Background Benign prostatic hyperplasia (BPH) is a common chronic urinary disease in middle-aged and older men, yet the impact of long-term exposure to atmospheric particulate matter (PM) on its pathogenesis remains unclear. Objective To investigate the association between PM exposure and the risk of incident BPH in middle-aged and older men. Methods Based on four waves of follow-up data (2011–2018) from the China Health and Retirement Longitudinal Study (CHARLS), 4766 participants were enrolled. Robust Poisson regression models were employed to assess the association between exposure to PM (PM₁, PM_2.5, and PM₁₀) and the risk of incident BPH. Relative risks (RR) and their corresponding 95% confidence intervals (95%CI) were calculated. Dose-response relationships were fitted using restricted cubic splines (RCS). Subgroup analyses were performed to explore potential effect modifications, and multiple imputation was used to handle missing data. Results Over a mean follow-up of 6 years, 914 incident BPH cases were identified among the4766 participants (cumulative incidence: 19.18%). After adjusting for confounders, each 10 μg·m⁻³ increase in PM₁, PM_2.5, and PM₁₀ concentrations was associated with a 13.1% (RR=1.131, 95%CI: 1.063, 1.203), 8.5% (RR=1.085, 95%CI: 1.050, 1.122), and 5.1% (RR=1.051, 95%CI: 1.034, 1.069) increased risk of BPH, respectively. RCS analysis showed that no nonlinear relationship was found between PM₁ and PM_2.5 and the risk of BPH (P>0.05); however, a nonlinear association was observed for PM₁₀ (P=0.03), with the risk increment slowing beyond 100 μg·m⁻³. Subgroup and sensitivity analyses confirmed the robustness of these findings. Conclusion Long-term exposure to ambient particulate matter may be associated with an increased risk of incident BPH in middle-aged and older men.

Background Benign prostatic hyperplasia (BPH) is a common chronic urinary disease in middle-aged and older men, yet the impact of long-term exposure to atmospheric particulate matter (PM) on its pathogenesis remains unclear. Objective To investigate the association between PM exposure and the risk of incident BPH in middle-aged and older men. Methods Based on four waves of follow-up data (2011–2018) from the China Health and Retirement Longitudinal Study (CHARLS), 4766 participants were enrolled. Robust Poisson regression models were employed to assess the association between exposure to PM (PM₁, PM_2.5, and PM₁₀) and the risk of incident BPH. Relative risks (RR) and their corresponding 95% confidence intervals (95%CI) were calculated. Dose-response relationships were fitted using restricted cubic splines (RCS). Subgroup analyses were performed to explore potential effect modifications, and multiple imputation was used to handle missing data. Results Over a mean follow-up of 6 years, 914 incident BPH cases were identified among the4766 participants (cumulative incidence: 19.18%). After adjusting for confounders, each 10 μg·m⁻³ increase in PM₁, PM_2.5, and PM₁₀ concentrations was associated with a 13.1% (RR=1.131, 95%CI: 1.063, 1.203), 8.5% (RR=1.085, 95%CI: 1.050, 1.122), and 5.1% (RR=1.051, 95%CI: 1.034, 1.069) increased risk of BPH, respectively. RCS analysis showed that no nonlinear relationship was found between PM₁ and PM_2.5 and the risk of BPH (P>0.05); however, a nonlinear association was observed for PM₁₀ (P=0.03), with the risk increment slowing beyond 100 μg·m⁻³. Subgroup and sensitivity analyses confirmed the robustness of these findings. Conclusion Long-term exposure to ambient particulate matter may be associated with an increased risk of incident BPH in middle-aged and older men.

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.

Next generation sequencing (NGS) technology is playing an increasingly important role in the diagnosis of genetic diseases. Whole exome sequencing (WES), which targets the coding regions of the genome, has been widely used in the diagnosis of genetic diseases for its low cost and high efficiency. However, compared to conventional methods, the Next Generation Sequencing (NGS) process is intricate, and there is variability in the expertise of data analysts and variant interpreters, which may lead to inconsistencies in the outcomes. To ensure the quality of testing and enhance the diagnostic rate of diseases, this consensus has provided recommendations regarding the laboratory setup, operational procedures, data analysis, result interpretation, and quality control for WES, with an aim to standardize its application in the detection of genetic disorders.

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.