Mesoporous Bioactive Glasses in Cancer Diagnosis and Therapy: Stimuli-Responsive, Toxicity, Immunogenicity, and Clinical Translation

Cancer is one of the top life-threatening dangers to the human survival, accounting for over 10 million deaths per year. Bioactive glasses have developed dramatically since their discovery 50 years ago, with applications that include therapeutics as well as diagnostics. A new system within the bioactive glass family, mesoporous bioactive glasses (MBGs), has evolved into a multifunctional platform, thanks to MBGs easy-to-functionalize nature and tailorable textural properties-surface area, pore size, and pore volume. Although MBGs have yet to meet their potential in tumor treatment and imaging in practice, recently research has shed light on the distinguished MBGs capabilities as promising theranostic systems for cancer imaging and therapy. This review presents research progress in the field of MBG applications in cancer diagnosis and therapy, including synthesis of MBGs, mechanistic overview of MBGs application in tumor diagnosis and drug monitoring, applications of MBGs in cancer therapy ( particularly, targeted delivery and stimuli-responsive nanoplatforms), and immunological profile of MBG-based nanodevices in reference to the development of novel cancer therapeutics.

What can artificial intelligence and machine learning tell us? A review of applications to equine biomechanical research

Artificial intelligence (AI) and machine learning (ML) are fascinating interdisciplinary scientific domains where machines are provided with an approximation of human intelligence. The conjecture is that machines are able to learn from existing examples, and employ this accumulated knowledge to fulfil challenging tasks such as regression analysis, pattern classification, and prediction. The horse biomechanical models have been identified as an alternative tool to investigate the effects of mechanical loading and induced deformations on the tissues and structures in humans. Many reported investigations into bone fatigue, subchondral bone damage in the joints of both humans and animals, and identification of vital parameters responsible for retaining integrity of anatomical regions during normal activities in all species are heavily reliant on equine biomechanical research. Horse racing is a lucrative industry and injury prevention in expensive thoroughbreds has encouraged the implementation of various measurement techniques, which results in massive data generation. ML substantially accelerates analysis and interpretation of data and provides considerable advantages over traditional statistical tools historically adopted in biomechanical research. This paper provides the reader with: a brief introduction to AI, taxonomy and several types of ML algorithms, working principle of a feedforward artificial neural network (ANN), and, a detailed review of the applications of AI, ML, and ANN in equine biomechanical research (i.e. locomotory system function, gait analysis, joint and bone mechanics, and hoof function). Reviewing literature on the use of these data-driven tools is essential since their wider application has the potential to: improve clinical assessments enabling real-time simulations, avoid and/or minimize injuries, and encourage early detection of such injuries in the first place.

Linear elastic and hyperelastic studies of equine hoof mechanical response at different hydration levels

Most simulation studies on equine hoof biomechanics employed linear elastic (LE) material models. However, the equine hoof wall's stress-strain relationship is nonlinear and varies with hydration level. Therefore, it is essential to investigate the accuracy of the LE model compared to more advanced material models, such as hyperelastic (HE) or viscoelastic models. The current research investigated performances of LE and three HE models (Mooney-Rivlin, Neo-Hookean, and Marlow) in describing equine hoof's mechanical behavior using finite element (FE) analysis. In the first attempt, a rectangular tissue specimen was simulated using the previously published experimental data. The Marlow HE model predicted the hoof wall stress-strain curve more accurately than the LE, Mooney-Rivlin, and Neo-Hookean models. The LE model accuracy, compared with the experimental results, varied within the reported range of the strain. However, the Marlow HE model perfectly matched the experimental data for a wide range of strains. In the second attempt, the entire hoof, including nine associated tissues, was modeled from computed tomography (CT) scans of an equine forelimb, and analyzed at trotting and standing modes of locomotion. The effect of environmental humidity on the hoof wall material properties was incorporated at four hydration levels; 0%, 53%, 75%, and 100%. The simulation results of the LE and HE models indicated that the minimum principal strain distribution on the hoof wall remained under 2% for various hydration levels and gait conditions. The numerical results of the Marlow HE model demonstrated better agreement with published experimental data compared to the LE, Mooney-Rivlin, and Neo-Hookean models. Higher hydration levels significantly increased the strains - a potential explanation could be the fact that the higher hydration levels decreased stiffness of the hoof wall tissues and ultimately increased strains. Higher ground reaction forces increased the von Mises stress at various points in the hoof wall, especially in the quarter regions and close to the coronet, where cracks and fractures are found more often in the physiological conditions.

Converging-diverging shape configuration of the diaphysis of equine third metacarpal bone through computer-aided design

The shape of the diaphysis of the equine third metacarpal bone (MC3) has a substantial influence on its mechanical properties. The connection between bone shape and bone adaptive responses is likely to be useful in forecasting the response of MC3 to a training program as well as predicting its internal loading. A variety of geometrical parameters including cortical area (A), width of dorsal cortex (D), palmar cortex (P), medial cortex (M), lateral cortex (L), medulla in dorsopalmar plane (Md), and medulla in lateromedial plane (M1) in three main cross sections (slices) within the diaphysis of 27 Thoroughbred horses aged from 12 hours to 15 years were measured using computer-aided-design and were analysed using t-tests and ANOVA test (performed in statistical MATLAB codes). Shape indices ([D/P] × [(D+P)/ Md]), H (D+Md +P), and V (M+ M1 +L) were also calculated. For all the samples, the values were plotted for a slice taken from around the mid-point of the shaft, and from two others taken at 3 cm proximally and distally from the middle slice. Cortical area decreased from proximal to distal slices in the majority of the specimens, except for all the foal samples where the area fluctuated and showed a converging-diverging shape. A similar trend was observed for one of the adult horses. To investigate converging-diverging shape configuration, a two-degree polynomial function was fitted to the plots of geometrical parameters and then the curvature (k) of these fitted curves was quantified and compared to assess the significant changes. Previous research showed that 0.5 mm differences in thickness of the midshaft dorsal cortex have a significant effect on local strain in vivo. Variations in the geometrical parameters of the midshaft metacarpus have a dramatic impact on the internal loading of the MC3 and should be considered in designing equine training programs in attempts to predict and prevent bone damage.

Prediction of load in a long bone using an artificial neural network prediction algorithm

The hierarchical nature of bone makes it a difficult material to fully comprehend. The equine third metacarpal (MC3) bone experiences nonuniform surface strains, which are a measure of displacement induced by loads. This paper investigates the use of an artificial neural network expert system to quantify MC3 bone loading. Previous studies focused on determining the response of bone using load, bone geometry, mechanical properties, and constraints as input parameters. This is referred to as a forward problem and is generally solved using numerical techniques such as finite element analysis (FEA). Conversely, an inverse problem has to be solved to quantify load from the measurements of strain and displacement. Commercially available FEA packages, without manipulating their underlying algebraic formulae, are incapable of completing a solution to the inverse problem. In this study, an artificial neural network (ANN) was employed to quantify the load required to produce the MC3 displacement and surface strains determined experimentally. Nine hydrated MC3 bones from thoroughbred horses were loaded in compression in an MTS machine. Ex-vivo experiments measured strain readings from one three-gauge rosette and three distinct single-element gauges at different locations on the MC3 midshaft, associated displacement, and load exposure time. Horse age and bone side (left or right limb) were also recorded for each MC3 bone. This information was used to construct input variables for the ANN model. The ability of this expert system to predict the MC3 loading was investigated. The ANN prediction offered excellent reliability for the prediction of load in the MC3 bones investigated, i.e. R² ≥ 0.98.

Tuning natural modes of vibration by prestress in the design of a harmonic gong

Prestresses are purposefully added to an object to improve its performance, such as tuning a guitar string by adding tension. This paper reports how the normal modes of a sheet metal component can be tuned through the prestresses generated by cold-forging small dimples. Finite element analysis showed that the frequencies of specific mode shapes were differentially affected by the location of residual stress fields due to dimple formation in relation to modal stress fields. The frequencies of overtones were most sensitive to the depth of the dimples located near the maxima of modal stresses. Using this approach a series of musical gongs were designed with up to the first five overtones tuned to within 5% of the harmonic series. The balance of harmonic and inharmonic overtones in these gongs that are well resolved by the human cochlea may constitute a set of recognizable musical timbres with sufficient harmonicity to produce an unambiguous pitch for most listeners. Since many other mechanical properties of sheet metal components are affected by residual stresses this manufacturing technique may have broader application in design engineering.