Enhancing patient education in cancer care: Intelligent cancer patient education model for effective communication.

BACKGROUND: The incidence of cancer is on the rise annually, whereas there exists a significant deficit of healthcare personnel. Inadequate communication between healthcare providers and patients may result in adverse emotional outcomes for the latter and interfere with their treatment progress. A viable solution to alleviate patient distress involves utilizing text generation models as an efficacious tool for delivering patient education. MATERIALS AND METHODS: In this study, we proposed an intelligent cancer patient education model (ICPEM) based on the pre-trained T5 model. Meanwhile, we presented a new method for optimizing the model's comprehension of the patient's intent through simulating the inquiries that the patient may ask. The datasets used include a doctor and patient dialogue dataset and a cancer patient education scenario dataset. After prompt-tuning, the model is capable of educating patients through four major aspects including medical examination, health care, radiotherapy, chemotherapy. RESULTS: We conducted a comprehensive evaluation of the model by employing both automated and manual metrics. Our findings indicate that the responses generated by the model effectively catered to the requirements of patient education. Furthermore, our visualization analysis demonstrated the model's proficiency in processing sentences that are prone to confusion while maintaining a robust comprehension of human intent. Finally, several shortcomings of the model are presented, such as the inadequate amount of knowledge and the limited range of responses. CONCLUSION: We proposed a method for training and deploying medical language models in a low-resource environment. The proposed model facilitated the comprehension of cancer patients' intentions, resulting in suitable responses, which promotes effective patient-provider communication.

STSRNet: Deep Joint Space-Time Super-Resolution for Vector Field Visualization.

We propose STSRNet, a joint space-time super-resolution deep learning based model for time-varying vector field data. Our method is designed to reconstruct high temporal resolution and high spatial resolution vector fields sequence from the corresponding low-resolution key frames. For large scale simulations, only data from a subset of time steps with reduced spatial resolution can be stored for post hoc analysis. In this article, we leverage a deep learning model to capture the nonlinear complex changes of vector field data with a two-stage architecture: the first stage deforms a pair of low spatial resolution (LSR) key frames forward and backward to generate the intermediate LSR frames, and the second stage performs spatial super-resolution to output the high-resolution sequence. Our method is scalable and can handle different datasets. We demonstrate the effectiveness of our framework with several datasets through quantitative and qualitative evaluations.

Helicobacter pylori inhibits autophagic flux and promotes its intracellular survival and colonization by down-regulating SIRT1.

Helicobacter pylori (H. pylori) is the strong risk factor for a series of gastric pathological changes. Persistent colonization of H. pylori leading to chronic infection is responsible for gastritis and malignancy. Autophagy is an evolutionary conserved process which can protect cells and organisms from bacterial infection. Here, we demonstrated that H. pylori infection induced autophagosome formation but inhibited autophagic flux. SIRT1, a class III histone deacetylase, was down-regulated at both mRNA and protein levels by H. pylori infection in gastric cells. Further investigation showed that the transcriptional factor RUNX3 accounted for down-regulation of SIRT1 in H. pylori-infected gastric cells. SIRT1 promoted autophagic flux in gastric cells and activation of SIRT1 restored the autophagic flux inhibited by H. pylori infection. Furthermore, SIRT1 exerted inhibitory effects on intracellular survival and colonization of H. pylori. And activation of autophagic flux in SIRT1-inhibited gastric cells could significantly reduce intracellular load of H. pylori. Moreover, the relationship between H. pylori infection and SIRT1 expression was identified in clinical specimen. Our findings define the importance of SIRT1 in compromised autophagy induced by H. pylori infection and bacterial intracellular colonization. These results provide evidence that SIRT1 can serve as a therapeutic target to eradicate H. pylori infection.

SIRT1 inhibits chemoresistance and cancer stemness of gastric cancer by initiating an AMPK/FOXO3 positive feedback loop.

Chemotherapy is the standard care for patients with gastric cancer (GC); however, resistance to existing drugs has limited its success. The persistence of cancer stem cells (CSCs) is considered to be responsible for treatment failure. In this study, we demonstrated that SIRT1 expression was significantly downregulated in GC tissues, and that a low SIRT1 expression level indicated a poor prognosis in GC patients. We observed a suppressive role of SIRT1 in chemoresistance of GC both in vitro and in vivo. In addition, we found that SIRT1 eliminated CSC properties of GC cells. Mechanistically, SIRT1 exerted inhibitory activities on chemoresistance and CSC properties through FOXO3 and AMPK. Furthermore, a synergistic effect was revealed between FOXO3 and AMPK. AMPK promoted nuclear translocation of FOXO3 and enhanced its transcriptional activities. In addition, FOXO3 increased the expression level and activation of AMPKα by directly binding to its promoter and activating the transcription of AMPKα. Similar to SIRT1, low expression levels of p-AMPKα and FOXO3a are also related to the poor prognosis of GC patients. Moreover, we revealed a correlation between the expression levels of SIRT1, p-AMPKα, and FOXO3a. These findings indicated the importance of the SIRT1-AMPK/FOXO3 pathway in reversing chemoresistance and CSC properties of GC. Thus, exploring efficient strategies to activate the SIRT1-AMPK/FOXO3 pathway may lead to improving the survival of GC patients.

SIRT1 suppresses the migration and invasion of gastric cancer by regulating ARHGAP5 expression.

Gastric cancer (GC) ranks among the top five malignant tumors worldwide by the incidence and mortality rate. However, the mechanisms underlying its progression are poorly understood. In this study, we investigated the role of SIRT1, a class III deacetylase, in the invasion and metastasis of GC. Here, we found that knockdown of SIRT1 promoted GC cell migration and invasion in vitro and metastasis in vivo. Forced expression of SIRT1 in GC cells had the opposite effects. Then, we used mRNA microarray to identify the target genes that are regulated by SIRT1 and found that ARHGAP5 was downregulated by SIRT1. The results of the mRNA microarray were confirmed in several GC cell lines. Furthermore, SIRT1 inhibited the expression of ARHGAP5 by physically associating with transcription factor c-JUN and deacetylating and inhibiting the transcriptional activity of c-JUN. Then the expression dynamics and clinical significance of ARHGAP5 were analyzed using clinical samples and database. The expression of ARHGAP5 was increased in GC, and positively correlated with tumor size, tumor infiltration, lymph node metastasis, and clinical stage. And multivariate analyses indicated that ARHGAP5 served as an independent prognostic marker of GC. In addition, the biological effects of ARHGAP5 in SIRT1-mediated inhibition of GC migration and invasion were investigated using both in vitro and in vivo models. Silencing of ARHGAP5 considerably inhibited the migration and invasion of GC, and ARHGAP5 was found to be involved in the SIRT1-mediated inhibition of GC migration and invasion. Our results indicate that SIRT1 suppresses migration and invasion of GC by downregulating ARHGAP5 through an interaction with c-JUN, and these phenomena represent a novel mechanism of the antitumor action of SIRT1.