Purpose: The aim of this study was to develop and train a machine learning (ML) algorithm to create a clinical decision support tool (i.e., ML-driven probability calculator) to be used in clinical practice to estimate recurrence rates following an arthroscopic Bankart repair (ABR). Methods: Data from 14 previously published studies were collected. Inclusion criteria were (1) patients treated with ABR without remplissage for traumatic anterior shoulder instability and (2) a minimum of 2 years follow-up. Risk factors associated with recurrence were identified using bivariate logistic regression analysis. Subsequently, four ML algorithms were developed and internally validated. The predictive performance was assessed using discrimination, calibration and the Brier score. Results: In total, 5591 patients underwent ABR with a recurrence rate of 15.4% (n = 862). Age <35 years, participation in contact and collision sports, bony Bankart lesions and full-thickness rotator cuff tears increased the risk of recurrence (all p < 0.05). A single shoulder dislocation (compared to multiple dislocations) lowered the risk of recurrence (p < 0.05). Due to the unavailability of certain variables in some patients, a portion of the patient data had to be excluded before pooling the data set to create the algorithm. A total of 797 patients were included providing information on risk factors associated with recurrence. The discrimination (area under the receiver operating curve) ranged between 0.54 and 0.57 for prediction of recurrence. Conclusion: ML was not able to predict the recurrence following ABR with the current available predictors. Despite a global coordinated effort, the heterogeneity of clinical data limited the predictive capabilities of the algorithm, emphasizing the need for standardized data collection methods in future studies. Level of Evidence: Level IV, retrospective cohort study.

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The self-assembly of materials driven by the inherent directionality of the constituent particles is of both practical and fundamental interest because it enables the fabrication of complex and hierarchical structures with tailored functionalities. By employing evaporation assisted self-assembly, we form opal-like structures with micro-sized magnetic superball particles. We study the structure formation of different superball shapes during evaporation of a dispersion droplet with in-situ small angle x-ray scattering with microradian resolution in the absence and presence of an external magnetic field. In the absence of a magnetic field, strong shape-dependent structures form as the water evaporates from the system. Applying a magnetic field to the droplet has a unique effect on the system; strong magnetic fields inhibit the growth of well-ordered assemblies due to the formation of out-of-equilibrium dipolar structures while lower magnetic fields allow particles to rearrange and orient without inhibition. In this work, we show how the superball assembly inside a droplet can be controlled by the magnetic field strength and the superball shape. The tunability of these parameters not only enables the controllable formation of macroscopic colloidal assemblies but also opens up possibilities for the development of functional materials with tailored properties on a macro-scale.@en

Introduction: Food loss and waste are urgent problems to address. Recent estimates highlighted that the highest quantities of waste are generated at the household level and for this reason, the interest in this sector has increased over years. Methods: To investigate if there is a connection between consumers’ behaviors aiming at reducing food waste and consumers’ choices in adopting healthy eating habits, a survey among a sample (n = 2,869) representative of the Italian population was carried out with the use of validated questionnaires. Results: Results demonstrated that the higher the adherence to the Italian dietary guidelines indicator (AIDGI) the higher the score measuring household food waste behaviors (HFWB). In particular, the highest AIDGI corresponds to a preponderance of respondents that was more able to plan the shopping and the use of food (38.9%, p < 0.001), to better evaluate the quantities to cook (40.4%, p < 0.001), to avoid impulsive buying (35.2%, p < 0.01), to have a high knowledge of the food stored (38.4%, p < 0.001), to reuse leftovers (35.4%, p < 0.001), to assess food safety (34.7%, p < 0.001), to plan accurately (34.9%, p < 0.01), to know how to prolong the shelf life of a product (34%, p < 0.05), and to cook creatively (32%, p < 0.01). In addition to that, half of the respondents with the lowest AIDGI score did not receive any education regarding food waste (51.1%, p < 0.001). HFWB indicators globally resulted in scores ranging from 40 to 80% revealing the attention of Italians to food waste issues. Regarding eating habits, in half of the sample (50.4%) a consumption pattern with low adherence to nutritional recommendations was found, in particular among men (34.4%), younger age groups (40%), and people living in large families (42.3%). Discussion: The overall results provided interesting information that could give input for planning nutrition education actions and identifying targets and topics to be addressed.

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Creating materials with structure that is independently controllable at a range of scales requires breaking naturally occurring hierarchies. Breaking these hierarchies can be achieved via the decoupling of building block attributes from structure during assembly. Here, we demonstrate, through computer simulations and experiments, that shape and interaction decoupling occur in colloidal cuboids suspended in evaporating emulsion droplets. The resulting colloidal clusters serve as “preassembled” mesoscale building blocks for larger-scale structures. We show that clusters of up to nine particles form mesoscale building blocks with geometries that are independent of the particles’ degree of faceting and dipolar magnetic interactions. To highlight the potential of these superball clusters for hierarchical assembly, we demonstrate, using computer simulations, that clusters of six to nine particles can assemble into high-order structures that differ from bulk self-assembly of individual particles. Our results suggest that preassembled building blocks present a viable route to hierarchical materials design.

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Understanding the relationship between colloidal building block shape and self-assembled material structure is important for the development of novel materials by self-assembly. In this regard, colloidal superballs are unique building blocks because their shape can smoothly transition between spherical and cubic. Assembly of colloidal superballs under spherical confinement results in macroscopic clusters with ordered internal structure. By utilizing Small Angle X-Ray Scattering (SAXS), we probe the internal structure of colloidal superball dispersion droplets during confinement. We observe and identify four distinct drying regimes that arise during compression via evaporating droplets, and we track the development of the assembled macrostructure. As the superballs assemble, we found that they arrange into the predicted paracrystalline, rhombohedral C₁-lattice that varies by the constituent superballs’ shape. This provides insights in the behavior between confinement and particle shape that can be applied in the development of new functional materials.

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Hematite microparticles are becoming increasingly important components in the soft matter field. The remarkable combination of magnetic and photocatalytic properties that characterize them, coupled with the variety of uniform and monodisperse shapes that they can be synthesized in, makes them a one of a kind colloidal model system. Thanks to these properties, hematite microparticles have been recently applied in several important soft matter applications, spanning from novel colloidal building blocks for self-assembly to necessary tools to investigate and understand fundamental problems. In this review article we provide a detailed overview of the traditional methods available for the preparation of hematite microparticles of different shapes, devoting special attention on some of the most common hiccups that could hider a successful synthesis. We furthermore review the particles' most important physico-chemical properties and their most relevant applications in the soft matter field.

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Manipulating the way in which colloidal particles self-organize is a central challenge in the design of functional soft materials. Meeting this challenge requires the use of building blocks that interact with one another in a highly specific manner. Their fabrication, however, is limited by the complexity of the available synthesis procedures. Here, we demonstrate that, starting from experimentally available magnetic colloids, we can create a variety of complex building blocks suitable for hierarchical self-organization through a simple scalable process. Using computer simulations, we compress spherical and cubic magnetic colloids in spherical confinement, and investigate their suitability to form small clusters with reproducible structural and magnetic properties. We find that, while the structure of these clusters is highly reproducible, their magnetic character depends on the particle shape. Only spherical particles have the rotational degrees of freedom to produce consistent magnetic configurations, whereas cubic particles frustrate the minimization of the cluster energy, resulting in various magnetic configurations. To highlight their potential for self-assembly, we demonstrate that already clusters of three magnetic particles form highly nontrivial Archimedean lattices, namely, staggered kagome, bounce, and honeycomb, when focusing on different aspects of the same monolayer structure. The work presented here offers a conceptually different way to design materials by utilizing preassembled magnetic building blocks that can readily self-organize into complex structures.

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Hypothesis: Our ability to dictate the colloid geometry is intimately related to self-assembly. The synthesis of anisotropic colloidal particles is currently dominated by wet chemistry and lithographic techniques. The wet chemical synthesis offers limited particle geometries at bulk quantities. Lithographic techniques, on the other hand, provide precise control over the particle shape, although at lower yields. In this respect, two-photon polymerization (2PP)¹ has attracted growing attention due to its ability to automatically fabricate complex micro/nano structures with high resolution. Experiments: We manufacture precisely designed colloids with sizes ranging from 1 µm to 10 µm with 2PP and optimize the process parameters for each dimension. Moreover, we study the shape dependent Brownian motion of these particles with video microscopy and estimate their diffusion coefficients. Findings: We observe that increasing the geometrical anisotropy leads to a pronounced deviation from the analytically predicted diffusion coefficient for disks with a given aspect ratio. The deviation is attributed to stronger hydrodynamic coupling with increasing anisotropy. We demonstrate, for the first time, 2PP manufacturing of colloids with tailored geometry. This study opens synthesis of colloidal building blocks to a broader audience with limited access to cleanrooms or wet-chemistry know-how.

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Assembling complex architectures with novel geometries and tailored properties requires the development of suitably designed colloidal building blocks that assemble through specific and directional interactions. Magnetic colloids have the potential to provide the necessary tools to obtain programmable building blocks due to the innate directionality of magnetic interactions. Magnetic dipoles can be permanently embedded into particles and they allow for remote control and manipulation via external fields independently of the chemical and physical composition of their dispersion medium. These properties put magnetic colloids at an advantage point over other currently available systems. In this chapter, we discuss recent advances on the synthesis of magnetic model colloids and their role in the preparation of rational structures via self-assembly.

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