La Trobe University

About: La Trobe University is a education organization based out in Melbourne, Victoria, Australia. It is known for research contribution in the topics: Population & Health care. The organization has 13370 authors who have published 41291 publications receiving 1138269 citations. The organization is also known as: LaTrobe University & LTU.

What is a clinical pathway? Development of a definition to inform the debate

Abstract: Background Clinical pathways are tools used to guide evidence-based healthcare that have been implemented internationally since the 1980s. However, there is widespread lack of agreement on the impact of clinical pathways on hospital resources and patient outcomes. This can be partially attributed to the confusion for both researchers and healthcare workers regarding what constitutes a clinical pathway. This paper describes efforts made by a team of Cochrane Review authors to develop criteria to assist in the objective identification of clinical pathway studies from the literature.

Volatile and Contradictory Punishment

Abstract: Criminologists have recognized that contemporary penal policy and practice are characterized by an unusual degree of incoherence and volatility. Garland (1996) sees this as evidence of the limits of the sovereign state, Simon (1995) as a sign of the postmodern disintegration of modern penality, while others explain it in terms of the emergence of advanced liberalism and neo-liberal politics. This article argues that such incoherence is better understood in terms of the contradictory elements of New Right politics. The nature of this political alliance extends the repertory of penality simultaneously in `nostalgic' (neo-conservative) and `innovative' (neo-liberal) directions, resulting in considerable incoherence. At the same time, the conservative orientation to state authoritarian strategies and the neo-liberal leaning toward market and private sector governance, could account for the volatility. This brings into question some of the accounts based on more fundamental social transformations.

Targeting Macrophages in Cancer: From Bench to Bedside.

Abstract: Macrophages are a major component of the tumor microenvironment and orchestrate various aspects of immunity. Within tumors, macrophages can reversibly alter their endotype in response to environmental cues, including hypoxia and stimuli derived from other immune cells, as well as the extracellular matrix. Depending on their activation status, macrophages can exert dual influences on tumorigenesis by either antagonizing the cytotoxic activity immune cells or by enhancing antitumor responses. In most solid cancers, increased infiltration with tumor-associated macrophages (TAMs) has long been associated with poor patient prognosis, highlighting their value as potential diagnostic and prognostic biomarkers in cancer. A number of macrophage-centered approaches to anticancer therapy have been investigated, and include strategies to block their tumor-promoting activities or exploit their antitumor effector functions. Integrating therapeutic strategies to target TAMs to complement conventional therapies has yielded promising results in preclinical trials and warrants further investigation to determine its translational benefit in human cancer patients. In this review, we discuss the molecular mechanisms underlying the pro-tumorigenic programming of macrophages and provide a comprehensive update of macrophage-targeted therapies for the treatment of solid cancers.

Probability Density Function Estimation Using Gamma Kernels

Abstract: We consider estimating density functions which have support on [0, ∞) using some gamma probability densities as kernels to replace the fixed and symmetric kernel used in the standard kernel density estimator. The gamma kernels are non-negative and have naturally varying shape. The gamma kernel estimators are free of boundary bias, non-negative and achieve the optimal rate of convergence for the mean integrated squared error. The variance of the gamma kernel estimators at a distance x away from the origin is O(n−4/5x−1/2) indicating a smaller variance as x increases. Finite sample comparisons with other boundary bias free kernel estimators are made via simulation to evaluate the performance of the gamma kernel estimators.