Snake venom contains many toxic proteins that can destroy the circulatory system or nervous system of prey. Studies have found that these snake venom proteins have the potential to treat cardiovascular and nervous system diseases. Therefore, the study of snake venom protein is conducive to the development of related drugs. The research technologies based on traditional biochemistry can accurately identify these proteins, but the experimental cost is high and the time is long. Artificial intelligence technology provides a new means and strategy for large-scale screening of snake venom proteins from the perspective of computing. In this paper, we developed a sequence-based computational method to recognize snake toxin proteins. Specially, we utilized three different feature descriptors, namely g-gap, natural vector and word 2 vector, to encode snake toxin protein sequences. The analysis of variance (ANOVA), gradient-boost decision tree algorithm (GBDT) combined with incremental feature selection (IFS) were used to optimize the features, and then the optimized features were input into the deep learning model for model training. The results show that our model can achieve a prediction performance with an accuracy of 82.00% in 10-fold cross-validation. The model is further verified on independent data, and the accuracy rate reaches to 81.14%, which demonstrated that our model has excellent prediction performance and robustness.

Trauma and bone loss from infections, tumors, and congenital diseases make bone repair and regeneration the greatest challenges in orthopedic, craniofacial, and plastic surgeries. The shortage of donors, intrinsic limitations, and complications in transplantation have led to more focus and interest in regenerative medicine. Structures that closely mimic bone tissue can be produced by this unique technology. The steady development of three-dimensional (3D)-printed bone tissue engineering scaffold therapy has played an important role in achieving the desired goal. Bioceramic scaffolds are widely studied and appear to be the most promising solution. In addition, 3D printing technology can simulate mechanical and biological surface properties and print with high precision complex internal and external structures to match their functional properties. Inkjet, extrusion, and light-based 3D printing are among the rapidly advancing bone bioprinting technologies. Furthermore, stem cell therapy has recently shown an important role in this field, although large tissue defects are difficult to fill by injection alone. The combination of 3D-printed bone tissue engineering scaffolds with stem cells has shown very promising results. Therefore, biocompatible artificial tissue engineering with living cells is the key element required for clinical applications where there is a high demand for bone defect repair. Furthermore, the emergence of various advanced manufacturing technologies has made the form of biomaterials and their functions, composition, and structure more diversified, and manifold. The importance of this article lies in that it aims to briefly review the main principles and characteristics of the currently available methods in orthopedic bioprinting technology to prepare bioceramic scaffolds, and finally discuss the challenges and prospects for applications in this promising and vital field.

The development of a safe and targetable drug carrier is a major challenge. An efficient delivery system should protect cargo from degradation and cleanup, and control of drug release in the target site. Metal–organic frameworks (MOFs), consisting of metal ions and a variety of organic ligands, have been applied for drug delivery due to their distinct structure. In this review, we summarized the synthesis strategies of MOFs, especially emphasizing the methods of pore creation in frameworks, which were based on recent literatures. Subsequently, the controlled size, biocompatibility, drug releasing performances, and imaging of MOFs were discussed, which would pave the road for the application in drug-delivery systems.

As a biomedical material, porous titanium alloy has gained widespread recognition and application within the field of orthopedics. Its remarkable biocompatibility, bioactivity, and mechanical properties establish it as a promising material for facilitating bone regeneration. A well-designed porous structure can lower the material’s modulus while retaining ample strength, rendering it more akin to natural bone tissue. The progression of additive manufacturing (AM) technology has significantly propelled the advancement of porous implants, simplifying the production of such structures. AM allows for the customization of porous implants with various shapes and sizes tailored to individual patients. Additionally, it enables the design of microscopic-scale porous structures to closely mimic natural bone, thus opening up avenues for the development of porous titanium alloy bone implants that can better stimulate bone regeneration. This article reviews the research progress on the structural design and preparation methods of porous titanium alloy bone implants, analyzes the porous structure design parameters that affect the performance of the implant, and discusses the application of porous medical titanium alloys. By comparing the effects of the parameters of different porosity, pore shape, and pore size on implant performance, it was concluded that pore diameters in the range of 500~800 μm and porosity in the range of 70%–90% have better bone-regeneration effects. At the same time, when the pore structure is a diamond, rhombohedral, or cube structure, it has better mechanical properties and bone-regeneration effects, providing a reference range for the application of clinical porous implants.

The advent of three-dimensional (3D) printing technology has revolutionized the production of customized titanium (Ti) alloy implants. The success rate of implantation and the long-term functionality of these implants depend not only on design and material selection but also on their surface properties. Surface modification techniques play a pivotal role in improving the biocompatibility, osseointegration, and overall performance of 3D-printed Ti alloy implants. Hence, the primary objective of this review is to comprehensively elucidate various strategies employed for surface modification to enhance the performance of 3D-printed Ti alloy implants. This review encompasses both conventional and advanced surface modification techniques, which include physical–mechanical methods, chemical modification methods, bioconvergence modification technology, and the functional composite method. Furthermore, it explores the distinct advantages and limitations associated with each of these methods. In the future, efforts in surface modification will be geared towards achieving precise control over implant surface morphology, enhancing osteogenic capabilities, and augmenting antimicrobial functionality. This will enable the development of surfaces with multifunctional properties and personalized designs. By continuously exploring and developing innovative surface modification techniques, we anticipate that implant performance can be further elevated, paving the way for groundbreaking advancements in the field of biomedical engineering.

Although some pyroptosis-related (PR) prognostic models for cancers have been reported, pyroptosis-based features have not been fully discovered at the single-cell level in hepatocellular carcinoma (HCC). In this study, by deeply integrating single-cell and bulk transcriptome data, we systematically investigated significance of the shared pyroptotic signature at both single-cell and bulk levels in HCC prognosis. Based on the pyroptotic signature, a robust PR risk system was constructed to quantify the prognostic risk of individual patient. To further verify capacity of the pyroptotic signature on predicting patients’ prognosis, an attention mechanism-based deep neural network classification model was constructed. The mechanisms of prognostic difference in the patients with distinct PR risk were dissected on tumor stemness, cancer pathways, transcriptional regulation, immune infiltration and cell communications. A nomogram model combining PR risk with clinicopathologic data was constructed to evaluate the prognosis of individual patients in clinic. The PR risk could also evaluate therapeutic response to neoadjuvant therapies in HCC patients. In conclusion, the constructed PR risk system enables a comprehensive assessment of tumor microenvironment characteristics, accurate prognosis prediction and rational therapeutic options in HCC.