ObjectiveA qualitative analysis method was established for the composition of Astragali Radix（AR） before and after rice stir-frying. On the basis of systematic characterization of the chemical compositions in AR and stir-fried AR with rice（ARR）， the structures of their major compounds were deduced and identified， and the differential compositions between them were analyzed. MethodsUltra performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS/MS） was used to detect the samples of AR and ARR in positive and negative ion modes， respectively. The compounds were analyzed and identified through self-constructed databases， literature， and reference standards， etc. And the data were analyzed by chemometrics， in order to screen for the differential components between AR and ARR. ResultsA total of 123 compounds were identified in AR and ARR， including 41 flavonoids， 19 terpenoids， 26 organic acids， 8 amino acids， 5 nucleotides， 5 carbohydrates and 19 other compounds. Among them， there were 95 common components in both， 18 unique components in AR， and 10 unique components in ARR. Principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA） results both showed that there were significant differences in the chemical constituents of AR before and after rice stir-frying， and a total of 26 constituents with differences in the content were screened out， including L-canavanine， L-pyroglutamic acid， L-phenylalanine， cis-caffeic acid， and malonylastragaloside Ⅰ. Among them， 19 constituents of ARR were down-regulated and 7 constituents were up-regulated by comparing with AR. ConclusionThis study clarifies that the chemical composition of AR and ARR is mainly composed of flavonoids， terpenoids， and organic acids， and analyzes the components with significant differences in content between the two in combination with chemometrics， and the differential components are dominated by amino acids， organic acids and terpenoids， which can provide reference for the subsequent quality control and material basis research.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

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.

Clinical practice guidelines represent the best recommendations for patient care. They are developed through systematically reviewing currently available clinical evidence and weighing the relative benefits and risks of various interventions. However, clinical practice guidelines have to go through a long translation cycle from development and revision to clinical promotion and application, facing problems such as scattered distribution, high duplication rate, and low actual utilization. At present, the clinical practice guideline information platform can directly or indirectly solve the problems related to the lengthy revision cycles, decentralized dissemination and limited application of clinical practice guidelines. Therefore, this paper systematically examines different types of clinical practice guideline information platforms and investigates their corresponding challenges and emerging trends in platform design, data integration, and practical implementation, with the aim of clarifying the current status of this field and providing valuable reference for future research on clinical practice guideline information platforms.

ObjectiveTo investigate the value of third lumbar skeletal muscle mass index （L3-SMI） in predicting the long-term prognosis of patients with acute-on-chronic liver failure （ACLF）， and to provide a useful tool for prognostic scoring of ACLF patients. MethodsA retrospective analysis was performed for the data of 126 patients who underwent abdominal computed tomography （CT） scanning and were diagnosed with ACLF in Shanxi Bethune Hospital from December 2017 to December 2021， including clinical indicators， biochemical parameters， and model for end-stage liver disease （MELD） score， and L3-SMI was calculated. The independent-samples t test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups； the chi-square test was used for comparison of categorical data between groups. The receiver operating characteristic （ROC） curve was used to assess the diagnostic value of L3-SMI and other variables （MELD score and Child-Pugh score）， and the DeLong test was used for comparison of the area under the ROC curve （AUC）. ResultsAmong the 126 patients enrolled， 44 （35%） died within 2 years and 82 （65%） survived. Compared with the survival group， the death group had significantly higher age， incidence rate of ascites， international normalized ratio， MELD score， and Child-Pugh score （all P<0.05） and a significantly lower value of L3-SMI ［38.40 （35.95‍ ‍—‍ ‍46.29） cm²/m² vs 44.19 （40.20‍ ‍—‍ ‍48.58） cm²/m²， Z=-2.855， P=0.004］. L3-SMI had an AUC of 0.720 in predicting 2-year mortality in ACLF patients， with a sensitivity of 63.6% and a specificity of 80.5%， and a combination of L3-SMI， MELD score， and Child-Pugh score had a significantly better AUC than a combination of MELD score and Child-Pugh score in predicting 2-year mortality （0.809 vs 0.757， Z=2.015， P<0.05）. ConclusionL3-SMI has a high predictive value for the prognosis of ACLF patients， and the combination of L3-SMI、MELD score and Child-Pugh score has a higher predictive value for ACLF patients， and the inclusion of L3-SMI or sarcopenia in the conventional prognostic scores of ACLF patients may increase the ability to predict disease progression.

Multiple system atrophy (MSA) is a rare neurodegenerative disease with complex clinical manifestations, presenting substantial challenges in clinical diagnosis and treatment. Its symptoms and the eight extraordinary meridians are potentially correlated; therefore, this article explores the association between MSA symptom clusters and the eight extraordinary meridians based on their circulation and physiological functions, as well as their treatment strategies. The progression from deficiency to damage in the eight extraordinary meridians aligns with the core pathogenesis of MSA, which is characterized by "the continuous accumulation of impacts from the vital qi deficiency leading to eventual damage". Liver and kidney deficiency and the emptiness of the eight extraordinary meridians are required for the onset of MSA; the stagnation of qi deficiency and the gradual damage to the eight extraordinary meridians are the key stages in the prolonged progression of MSA. The disease often begins with the involvement of the yin and yang qiao mai, governor vessel, thoroughfare vessel, and conception vessel before progressing to multiple meridian involvements, ultimately affecting all eight extraordinary meridians simultaneously. The treatment approach emphasizes that "the direct method may be used for joining battle, but indirect method will be needed in order to secure victory" and focuses on "eliminate pathogenic factors and reinforce healthy qi". Distinguishing the extraordinary meridians and focusing on the primary symptoms are pivotal to improving efficacy. Clinical treatment is aimed at the target, and tailored treatment based on careful clinical observation ensures precision in targeting the disease using the eight extraordinary meridians as the framework and core symptoms as the specific focus. Additionally, combining acupuncture, daoyin therapy, and other method may help prolong survival. This article classifies clinical manifestations based on the theory of the eight extraordinary meridians and explores treatment.

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.