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OBJECTIVES Acute liver failure (ALF) is classically defined by coagulopathy and hepatic encephalopathy (HE); however, acute liver injury (ALI), i.e., severe acute hepatocyte necrosis without HE, has not been carefully defined nor studied. Our aim is to describe the clinical course of specifically defined ALI, including the risk and clinical predictors of poor outcomes, namely progression to ALF, the need for liver transplantation (LT) and death. METHODS 386 subjects prospectively enrolled in the Acute Liver Failure Study Group registry between 1 September 2008 through 25 October 2013, met criteria for ALI: International Normalized Ratio (INR)≥2.0 and alanine aminotransferase (ALT)≥10× elevated (irrespective of bilirubin level) for acetaminophen (N-acetyl-p-aminophenol, APAP) ALI, or INR≥2.0, ALT≥10× elevated, and bilirubin≥3.0 mg/dl for non-APAP ALI, both groups without any discernible HE. Subjects who progressed to poor outcomes (ALF, death, LT) were compared, by univariate analysis, with those who recovered. A model to predict poor outcome was developed using the random forest (RF) procedure. RESULTS Progression to a poor outcome occurred in 90/386 (23%), primarily in non-APAP (71/179, 40%) vs. only 14/194 (7.2%) in APAP patients comprising 52% of all cases (13 cases did not have an etiology assigned; 5 of whom had a poor outcome). Of 82 variables entered into the RF procedure: etiology, bilirubin, INR, APAP level and duration of jaundice were the most predictive of progression to ALF, LT, or death. CONCLUSIONS A majority of ALI cases are due to APAP, 93% of whom will improve rapidly and fully recover, while non-APAP patients have a far greater risk of poor outcome and should be targeted for early referral to a liver transplant center.

Understanding the processes of mitochondrial dynamics (fission, fusion, biogenesis, and mitophagy) has been hampered by the lack of automated, deterministic methods to measure mitochondrial morphology from microscopic images. A method to quantify mitochondrial morphology and function is presented here using a commercially available automated high-content wide-field fluorescent microscopy platform and R programming-language-based semi-automated data analysis to achieve high throughput morphological categorization (puncta, rod, network, and large & round) and quantification of mitochondrial membrane potential. In conjunction with cellular respirometry to measure mitochondrial respiratory capacity, this method detected that increasing concentrations of toxicants known to directly or indirectly affect mitochondria (t-butyl hydroperoxide [TBHP], rotenone, antimycin A, oligomycin, ouabain, and carbonyl cyanide-p-trifluoromethoxyphenylhydrazone [FCCP]), decreased mitochondrial networked areas in cultured 661w cells to 0.60-0.80 at concentrations that inhibited respiratory capacity to 0.20-0.70 (fold change compared to vehicle). Concomitantly, mitochondrial swelling was increased from 1.4- to 2.3-fold of vehicle as indicated by changes in large & round areas in response to TBHP, oligomycin, or ouabain. Finally, the automated identification of mitochondrial location enabled accurate quantification of mitochondrial membrane potential by measuring intramitochondrial tetramethylrhodamine methyl ester (TMRM) fluorescence intensity. Administration of FCCP depolarized and administration of oligomycin hyperpolarized mitochondria, as evidenced by changes in intramitochondrial TMRM fluorescence intensities to 0.33- or 5.25-fold of vehicle control values, respectively. In summary, this high-content imaging method accurately quantified mitochondrial morphology and membrane potential in hundreds of thousands of cells on a per-cell basis, with sufficient throughput for pharmacological or toxicological evaluation.

Often repeated measures data are summarized into pre-post-treatment measurements. Various methods exist in the literature for estimating and testing treatment effect, including ANOVA, analysis of covariance (ANCOVA), and linear mixed modeling (LMM). Under the first two methods, outcomes can either be modeled as the post treatment measurement (ANOVA-POST or ANCOVA-POST), or a change score between pre and post measurements (ANOVA-CHANGE, ANCOVA-CHANGE). In LMM, the outcome is modeled as a vector of responses with or without Kenward-Rogers adjustment. We consider five methods common in the literature, and discuss them in terms of supporting simulations and theoretical derivations of variance. Consistent with existing literature, our results demonstrate that each method leads to unbiased treatment effect estimates, and based on precision of estimates, 95% coverage probability, and power, ANCOVA modeling of either change scores or post-treatment score as the outcome, prove to be the most effective. We further demonstrate each method in terms of a real data example to exemplify comparisons in real clinical context.

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