Objective To explore the influence of salinomycin in the growth, apoptosis and invasion of human bladder cancer 5637 cells,and to clarify its possible mechanism.Methods The human bladder cancer 5637 cells cultured invitro at logarithmic growth phase were divided into control group and different doses of salinomycin(15, 30 and 60μmol·L-1 )groups.The inhibitory rate of the growth of 5637 cells in various groups was measured by MTT assay. Flow cytometry was used to detect the apoptotic rates of 5637 cells in various groups. The invasiveness of 5637 cells was tested by Matrigel Invasion Assay.The expression levels ofβ-catenin protein in 5637 cells in various groups were determined by Western blotting method. Results Compared with control group,the inhibitory rates of growth of human bladder cancer 5637 cells in different doses of salinomycin groups were increased significantly(P<0.05);the apoptotic rates were increased(P<0.05).the number of cells passed the Matrigel was decreased(P<0.05),and the expression level ofβ-catenin protein was decreased (P<0.05).Compared with low dose of salinomycin group,the inhibitory rate of growth of 5637 cells in high dose of salinomycin group was increased(P<0.05);the apoptotic rate was increased(P<0.05),the number of cells passed the Matrigel was decreased (P < 0.05 ), and the expression levels of β-catenin protein was decreased (P<0.05). Conclusion Salinomycin can inhibit the growth of 5637 cells significantly,increase the apoptosis,and decrease the cell invasion;the inhibitory effect may act by inhibiting the Wnt/β-catenin pathway.

Objective To study the expression of tumor stem cell marker aldehyde dehydrogenase 1 (ALDH1)in invasive bladder cancer tissue and to clarify its relationship with the biological behavior of bladder cancer. Methods The ALDH1 expression in 109 cases of primary invasive carcinomas specimens (case group)and 20 cases of normal bladder tissue surrounding cancer (control group)was detected by immunohistochemistry. At the same time,the ALDH1 expression in 6 cases of metastatic pelvic lymph node tissue and 20 cases of non-metastatic pelvic lymph node tissue was detected. The relationship between the ALDH1 expression and the chinicopathological charateristics of invasive bladder cancer and its influence in the survival rate and disease-free survival were analyzed. Results The positive rates of ALDH1 expression in bladder cancer tissue and normal bladder tissue were 33.94%(37/109)and 5.00% (1/20),respectively,there was significant different between them (P<0.01);they were 19.05% (8/42)and 43.28% (29/67)in the cases with non muscle invasive and nmuscle invasive bladder cancer, respectively,there was significant difference (P<0.01);they were 13.04% (3/23)and 39.53% (34/86)in the cases of bladder cancer with low grade and high grade,respectively,there was significant difference (P<0.05);they were 50.00% (3/6)and 12.90% (4/31)in the tissue of bladder cancer with metastatic lymph nodes and non metastatic ones,respectively,there was significant difference (P<0.05);they were 50.00% (3/6)and 0.00%(0/20)in the metastatic lymph nodes and non metastatic ones,respectively,there was significant difference (P<0.01).The overall survival rate in the patients with positive ALDH1 expression was 64.9% while it was 84.7% in negative ones,there was significant difference (P<0.05);the disease-free Survival was 51.4% and 75% in the patients with positive and negative ALDH1 groups,respectively,there was significant difference (P<0.05). Conclusion The high expression of tumor stem cell marker ALDH1 is associated with staging, grading and prognosis of invasive bladder cancer.ALDH1 may play a role in the tumorigenesis,progression and metastasis of bladder cancer.

The ChatGPT, a lite and conversational variant of Generative Pretrained Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large Language Models (LLMs) with billions of parameters. LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks, which profoundly impact various fields. This paper mainly discusses the future applications of LLMs in dentistry. We introduce two primary LLM deployment methods in dentistry, including automated dental diagnosis and cross-modal dental diagnosis, and examine their potential applications. Especially, equipped with a cross-modal encoder, a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations. We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application. While LLMs offer significant potential benefits, the challenges, such as data privacy, data quality, and model bias, need further study. Overall, LLMs have the potential to revolutionize dental diagnosis and treatment, which indicates a promising avenue for clinical application and research in dentistry.
Dentistry ; Artificial Intelligence