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: Negative symptoms in schizophrenia indicate a poor prognosis. However, the mechanisms underlying the development of negative symptoms remain unclear. This study investigated the relationship between negative symptoms in schizophrenia and frontal alpha asymmetry (FAA). Methods: The study used a 32-channel electroencephalography to acquire alpha power in 4 target-paired sites in each patient. Regional alpha asymmetry was calculated based on the alpha power using EEGLAB Frontal Alpha Asymmetry Toolbox. Results: Sixty schizophrenia patients with predominant negative symptoms (PNS), 72 stabilized schizophrenia (SS) patients, and 73 healthy control (HC) participants were enrolled in this study. No significant differences were observed in FAA between the PNS and SS groups, although both groups exhibited reduced P3-P4 alpha asymmetry compared to HCs. A positive correlation was found between F7-F8 alpha asymmetry and illness duration. Additionally, a predictive model based on P3-P4 alpha asymmetry scores was able to differentiate schizophrenia patients from HCs, achieving a sensitivity of 71.2% and a specificity of 72.6%. Conclusion This study highlighted that parietal alpha asymmetry could serve as a valuable diagnostic tool for schizophrenia.

Objective To investigate the correlation between spread through air space (STAS) of sub-centimeter non-small cell lung cancer and clinical characteristics and radiological features, constructing a nomogram risk prediction model for STAS to provide a reference for the preoperative planning of sub-centimeter non-small cell lung cancer patients. Methods The data of patients with sub-centimeter non-small cell lung cancer who underwent surgical treatment in Nanjing Drum Tower Hospital from January 2022 to October 2023 were retrospectively collected. According to the pathological diagnosis of whether the tumor was accompanied with STAS, they were divided into a STAS positive group and a STAS negative group. The clinical and radiological data of the two groups were collected for univariate logistic regression analysis, and the variables with statistical differences were included in the multivariate analysis. Finally, independent risk factors for STAS were screened out and a nomogram model was constructed. The sensitivity and specificity were calculated based on the Youden index, and area under the curve (AUC), calibration plots and decision curve analysis (DCA) were used to evaluate the performance of the model. Results A total of 112 patients were collected, which included 17 patients in the STAS positive group, consisting of 11 males and 6 females, with a mean age of (59.0±10.3) years. The STAS negative group included 95 patients, with 30 males and 65 females, and a mean age of (56.8±10.3) years. Univariate logistic regression analysis showed that male, anti-GAGE7 antibody positive, mean CT value and spiculation were associated with the occurrence of STAS (P＜0.05). Multivariate regression analysis showed that associations between STAS and male (OR=5.974, 95%CI 1.495 to 23.872), anti-GAGE7 antibody positive (OR=11.760, 95%CI 1.619 to 85.408) and mean CT value (OR=1.008, 95%CI 1.004 to 1.013) were still significant (P＜0.05), while the association between STAS and spiculation was not significant anymore (P=0.438). Based on the above three independent predictors, a nomogram model of STAS in sub-centimeter non-small cell lung cancer was constructed. The AUC value of the model was 0.890, the sensitivity was 76.5%, and the specificity was 91.6%. The calibration curve was well fitted, suggesting that the model had a good prediction efficiency for STAS. The DCA plot showed that the model had a good clinically utility. Conclusion Male, anti-GAGE7 antibody positive and mean CT value are independent predictors of STAS positivity of sub-centimeter non-small cell lung cancer, and the nomogram model established in this study has a good predictive value and provides reference for preoperative planning of patients.

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.

The alternative pathway (AP) of the complement system is a key contributor to the pathogenesis of several diseases including paroxysmal nocturnal hemoglobinuria (PNH), atypical hemolytic uremic syndrome (aHUS), C3 glomerular disease (C3G) and age-related macular degeneration (AMD). Complement factor B (CFB) is a trypsin-like serine protein that circulates in the human bloodstream in a latent form. As a key node of the alternative pathway, it is an important target for the treatment of diseases mediated by the complement system. With the successful launch of iptacopan, the CFB small molecule inhibitors has become a current research hotspot, a number of domestic and foreign pharmaceutical companies are actively developing CFB small molecule inhibitors. In this paper, the research progress of CFB small molecule inhibitors in recent years is systematically summarized, the representative compounds and their activities are introduced according to structural types and design ideas, so as to provide reference and ideas for the subsequent research on CFB small molecule inhibitors.

ObjectiveTo observe the clinical effect of modified Huanglian Wendantang on cardiovascular risk factors in patients with metabolic syndrome under the guidance of treating disease before its onset. MethodsA total of 82 patients with metabolic syndrome treated in the First Affiliated Hospital of Heilongjiang University of Chinese Medicine from July 2023 to July 2024 were selected and allocated into an observation group （41 cases） and a control group （41 cases） by the random number table method. The control group received routine treatment， and the observation group was treated with modified Huanglian Wendantang on the basis of routine treatment. Both groups were treated for 8 weeks. The therapeutic effects on TCM symptoms after treatment in the two groups were evaluated. The levels of obesity degree indicators， blood pressure indicators， glucose and lipid metabolism indicators， inflammatory factors， and vascular endothelial function indicators before and after treatment in the two groups were measured， and the treatment safety was evaluated. ResultsAfter treatment， the total response rate of TCM symptoms in the observation group was 97.56% （40/41）， which was higher than that （87.80%， 36/41） in the control group （χ²=5.205， P<0.05）. After treatment， both groups showed declines （P<0.05） in systolic blood pressure （SBD）， diastolic blood pressure （DBP）， triglyceride （TG）， total cholesterol （TC）， low density lipoprotein cholesterol （LDL-C）， fasting blood glucose， 2-hour postprandial blood glucose （2 h PG）， glycosylated hemoglobin （HbA1c）， fasting insulin （FINS）， Homeostatic Model Assessment for Insulin Resistance （HOMA-IR）， body mass index （BMI）， waist circumference （WC）， waist-to-hip ratio （WHR）， leptin （LEP）， interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， endothelin-1 （ET-1）， and inducible nitric oxide synthase （iNOS）. Moreover， the declines in the observation group were more obvious than those in the control group （P<0.05， P<0.01）. After treatment， both groups showed elevated levels of high density lipoprotein cholesterol （HDL-C）， adiponectin （ADP）， nitric oxide （NO）， and endothelial nitric oxide synthase （eNOS） （P<0.05）， and the above indexes in the observation group were higher than those in the control group （P<0.01）. ConclusionUnder the guidance of the thought of treating disease before its onset， modified Huanglian Wendantang was used to treat patients with metabolic syndrome. The decoction improved the clinical efficacy by ameliorating IR to improve insulin sensitivity， reducing inflammation， and protecting the vascular endothelial function. It inhibits cardiovascular risk factors without inducing adverse reactions， being worthy of clinical application and promotion.

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: Negative symptoms in schizophrenia indicate a poor prognosis. However, the mechanisms underlying the development of negative symptoms remain unclear. This study investigated the relationship between negative symptoms in schizophrenia and frontal alpha asymmetry (FAA). Methods: The study used a 32-channel electroencephalography to acquire alpha power in 4 target-paired sites in each patient. Regional alpha asymmetry was calculated based on the alpha power using EEGLAB Frontal Alpha Asymmetry Toolbox. Results: Sixty schizophrenia patients with predominant negative symptoms (PNS), 72 stabilized schizophrenia (SS) patients, and 73 healthy control (HC) participants were enrolled in this study. No significant differences were observed in FAA between the PNS and SS groups, although both groups exhibited reduced P3-P4 alpha asymmetry compared to HCs. A positive correlation was found between F7-F8 alpha asymmetry and illness duration. Additionally, a predictive model based on P3-P4 alpha asymmetry scores was able to differentiate schizophrenia patients from HCs, achieving a sensitivity of 71.2% and a specificity of 72.6%. Conclusion This study highlighted that parietal alpha asymmetry could serve as a valuable diagnostic tool for schizophrenia.

Objective: Negative symptoms in schizophrenia indicate a poor prognosis. However, the mechanisms underlying the development of negative symptoms remain unclear. This study investigated the relationship between negative symptoms in schizophrenia and frontal alpha asymmetry (FAA). Methods: The study used a 32-channel electroencephalography to acquire alpha power in 4 target-paired sites in each patient. Regional alpha asymmetry was calculated based on the alpha power using EEGLAB Frontal Alpha Asymmetry Toolbox. Results: Sixty schizophrenia patients with predominant negative symptoms (PNS), 72 stabilized schizophrenia (SS) patients, and 73 healthy control (HC) participants were enrolled in this study. No significant differences were observed in FAA between the PNS and SS groups, although both groups exhibited reduced P3-P4 alpha asymmetry compared to HCs. A positive correlation was found between F7-F8 alpha asymmetry and illness duration. Additionally, a predictive model based on P3-P4 alpha asymmetry scores was able to differentiate schizophrenia patients from HCs, achieving a sensitivity of 71.2% and a specificity of 72.6%. Conclusion This study highlighted that parietal alpha asymmetry could serve as a valuable diagnostic tool for schizophrenia.

Objective: Negative symptoms in schizophrenia indicate a poor prognosis. However, the mechanisms underlying the development of negative symptoms remain unclear. This study investigated the relationship between negative symptoms in schizophrenia and frontal alpha asymmetry (FAA). Methods: The study used a 32-channel electroencephalography to acquire alpha power in 4 target-paired sites in each patient. Regional alpha asymmetry was calculated based on the alpha power using EEGLAB Frontal Alpha Asymmetry Toolbox. Results: Sixty schizophrenia patients with predominant negative symptoms (PNS), 72 stabilized schizophrenia (SS) patients, and 73 healthy control (HC) participants were enrolled in this study. No significant differences were observed in FAA between the PNS and SS groups, although both groups exhibited reduced P3-P4 alpha asymmetry compared to HCs. A positive correlation was found between F7-F8 alpha asymmetry and illness duration. Additionally, a predictive model based on P3-P4 alpha asymmetry scores was able to differentiate schizophrenia patients from HCs, achieving a sensitivity of 71.2% and a specificity of 72.6%. Conclusion This study highlighted that parietal alpha asymmetry could serve as a valuable diagnostic tool for schizophrenia.