Absorbable polymers have attracted increasing attention in the field of bone regeneration in recent years for their degradation. Compared with other degradable polymers, polypropylene carbonate (PPC) has several advantages such as biodegradation and relatively cheap raw materials. Most importantly, PPC can degrade into water and carbon dioxide totally which does not give rise to local inflammation and bone resorption in vivo. However, pure PPC has not presented excellent osteoinductivity properties. In order to enhance the osteoinductivity of PPC, silicon nitride (SiN) was employed due to its excellent mechanical properties, biocompatibility and osteogenesis compared with the other common materials such as hydroxyapatite and calcium phosphate ceramics. In this study, composites of PPC mixed with different contents of SiN were prepared successfully (PSN10 with 10 wt% SiN content, and PSN20 with 20 wt% SiN content). The characterization of the composites suggested that PPC mixed with SiN evenly and PSN composites presented stable properties. The results in vitro revealed that the PSN20 composite possessed satisfactory biocompatibility and exerted better osteogenic differentiation effects on adipose-derived stem cells (ADSCs). In particular, the PSN20 composite accelerated the healing of bone defects better and degraded with the process of bone healing in vivo. Overall, the PSN20 composite exhibited better biocompatibility, induced osteogenic differentiation of ADSCs and promoted healing of bone defects, due to which the PSN composite is considered as a potential candidate for treating bone defects in the field of bone tissue engineering.

Accurate automatic sleep stage classification from single-channel EEG remains challenging due to the need for effective extraction of multiscale neurophysiological features and modeling of long-range temporal dependencies. This study aims to address these limitations by developing an efficient and compact deep learning architecture tailored for wearable and edge device applications. We propose MultiScaleSleepNet, a hybrid convolutional neural network–bidirectional long short-term memory–transformer architecture that extracts multiscale temporal and spectral features through parallel convolutional branches, followed by sequential modeling using a BiLSTM memory network and transformer-based attention mechanisms. The model obtained an accuracy, macro-averaged F1 score, and kappa coefficient of 88.6%, 0.833, and 0.84 on the Sleep-EDF dataset; 85.6%, 0.811, and 0.80 on the Sleep-EDF Expanded dataset; and 84.6%, 0.745, and 0.79 on the SHHS dataset. Ablation studies indicate that attention mechanisms and spectral fusion consistently improve performance, with the most notable gains observed for stages N1, N3, and rapid eye movement. MultiScaleSleepNet demonstrates competitive performance across multiple benchmark datasets while maintaining a compact size of 1.9 million parameters, suggesting robustness to variations in dataset size and class distribution. The study supports the feasibility of real-time, accurate sleep staging from single-channel EEG using parameter-efficient deep models suitable for portable systems.

To investigate the effect of inhibitor κBα (IκBα) on severe pneumonia and explain the mechanisms of nuclear factor κB (NF‑κB), the activation of NF‑κB was induced in Sprague-Dawley (SD) rats infected with Klebsiella pneumoniae (K. pneumoniae). The rats were then treated with differing concentrations of IκBα protein. A histological analysis was performed to compare the lung structure prior to and following treatment, and an immunohistochemistry assay was used to detect NF‑κB activity. In addition, the expression of certain inflammatory factors was detected using a protein chip assay. The severe pneumonia rat model was successfully produced and in model rats, NF‑κB was activated by K. pneumoniae. Following treatment with IκBα, the activity of NF‑κB was inhibited and pneumonia symptoms in model rats were alleviated. Furthermore, the expression of a number of inflammatory factors including tumor necrosis factor α (TNF-α), interleukin 6 (IL-6), interferon γ (IFN‑γ) and monocyte chemoattractant protein‑1 (MCP‑1) were also inhibited. The current study demonstrates that NF‑κB inhibition with IκBα protein therapy prevents the development of pneumonia in a K. pneumoniae rat model. The therapeutic effect is indicated by the responses of proinflammatory factors, including TNF-α, IL-6, IFN-γ and MCP-1.