ABSTRACT The design and operation of blood pumps are very complex and expensive to save lives of patients suffering from heart ailments. Machine learning approaches may be those better affordable ones which can be used to enhance understanding of correlation between operating parameters and performance of the pumps that is between pressure rise in blood pumps and clearance, blade height and rotational speed of the pump. Four types of machine learning algorithms, that is multiple linear regression, polynomial regression, artificial neural network and random forest regression have been employed in the current study to predict pressure rise. Performance evaluation metrics have shown that machine learning approaches are efficiently able to predict the pressure rise in blood pumps using its operational parameters. Among all of the models used, the ANN (3‐12‐12‐1) model has shown the highest accuracy of 99.9% with RELU activation function for input and hidden layers, linear activation for the output layer using ADAMAX as an optimiser. The ANN model is found to be accurate with 0.0012 MSE, 0.023 MAE and 0.999 R 2 , which are significantly lower while predicting the pressure rise in the blood pump. The ANN model is also found to predict the physical behaviour of the blood pump efficiently.
ABSTRACT Chronic wounds represent a profound global healthcare burden, characterised by stalled healing cascades, excessive reactive oxygen species (ROS) accumulation and the severe attenuation of endogenous electric fields (EF). While conductive hydrogels have emerged as promising bioelectronic interfaces to restore impaired EF and enhance electrical signal transduction, conventional designs are fundamentally limited by heterogeneous filler dispersion, high interfacial impedance and suboptimal biocompatibility. This review systematically summarises the design paradigms and therapeutic mechanisms of polyphenol‐based conductive hydrogels for chronic wound management. By leveraging the versatile coordination chemistry of catechol and pyrogallol moieties, natural polyphenols function as structural architects that ensure uniform conductive networks, robust wet tissue adhesion and enhanced mechanical toughness. Moreover, they impart intrinsic bioactive attributes, including antioxidative and anti‐inflammatory effects. Furthermore, we critically detail how the synergistic interplay between the intrinsic bioactivities of polyphenols and the biophysical cues from the conductive network orchestrates the entire healing trajectory. This synergy actively resolves pathological inflammation, accelerates angiogenesis and guides organised extracellular matrix remodelling. Finally, we highlight the current translational bottlenecks and propose future directions, emphasising the integration of artificial intelligence (AI) and machine learning for the inverse design of smart hydrogels, alongside the imperative for standardised large‐animal clinical evaluations.
ABSTRACT Medicated moxa sticks were prepared by mixing the herbs in Juanbi decoction with moxa wool. The components of smoke produced by combustion of these medicated moxa sticks and pure moxa sticks were analysed by gas chromatography‐mass spectrometry. The smoke of the medicated moxa sticks contained 34 more active ingredients than that of the pure moxa sticks. Six of the active ingredients screened using network pharmacology and molecular docking can regulate joint diseases via the tumour necrosis factor pathway and have good binding affinities with core targets. The preparation process of the medicated moxa sticks was optimised using Box–Behnken design of response surface methodology. The smoke of the optimised product contained a high concentration of active ingredients and a low tar concentration. Molecular dynamics simulations and rat experiments demonstrated that the smoke derived from medicated moxa sticks possessed good skin permeability and detumescence activity. The developed medicated moxa sticks are environmentally friendly and show good prospects for the treatment of joint disease.
ABSTRACT Cancer progression has been shown to be accompanied by alterations in physical features of its microenvironment. In the study, we fabricated an aligned collagen fibre matrix, whose architecture in both micro‐level and nano‐level was similar to that of the matrix found around the breast tumour tissue. The modulus of matrix exhibited a stiffness similarly to that found in metastatic breast tumour tissue. On the tough substrate, MDA‐MB‐231 cells presented an increased migration rate along the alignment of fibre. The biomechanical properties of tumour cells were explored in situ by AFM. The stiffness of a single MDA‐MB‐231 cell on the aligned matrix was reduced when compared with that on the nonaligned one. Wound healing assay showed that tumour cells with compliant modulus moved fast along the alignment of collagen fibre matrix. The in vitro model effectively mimicked the tumour cell behaviour, which would be helpful for new therapeutic strategies to detect cancer progress.
Bioactive glass nanoparticles (BGNs) have attracted widespread attention in regenerative medicine owing to their excellent biocompatibility and tissue repair capacity. However, the ion release behaviour associated with the long-term degradation of BGNs doped with different functional elements remains unclear. Herein, binary silicate BGNs (BGN-Ca, BGN-Mg, BGN-Zn, BGN-Cu and BGN-Mn) were prepared, and their degradation behaviour in normal saline was systematically evaluated over a period of up to 150 days. The evolution of particle morphology and size, ion release and pH changes in solution was characterised in detail. The results showed that the degradation behaviours of BGNs doped with different elements differed markedly. BGN-Ca and BGN-Mg exhibited the fastest degradation rates characterised by rapid degradation accompanied by higher Si release, whereas BGN-Zn and BGN-Mn showed comparable total Si release but displayed 'fast-then-slow' and 'slow-then-fast' release patterns, respectively. Overall, the reactivity of metal ions can provide a preliminary prediction of early ion-exchange trends, but the actual degradation behaviour is also co-regulated by multiple factors, including doping content, crystalline structure and solubility. This study elucidates the intrinsic relationship among doped elements, structural state and degradation behaviour and provides an experimental support for the precise design and stability regulation of multicomponent functionalised BGNs.
