Malignant tumors represent a major threat to global health. Conventional anti-tumor pharmacotherapy often encounters challenges such as drug resistance, highlighting an urgent need for the development of novel therapeutic strategies. Fatty acid synthase (FASN), the key enzyme catalyzing de novo fatty acid synthesis, is subject to precise regulation at multiple levels, including transcriptional control, various post-translational modifications such as ubiquitination and phosphorylation, as well as modulation by diverse signaling pathways. Recent studies have revealed that FASN is aberrantly overexpressed in various malignant tumors and is closely associated with tumor progression and poor patient prognosis. FASN is a homodimer composed of seven functional domains that catalyzes the NADPH-dependent condensation of acetyl-CoA and malonyl-CoA to generate saturated fatty acids, primarily palmitic acid. Its stability is regulated by multiple ubiquitin ligases and deubiquitinating enzymes. Additionally, FASN is subject to upstream regulation via neural precursor cell-expressed developmentally downregulated 8 (Nedd8) modification and the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) pathway, thereby establishing a metabolic-signaling positive feedback loop. As a core executor of metabolic reprogramming, FASN promotes tumorigenesis through dual mechanisms. First, its fatty acid synthesis product, palmitate, participates in membrane phospholipid synthesis, lipid raft formation, and protein palmitoylation, thereby activating several key oncogenic signaling pathways, including PI3K/AKT/mTOR, wingless-type MMTV integration site family member (Wnt)/β‑catenin, and signal transducer and activator of transcription 3 (STAT3)/matrix metalloproteinase (MMP), leading to tumor development and progression. Second, FASN plays a pivotal role in modulating the anti-tumor functions of immune cells and remodeling the tumor immune microenvironment. Specifically, FASN enhances immune checkpoint inhibition by inducing programmed death-ligand 1 (PD-L1) palmitoylation, suppresses the activation of cytotoxic T lymphocytes and natural killer cells, and promotes the polarization of M2-type macrophages, consequently facilitating tumor immune evasion and malignant progression. Precisely due to its significant overexpression in tumor cells, its critical functional role, and its differential expression compared to normal cells, FASN has emerged as a highly promising target for anti-tumor drug development. Highly selective small-molecule inhibitors, notably represented by TVB-2640, have advanced to clinical trial stages and demonstrated favorable anti-tumor activity. Furthermore, the combination of FASN inhibitors with other chemotherapeutic agents or targeted drugs can overcome the limitations of monotherapy through synergistic effects or by resensitizing tumor cells to conventional drugs, achieving a “1+1>2” therapeutic outcome. With the advancement of modern traditional Chinese medicine (TCM), numerous active ingredients derived from TCM have been confirmed to exert anti-tumor effects by modulating FASN-related pathways. This integrated approach leverages the precision of Western medicine while simultaneously harnessing the holistic regulatory benefits of TCM to alleviate the side effects of radiotherapy and chemotherapy. Despite the promising prospects of FASN-targeted therapies, challenges remain, including tumor cell metabolic plasticity, tumor context-dependent responses, and heterogeneity. This review systematically summarizes the molecular structure, physiological functions, and mechanisms of FASN in tumorigenesis, as well as recent advances in targeted therapies. Future directions—including the precise identification of responsive patient populations using spatial transcriptomics, the development of novel combination regimens, and the active exploration of integrative strategies combining traditional Chinese and Western medicine—will facilitate the clinical translation of FASN-targeted therapies and open new avenues for improving the quality of life and prognosis of cancer patients.

Background: Patients with coarctation of the aorta (CoA) and an interrupted aortic arch (IAA) may present with small mitral valves (MVs) and a reduced left ventricular (LV) volume. Biventricular repair (BVR) in these patients is dependent on adequate size of the left cardiac structures.Purpose: This study evaluated the impact of the hemodynamic characteristics of atrial septal defects (ASDs) on MV growth following surgical repair. Methods: We retrospectively reviewed the data of patients diagnosed with CoA or IAA between 2007 and 2024. The z score for MV size measured 6 months postoperatively (Z2) was compared with the preoperative MV size (Z1). The factors associated with MV growth were also studied. Results: A total of 161 patients with CoA or IAA were included. Transthoracic echocardiography was used to assess the MV and LV dimensions preoperatively and 6 months postoperatively. Of the cohort, 155 (96.3%) underwent initial BVR and 6 underwent single-ventricle palliation. MV z scores significantly increased following BVR (mean change: +0.45±1.35; P<0.001) but decreased after single-ventricle repair (-0.56±0.49, P=0.04). Multivariate analysis identified the initial MV z score and ASD pressure gradient as independent predictors of MV growth (R2=0.39). Conclusion Annular growth of the MV was not observed in patients who underwent single-ventricle palliation. In contrast, among patients who achieved BVR, those with a small preoperative MV annulus and low ASD pressure gradient demonstrated subsequent catch-up MV growth, suggesting that adequate left-sided preload is essential for MV development.

Objective To design a VR-based air evacuation training system for simulating the on-board medical treatment process during air evacuation.Methods A VR-based air evacuation training system was developed which used 3D modeling technology to construct models of the medical aircraft cabin,medical devices and virtual characters to achieve scene interaction.The hardware part of the system included server computers,training terminal computers,VR equipment,3D fusion projection equipment,motion capture equipment,etc.The software of the system was developed using C++,UE4 Blueprint and C# programming languages,including two modules for medical treatment unit and medical treatment training process evaluation.The efficacy of the system was verified by the trials in air evacuation.Results The system developed successfully simulated the scenarios of tracheal tube dislodgement and increased intracranial pressure in the scenario model of open severe craniocerebral injury.The expert evaluation showed that the system gained advantages in training efficiency,low cost,safety,sense of immersion and recorded the operation data in real time to optimize the follow-up training.Conclusion The system developed delivers a virtual training environment with high-fidelity replication of real-mission conditions,enabling whole-course and immersive air evacuation drills.[Chinese Medical Equipment Journal,2025,46(3):15-20]

Objective To design a VR-based air evacuation training system for simulating the on-board medical treatment process during air evacuation.Methods A VR-based air evacuation training system was developed which used 3D modeling technology to construct models of the medical aircraft cabin,medical devices and virtual characters to achieve scene interaction.The hardware part of the system included server computers,training terminal computers,VR equipment,3D fusion projection equipment,motion capture equipment,etc.The software of the system was developed using C++,UE4 Blueprint and C# programming languages,including two modules for medical treatment unit and medical treatment training process evaluation.The efficacy of the system was verified by the trials in air evacuation.Results The system developed successfully simulated the scenarios of tracheal tube dislodgement and increased intracranial pressure in the scenario model of open severe craniocerebral injury.The expert evaluation showed that the system gained advantages in training efficiency,low cost,safety,sense of immersion and recorded the operation data in real time to optimize the follow-up training.Conclusion The system developed delivers a virtual training environment with high-fidelity replication of real-mission conditions,enabling whole-course and immersive air evacuation drills.[Chinese Medical Equipment Journal,2025,46(3):15-20]

Objective:To investigate the predictive value of radiomics features derived from dynamic contrast-enhanced MRI (DCE-MRI) based on habitat imaging technology for pathological complete response after neoadjuvant therapy (NAT) for breast cancer.Methods:All patients were female, aged 25-67 years. Patients were stratified into training ( n=83) and validation ( n=36) sets via stratified random sampling (7∶3 ratio). Pathological complete remission (pCR) and non-pathological complete remission (non-pCR) were defined using the Miller-Payne grading system. All patients underwent DCE-MRI before NAT. ITK-Snap software was used to outline the region of interest (ROI), the imaging histological features of the entire tumor region were extracted and screened, a traditional imaging histological model for predicting post-NAT pCR (ROI overall model) was constructed; the tumor region was divided into three subregions using habitat imaging technology, and the imaging histological features within ROI subregion 1, ROI subregion 2, and ROI subregion 3 were extracted and screened, and the habitat imaging model for predicting post-NAT pCR were constructed (ROI subregion 1 model, ROI subregion 2 model, ROI subregion 3 model). Univariate logistic regression identified clinical predictors of pCR for clinical model construction. Combined models integrating clinical predictors and habitat imaging features were established. The efficacy of each model in predicting pCR after NAT in breast cancer was evaluated using receiver operating characteristic curves and area under the curve (AUC), and the efficacy of clinical application of the models was evaluated using decision curve analysis (DCA). Results:Of the 119 patients, 74 were pCR patients, with 52 in the training set and 22 in the validation set, and 45 were non-pCR patients, with 31 in the training set and 14 in the validation set. Logistic regression analysis showed that human epidermal growth factor receptor 2 status ( OR=0.254, 95% CI 0.093-0.697, P=0.008) was an independent predictor of pCR after NAT, and this was used to construct a clinical prediction model. The predictive efficacy of ROI subregion 1 model and ROI subregion 2 model in the habitat model was higher than that of the traditional imaging histology model (ROI overall model), with AUCs of 0.805, 0.748,0.728 for the training set and 0.776,0.718,0.708 for the validation set, respectively. The combined clinical prediction model for predicting pCR after NAT in breast cancer had AUCs of 0.877 and 0.818 for the training and validation sets, respectively. DCA showed a higher net benefit for the combined model than for the traditional imaging histology model and the habitat imaging histology model. Conclusion:Compared with the traditional method of extracting the entire tumor region, extracting radiomics features from DCE-MRI subregions based on habitat imaging technology can improve the predictive performance of NAT efficacy in breast cancer.

Single-cell metabolite analysis at the small molecule level reveals intercellular heterogeneity and molecular diversity,especially living cell metabolite analysis which can provide more accurate biochemical information.In this study,a comprehensive single-cell metabolomics mass spectrometry analysis platform was constructed based on continuous ultrasonic sample introduction,aiming to improve the utilization rate of single cells and the efficiency of mass spectrometry detection.This platform utilized mechanical motion generated by a miniaturized ultrasound module,which minimally affected cell integrity and viability,enabling cell suspension and dispersion for up to 60 min,with cell viability exceeding 70%.By comparing cell suspension densities and the cell number of mass spectrometry detections between static and ultrasound groups,the results showed that the ultrasound treatment significantly reduced cell sedimentation rate and increased single-cell mass spectrometry detection efficiency.Applying this platform to single-cell analysis of cell line of mouse cerebellar astrocytes(C8D1A)and mouse glioma(GL261)cells achieved clustering and differential analysis of different cell types,demonstrating the method's potential in analyzing cellular heterogeneity and identifying cells.This approach promised to provide new insights and solutions for single-cell analysis.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.

The aim of this study was to investigate the improvement effect of Wenpi Pills(WPP) on non-alcoholic fatty liver disease(NAFLD). The experiment was divided into two parts, using C57BL/6 mouse models induced by a high-fat diet(HFD) and a methionine and choline deficiency diet(MCD). The HFD-induced experiment lasted for 16 weeks, while the MCD-induced experiment lasted for 6 weeks. Mice in both parts were divided into four groups: control group, model group, low-dose WPP group(3.875 g·kg~(-1), WPP＿L), and high-dose WPP group(15.5 g·kg~(-1), WPP＿H). After sample collection from the HFD-induced mice, lipid content in the serum and liver, liver function indexes in the serum, and hepatic pathology were examined. Real-time fluorescent quantitative reverse transcription PCR(qRT-PCR) was used to detect the expression of lipid-related genes. After sample collection from the MCD-induced mice, serum liver function indexes and inflammatory factors were measured, and hepatic pathology and lipid changes were analyzed by hematoxylin-eosin(HE) staining and widely targeted lipidomic profiling, respectively. The results from the HFD-induced experiment showed that, compared with the HFD group, WPP administration significantly reduced the levels of aspartate aminotransferase(AST), alanine aminotransferase(ALT), triglyceride(TG), and total cholesterol(TC) in the serum, with the WPP＿H group showing the most significant improvement. HE staining results indicated that, compared with the HFD group, WPP treatment improved the morphology of white adipocytes, reducing their size, and alleviated hepatic steatosis and lipid droplet accumulation. The qRT-PCR results suggested that WPP might increase the mRNA expression of liver cholesterol-converting genes, such as liver X receptor α(LXRα) and cytochrome P450 family 27 subfamily A member 1(CYP27A1), as well as lipid consumption genes like peroxisome proliferator-activated receptor α(PPARα) and adenosine mono-phosphate-activated protein kinase(AMPK). Meanwhile, WPP decreased the mRNA expression of lipid synthesis genes, including fatty acid synthetase(FAS), stearoyl-CoA desaturase 1(SCD1), and sterol regulatory element-binding protein 1c(SREBP-1c), thereby reducing liver lipid accumulation. The results from the MCD-induced experiment showed that, compared with the MCD group, WPP administration reduced the levels of ALT, AST, and inflammatory factors in the serum, thereby alleviating liver injury and the inflammatory response. HE staining of liver tissue indicated that WPP effectively improved hepatic steatosis. Non-targeted lipidomics analysis showed that WPP improved lipid metabolism disorders in the liver, mainly by affecting the metabolism of TG and cholesterol esters. In conclusion, WPP can improve hepatic lipid accumulation in NAFLD mice induced by both HFD and MCD. This beneficial effect is primarily achieved by alleviating liver injury and inflammation, as well as regulating lipid metabolism.
Animals ; Non-alcoholic Fatty Liver Disease/genetics* ; Lipid Metabolism/drug effects* ; Mice ; Mice, Inbred C57BL ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Diet, High-Fat/adverse effects* ; Liver/drug effects* ; Humans ; Disease Models, Animal ; Methionine

Objective:To explore the correlation between the total cholesterol/high-density lipoprotein cholesterol (TC/HDL-C) ratio during pregnancy and the onset of gestational metabolic syndrome (GMS), as well as its association with pregnancy outcomes.Methods:Clinical data of 153 pregnant women diagnosed with GMS in the third trimester admitted to Women′s Hospital of Nanjing Medical University from January 2020 to December 2022 (observation group) were collected, and data of 153 healthy pregnant women (control group) were randomly selected for a comparative study. Indicators of the two groups were observed and compared. Multiple linear regression and restricted cubic spline analysis were used to analyze the correlation between TC/HDL-C in the first and second trimesters and metabolic indicators in the third trimester and GMS risk, respectively. According to the pregnancy outcomes of GMS pregnant women with different TC/HDL-C ratios, the association between TC/HDL-C ratio and preterm birth subtypes was analyzed.Results:There were statistically significant differences between the two groups in age, gestational week, pre-pregnancy body mass index (BMI), weight gain during pregnancy, abdominal circumference in the third trimester, whether to deliver vaginally, history of preterm birth, history of cesarean section, history of abortion, neonatal birth weight, and neonatal length (all P<0.05). There were statistically significant differences in blood pressure and BMI between the two groups in the second and third trimesters (all P<0.01). In the first trimester (gestational week <14 weeks), there were statistically significant differences in fasting plasma glucose (FPG), HDL-C, and TC/HDL-C ratio between the two groups (all P<0.05). In the second trimester (gestational weeks 14-27) and third trimester (gestational week ≥28 weeks), the levels of FPG, fasting insulin (FINS), triglyceride (TG), TC, low-density lipoprotein cholesterol (LDL-C), and TC/HDL-C ratio in the observation group were higher than those in the control group, while HDL-C was lower (all P<0.05). There were statistically significant differences between the two groups in postpartum abnormal blood pressure, abnormal BMI, abnormal glucose metabolism, and abnormal lipid metabolism (all P<0.05). After adjusting for age, gestational week, and BMI, first-trimester TC/HDL-C ratio was correlated with third-trimester FPG, systolic blood pressure, diastolic blood pressure, TG, TC, and HDL-C (all P<0.05); second-trimester TC/HDL-C ratio was correlated with third-trimester FPG, FINS, systolic blood pressure, diastolic blood pressure, TG, TC, HDL-C, and LDL-C (all P<0.05), and both were linearly related to third-trimester GMS risk ( Pfor trend<0.01). The TC/HDL-C ratio was positively correlated with the preterm birth rate ( RR>1). Conclusions:An increased TC/HDL-C ratio is closely related to the occurrence of GMS, which is prone to cause preterm birth and affect fetal growth and development.