Analysis of the oxygen evolution activity of layered double hydroxides (LDHs) using machine learning guidance.

Layered double hydroxides (LDHs) are excellent catalysts for the oxygen evolution reaction (OER) because of their tunable properties, including chemical composition and structural morphology. An interplay between these adjustable properties and other (including external) factors might not always benefit the OER catalytic activity of LDHs. Therefore, we applied machine learning algorithms to simulate the double-layer capacitance to understand how to design/tune LDHs with targeted catalytic properties. The key factors of solving this task were identified using the Shapley Additive explanation and cerium was identified as an effective element to modify the double-layer capacitance. We also compared different modelling methods to identify the most promising one and the results revealed that binary representation is better than directly applying atom numbers as inputs for chemical compositions. Overpotentials of LDH-based materials as predicted targets were also carefully examined and evaluated, and it turns out that overpotentials can be predicted when measurement conditions about overpotentials are added as features. Finally, to confirm our findings, we reviewed additional experimental literature data and used them to test our machine algorithms to predict LDH properties. This analysis confirmed the very credible and robust generalization ability of our final model capable of achieving accurate results even with a relatively small dataset.

Rotator cuff tear reaching the superior half portion of the humeral head causes shoulder abduction malfunction.

PURPOSE: To examine the biomechanical properties governing posterosuperior rotator cuff (RC) tear progression and dynamic shoulder abduction function, in the absence of excess loading. METHODS: Twelve freshly frozen cadaveric shoulders were evaluated via an established dynamic shoulder abduction stimulator. The shoulder abduction functions were primarily evaluated using subacromial contact pressure (SACP) during an abduction procedure, and subsequent middle deltoid force (MDF) under 5 conditions: (1) intact, (2) anterior 1/3 posterosuperior rotator cuff (PSRC) tear, (3) anterior 2/3 PSRC tear, (4) entire PSRC tear, and (5) global RC tear (tear involving the entire superior RC). RESULTS: No obvious differences were observed in the peak MDF required for abduction, and in the peak SACP among the four PSRC tear statuses (49.8 ± 9.2 N, 0.39 ± 0.05 mPa [1/3 PSRC tear]; 49.3 ± 6.8 N, 0.40 ± 0.06 mPa [2/3 PSRC tear]; 51.6 ± 7.0 N, 0.44 ± 0.08 mPa [entire PSRC tear]), as well as intact statuses (48.3 ± 9.8 N, 0.40 ± 0.05 mPa). However, significant elevations in the peak MDF and peak SACP levels were observed among the four PSRC tear statuses and global RC tear (68.1 ± 9.3 N; 4.12 ± 1.50 mPa, P < 0.01). CONCLUSION: In the absence of excess loading, the biomechanical function of the shoulder was not impaired by a simple PSRC tear. However, once the tear size reached the half superior portion of the humeral head, the humeral head migrated to the surface of the subacromion, and this action markedly decreased shoulder abduction function.

Deep insights into the viscosity of deep eutectic solvents by an XGBoost-based model plus SHapley Additive exPlanation.

Deep eutectic solvents (DESs) are emerging as novel green solvents for the processes of mass transport and heat transfer, in which the viscosity of DESs is important for their industrial applications. However, for DESs, the measurement of viscosity is time-consuming, and there are many factors influencing the viscosity, which impedes their wider application. This study aims to develop a data-driven model which could accurately and rapidly predict the viscosity of diverse DESs at different temperatures, and furthermore boost the design and screening of novel DESs. In this work, we collected 107 DESs with 994 experimental values of viscosity from published works. Given the significant effect of water on viscosity, the water content of each collected DES was labeled. The Morgan fingerprint was first employed as a feature to describe the chemical environment of DESs. And four machine learning algorithms were used to train models: support vector regression (SVR), random forest (RF), neural network (NN), and extreme gradient boosting (XGBoost), and XGBoost showed the best predictive performance. In combination with the powerful interpretation method SHapley Additive exPlanation (SHAP), we further revealed the positive or negative effect of features on viscosity. Overall, this work provides a machine learning model which could predict viscosity precisely and facilitate the design and application of DESs.

Fluffy hybrid nanoadjuvants for reversing the imbalance of osteoclastic and osteogenic niches in osteoporosis.

Osteoporosis is majorly caused by an imbalance between osteoclastic and osteogenic niches. Despite the development of nationally recognized first-line anti-osteoporosis drugs, including alendronate (AL), their low bioavailability, poor uptake rate, and dose-related side effects present significant challenges in treatment. This calls for an urgent need for more effective bone-affinity drug delivery systems. In this study, we produced hybrid structures with bioactive components and stable fluffy topological morphology by cross-linking calcium and phosphorus precursors based on mesoporous silica to fabricate nanoadjuvants for AL delivery. The subsequent grafting of -PEG-DAsp8 ensured superior biocompatibility and bone targeting capacity. RNA sequencing revealed that these fluffy nanoadjuvants effectively activated adhesion pathways through CARD11 and CD34 molecular mechanisms, hence promoting cellular uptake and intracellular delivery of AL. Experiments showed that small-dose AL nanoadjuvants effectively suppress osteoclast formation and potentially promote osteogenesis. In vivo results restored the balance between osteogenic and osteoclastic niches against osteoporosis as well as the consequent significant recovery of bone mass. Therefore, this study constructed a drug nanoadjuvant with peculiar topological structures and high bone targeting capacities, efficient intracellular drug delivery as well as bone bioactivity. This provides a novel perspective on drug delivery for osteoporosis and treatment strategies for other bone diseases.

Relationship between the progression of posterosuperior rotator cuff tear size and shoulder abduction function: A cadaveric study via dynamic shoulder simulator.

Posterosuperior rotator cuff tear (PSRCT) is one of the most common shoulder disorders in elderly people's daily life; however, the biomechanical relationship between PSRCT and shoulder abduction function is still controversial. In this study, a total of twelve freshly frozen cadaveric shoulders were included and tested in five conditions: intact rotator cuff, 1/3 PSRCT, 2/3 PSRCT, entire PSRCT, and global RCT. In each condition, extra load (0%, 45%, and 90% failure load) was sequentially added to the distal humerus, and the function of the remaining rotator cuff was mainly evaluated via the middle deltoid force (MDF) required for abduction. It is found that the peak MDF is required for abduction did not differ among the three PSRCT conditions (1/3 PSRCT: 29.30 ± 5.03 N, p = 0.96; 2/3 PSRCT: 29.13 ± 9.09 N, p = 0.98; entire PSRCT: 28.85 ± 7.12 N, p = 0.90) and the intact condition (29.18 ± 4.99 N). However, the peak MDF significantly differed between the global RCT (76.27 ± 4.94 N, p < 0.01) and all PSRCT and intact conditions. Under 45% failure load, the MDF of the entire PSRCT and global tear conditions were significantly increased compared with another status. With the 90% failure load, only the 1/3 PSRCT condition maintained the same shoulder function as the intact rotator cuff. These biomechanical testing jointly suggested that the weight-bearing ability of the shoulder significantly decreased as PSRCT progressed.

Regulating Macrophages through Immunomodulatory Biomaterials Is a Promising Strategy for Promoting Tendon-Bone Healing.

The tendon-to-bone interface is a special structure connecting the tendon and bone and is crucial for mechanical load transfer between dissimilar tissues. After an injury, fibrous scar tissues replace the native tendon-to-bone interface, creating a weak spot that needs to endure extra loading, significantly decreasing the mechanical properties of the motor system. Macrophages play a critical role in tendon-bone healing and can be divided into various phenotypes, according to their inducing stimuli and function. During the early stages of tendon-bone healing, M1 macrophages are predominant, while during the later stages, M2 macrophages replace the M1 macrophages. The two macrophage phenotypes play a significant, yet distinct, role in tendon-bone healing. Growing evidence shows that regulating the macrophage phenotypes is able to promote tendon-bone healing. This review aims to summarize the impact of different macrophages on tendon-bone healing and the current immunomodulatory biomaterials for regulating macrophages, which are used to promote tendon-bone healing. Although macrophages are a promising target for tendon-bone healing, the challenges and limitations of macrophages in tendon-bone healing research are discussed, along with directions for further research.