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.

Bacillus coagulans can utilize the hydrolyzed carbon source of agricultural waste to produce lactic acid via a homofermentative pathway. However, a significant carbon source metabolic repression effect was observed when the strain metabolized mixed sugars (glucose and xylose), reducing the productivity of lactic acid. In this study, we obtained the fermentation conditions for the simultaneous utilization of the mixed sugars by B. coagulans by changing the ratio of glucose to xylose in the medium. Through transcriptome sequencing, several key genes responsible for xylose utilization were identified. The critical role of xylose isomerase (XylA, EC 5.3.1.5) in the synchronous utilization of glucose/xylose in B. coagulans was investigated via qRT-PCR (quantitative real-time polymerase chain reaction). Subsequently, the heterologous expression and characterization of the XylA-encoding gene (XylA) were conducted. It was determined that the gene encoded a protein composed of 440 amino acid residues. The secondary structure of the encoded protein was predominantly composed of α-helixes and random coils, while the higher structure of the protein was identified as a homotetramer. Then, XylA was cloned and expressed in Escherichia coli BL21(DE3), and the recombinant protein Bc-XlyA was obtained with a molecular weight of approximately 50 kDa. The optimal pH and temperature of Bc-XylA were 8.0 and 60 ℃, respectively, and Mn²⁺, Mg²⁺, and Co²⁺ had positive effects on the activity of Bc-XlyA. The present study provides scientific data on the molecular modification of B. coagulans, offering theoretical support for the efficient utilization of xylose in the strain.
Xylose/metabolism* ; Cloning, Molecular ; Bacillus coagulans/enzymology* ; Aldose-Ketose Isomerases/metabolism* ; Fermentation ; Bacterial Proteins/metabolism* ; Glucose/metabolism*

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.

This study aims to develop an abdominal acupoint localization system based on computer vision and convolutional neural networks (CNNs). To address the challenge of abdominal acupoint localization, a multi-task CNNs architecture was constructed and trained to locate the Shenque (CV8) and human body boundaries. Based on the identified Shenque (CV8), the system further deduces key characteristics of four acupoints: Shangwan (CV13), Qugu (CV2), and bilateral Daheng (SP15). An affine transformation matrix is applied to accurately map image coordinates to an acupoint template space, achieving precise localization of abdominal acupoints. Testing has verified that this system can accurately identify and locate abdominal acupoints in images. The development of this localization system provides technical support for TCM remote education, diagnostic assistance, and advanced TCM equipment, such as intelligent acupuncture robots, facilitating the standardization and intelligent advancement of acupuncture.
Acupuncture Points ; Humans ; Deep Learning ; Abdomen/diagnostic imaging* ; Neural Networks, Computer ; Acupuncture Therapy ; Image Processing, Computer-Assisted

BACKGROUND: The effect of the interval between previous Helicobacter pylori (H. pylori) eradication and rescue treatment on therapeutic outcomes remains unknown. The aim of this study was to investigate the association between eradication rates and treatment interval durations in H. pylori infections. METHODS: This prospective observational study was conducted from December 2021 to February 2023 at six tertiary hospitals in Shandong, China. We recruited patients who were positive for H. pylori infection and required rescue treatment. Demographic information, previous times of eradication therapy, last eradication therapy date, and history of antibiotic use data were collected. The patients were divided into four groups based on the rescue treatment interval length: Group A, ≥4 weeks and ≤3 months; Group B, >3 and ≤6 months; Group C, >6 and ≤12 months; and Group D, >12 months. The primary outcome was the eradication rate of H. pylori . Drug compliance and adverse events (AEs) were also assessed. Pearson's χ2 test or Fisher's exact test was used to compare eradication rates between groups. RESULTS: A total of 670 patients were enrolled in this study. The intention-to-treat (ITT) eradication rates were 88.3% (158/179) in Group A, 89.6% (120/134) in Group B, 89.1% (123/138) in Group C, and 87.7% (192/219) in Group D. The per-protocol (PP) eradication rates were 92.9% (156/168) in Group A, 94.5% (120/127) in Group B, 94.5% (121/128) in Group C, and 93.6% (190/203) in Group D. There was no statistically significant difference in the eradication rates between groups in either the ITT ( P = 0.949) or PP analysis ( P = 0.921). No significant differences were observed in the incidence of AEs ( P = 0.934) or drug compliance ( P = 0.849) between groups. CONCLUSION: The interval duration of rescue treatment had no significant effect on H. pylori eradication rates or the incidence of AEs. REGISTRATION ClinicalTrials.gov , NCT05173493.
Humans ; Helicobacter Infections/drug therapy* ; Helicobacter pylori/pathogenicity* ; Male ; Female ; Prospective Studies ; Middle Aged ; Anti-Bacterial Agents/adverse effects* ; Adult ; Aged ; Treatment Outcome ; Proton Pump Inhibitors/therapeutic use*

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*

Rosacea is a common chronic inflammatory skin disease that predominantly affects the central face. It can impair appearance and cause various discomforts, thus negatively impacting patients' physical and mental well-being as well as their quality of life. Its pathophysiological mechanisms involve multiple factors. Studies have confirmed that ultraviolet radiation plays a significant role in the pathogenesis of rosacea, affecting skin tissues, cells, DNA, and proteins, and inducing oxidative damage. Ultraviolet can lead to the occurrence and development of rosacea by up-regulating the expression of LL-37, matrix metalloproteinase, vascular endothelial growth factor, and reactive oxygen species, and influence their interactions, thereby triggering inflammatory responses, altering the dermal matrix, and promoting capillary dilation and neovascularization, which contribute to the onset and progression of rosacea. Exploring the role of ultraviolet in the pathogenesis of rosacea can provide new strategies for protection and treatment, and enhance awareness of ultraviolet protection among patients with rosacea.
Humans ; Rosacea/metabolism* ; Ultraviolet Rays/adverse effects* ; Cathelicidins ; Reactive Oxygen Species/metabolism* ; Antimicrobial Cationic Peptides/metabolism* ; Matrix Metalloproteinases/metabolism* ; Vascular Endothelial Growth Factor A/metabolism* ; Skin/metabolism*