Role of High-Mobility Group Box-1 in Liver Pathogenesis

High-mobility group box 1 (HMGB1) is a highly abundant DNA-binding protein that can relocate to the cytosol or undergo extracellular release during cellular stress or death. HMGB1 has a functional versatility depending on its cellular location. While intracellular HMGB1 is important for DNA structure maintenance, gene expression, and autophagy induction, extracellular HMGB1 acts as a damage-associated molecular pattern (DAMP) molecule to alert the host of damage by triggering immune responses. The biological function of HMGB1 is mediated by multiple receptors, including the receptor for advanced glycation end products (RAGE) and Toll-like receptors (TLRs), which are expressed in different hepatic cells. Activation of HMGB1 and downstream signaling pathways are contributing factors in the pathogenesis of non-alcoholic fatty liver disease (NAFLD), alcoholic liver disease (ALD), and drug-induced liver injury (DILI), each of which involves sterile inflammation, liver fibrosis, ductular reaction, and hepatic tumorigenesis. In this review, we will discuss the critical role of HMGB1 in these pathogenic contexts and propose HMGB1 as a bona fide and targetable DAMP in the setting of common liver diseases

Note On Certain Inequalities for Neuman Means

In this paper, we give the explicit formulas for the Neuman means $N_{AH}$, $N_{HA}$, $N_{AC}$ and $N_{CA}$, and present the best possible upper and lower bounds for theses means in terms of the combinations of harmonic mean $H$, arithmetic mean $A$ and contraharmonic mean $C$.Comment: 9 page

Autophagy in Alcoholic Liver Disease, Self-eating Triggered by Drinking

Macroautophagy (autophagy) is an evolutionarily conserved mechanism. It is important for normal cellular function and also plays critical roles in the etiology and pathogenesis of a number of human diseases. In alcohol-induced liver disease, autophagy is a protective mechanism against the liver injury caused by alcohol. Autophagy is activated in acute ethanol treatment but could be suppressed in chronic and/or high dose treatment of alcohol. The selective removal of lipid droplets and/or damaged mitochondria is likely the major mode of autophagy in reducing liver injury. Understanding the dynamics of the autophagy process and the approach to modulate autophagy could help finding new ways to battle against alcohol-induced liver injury

Relevance of autophagy to fatty liver diseases and potential therapeutic applications

Autophagy is an evolutionarily conserved lysosome-mediated cellular degradation program. Accumulating evidence shows that autophagy is important to the maintenance of liver homeostasis. Autophagy involves recycling of cellular nutrients recycling as well as quality control of subcellular organelles. Autophagy deficiency in the liver causes various liver pathologies. Fatty liver disease (FLD) is characterized by the accumulation of lipids in hepatocytes and the dysfunction in energy metabolism. Autophagy is negatively affected by the pathogenesis of FLD and the activation of autophagy could ameliorate steatosis, which suggests a potential therapeutic approach to FLD. In this review, we will discuss autophagy and its relevance to liver diseases, especially FLD. In addition, we will discuss recent findings on potential therapeutic applications of autophagy modulators for FLD

Diverse Consequences in Liver Injury in Mice with Different Autophagy Functional Status Treated with Alcohol

Alcoholic fatty liver disease is often complicated by other pathologic insults, such as viral infection or high-fat diet. Autophagy plays a homeostatic role in the liver but can be compromised by alcohol, high-fat diet, or viral infection, which in turn affects the disease process caused by these etiologies. To understand the full impact of autophagy modulation on alcohol-induced liver injury, several genetic models of autophagy deficiency, which have different levels of functional alterations, were examined after acute binge or chronic-plus-binge treatment. Mice given alcohol with either mode and induced with deficiency in liver-specific autophagy-related protein (Atg)-7 shortly after the induction of Atg7 deletion had elevated liver injury, indicating the protective role of autophagy. Constitutive hepatic Atg7–deficient mice, in which Atg7 was deleted in embryos, were more susceptible with chronic-plus-binge but not with acute alcohol treatment. Constitutive hepatic Atg5–deficient mice, in which Atg5 was deleted in embryos, were more susceptible with acute alcohol treatment, but liver injury was unexpectedly improved with the chronic-plus-binge regimen. A prolonged Atg deficiency may complicate the hepatic response to alcohol treatment, likely in part due to endogenous liver injury. The complexity of the relationship between autophagy deficiency and alcohol-induced liver injury can thus be affected by the timing of autophagy dysfunction, the exact autophagy gene being affected, and the alcohol treatment regimen

Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation

Instruction tuning has emerged to enhance the capabilities of large language models (LLMs) to comprehend instructions and generate appropriate responses. Existing methods either manually annotate or employ LLM (e.g., GPT-series) to generate data for instruction tuning. However, they often overlook associating instructions with existing annotated datasets. In this paper, we propose Dynosaur, a dynamic growth paradigm for the automatic curation of instruction-tuning data. Based on the metadata of existing datasets, we use LLMs to automatically construct instruction-tuning data by identifying relevant data fields and generating appropriate instructions. By leveraging the existing annotated datasets, Dynosaur offers several advantages: 1) it reduces the API cost for generating instructions (e.g., it costs less than $12 USD by calling GPT-3.5-turbo for generating 800K instruction tuning samples; 2) it provides high-quality data for instruction tuning (e.g., it performs better than Alpaca and Flan on Super-NI and Longform with comparable data sizes); and 3) it supports the continuous improvement of models by generating instruction-tuning data when a new annotated dataset becomes available. We further investigate a continual learning scheme for learning with the ever-growing instruction-tuning dataset, and demonstrate that replaying tasks with diverse instruction embeddings not only helps mitigate forgetting issues but generalizes to unseen tasks better. Code and data are available at https://github.com/WadeYin9712/Dynosaur.Comment: EMNLP 2023. Code and data are available at https://github.com/WadeYin9712/Dynosau