Objective To develop a predictive model integrating clinical features,deep learning(DL),and radiomics based on T2-weighted imaging for prenatal assessment of postpartum hemorrhage(PPH)risk in high-risk pregnant women.Methods A total of 538 pregnant women with ultrasound-reported high-risk placenta accrete were retrospectively enrolled and divided into training,internal test,and external test cohorts.A nnUNet model was trained for automatic placental segmentation.Univariate and multivariate analyses were conducted on clinical features to identify those associated with PPH.Quantitative radiomic features were extracted from the placental region,and a random forest model was developed to predict estimated blood loss(EBL)and PPH risk.A DenseNet-based multi-task DL model was trained to predict PPH risk,EBL,and placenta previa status.Finally,a DL-radiomics ensemble(DRE)model was constructed by integrating clinical features,DL outputs,and radiomics scores.Diagnostic performance was evaluated using the area under the receiver operating characteristic curve(AUC)and DeLong test.Results The DRE model achieved AUC values of 0.874(95%CI:0.792-0.951)and 0.836(95%CI:0.648-0.974)in the internal and external test cohorts,respectively,significantly outperforming the standalone clinical,DL,and radiomics models.Incorporation of EBL regression improved the performance of the PPH classification model,with the external test AUC increasing from 0.261-0.788 to 0.836.Conclusion The DRE model integrating DL and radiomics can efficiently predict PPH risk and assist in the clinical management of high-risk pregnancies.

Objective To develop a predictive model integrating clinical features,deep learning(DL),and radiomics based on T2-weighted imaging for prenatal assessment of postpartum hemorrhage(PPH)risk in high-risk pregnant women.Methods A total of 538 pregnant women with ultrasound-reported high-risk placenta accrete were retrospectively enrolled and divided into training,internal test,and external test cohorts.A nnUNet model was trained for automatic placental segmentation.Univariate and multivariate analyses were conducted on clinical features to identify those associated with PPH.Quantitative radiomic features were extracted from the placental region,and a random forest model was developed to predict estimated blood loss(EBL)and PPH risk.A DenseNet-based multi-task DL model was trained to predict PPH risk,EBL,and placenta previa status.Finally,a DL-radiomics ensemble(DRE)model was constructed by integrating clinical features,DL outputs,and radiomics scores.Diagnostic performance was evaluated using the area under the receiver operating characteristic curve(AUC)and DeLong test.Results The DRE model achieved AUC values of 0.874(95%CI:0.792-0.951)and 0.836(95%CI:0.648-0.974)in the internal and external test cohorts,respectively,significantly outperforming the standalone clinical,DL,and radiomics models.Incorporation of EBL regression improved the performance of the PPH classification model,with the external test AUC increasing from 0.261-0.788 to 0.836.Conclusion The DRE model integrating DL and radiomics can efficiently predict PPH risk and assist in the clinical management of high-risk pregnancies.

Magnetic resonance angiography(MRA)is a non-invasive imaging technique used to observe blood vessels.Quantitative analysis of MRA images enables visualization of vascular pathways,condition,and blood flow dynamics,which is essential for diagnosing vascular diseases such as vascular lesions,stenosis,and occlusions.Vessel segmentation serves as the fundamental basis for quantitative vascular analysis.However,the complex morphology of vessels,difficulties in labeling,and scarcity of accurate 3D vascular annotations pose significant challenges for MRA-based vessel segmentation.A strategy of selectively occluding vessels during model training is proposed to enhance the algorithm's capacity to capture the topological structure of blood vessels,thereby improving the continuity of vessel segmentation results.Additionally,a Refine network is incorporated to refine the binary segmentation results of the segmentation network,thereby further improving segmentation accuracy.Model training and testing are carried out using 42 cases of 3D MRA data from the public MIDAS dataset.For the test set,the 3D U-Net baseline model with vessel occlusion strategy shows a β0 Error of 1.2742±0.2103 and a β1 Error of 0.3393±0.0818,respectively,which are 0.1136 and 0.0280 lower than the baseline.The model integrating vessel occlusion strategy and Refine network achieves an average Dice score of 0.7105±0.0125,which is 0.0028 higher than the baseline.These results demonstrate that the proposed method effectively improves both vascular connectivity and segmentation accuracy.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To understand the coverage and information consistency rate of National Immunization Program (NIP) vaccines among children born during 2020-2021 in Zhejiang Province, Chongqing City, and Shanxi Province (3 provinces) of China .Methods:A simple random sampling method was used to randomly select 3 counties (districts) from each of the 3 provinces, 5 townships from each county (district), and 5 villages from each township. Vaccination information for seven NIP vaccines was collected for children born between 2020 and 2021 in each village. The vaccination coverage, timely coverage, and consistency rates between the survey data and the Immunization Planning Information System data were analyzed.Results:A total of 1 117 children were investigated. The vaccination coverage for each dose of NIP vaccine ranged from 99.10% to 100.00%, with those in Zhejiang Province, Chongqing City, and Shanxi Province ranging from 99.19% to 100.00%, 98.92% to 100.00%, and 99.20% to 100.00%, respectively. The timely coverage of each dose of NIP vaccine ranged from 89.79% to 99.82%, with those in Zhejiang Province, Chongqing City, and Shanxi Province ranging from 94.09% to 99.73%, 89.52% to 99.73%, and 78.55% to 100.00%, respectively. The consistency rate of information on each dose of NIP vaccine ranged from 94.36% to 99.91%, with those in Zhejiang Province, Chongqing City, and Shanxi Province ranging from 97.85% to 99.73%, 98.92% to 100.00%, and 86.06% to 100.00%, respectively.Conclusions:Coverage of NIP vaccines was generally high among children born during 2020-2021 in the 3 provinces of China, but there were regional differences in the timely coverage of some vaccine doses and the vaccination information consistency rate. It is necessary to strengthen the timely vaccination of children's vaccine booster doses and optimize the management of vaccination services.

OBJECTIVES: This systematic review examines recent pharmacoeconomic literature on denosumab' cost-effectiveness for bone metastasis treatment, providing evidence-based insights to guide healthcare policy decisions. METHODS: A comprehensive literature search was performed across Cochrane, PubMed, EMBASE (Ovid), CNKI, and Wanfang databases to identify original articles published between 2017 and 2023. Key words consisted of bone metastases, denosumab, and cost-effectiveness in the search strategy. The methodological quality of the included studies was assessed utilizing the revised Consolidated Health Economic Evaluation Reporting Standards (CHEERS 2022). Data was extracted regarding methodological characteristics and cost-effectiveness analyses. RESULTS: A total of 111 studies were retrieved, of which 6 met the inclusion criteria. All included studies were based on clinical trials and published literature data and exhibited high methodological quality. Up to 83% (5 out of 6) of comparisons demonstrated that denosumab was more cost-effective or dominant compared to zoledronic acid. The adjusted incremental cost-effectiveness ratios varied substantially by tumor type, ranging from CZK 436,339.09 to USD 136,234 per skeletal-related event avoided and from CZK 61,580.95 to USD 118,392.11 per quality-adjusted life year gained. CONCLUSIONS The majority of the included studies support denosumab as a more cost-effective treatment option for bone metastases in solid tumors compared to zoledronic acid. The application of CHEER (2022) enhances the reliability of pharmacoeconomic evaluations.
Denosumab/therapeutic use* ; Humans ; Bone Neoplasms/economics* ; Cost-Benefit Analysis

Objective To construct a finite element analysis model for the treatment of calcaneal fracture for Sanders Ⅲ type calcaneal by using simulated sustentaculum tali sustentacular screw placement,and to explore the effectiveness of the treatment of Sanders typeⅢcalcaneal fracture.Methods The finite element analysis method was used to study the effectiveness of the treatment of Sanders Ⅲ calcaneal fracture.Three calcaneal specimens of a healthy adult were taken for Micro-CT scanning to obtain the CT sections data,then Mimics 21.0 and Geomagic Wrap 2017 software were used to reconstruct the three-dimensional model of normal calcaneal bone,and SolidWorks 2017 software was used to map the internal fixation and fracture model according to the clinical fracture cases,and simulated surgery was performed.The data obtained were imported into ANSYS 17.0 finite element analysis software for material assignment and mesh division to establish a three-dimensional finite element model.The load and boundary constraints were applied to each model to perform finite element analysis and calculation,and then the stress of the finite element model and the maximum displacement of the fracture end were extracted.Results The maximum stress of the overall structure of each model was concentrated on internal fixation,in which the maximum stress of sustentaculum tali fracture and the maximum stress of screws in all fracture models are located on the sustentaculum tali sustentacular screw.The displacement of fracture end in each finite element model was less than 0.15 mm,and no internal fixation failure occurred.Conclusion It is effective to simulate the treatment of calcaneal fracture of Sanders type Ⅲ by using of sustentaculum tali sustentacular screw.

The prelimbic cortex (PL) plays a critical role in processing both the sensory and affective components of pain. However, the underlying molecular mechanisms remain poorly understood. In this study, we observed a reduction in hyperpolarization-activated cation current (I_h) in layer V pyramidal neurons of the contralateral PL in a mouse model of spared nerve injury (SNI). The expression of hyperpolarization-activated cyclic nucleotide-gated 2 (HCN2) channels was also decreased in the contralateral PL. Conversely, microinjection of fisetin, a partial agonist of HCN2, produced both analgesic and anxiolytic effects. Additionally, we found that cyclin-dependent kinase 5 (CDK5) was activated in the contralateral PL, where it formed a complex with HCN2 and phosphorylated its C-terminus. Knockdown of CDK5 restored HCN2 expression and alleviated both pain hypersensitivity and anxiety-like behaviors. Collectively, these results indicate that CDK5-mediated dysfunction of HCN2 in the PL underlies nerve injury-induced mechanical hypersensitivity and anxiety.
Animals ; Hyperpolarization-Activated Cyclic Nucleotide-Gated Channels/metabolism* ; Hyperalgesia/metabolism* ; Cyclin-Dependent Kinase 5/metabolism* ; Neuralgia/metabolism* ; Male ; Anxiety/metabolism* ; Mice ; Potassium Channels/metabolism* ; Mice, Inbred C57BL ; Disease Models, Animal ; Pyramidal Cells/metabolism*

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Reproductive system diseases are among the primary contributors to the decline in social fertility rates and the intensification of aging, posing significant threats to both physical and mental health, as well as quality of life. Recent research has revealed the substantial potential of the gut microbiota in improving reproductive system diseases. Under healthy conditions, the gut microbiota maintains a dynamic balance, whereas dysfunction can trigger immune-inflammatory responses, metabolic disorders, and other issues, subsequently leading to reproductive system diseases through the gut-gonadal axis. Reproductive diseases, in turn, can exacerbate gut microbiota imbalance. This article reviews the impact of the gut microbiota and its metabolites on both male and female reproductive systems, analyzing changes in typical gut microorganisms and their metabolites related to reproductive function. The composition, diversity, and metabolites of gut bacteria, such as Bacteroides, Prevotella, and Firmicutes, including short-chain fatty acids, 5-hydroxytryptamine, γ-aminobutyric acid, and bile acids, are closely linked to reproductive function. As reproductive diseases develop, intestinal immune function typically undergoes changes, and the expression levels of immune-related factors, such as Toll-like receptors and inflammatory cytokines (including IL-6, TNF-α, and TGF-β), also vary. The gut microbiota and its metabolites influence reproductive hormones such as estrogen, luteinizing hormone, and testosterone, thereby affecting folliculogenesis and spermatogenesis. Additionally, the metabolism and absorption of vitamins can also impact spermatogenesis through the gut-testis axis. As the relationship between the gut microbiota and reproductive diseases becomes clearer, targeted regulation of the gut microbiota can be employed to address reproductive system issues in both humans and animals. This article discusses the regulation of the gut microbiota and intestinal immune function through microecological preparations, fecal microbiota transplantation, and drug therapy to treat reproductive diseases. Microbial preparations and drug therapy can help maintain the intestinal barrier and reduce chronic inflammation. Fecal microbiota transplantation involves transferring feces from healthy individuals into the recipient’s intestine, enhancing mucosal integrity and increasing microbial diversity. This article also delves into the underlying mechanisms by which the gut microbiota influences reproductive capacity through the gut-gonadal axis and explores the latest research in diagnosing and treating reproductive diseases using gut microbiota. The goal is to restore reproductive capacity by targeting the regulation of the gut microbiota. While the gut microbiota holds promise as a therapeutic target for reproductive diseases, several challenges remain. First, research on the association between gut microbiota and reproductive diseases is insufficient to establish a clear causal relationship, which is essential for proposing effective therapeutic methods targeting the gut microbiota. Second, although gut microbiota metabolites can influence lipid, glucose, and hormone synthesis and metabolism via various signaling pathways—thereby indirectly affecting ovarian and testicular function—more in-depth research is required to understand the direct effects of these metabolites on germ cells or granulosa cells. Lastly, the specific efficacy of gut microbiota in treating reproductive diseases is influenced by multiple factors, necessitating further mechanistic research and clinical studies to validate and optimize treatment regimens.