OBJECTIVE: Baicalein has been reported to have wide therapeutic effects that act through its anti-inflammatory activity. This study examines the effect and mechanism of baicalein on sepsis-induced cardiomyopathy (SIC). METHODS: A thorough screening of a small library of natural products, comprising 100 diverse compounds, was conducted to identify the most effective drug against lipopolysaccharide (LPS)-treated H9C2 cardiomyocytes. The core target proteins and their associated signaling pathways involved in baicalein's efficacy against LPS-induced myocardial injury were predicted by network pharmacology. RESULTS: Baicalein was identified as the most potent protective agent in LPS-exposed H9C2 cardiomyocytes. It exhibited a dose-dependent inhibitory effect on cell injury and inflammation. In the LPS-induced septic mouse model, baicalein demonstrated a significant capacity to mitigate LPS-triggered myocardial deficits, inflammatory responses, and ferroptosis. Network pharmacological analysis and experimental confirmation suggested that hypoxia-inducible factor 1 subunit α (HIF1-α) is likely to be the crucial factor in mediating the impact of baicalein against LPS-induced myocardial ferroptosis and injury. By combining microRNA (miRNA) screening in LPS-treated myocardium with miRNA prediction targeting HIF1-α, we found that miR-299b-5p may serve as a regulator of HIF1-α. The reduction in miR-299b-5p levels in LPS-treated myocardium, compared to the control group, was reversed by baicalein treatment. The reverse transcription quantitative polymerase chain reaction, Western blotting, and dual-luciferase reporter gene analyses together identified HIF1-α as the target of miR-299b-5p in cardiomyocytes. CONCLUSION Baicalein mitigates SIC at the miRNA level, suggesting the therapeutic potential of it in treating SIC through the regulation of miR-299b-5p/HIF1-α/ferroptosis pathway. Please cite this article as: Zhou WY, Du JK, Liu HH, Deng L, Ma K, Xiao J, Zhang S, Wang CN. Baicalein attenuates lipopolysaccharide-induced myocardial injury by inhibiting ferroptosis via miR-299b-5p/HIF1-α pathway. J Integr Med. 2025; 23(5):560-575.
Flavanones/pharmacology* ; Animals ; MicroRNAs/genetics* ; Lipopolysaccharides ; Hypoxia-Inducible Factor 1, alpha Subunit/genetics* ; Ferroptosis/drug effects* ; Mice ; Myocytes, Cardiac/metabolism* ; Signal Transduction/drug effects* ; Rats ; Male ; Mice, Inbred C57BL ; Cardiomyopathies/etiology* ; Cell Line ; Sepsis/complications*

OBJECTIVES: Renal cell carcinoma (RCC) is a malignant renal tumor that poses a significant threat to patient health. Accurate preoperative pathological grading plays a crucial role in determining the appropriate treatment for this disease. Currently, deep learning technology has become an important method for pathological grading of RCC. However, existing methods primarily rely on single-phase computed tomography (CT) imaging for analysis and prediction, which has limitations such as missing small lesions, one-sided evaluation, and local focusing issues. Therefore, this study proposes a multi-modal deep learning algorithm that integrates multi-phase enhanced CT images with clinical variable data, aiming to provide a basis for predicting the pathological grading of RCC. METHODS: First, the algorithm took four-phase enhanced CT images from the plain scan, arterial phase, venous phase, and delayed phase, along with clinical variables, as inputs. Then, an embedding encoding module was used to extract heterogeneous information from the clinical variables, and a 3-dimensional (3D) ResNet50 model was employed to capture spatial information from the multi-phase enhanced CT image data. Finally, a Fusion module deeply integrated the feature information from clinical variables and each phase's CT image features, further utilizing a cross-self-attention mechanism to achieve multi-phase feature fusion. This approach comprehensively captures the deep semantic information from the patient data, fully leveraging the complementary advantages of multi-modal and multi-phase data. To validate the effectiveness of the proposed method, a total of 1 229 RCC patients were approved by ethics review were included to train the model. RESULTS: Experimental results demonstrated superior performance compared to traditional radiomics and state-of-the-art deep learning methods, achieving an accuracy of 83.87%, a recall rate of 95.04%, and an F1-score of 82.23%. CONCLUSIONS The proposed algorithm exhibits strong stability and sensitivity, significantly enhancing the predictive performance of RCC pathological grading. It offers a novel approach for accurate RCC diagnosis and personalized treatment planning.
Humans ; Carcinoma, Renal Cell/pathology* ; Deep Learning ; Kidney Neoplasms/diagnostic imaging* ; Tomography, X-Ray Computed/methods* ; Algorithms ; Neoplasm Grading ; Male ; Female ; Middle Aged

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.

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 construct a machine-learning model for predicting the progression of pancreatic cystic lesions (PCLs) based on clinical and CT features, and to evaluate its predictive performance in internal/external testing cohorts.Methods:Baseline clinical and radiological data of 200 PCLs in 177 patients undergoing abdominal thin slice enhanced CT examination at Peking Union Medical College Hospital from July 2014 to December 2022 were retrospectively collected. PCLs were divided into progressive and non-progressive groups according to whether the signs indicated for surgery by the guidelines of the European study group on PCLs were present during three-year follow-up. 200 PCLs were randomly divided into training (150 PCLs) and internal testing cohorts (50 PCLs) at the ratio of 1∶3. 15 PCLs in 14 patients at Jinling Affiliated Hospital of Medical School of Nanjing University from October 2011 to May 2020 were enrolled as external testing cohort. The clinical and CT radiological features were recorded. Multiple feature selection methods and machine-learning models were implemented and combined to identify the optimal machine-learning model based on the 10-fold cross-validation method. Receiver operating characteristics (ROC) curve was drawn and area under curve (AUC) was calculated. The model with the highest AUC was determined as the optimal model. The optimal model's predictive performance was evaluated on testing cohort by calculating AUC, sensitivity, specificity and accuracy. Permutation importance was used to assess the importance of optimal model features. Calibration curves of the optimal model were established to evaluate the model's clinical applicability by Hosmer-Lemeshow test.Results:In training and internal testing cohorts, the progressive and non-progressive groups were significantly different on history of pancreatitis, lesions size, main pancreatic duct diameter and dilation, thick cyst wall, presence of septation and thick septation (all P value <0.05) In internal testing cohort, the two groups were significantly different on gender, lesion calcification and pancreatic atrophy (all P value <0.05). In external testing cohort, the two groups were significantly different on lesions size and pancreatic duct dilation (both P<0.05). The support vector machine (SVM) model based on five features selected by F test (lesion size, thick cyst wall, history of pancreatitis, main pancreatic duct diameter and dilation) achieved the highest AUC of 0.899 during cross-validation. SVM model for predicting the progression of PCLs demonstrated an AUC of 0.909, sensitivity of 82.4%, specificity of 72.7%, and accuracy of 76.0% in the internal testing cohort, and 0.944, 100%, 77.8%, and 86.7% in the external testing cohort. Calibration curved showed that the predicted probability by the model was comparable to the real progression of PCLs. Hosmer-Lemeshow goodness-of-fit test affirmed the model's consistency with actual PCLs progression in testing cohorts. Conclusions:The SVM model based on clinical and CT features can help doctors predict the PCLs progression within three-year follow-up, thus achieving efficient patient management and rational allocation of medical resource.

Objective To understand the prevalence characteristics of influenza and changes of influenza virus strains,and provide reference for the prevention and control of influenza in the province.Methods Surveillance da-ta about influenza in Hunan Province from 2014 to 2023 were exported from China Influenza Surveillance Informa-tion System.Differences in the percentage of influenza-like illness(ILI)cases(percentage of influenza-like cases[ILI％]in outpatient and emergency department visits)among different years and different populations,as well as the positive rate of influenza virus in ILI specimens were compared.Results From 2014 to 2023,over 2.65 million cases of ILI were reported,with an ILI％of 4.70％.ILI％among different years presented statistically significant differences(P＜0.001).People aged 0-14 years old were the main population with ILI,accounting for 82.90％.The positive rate of influenza virus in ILI specimens was 14.14％,the positive rate of influenza virus among diffe-rent years and age groups were both significantly different(both P＜0.001).The main prevalent influenza strains from 2014 to 2023 included types A(H1N1),A(H3N2),B(Victoria),and B(Yamagata),alternating among di-fferent years.However,type B(Yamagata)strains were not detected from 2020 to 2023.There were basically two influenza prevalence seasons every year,namely winter-spring and summer.Conclusion People＜15 years old are the main population of influenza,and the prevalence peaks are in winter-spring and summer.From 2021 to 2023,the prevalence alternates mainly among 3 types:A(H1N1),A(H3N2),and B(Victoria).

PI3Kγ and PI3Kδ have important regulatory roles in the immune system, and targeting these two subtypes helps to reshape the tumor microenvironment. PI3Kγ and PI3Kδ are potential targets for tumor immunotherapy. In this study, a series of new pyrazolopyrimidine derivatives were designed and synthesized on the basis of our previously reported PI3K inhibitors, resulting in the discovery of compound 16l as a potent and selective PI3Kγ/δ dual inhibitor. Compound 16l demonstrated strong biochemical potencies against PI3Kγ and PI3Kδ with IC₅₀ values of 0.11 and 0.79 nmol·L^-1. In cell-based assays, it potently inhibited the PI3Kγ and PI3Kδ mediated Akt S473 phosphorylation with EC₅₀ values of 3 and 7 nmol·L^-1. In vivo, compound 16l exhibited acceptable pharmacokinetic properties in Sprague-Dawley (SD) rats and suppressed the tumor growth in a MC38 syngeneic mouse model. The animal experiments were approved by the Animal Ethics Committee of Hefei Institutes of Physical Science, Chinese Academy of Sciences (approval number: DWLL-2000-06). In addition, no appreciable human ether-a-go-go-related gene (hERG) inhibition was observed for compound 16l even at 30 μmol·L^-1. These results suggested that compound 16l might be a potential research tool for studying the PI3Kγ/δ mediated signaling pathways.

Objective: To explore the utility of stool-based DNA test of methylated SDC2 (mSDC2) for colorectal cancer (CRC) screening in residents of Shipai Town, Dongguan City. Methods: This was a cross-sectional study. Using a cluster sampling method, residents of 18 villages in Shipai Town, Dongguan City were screened for CRC from May 2021 to February 2022. In this study, mSDC2 testing was employed as a preliminary screening method. Colonoscopy examination was recommended for individuals identified as high-risk based on the positive mSDC2 tests. The final screening results, including the rate of positive mSDC2 tests, the rate of colonoscopy compliance, the rate of lesions detection, and the cost-effectiveness of screening, were analyzed to explore the benefits of this screening strategy. Results: A total of 10 708 residents were enrolled and completed mSDC2 testing, giving a participation rate of 54.99% (10 708/19 474) and a pass rate of 97.87% (10 708/10 941). These individuals included 4 713 men (44.01%) and 5 995 women (55.99%) with a mean age of (54.52±9.64) years. The participants were allocated to four age groups (40-49, 50-59, 60-69, and 70-74 years), comprising 35.21%(3770/10 708), 36.25% (3882/10 708), 18.84% (2017/10 708), and 9.70% (1039/10 708) of all participants, respectively. mSDC2 testing was positive in 821/10 708 (7.67%) participants, 521 of whom underwent colonoscopy, resulting in a compliance rate of 63.46% (521/821). After eliminating of 8 individuals without pathology results, data from 513 individuals were finally analyzed. Colonoscopy detection rate differed significantly between age groups (χ²=23.155, P<0.001),ranging from a low of 60.74% in the 40-49 year age group to a high of 86.11% in the 70-74 year age group. Colonoscopies resulted in the diagnosis of 25 (4.87%) CRCs, 192 (37.43%) advanced adenomas, 67 (13.06%) early adenomas, 15 (2.92%) serrated polyps, and 86 (16.76%) non- adenomatous polyps. The 25 CRCs were Stage 0 in 14 (56.0%) individuals, stage I in 4 (16.0%), and Stage II in 7(28.0%). Thus, 18 of the detected CRCs were at an early stage. The early detection rate of CRCs and advanced adenomas was 96.77% (210/217). The rate of mSDC2 testing for all intestinal lesions was 75.05% (385/513). In particular, the financial benefit of this screening was 32.64 million yuan, and the benefit-cost ratio was 6.0. Conclusion: Screening for CRCs using stool-based mSDC2 testing combined with colonoscopy has a high lesion detection rate and a high cost-effectiveness ratio. This is a CRC screening strategy that deserves to be promoted in China.
Male ; Humans ; Female ; Adult ; Middle Aged ; Cross-Sectional Studies ; Early Detection of Cancer/methods* ; Colorectal Neoplasms/pathology* ; Colonoscopy/methods* ; Mass Screening/methods* ; Adenoma/diagnosis* ; DNA ; Syndecan-2/genetics*

Objective: To evaluate the prognostic value of left ventricular ejection fraction (LVEF) reserve assessed by gated SPECT myocardial perfusion imaging (SPECT G-MPI) for major adverse cardiovascular event (MACE) in patients with coronary artery disease. Methods: This is a retrospective cohort study. From January 2017 to December 2019, patients with coronary artery disease and confirmed myocardial ischemia by stress and rest SPECT G-MPI, and underwent coronary angiography within 3 months were enrolled. The sum stress score (SSS) and sum resting score (SRS) were analyzed by the standard 17-segment model, and the sum difference score (SDS, SDS=SSS-SRS) was calculated. The LVEF at stress and rest were analyzed by 4DM software. The LVEF reserve (ΔLVEF) was calculated (ΔLVEF=stress LVEF-rest LVEF). The primary endpoint was MACE, which was obtained by reviewing the medical record system or by telephone follow-up once every twelve months. Patients were divided into MACE-free and MACE groups. Spearman correlation analysis was used to analyze the correlation between ΔLVEF and all MPI parameters. Cox regression analysis was used to analyze the independent factors of MACE, and the optimal SDS cutoff value for predicting MACE was determined by receiver operating characteristic curve (ROC). Kaplan-Meier survival curves were plotted to compare the difference in the incidence of MACE between different SDS groups and different ΔLVEF groups. Results: A total of 164 patients with coronary artery disease [120 male; age (58.6±10.7) years] were included. The average follow-up time was (26.5±10.4) months, and a total of 30 MACE were recorded during follow-up. Multivariate Cox regression analysis showed that SDS (HR=1.069, 95%CI: 1.005-1.137, P=0.035) and ΔLVEF (HR=0.935, 95%CI: 0.878-0.995, P=0.034) were independent predictors of MACE. According to ROC curve analysis, the optimal cut-off to predict MACE was a SDS of 5.5 with an area under the curve of 0.63 (P=0.022). Survival analysis showed that the incidence of MACE was significantly higher in the SDS≥5.5 group than in the SDS<5.5 group (27.6% vs. 13.2%, P=0.019), but the incidence of MACE was significantly lower in the ΔLVEF≥0 group than in theΔLVEF<0 group (11.0% vs. 25.6%, P=0.022). Conclusions: LVEF reserve (ΔLVEF) assessed by SPECT G-MPI serves as an independent protective factor for MACE, while SDS is an independent risk predictor in patients with coronary artery disease. SPECT G-MPI is valuable for risk stratification by assessing myocardial ischemia and LVEF.
Humans ; Male ; Middle Aged ; Aged ; Coronary Artery Disease/diagnostic imaging* ; Stroke Volume ; Myocardial Perfusion Imaging ; Retrospective Studies ; Ventricular Function, Left ; Myocardial Ischemia

OBJECTIVE: To explore whether casticin (CAS) suppresses stemness in cancer stem-like cells (CSLCs) obtained from human cervical cancer (CCSLCs) and the underlying mechanism. METHODS: Spheres from HeLa and CaSki cells were used as CCSLCs. DNA methyltransferase 1 (DNMT1) activity and mRNA levels, self-renewal capability (Nanog and Sox2), and cancer stem cell markers (CD133 and CD44), were detected by a colorimetric DNMT activity/inhibition assay kit, quantitative real-time reverse transcription-polymerase chain reaction, sphere and colony formation assays, and immunoblot, respectively. Knockdown and overexpression of DNMT1 by transfection with shRNA and cDNA, respectively, were performed to explore the mechanism for action of CAS (0, 10, 30, and 100 nmol/L). RESULTS: DNMT1 activity was increased in CCSLCs compared with HeLa and CaSki cells (P<0.05). In addition, HeLa-derived CCSLCs transfected with DNMT1 shRNA showed reduced sphere and colony formation abilities, and lower CD133, CD44, Nanog and Sox2 protein expressions (P<0.05). Conversely, overexpression of DNMT1 in HeLa cells exhibited the oppositive effects. Furthermore, CAS significantly reduced DNMT1 activity and transcription levels as well as stemness in HeLa-derived CCSLCs (P<0.05). Interestingly, DNMT1 knockdown enhanced the inhibitory effect of CAS on stemness. As expected, DNMT1 overexpression reversed the inhibitory effect of CAS on stemness in HeLa cells. CONCLUSION CAS effectively inhibits stemness in CCSLCs through suppression of DNMT1 activation, suggesting that CAS acts as a promising preventive and therapeutic candidate in cervical cancer.
Female ; Humans ; Cell Line, Tumor ; HeLa Cells ; Neoplastic Stem Cells/metabolism* ; RNA, Small Interfering/metabolism* ; Uterine Cervical Neoplasms/metabolism*