Triple-negative breast cancer (TNBC) remains a refractory subtype of breast cancer due to its resistance to various therapeutic strategies. In this study, we introduce a "brake-release and accelerator-pressing" approach to engineer a microneedle patch embedded with copper-doped Prussian blue nanoparticles (Cu-PB) and the ferroptosis inducer sorafenib (SRF) for raised chemodynamic (CDT)/photothermal (PTT) combination therapy against TNBC. Upon transdermal insertion, the dissolving microneedles swiftly disintegrate and facilitate the release of SRF. Under gentle external light exposure, copper ions (Cu²⁺) and iron ions (Fe³⁺) were liberated from Cu-PB. The direct chelation of Cu²⁺ and the indirect suppression by SRF, collectively attenuate glutathione peroxidase 4 (GPX4) enzymatic function, destabilizing the cellular redox equilibrium (referred to as the "brake-release" strategy). The release of Cu²⁺ and Fe³⁺ ions instigates a Fenton/Fenton-like reaction within tumor cells, further yielding hydroxyl radicals and elevating reactive oxygen species (ROS) concentrations (referred to as the "accelerator-pressing" strategy). This overwhelming ROS accumulation, coupled with the impaired clearance of resultant lipid peroxides (LPO), ultimately triggers a robust ferroptosis cell death response. In summary, this study presents an innovative combinatorial therapeutic strategy based on dual-ferroptosis induction for TNBC, implying a promising therapeutic platform for developing ferroptosis-centered treatments for this aggressive breast cancer subtype.

OBJECTIVES: To explore the mechanism of Circ-MYOCD back-splicing and its regulatory role in myocardial hypertrophy. METHODS: Sanger sequencing and RNase R assays were performed to verify the circularity and stability of Circ-MYOCD, whose subcellular distribution was determined by nuclear-cytoplasmic fractionation. Bioinformatics analysis and mass spectrometry from pull-down assays were conducted to predict the RNA-binding proteins (RBPs) interacting with Circ-MYOCD. In rat cardiomyocytes H9C2 cells, the effects of HNRNPH1 and HNRNPL knockdown and overexpression on Circ-MYOCD back-splicing were evaluated. In a H9C2 cell model of angiotensin II (Ang II)-induced myocardial hypertrophy, the expression of HNRNPH1 was detected, the effects of HNRNPH1 knockdown and overexpression on progression of myocardial hypertrophy were assessed, and the regulatory effect of HNRNPH1 on Circ-MYOCD back-splicing was analyzed. RESULTS: Sanger sequencing confirmed that the junction primers could amplify the correct Circ-MYOCD sequence. RNase R and nuclear-cytoplasmic fractionation assays showed that Circ-MYOCD was stable and predominantly localized in the cytoplasm. Bioinformatics analysis and mass spectrometry from the Circ-MYOCD pull-down assay identified HNRNPH1 and HNRNPL as the RBPs interacting with Circ-MYOCD. In H9C2 cells, HNRNPH1 knockdown significantly enhanced while its overexpression inhibited Circ-MYOCD back-splicing; HNRNPH1 overexpression obviously increased the expressions of myocardial hypertrophy markers ANP and BNP, while its knockdown produced the opposite effect. In Ang II-induced H9C2 cells, which exhibited a significant increase of HNRNPH1 expression and increased expressions of ANP and BNP, HNRNPH1 knockdown obviously increased Circ-MYOCD expression, decreased MYOCD expression and lowered both ANP and BNP expressions. CONCLUSIONS HNRNPH1 regulates Circ-MYOCD back-splicing to influence the progression of myocardial hypertrophy.
Animals ; Rats ; RNA, Circular/genetics* ; Cardiomegaly/metabolism* ; Myocytes, Cardiac/metabolism* ; Heterogeneous-Nuclear Ribonucleoprotein Group F-H/metabolism* ; Cell Line ; RNA Splicing ; Angiotensin II ; RNA-Binding Proteins

Placenta，fetal heart and brain affect each other in the process of fetal growth. They are influenced by genetic，environmental，epigenetic and hemodynamic factors，and share several key developmental pathways. Fetal heart defect in ongenital heart disease（CHD）is associated with abnormal development of placenta and brain. One-stop-shop ‘heart-brain-placenta’ imaging is of great value in prenatal diagnosis of CHD fetuses. This review discusses the current research on the one-stop-shop ‘heart-brain-placenta’ imaging of CHD fetuses.

Pulmonary siderosis caused by iron and its compound dust is prone to misdiagnosis and underdiagnosis due to its insidious exposure pathways and non-specific imaging manifestations. This study analyzes the occupational histories and clinical data of six patients with occupational pulmonary siderosis diagnosed at Qingdao Central Hospital between January 2017 and December 2023, summarizes its characteristics, and evaluates the value of AI-assisted diagnosis. All six patients were male, with five being welders. The median dust exposure duration was 9.4 years, and the median latency period was 8.4 years. The main symptoms were chest tightness, cough, and shortness of breath. High-kilovolt chest radiographs were negative in four cases and showed thickened bronchovascular markings in two cases. High-resolution computed tomography (HRCT) revealed centrilobular nodules and tree-in-bud opacities in all cases. Pulmonary siderosis caused by iron and its compound dust is characterized by mild symptoms and a favorable prognosis. Comprehensive assessment and HRCT are crucial for early diagnosis. The development of AI models could enhance diagnostic recognition efficiency and promote precision diagnosis in the future.

Public hospitals have formed a relatively perfect working foundation in the introduction and training of young talents,but the evaluation system of young talents is not perfect.Based on the requirements of high-quality development,grasp the principle of party management of talents,combine the talents development situation in Jiading District Maternal and Child Health Care Hospital,takes the special training of young talents in administration as the starting point,comprehensively uses the literature method,interview method and Delphi method to establish the index database,uses the exploratory factor analysis meth-od to calculate the index weight,and constructs the evaluation model of young talents in hospital administrative management,so as to help hospitals better screen and evaluate talents and give full play to the value and role of talents as the first resource.

Artificial intelligence(AI)technology is advancing rapidly and has demonstrated significant potential in medical image processing.In recent years,various research initiatives and products based on AI technology have been implemented in the diagnosis and treatment of strokes,enhancing both efficiency and accuracy to some extent.However,AI technology still encounters several challenges in the diagnosis and treatment of strokes.This paper reviewed the existing related AI technologies and applications and explores future research directions.

Objective:To investigate the diagnostic value and influencing factors of endoscopic ultrasonography (EUS) for detecting rectal neuroendocrine neoplasms (R-NENs).Methods:A retrospective case-control study was performed on data of patients with suspected R-NENs by white light endoscopy who underwent endoscopic diagnosis and treatment or surgical operation and obtained pathological diagnosis at the Affiliated Hospital of Qingdao University from March 2016 to June 2023. Clinical data, EUS characteristics and pathological results were statistically analyzed, and the diagnostic accuracy of EUS for R-NENs were obtained by comparing the EUS results with the pathological results. Influencing factors affecting accuracy were analyzed by using the binary logistic regression model.Results:A total of 317 patients were included. The sensitivity, the specificity, the positive predictive value and the negative predictive value of EUS in diagnosing R-NENs were 98.03% (249/254), 34.92% (22/63), 85.86% (249/290) and 81.48% (22/27) respectively. The accuracy was 85.49% (271/317) and the Jorden index was 0.33. Tumor size ≤5 mm ( P=0.002, OR=2.892, 95% CI: 1.464-5.713), absence of surface vascular dilation ( P=0.019, OR=2.613, 95% CI: 1.170-5.837), normal tumor coloration ( P=0.001, OR=3.460, 95% CI: 1.645-7.279) and erythematous surface appearance ( P=0.048, OR=7.242, 95% CI: 1.015-51.680) were independent risk factors affecting the accuracy of R-NENs diagnosis by EUS. Depth assessment accuracy of EUS was 76.77% (195/254), with echo heterogeneity ( P<0.001, OR=4.008, 95% CI: 1.980-8.113) and surface depression ( P=0.035, OR=2.664, 95% CI: 1.073-6.615) emerging as significant factors affecting invasion depth evaluation. Conclusion:EUS demonstrates substantial clinical utility for R-NENs assessment, with diagnostic performance being significantly associated with tumor morphology and sonographic features. Macroscopic characteristics including tumor size, vascular patterns, and chromatic features influence diagnostic accuracy, while echo-textural heterogeneity and surface depression affect invasion depth precision. These findings underscore the clinical relevance of comprehensive EUS evaluation in R-NENs management.

Objective To evaluate the modeling characteristics and predictive performance of models for predicting post-stroke neurological deterioration(ND)published in existing literatures.Methods Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidance for scoping reviews,a comprehensive search was conducted in PubMed,CINAHL,Cochrane Library,Embase,Web of Science,Scopus,CNKI,Wanfang Data,and VIP databases from inception to December 15,2024.The search strategy combined Medical Subject Headings(MeSH)and free-text terms,with key words including"Stroke""Ischemic Stroke""Neurological Deterioration""Nomograms""Risk Prediction""Predictive Models""卒中""脑梗死""脑出血""神经功能恶化"and"预测模型".Base on the data extraction checklist and critical appraisal,data extraction covered three domains:(1)basic characteristics,including author,publication year,country,study design(retrospective,prospective,registry-based),sample source(single-center,multicenter),stroke subtypes(acute ischemic stroke[AIS]-conservative therapy,AIS-intravenous thrombolysis[IVT],AIS-endovascular therapy[EVT],intracerebral hemorrhage[ICH]),ND time windows(acute[≤72 h],subacute[≤ 7 d],long-term[≤90 d]),and outcome types(single/composite endpoints);(2)model evaluation metrics,including missing data handling(complete-case analysis,multiple imputation),model development methodologies(multivariate Logistic regression,least absolute shrinkage and selection operator regression,machine learning),presentation formats(nomograms,web calculators,risk prediction tool),discrimination(area under the curve,C-index),calibration(Hosmer-Lemeshow test,calibration curve and slope),clinical utility(decision curve analysis[DCA],global metrics Brier score,R2,AIC),sample size(training set,internal validation set,external validation set),sample size requirements(events per variable[EPV]≥10 to mitigate overfitting),and validation(internal/external);(3)predictor features,including selection strategies(prior knowledge-driven,univariate analysis),quantity,and attributes(demographics,medical history,physical examination,treatment intervention information,imaging/laboratory indicators).Predictive models that meet exclusion criteria from prior literature were analyzed by their discrimination,calibration,clinical utility and global metrics.Forest plots were utilized to visualize discrimination(evaluated via difference in area under the curve)of the extracted models.The prediction model risk of bias assessment tool(PROBAST)was applied to assess bias risk and clinical applicability.Occurrence frequencies of the post-stroke neurological deterioration predictors were ranked and the top 6 high-frequency predictors were extracted.Results(1)Among 3 728 screened studies,25 were included based on the inclusion and exclusion criteria.(2)Basic characteristics:retrospective(72％[18/25])and single-center(64％[16/25])designs dominated.With most models targeted on AIS(92％[23/25]),and the rest(8％[2/25])on ICH.ND was primarily defined by neurological scale changes(60％[15/25];e.g.,National Institutes of Health stroke scale[NIHSS]score increase or Glasgow coma scale[GCS]score decrease),with time windows categorized as acute(36％[9/25]),subacute(48％[12/25]),or long-term(16％[4/25]).(3)Model evaluation:multivariate Logistic regression(96％[24/25])and nomograms(88％[22/25])were predominant.Only 24％(6/25)explicitly addressed missing data handling methods,and 52％(13/25)with EPV≥10.The median area under the curve was 0.865(range:0.650-0.981).44％(11/25)of the studies reported calibration curves,and 4％(1/25)reported calibration slopes.All studies utilized DCA to validate their clinical applicability,84％(21/25)of the studies conducted internal validation,while only 32％(8/25)conducted external validation.PROBAST evaluation revealed low overall bias risk in 8％(2/25;no error across participant,predictor,outcome,or analysis domains)and low clinical applicability risk in 44％(11/25;alignment with target populations,accessible predictors,and clinically relevant outcomes)of the studies.(4)Predictors:64％(16/25)of the predictor were screened predominantly through the prior knowledge-driven based strategy.The top 6 high-frequency predictors are NIHSS score(64％[16/25]),age(36％[9/25]),blood glucose/diabetes(36％[9/25]),blood pressure/hypertension(32％[8/25]),the Alberta stroke program early CT score(20％[5/25]),and neutrophil-to-lymphocyte ratio(20％[5/25]).AIS-ND predictors emphasized readily available metrics,such as NIHSS(65％[15/23]),age(35％[8/23]),while ICH-ND primarily relied on imaging markers(e.g.,baseline hematoma volume[2/2],location[1/2]).Conclusion Current post-stroke ND predictive models demonstrate satisfactory performance on discrimination and multimodal integration,but their practical application are hindered by insufficient calibration quantification,high bias risk,and limited clinical translatability.

As one of the core theories of spleen governing transportation and transformation in the traditional Chinese medicine visceral manifestation theory,the modern biological basis of"spleen qi disperses essence"has not been fully elucidated.Lipids are one of the three major nutrients in the body,which are derived from exogenous absorption or endogenous transformation,and belong to the category of"grease"and"essence"substances in traditional Chinese medicine.Because of their hydrophobic nature,lipids require apolipoproteins to be transported in the bloodstream and used by the body;similarly,essence also needs spleen qi transformation to be distributed throughout the body and exert their nourishing effects,revealing a certain degree of inherent unity between the two.When the spleen qi functions properly,essence dispersal is orderly and lipid metabolism remains in homeostatic balance;if spleen deficient leads to impaired transportation,the essence will not be distributed,and the lipid turbidity will accumulate,causing disease.Classic strengthening spleen prescriptions such as Zexie Decoction,can reshape lipid homeostasis by regulating apolipoproteins.Based on apolipoprotein-mediated lipid metabolism,this paper explores the modern molecular biology basis of the theory of"spleen qi disperses essence,"which provides novel insights for enriching the modern research of traditional Chinese medicine visceral manifestation theory,and lays the foundation for clinical practice and theoretical innovation in the treatment of metabolic diseases from the spleen.

Objective To evaluate the modeling characteristics and predictive performance of models for predicting post-stroke neurological deterioration(ND)published in existing literatures.Methods Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidance for scoping reviews,a comprehensive search was conducted in PubMed,CINAHL,Cochrane Library,Embase,Web of Science,Scopus,CNKI,Wanfang Data,and VIP databases from inception to December 15,2024.The search strategy combined Medical Subject Headings(MeSH)and free-text terms,with key words including"Stroke""Ischemic Stroke""Neurological Deterioration""Nomograms""Risk Prediction""Predictive Models""卒中""脑梗死""脑出血""神经功能恶化"and"预测模型".Base on the data extraction checklist and critical appraisal,data extraction covered three domains:(1)basic characteristics,including author,publication year,country,study design(retrospective,prospective,registry-based),sample source(single-center,multicenter),stroke subtypes(acute ischemic stroke[AIS]-conservative therapy,AIS-intravenous thrombolysis[IVT],AIS-endovascular therapy[EVT],intracerebral hemorrhage[ICH]),ND time windows(acute[≤72 h],subacute[≤ 7 d],long-term[≤90 d]),and outcome types(single/composite endpoints);(2)model evaluation metrics,including missing data handling(complete-case analysis,multiple imputation),model development methodologies(multivariate Logistic regression,least absolute shrinkage and selection operator regression,machine learning),presentation formats(nomograms,web calculators,risk prediction tool),discrimination(area under the curve,C-index),calibration(Hosmer-Lemeshow test,calibration curve and slope),clinical utility(decision curve analysis[DCA],global metrics Brier score,R2,AIC),sample size(training set,internal validation set,external validation set),sample size requirements(events per variable[EPV]≥10 to mitigate overfitting),and validation(internal/external);(3)predictor features,including selection strategies(prior knowledge-driven,univariate analysis),quantity,and attributes(demographics,medical history,physical examination,treatment intervention information,imaging/laboratory indicators).Predictive models that meet exclusion criteria from prior literature were analyzed by their discrimination,calibration,clinical utility and global metrics.Forest plots were utilized to visualize discrimination(evaluated via difference in area under the curve)of the extracted models.The prediction model risk of bias assessment tool(PROBAST)was applied to assess bias risk and clinical applicability.Occurrence frequencies of the post-stroke neurological deterioration predictors were ranked and the top 6 high-frequency predictors were extracted.Results(1)Among 3 728 screened studies,25 were included based on the inclusion and exclusion criteria.(2)Basic characteristics:retrospective(72％[18/25])and single-center(64％[16/25])designs dominated.With most models targeted on AIS(92％[23/25]),and the rest(8％[2/25])on ICH.ND was primarily defined by neurological scale changes(60％[15/25];e.g.,National Institutes of Health stroke scale[NIHSS]score increase or Glasgow coma scale[GCS]score decrease),with time windows categorized as acute(36％[9/25]),subacute(48％[12/25]),or long-term(16％[4/25]).(3)Model evaluation:multivariate Logistic regression(96％[24/25])and nomograms(88％[22/25])were predominant.Only 24％(6/25)explicitly addressed missing data handling methods,and 52％(13/25)with EPV≥10.The median area under the curve was 0.865(range:0.650-0.981).44％(11/25)of the studies reported calibration curves,and 4％(1/25)reported calibration slopes.All studies utilized DCA to validate their clinical applicability,84％(21/25)of the studies conducted internal validation,while only 32％(8/25)conducted external validation.PROBAST evaluation revealed low overall bias risk in 8％(2/25;no error across participant,predictor,outcome,or analysis domains)and low clinical applicability risk in 44％(11/25;alignment with target populations,accessible predictors,and clinically relevant outcomes)of the studies.(4)Predictors:64％(16/25)of the predictor were screened predominantly through the prior knowledge-driven based strategy.The top 6 high-frequency predictors are NIHSS score(64％[16/25]),age(36％[9/25]),blood glucose/diabetes(36％[9/25]),blood pressure/hypertension(32％[8/25]),the Alberta stroke program early CT score(20％[5/25]),and neutrophil-to-lymphocyte ratio(20％[5/25]).AIS-ND predictors emphasized readily available metrics,such as NIHSS(65％[15/23]),age(35％[8/23]),while ICH-ND primarily relied on imaging markers(e.g.,baseline hematoma volume[2/2],location[1/2]).Conclusion Current post-stroke ND predictive models demonstrate satisfactory performance on discrimination and multimodal integration,but their practical application are hindered by insufficient calibration quantification,high bias risk,and limited clinical translatability.