Objective To compare the dosimetric differences in the heart and its substructures between two hybrid plans for hypofractionated whole-breast radiotherapy after breast-conserving surgery in patients with early-stage left-sided breast cancer. Methods A total of 46 patients with early-stage left-sided breast cancer who underwent hypofractionated whole-breast radiotherapy were randomly selected. Two hybrid radiotherapy plans were used, including hybrid intensity-modulated radiotherapy (H_IMRT) and hybrid volumetric-modulated arc therapy (H_VMAT). The heart and its substructures were contoured, including left anterior descending (LAD), left ventricle (LV), right coronary artery (RCA), and right ventricle (RV). The heart and substructure doses, as well as monitor units, were compared between H_IMRT and H_VMAT. Results Both hybrid plans met the clinical requirements. H_IMRT significantly outperformed H_VMAT for the heart (V₁₀, V₃₀, and D_mean), LAD (V₃₀, V₄₀, D_max and D_mean), LV (V₁₀, V₂₀ and D_mean), RCA (D_max, D_mean), and RV (V₅, V₁₀, D_mean) (P < 0.001). Additionally, H_IMRT was significantly superior to H_VMAT for heart V₅, LAD V₂₀, and RV V₂₀ (P = 0.005, 0.035 and 0.037). For LAD (V₁₅, V₄₀) and LV (V₅, V₂₅), H_IMRT was slightly better than H_VMAT, and the difference was not statistically significant. Conclusion Both H_IMRT and H_VMAT hybrid radiotherapy plans are suitable for hypofractionated whole-breast radiotherapy after breast-conserving surgery in patients with early-stage left-sided breast cancer. H_IMRT is slightly better than H_VMAT in dose sparing for the heart and its substructures.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Traditional Chinese Medicine (TCM), as a treasure of the Chinese nation, plays a significant role in maintaining public health. In 2019, the Central Committee of the Communist Party of China and the State Council proposed for the first time the establishment of a TCM registration and evaluation evidence system that integrates TCM theory, "personal experience" and clinical trials (referred to as the "Three-in-One" System) to promote the inheritance and innovation of TCM. Subsequently, the National Medical Products Administration issued several guiding principles to advance the improvement and implementation of this system. Owing to the complexity of its implementation, there are still differing understandings within the TCM industry regarding the positioning of the "Three-in-One" Registration and Evaluation Evidence System, as well as the connotation and value orientation of the "personal experience." To address this, Academician WANG Qi, President of the TCM Association, China International Exchange and Promotion Association for Medical and Healthcare and TCM master, led a group of academicians, TCM masters, TCM pharmacology experts and clinical TCM experts to convene a "Seminar on Promoting the Implementation of the ′Three-in-One′ Registration and Evaluation Evidence System for Chinese Medicinals." Through extensive discussions, an expert consensus was formed, clarifying the different roles of the TCM theory, "personal experience" and clinical trials within the system. It was further emphasized that the "personal experience" is the core of this system, and its data should be derived from clinical practice scenarios. In the future, the improvement of this system will require collaborative efforts across multiple fields to promote the high-quality development of the Chinese medicinal industry.

Objective To evaluate the efficacy and safety of dupilumab combined with 2％cleboride ointment in the treatment of moderate to severe atopic dermatitis,as well as its impact on serological indica-tors.Methods A retrospective analysis was conducted on the clinical data of 67 patients with moderate to se-vere atopic dermatitis(AD)admitted to the Department of Dermatology of Shaoxing University Affiliated Hospital from March 2021 to December 2024.The study subjects were divided into an experimental group(n=35)and a control group(n=32)according to the treatment method.The experimental group was treated with Dupilumab injection and 2％Cleboride ointment,while the control group was treated with ebastine tab-lets and 2％cleboride ointment.Clinical and related serological indicators of patients after 16 weeks of treat-ment were collected,and the itch digital scale score,eczema area and severity(EASI)score,IL-4,IL-13 levels,and incidence of local skin adverse reactions were analyzed before and after treatment in both groups.Results The total effective rate of the experimental group after treatment was 94.29％(33/35),which was higher than the control group[53.13％(17/32)],and the difference was statistically significant(x2=12.862,P＜0.001).There was no statistically significant difference in symptom scores,IL-4,and IL-13 levels between the two groups before treatment(P＞0.05).After treatment,the symptom scores of the experimental group were lower than those of the control group,and the difference was statistically significant(P＜0.05).The lev-els of IL-4 and IL-13 in the experimental group were lower than those in the control group,and the difference was statistically significant(P＜0.05).The incidence of adverse reactions in the experimental group was 2.86％(1/35),significantly lower than the 34.38％(11/32)in the control group,and the difference was sta-tistically significant(x2=9.252,P=0.002).Conclusion The combination of dupilumab injection and 2％cleboride ointment is effective in relieving skin symptoms,regulating cellular immune function,reducing in-flammatory reactions,and minimizing local skin adverse reactions in patients with moderate to severe AD.It is worthy of clinical promotion and use.

ObjectiveTo observe the effects of Coptidis Rhizoma-Fermentum Rubrum on non-alcoholic fatty liver disease （NAFLD） in mice and explore its possible mechanisms. MethodSixty male SPF C57BL/6J mice were randomly divided into six groups： control group， model group， low-， medium-， and high-dose Coptidis Rhizoma-Fermentum Rubrum group （0.75， 1.5， 3 g·kg^-1）， and metformin group （0.075 g·kg^-1）， with 10 mice in each group. NAFLD mouse models were induced by high-fat diet feeding for 24 weeks. The low， medium， and high-dose Coptidis Rhizoma-Fermentum Rubrum groups were administered corresponding doses of Coptidis Rhizoma-Fermentum Rubrum by gavage， while the control and model groups received an equivalent amount of saline for four weeks. Serum total cholesterol （TC）， triglycerides （TG）， free fatty acids （FFA）， and liver function markers including alanine aminotransferase （ALT） and aspartate aminotransferase （AST） were measured using an automatic biochemical analyzer. Hematoxylin-eosin （HE） and oil red O staining were used to detect liver lipid deposition， and Prussian blue staining was used to measure liver ferrous ion levels. Western blot was performed to detect the expression of key proteins in the nuclear factor erythroid-2-related factor 2 （Nrf2）-glutathione peroxidase 4 （GPX4） axis. ResultAfter 24 weeks of high-fat feeding， compared with the control group， the model group showed significant increases in body weight， liver weight and liver index， and serum lipid levels （P<0.01）， as well as substantial hepatic lipid deposition with marked steatosis. Compared with the model group， Coptidis Rhizoma-Fermentum Rubrum intervention reduced body weight （P<0.01）， liver weight and liver index （P<0.01）， and serum lipid levels （P<0.05， P<0.01）， improved liver function （P<0.01）， and decreased hepatic lipid deposition， with the low-dose Coptidis Rhizoma-Fermentum Rubrum group showing the best effect. Western blot results showed that compared with those in the control group， the expression levels of Nrf2， heme oxygenase-1 （HO-1）， kelch-like ECH-associated protein 1（Keap1）， and GPX4 proteins in the model group were decreased （P<0.05， P<0.01）. Compared with the model group， Coptidis Rhizoma-Fermentum Rubrum increased the expression levels of these proteins （P<0.05， P<0.01）. ConclusionCoptidis Rhizoma-Fermentum Rubrum can alleviate fatty liver in mice， improve liver function， and reduce hepatic lipid deposition， possibly by regulating the Nrf2/GPX4 ferroptosis axis.

Primary liver cancer is a malignant tumor with high morbidity and mortality. It also ranks in the forefront in the incidence and mortality of malignant tumors in China, which seriously threatens the lives and health of Chinese people. Most patients have already been in the intermediate and late stage when they are diagnosed, thus the chance of surgery is lost, and the prognosis is poor. In recent years, with the advancement of vascular interventional therapy technologies such as hepatic arterial chemoembolization and hepatic arterial infusion chemotherapy, the emergence of new tyrosine kinase inhibitors, immune checkpoint inhibitors, and especially the development of multimodal combination therapy, the treatment effect of unresectable hepatocellular carcinoma has been continuously improved, and it also provides a potential possibility for sequential surgical treatment. This article reviews the research progress of vascular interventional therapy combined with systemic therapy in unresectable hepatocellular carcinoma, in order to provide a reference for the clinical treatment of unresectable hepatocellular carcinoma.

The health effects associated with environmental pollutants remain one of the major public health issues at present. The research method focusing on the population as the research subjects is limited by reliable cohorts, and the research method targeting individual molecules cannot fully reflect the biological health effects under environmental pollutant stress. Using high-throughput multi-omics, machine learning, and epigenetic detection to conduct targeted research and joint analysis on cells, organoids, organs, animals, and humans in different biological dimensions will help provide data support for the study of potential targets and biological effects of environmental pollutants, providing a theoretical basis for the risk assessment and safety evaluation of environmental pollutants.