The diaphragm muscle (DIAm) is the primary inspiratory muscle in mammals and is active during ventilatory behaviors, but it is also involved in higher‐force behaviors such as those necessary for clearing the airway. Our laboratory has previously reported DIAm sarcopenia in rats and mice characterized by DIAm atrophy and a reduction in maximum specific force at 24 months of age. In Fischer 344 rats, these studies were limited to male animals, although in other studies, we noted a more rapid increase in body mass from 6 to 24 months of age in females (~140%) compared to males (~110%). This difference in body weight gain suggests a possible sex difference in the manifestation of sarcopenia. In mice, we previously measured transdiaphragmatic pressure (Pdi) to evaluate in vivo DIAm force generation across a range of motor behaviors, but found no evidence of sex‐related differences. The purpose of this study in Fischer 344 rats was to evaluate if there are sex‐related differences in DIAm sarcopenia, and if such differences translate to a functional impact on Pdi generation across motor behaviors and maximal Pdi (Pdimax) elicited by bilateral phrenic nerve stimulation. In both males and females, DIAm sarcopenia was apparent in 24‐month‐old rats with a ~30% reduction in both maximum specific force and the cross‐sectional area of type IIx and/or IIb fibers. Importantly, in both males and females, Pdi generated during ventilatory behaviors was unimpaired by sarcopenia, even during more forceful ventilatory efforts induced via airway occlusion. Although ventilatory behaviors were preserved with aging, there was a ~20% reduction in Pdimax, which likely impairs the ability of the DIAm to generate higher‐force expulsive airway clearance behaviors necessary to maintain airway patency.
Drug labeling contains an ‘INDICATIONS AND USAGE’ that provides vital information to support clinical decision making and regulatory management. Effective extraction of drug indication information from free-text based resources could facilitate drug repositioning projects and help collect real-world evidence in support of secondary use of approved medicines. To enable AI-powered language models for the extraction of drug indication information, we used manual reading and curation to develop a Drug Indication Classification and Encyclopedia (DICE) based on FDA approved human prescription drug labeling. A DICE scheme with 7,231 sentences categorized into five classes (indications, contradictions, side effects, usage instructions, and clinical observations) was developed. To further elucidate the utility of the DICE, we developed nine different AI-based classifiers for the prediction of indications based on the developed DICE to comprehensively assess their performance. We found that the transformer-based language models yielded an average MCC of 0.887, outperforming the word embedding-based Bidirectional long short-term memory (BiLSTM) models (0.862) with a 2.82% improvement on the test set. The best classifiers were also used to extract drug indication information in DrugBank and achieved a high enrichment rate (>0.930) for this task. We found that domain-specific training could provide more explainable models without performance sacrifices and better generalization for external validation datasets. Altogether, the proposed DICE could be a standard resource for the development and evaluation of task-specific AI-powered, natural language processing (NLP) models.
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