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.

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 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.

Objective:To explore the impacts of different frequencies of orthogonal image-guided verification on set-up error correction and the reference values of planning target volume (PTV) margins in postoperative radiotherapy for breast cancer, in order to provide recommendations of the verification frequency in clinical practice.Methods:A total of 80 breast cancer patients who received postoperative intensity-modulated radiotherapy for breast cancer at the Affiliated Union Hospital of Fujian Medical University from January 2021 to January 2022 were enrolled. Orthogonal image-guided verification was conducted before each radiation treatment to determine initial and residual set-up errors. The error data were statistically analyzed by categorizing patients into groups A, B, C, D, E, and F, based on assumed verification frequencies (daily, every 2, 3, 4, and 5 d, and no correction). The magnitude, distribution, and differences of set-up errors of various groups were analyzed, and the PTV margins were calculated using a reference formula.Results:The collected initial set-up errors of the 80 patients were (3.49±3.43), (2.87±2.88), and (2.75±2.35) mm, in x, y, and z directions, respectively. The residual set-up errors decreased gradually with increasing verification frequency. In the case of daily image-guided verification, the residual set-up errors in x, y, and z directions decreased to (1.63±0.90), (1.63±0.93), and (1.55±0.90)mm, respectively. The reference values of PTV margins calculated under different verification frequencies decreased with an increase in the verification frequency. Under daily verification, the reference values of PTV margins calculated based on set-up errors decreased from initial 6.60, 6.29, and 4.22 mm (without correction) to 1.27, 1.37, and 1.28 mm, respectively. Differences in the reference values under verification frequencies every 3, 4, and 5 d were less than 0.81 mm. Conclusions:Daily image-guided verification (including correction) can effectively reduce set-up errors in image-guided radiotherapy for breast cancer. The set-up errors differ under varying verification frequencies, with a higher frequency corresponding to better correction effects and smaller PTV margins required.

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.