Cancer is a highly deadly disease, with breast cancer, cervical cancer, endometrial cancer, and ovarian cancer being the most prevalent in women. However, traditional cancer treatments present challenges due to their strong toxic side effects and adverse reactions. Numerous studies have demonstrated that natural products derived from various plants possess therapeutic and preventive properties against cancer. These phytochemicals have been extensively investigated as a potential alternative to conventional chemotherapy drugs, owing to their safety and efficacy. This article provides a comprehensive review of the recent advances in the chemoprevention and mechanisms of phytochemicals against the four major female cancers. The focus will be on how these phytochemicals regulate cancer cell proliferation, apoptosis, invasion, and metastasis to impede cancer progression. Given their extensive clinical applications, phytochemicals hold great promise in the field of cancer treatment. It hopes that this review will inspire more researchers to explore the potential of these natural compounds in combating female cancers.

Type 2 diabetes mellitus is a metabol-ic disease characterized by high blood sugar levels.Traditionally,amino acids are primarily viewed as the basic building blocks for proteins and peptide synthesis.However,in recent years,amino acids have gained increased attention as signaling mole-cules that play crucial roles in the maintenance and regulation of metabolic homeostasis.It has been found that there is a close correlation between the metabolism of branched-chain and/or aromatic amino acids and the occurrence or development of type 2 diabetes mellitus.Furthermore,there have been successive reports on the regulation mecha-nism involved.This article will focus on the meta-bolic processes,mechanisms and clinical value of branched-chain and aromatic amino acids in type 2 diabetes mellitus.It will also summarize and pro-vide an outlook on the current state of amino acid metabolism in the treatment of diabetes mellitus,with the aim of offering new ideas for the treat-ment of type 2 diabetes mellitus.

Objective To investigate the effect and mechanism of dabrafenib （DAB） combined with tremelimumab （TREM） on melanoma. Methods The effects of DAB combined with TREM on cell viability, cytotoxicity and cell migration of A375 cells were evaluated by Cell Counting Kit-8 （CCK-8） method, lactate dehydrogenase （LDH） method and scratch assay. The levels of reactive oxygen species （ROS）, adenosine triphosphate （ATP）, malondialdehyde （MDA）, and superoxide dismutase （SOD） were detected to evaluate the effects of combined drugs on oxidative stress and energy metabolism. In addition, A375 tumor-bearing nude mice model was used to evaluate the inhibitory effect of the combined treatment on tumor growth in vivo, and the degree of cell apoptosis and cell proliferation in tumor tissues were analyzed by terminal deoxynucleotidyl transferase-mediated dutP Nick end labeling （TUNEL） and proliferating cell nuclear antigen （PCNA） immunohistochemical staining. Results The combined treatment significantly inhibited the survival rate and migration ability of A375 cells and enhanced the cytotoxicity. The combined intervention also significantly increased ROS level, decreased ATP, SOD and MDA levels. It effectively inhibited tumor growth in tumor-bearing nude mice, increased the apoptosis rate of tumor cells and inhibited cell proliferation. Conclusion DAB combined with TREM may improve the therapeutic effect of melanoma by enhancing oxidative stress, inhibiting energy metabolism, and promoting cell apoptosis. This combination therapy may provide a new therapeutic strategy to overcome the limitations of singledrug therapy.

Objective:To construct and evaluate the predictive performance of a multiphase ultrasound radiomics model for microvascular invasion（MVI）in hepatocellular carcinoma（HCC）.Methods:A total of 126 patients with pathologically confirmed HCC were retrospectively enrolled from 4 medical centers between May 2018 and July 2025，including the First Hospital of Shanxi Medical University，Shanxi Province Third People's Hospital，Changzhi People's Hospital，and the Organ Transplant Center of the Affiliated Hospital of Qingdao University. A total of 630 ultrasound images of the lesions in different phases were collected，from which 1 561 radiomic features were extracted. The patients from medical institutions in Shanxi Province were chosen as the training set（ n=91），and the patients from the Organ Transplant Center of the Affiliated Hospital of Qingdao University were chosen as the validation set（ n=35）. In the training set，37.4%（34/91）patients presented MVI（+），whereas in the validation set，54.3%（19/35）patients presented MVI（+）. Radiomics features were extracted from ultrasound images，and features related to the MVI（+）were selected through dimensionality reduction analysis. Five multiple machine learning algorithms were used to construct predictive models，which were then evaluated using an external validation set. The Radscore was calculated，and a nomogram was constructed combining Radscore with ultrasound and clinical characteristics to predict MVI. Results:The model combining radiomics features from the portal venous phase and the delay phase showed the best predictive performance in both the training and validation sets，with area under curve（AUC）values of 0.835 and 0.727，respectively. The prediction model developed using radiomics Radscore and clinical indicators could be represented and presented as a nomogram.Conclusions:The radiomics model based on multi-phase ultrasound offers a novel approach for non-invasive preoperative prediction of MVI in liver cancer. Furthermore，its integration with clinical features aids in optimizing clinical treatment strategies.

Objective·To identify and evaluate novel and reliable non-invasive serological biomarkers for detecting pancreatic ductal adenocarcinoma(PDAC).Methods·Sixty-seven PDAC patients(Ruijin cohort Ⅰ)were recruited at Pancreatic Center,Ruijin Hospital,Shanghai Jiao Tong University School of Medicine,from May 2018 to December 2019.Global proteome profiling of 67 PDAC tumor tissues and 67 matched adjacent normal tissues was performed using mass spectrum.Bioinformatics analysis on the proteomics data was conducted to identify new biomarkers,and receiver operating characteristic(ROC)curves and the area under the curve(AUC)were used to evaluate their value of detecting PDAC.The proteomic and mRNA sequencing data were further downloaded and analysed from the Clinical Proteomic Tumor Analysis Consortium(CPTAC)cohort for validation.In addition,the Ruijin Cohort Ⅱ,consisting of 47 PDAC patients and 75 healthy individuals,was recruited for a case-control study from June 2021 to June 2022.Enzyme-linked immunosorbent assay(ELISA)was used to detect the expression level of new biomarkers in the serum of patients and healthy individuals to evaluate the serological diagnostic values of them.Results·Collagen type Ⅻ α1 chain(COL12A1)was identified as a candidate biomarker for PDAC diagnosis based on differential expression analysis on the proteomic data and was validated to be higher in tumor tissues than in adjacent normal tissues in the CPTAC cohort.In addition,COL12A1 protein levels were significantly higher in the sera of PDAC patients than in those of healthy controls,showing good diagnostic performance with an AUC of 0.82,a sensitivity of 81%,and a specificity of 83%.ROC analysis revealed that COL12A1 improved the performance of carbohydrate antigen 199(CA199)in distinguishing PDAC patients from healthy individuals(AUCCA199=0.91 vs AUCCA199+COL12A1=0.95,P<0.05).Furthermore,COL12A1 also showed excellent ability to distinguish early-stage PDAC patients(stage Ⅰ?Ⅱ)from healthy individuals(AUCCOL12A1=0.83),and significantly improved the AUC of CA199 in early-stage PDAC patients(AUCCA199=0.92 vs AUCCA199+COL12A1=0.97,P<0.05).Conclusion·COL12A1 is a potential serological diagnostic marker that complements CA199 in detecting early-stage PDAC.

Objective·To identify and evaluate novel and reliable non-invasive serological biomarkers for detecting pancreatic ductal adenocarcinoma(PDAC).Methods·Sixty-seven PDAC patients(Ruijin cohort Ⅰ)were recruited at Pancreatic Center,Ruijin Hospital,Shanghai Jiao Tong University School of Medicine,from May 2018 to December 2019.Global proteome profiling of 67 PDAC tumor tissues and 67 matched adjacent normal tissues was performed using mass spectrum.Bioinformatics analysis on the proteomics data was conducted to identify new biomarkers,and receiver operating characteristic(ROC)curves and the area under the curve(AUC)were used to evaluate their value of detecting PDAC.The proteomic and mRNA sequencing data were further downloaded and analysed from the Clinical Proteomic Tumor Analysis Consortium(CPTAC)cohort for validation.In addition,the Ruijin Cohort Ⅱ,consisting of 47 PDAC patients and 75 healthy individuals,was recruited for a case-control study from June 2021 to June 2022.Enzyme-linked immunosorbent assay(ELISA)was used to detect the expression level of new biomarkers in the serum of patients and healthy individuals to evaluate the serological diagnostic values of them.Results·Collagen type Ⅻ α1 chain(COL12A1)was identified as a candidate biomarker for PDAC diagnosis based on differential expression analysis on the proteomic data and was validated to be higher in tumor tissues than in adjacent normal tissues in the CPTAC cohort.In addition,COL12A1 protein levels were significantly higher in the sera of PDAC patients than in those of healthy controls,showing good diagnostic performance with an AUC of 0.82,a sensitivity of 81%,and a specificity of 83%.ROC analysis revealed that COL12A1 improved the performance of carbohydrate antigen 199(CA199)in distinguishing PDAC patients from healthy individuals(AUCCA199=0.91 vs AUCCA199+COL12A1=0.95,P<0.05).Furthermore,COL12A1 also showed excellent ability to distinguish early-stage PDAC patients(stage Ⅰ?Ⅱ)from healthy individuals(AUCCOL12A1=0.83),and significantly improved the AUC of CA199 in early-stage PDAC patients(AUCCA199=0.92 vs AUCCA199+COL12A1=0.97,P<0.05).Conclusion·COL12A1 is a potential serological diagnostic marker that complements CA199 in detecting early-stage PDAC.

Type 2 diabetes mellitus is a metabol-ic disease characterized by high blood sugar levels.Traditionally,amino acids are primarily viewed as the basic building blocks for proteins and peptide synthesis.However,in recent years,amino acids have gained increased attention as signaling mole-cules that play crucial roles in the maintenance and regulation of metabolic homeostasis.It has been found that there is a close correlation between the metabolism of branched-chain and/or aromatic amino acids and the occurrence or development of type 2 diabetes mellitus.Furthermore,there have been successive reports on the regulation mecha-nism involved.This article will focus on the meta-bolic processes,mechanisms and clinical value of branched-chain and aromatic amino acids in type 2 diabetes mellitus.It will also summarize and pro-vide an outlook on the current state of amino acid metabolism in the treatment of diabetes mellitus,with the aim of offering new ideas for the treat-ment of type 2 diabetes mellitus.

Objective:To construct and evaluate the predictive performance of a multiphase ultrasound radiomics model for microvascular invasion（MVI）in hepatocellular carcinoma（HCC）.Methods:A total of 126 patients with pathologically confirmed HCC were retrospectively enrolled from 4 medical centers between May 2018 and July 2025，including the First Hospital of Shanxi Medical University，Shanxi Province Third People's Hospital，Changzhi People's Hospital，and the Organ Transplant Center of the Affiliated Hospital of Qingdao University. A total of 630 ultrasound images of the lesions in different phases were collected，from which 1 561 radiomic features were extracted. The patients from medical institutions in Shanxi Province were chosen as the training set（ n=91），and the patients from the Organ Transplant Center of the Affiliated Hospital of Qingdao University were chosen as the validation set（ n=35）. In the training set，37.4%（34/91）patients presented MVI（+），whereas in the validation set，54.3%（19/35）patients presented MVI（+）. Radiomics features were extracted from ultrasound images，and features related to the MVI（+）were selected through dimensionality reduction analysis. Five multiple machine learning algorithms were used to construct predictive models，which were then evaluated using an external validation set. The Radscore was calculated，and a nomogram was constructed combining Radscore with ultrasound and clinical characteristics to predict MVI. Results:The model combining radiomics features from the portal venous phase and the delay phase showed the best predictive performance in both the training and validation sets，with area under curve（AUC）values of 0.835 and 0.727，respectively. The prediction model developed using radiomics Radscore and clinical indicators could be represented and presented as a nomogram.Conclusions:The radiomics model based on multi-phase ultrasound offers a novel approach for non-invasive preoperative prediction of MVI in liver cancer. Furthermore，its integration with clinical features aids in optimizing clinical treatment strategies.

Objective To compare the cleaning abilities of ProTaper Gold(PTG)and TF-Adaptive(TFA).Methods Forty single-abstracted premolars were randomly divided into PTG and TFA groups.The debris and smear layer of the root coronal 1/3,middle 1/3 and apical 1/3 in the two groups were observed using scanning electron microscope(SEM).The cleaning abilities of the PTG and TFA rotary systems were scored according to the scoring standard developed by YAMASHITA.The scores of the two groups were compared and then analyzed.Results There was no significant difference found in the scores of the smear layer and debris at the root coronal 1/3 and the middle 1/3.The scores of the debris and smear layer at the apical 1/3 in the group PTG were higher than those in the TFA group(P<0.05).Conclusion PTG and TFA can effectively remove the root canal smear layer and debris at the root coronal 1/3,and the middle 1/3.However,at the apical 1/3,the cleaning efficiency of TFA is better than that of PTG.

OBJECTIVE To provide reference for Smilax glabra quality control. METHODS UPLC method was established to simultaneously determine the contents of 5-O-caffeoylshikimic acid， neoastilbin， astilbin， neoisoastilbin， isoastilbin， engelitin， resveratrol and isoengelitin in 20 batches of S. glabra from different areas （No. S1-S20）. The quality evaluation of 20 batches of samples was performed by chemometrics； the differential biomarkers that affected the quality of S. glabra were screened. RESULTS The measured 8 components had good linear relationship within the range of measured concentration （r≥0.999 6）. RSDs of precision， repeatability and stability tests （24 h） were all lower than 2.00% （n＝6）. The average recoveries varied between 97.60% and 106.40% （RSDs were all lower than 2.00%， n＝6）. Cluster analysis showed that the samples produced in Zhejiang （S1-S5） and Jiangxi （S6-S10） were clustered into one category； the samples produced in Hunan （S11-S15） were clustered into one category； the samples produced in Yunnan （S16-S20） were clustered into one category. Principal component analysis showed that the first two principal components could represent 85.60% information of 8 components in S. glabra. Partial least squares-discriminant analysis showed that variable importance projection values of 5-O-caffeoylshikimic acid， astilbin， isoastilbin and neoastilbin were all greater than 1. CONCLUSIONS There are differences in the contents of the above 8 components in S. glabra from different origins； 5-O-caffeoylshikimic acid， astilbin， isoastilbin and neoastilbin may be differential markers affecting the quality of S. glabra.