T). The tanks were made of either glass, steel, or aluminium, and each tank was equipped with a Rushton turbine of diameter, D = 0.35 T. The investigated flow regimes, flooding, loading, complete dispersion, and un-gassed, were obtained by changing the air feed flow rates and by varying the impeller speed. The acoustic spectra obtained were processed to select an optimal number of features characterizing each of the regimes, and these were used to train three different ML algorithms. The pre-processing includes a principal component analysis (PCA) step, which reduces the volume of data fed to the ML algorithms, saving computational time up to a factor of 5. The algorithms (decision tree, k-nearest neighbour, and support vector machines) were challenged to use these features to identify the correct flow regime. Accurate predictions of the three gas–liquid regimes of interest have been achieved. The accuracy of the prediction ranges from 90% to 99%, and this difference is related to the material used for the vessel.