Second-harmonic generation (SHG) microscopy and hepatic venous pressure gradient-based validation of a novel histological staging system for alcoholic hepatitis.

Alcoholic hepatitis (AH) lacks specific histological staging. A novel fibrosis staging that encompasses perisinusoidal fibrosis and cirrhosis sub-stages, substantiated by Hepatic venous pressure gradient (HVPG) and automated fibrosis quantification, is imperative. To correlate novel histological staging system of AH with second-harmonic generation microscopy (SHG)-based q-fibrosis, HVPG, and activated hepatic stellate cells (HSCs). Liver biopsies of AH (n = 175) were staged semi-quantitatively as F0, F1, F2, F3A and F3B and Laennec substages of cirrhosis 4A, 4B and 4C. Stages were correlated with SHG q-fibrosis parameters, HVPG and HSCs. Mean age 41.2 ± 9.4 years, 96.6% males, bilirubin 20.58 ± 8.0 mg/dl and Maddrey's discriminant function 78.9 ± 36.7 displayed advanced fibrosis in 98.6%. With increasing histological stages, an increase in q-fibrosis indices and mean HVPG (p < 0.0001) were recorded; stage 4C showed the most significant difference from other stages (p < 0.000). Stages 3A and 3B were comparable with the stages 4A and 4B, respectively, for q-fibrosis (p = 1) and HVPG (p = 1). HSCs (> 30%) were significantly higher in stage 3 (75%) compared with 4 (49%) and 2 (59%), p = 0.018. Overall agreement for histological staging was excellent for all stages (0.82). SHG quantified fibrosis and HVPG corroborates the novel histological staging of AH. Expansive PCF matches with collagen content and clinical severity to early sub-stages of cirrhosis. This highlights the need for an accurate quantification and inclusion of PCF as a separate stage. SHG-based quantification can be a useful adjunct to histological fibrosis staging systems.

Hierarchy and extremes in selections from pools of randomized proteins.

Variation and selection are the core principles of Darwinian evolution, but quantitatively relating the diversity of a population to its capacity to respond to selection is challenging. Here, we examine this problem at a molecular level in the context of populations of partially randomized proteins selected for binding to well-defined targets. We built several minimal protein libraries, screened them in vitro by phage display, and analyzed their response to selection by high-throughput sequencing. A statistical analysis of the results reveals two main findings. First, libraries with the same sequence diversity but built around different "frameworks" typically have vastly different responses; second, the distribution of responses of the best binders in a library follows a simple scaling law. We show how an elementary probabilistic model based on extreme value theory rationalizes the latter finding. Our results have implications for designing synthetic protein libraries, estimating the density of functional biomolecules in sequence space, characterizing diversity in natural populations, and experimentally investigating evolvability (i.e., the potential for future evolution).