Transintestinal transport of the anti-inflammatory drug 4F and the modulation of transintestinal cholesterol efflux.

The site and mechanism of action of the apoA-I mimetic peptide 4F are incompletely understood. Transintestinal cholesterol efflux (TICE) is a process involved in the clearance of excess cholesterol from the body. While TICE is responsible for at least 30% of the clearance of neutral sterols from the circulation into the intestinal lumen, few pharmacological agents have been identified that modulate this pathway. We show first that circulating 4F selectively targets the small intestine (SI) and that it is predominantly transported into the intestinal lumen. This transport of 4F into the SI lumen is transintestinal in nature, and it is modulated by TICE. We also show that circulating 4F increases reverse cholesterol transport from macrophages and cholesterol efflux from lipoproteins via the TICE pathway. We identify the cause of this modulation of TICE either as 4F being a cholesterol acceptor with respect to enterocytes, from which 4F enhances cholesterol efflux, or as 4F being an intestinal chaperone with respect to TICE. Our results assign a novel role for 4F as a modulator of the TICE pathway and suggest that the anti-inflammatory functions of 4F may be a partial consequence of the codependent intestinal transport of both 4F and cholesterol.

Source and role of intestinally derived lysophosphatidic acid in dyslipidemia and atherosclerosis.

We previously reported that i) a Western diet increased levels of unsaturated lysophosphatidic acid (LPA) in small intestine and plasma of LDL receptor null (LDLR(-/-)) mice, and ii) supplementing standard mouse chow with unsaturated (but not saturated) LPA produced dyslipidemia and inflammation. Here we report that supplementing chow with unsaturated (but not saturated) LPA resulted in aortic atherosclerosis, which was ameliorated by adding transgenic 6F tomatoes. Supplementing chow with lysophosphatidylcholine (LysoPC) 18:1 (but not LysoPC 18:0) resulted in dyslipidemia similar to that seen on adding LPA 18:1 to chow. PF8380 (a specific inhibitor of autotaxin) significantly ameliorated the LysoPC 18:1-induced dyslipidemia. Supplementing chow with LysoPC 18:1 dramatically increased the levels of unsaturated LPA species in small intestine, liver, and plasma, and the increase was significantly ameliorated by PF8380 indicating that the conversion of LysoPC 18:1 to LPA 18:1 was autotaxin dependent. Adding LysoPC 18:0 to chow increased levels of LPA 18:0 in small intestine, liver, and plasma but was not altered by PF8380 indicating that conversion of LysoPC 18:0 to LPA 18:0 was autotaxin independent. We conclude that i) intestinally derived unsaturated (but not saturated) LPA can cause atherosclerosis in LDLR(-/-) mice, and ii) autotaxin mediates the conversion of unsaturated (but not saturated) LysoPC to LPA.

Evaluating the predictive ability of natural language processing in identifying tertiary/quaternary cases in prioritization workflows for interhospital transfer.

Objectives: Tertiary and quaternary (TQ) care refers to complex cases requiring highly specialized health services. Our study aimed to compare the ability of a natural language processing (NLP) model to an existing human workflow in predictively identifying TQ cases for transfer requests to an academic health center. Materials and methods: Data on interhospital transfers were queried from the electronic health record for the 6-month period from July 1, 2020 to December 31, 2020. The NLP model was allowed to generate predictions on the same cases as the human predictive workflow during the study period. These predictions were then retrospectively compared to the true TQ outcomes. Results: There were 1895 transfer cases labeled by both the human predictive workflow and the NLP model, all of which had retrospective confirmation of the true TQ label. The NLP model receiver operating characteristic curve had an area under the curve of 0.91. Using a model probability threshold of ≥0.3 to be considered TQ positive, accuracy was 81.5% for the NLP model versus 80.3% for the human predictions (P = .198) while sensitivity was 83.6% versus 67.7% (P<.001). Discussion: The NLP model was as accurate as the human workflow but significantly more sensitive. This translated to 15.9% more TQ cases identified by the NLP model. Conclusion: Integrating an NLP model into existing workflows as automated decision support could translate to more TQ cases identified at the onset of the transfer process.