OBJECTIVE To investigate the effects of baicalin （BC） on insulin resistance in rats with gestational diabetes mellitus （GDM） and its underlying mechanism based on the adenosine monophosphate-activated protein kinase （AMPK）/suppressor of variegation 3-9 homolog 1 （SUV39H1）/histone H3 lysine 9 trimethylation （H3K9me3） axis. METHODS A GDM rat model was established by a combination of a high-fat diet and streptozotocin injection. The successfully modeled rats were divided into the GDM group， BC low-dose group， BC high-dose group， and high-dose of BC+AMPK inhibitor （Compound C） group， with 10 rats in each group. Another 10 pregnant rats fed a normal diet served as the control group. Rats in each group were given corresponding drugs/normal saline intragastrically and/or intraperitoneally， once daily for 2 consecutive weeks. After the last administration， the levels of fasting blood glucose （FBG）， pancreatic function indexes [fasting insulin （FINS）， homeostasis model assessment of insulin resistance （HOMA-IR）， insulin sensitivity index （ISI）]， blood lipid indexes （total cholesterol， triglyceride， low-density lipoprotein cholesterol）， liver function indexes （alanine transferase， aspartate transferase， alkaline phosphatase）， inflammatory indicators （C-reactive protein， interleukin-1β， interleukin-6）， metabolic regulatory protein [complement-C1q/tumor necrosis factor-related protein 3 （CTRP3）]， insulin sensitivity related factors [glucose transporter 4 （GLUT4）， adiponectin]， and oxidative stress indicators [superoxide dismutase （SOD）， catalase （CAT）， malondialdehyde （MDA）] were measured. Pathological changes in liver tissue were observed， and the expressions of proteins related to the AMPK/SUV39H1/H3K9me3 axis in liver tissue were detected. RESULTS Compared with the GDM group， rats in the BC low- and high-dose groups showed varying degrees of improvement in pathological changes such as disordered cell arrangement， vacuolar degeneration， lipid deposition， and inflammatory cell infiltration in liver tissue. Their FBG and FINS levels， HOMA-IR， the levels of blood lipid indexes， liver function indexes， inflammatory indicators and MDA， and the expressions of SUV39H1 and H3K9me3 were significantly decreased or down-regulated， while metabolic regulatory protein， insulin sensitivity-related factors and AMPK protein phosphorylation levels were significantly increased （ P ＜0.05）. The improvement was more significant in the BC high-dose group （ P ＜0.05）. Compound C could significantly reverse the ameliorative effects of high-dose BC on the above quantitative indicators （ P ＜0.05）. CONCLUSIONS BC can significantly reduce oxidative stress and inflammatory responses， increase serum levels of CTRP3， GLUT4 and adiponectin， thereby improving insulin resistance in GDM rats. These effects may be related to the activation of AMPK and inhibition of SUV39H1-mediated H3K9me3 modification.

[摘 要] 目的：通过多组学整合分析，解析肠道菌群在胆管癌（CCA）发生发展中的潜在作用机制并识别相关关键基因。方法：基于SRA数据库的16S rRNA测序数据，比较CCA患者与健康对照者的肠道菌群组成；采用孟德尔随机化（MR）分析评估菌群与CCA风险的遗传关联。通过gutMGene和GeneCards数据库获取相关代谢物与基因，进行代谢和功能富集分析。整合GEO单细胞转录组数据（GSE213452），解析肿瘤微环境的细胞组成，重点关注T细胞亚群及其功能状态，并结合TCGA-CHOL数据集验证关键候选基因的表达差异。结果：与健康对照组相比，CCA患者肠道菌群组成发生显著改变，变形菌门下菌群异常富集（LDA > 4）。MR分析进一步证实，肠杆菌目与肠杆菌科的遗传易感性均与CCA风险呈正向关联。代谢通路富集分析提示，菌群相关代谢物主要参与嘌呤代谢及糖酵解/糖异生等通路；功能富集分析显示，相关基因显著富集于NOD样受体、IL-17、Toll样受体及NF-κB信号通路等炎症免疫通路。单细胞转录组分析结果显示，CCA组织中肿瘤细胞比例显著升高（P < 0.05），T细胞比例由20.7%增至39.2%；拟时序分析结果表明，MKI67⁺ T细胞处于分化末期并呈高增殖特征，其差异基因与菌群相关基因存在交集，其中SERPINA1和IFNG表达在肿瘤免疫微环境中显著变化（P < 0.001），可能发挥核心调控作用。TCGA-CHOL数据集验证显示，SERPINA1在CCA肿瘤组织中显著下调（P < 0.001），而IFNG在肿瘤与正常组织间无显著差异（P > 0.05）。结论：肠道菌群失衡（尤其是肠杆菌科异常增殖）可能通过代谢-免疫调控网络促进CCA进展，T细胞功能变化与关键基因（SERPINA1和IFNG）在MKI67+ T细胞中的差异化表达模式密切相关。

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective To analyze the predictive value of different obesity indicators on acute cerebral infarction in patients with transient ischemic attack (TIA). Methods A total of 310 patients with TIA admitted to Tangdu Hospital of Air Force Medical University from March 2022 to September 2025 were selected and classified into non-event group (276 cases) and cerebral infarction group (34 cases) based on acute cerebral infarction within 90 days. The basic data and obesity-related indicators [body mass index (BMI), waist-to-hip ratio, body adiposity index (BAI), conicity index (C-index) and a body shape index (ABSI were compared. Multivariate logistic regression analysis was utilized to screen the independent influencing factors. Receiver operating characteristic (ROC) curve was drawn to analyze the predictive efficiency. Results The age, proportions of concurrent hypertension and concurrent diabetes mellitus, ABCD2 score, waist-to-hip ratio, ABSI, C-index, BMI and BAI in the cerebral infarction group were older or higher than those in the non-event group, and the symptoms duration was longer (P<0.05). The above indicators were all related factors for acute cerebral infarction in patients with TIA within 90 days (P<0.05), and ABSI and ABCD² score were independent influencing factors (P<0.05). The prediction model was manifested as Logit (P)=− 4.205 + 0.482 × ABCD² score + 1.120 × ABSI × 100. ABSI and ABCD2 score had predictive value on the occurrence of acute cerebral infarction (P<0.05), and the predictive efficiency of combined model was higher than that of single indicator (P<0.05). Conclusion Among different obesity indicators, ABSI has the highest value on predicting recent acute cerebral infarction in TIA. The prediction model based on ABSI and ABCD2 score exhibits the best predictive efficiency.

Owing to its inherent advantages—such as being non-destructive, rapid, highly molecule-specific, and minimally interfered with by moisture—Raman spectroscopy has been widely adopted in the fields of skin barrier function assessment, monitoring the transdermal penetration of active cosmetic ingredients, and the identification and quality control of cosmetic products. Despite these strengths, the practical application of this technique faces a significant bottleneck: the strong fluorescence background generated by endogenous skin components and exogenous cosmetic additives. Endogenous skin substances, such as structural proteins (e.g., collagen and elastin), metabolic coenzymes (e.g., nicotinamide adenine dinucleotide), and pigments (e.g., melanin), together with exogenous cosmetic constituents like organic colorants, chemical sunscreens, and fragrances, often possess strong absorption and emission characteristics. When excited by lasers, these components produce a fluorescence background that can be 10⁶ to 10⁸ times stronger than the Raman scattering signals, effectively masking the inherently weak vibrational fingerprint information. In recent years, driven by the rapid development of optoelectronic hardware and artificial intelligence algorithms, fluorescence suppression strategies have evolved from isolated, single-method approaches into comprehensive, multi-level synergistic systems. These systems are categorized into three distinct tiers: sample preparation, signal acquisition, and data processing. At the sample preparation level, techniques such as photobleaching and surface-enhanced Raman spectroscopy (SERS) are employed to eliminate or bypass the generation of fluorescence at the source. At the signal acquisition level, instrumental improvements—including the use of long-wavelength near-infrared excitation (typically 785 nm or 1 064 nm), confocal spatial filtering, and shifted excitation Raman difference spectroscopy (SERDS)— are utilized to physically isolate Raman signals from the fluorescence background. Furthermore, at the data processing level, numerical baseline correction methods such as polynomial fitting, penalized least squares (e.g., airPLS, arPLS), wavelet transform, and derivative algorithms are increasingly integrated into the analytical pipeline to extract Raman spectral features from mixed signals without increasing hardware costs or acquisition time. This review provides a systematic categorization and critical evaluation of these fluorescence suppression methods, detailing their underlying principles, technical advantages, and inherent limitations in diverse experimental setups. By focusing on critical application scenarios—including skin barrier assessment, percutaneous absorption monitoring, the routine quality control of cosmetics, and the emerging field of portable on-site detection—this paper explores the current state of technique selection and optimization. Finally, the article discusses future development trends, emphasizing the necessity of constructing adaptive, tiered suppression strategies, developing intelligent and automated data processing algorithms, and promoting the integration of portable, multi-modal diagnostic devices. The objective of this review is to provide a comprehensive technical reference to facilitate the transition of Raman spectroscopy from a specialized laboratory tool into a routine, robust analytical platform for advancements in skin science and cosmetic research.

Congenital heart disease (CHD) is a congenital malformation resulting from abnormal embryonic development of the heart and great vessels, accounting for approximately 25% of all congenital malformations. Children with CHD are often complicated by short stature. Although surgical treatment can improve their growth and development to a certain extent, some children still experience growth retardation after surgery. Recombinant human growth hormone (rhGH) is the main drug for treating short stature, but its efficacy and safety in the treatment of patients with concomitant CHD warrant further investigation. This article reports six cases of children with CHD and short stature who were treated with rhGH. Through a literature review, we summarize and discuss the therapeutic efficacy, follow-up experiences, and adverse reactions of rhGH treatment, aiming to provide references for clinicians in applying rhGH to treat patients with CHD and short stature.

Objective To develop a melanoma diagnosis framework based on large-scale vision-language models,and to explore the feasibility and accuracy of the framework for melanoma diagnosis.Methods The publicly available Derm7pt dataset,which was divided into a training set(346 cases),a validation set(161 cases),and a test set(320 cases)was utilized.A melanoma diagnosis framework based on large-scale vision-language models was proposed,comprising two text branches and one visual branch.In the text branches,one branch processed fixed clinical prompts,while the other handled learnable prompts.This design aimed to optimize the effectiveness of learnable prompts through guidance from fixed clinical prompts.The visual branch processed dermoscopic images and enhanced melanoma feature recognition through fine-tuning the image encoder.Results On the Derm7pt dataset,our method outperformd other existing method.It achieved an area under the receiver operating characteristic curve(AUC)of 87.35％,an accuracy of 84.17％,and an F1-score of 84.01％.Conclusion The study demonstrates that with appropriate fine-tuning strategies,methods based on large-scale vision-language pre-trained models can effectively adapt to melanoma diagnosis tasks.This approach can serve as a powerful auxiliary tool for doctors,helping them make more accurate diagnostic decisions.

Objective To explore the predictive model and its value of irAEs based on peripheral blood markers.Methods The baseline clinical data,laboratory tests,and irAEs follow-up results of 825 malignant tumor patients treated with PD-1/PD-L1 antibodies in the First Affiliated Hospital of Kunming Medical University were retrospectively collected from December 2020 to December 2023.The patients were divided into irAEs group and non-irAEs group according to the presence or absence of irAEs.The differences between and within groups were analyzed by t-test,rank-sum test,chi-square test and Fisher exact probability method.LASSO,Ridge and Elastic-net logistic regressions were used to screen the predictors and establish the risk prediction models for irAEs.Results 136 patients experienced 178 irAEs,of which endocrine toxicity accounted for 42.64％,hepatitis 35.29％,pneumonia 20.58％,grade≥G3 accounted for 19.07％,involving more than two organs accounted for 24.26％of the total number of irAEs.Univariate analysis showed that baseline CD4+T cell count,IL-6,IL-17,TSH,GLB and ALB were associated with irAEs.GLB,ALB,IL-17 and TSH were selected as the important risk factors by Ridge,LASSO and Elastic-Net logistic regression.The results showed that the AUC of the three algorithms were over 0.800.The AUC of internal validation set by LASSO-Logistic was 0.800(95％CI 0.739～0.862).The AUC of external validation set was 0.800(95％CI 0.739～0.861)and the DCA curve results indicated the highest net return for this predictive model.Conclusion GLB,ALB,IL-17 and TSH are independent predictors of irAEs,and the predictive model of irAEs based on them is effective.

The chemical constituents of the petroleum ether extract derived from the 90% ethanol extract of Elephantopus scaber were investigated. By silica gel column chromatography, C_(18), MCI column chromatography and semi-preparative high performance liquid chromatography, ten compounds were isolated. Their structures were identified as 3β-hydroxy-6β,7β-epoxytaraxeran-14-ene(1), 3β-hydroxyolean-12-en-28-oic acid(2), D-friedoolean-14-ene-3β,7α-diol(3), 3β-hydroxy-11α-methoxyolean-12-ene(4), 3β-hydroxyolean-11,13(18)-diene(5), 11α-hydroxy-β-amyrin(6), betulinic acid(7), 3β-hydroxy-30-norlupan-20-one(8), 6-acetonylchelerythrine(9), and 4',5'-dehydrodiodictyonema A(10) by analysis of the 1D NMR, 2D NMR, MS, and IR spectral data. Among them, compound 1 was a new triterpene and other compounds except compounds 2 and 7 were isolated from this plant for the first time.
Triterpenes/isolation & purification* ; Drugs, Chinese Herbal/isolation & purification* ; Molecular Structure ; Asteraceae/chemistry* ; Chromatography, High Pressure Liquid ; Magnetic Resonance Spectroscopy