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BACKGROUND: Myofibroblast differentiation, characterized by α-smooth muscle actin (α-SMA) expression, is a key process in organ fibrosis, and is induced by TGF-β. Here we examined whether an anti-fibrotic agent, N-acetyl-seryl-aspartyl-lysylproline (Ac-SDKP), can regulate induction of TGF-β signaling and myofibroblast differentiation as a potential key component of its anti-fibrotic mechanism in vivo and in vitro. METHODOLOGY/PRINCIPAL FINDINGS: Rat pulmonary fibroblasts were cultured in vitro and divided to 4 groups 1) control; 2) TGF-β1; 3) TGF-β1+ LY364947; 4) TGF-β1+Ac-SDKP. For in vivo studies, six groups of animals were utilized 1) control 4w; 2) silicotic 4w; 3) control 8w; 4) silicotic 8w; 5) Ac-SDKP post-treatment; 6)Ac-SDKP pre-treatment. SiO(2) powders were douched in the trachea of rat to make the silicotic model. Myofibroblast differentiation was measured by examining expression of α-SMA, as well as expression of serum response factor (SRF), a key regulator of myofibroblast differentiation. The expressions of collagen, TGF-β1 and RAS signaling were also assessed. The results revealed that TGF-β1 strongly induced myofibroblast differentiation and collagen synthesis in vitro, and that pre-treatment with Ac-SDKP markedly attenuated myofibroblast activation, as well as induction of TGF-β1 and its receptor. Similar results were observed in vivo in the pathologically relevant rat model of silicosis. Ac-SDKP treatment in vivo strongly attenuated 1) silicosis-induced increased expressions of TGF-β1 and RAS signaling, 2) myofibroblast differentiation as indicated by a robust decrease of SRF and α-SMA-positive myofibroblast localization in siliconic nodules in the lung, 3) collagen deposition. CONCLUSION/SIGNIFICANCE: The results of the present study suggest a novel mechanism of action for Ac-SDKP's beneficial effect in silicosis, which involves attenuation of TGF-β1 and its receptors, SRF and Ang II type 1 receptor (AT(1)) expression, collagen deposition and myofibroblast differentiation. The results further suggest that therapies targeting myofibroblast differentiation may have therapeutic efficacy in treatment of silicosis of the lung.

Tax audits are a crucial process adopted in all tax departments to ensure tax compliance and fairness. Traditionally, tax audit leads have been selected based on empirical rules and randomization methods, which are not adaptive, may miss major cases and can introduce bias. Here, we present an audit lead tool based on artificial neural networks that have been trained and evaluated on an integrated dataset of 93,413 unique tax records from 8,647 restaurant businesses over 10 years in the Northern California, provided by the California Department of Tax and Fee Administration (CDTFA). The tool achieved a 40.1% precision and 58.7% recall (F1-score of 0.42) on classifying positive audit leads, and the corresponding regressor provided estimated audit gains (MAE of $155,490). Finally, we evaluated the statistical significance of various empirical rules for use in lead selection, with two out of five being supported by the data. This work demonstrates how data can be leveraged for creating evidence-based models of audit selection and validating empirical hypotheses, resulting in higher audit yields and more fair audit selection processes.