Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

Objective This research was performed to identify rodent-borne pathogens in Hekou Port,Yunnan Province.Methods Rodents were captured using cages and dissected to collect their lungs,liver,spleen,and other viscera.Eight pathogens,including Yersinia pestis,Leptospira,Bartonella,and Anaplasmataceae,were identified using polymerase chain reaction amplification.Amplified pathogen sequences from positive samples were sequenced,and BLAST homology searches were conducted using GenBank to confirm pathogen identities.A phylogenetic tree of the identified pathogens was constructed using the neighbor joining method.Results The total of 31 rodents,identified as Rattus tanezumi,R.norvegicus,and Mus musculus,were captured.Among these,R.tanezumi was the dominant species,accounting for 64.52％of the total.Two pathogens,Leptospira interrogans and Neoehrlichia mikurensis,were detected,with positivity rates of 9.68％and 29.03％,respectively.No other pathogens were detected.The overall positivity rate for rodent-borne pathogens was 35.48％.Conclusions The single 16S rRNA gene fragment is insufficient for the molecular identification of all Neoehrlichia species.Accurate species identification should be based on a combined analysis of multiple genes.The prevalence of rodent-borne pathogens in Hekou Port indicates the necessity for enhanced surveillance of rodent-borne diseases and implementation of additional prevention and control measures in border ports.

The presence of a positive deep surgical margin in tongue squamous cell carcinoma (TSCC) significantly elevates the risk of local recurrence. Therefore, a prompt and precise intraoperative assessment of margin status is imperative to ensure thorough tumor resection. In this study, we integrate Raman imaging technology with an artificial intelligence (AI) generative model, proposing an innovative approach for intraoperative margin status diagnosis. This method utilizes Raman imaging to swiftly and non-invasively capture tissue Raman images, which are then transformed into hematoxylin-eosin (H&E)-stained histopathological images using an AI generative model for histopathological diagnosis. The generated H&E-stained images clearly illustrate the tissue's pathological conditions. Independently reviewed by three pathologists, the overall diagnostic accuracy for distinguishing between tumor tissue and normal muscle tissue reaches 86.7%. Notably, it outperforms current clinical practices, especially in TSCC with positive lymph node metastasis or moderately differentiated grades. This advancement highlights the potential of AI-enhanced Raman imaging to significantly improve intraoperative assessments and surgical margin evaluations, promising a versatile diagnostic tool beyond TSCC.
Humans ; Spectrum Analysis, Raman/methods* ; Tongue Neoplasms/diagnostic imaging* ; Carcinoma, Squamous Cell/diagnostic imaging* ; Artificial Intelligence ; Margins of Excision

Inflammatory diseases (IDs) are a general term of diseases characterized by chronic inflammation as the primary pathogenetic mechanism, which seriously affect the quality of patient′s life and cause significant social and medical burden. Current drugs for IDs include nonsteroidal anti-inflammatory drugs, corticosteroids, immunomodulators, biologics, and antioxidants, but these drugs may cause gastrointestinal side effects, induce or worsen infections, and cause non-response or intolerance. Given the outstanding performance of metal polyphenol network (MPN) in the fields of drug delivery, biomedical imaging, and catalytic therapy, its application in the diagnosis and treatment of IDs has attracted much attention and significant progress has been made. In this paper, we first provide an overview of the types of IDs and their generating mechanisms, then sort out and summarize the different forms of MPN in recent years, and finally discuss in detail the characteristics of MPN and their latest research progress in the diagnosis and treatment of IDs. This research may provide useful references for scientific research and clinical practice in the related fields.

Five saponins were isolated from the kernels of Momordica cochinchinensis, by macroporous resin, silica gel, ODS column chromatography, and semi preparative HPLC. Based on MS and NMR analysis, combining with alkaline hydrolysis and acid hydrolysis, their structures were identified as: gypsogenin-3-O-{β-D-galactopyranosyl (1→2)-[α-L-rhamnopyranosyl (1→3)]-β-D-glucuropyranonosyl}-28-O-β-D-xylopyranosyl (1→3)-[β-D-xylopyranosyl (1→4)]-α-L-rhamnopyranosyl (1→2)-β-D-fucopyranoside (1), quillaic acid-3-O-{β-D-galactopyranosyl (1→2)-[α-L-rhamnopyranosyl (1→3)]-β-D-glucuropyranonosyl}-28-O-β-D-xylopyranosyl (1→3)-[β-D-xylopyranosyl (1→4)]-α-L-rhamnopyranosyl (1→2)-β-D-fucopyranoside (2), gypsogenin-3-O-β-D-galactopyranosyl (1→2)-[α-L-rhamnopyranosyl (1→3)]-β-D-glucuropyranonoside sodium (3), quillaic acid-3-O-β-D-galactopyranosyl (1→2)-[α-L-rhamnopyranosyl (1→3)]-β-D-glucuropyranonoside sodium (4), 18α-quillaic acid-3-O-β-D-galactopyranosyl (1→2)-[α-L-rhamnopyranosyl (1→3)]-β-D-glucuropyranonoside (5). Compounds 1-5 were new compounds, and named as mubezhisides A, B, C, D, E, respectively. They all could obviously inhibited the growth of Candida albicans, C. parapsilosis, and C. tropicalis.

Biomolecular condensates are formed through phase separation of biomacromolecules such as proteins and RNAs. These condensates exhibit liquid-like properties that can futher transition into more stable material states. They form complex internal structures via multivalent weak interactions, enabling precise spatiotemporal regulations. However, the use of inconsistent and non-standardized terminology has become increasingly problematic, hindering academic exchange and the dissemination of scientific knowledge. Therefore, it is necessary to discuss the terminology related to biomolecular condensates in order to clarify concepts, promote interdisciplinary cooperation, enhance research efficiency, and support the healthy development of this field.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

Objective : To investigate the prevalence of negative emotions in hospitalized youth patients with type 2 diabetes(T2DM) and its correlation with coping strategies and psychological resilience. Methods : 141 youth T2DM patients who met the research standards were selected. Blood glucose related indicators, blood pressure, body mass index(BMI), diabetes chronic complications screening results and other data were collected. The basic information and disease related information questionnaire, self-rating depression scale(SDS), self-rating anxiety scale(SAS), diabetes distress scale(DDS), medical coping modes questionnaire(MCMQ) and Connor-Davidson resilience scale(CD-RISC) were completed. Results: Among 141 hospitalized youth T2DM patients, 37.6% were combined with depression, 32.6% were combined with anxiety, and 35.5% were combined with diabetic distress(DD). Univariate analysis showed that systolic blood pressure(P<0.01), educational level, and the form of hospitalization expenses(P<0.05) were significantly correlated with depression. Marital status(P<0.01), family residence, blood glucose monitoring methods, and the last fasting blood glucose(P<0.05) were significantly correlated with anxiety. BMI, whether it was first diagnosed or treated(P<0.01), gender, occupation, disease course, weekly blood glucose monitoring frequency, and the presence of chronic complications(P<0.05) were significantly correlated with DD. In multivariate analysis, systolic blood pressure(P<0.01), educational level, and the form of hospitalization expenses were significantly correlated with depression, marital status(P<0.05) was significantly correlated with anxiety; BMI and weekly blood glucose monitoring frequency(P<0.01) were significantly correlated with DD. SDS, SAS, total scores and dimensions of DDS were negatively correlated with the total score and dimensions of CD-RISC(rs=-0.182--0.467, P<0.05 or 0.01), and positively correlated with the yielding coping strategies(rs=0.177-0.271,P<0.05 or 0.01). SAS,total scores and dimensions of DDS were positively correlated with avoiding coping strategies(rs=0.237-0.419,P<0.05 or 0.01). The total and dimensions of CD-RISC were positively correlated with facing coping strategies(rs=0.215-0.349,P<0.05 or 0.01),and negatively correlated with yielding coping strategies(rs=-0.234--0.325,P<0.01). Conclusion More than 30% of hospitalized youth T2DM may experience negative emotions such as depression,anxiety,and DD. The occurrence of negative emotions in such patients may be related to disease management or socio-economic issues such as systolic blood pressure,educational level,hospitalization expenses,marital status,BMI,and frequency of blood glucose monitoring,as well as decreased psychological resilience and negative coping strategies.

BACKGROUND: Death burden of stroke is severe with over one-third rural residents in China, but there is still a lack of specific national and high-quality reports on the urban-rural differences in stroke burden, especially for subtypes. We aimed to update the understanding of urban-rural differences in stroke deaths. METHODS: This is a descriptive observational study. Data from the national mortality surveillance system, which covers 323.8 million with 605 disease surveillance points (DSPs) across all 31 provinces, municipalities, and autonomous regions in China. All deaths from stroke as the underlying cause from 2015 to 2020 according to DSPs. Crude mortality rate and age-standardized mortality rate (ASMR) were estimated through DSPs. Average annual percentage change was used to explain the change in mortality rate. RESULTS: From 2015 to 2020, the majority of deaths from all stroke subtypes occurred in rural areas. There were significant differences between the changes of urban and rural ASMRs. On the whole, the changes in urban areas were evidently better, and the ASMR differences were basically expanding. Stroke ASMR in urban China decreased by 15.5%. The rural ASMR of ischemic stroke increased by 12.9%. The rural and urban ASMRs of intracerebral hemorrhage decreased by 24.9% and 27.4%, and those of subarachnoid hemorrhage decreased by 29.5% and 40.4%, respectively. The highest ASMRs of all stroke subtypes and the increasing trend of ischemic stroke ASMR make rural males the focus of stroke management. CONCLUSIONS The death burden of stroke varies greatly between urban and rural China. Rural residents face unique challenges.
Humans ; China/epidemiology* ; Stroke/mortality* ; Rural Population/statistics & numerical data* ; Male ; Female ; Urban Population/statistics & numerical data* ; Middle Aged ; Aged ; Aged, 80 and over ; Adult