Material Design of Porous Hydroxyapatite Ceramics via Inverse Analysis of an Estimation Model for Bone-Forming Ability Based on Machine Learning and Experimental Validation of Biological Hard Tissue Responses.

Hydroxyapatite and ß-tricalcium phosphate have been clinically applied as artificial bone materials due to their high biocompatibility. The development of artificial bones requires the verification of safety and efficacy through animal experiments; however, from the viewpoint of animal welfare, it is necessary to reduce the number of animal experiments. In this study, we utilized machine learning to construct a model that estimates the bone-forming ability of bioceramics from material fabrication conditions, material properties, and in vivo experimental conditions. We succeeded in constructing two models: 'Model 1', which predicts material properties from their fabrication conditions, and 'Model 2', which predicts the bone-formation rate from material properties and in vivo experimental conditions. The inclusion of full width at half maximum (FWHM) in the feature of Model 2 showed an improvement in accuracy. Furthermore, the results of the feature importance showed that the FWHMs were the most important. By an inverse analysis of the two models, we proposed candidates for material fabrication conditions to achieve target values of the bone-formation rate. Under the proposed conditions, the material properties of the fabricated material were consistent with the estimated material properties. Furthermore, a comparison between bone-formation rates after 12 weeks of implantation in the porcine tibia and the estimated bone-formation rate. This result showed that the actual bone-formation rates existed within the error range of the estimated bone-formation rates, indicating that machine learning consistently predicts the results of animal experiments using material fabrication conditions. We believe that these findings will lead to the establishment of alternative animal experiments to replace animal experiments in the development of artificial bones.

Effects of galvanic vestibular stimulation on postural righting reactions in hemiplegia.

Patients with post-stroke hemiplegia often exhibit reduced ability to maintain sitting balance, a crucial factor for predicting prognosis. Galvanic vestibular stimulation (GVS) influences postural control by stimulating vestibular organ. Although several studies have focused on GVS in static postures, no studies have demonstrated the influence of GVS on righting reactions. Therefore, we aimed to investigate the effects of GVS on postural righting reactions in seated patients with stroke-induced hemiplegia. Using a vertical board (VB), righting reactions were induced by tilting the VB at 10° after patients sat for 1 min. Patients adjusted their bodies until feeling vertical upon prompt. Twenty-two left hemiplegic patients with cerebrovascular disease participated, divided into two groups undergoing right cathode GVS (RC-GVS) followed by left cathode GVS or vice versa, preceded by sham stimulation. Centre of pressure and the joint angle were measured. During the postural righting reactions towards the paralysed side, RC-GVS enhanced the righting reactions and moved the mean position on the x-axis (COPx) to the right and the mean position on the y-axis (COPy) to the front. During the postural righting reaction towards the right side, RC-GVS induced resistance against the righting reaction, COPx was deflected to the right, COPy was deflected backward, and the angle of the neck tilt increased. The findings revealed that GVS with anodal stimulation on the paralysed side could promote righting reactions in patients with post-stroke hemiplegia. SIGNIFICANCE STATEMENT: The study findings suggest that using the contralesional placement of the anode promotes righting reactions, and galvanic vestibular stimulation can induce joint movements in the neck and trunk by polarising it to act as resistance against righting reactions.