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Constructing a Novel Simple LEEP Training Model.

ABSTRACT: BACKGROUND: Duty hour restrictions and enhanced focus on patient safety have prompted the development of new instruction models for practice of surgical techniques outside the operating room, including models for teaching loop electrosurgical excisional procedure (LEEP), a common procedure that gynecology residents perform to diagnose and manage cervical disease. OBJECTIVE: We sought to develop an inexpensive and reusable training model for guided practice opportunities that will improve gynecology residents' LEEP technique. METHODS: Polyvinyl chloride, foam, and a polish sausage are used to simulate the basic anatomy of the vagina and cervix. A 2-in-diameter polyvinyl chloride pipe and high-density foam are used to create a realistic representation with the sausage simulating the cervix. An electrosurgical pad is attached to the sausage and a standard operating room electrosurgical generator is used. RESULTS: After a brief lecture and demonstration of the LEEP procedure, gynecology residents are positioned at individual stations. Use of 2 to 3 instructors allows for the provision of directions and feedback to residents as they perform the simulated LEEP. During the last 6 years, this model has continued to improve residents' confidence and skills with the procedure. CONCLUSIONS: An anatomically accurate LEEP model can not only improve resident knowledge, skills, and confidence, but also improve quality and patient safety. This training model allows residents to refine their surgical skills through guided practice and instructors to monitor performance before residents to perform the procedure on patients.

SUBMITTER: Walters CL

PROVIDER: S-EPMC3693702 | biostudies-other | 2013 Jun

REPOSITORIES: biostudies-other

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Project description:BackgroundThe concept of overreaching and super compensation is widely in use by athletes and coaches seeking to maximize performance and adaptations to exercise training. The physiological aspects of acute fatigue, overreaching and non-functional overreaching are, however, not well understood, and well-defined negative physiological outcomes are missing. Instead, the concept relies heavily on performance outcomes for differentiating between the states. Recent advancements in the field of integrated exercise physiology have associated maladaptations in muscular oxidative function to high loads of exercise training.MethodEleven female and male subjects that exercised regularly but did not engage in high-intensity interval training (HIIT) were recruited to a 4-week long training intervention where the responses to different training loads were studied. Highly monitored HIIT sessions were performed on a cycle ergometer in a progressive fashion with the intent to accomplish a training overload. Throughout the intervention, physiological and psychological responses to HIIT were assessed, and the results were used to construct a diagnostic model that could indicate maladaptations during excessive training loads.ResultsWe here use mitochondrial function as an early marker of excessive training loads and show the dynamic responses of several physiological and psychological measurements during different training loads. During HIIT, a loss of mitochondrial function was associated with reduced glycolytic, glucoregulatory and heart rate responses and increased ratings of perceived exertion in relation to several physiological measurements. The profile of mood states was highly affected after excessive training loads, whereas performance staled rather than decreased. By implementing five of the most affected and relevant measured parameters in a diagnostic model, we could successfully, and in all the subjects, identify the training loads that lead to maladaptations.ConclusionsAs mitochondrial parameters cannot be assessed without donating a muscle biopsy, this test can be used by coaches and exercise physiologists to monitor adaptation to exercise training for improving performance and optimizing the health benefits of exercise. Clinical trial registry number NCT04753021 . Retrospectively registered 2021-02-12.

Project description:BackgroundUncovering the evolutionary principles of gene coexpression network is important for our understanding of the network topological property of new genes. However, most existing evolutionary models only considered the evolution of duplication genes and only based on the degree of genes, ignoring the other key topological properties. The evolutionary mechanism by which how are new genes integrated into the ancestral networks are not yet to be comprehensively characterized. Herein, based on the human ribonucleic acid-sequencing (RNA-seq) data, we develop a new evolutionary model of gene coexpression network which considers the evolutionary process of both duplication genes and de novo genes.ResultsBased on the human RNA-seq data, we construct a gene coexpression network consisting of 8061 genes and 638624 links. We find that there are 1394 duplication genes and 126 de novo genes in the network. Then based on human gene age data, we reproduce the evolutionary process of this gene coexpression network and develop a new evolutionary model. We find that the generation rates of duplication genes and de novo genes are approximately 3.58/Myr (Myr=Million year) and 0.31/Myr, respectively. Based on the average degree and coreness of parent genes, we find that the gene duplication is a random process. Eventually duplication genes only inherit 12.89% connections from their parent genes and the retained connections have a smaller edge betweenness. Moreover, we find that both duplication genes and de novo genes prefer to develop new interactions with genes which have a large degree and a large coreness. Our proposed model can generate an evolutionary network when the number of newly added genes or the length of evolutionary time is known.ConclusionsGene duplication and de novo genes are two dominant evolutionary forces in shaping the coexpression network. Both duplication genes and de novo genes develop new interactions through a "rich-gets-richer" mechanism in terms of degree and coreness. This mechanism leads to the scale-free property and hierarchical architecture of biomolecular network. The proposed model is able to construct a gene coexpression network with comprehensive biological characteristics.

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Constructing a Novel Simple LEEP Training Model.

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