A comparative study on machine learning-based classification to find photothrombotic lesion in histological rabbit brain images

Recently,research has been conducted to assist in the processing and analysis of histopathological images using machine learning algorithms.In this study,we established machine learning-based algorithms to detect photothrombotic lesions in histological images of photothrombosis-induced rabbit brains.Six machine learning-based algorithms for binary classification were applied,and the accu-racies were compared to classify normal tissues and photothrombotic lesions.The lesion classification model consisting of a 3-layered neural network with a rectified linear unit(ReLU)activation function,Xavier initialization,and Adam optimization using datasets with a unit size of 128×128 pixels yielded the highest accuracy(0.975).In the validation using the tested histological images,it was confirmed that the model could identify regions where brain damage occurred due to photochemical ischemic stroke.Through the development of machine learning-based photothrombotic lesion classi-fication models and performance comparisons,we confirmed that machine learning algorithms have the potential to be utilized in histopathology and various medical diagnostic techniques.

3D-printed gelatin methacrylate (GelMA)/ silanated silica scaffold assisted by two-stage cooling system for hard tissue regeneration

Among many biomaterials,gelatin methacrylate(GelMA),a photocurable protein,has been widely used in 3D bioprinting process owing to its excellent cellular responses,biocompatibility and biodegradability.However,GelMA still shows a low processability due to the severe temperature dependence of viscosity.To overcome this obstacle,we propose a two-stage temperature control system to effectively control the viscosity of GelMA.To optimize the process conditions,we evaluated the temperature of the cooling system(jacket and stage).Using the established system,three GelMA scaffolds were fabricated in which different concentrations(0,3 and 10 wt%)of silanated silica particles were embedded.To evaluate the performances of the prepared scaffolds suitable for hard tissue regeneration,we analyzed the physical(viscoelasticity,surface roughness,compressive modulus and wettability)and biological(human mesenchymal stem cells growth,western blotting and osteogenic differentiation)properties.Consequently,the composite scaffold with greater silica contents(10 wt%)showed enhanced physical and biological performances including mechanical strength,cell initial attachment,cell proliferation and osteogenic differentiation compared with those of the controls.Our results indicate that the GelMA/silanated silica composite scaffold can be potentially used for hard tissue regeneration.