Background Hemostasis and repair are two essential processes in wound healing, yet early hemostasis and following vascularization are challenging to address in an integrated manner. Results In this study, we constructed a hemostatic sponge OBNC-DFO by fermentation of Komagataeibacterxylinus combined with TEMPO oxidation to obtain oxidized bacterial nanocellulose (OBNC). Then angiogenetic drug desferrioxamine (DFO) was grafted through an amide bond, and it promoted clot formation and activated coagulation reaction by rapid blood absorption due to the high total pore area (approximately 42.429 m2/g measured by BET). The further release of DFO stimulated the secretion of HIF-1α and the reconstruction of blood flow, thus achieving rapid hemostasis and vascularization in damaged tissue. This new hemostatic sponge can absorb water at a rate of approximate 1.70 g/s, rapidly enhancing clot formation in the early stage of hemostasis. In vitro and in vivo coagulation experiments (in rat tail amputation model and liver trauma model) demonstrated superior pro-coagulation effects of OBNC and OBNC-DFO to clinically used collagen hemostatic sponges (COL). They promoted aggregation and activation of red blood cells and platelets with shorter whole blood clotting time, more robust activation of endogenous coagulation pathways and less blood loss. In vitro cellular assays showed that OBNC-DFO prevailed over OBNC by promoting the proliferation of human umbilical vein endothelial cells (HUVECs). In addition, the release of DFO enhanced the secretion of HIF-1α, further strengthening vascularization in damaged skin. In the rat skin injury model, 28 days after being treated with OBNC-DFO, skin appendages (e.g., hair follicles) became more intact, indicating the achievement of structural and functional regeneration of the skin. Conclusion This hemostatic and vascularization-promoting oxidized bacterial nanocellulose hemostatic sponge, which rapidly activates coagulation pathways and enables skin regeneration, is a highly promising hemostatic and pro-regenerative repair biomaterial. Graphical Abstract

The “double‐edged sword” effect of macrophages under the influence of different microenvironments determines the outcome and prognosis of tissue injury. Accurate and stable reprogramming macrophages (Mφ) are the key to rapid wound healing. In this study, an immunized microsphere‐engineered GelMA hydrogel membrane is constructed for oral mucosa treatment. The nanoporous poly(lactide‐co‐glycolide) (PLGA) microsphere drug delivery system combined with the photo‐cross‐linkable hydrogel is used to release the soybean lecithin (SL)and IL‐4 complexes (SL/IL‐4) sustainedly. In this way, it is realized effective wound fit, improvement of drug encapsulation, and stable triphasic release of interleukin‐4 (IL‐4). In both in vivo and in vitro experiments, it is demonstrated that the hydrogel membrane can reprogram macrophages in the microenvironment into M2Mφ anti‐inflammatory types, thereby inhibiting the local excessive inflammatory response. Meanwhile, high levels of platelet‐derived growth factor (PDGF) secreted by M2Mφ macrophages enhanced neovascular maturation by 5.7‐fold, which assisted in achieving rapid healing of oral mucosa. These findings suggest that the immuno‐engineered hydrogel membrane system can re‐modulating the biological effects of Mφ, and potentiating the maturation of neovascularization, ultimately achieving the rapid repair of mucosal tissue. This new strategy is expected to be a safe and promising immunomodulatory biomimetic material for clinical translation.

The ultraviolet-visible (UV-Vis) spectroscopy measurement method of Chemical Oxygen Demand (COD) in water is a simple physical method that can measure water without secondary pollution from chemical reagents. To solve the problems of low accuracy and insufficient generalization capability of the COD prediction model, an improved Bagging algorithm is proposed and evaluated in this study. The Improved-Bagging algorithm can reduce model variance and bias concurrently, and improves the accuracy and stability of the traditional Bagging algorithm. Results show that the Improved-Bagging algorithm achieves a better prediction ability on different preprocessed data than the traditional Bagging algorithm. After ensemble empirical mode decomposition based (EEMD-Based) algorithm denoising and stability competitive adaptive reweighted sampling (SCARS) algorithm dimension reduction, Improved-Bagging model achieves the best prediction performance. Its coefficient of determination (R 2 ) on the prediction set reached 0.9317, its root mean square error of prediction (RMSEP) reached 5.39 mg/L, and its variance reached 5.53 mg 2 . Results also show that the Improved-Bagging algorithm can accurately measure the COD concentration in water, which lays the foundation for the wide application of spectroscopy to measure water quality parameters.