Publications

Recent in vitro studies have demonstrated that amyloid fibrils found in semen from healthy and HIV-infected men, as well as semen itself, can markedly enhance HIV infection rates. Semen fibrils are made up of multiple naturally occurring peptide fragments derived from semen. The best characterized of these fibrils are SEVI (semen-derived enhancer of viral infection), made up of residues 248-286 of prostatic acidic phosphatase, and the SEM1 fibrils, made up of residues 86-107 of semenogelin 1. A small molecule screen for antagonists of semen fibrils identified four compounds that lowered semen-mediated enhancement of HIV-1 infectivity. One of the four, gallic acid, was previously reported to antagonize other amyloids and to exert anti-inflammatory effects. To better understand the mechanism by which gallic acid modifies the properties of semen amyloids, we performed biophysical measurements (atomic force microscopy, electron microscopy, confocal microscopy, thioflavin T and Congo Red fluorescence assays, zeta potential measurements) and quantitative assays on the effects of gallic acid on semen-mediated enhancement of HIV infection and inflammation. Our results demonstrate that gallic acid binds to both SEVI and SEM1 fibrils and modifies their surface electrostatics to render them less cationic. In addition, gallic acid decreased semen-mediated enhancement of HIV infection but did not decrease the inflammatory response induced by semen. Together, these observations identify gallic acid as a non-polyanionic compound that inhibits semen-mediated enhancement of HIV infection and suggest the potential utility of incorporating gallic acid into a multicomponent microbicide targeting both the HIV virus and host components that promote viral infection.

BACKGROUND

In the United States, 795,000 people suffer strokes each year; 10-15 % of these strokes can be attributed to stenosis caused by plaque in the carotid artery, a major stroke phenotype risk factor. Studies comparing treatments for the management of asymptomatic carotid stenosis are challenging for at least two reasons: 1) administrative billing codes (i.e., Current Procedural Terminology (CPT) codes) that identify carotid images do not denote which neurovascular arteries are affected and 2) the majority of the image reports are negative for carotid stenosis. Studies that rely on manual chart abstraction can be labor-intensive, expensive, and time-consuming. Natural Language Processing (NLP) can expedite the process of manual chart abstraction by automatically filtering reports with no/insignificant carotid stenosis findings and flagging reports with significant carotid stenosis findings; thus, potentially reducing effort, costs, and time.

METHODS

In this pilot study, we conducted an information content analysis of carotid stenosis mentions in terms of their report location (Sections), report formats (structures) and linguistic descriptions (expressions) from Veteran Health Administration free-text reports. We assessed an NLP algorithm, pyConText's, ability to discern reports with significant carotid stenosis findings from reports with no/insignificant carotid stenosis findings given these three document composition factors for two report types: radiology (RAD) and text integration utility (TIU) notes.

RESULTS

We observed that most carotid mentions are recorded in prose using categorical expressions, within the Findings and Impression sections for RAD reports and within neither of these designated sections for TIU notes. For RAD reports, pyConText performed with high sensitivity (88 %), specificity (84 %), and negative predictive value (95 %) and reasonable positive predictive value (70 %). For TIU notes, pyConText performed with high specificity (87 %) and negative predictive value (92 %), reasonable sensitivity (73 %), and moderate positive predictive value (58 %). pyConText performed with the highest sensitivity processing the full report rather than the Findings or Impressions independently.

CONCLUSION

We conclude that pyConText can reduce chart review efforts by filtering reports with no/insignificant carotid stenosis findings and flagging reports with significant carotid stenosis findings from the Veteran Health Administration electronic health record, and hence has utility for expediting a comparative effectiveness study of treatment strategies for stroke prevention.