Background: Retinal diseases are becoming an important public health issue, with early diagnosis and timely intervention playing a key role in preventing vision loss. Optical coherence tomography (OCT) remains the leading non-invasive imaging technique for identifying retinal conditions. However, distinguishing between diabetic macular edema (DME) and macular edema resulting from retinal vein occlusion (RVO) can be particularly challenging, especially for clinicians without specialized training in retinal disorders, as both conditions manifest through increased retinal thickness. Due to the limited research exploring the application of deep learning methods, particularly for RVO detection using OCT scans, this study proposes a novel diagnostic approach based on stacked convolutional neural networks. This architecture aims to enhance classification accuracy by integrating multiple neural network layers, enabling more robust feature extraction and improved differentiation between retinal pathologies. Methods: The VGG-16, VGG-19, and ResNet50 models were fine-tuned using the Kermany dataset to classify the OCT images and afterwards were trained using a private OCT dataset. Four stacked models were then developed using these models: a model using the VGG-16 and VGG-19 networks, a model using the VGG-16 and ResNet50 networks, a model using the VGG-19 and ResNet50 models, and finally a model using all three networks. The performance metrics of the model includes accuracy, precision, recall, $F_2$-score, and area under of the receiver operating characteristic curve (AUROC). Results: The stacked neural network using all three models achieved the best results, having an accuracy of 90.7%, precision of 99.2%, a recall of 90.7%, and an $F_2$-score of 92.3%. Conclusions: This study presents a novel method for distinguishing retinal disease by using stacked neural networks. This research aims to provide a reliable tool for ophthalmologists to improve diagnosis accuracy and speed.

This study researches the viscous and hyperelastic mechanical behaviors of hydrogel-based nanocomposite materials, including: (i) a composite of human methacryloyl platelet lysate (hPLMA), human platelet lysates (hPL), and nanohydroxyapatite (nHA); (ii) bovine serum albumin methacryloyl (BSAMA); and (iii) hyaluronic acid methacryloyl (HAMA). These materials stand out for their enhanced bioactivity and mechanical strength compared to traditional hydrogels. Their potential applications in various scaffold architectures and the biomechanical restoration of the mandibular joint are investigated. Mechanical compression and relaxation tests are performed on the hydrogel-based samples, with varying nHA content (0%, 1%, and 5%) in the hPL/hPLMA composite, to characterize their visco-hyperelastic behavior. Mechanical parameters are optimized using a micro-genetic algorithm. Results show that increasing nHA content raises compressive stress and intensifies the viscoelastic response. Besides, higher strut density and staggered orthogonal patterns produce more uniform stress distribution despite a less stable viscous response. In in silico simulations of the human mandible, a multimaterial scaffold was created, featuring a hydrogel core with ceramic-based scaffold ends (hydroxyapatite-based cement enriched with hPL). The ceramic phase provided effective mechanical protection for the hydrogel. Besides, substituting the hPL/hPLMA core with HAMA reduced the stress values in the hydrogel phase, while using BSAMA led to increased stress. This study introduces a novel framework integrating experimental data, mechanical optimization, and numerical simulations to elucidate the viscoelastic behavior of hydrogel-based nanocomposite inks and multimaterial scaffolds. This research advances the development of next-generation biomaterials with improved durability and functionality, promoting regenerative medicine and personalized tissue engineering.

Multi‐tissue regeneration remains a critical clinical challenge due to the lack of solutions that can replicate the hierarchical heterogeneity of such complex interfaces. While biofabrication approaches, such as extrusion‐based, allow replicating robust, biomimetic, and layered designs, constructs are usually hindered by inadequate phase/layer integration, poor filler dispersion, and mismatched rheological and mechanical performances. This study introduces an ink engineering strategy as a solution for integrating natural‐based nanocomposites in multi‐tissue regenerative approaches. For that, two photocrosslinkable natural matrices: a protein–bovine serum albumin methacrylate (BSAMA), and a polysaccharide–hyaluronic acid methacrylate (HAMA)–are selected for their complementary mechanical and cytocompatibility profiles. Bioactive glass nanoparticles, known for osteoconductive potential, are functionalized and covalently immobilized within both matrices through EDC/NHS chemical coupling. This primary crosslinking enables uniform distribution of inorganic phases, unlocking tuneable rheological properties, adequate for extrusion 3D printing. Then, a secondary crosslinking, leveraging the photo‐responsive moieties for post‐printing photocuring, enables the obtention of seamlessly integrated robust multi‐material constructs. Overall, BSAMA‐based inks offer higher cytocompatibility, while HAMA‐based inks provide superior mechanical strength. Their combination in multi‐material constructs supports hASCs’ metabolic activity and proliferation, confirming bioactivity and cytocompatibility; and finite element modeling validates their mechanical performance, supporting clinical potential for multi‐tissue regeneration.