A centrifugation-free,single-reaction colorimetric method for detection of glucose,utilizing a dual nanozyme cascade system based on gold nanoparticles(AuNPs)and iron-doped carbon dots(FeCDs),was developed in this work.The AuNPs exhibited glucose oxidase-like activity to catalyze glucose oxidation for generation of H2O2,while the FeCDs demonstrated peroxidase-like activity to subsequently catalyze the H2O2-mediated oxidation of 3,3',5,5'-tetramethylbenzidine(TMB).To prevent interference from the blue signal generated by self-aggregation of AuNPs in subsequent quantitative detection,the reaction system was terminated with HCl,converting oxTMB into a stable yellow product.Based on changes in the absorbance at 450 nm of this yellow solution,a quantitative relationship was established between glucose concentration and absorbance at 450 nm(A450).Experimental results demonstrated that this sensor achieved a linear detection range of 44 μmol/L to 11.11 mmol/L(R2=0.993)with a detection limit of 30.68 μmol/L and spiked recoveries of 97.9%-104.7%.By integrating smartphone-based color recognition capabilities,a rapid visual detection platform was established for quantification of glucose through RGB analysis.The validation experimental results using commercial glucose injection samples further confirmed the practical application potential of this methodology.

Water-soluble silver nanoclusters(DHLA-AgNCs)with red fluorescence emission were synthesized using silver nitrate(AgNO3)as silver source,dihydrolipoic acid(DHLA)as ligand and sodium borohydride(NaBH4)as reducing agent by one-pot method.Based on the selective quenching of DHLA-AgNCs by tetracycline(TC),a rapid and selective fluorescence analysis method for detection of TC was constructed by monitoring the fluorescence intensity change at 650 nm.Under the optimal detection conditions,the fluorescence quenching efficiency of DHLA-AgNCs showed good linear relationship with concentration of TC within the range of 10.0-120.0 μmol/L,and the limit of detection(LOD)was 0.39 μmol/L.This method was successfully applied to detection of TC in milk samples,with spiked recoveries ranging from 99.5%to 102.5%,and relative standard deviations(RSDs)less than 5%.This method had the advantages of simplicity,rapidity,strong specificity and low cost,and thus provided a simple and feasible strategy for selective detection of TC.

Nano/microplastics(NMPs),due to their environmental persistence and resistance to degradation,have emerged as a major contributor to global pollution.NMPs are capable of adsorbing various hazardous chemicals and heavy metals,thereby posing threats to aquatic ecosystem health,which may ultimately cause potential risks to human health.Conventional analytical methods suffered from limited resolution,insufficient chemical information,or destruction of sample,invalidating these assays for on-site detection of NMPs.Surface-enhanced Raman scattering(SERS)offers distinct advantages such as high sensitivity,superior specificity,rich fingerprint information,and non-destructive analysis,thus facilitating the on-site analysis of NMPs in complex matrices.This review summarized recent advances in SERS substrates for detection of NMPs,discussed the construction and applications of SERS-based multimodal detection strategies,and introduced the research progress of SERS detection of NMPs in food safety,environmental pollution,and bioanalysis.Moreover,the main challenges and future directions of SERS-based NMP detection were outlined.

A portable molecularly imprinted(MIP)surface-enhanced Raman scattering(SERS)chip was fabricated via a green electrochemical approach for highly selective detection of bisphenol A(BPA).This MIP-AuNP/UIO-66/SPE sensor was fabricated through a single-step co-deposition process.The process involved electropolymerization onto a UIO-66 modified screen-printed electrode(SPE),by usingo-phenylenediamine(OPD)as functional monomer and BPA as template,and simultaneously electro-reduction generated gold nanoparticles(AuNPs),which served as the SERS-active substrate.Ultimately,this one-step method formed a three-dimensional porous architecture on the electrode surface.Under 785 nm laser excitation,the sensing chip exhibited a highly sensitive SERS response towards BPA.The intensity of its characteristic peak at 850 cm-1 showed a good linear relationship with logarithm of BPA concentration in the range of 1.0×10-10 to 1.0×10-6 mol/L,with a detection limit of 1.0×10-12 mol/L.More importantly,the fabricated chips maintained highly selective binding affinity for BPA in water samples even in the presence of structural analogs bisphenol F(BPF)and bisphenol S(BPS).When the chip was applied to detection of BPA in water samples from plastic bottle and paper cup,the recovery rates ranged from 94.0%to 103.0%with relative standard deviations(RSD)less than 4.7%.The developed chip offered a highly sensitive and selective solution for detection of trace BPA in complex water samples.

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.

Objective:To establish the quality standard of Galla Turcica standard decoction; To provide a reference for the quality evaluation of its decoction granules.Methods:The moisture, total ash, acid-insoluble ash, and extract of Galla Turcica were assessed according to the guidelines specified in the Chinese Pharmacopoeia (Volume Ⅳ); high-performance liquid chromatography (HPLC) was employed to quantify the gallic acid content in sixteen batches of Galla Turcica and standard decoction, from which the transfer rate was calculated; the paste rate, moisture, and extract yield were quantitatively analyzed, establishing the comprehensive fingerprint of Galla Turcica standard decoction. The quantitative value transfer rules of common peaks from medicinal materials to standard decoction were studied through similarity evaluation and stoichiometric analysis. Results:The gallic acid content of the sixteen batches of Galla Turcica standard decoction ranged from 76.79 mg/g to 115.04 mg/g, the range of transfer rate was 24.02% to 47.82%, the extraction rate was 34.04% to 63.23%, the moisture was 1.11% to 2.06%, and the extract was 94.44% to 101.75%. A total of nine characteristic peaks were identified in the fingerprints of the sixteen batches of Galla Turcica and standard decoction, and four characteristic peaks were identified: gallic acid (GA), 1, 2, 3, 6 - tetragalloylglucose (TeGG), ellagic acid (EA) and 1, 2, 3, 4, 6, - pentagalloylglucose (PGG). Clustering analysis revealed that at a measurement distance of fifteen, the sixteen batches could be grouped into two categories. Principal component analysis (PCA) showed that three principal components were extracted with a cumulative variance contribution rate of 84.204%, which could represent most of the information of the samples.Conclusions:The standard decoction preparation process developed in this study adheres to the requirements of traditional decoction methods and has proven to be both effective and feasible. This method can be used for the research and quality evaluation of standard decoction of Galla Turcica, and provide a basis for subsequent formulation granules and related research.

This study aims to explore the mechanism of lung and gut protection by Tanreqing Capsules on the mice infected with influenza virus based on "the lung-gut axis". A total of 110 C57BL/6J mice were randomized into control group, model group, oseltamivir group, and low-and high-dose Tanreqing Capsules groups. Ten mice in each group underwent body weight protection experiments, and the remaining 12 mice underwent experiments for mechanism exploration. Mice were infected with influenza virus A/Puerto Rico/08/1934(PR8) via nasal inhalation for the modeling. The lung tissue was collected on day 3 after gavage, and the lung tissue, colon tissue, and feces were collected on day 7 after gavage for subsequent testing. The results showed that Tanreqing Capsules alleviated the body weight reduction and increased the survival rate caused by PR8 infection. Compared with model group, Tanreqing Capsules can alleviate the lung injury by reducing the lung index, alleviating inflammation and edema in the lung tissue, down-regulating viral gene expression at the late stage of infection, reducing the percentage of neutrophils, and increasing the percentage of T cells. Tanreqing Capsules relieved the gut injury by restoring the colon length, increasing intestinal lumen mucin secretion, alleviating intestinal inflammation, and reducing goblet cell destruction. The gut microbiota analysis showed that Tanreqing Capsules increased species diversity compared with model group. At the phylum level, Tanreqing Capsules significantly increased the abundance of Firmicutes and Actinobacteria, while reducing the abundance of Bacteroidota and Proteobacteria to maintain gut microbiota balance. At the genus level, Tanreqing Capsules significantly increased the abundance of unclassified＿f＿Lachnospiraceae while reducing the abundance of Bacteroides, Eubacterium, and Phocaeicola to maintain gut microbiota balance. In conclusion, Tanreqing Capsules can alleviate mouse lung and gut injury caused by influenza virus infection and restore the balance of gut microbiota. Treating influenza from the lung and gut can provide new ideas for clinical practice.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Lung/metabolism* ; Mice, Inbred C57BL ; Capsules ; Orthomyxoviridae Infections/virology* ; Gastrointestinal Microbiome/drug effects* ; Male ; Humans ; Female ; Influenza A virus/physiology* ; Influenza, Human/virology*

Colorectal cancer(CRC) is one of the most common malignant tumors worldwide, primarily originating from recurrent inflammatory bowel disease(IBD). Therefore, blocking the inflammation-cancer transformation in the colon has become a focus in the early prevention and treatment of CRC. The inflammation-cancer transformation in the colon involves multiple types of cells and complex pathological processes, including inflammatory responses and tumorigenesis. In this complex pathological process, immune cells(including non-specific and specific immune cells) and non-immune cells(such as tumor cells and fibroblasts) interact with each other, collectively promoting the progression of the disease. In traditional Chinese medicine(TCM), inflammation-cancer transformation in the colon belongs to the categories of dysentery and diarrhea, with the main pathogenesis being cold and heat in complexity. This paper first elaborates on the complex molecular mechanisms involved in the inflammation-cancer transformation process in the colon from the perspectives of inflammation, cancer, and their mutual influences. Subsequently, by comparing the pathogenic characteristics and clinical manifestations between inflammation-cancer transformation and the TCM pathogenesis of cold and heat in complexity, this paper explores the intrinsic connections between the two. Furthermore, based on the correlation between inflammation-cancer transformation in the colon and the TCM pathogenesis, this paper delves into the importance of the interaction between inflammation and cancer. Finally, it summarizes and discusses the clinical and basic research progress in the TCM intervention in the inflammation-cancer transformation process, providing a theoretical basis and treatment strategy for the treatment of CRC with integrated traditional Chinese and Western medicine.
Humans ; Colon/pathology* ; Integrative Medicine ; Animals ; Cold Temperature ; Cell Transformation, Neoplastic/drug effects* ; Medicine, Chinese Traditional ; Hot Temperature ; Inflammation ; Drugs, Chinese Herbal/therapeutic use* ; Colonic Neoplasms/drug therapy*

The medicinal materials of Bawei Chenxiang Pills(BCPs) were extracted via three methods: reflux extraction by water, reflux extraction by 70% ethanol, and extraction by pure water following reflux extraction by 70% ethanol, yielding three extracts of ST, CT, and CST. The efficacy of ST(760 mg·kg~(-1)), CT(620 mg·kg~(-1)), and CST(1 040 mg·kg~(-1)) were evaluated by acute myocardial ischemia(AMI) and p-chlorophenylalanine(PCPA)-induced insomnia in mice, respectively. Western blot was further utilized to investigate their hypnosis mechanisms. The main chemical components of different extracts were identified by the UPLC-Q-Exactive-MS technique. The results showed that CT and CST significantly increased the ejection fraction(EF) and fractional shortening(FS) of myocardial infarction mice, reduced left ventricular internal dimension at end-diastole(LVIDd) and left ventricular internal dimension at end-systole(LVIDs). In contrast, ST did not exhibit significant effects on these parameters. In the insomnia model, CT significantly reduced sleep latency and prolonged sleep duration, whereas ST only prolonged sleep duration without shortening sleep latency. CST showed no significant effects on either sleep latency or sleep duration. Additionally, both CT and ST upregulated glutamic acid decarboxylase 67(GAD67) protein expression in brain tissue. A total of 15 main chemical components were identified from CT, including 2-(2-phenylethyl) chromone and 6-methoxy-2-(2-phenylethyl) chromone. Six chemical components including chebulidic acid were identified from ST. The results suggested that chromones and terpenes were potential anti-myocardial ischemia drugs of BCPs, and tannin and phenolic acids were potential hypnosis drugs. This study enriches the pharmacological and chemical research of BCPs, providing a basis and reference for their secondary development, quality standard improvement, and clinical application.
Animals ; Drugs, Chinese Herbal/isolation & purification* ; Mice ; Male ; Sleep Initiation and Maintenance Disorders/physiopathology* ; Humans ; Myocardial Infarction/drug therapy* ; Myocardial Ischemia/drug therapy*