| 1. |
- Faux, Pierre, et al.
(författare)
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Neanderthal introgression in SCN9A impacts mechanical pain sensitivity
- 2023
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Ingår i: Communications Biology. - 2399-3642. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- The Nav1.7 voltage-gated sodium channel plays a key role in nociception. Three functional variants in the SCN9A gene (encoding M932L, V991L, and D1908G in Nav1.7), have recently been identified as stemming from Neanderthal introgression and to associate with pain symptomatology in UK BioBank data. In 1000 genomes data, these variants are absent in Europeans but common in Latin Americans. Analysing high-density genotype data from 7594 Latin Americans, we characterized Neanderthal introgression in SCN9A. We find that tracts of introgression occur on a Native American genomic background, have an average length of similar to 123kb and overlap the M932L, V991L, and D1908G coding positions. Furthermore, we measured experimentally six pain thresholds in 1623 healthy Colombians. We found that Neanderthal ancestry in SCN9A is significantly associated with a lower mechanical pain threshold after sensitization with mustard oil and evidence of additivity of effects across Nav1.7 variants. Our findings support the reported association of Neanderthal Nav1.7 variants with clinical pain, define a specific sensory modality affected by archaic introgression in SCN9A and are consistent with independent effects of the Neanderthal variants on Nav1.7 function. Neanderthal-derived variants in the SCN9A gene (encoding the voltage gated sodium channel, Nav1.7) are associated with enhanced experimental mechanical pain sensitivity in modern humans.
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| 2. |
- Rojas, Ruben, et al.
(författare)
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Mean Glucose Slope, Principal Component Analysis Classification to Detect Insulin Infusion Set Failure
- 2011
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Ingår i: 18th IFAC World Congress. - Milan, Italy.
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Konferensbidrag (refereegranskat)abstract
- The bivariate classification technique using the mean glucose slope (MGS) and the first component of the principal component analysis (PCA), is applied to insulin infusion set failure detection (IISF), a challenging problem faced by individuals with type 1 diabetes that are on continuous insulin infusion pump therapy. The objective of this study was to determine if the proposed approach could be used to distinguish between normal patient data and data from patients under IISF online, in a reasonably short period of time. The proposed approach was applied to simulated glucose concentrations for 10 patients, based on a nonlinear physiological model of insulin and glucose dynamics. Although it presents few false alarms, it was capable of detecting most drifting (ramp) infusion set failures before complete failure occurred.
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| 3. |
- Rojas, Ruben, et al.
(författare)
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Multivariate Statistical Analysis to Detect Insulin Infusion Set Failure
- 2011
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Ingår i: 2011 American Control Conference, ACC 2011. - San Francisco, CA, USA : IEEE. - 9781457700804 ; , s. 1952-1957
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Konferensbidrag (refereegranskat)abstract
- Multivariate statistical analysis techniques are applied to insulin infusion set failure detection (IISF), a challenging problem faced by individuals with type 1 diabetes that are on continuous insulin infusion pump therapy. Bivariate classification (BC), principal component analysis (PCA), and a combined approach were applied to simulated glucose concentrations for 10 patients, based on a nonlinear physiological model of insulin and glucose dynamics. The PCA algorithm had fewer false alarms than BC, while detecting most drifting (ramp) infusion set failures before complete failure occurred.
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