ABSTRACT
A crucial factor in the pathogenesis of orthopedics associated infections is that bacteria do not only colonize the implant surface but also the surrounding tissues. This study aimed to engineer an antimicrobial release coating for stainless steel (SS) surfaces, to impart them with the ability to prevent Staphylococci colonization. Chlorhexidine (CHX) was immobilized using two polydopamine (pDA)-based approaches: a one-pot synthesis, where CHX is dissolved together with dopamine before its polymerization; and a two-step methodology, comprising the deposition of a pDA layer to which CHX is immobilized. To modulate CHX release, an additional layer of pDA was also added for both strategies. Immobilization of CHX using a one-step approach yielded surfaces with a more homogenous coating and less roughness than the other strategies. The amount of released CHX was lower for the one-step approach, as opposed to the two-step approach yielding the higher release, which could be decreased by applying an outward layer of pDA. Both one and two-step approaches provided the surfaces with the ability to prevent bacterial colonization of the surface itself and kill most of bacteria in the bulk phase up to 10 days. This long-term antimicrobial performance alluded a stable and enduring immobilization of CHX. In terms of biocompatibility, the amount of CHX released from the one-step approach did not compromise the growth of mammalian cells, contrary to the two-step strategy. Additionally, the few bacteria that managed to adhere to surfaces modified with one-step approach did not show evidence of resistance towards CHX. Overall data underline that one-step immobilization of CHX holds great potential to be further applied in the fight against orthopedic devices associated infections.
Subject(s)
Anti-Infective Agents , Chlorhexidine , Animals , Anti-Bacterial Agents , Chlorhexidine/pharmacology , Dopamine , Stainless SteelABSTRACT
The hazard function plays an important role in cancer patient survival studies, as it quantifies the instantaneous risk of death of a patient at any given time. Often in cancer clinical trials, unimodal hazard functions are observed, and it is of interest to detect (estimate) the turning point (mode) of hazard function, as this may be an important measure in patient treatment strategies with cancer. Moreover, when patient cure is a possibility, estimating cure rates at different stages of cancer, in addition to their proportions, may provide a better summary of the effects of stages on survival rates. Therefore, the main objective of this paper is to consider the problem of estimating the mode of hazard function of patients at different stages of cervical cancer in the presence of long-term survivors. To this end, a mixture cure rate model is proposed using the log-logistic distribution. The model is conveniently parameterized through the mode of the hazard function, in which cancer stages can affect both the cured fraction and the mode. In addition, we discuss aspects of model inference through the maximum likelihood estimation method. A Monte Carlo simulation study assesses the coverage probability of asymptotic confidence intervals.
ABSTRACT
In lifetimes studies, the occurrence of an event (such as tumor detection or death) might be caused by one of many competing causes. Moreover, both the number of causes and the time-to-event associated with each cause are not usually observable. The number of causes can be zero, corresponding to a cure fraction. In this article, we propose a method of estimating the numerical characteristics of unobservable stages (such as initiation, promotion and progression) of carcinogenesis from data on tumor size at detection in the presence of latent competing causes. To this end, a general survival model for spontaneous carcinogenesis under a hybrid latent activation scheme has been developed to allow for a simple pattern of the dynamics of tumor growth. It is assumed that a tumor becomes detectable when its size attains some threshold level (proliferation of tumorais cells (or descendants) generated by the malignant cell), which is treated as a random variable. We assume the number of initiated cells and the number of malignant cells (competing causes) both to follow weighted Poisson distributions. The advantage of this model is that it incorporates into the analysis characteristics of the stage of tumor progression as well as the proportion of initiated cells that had been 'promoted' to the malignant ones and the proportion of malignant cells that die before tumor induction. The lifetimes corresponding to each competing cause are assumed to follow a Weibull distribution. Parameter estimation of the proposed model is discussed through the maximum likelihood estimation method. A simulation study has been carried out in order to examine the coverage probabilities of the confidence intervals. Finally, we illustrate the usefulness of the proposed model by applying it to a real data involving malignant melanoma.
