Obesity is a multi-factorial disease involving genetics, individual behavior, socio-economic status, and environmental factors, and has become a global public health issue. The food environment, as an external factor amenable to direct intervention, affects the development of obesity by shaping individual food acquisition and consumption behaviors. The food environment refers to the physical and social environment where food is accessible, and can be assessed from dimensions such as availability, accessibility, and affordability through geographic information system spatial analysis, field surveys, commercial databases, and questionnaires. Studies indicate that the food environment can influence obesity through the spatial shaping effects of dietary structure and sociobehavioral pathways. A healthy food environment is negatively correlated with the risk of obesity, whereas an unhealthy food environment is positively correlated with the risk of obesity. This paper reviews studies related to the correlation between the food environment and obesity, covering the prevalence of obesity, the definition and assessment methods of the food environment, and the mechanisms by which the food environment affects obesity. It summarizes food environment intervention strategies centered on urban planning, policies and regulations, and community education to provide a reference for obesity prevention and control.

ObjectiveTo address the key challenges in breast electrical impedance tomography (EIT) reconstruction including the severe ill‑posedness of the inverse problem, limited measurement information from the electrodes, low contrast of small lesions, as well as the common drawbacks of traditional reconstruction methods such as boundary blurring, low spatial resolution, and weak small-target detection capability, and to overcome the limitations of existing deep learning approaches namely low training efficiency, insufficient physical consistency, and substantial information loss during the low‑to‑high dimensional mapping process, this study proposes a multi‑scale attention and dynamic sparse mask fusion network (MADS‑Net). The proposed method aims to enhance high‑resolution reconstruction accuracy, small-lesion detectability, noise robustness, and computational efficiency in breast‑screening-oriented EIT, thereby providing a feasible solution for the application of EIT in breast functional imaging. MethodsThe proposed network adopts a strategy of “locate first, reconstruct later”. It realizes adaptive allocation of computational resources through a dynamic mask module, alleviates information loss in the low-to-high dimensional mapping process via a multi-scale attention mechanism, and embeds physical constraints to improve the physical rationality of reconstruction results. The performance of the proposed method was validated through systematic numerical simulations, agar phantom experiments, and porcine tissue experiments. Comparisons were made with mainstream methods including the Gauss-Newton algorithm, convolutional neural networks, U-Net, and Deep D-Bar. The image correlation coefficient (ICC), root mean square error (RMSE), and peak signal-to-noise ratio (PSNR) were adopted as quantitative evaluation metrics. Furthermore, lesion‑level evaluation metrics were introduced to assess the capability of different methods in reconstructing abnormal target boundaries and small‑sized target regions. ResultsExperimental results demonstrate that MADS-Net outperforms all comparison methods across all test scenarios. In numerical simulations, it achieves higher accuracy in target morphology and spatial location recovery as well as better background suppression. In agar phantom experiments, its detection and imaging reliability for small targets is significantly superior to the comparison methods. In porcine tissue experiments, the model maintains good generalization performance and can adapt to complex biological tissue imaging scenarios. Quantitative results show that MADS-Net achieves an average ICC of 0.917 6±0.048 8, an RMSE as low as0.060 6±0.031 6, and a PSNR of (23.893 9±2.685 3) dB. It successfully detects tiny targets as small as 5 mm within a 100 mm diameter sensing field, reduces the total training duration by 23.5% compared with the baseline method, and maintains stable reconstruction performance even under strong noise interference. ConclusionThe results demonstrate that MADS-Net achieves favorable EIT reconstruction performance in numerical simulations, agar phantom experiments, and porcine tissue experiments, even under common practical imaging challenges including low signal-to-noise ratio and blurred tissue boundaries. This study provides a methodological reference for further research on EIT in breast-screening-related scenarios and other medical functional imaging tasks, and also offers a low-cost, radiation-free alternative for breast lesion screening that aligns with the clinical demand for non-invasive diagnostic tools. It should be noted that real clinical breast data have not yet been used for validation in this study, and the clinical applicability of the proposed method requires further investigation. Future work will prioritize collecting multi-center real clinical breast datasets, and further optimize the model’s generalization ability to adapt to individual differences in breast tissue composition to promote its clinical application.

AIM To establish a UPLC-MS/MS method for the simultaneous content determination of alisol A,23-acetyl alisol B,alisol A-24-acetate,alisol B,alisol C,23-acetyl alisol C,alisol F,alisol G,atractylenolideⅠ,atractylenolide Ⅱ,atractylenolide Ⅲ,atractylon and ferulic acid in Zexie Decoction.METHODS The analysis was performed on a 30 ℃ thermostatic Waters ACQUITY UPLC HSS C18 column(100 mm×2.1 mm,1.8μm),with the mobile phase comprising of water(containing 0.1％formic acid)-acetonitrile(containing 0.1％formic acid)flowing at 0.3 mL/min in a gradient elution manner,and electron spray ionization source was adopted in positive and negative ion scanning with multiple reaction monitoring mode.RESULTS Thirteen constituents showed good linear relationships within their own ranges(r＞0.999 1),whose average recoveries were 95.05％-103.8％with the RSDs of 1.4％-6.7％.CONCLUSION This simple,rapid and accurate method can provide experimental basis for the quality control of Zexie Decoction.

Objective:To explore the research hotspots and trends in the radiomics field based on bibliometric for breast cancer.Methods:Publications related to radiomics for breast cancer during January 2012 to June 2024 were retrieved from the Web of Science Core Collection databases.Then,the R software's package"bibliometric"tool,VOSviewer and CiteSpace softwares were used to conduct visual analysis of radiomics-related bibliometric for breast cancer,which involved to publication trends,countries,institutions,journals,authors,keywords,and highlight keywords.Results:A total of 683 radiomics-related literatures for breast cancer were obtained,and 518 articles were finally included after non-English literatures and non-Article literatures were excluded.China and Sun Yat-sen University emerged as the leading country(326 articles)and institution(83 articles).The top three journals with the most of published articles were respectively,and.Professor Mao Ning has the largest number of articles,followed by Pinker Katja and Liang Changhong.The results of keywords and highlight keywords showed that current radiomics researches for breast cancer mainly focused on the applications of magnetic resonance imaging(MRI),machine learning,deep learning,artificial intelligence,and radiogenomics in prediction for diseases'prognosis,and assessment for treatment's response.Conclusion:The applications of machine learning,deep learning and multi-parameter imaging belongs to hot-research fields in assessment for personalized treatment.Future research directions would more and more tend to integrate artificial intelligence and multi-omics analysis,so as to promote prognostic assessment and personalized treatment.

On the basis of that the fluorescence wavelength of copper nanoclusters(CuNCs)could cover the entire visible region,multicolor fluorescent CuNCs/starch composites were prepared and applied in fingermark development.With L-glutathione as the reducing agent and protective ligand,blue emissive and orange emissive CuNCs solutions were obtained in alkaline solutions at 90℃and 25℃,respectively.With the aggregation-induced emission effect induced by ethanol as a poor solvent,the fluorescence of orange emissive CuNCs with a higher intensity was achieved in an ethanol-water solution.With ascorbic acid as the reducing agent and 3-mercaptopropionic acid as the protective agent,green emissive CuNCs solution was prepared in an acid solution.Particle morphologies,chemical compositions and optical properties of these three CuNCs above were investigated using physical characterization and spectroscopic analysis,indicating that well-dispersed CuNCs had excellent photoluminescent properties.These CuNCs solutions were combined with starch to form composite powders by simply drying.The influences of the type of CuNCs and the ratio of CuNCs to starch on the emission wavelength and fluorescence intensity of the products were studied.The obtained CuNCs/starch composites could emit blue,green and orange fluorescence under 365 nm ultraviolet light,respectively,which were suitable for fingermark development.Minutiae and partial level-3 features of latent fingermarks could be effectively developed.High-quality fluorescence fingermark images would be captured using appropriate optical filters to eliminate background interference of various substrates.

Compared to traditional machine learning algorithms,which often suffer from low feature extraction efficiency,insufficient nonlinear pattern recognition capabilities and slow training speeds,the broad learning system(BLS)enhances the learning ability and efficiency by horizontally expanding the network structure.BLS offers advantages such as a simple structure,fast training speed,and strong generalization capabilities.While BLS has demonstrated potential in various fields,but its application in identification of complex samples has not been fully explored.This research investigated the feasibility of using BLS algorithm for identification of complex samples based on physicochemical indicators.Using the iris,wine,and breast cancer datasets,the length and width of petals and sepals of iris flowers,the physicochemical properties of wine,and the nuclear characteristics of breast cancer cells were used as input variables to establish BLS models for identifying iris species,wine varieties,and benign versus malignant nucleus.The model performance was evaluated by confusion matrices,accuracy,and runtime.Compared with partial least squares-discriminant analysis(PLS-DA),soft independent modeling of class analogies(SIMCA),and artificial neural networks(ANN),the results indicated that BLS demonstrated significant advantages in computational efficiency and recognition accuracy.Thus,BLS was an efficient and reliable method for identification of complex samples.

Hepatocellular carcinoma(HCC)is one of common cancer that seriously endangers human health.Designing methods for early,rapid,and accurate diagnosis of HCC has become the key point.Golgi protein 73(GP73),a novel potential biomarker for HCC,is crucial for diagnosis and treatment of HCC.In this study,a colorimetric sensor with rapidity,smplicity and high specificity was established for detection of GP73 based on peroxidase-like activity of hemin-reduced graphene oxide-manganese dioxide(H-rGO-MnO2).The H-rGO-MnO2-GP73Apt1 signal probe was synthesized by carboxyl of H-rGO-MnO2 nanozyme and amination of GP73 aptamer(GP73Apt1)though amide reaction.In the presence of GP73,the sulfhydryl-modifed GP73 aptamer(GP73Apt2),as the capture probe,and the signal probe both specifically recognized GP73,forming a sandwich structure(GP73Apt2-GP73-H-rGO-MnO2-GP73Apt1).This structure could catalyze the oxidation of H2O2 to produce hydroxyl radical(·OH),thereby oxidizing the colorless phthalenediamine(OPD)into the yellow 2,3-diaminophenazine(DPA).The quantitative detection of GP73 was achieved by measuring the characteristic absorbance of DPA at 450 nm.In the GP73 concentration range of 10-150 ng/mL,there was a good linear relationship between the DPA absorbance at 450 nm(A450 nm)and the GP73 concentration under optimal conditions.The linear equation was A450 nm=0.00321CGP73+0.8988,with the correlation coefficient(R2)of 0.9960 and the detection limit(LOD)of 5.38 ng/mL.The colorimetric sensor was applied to detection of GP73 in human serum samples,with recoveries of 88.4%?98.8%.This sensor showed high specificity,sensitivity,and stability,and had potential for clinical detection of GP73,providing a new approach for the early diagnosis of HCC.

Solid-phase extraction(SPE)combined with surface-enhanced Raman spectroscopy(SERS)has emerged as a promising analytical technique for detection and analysis of trace components in complex sample matrices.SPE enriches analytes through selective adsorption and solvent elution,effectively increasing the concentration and signal intensity.SERS enables ultra-sensitive and highly selective molecular analysis through the use of SERS-active substrates engineered to amplify Raman signals.The integration of these two techniques overcomes the limitations of conventional Raman spectroscopy in low-concentration detection field,while significantly improving sample preparation efficiency and analytical accuracy.This review provided a comprehensive overview of the characteristics of three SPE-SERS coupling modes,including two-step,one-step,and online integration.Special emphasis was placed on recent advancements in one-step SPE-SERS approaches based on novel functional adsorbent materials such as graphene,metal-organic frameworks,covalent organic frameworks,and molecularly imprinted polymers.Furthermore,future directions and development prospects of SPE-SERS technology were discussed.

Objective To analyze the value of combined prediction of peripheral blood CD4+,CD3-CD16+CD56+,CD8+,CD19+for the risk of death in patients with Epstein-Barr virus-associated hemophagocytic syn-drome(EBV-HLH).Methods A total of 84 patients with EBV-HLH who were treated in Beijing Friendship Hospital Affiliated to Capital Medical University and Peking University First Hospital from May 2021 to A-pril 2023 were selected and divided into a death group(17 cases)and a survival group(67 cases)according to their survival outcomes within 6 months.The general information and peripheral blood T lymphocyte subsets of the two groups were compared,multivariate Logistic regression analysis was conducted to investigate the influencing factors of death in EBV-HLH patients,and receiver operating characteristic(ROC)curve was used to evaluate the predictive value of peripheral blood T lymphocyte subsets on death in EBV-HLH patients.Results The platelet count,hemoglobin,CD4+and CD3-CD16+CD56+levels in the death group were lower than those in the survival group,while the levels of CD8+and CD19+were higher than those in the survival group(P＜0.05).Multivariate Logistic regression analysis showed that platelet count,hemoglobin,CD4+,and CD3-CD16+CD56+were all independent protective factors for the death of EBV-HLH patients,while CD8+and CD19+were independent risk factors for the death of EBV-HLH patients(P＜0.05).The ROC curve results showed that the area under the curve of combined prediction of CD4+,CD8+,CD19+,CD3 CD16+CD56+for death in EBV-HLH patients was 0.923,with sensitivity and specificity of 94.12％and 80.60％,respectively,which were significantly higher than those predicted by each individual indicator(P＜0.05).Conclusion The abnormal expression of CD4+,CD3-CD16+CD56+,CD8+,and CD19+in peripheral blood of EBV-HLH patients is associated with their survival outcomes,and the combined prediction of CD4+,CD3-CD16+CD56+,CD8+,and CD19+has high reference value for predicting patients'death.

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.