Effect of atractylenolide III on interstitial cells of Cajal and C-kit/SCF pathway of rats with loperamide-induced slow transit constipation

Purpose: To determine the effect of atractylenolide-III (ATL-III) on loperamide-induced slow transit constipation (STC) in a rat STC model, and to elucidate the mechanisms involved. Methods: Male Wistar rats were divided into five groups (n=6 per group): normal control group (NG), model group, and three STC rat groups treated with different doses of ATL-III, viz, 5, 10 and 15 mg/kg. The rats were treated for 15 days. Feed consumption, fecal excretion and intestinal transit rate were determined. Nitric oxide synthase (NOS), somatostatin (SS), serotonin (5-HT), and vasoactive intestinal peptide (VIP) were measured with enzyme-linked immunosorbent assay (ELISA). The protein and mRNA expressions of C-kit, SCF, PKC, and PI-3K were assayed using Western blot analysis and realtime reverse transcription polymerase chain reaction (RT-PCR), respectively. Results: The amount, weight, and moisture content of stool, and water consumption were significantly higher in ATL-III-treated groups than in the untreated (model) group (p < 0.05), whereas no difference was observed in feed intake. Intestinal transit rate was higher in the ATL-III-treated groups (p < 0.05). Decreased NOS, SS and VIP levels and increased 5-HT level were seen in the ATL-III-treated groups (p < 0.05). ATL-III treatment also induced increases in smooth muscle cells, neuronal cells, and mucous layer (p<0.05). Results from RT-PCR and Western blot revealed that ATL-III–treated groups had elevated c-kit, SCF, PKC, as well as PI-3K mRNA and protein expressions (p < 0.05). Conclusion: These results suggest that ATL-III mitigates loperamide-induced STC in rats via stimulation of NOS, SS, VIP, and 5-HT secretions. It also increases smooth muscle cells, neuronal cells, and mucous layer, and regulates the signaling pathways involving PKC, PI3K, SCF, and c-kit

PEGylated graphene oxide for tumor-targeted delivery of paclitaxel.

AIM: The graphene oxide (GO) sheet has been considered one of the most promising carbon derivatives in the field of material science for the past few years and has shown excellent tumor-targeting ability, biocompatibility and low toxicity. We have endeavored to conjugate paclitaxel (PTX) to GO molecule and investigate its anticancer efficacy. MATERIALS & METHODS: We conjugated the anticancer drug PTX to aminated PEG chains on GO sheets through covalent bonds to get GO-PEG-PTX complexes. The tissue distribution and anticancer efficacy of GO-PEG-PTX were then investigated using a B16 melanoma cancer-bearing C57 mice model. RESULTS: The GO-PEG-PTX complexes exhibited excellent water solubility and biocompatibility. Compared with the traditional formulation of PTX (Taxol®), GO-PEG-PTX has shown prolonged blood circulation time as well as high tumor-targeting and -suppressing efficacy. CONCLUSION: PEGylated graphene oxide is an excellent nanocarrier for paclitaxel for cancer targeting

On the generalized Cochrane sum with Dirichlet characters

In this paper, we defined a new generalized Cochrane sum with Dirichlet characters, and gave the upper bound of the generalized Cochrane sum with Dirichlet characters. Moreover, we studied the asymptotic estimation problem of the mean value of the generalized Cochrane sum with Dirichlet characters and obtained a sharp asymptotic formula for it. By using this asymptotic formula, we also gave the mean value of the generalized Dedekind sum

Latent User Intent Modeling for Sequential Recommenders

Sequential recommender models are essential components of modern industrial recommender systems. These models learn to predict the next items a user is likely to interact with based on his/her interaction history on the platform. Most sequential recommenders however lack a higher-level understanding of user intents, which often drive user behaviors online. Intent modeling is thus critical for understanding users and optimizing long-term user experience. We propose a probabilistic modeling approach and formulate user intent as latent variables, which are inferred based on user behavior signals using variational autoencoders (VAE). The recommendation policy is then adjusted accordingly given the inferred user intent. We demonstrate the effectiveness of the latent user intent modeling via offline analyses as well as live experiments on a large-scale industrial recommendation platform.Comment: The Web Conference 2023, Industry Trac