The recurrence of biofilm‐associated infections (BAIs) remains high after implant‐associated surgery. Biofilms on the implant surface reportedly shelter bacteria from antibiotics and evade innate immune defenses. Moreover, little is currently known about eliminating residual bacteria that can induce biofilm reinfection. Herein, novel “interference‐regulation strategy” based on bovine serum albumin–iridium oxide nanoparticles (BIONPs) as biofilm homeostasis interrupter and immunomodulator via singlet oxygen (1O2)‐sensitized mild hyperthermia for combating BAIs is reported. The catalase‐like BIONPs convert abundant H2O2 inside the biofilm‐microenvironment (BME) to sufficient oxygen gas (O2), which can efficiently enhance the generation of 1O2 under near‐infrared irradiation. The 1O2‐induced biofilm homeostasis disturbance (e.g., sigB, groEL, agr‐A, icaD, eDNA) can disrupt the sophisticated defense system of biofilm, further enhancing the sensitivity of biofilms to mild hyperthermia. Moreover, the mild hyperthermia‐induced bacterial membrane disintegration results in protein leakage and 1O2 penetration to kill bacteria inside the biofilm. Subsequently, BIONPs‐induced immunosuppressive microenvironment re‐rousing successfully re‐polarizes macrophages to pro‐inflammatory M1 phenotype in vivo to devour residual biofilm and prevent biofilm reconstruction. Collectively, this 1O2‐sensitized mild hyperthermia can yield great refractory BAIs treatment via biofilm homeostasis interference, mild‐hyperthermia, and immunotherapy, providing a novel and effective anti‐biofilm strategy.

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Since the breakout of COVID-19 pandemic in December 2019, effective modelling of the spreading of the virus has become an essential reference for the epidemic controlling. In a bid to solve the problem of Epidemic prediction, susceptible-exposed-infected-recovered (SEIR) model are widely applied. However, this model seems lack the ability to handle random events which may occur during the spreading of the pandemic and the ability to simulate the pandemic spreading between different subdivided regions. Therefore, we propose an early version of susceptible–exposed–infected–recovered–deceased (SEIRD) model that combines the classic compartmental concepts of SEIRD and the random walk methodology to forecast COVID-19 in real time. Specifically, this study will focus on improvement of the exposed–infected part of SEIRD model. First, the exposed–infected section of SEIRD model will be applied to each subdivided regions separated. Then, instead of entering infected–recovered part directly, the infected of each district will be selected and sent to linked districts by random walk system to mimic the commuting and irregular trips between regions. Eventually, after the re-distribution of infected patients, the model will enter the infected–recovered section. This argued model adopt the SEIRD model to forecasting of virus spreading between small regions and taking irregular moving of citizens into consideration via random walk system, thus provide an effective reference for countries which aim to respond to the post-epidemic era.

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