ObjectiveThis study aims to investigate the role of fast-track surgery in preventing the development of postoperative delirium and other complications in elderly patients with colorectal carcinoma.MethodsA total of 240 elderly patients with colorectal carcinoma (aged ≥70 years) undergoing open colorectal surgery was randomly assigned into two groups, in which the patients were managed perioperatively either with traditional or fast-track approaches. The length of hospital stay (LOS) and time to pass flatus were compared. The incidence of postoperative delirium and other complications were evaluated. Serum interleukin-6 (IL-6) levels were determined before and after surgery.ResultsThe LOS was significantly shorter in the fast-track surgery (FTS) group than that in the traditional group. The recovery of bowel movement (as indicated by the time to pass flatus) was faster in the FTS group. The postoperative complications including pulmonary infection, urinary infection and heart failure were significantly less frequent in the FTS group. Notably, the incidence of postoperative delirium was significantly lower in patients with the fast track therapy (4/117, 3.4 %) than with the traditional therapy (15/116, 12.9 %; p = 0.008). The serum IL-6 levels on postoperative days 1, 2, and 3 in patients with the fast-track therapy were significantly lower than those with the traditional therapy (p < 0.001).ConclusionsCompared to traditional perioperative management, fast-track surgery decreases the LOS, facilitates the recovery of bowel movement, and reduces occurrence of postoperative delirium and other complications in elderly patients with colorectal carcinoma. The lower incidence of delirium is at least partly attributable to the reduced systemic inflammatory response mediated by IL-6.
Modern deep neural networks are highly vulnerable to adversarial examples, which attracts more and more researchers' attention to craft powerful adversarial examples. Most of these generation algorithms create global perturbations that would affect the visual quality of adversarial examples. To mitigate such drawbacks, some attacks attempt to generate local perturbations. However, existing local adversarial attacks are time‐consuming and the generated adversarial examples are still distinguishable from clean images. In this paper, we propose a novel efficient local adversarial attack (ELAA) using model interpreters to generate severe local perturbations and improve the imperceptibly of the generated adversarial examples. Specifically, we take advantage of model interpretation methods to search the discriminative regions of clean images. Then, we generate local adversarial examples by adding masks to original clean images. We also propose a new optimization method to reduce the redundancy of local perturbations. Through extensive experiments, we show our ELAA can maintain a high attack ability while preserving the visual quality of clean images. Experimental results also demonstrate our local attack outperforms state‐of‐the‐art local attack methods under various system settings.
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