ObjectiveTo establish an ultra-performance liquid fingerprint and multi-components determination method for Naomaili granules. To evaluate the quality of different batches by chemometrics， and the anti-neuroinflammatory effects of water extract and main components of Naomaili granules were tested in vitro. MethodsThe similarity and common peaks of 27 batches of Naomaili granules were evaluated by using Ultra performance liquid chromatography （UPLC） fingerprint detection. Ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） technology was used to determine the content of the index components in Naomaili granules and to evaluate the quality of different batches of Naomaili granules by chemometrics. LPS-induced BV-2 cell inflammation model was used to investigate the anti-neuroinflammatory effects of the water extract and main components of Naomaili granules. ResultsThe similarity of fingerprints of 27 batches of samples was > 0.90. A total of 32 common peaks were calibrated， and 23 of them were identified and assigned. In 27 batches of Naomaili granules， the mass fractions of 14 components that were stachydrine hydrochloride， leonurine hydrochloride， calycosin-7-O-glucoside， calycosin，tanshinoneⅠ， cryptotanshinone， tanshinoneⅡ_A， ginsenoside Rb₁， notoginsenoside R₁， ginsenoside Rg₁， paeoniflorin， albiflorin， lactiflorin， and salvianolic acid B were found to be 2.902-3.498， 0.233-0.343， 0.111-0.301， 0.07-0.152， 0.136-0.228， 0.195-0.390， 0.324-0.482， 1.056-1.435， 0.271-0.397， 1.318-1.649， 3.038-4.059， 2.263-3.455， 0.152-0.232， 2.931-3.991 mg∙g^-1， respectively. Multivariate statistical analysis showed that paeoniflorin， ginsenoside Rg₁， ginsenoside Rb₁ and staphylline hydrochloride were quality difference markers to control the stability of the preparation. The results of bioactive experiment showed that the water extract of Naomaili granules and the eight main components with high content in the prescription had a dose-dependent inhibitory effect on the release of NO in the cell supernatant. Among them， salvianolic acid B and ginsenoside Rb₁ had strong anti-inflammatory activity， with IC₅₀ values of （36.11±0.15） mg∙L^-1 and （27.24±0.54） mg∙L^-1， respectively. ConclusionThe quality evaluation method of Naomaili granules established in this study was accurate and reproducible. Four quality difference markers were screened out， and eight key pharmacodynamic substances of Naomaili granules against neuroinflammation were screened out by in vitro cell experiments.

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.

Objective To explore the risk factors of painful diabetic neuropathy (PDN) in patients with type 2 diabetes mellitus (T2DM). Methods A total of 269 patients with type 2 diabetes mellitus (T2DM) who were treated in the Department of Endocrinology at Zhangjiagang Hospital Affiliated to Soochow University from January 2020 to December 2024 were selected. The patients were divided into two groups: T2DM without PDN (n=190) and T2DM with PDN (n=79). The general characteristics and biochemical indicators of the two groups of patients were compared. Multivariate logistic regression was used to analyze the associated factors with PDN in T2DM. The receiver operating characteristic (ROC) curve was used to evaluate fasting C-peptide (FC-P), body mass index (BMI), and disease duration to predict the risk of PDN. Results Compared with the T2DM group without concurrent PDN, the T2DM group with concurrent PDN had a longer disease course, lower BMI, higher HDL-C, lower FC-P, and a higher proportion of diabetic retinopathy. The differences between the two groups were statistically significant (P<0.05). The results of multivariate logistic regression analysis showed that BMI, duration, and FC-P were associated factors of PDN. Conclusion BMI, duration and FC-P are associated factors of painful neuropathy complicated with type 2 diabetes.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

After the wooden shipwreck was recovered from the marine underwater environment,the wooden components undergo varying degrees of degradation,therefore,accurately determining the extent of degradation is a fundamental scientific issue for implementing effective preservation strategies.In this work,the wooden remains of Pinus massoniana excavated from the"Nanhai No.1"shipwreck(Southern Song Dynasty)were investigated and compared with the modern wood to discriminate the degradation levels of archaeological wood using attenuated total reflection Fourier transform infrared(ATR-FTIR)spectroscopy.The residual sugar content within wood cell walls was determined using a non-invasive automated microscale ATR-FTIR method to extract chemical information from the wood tangential section.Microstructural characterization of wood samples was conducted by super depth of field microscopy and scanning electron microscopy.FTIR spectral analysis was performed to evaluate the degradation state and elucidate changes in cellulose crystallinity.Finally,the combination of FTIR spectroscopy with the sparse partial least squares discriminant analysis(sPLS-DA)model facilitated the rapid discrimination of degradation levels in shipwreck archaeological wood,and the performance of the model was evaluated using receiver operating characteristic(ROC)curves and area under the curve(AUC).The results showed that the higher the degree of wood degradation,the lower the residual sugar content in the wood cell wall,and the residual glucose content of highly degraded wood was only 4.7％.Significant differences were observed in both the tangential section microstructure and FTIR characteristic absorption patterns across degradation levels,and as the degradation advanced,progressive cell wall loosening occurred alongside selective removal of polysaccharide components,and the relative lignin content was increased,resulting in an elevated A1509/A1370 ratio in FTIR spectra.The sPLS-DA model achieved excellent discrimination performance with AUC values exceeding 0.9,confirming that the combination of FTIR spectroscopy with sPLS-DA enabled accurate assessment of degradation levels in shipwreck archaeological wood.This study developed a rapid and accurate methodology for assessing degradation levels in shipwreck archaeological wood based on microscale ATR-FTIR spectroscopy,which would help to promote the accurate assessment of the preservation state of waterlogged wooden artifacts.

Objective: To explore the risk of endometrial polyp (EP) based on lipid metabolism and vaginal micro- ecology combined with uterine volume line drawing model. Methods: 143 EP patients treated by hysteroscopic sur- gery were selected as the experimental group , and 113 healthy women were selected as the control group at the same time. The data were randomly divided into training set and validation set according to the ratio of 7 : 3. The clinical data of the two groups were collected and recorded , and t/χ2 test , LASSO regression and multifactorial lo- gistic regression analysis were used to screen the independent risk factors , construct the prediction model , and draw the column line graph. The performance of the model was evaluated by applying subject operating characteristic (ROC) curves , calibration curves , Hosmer-Lemeshow test and clinical decision-making (DCA) curves. Results: Multifactorial logistic regression analysis showed that total cholesterol ( TC) , low-density lipoprotein cholesterol (LDL-C) , vaginal microecological balance , and uterine volume were independent risk factors for the development of EP. ROC curve analysis showed that the AUC values of the training and validation sets of the column line graph model were 0. 935 and 0. 887 , respectively , and its sensitivity and specificity were 90. 21% , 83. 46% and 86. 29% , 80. 66% respectively , The Hosmer-Lemeshow test showed that the model fits well ( training set : χ2 = 2. 261 , P = 0. 840 ; validation set : χ2 = 4. 837 , P = 0. 441) and the calibration curves of the training and validation sets were close to the ideal curves , which indicated that the model had good prediction accuracy; the analysis of DCA curves of the training and validation sets both showed that the column-line graph model had a good clinical benefit rate in predicting EP. Conclusion TC , LDL-C , vaginal microecological balance and uterine volume are independent risk factors for EP , and the column-line diagram model constructed by the model has high clinical ben- efit , calibration and accuracy in predicting the risk of EP.

BACKGROUND:Various proteins,signaling pathways,and inflammatory mediators are involved in the pathophysiological process of osteoarthritis.The development of small molecule drugs targeting these proteins,signaling pathways,and inflammatory mediators can effectively delay the progression of osteoarthritis and ameliorate its clinical manifestations. OBJECTIVE:To review the research progress of small molecule drugs in the treatment of osteoarthritis based on the pathogenesis of osteoarthritis. METHODS:PubMed,CNKI,and WanFang databases were searched with English search terms"osteoarthritis,arthritis,osteoarthrosis,degenerative,arthritides,deformans,small molecule drugs,small molecule inhibitors,small molecule agents"and Chinese search terms"osteoarthritis,small molecule drugs,small molecule inhibitors."A total of 68 articles were included for review according to the inclusion and exclusion criteria. RESULTS AND CONCLUSION:(1)Currently,studies concerning the pathogenesis of osteoarthritis remain unclear.The occurrence and development of osteoarthritis are strongly associated with proteins,cytokines,and signal transduction pathways,so its therapeutic mechanism is relatively complex.Currently,targeting proteins,cytokines,and signal transduction pathways related to osteoarthritis with small molecule drugs has become a major research focus.(2)Small molecule drugs frequently possess visible intracellular or extracellular targets and efficacy,containing enhancing cartilage repair,resisting joint degradation,attenuating inflammation,and relieving pain.Other anti-osteoarthritis small molecule drugs have shown promise in promoting stem cell chondrogenic differentiation and cartilage matrix reconstruction.(3)At present,small molecule drugs targeting the pathophysiological process of osteoarthritis to delay the progression of osteoarthritis are still in the experimental stage,but most of these small molecule drugs have shown the expected results in the experimental process,and there are no relevant studies to illustrate the efficacy of small molecule drugs in the treatment of osteoarthritis.(4)Small molecule drugs for the treatment of osteoarthritis have reached the expected experimental results in the basic experimental stage.Numerous studies have exhibited that small molecule drugs can target the suppression of specific proteins,cytokines,and signal transduction pathways that cause osteoarthritis,so as to treat osteoarthritis.Nevertheless,its safety and effectiveness still need to be identified by further basic and clinical studies.This process needs to be investigated and studied by more scholars.(5)At present,many scholars in and outside China have made contributions to the treatment of osteoarthritis.Compared with traditional treatment methods,small molecule drugs reveal better efficacy and safety in the basic experimental stage,and it is expected to become an emerging method for the treatment of osteoarthritis in the future to rid patients of pain.

Background: The association of changes in metabolic syndrome (MetS) with cognitive function remains unclear. We explored this association using prospective and Mendelian randomization (MR) studies. Methods: MetS components including high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), waist circumference (WC), fasting plasma glucose (FPG), and triglycerides were measured at baseline and two follow-ups, constructing a MetS index. Immediate, delayed memory recall, and cognitive function along with its dimensions were assessed by immediate 10- word recall test (IWRT) and delayed 10-word recall test (DWRT), and mini-mental state examination (MMSE), respectively, at baseline and follow-ups. Linear mixed-effect model was used. Additionally, the genome-wide association study (GWAS) of MetS was conducted and one-sample MR was performed to assess the causality between MetS and cognitive function. Results: Elevated MetS index was associated with decreasing annual change rates (decrease) in DWRT and MMSE scores, and with decreases in attention, calculation and recall dimensions. HDL-C was positively associated with an increase in DWRT scores, while SBP and FPG were negatively associated. HDL-C showed a positive association, whereas WC was negatively associated with increases in MMSE scores, including attention, calculation and recall dimensions. Interaction analysis indicated that the association of MetS index on cognitive decline was predominantly observed in low family income group. The GWAS of MetS identified some genetic variants. MR results showed a non-significant causality between MetS and decrease in DWRT, IWRT, nor MMSE scores. Conclusion Our study indicated a significant association of MetS and its components with declines in memory and cognitive function, especially in delayed memory recall.

Rubi Fructus has a long history of medicinal and edible use in China. It contains chemical components such as terpenes, flavonoids, phenolic acids, fatty acids, and alkaloids, and possesses various pharmacological activities, including antioxidant, anti-inflammatory, hypoglycemic, anti-tumor, anti-osteoporosis, and liver-protective effects. Rubi Fructus is widely applied in medical, health, and food fields. The quality of Rubi Fructus can directly affect the safety and effectiveness of clinical medication. Therefore, this article reviews the research progress on the chemical constituents and pharmacological effects of Rubi Fructus. Based on the concept of traditional Chinese medicine(TCM) quality markers(Q-markers), the article explores the screening and determination of Q-markers for Rubi Fructus from various aspects, including plant kinship, traditional efficacy, medicinal properties, measurability of chemical composition, different processing methods, producing areas, harvesting periods, and planting conditions. The components ellagic acid, kaempferol, quercetin, kaempferol-3-O-rutinoside, rutin, astragalin, tiliroside, and hyperoside are preliminarily proposed as Q-markers for Rubi Fructus, providing a reference for the quality control of Rubi Fructus.
Drugs, Chinese Herbal/pharmacology* ; Humans ; Rubus/chemistry* ; Fruit/chemistry* ; Quality Control ; Animals