1,721,543 research outputs found
Adhesive Small Bowel Obstruction and the six w's: Who, How, Why, When, What, and Where to diagnose and operate?
No full textBackground and aims: Approximately 75% of patients admitted with small bowel obstruction have intra-abdominal adhesions as their cause (adhesive small bowel obstruction). Up to 70% of adhesive small bowel obstruction cases, in the absence of strangulation and bowel ischemia, can be successfully treated with conservative management. However, emerging evidence shows that surgery performed early during the first episode of adhesive small bowel obstruction is highly effective. The objective of this narrative review is to summarize the current evidence on adhesive small bowel obstruction management strategies. Materials and methods: A review of the literature published over the last 20 years was performed to assess Who, hoW, Why, When, What, and Where diagnose and operate on patients with adhesive small bowel obstruction. Results: Adequate patient selection through physical examination and computed tomography is the key factor of the entire management strategy, as failure to detect patients with strangulated adhesive small bowel obstruction and bowel ischemia is associated with significant morbidity and mortality. The indication for surgical exploration is usually defined as a failure to pass contrast into the ascending colon within 8-24 h. However, operative management with early adhesiolysis, defined as operative intervention on either the calendar day of admission or the calendar day after admission, has recently shown to be associated with an overall long-term survival benefit compared to conservative management. Regarding the surgical technique, laparoscopy should be used only in selected patients with an anticipated single obstructing band, and there should be a low threshold for conversion to an open procedure in cases of high risk of bowel injuries. Conclusion: Although most adhesive small bowel obstruction patients without suspicion of bowel strangulation or gangrene are currently managed nonoperatively, the long-term outcomes following this approach need to be analyzed in a more exhaustive way, as surgery performed early during the first episode of adhesive small bowel obstruction has shown to be highly effective, with a lower rate of recurrence
A Deep Generative Model for Fragment-Based Molecule Generation
Full text linkMolecule generation is a challenging open problem in cheminformatics. Currently, deep generative approaches addressing the challenge belong to two broad categories, differing in how molecules are represented. One approach encodes molecular graphs as strings of text, and learns their corresponding character-based language model. Another, more expressive, approach operates directly on the molecular graph. In this work, we address two limitations of the former: generation of invalid and duplicate molecules. To improve validity rates, we develop a language model for small molecular substructures called fragments, loosely inspired by the well-known paradigm of Fragment-Based Drug Design. In other words, we generate molecules fragment by fragment, instead of atom by atom. To improve uniqueness rates, we present a frequency-based masking strategy that helps generate molecules with infrequent fragments. We show experimentally that our model largely outperforms other language model-based competitors, reaching state-of-the-art performances typical of graph-based approaches. Moreover, generated molecules display molecular properties similar to those in the training sample, even in absence of explicit task-specific supervision
Response to comment on “Antibiotic treatment and appendectomy for uncomplicated acute appendicitis in adults and children: A Systematic Review and Meta-analysis” by Minneci PC et al
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Edge-based sequential graph generation with recurrent neural networks
Full text linkGraph generation with Machine Learning is an open problem with applications in various research fields. In this work, we propose to cast the generative process of a graph into a sequential one, relying on a node ordering procedure. We use this sequential process to design a novel generative model composed of two recurrent neural networks that learn to predict the edges of graphs: the first network generates one endpoint of each edge, while the second network generates the other endpoint conditioned on the state of the first. We test our approach extensively on five different datasets, comparing with two well-known baselines coming from graph literature, and two recurrent approaches, one of which holds state of the art performances. Evaluation is conducted considering quantitative and qualitative characteristics of the generated samples. Results show that our approach is able to yield novel, and unique graphs originating from very different distributions while retaining structural properties very similar to those in the training sample. Under the proposed evaluation framework, our approach is able to reach performances comparable to the current state of the art on the graph generation task
Intermittent bile acid therapy for recurrent cholesterol gallstones: a 5 year prospective multicentre trial.
No full textGraph generation by sequential edge prediction
No full textGraph generation with Machine Learning models is a challenging problem with applications in various research fields. Here, we propose a recurrent Deep Learning based model to generate graphs by learning to predict their ordered edge sequence. Despite its simplicity, our experiments on a wide range of datasets show that our approach is able to generate graphs originating from very different distributions, outperforming canonical graph generative models from graph theory, and reaching performances comparable to the current state of the art on graph generation
Beware of the difference between randomized controlled trials and daily clinical practice (in reply to “Surgery or antibiotics for acute appendicitis? Take care about study’s design and methodology!”, by Ceresoli M, et al.)
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Improved immediate postoperative pain following laparoscopic inguinal herniorrhaphy using self-adhering mesh: the importance of clinical practice guidelines
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