OBJECTIVE: Coptis chinensis (Huanglian in Chinese, HL) is commonly utilized in clinical settings to counteract dyslipidemia in patients with hot syndrome. Its lipid-reducing efficacy has been consistently demonstrated in high-fat diet (HFD)-induced hyperlipidemic animal models. However, whether HL's efficacy differs in HFD-fed animals with hot or cold syndromes remains unclear. This study aims to discern the variations in the anti-hyperlipidemic effects of HL in HFD-fed mice with hot or cold syndromes. METHODS: HFD-induced C57BL/6 mice were subjected to cold or hot syndrome via two weeks of ice water (0 °C) and levothyroxine sodium (240 µg/kg) treatment, respectively. Then, an aqueous extract of HL was administered to the mice via oral gavage over the following four-week period. Lipid levels in the serum and liver were gauged to determine the lipid-reducing effects of HL. Furthermore, gut microbiota composition was elucidated using full-length 16S rRNA gene sequencing. RESULTS: HL notably reduced lipid levels in HFD-induced hyperlipidemic mice. Its efficacy was amplified in hyperlipidemic mice with a hot syndrome but was markedly reduced in those with a cold syndrome. HL treatment led to a decline in alpha-diversity (characterized by ACE, Chao1, Shannon and Simpson index) of the gut microbiota in both sets of mice but affected specific microbial populations based on the syndrome. Specifically, while HL led to a notable increase in Eubacterium, Robinsoniella, and Lachnoclostridium genera, along with the enhancement of Clostridium innocuum and Bacteroides thetaiotaomicron species across all conditions, it syndrome-dependently stimulated Romboutsia ilealis and Parabaceroides_sp_HGS0025 species in mice with hot syndrome. CONCLUSION HL shows stronger lipid-lowering effect on hyperlipidemic mice with hot syndrome, which is in accordance with its traditional usage in clinic. The therapeutic outcomes of HL are intrinsically tied, at least in part, to its modulatory effects on the gut microbiota, offering fresh insights into the foundational principles of traditional Chinese medicine.

BACKGROUND: In light of the significant clinical benefits of antibody-drug conjugates in clinical trials, the human epidermal growth factor receptor 2 (HER2)-low category in breast cancers has gained increasing attention. Therefore, we studied the clinicopathological characteristics of Chinese patients with hormone receptor (HR)-positive/HER2-low early-stage breast cancer and developed a recurrence risk prediction model. METHODS: Female patients with HR-positive/HER2-low early-stage breast cancer treated in 29 hospitals of the Chinese Society of Breast Surgery (CSBrS) from Jan 2015 to Dec 2016 were enrolled. Their clinicopathological data and prognostic information were collected, and machine learning methods were used to analyze the prognostic factors. RESULTS: In total, 25,096 patients were diagnosed with breast cancer in 29 hospitals of CSBrS from Jan 2015 to Dec 2016, and clinicopathological data for 6486 patients with HER2-low early-stage breast cancer were collected. Among them, 5629 patients (86.79%) were HR-positive. The median follow-up time was 57 months (4, 76 months); the 5-year disease-free survival (DFS) rate was 92.7%, and the 5-year overall survival (OS) rate was 97.7%. In total, 412 cases (7.31%) of metastasis were observed, and 124 (2.20%) patients died. Multivariate Cox regression analysis revealed that T stage, N stage, lymphovascular thrombosis, Ki-67 index, and prognostic stage were associated with recurrence and metastasis ( P <0.05). A recurrence risk prediction model was established using the random forest method and exhibited a sensitivity of 81.1%, specificity of 71.7%, positive predictive value of 74.1%, and negative predictive value of 79.2%. CONCLUSION: Most of patients with HER2-low early-stage breast cancer were HR-positive, and patients had favorable outcome; tumor N stage, lymphovascular thrombosis, Ki-67 index, and tumor prognostic stage were prognostic factors. The HR-positive/HER2-low early-stage breast cancer recurrence prediction model established based on the random forest method has a good reference value for predicting 5-year recurrence events. REGISTRITATION ChiCTR.org.cn, ChiCTR2100046766.
Humans ; Female ; Breast Neoplasms/diagnosis* ; Ki-67 Antigen ; Receptor, ErbB-2 ; Prognosis ; Thrombosis ; Receptors, Progesterone

BACKGROUND: : Breast cancer with low-positive human epidermal growth factor receptor 2 (HER2) expression has triggered further refinement of evaluation criteria for HER2 expression. We studied the clinicopathological features of early-stage breast cancer with low-positive HER2 expression in China and analyzed prognostic factors. METHODS: : Clinical and pathological data and prognostic information of patients with early-stage breast cancer with low-positive HER2 expression treated by the member units of the Chinese Society of Breast Surgery and Chinese Society of Surgery of Chinese Medical Association, from January 2015 to December 2016 were collected. The prognostic factors of these patients were analyzed. RESULTS: : Twenty-nine hospitals provided valid cases. From 2015 to 2016, a total of 25,096 cases of early-stage breast cancer were treated, 7642 (30.5%) of which had low-positive HER2 expression and were included in the study. After ineligible cases were excluded, 6486 patients were included in the study. The median follow-up time was 57 months (4-76 months). The disease-free survival rate was 92.1% at 5 years, and the overall survival rate was 97.4% at 5 years. At the follow-up, 506 (7.8%) cases of metastasis and 167 (2.6%) deaths were noted. Multivariate Cox regression analysis showed that tumor stage, lymphvascular invasion, and the Ki67 index were related to recurrence and metastasis (P < 0.05). The recurrence risk prediction model was established using a machine learning model and showed that the area under the receiving operator characteristic curve was 0.815 (95% confidence interval: 0.750-0.880). CONCLUSIONS: : Early-stage breast cancer patients with low-positive HER2 expression account for 30.5% of all patients. Tumor stage, lymphvascular invasion, and the Ki67 index are factors affecting prognosis. The recurrence prediction model for breast cancer with low-positive HER2 expression based on a machine learning model had a good clinical reference value for predicting the recurrence risk at 5 years. TRIAL REGISTRATION : ChiCTR.org.cn, ChiCTR2100046766.
Breast Neoplasms/metabolism* ; Female ; Humans ; Ki-67 Antigen ; Mastectomy ; Receptor, ErbB-2/metabolism*