No abstract

Single-atom nanozymes (SAzymes), with individually isolated metal atom as active sites, have shown tremendous potential as enzyme-based drugs for enzymatic therapy. However, using SAzymes in tumor theranostics remains challenging because of deficient enzymatic activity and insufficient endogenous H2O2. We develop an external-field-enhanced catalysis by an atom-level engineered FeN4-centered nanozyme (FeN4-SAzyme) for radio-enzymatic therapy. This FeN4-SAzyme exhibits peroxidase-like activity capable of catalyzing H2O2 into hydroxyl radicals and converting single-site FeII species to FeIII for subsequent glutathione oxidase-like activity. Density functional theory calculations are used to rationalize the origin of the single-site self-cascade enzymatic activity. Importantly, using X-rays can improve the overall single-site cascade enzymatic reaction process via promoting the conversion frequency of FeII/FeIII. As a H2O2 producer, natural glucose oxidase is further decorated onto the surface of FeN4-SAzyme to yield the final construct GOD@FeN4-SAzyme. The resulting GOD@FeN4-SAzyme not only supplies in situ H2O2 to continuously produce highly toxic hydroxyl radicals but also induces the localized deposition of radiation dose, subsequently inducing intensive apoptosis and ferroptosis in vitro. Such a synergistic effect of radiotherapy and self-cascade enzymatic therapy allows for improved tumor growth inhibition with minimal side effects in vivo. Collectively, this work demonstrates the introduction of external fields to enhance enzyme-like performance of nanozymes without changing their properties and highlights a robust therapeutic capable of self-supplying H2O2 and amplifying self-cascade reactions to address the limitations of enzymatic treatment.

In this paper, we present a parameter estimation procedure for a condition-based maintenance model under partial observations. Systems can be in a healthy or unhealthy operational state, or in a failure state. System deterioration is driven by a continuous time homogeneous Markov chain and the system state is unobservable, except the failure state. Vector information that is stochastically related to the system state is obtained through condition monitoring at equidistant sampling times. Two types of data histories are available -data histories that end with observable failure, and censored data histories that end when the system has been suspended from operation but has not failed. The state and observation processes are modeled in the hidden Markov framework and the model parameters are estimated using the expectation-maximization algorithm. We show that both the pseudolikelihood function and the parameter updates in each iteration of the expectation-maximization algorithm have explicit formulas. A numerical example is developed using real multivariate spectrometric oil data coming from the failing transmission units of 240-ton heavy hauler trucks used in the Athabasca oil sands of Alberta, Canada. Bus. Ind. 2013, 29 279-294 Appl. Stochastic Models Model assumptionsWe assume that a technical system's condition can be categorized into one of three states: a healthy or 'good as new' state (state 0), an unhealthy or deteriorated state (state 1), and a failure state (state 2). In many real world applications the state of an operational system is unobservable, and only the failure state is observable. For example, the state of an operational transmission unit in a heavy hauler truck cannot be observed without full system inspection, which is typically quite costly.Appl. Stochastic Models Bus. Ind. 2013, 29 279-294 As detailed in Section 2, to satisfy the assumption of independence and normality, we first need to fit a model that accounts for autocorrelation in the data histories, and choose as the observation process, in the hidden Markov model, the residuals of the fitted model. Before fitting a model to the data histories, we have to approximate the healthy portions of the