The Role of PPARγ Receptors and Leukotriene B4 Receptors in Mediating the Effects of LY293111 in Pancreatic Cancer

Pancreatic cancer is a devastating disease in which current therapies are inadequate. Separate lines of research have identified the 5-lipoxygenase/leukotriene B4 receptor pathway and the PPARγ pathway as potential targets for prevention or treatment of this disease. LY293111 was originally designed as a potent leukotriene B4 receptor antagonist for treatment of inflammatory conditions. LY293111 was also known to have inhibitory effects on 5-lipoxygenase, which is upstream of the production of leukotrienes. LY293111 was shown to have potent anticancer effects in pancreatic cancer and several other solid malignancies, where it caused cell cycle arrest and marked apoptosis. Subsequently, it came to light that LY293111 exhibited PPARγ agonist activity in addition to its effects on the 5-lipoxygenase pathway. This raises the question of which of the two targets is of greatest importance with regard to the anticancer effects of this agent. The evidence to date is not conclusive, but suggests that the effects of LY293111 may be mediated by both LTB4 receptors and PPARγ

Data Race Detection Using Large Language Models

Large language models (LLMs) are demonstrating significant promise as an alternate strategy to facilitate analyses and optimizations of high-performance computing programs, circumventing the need for resource-intensive manual tool creation. In this paper, we explore a novel LLM-based data race detection approach combining prompting engineering and fine-tuning techniques. We create a dedicated dataset named DRB-ML, which is derived from DataRaceBench, with fine-grain labels showing the presence of data race pairs and their associated variables, line numbers, and read/write information. DRB-ML is then used to evaluate representative LLMs and fine-tune open-source ones. Our experiment shows that LLMs can be a viable approach to data race detection. However, they still cannot compete with traditional data race detection tools when we need detailed information about variable pairs causing data races

Caspase-3 suppresses diethylnitrosamine-induced hepatocyte death, compensatory proliferation and hepatocarcinogenesis through inhibiting p38 activation

It is critical to understand the molecular mechanisms of hepatocarcinogenesis in order to prevent or treat hepatocellular carcinoma (HCC). The development of HCC is commonly associated with hepatocyte death and compensatory proliferation. However, the role of Caspase-3, a key apoptotic executor, in hepatocarcinogenesis is unknown. In this study, we used Caspase-3-deficient mice to examine the role of Caspase-3 in hepatocarcinogenesis in a chemical (diethylnitrosamine, DEN)-induced HCC model. We found that Caspase-3 deficiency significantly increased DEN-induced HCC. Unexpectedly, Caspase-3 deficiency increased apoptosis induced by DEN and the subsequent compensatory proliferation. Intriguingly, we discovered that Caspase-3 deficiency increased the activation of p38 with and without DEN treatment. Moreover, we demonstrated that TNFα and IL1α stimulated increased activation of p38 in Caspase-3 KO hepatocytes compared with wild-type hepatocytes. Finally, we found that inhibition of p38 by SB202190 abrogated enhanced hepatocyte death, compensatory proliferation and HCC induced by DEN in Caspase-3-deficient mice. Overall, our data suggest that Caspase-3 inhibits chemical-induced hepatocarcinogenesis by suppressing p38 activation and hepatocyte death

A quantitative evaluation of gross versus histologic neuroma formation in a rabbit forelimb amputation model: potential implications for the operative treatment and study of neuromas

<p>Abstract</p> <p>Background</p> <p>Surgical treatment of neuromas involves excision of neuromas proximally to the level of grossly "normal" fascicles; however, proximal changes at the axonal level may have both functional and therapeutic implications with regard to amputated nerves. In order to better understand the retrograde "zone of injury" that occurs after nerve transection, we investigated the gross and histologic changes in transected nerves using a rabbit forelimb amputation model.</p> <p>Methods</p> <p>Four New Zealand White rabbits underwent a forelimb amputation with transection and preservation of the median, radial, and ulnar nerves. After 8 weeks, serial sections of the amputated nerves were then obtained in a distal-to-proximal direction toward the brachial plexus. Quantitative histomorphometric analysis was performed on all nerve specimens.</p> <p>Results</p> <p>All nerves demonstrated statistically significant increases in nerve cross-sectional area between treatment and control limbs at the distal nerve end, but these differences were not observed 10 mm more proximal to the neuroma bulb. At the axonal level, an increased number of myelinated fibers were seen at the distal end of all amputated nerves. The number of myelinated fibers progressively decreased in proximal sections, normalizing at 15 mm proximally, or the level of the brachial plexus. The cross-sectional area of myelinated fibers was significantly decreased in all sections of the treatment nerves, indicating that atrophic axonal changes proceed proximally at least to the level of the brachial plexus.</p> <p>Conclusions</p> <p>Morphologic changes at the axonal level extend beyond the region of gross neuroma formation in a distal-to-proximal fashion after nerve transection. This discrepancy between gross and histologic neuromas signifies the need for improved standardization among neuroma models, while also providing a fresh perspective on how we should view neuromas during peripheral nerve surgery.</p

HPC-GPT: Integrating Large Language Model for High-Performance Computing

Large Language Models (LLMs), including the LLaMA model, have exhibited their efficacy across various general-domain natural language processing (NLP) tasks. However, their performance in high-performance computing (HPC) domain tasks has been less than optimal due to the specialized expertise required to interpret the model responses. In response to this challenge, we propose HPC-GPT, a novel LLaMA-based model that has been supervised fine-tuning using generated QA (Question-Answer) instances for the HPC domain. To evaluate its effectiveness, we concentrate on two HPC tasks: managing AI models and datasets for HPC, and data race detection. By employing HPC-GPT, we demonstrate comparable performance with existing methods on both tasks, exemplifying its excellence in HPC-related scenarios. Our experiments on open-source benchmarks yield extensive results, underscoring HPC-GPT's potential to bridge the performance gap between LLMs and HPC-specific tasks. With HPC-GPT, we aim to pave the way for LLMs to excel in HPC domains, simplifying the utilization of language models in complex computing applications.Comment: 9 page

Inhibition of Insulin‐Like Growth Factor 1 Receptor Enhances the Efficacy of Sorafenib in Inhibiting Hepatocellular Carcinoma Cell Growth and Survival

Hepatocellular carcinoma (HCC) is the fifth most common primary cancer and second largest cause of cancer‐related death worldwide. The first‐line oral chemotherapeutic agent sorafenib only increases survival in patients with advanced HCC by less than 3 months. Most patients with advanced HCC have shown limited response rates and survival benefits with sorafenib. Although sorafenib is an inhibitor of multiple kinases, including serine/threonine‐protein kinase c‐Raf, serine/threonine‐protein kinase B‐Raf, vascular endothelial growth factor receptor (VEGFR)‐1, VEGFR‐2, VEGFR‐3, and platelet‐derived growth factor receptor β, HCC cells are able to escape from sorafenib treatment using other pathways that the drug insufficiently inhibits. The aim of this study was to identify and target survival and proliferation pathways that enable HCC to escape the antitumor activity of sorafenib. We found that insulin‐like growth factor 1 receptor (IGF1R) remains activated in HCC cells treated with sorafenib. Knockdown of IGF1R sensitizes HCC cells to sorafenib treatment and decreases protein kinase B (AKT) activation. Overexpression of constitutively activated AKT reverses the effect of knockdown of IGF1R in sensitizing HCC cells to treatment with sorafenib. Further, we found that ceritinib, a drug approved by the U.S. Food and Drug Administration for treatment of non‐small cell lung cancer, effectively inhibits the IGF1R/AKT pathway and enhances the inhibitory efficacy of sorafenib in human HCC cell growth and survival in vitro, in a xenograft mouse model and in the c‐Met/β‐catenin‐driven HCC mouse model. Conclusion: Our study provides a biochemical basis for evaluation of a new combination treatment that includes IGF1R inhibitors, such as ceritinib and sorafenib, in patients with HCC

ABL1, Overexpressed in Hepatocellular Carcinomas, Regulates Expression of NOTCH1 and Promotes Development of Liver Tumors in Mice

Background & Aims We investigated whether ABL proto-oncogene 1, non-receptor tyrosine kinase (ABL1) is involved in development of hepatocellular carcinoma (HCC). Methods We analyzed clinical and gene expression data from The Cancer Genome Atlas. Albumin-Cre (HepWT) mice and mice with hepatocyte-specific disruption of Abl1 (HepAbl–/– mice) were given hydrodynamic injections of plasmids encoding the Sleeping Beauty transposase and transposons with the MET gene and a catenin β1 gene with an N-terminal truncation, which induces development of liver tumors. Some mice were then gavaged with the ABL1 inhibitor nilotinib or vehicle (control) daily for 4 weeks. We knocked down ABL1 with short hairpin RNAs in Hep3B and Huh7 HCC cells and analyzed their proliferation and growth as xenograft tumors in mice. We performed RNA sequencing and gene set enrichment analysis of tumors. We knocked down or overexpressed NOTCH1 and MYC in HCC cells and analyzed proliferation. We measured levels of phosphorylated ABL1, MYC, and NOTCH1 by immunohistochemical analysis of an HCC tissue microarray. Results HCC tissues had higher levels of ABL1 than non-tumor liver tissues, which correlated with shorter survival times of patients. HepWT mice with the MET and catenin β1 transposons developed liver tumors and survived a median 64 days; HepAbl–/– mice with these transposons developed tumors that were 50% smaller and survived a median 81 days. Knockdown of ABL1 in human HCC cells reduced proliferation, growth as xenograft tumors in mice, and expression of MYC, which reduced expression of NOTCH1. Knockdown of NOTCH1 or MYC in HCC cells significantly reduced cell growth. NOTCH1 or MYC overexpression in human HCC cells promoted proliferation and rescued the phenotype caused by ABL1 knockdown. The level of phosphorylated (activated) ABL1 correlated with levels of MYC and NOTCH1 in human HCC specimens. Nilotinib decreased expression of MYC and NOTCH1 in HCC cell lines, reduced the growth of xenograft tumors in mice, and slowed growth of liver tumors in mice with MET and catenin β1 transposons, reducing tumor levels of MYC and NOTCH1. Conclusions HCC samples have increased levels of ABL1 compared with nontumor liver tissues, and increased levels of ABL1 correlate with shorter survival times of patients. Loss or inhibition of ABL1 reduces proliferation of HCC cells and slows growth of liver tumors in mice. Inhibitors of ABL1 might be used for treatment of HCC

High-Throughput Chemical Screen Identifies a Novel Potent Modulator of Cellular Circadian Rhythms and Reveals CKIα as a Clock Regulatory Kinase

A novel compound “longdaysin” was found to dramatically slow down the speed of the circadian clock through simultaneous inhibition of protein kinases CKIδ, CKIα, and ERK2

Cyber-Physical Systems Optimization with Reinforcement Learning: Methods and Applications

Cyber-physical systems rely on many control and decision-making algorithms. However, the majority of cyber-physical systems today are still operated by simple rule-based and feedback controls such as on-off control or proportional-integral derivative (PID) control. These prescriptive and reactive control strategies do not take into consideration predictive information on disturbances, such as weather changes, making their performance sub-optimal. Optimal control strategies such as MPC address these drawbacks by iteratively optimizing an objective function over a receding time horizon. However, despite many successful cyber-physical systems applications of MPC, its wide-spread adoption has been limited by the need of accurate models. This is especially challenging because cyber-physical systems are heterogeneous. Thus, custom models are required for cyber-physical systems, limiting the scalability of MPC. To overcome the above challenges, we investigate deep reinforcement learning (DRL) for solving challenging cyber-physical system optimization problems. Deep reinforcement learning is a data-driven control method and learns an optimal control policy directly from data, without using any pre-programmed control rules or explicit assumptions about the cyber-physical systems. DRL also allows us to use domain knowledge to train a control agent (a neural network) without labeled data. The generalization ability of the neural network enables the control agent to handle the dynamically-varying factors (e.g., weather changing). We use this approach to build a series of cyber-physical systems for important applications including building control, irrigation control, and virtual machine rescheduling. In the first building control application, we take a holistic approach to deal with the trade-offs between energy use and comfort in commercial buildings. Further, we investigate the common data-efficiency problem when leveraging DRL to cyber-physical systems and propose two data-efficient model-based RL methods for multi-zone building control. In the second irrigation control application, we design a DRL-based irrigation system that generates optimal irrigation control commands according to current soil water content, current weather data, and forecastedweather information. In the third virtual machine rescheduling application, we design a two-stage DRL agent framework to balance rescheduling performance and solution speed. In future work, we will use the insights from these systems to identify common problems and develop new data-efficient reinforcement learning techniques for cyber-physical systems