Objective To determine the rules of acupoint selection for hiccup based on data mining.Methods From the acupuncture and moxibustion sections of 53 medical books included in the 5th edition of Chinese Medical Classics,entries related to acupuncture treatment for hiccups was screened and constructed a"Classical Chinese Medicine Database for Acupuncture Treatment of Hiccups."Data mining techniques,including cluster analysis and association rule analysis,were employed to study the characteristics and patterns of acupoint selection for hiccup treatment in ancient medical texts.Results A total of 126 prescriptions were collected,involving 65 acupoints with a total application frequency of 172 times.The most frequently selected acupoints were Taiyuan(LU9),Shaoshang(LU11),Shiguan(KI18),Wenliu(LI7),Laogong(PC8),and Zusanli(ST36).The primary meridians used were the conception vessel(CV1)and the lung meridian of hand-taiyin.The most commonly stimulated body regions were the upper limbs and the chest/abdomen.Among special acupoint categories,the five-shu points and crossing points were predominantly utilized.Cluster analysis identified two significant clusters:Daling(PC7)-Ximen(PC4)-Yuji(LU10),and Qimen(LR14)-Chengjiang(CV24)-Dadun(LR1)-Guanyuan(CV4)-Juque(CV14).Association rule analysis revealed the strongest acupoint correlation between Danzhong(CV17)and Zhongwan(CV12),followed by the combination of Danzhong-Zhongwan-Zusanli.Conclusion In ancient acupuncture treatment of hiccups,particular emphasis was placed on the flexible combination of Taiyuan-Shaoshang and Danzhong-Zhongwan with other acupoints.This approach fully embodies the meridian syndrome differentiation principle of"where the meridian passes,it can treat"and"where the disease locates,it can treat."The selection follows the acupoint pairing principles of"combining distal and local points"and"coordinating upper and lower regions",while also emphasizing the application of specific acupoint categories.

Objective:To explore the predictive potential of dual-energy CT (DECT) quantitative parameters in identifying breast cancer molecular subtypes and the expression of the epidermal growth factor receptor (EGFR) .Methods:A cohort of 97 breast cancer patients, treated between Jun. 2022 and Jun. 2024 were selected. The study compared DECT parameters-such as iodine concentration (IC) , normalized iodine concentration (NIC) , spectral curve slope (λ HU) , and effective atomic number (Z eff) in both arterial and venous phases across different molecular subtypes. A multiclass logistic regression model was employed to assess the parameters' value in predicting molecular subtypes, while a binary logistic regression model was used to evaluate their predictive value for EGFR expression. Results:Multiple Logistic regression analysis showed that after adjusting for confounder age and family history, IC ( OR=1.72, 2.78, 3.05) , NIC ( OR=2.52, 1.94, 2.93) , λ HU ( OR=2.08, 2.54, 3.17) and Z eff ( OR=2.03, 2.30, 2.37) at arterial stage were independently correlated with the molecular subtypes of breast cancer ( P<0.05) . Binary logistic regression analysis, adjusted for tumor size and lymph node metastasis, identified arterial phase IC ( OR=3.45) , NIC ( OR=2.73) , λ HU ( OR=2.59) , and Z eff ( OR=1.76) as independent risk factors for EGFR-positive breast cancer ( P<0.05) . Conclusion:DECT quantitative parameters, particularly arterial phase IC, NIC, λ HU, and Zeff, offer valuable insights into the molecular subtyping of breast cancer and EGFR expression, thereby assisting in the development of personalized treatment strategies.

Objective:To explore the predictive potential of dual-energy CT (DECT) quantitative parameters in identifying breast cancer molecular subtypes and the expression of the epidermal growth factor receptor (EGFR) .Methods:A cohort of 97 breast cancer patients, treated between Jun. 2022 and Jun. 2024 were selected. The study compared DECT parameters-such as iodine concentration (IC) , normalized iodine concentration (NIC) , spectral curve slope (λ HU) , and effective atomic number (Z eff) in both arterial and venous phases across different molecular subtypes. A multiclass logistic regression model was employed to assess the parameters' value in predicting molecular subtypes, while a binary logistic regression model was used to evaluate their predictive value for EGFR expression. Results:Multiple Logistic regression analysis showed that after adjusting for confounder age and family history, IC ( OR=1.72, 2.78, 3.05) , NIC ( OR=2.52, 1.94, 2.93) , λ HU ( OR=2.08, 2.54, 3.17) and Z eff ( OR=2.03, 2.30, 2.37) at arterial stage were independently correlated with the molecular subtypes of breast cancer ( P<0.05) . Binary logistic regression analysis, adjusted for tumor size and lymph node metastasis, identified arterial phase IC ( OR=3.45) , NIC ( OR=2.73) , λ HU ( OR=2.59) , and Z eff ( OR=1.76) as independent risk factors for EGFR-positive breast cancer ( P<0.05) . Conclusion:DECT quantitative parameters, particularly arterial phase IC, NIC, λ HU, and Zeff, offer valuable insights into the molecular subtyping of breast cancer and EGFR expression, thereby assisting in the development of personalized treatment strategies.