In recent years, China has been facing the dual challenges of declining fertility rates and births, with male reproductive health issues, especially the decline in semen quality, identified as a pivotal contributor to this phenomenon. Meanwhile, accumulating evidence indicates that air pollutants, an increasingly severe environmental problem, can damage semen quality not only directly through their biological toxicity but also indirectly by disrupting the composition of microbial communities in the gut and semen, thereby dysregulating immune function, endocrine homeostasis, and oxidative stress responses. The gut microbiota and semen microbiota, as important components of the human microecosystem, play crucial roles in maintaining reproductive health. This article comprehensively reviewed the research progress on the potential effects of air pollutants (particulate matter and gaseous pollutants), gut microbiota, and semen microbiota on semen quality. Specifically, it elucidated the mechanisms of interaction between these factors and explored how they affect male fertility.

Pulmonary hypertension (PH) is a group of progressive diseases characterized by pulmonary vascular remodeling,and some patients already have right heart insufficiency at the time of diagnosis. Therefore,early diagnosis of PH is essential to improve patients' quality of life and prolong survival. Biomarkers are an important indicator for early diagnosis of the disease,and there are many traditional biomarkers for PH diagnosis,but the sensitivity and specificity are low. With the progress of research,some new biomarkers have been shown to predict disease progression in early PH and play an important role in the early diagnosis of PH. This study reviews the research progress of biomarkers of PH from the aspects of right heart insufficiency,endothelial dysfunction,pulmonary artery smooth muscle dysfunction,inflammation,and in situ thrombosis to provide exploration direction and reference value for early diagnosis of PH.

Pulmonary hypertension(PH)is a fatal disease characterized by pulmonary vascular remodeling,ultimately leading to right heart failure and death.Current treatments for PH are suboptimal,with no substantial improvement in overall survival among patients with advanced PH.Despite some progress in understanding the pathogenesis of PH,further studies at the molecular level are needed to develop more effective treatments for PH.Recent research has shown that long non-coding RNAs(lncRNAs)have an important regulatory function in the pathophysiological process of PH,and may thus be potential disease biomarkers and therapeutic targets.In this paper,we review recent progress in our understanding of the molecular mechanisms of lncRNAs in PH.

Objective:To explore how the teaching of ideological and political courses in colleges and universities can effectively guide online public opinion under the background of melted media,so as to cope with the challenges posed by diversified information transmission channels and complex and changeable online public opinion,thereby promoting the formation of correct values and ideological cognition among students.Methods:A total of 1 353 students who volunteered to participate in this survey in a university that offers courses related to online public opinion guidance were selected as the survey subjects.Multiple linear regression analysis was used to analyze how universities should teach students to deal with online hot topics through relevant ideological and political courses.Results:Results of multiple linear regression analysis indicated that the teaching of ideological and political courses in colleges and universities under the background of melted media significantly enhanced students'awareness of online public opinion,and the courses played a positive role in the guidance.The improvement of media literacy facilitated the formation of students'correct values and ideological cognition,effectively responding to the challenges of diversified information dissemination.Variables including the infringement of personal privacy by online public opinion,the enhancement of civic consciousness,the impact on learning,and the distortion of reality understanding all had an impact on students'perception of the teaching effect of ideological and political courses.Conclusions:The teaching of ideological and political courses in colleges and universities plays a significant role in guiding online public opinion under the background of melted media.By enhancing students'media literacy,discernment,and emotional management ability,it effectively promotes the formation of students'correct values and ideological ideological,and provides strong support for the healthy growth of students in the complex network environment.The important role of ideological and political teaching in cultivating students'comprehensive abilities and guiding them to correctly deal with online public opinion is emphasized.

With the emergence of deep learning techniques based on convolutional neural networks, artificial intelligence (AI) has driven transformative developments in the field of medical image analysis. Recently, large language models (LLMs) such as ChatGPT have also started to achieve distinction in this domain. Increasing research shows the undeniable role of AI in reshaping various aspects of medical image analysis, including processes such as image enhancement, segmentation, detection in image preprocessing, and postprocessing related to medical diagnosis and prognosis in clinical settings. However, despite the significant progress in AI research, studies investigating the recent advances in AI technology in the aforementioned aspects, the changes in research hotspot trajectories, and the performance of studies in addressing key clinical challenges in this field are limited. This article provides an overview of recent advances in AI for medical image analysis and discusses the methodological profiles, advantages, disadvantages, and future trends of AI technologies.
Artificial Intelligence ; Humans ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Deep Learning ; Diagnostic Imaging/methods*

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.

Objective:To explore the relationship between long-term blood pressure variability and arteriosclerosis in women with a history of hypertensive disorders in pregnancy (HDP).Methods:This study was a retrospective cohort study. Data were obtained from the Kailuan Research Database. Women with a history of HDP who delivered between January 1990 and December 2020 and completed brachial-ankle pulse wave velocity (baPWV) measurement in the postpartum period were enrolled. Baseline data were obtained from the first post-delivery health examination, while the outcome measure was the baPWV recorded during the last follow-up visit, synchronized with blood pressure measurements. Based on long-term blood pressure variability, the enrolled study subjects were divided into the first, second, and third tertile groups in ascending order using the tertile method, and intergroup differences in clinical characteristics were compared. Multivariable logistic regression was performed to evaluate the impact of long-term blood pressure variability levels on arteriosclerosis risk in women with a history of HDP. Sensitivity analyses excluded individuals with multiple deliveries to validate the robustness of findings. Subgroup analyses were conducted based on delivery age (<40 vs. ≥40 years) and blood pressure measurement frequency (3 vs. >3 times) to explore the potential impact of different population characteristics on the study results.Results:A total of 421 study subjects were enrolled, aged (36.07±6.05) years, with a baPWV value of (1 376.80±238.18) cm/s. Long-term blood pressure variability was 4.66 (3.41, 6.50) mmHg (1 mmHg=0.133 kPa). The first, second and third quartile group included 140, 141 and 140 individuals, respectively. In the total population, the incidence of arteriosclerosis was 40.4% (170/421). The incidence rates in the first, second, and third tertile groups were 34.3% (48/140), 39.0% (55/141), and 47.9% (67/140), respectively. Multivariate logistic regression analysis showed that increased long-term blood pressure variability was an independent risk factor for arteriosclerosis in women with a history of HDP ( OR=1.702, 95% CI 1.018-2.844, P=0.043). The results of sensitivity analyses were consistent with that of the primary analysis ( OR=1.758, 95% CI 1.044-2.959, P=0.034). Subgroup analyses further indicated that in the subgroups with delivery age <40 years ( OR=2.116, 95% CI 1.153-3.885, P=0.016) and blood pressure measurement frequency >3 times ( OR=1.894, 95% CI 1.069-3.355, P=0.029), the association between long-term blood pressure variability and arterial stiffness risk was more significant. Conclusions:For women with a history of HDP, elevated long-term blood pressure variability may increase the risk of arteriosclerosis, and this effect is more pronounced in younger women (delivery age <40 years) and those with high-frequency blood pressure measurements (>3 times). Enhanced monitoring and management of blood pressure variability in this population are crucial to improving long-term cardiovascular health outcomes.