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International audienceThe acoustic vibration of single gold nanoprism pairs on a glass substrate has been investigated in the time-domain combining a spatial modulation spectroscopy microscope with a high-sensitivity femtosecond pump−probe setup. Three modes were observed and ascribed to two in-plane and one out-of-plane vibration of the nanoprisms forming the pair, in agreement with a theoretical analysis. The periods of the two former modes with similar nature show weak (about 10%) and well correlated pair to pair fluctuations that can be unambiguously ascribed to variation of the prism geometry. In contrast, strong fluctuations, by almost a factor of 6, of the mode damping are evidenced with no correlation with their period. This indicates large variations of the prism-substrate coupling, providing a unique way for its local investigation

The isomeric composition of retinal was measured in a number of bacteriorhodopsin (bR) mutants (D85N, D212N, R82A, Y185F, and D115N) under various conditions, using a rapid retinal extraction technique followed by HPLC analysis. Besides the 13-cis and the all-trans retinal isomers observed in wild type (wt) bR under physiological conditions, the 1 1-cis and 9-cis retinal isomers were observed in variable but minor amounts in the bR mutants. In addition, the values of the equilibrium constant at two temperatures and the enthalpy change for the all-trans to 13-cis isomerization process in the dark-adapted state of D212N, DMN, deionized blue bR, and wt bR were determined. We find that perturbation of the retinal cavity (pocket) by residue replacement changes the relative thermal stability of the different retinal isomers, allowing for thermaland/or photoisomenzation of the retinal chromophore along C9-Cl0 and C1 1 -C12 bonds to moderately compete with the isomerization around the C I~-C I~ bond. The bR mutants expressed in Halobacterium salinarium studied in the present work showed normal 13-cis to all-trans light adaptation, in contrast with abnormal all-trans to 13-cis light adaptation observed for D212E, D212A, and D212N expressed in Escherichia coli, suggesting an influence of the purple membrane lattice and/or the lipids on the stability of the different retinal isomers within the protein.

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Patient to patient variability is one of the issues when administering medications to individuals with different health conditions, pharmacokinetic, age, fitness, gender, and race. This requires introducing smart and personalised drug delivery systems with controlled release profile manufactured using novel approaches. Additive manufacturing (AM) provides opportunities such as full customisation, design freedom, and on-site manufacturing, and materials recycling.As a result, the academic and industrial demand for additive manufacturing for drug delivery has been continuously increasing and showing impressive results for a wide range of products.This paper provides an extensive overview of AM technologies and their applications for drug delivery. The review discusses AM technologies including their working principles, processed materials, as well as current progress in drug delivery to produce personalized dosages for every patient with controlled release profile. AM potentials, industrial scale, and challenges are investigated with regards to practice and industrial applications. The paper covers novel possibilities of AM technologies and their pharmaceuticals applications, which indicate a promising healthcare future.

Cellular structures are lightweight‐engineered materials that have gained much attention with the development of additive manufacturing technologies. This article introduces a precise approach to predict the mechanical properties of additively manufactured lattice structures using deep‐learning approaches. Diamond‐shaped nodal lattice structures are designed by varying strut length, strut diameter, and strut orientation angle. The samples are manufactured using laser powder bed fusion (LPBF) of Ti−64 alloy and subjected to compression testing to measure the ultimate strength, elastic modulus, and specific strength. Machine learning approaches such as shallow neural network (SNN), deep neural network (DNN), and deep learning neural network (DLNN) are developed and compared to the statistical design of experiment (DoE) approach. The trained DLNN model show the highest performance when compared with DNN, DoE, and SNN with a mean percentage error of 5.26%, 14.60%, and 9.39% for the ultimate strength, elastic modulus, and specific strength, respectively. The DLNN model is used to create process maps, and is further validated. The results show that although deep learning is preferred for big data, the optimized DLNN model outperform the statistical DoE approach and can be a favorable tool for lattice structure prediction with limited data.