Targeting Heat Shock 27 Kda Protein Induces Androgen Receptor Degradation

Glioblastoma (GBM) is the most common and aggressive brain tumor, with very poor prognosis. Androgen receptor (AR) plays a significant role in the progression of GBM, and anti-androgen agents have the potential to be used for the treatment of GBM. However, AR mutation commonly happens in GBM, which makes the anti-androgen agents less effective. Heat shock 27 kDa protein (HSP27) is a well-documented chaperone protein to stabilize AR. Inhibition of HSP27 results in AR degradation regardless the mutation status of AR, which makes HSP27 a good target to abolish AR in GBM. Identified compound I ((N-(3-((2,5-dimethoxybenzyl)oxy)-4-(methylsulfonamido) phenyl)-4-methoxybenzamide) inhibits GBM cell growth with IC50s around 5 nM, and also shows significant inhibition in an in vivo GBM xenograft model at 20 mg/kg. Furthermore, it does not show toxicity to mice up to 80 mg/kg, 4-fold higher than the active in vivo dose. The compound significantly induces AR degradation in GBM cells via the proteasomal pathway. These results suggest that targeting HSP27 chaperone function to induce AR degradation in GBM is a promising and novel treatment. Additionally, a sensitive and rapid high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method was developed and validated to investigate the pharmacokinetics and brain distribution of compound I in mice. The method was successfully applied to evaluate the pharmacokinetic of compound I in mouse plasma and brain tissue. The apparent elimination half-life (t1/2) was 4.06 h. The Cmax of compound I in brain tissue was 0.88 μg/g. The results indicated that compound I was rapidly distributed and compound I could cross the blood-brain barrier (BBB). The pharmacokinetic profile summarized provides valuable information for the further investigation of compound I as a potential anti-glioblastoma agent. Ligand based structural optimization in the project identified two novel compounds (compounds 4 and 26) which have potent anti-GBM activity and significantly increased BBB permeability in comparison to the lead compound I. This study indicated that compounds 4 and 26 could be the promising drugs to treat AR over expressed GBM, also provided a meaningful insight for the further structural modification to retain or improve the potency and BBB permeability

Capturing Word Semantics From Co-occurrences Using Dynamic Mutual Information

Semantic relations between words are crucial for information retrieval and natural language processing tasks. Distributional representations are based on word co-occurrence, and have been proven successful. Recent neural network approaches such as Word2vec and Glove are all derived from co-occurrence information. In particular, they are based on Shifted Positive Pointwise Mutual Information (SPPMI). In SPPMI, PMI values are shifted uniformly by a constant, which is typically five. Although SPPMI is effective in practice, it lacks theoretical explanation, and has space for improvement. Intuitively, shifting is to remove co-occurrence pairs that could have co-occurred due to randomness, i.e., the pairs whose expected co-occurrence count is close to its observed appearances. We propose a new shifting scheme, called Dynamic Mutual Information (DMI), where the shifting is based on the variance of co-occurrences and Chebyshev\u27s Inequality. Intuitively, DMI shifts more aggressively for rare word pairs. We demonstrate that DMI outperforms the state-of-the-art SPPMI in a variety of word similarity evaluation tasks

Off targets toxicological investigation of anti-cancer tubulin inhibitors

We have developed a class of novel tubulin inhibitors based on NSC751382 (Figure 1), Benzo[1,3]dioxole-5- carboxylic acid [3-(2,5-dimethyl- benzyloxy)-4- (methanesulfonyl-methyl-amino)-phenyl] -amide, as the lead compound. This compound showed potent tubulin polymerization inhibitory activity by binding at the colchicine’s binding domain, and suppressed cancer cell growth with an IC50 of 200nM. It has molecular weight of 482, logP of 4.1, only one hydrogen bond donor, and eight hydrogen bond acceptors. The compound meets the Lipinski\u27s Rule of Five and is a highly drug-like molecule. In addition, NSC751382 significantly inhibited the growth of Taxol resistant cancer cells, suggesting it is not a substrate for P-glycoprotein. Furthermore, it exhibited potent in vivo anti-cancer activity and excellent pharmacokinetic parameters. We further optimize the structure of the compound and generated a new analog with much improved potency (IC50 of 1nM) to inhibit cancer cell growth (Figure 1). The new compound also showed much better potency to inhibit tubulin polymerization, cause cell cycle arrest, and inhibit in vivo tumor growth as well. However, we did notice mouse weight lost during the treatment, suggesting toxicity to the animals. We speculate that the lead optimization may result in off target effect, i.e., the new compound possibly bind to other proteins besides tubulin and cause toxicity. It seems that the structural optimization might cause target changing of the lead compound, and the new off target proteins may cause the toxicity. To investigate the toxicity, we synthesized 6 structure very similar analogs to compound A, and all the compounds showed similar in vitro activity to inhibit cancer cell growth. These compounds will be tested in the animals to correlate the toxicity to the structures, and elucidate the toxic moiety of the compounds. We also synthesized a biotinylated compound A to investigate the potential off target proteins that bind to the compounds, and explore the toxic inducing factors. This analysis can help us understand what structural characteristics lead to the target switching phenomenon. Understanding the structural difference correlated to the molecular targets will help us to design new analogs with reduced toxicity.https://engagedscholarship.csuohio.edu/u_poster_2018/1029/thumbnail.jp

Strategies for Talent’s Digital Competence Development at Higher Vocational Colleges for Digital Transformation

Digital transformation has brought unprecedented opportunities and challenges to economic and social development, and the development of talent’s digital competence is of growing importance. Under an ideal situation, digitization, digitalization and digital transformation are three stages of gradual digital development. However, influenced by the difference in social and economic development among regions, higher vocational colleges, as an educational system closest to the labor market, vary by their own strengths and digital competence. From the perspective of game theory and based on the model of the boxed pig game, this paper provides strategies and suggestions for the cultivation of talent’s digital competence in terms of non-cooperative strategy at higher vocational colleges. The supply-demand model of talent’s digital competence cultivation is established to provide further suggestions for the top-down cooperation strategy among higher vocational colleges

Understanding Distributed Leadership and Insights for Chinese Educational Institutions in the Context of Digital Transformation: A Literature Review

When education across all levels, is no exception for meeting the needs of industry 4.0 and the new demand of the digital economy and society, distributed leadership is an effective reform strategy for organization's transition to digital transformation. 174 articles related to distributed leadership were selected from eight core-international journals in the field of educational leadership and management with an average h-index of 45, and 64 articles with the keywords of distributed leadership published in the CSSCI and core journals were found. The 248 articles in total were reviewed for analysis with three aspects (research themes and theories; research methodology and analytical methods; discovery and revelation) which were synthesized from the systematic conceptual framework of literature review by Hallinger (2013,2014), the research conclusion frameworks by Bennett et al. (2003) and Tian et al. (2016). The literature review was conducted on four aspects (who, why, what and how) for knowing which most scholars are concerned and for informing educational institutions with insights on distributed leadership for future development

p-sylowizers and p-nilpotency of finite groups

In this paper, we investigate the structure of finite group G by assuming that the intersections between p-sylowizers of some p-subgroups of G and $O^p(G)$ are S-permutable in G. We obtain some criterions for p-nilpotency of a finite group

GrammarGPT: Exploring Open-Source LLMs for Native Chinese Grammatical Error Correction with Supervised Fine-Tuning

Grammatical error correction aims to correct ungrammatical sentences automatically. Recently, some work has demonstrated the excellent capabilities of closed-source Large Language Models (LLMs, e.g., ChatGPT) in grammatical error correction. However, the potential of open-source LLMs remains unexplored. In this paper, we introduced GrammarGPT, an open-source LLM, to preliminary explore its potential for native Chinese grammatical error correction. The core recipe of GrammarGPT is to leverage the hybrid dataset of ChatGPT-generated and human-annotated. For grammatical errors with clues, we proposed a heuristic method to guide ChatGPT to generate ungrammatical sentences by providing those clues. For grammatical errors without clues, we collected ungrammatical sentences from publicly available websites and manually corrected them. In addition, we employed an error-invariant augmentation method to enhance the ability of the model to correct native Chinese grammatical errors. We ultimately constructed about 1k parallel data and utilized these data to fine-tune open-source LLMs (e.g., Phoenix, released by The Chinese University of Hong Kong, Shenzhen) with instruction tuning. The experimental results show that GrammarGPT outperforms the existing SOTA system significantly. Although model parameters are 20x larger than the SOTA baseline, the required amount of data for instruction tuning is 1200x smaller, illustrating the potential of open-source LLMs on native CGEC. Our GrammarGPT ranks $3^{rd}$ on NLPCC2023 SharedTask1, demonstrating our approach's effectiveness. The code and data are available at \url{https://github.com/FreedomIntelligence/GrammarGPT}

Enforcing Levy relaxation for multi-mode fibers with correlated disorder

Environmental perturbations and noise are source of mode mixing and interferences between the propagating modes of a complex multi-mode fiber. Typically, they are characterized by their correlation (paraxial) length, and their spectral content which describes the degree of coupling between various modes. We show that an appropriate control of these quantities allows to engineer Levy-type relaxation processes of an initial mode excitation. Our theory, based on Random Matrix Theory modeling, is tested against realistic simulations with multi-mode fibers.Comment: 5 pages, 5 figure

Quantifying Self-diagnostic Atomic Knowledge in Chinese Medical Foundation Model: A Computational Analysis

Foundation Models (FMs) have the potential to revolutionize the way users self-diagnose through search engines by offering direct and efficient suggestions. Recent studies primarily focused on the quality of FMs evaluated by GPT-4 or their ability to pass medical exams, no studies have quantified the extent of self-diagnostic atomic knowledge stored in FMs' memory, which is the basis of foundation models to provide factual and reliable suggestions. In this paper, we first constructed a benchmark of Self-diagnostic Atomic Knowledge (SdAK), including the most common types of atomic knowledge involved in self-diagnostic queries, with 17 atomic types and a total of 14, 048 pieces of atomic knowledge. Then, we evaluated both generic and open-source Chinese medical FMs on the benchmark. The experimental results showcase that generic FMs perform better than medical FMs in terms of self-diagnostic atomic knowledge. Error analysis revealed that both generic and medical FMs are sycophantic, e.g., always catering to users' claims when it comes to unknown knowledge. We further explored different types of data commonly adopted for fine-tuning medical FMs, i.e., real-world, semi-distilled, and distilled data, and found that distilled data can benefit FMs most. The code and data are available at https://github.com/FreedomIntelligence/SDAK