Experimental models for peri-implant diseases: a narrative review.

OBJECTIVES: Peri-implant diseases, being the most common implant-related complications, significantly impact the normal functioning and longevity of implants. Experimental models play a crucial role in discovering potential therapeutic approaches and elucidating the mechanisms of disease progression in peri-implant diseases. This narrative review comprehensively examines animal models and common modeling methods employed in peri-implant disease research and innovatively summarizes the in vitro models of peri-implant diseases. MATERIALS AND METHODS: Articles published between 2015 and 2023 were retrieved from PubMed/Medline, Web of Science, and Embase. All studies focusing on experimental models of peri-implant diseases were included and carefully evaluated. RESULTS: Various experimental models of peri-implantitis have different applications and advantages. The dog model is currently the most widely utilized animal model in peri-implant disease research, while rodent models have unique advantages in gene knockout and systemic disease induction. In vitro models of peri-implant diseases are also continuously evolving to meet different experimental purposes. CONCLUSIONS: The utilization of experimental models helps simplify experiments, save time and resources, and promote advances in peri-implant disease research. Animal models have been proven valuable in the early stages of drug development, while technological advancements have brought about more predictive and relevant in vitro models. CLINICAL RELEVANCE: This review provides clear and comprehensive model selection strategies for researchers in the field of peri-implant diseases, thereby enhancing understanding of disease pathogenesis and providing possibilities for developing new treatment strategies.

Role of mechano-sensitive non-coding RNAs in bone remodeling of orthodontic tooth movement: recent advances.

Orthodontic tooth movement relies on bone remodeling and periodontal tissue regeneration in response to the complicated mechanical cues on the compressive and tensive side. In general, mechanical stimulus regulates the expression of mechano-sensitive coding and non-coding genes, which in turn affects how cells are involved in bone remodeling. Growing numbers of non-coding RNAs, particularly mechano-sensitive non-coding RNA, have been verified to be essential for the regulation of osteogenesis and osteoclastogenesis and have revealed how they interact with signaling molecules to do so. This review summarizes recent findings of non-coding RNAs, including microRNAs and long non-coding RNAs, as crucial regulators of gene expression responding to mechanical stimulation, and outlines their roles in bone deposition and resorption. We focused on multiple mechano-sensitive miRNAs such as miR-21, - 29, -34, -103, -494-3p, -1246, -138-5p, -503-5p, and -3198 that play a critical role in osteogenesis function and bone resorption. The emerging roles of force-dependent regulation of lncRNAs in bone remodeling are also discussed extensively. We summarized mechano-sensitive lncRNA XIST, H19, and MALAT1 along with other lncRNAs involved in osteogenesis and osteoclastogenesis. Ultimately, we look forward to the prospects of the novel application of non-coding RNAs as potential therapeutics for tooth movement and periodontal tissue regeneration.

5G Massive MIMO Signal Detection Algorithm Based on Deep Learning.

Aiming at the problems of poor signal detection effect caused by many interference factors in large-scale MIMO technology scene, this paper proposes a 5G massive MIMO signal detection algorithm based on deep learning. Firstly, the MIMO system model based on neural network is constructed, and Deep Neural Network (DNN) detection is introduced into the receiver of the traditional MIMO system to obtain the information bits or codewords and channel state information transmitted by transmitters. Then, the end-to-end training method is adopted to make neural network learn the mapping relationship of information bits or codewords transmitted by system transceivers. Furthermore, DNN detector is improved based on Simplified Message Passing Detection (sMPD) algorithm, and the correction factor is updated continuously to optimize network parameters to realize the accurate detection and decoding of the MIMO system. Finally, the proposed algorithm is experimentally analyzed based on the TensorFlow deep learning framework. Experimental results show that when signal-to-noise ratio is 10 dB, the bit error rate and mean square error are lower than 0.005 and 0.1, respectively.