Electroencephalogram (EEG) is a non-invasive, high temporal-resolution technique for monitoring brain activity. However, affected by the volume conduction effect, EEG has a low spatial resolution and is difficult to locate brain neuronal activity precisely. The surface Laplacian (SL) technique obtains the Laplacian EEG (LEEG) by estimating the second-order spatial derivative of the scalp potential. LEEG can reflect the radial current activity under the scalp, with positive values indicating current flow from the brain to the scalp (“source”) and negative values indicating current flow from the scalp to the brain (“sink”). It attenuates signals from volume conduction, effectively improving the spatial resolution of EEG, and is expected to contribute to breakthroughs in neural engineering. This paper provides a systematic overview of the principles and development of SL technology. Currently, there are two implementation paths for SL technology: current source density algorithms (CSD) and concentric ring electrodes (CRE). CSD performs the Laplace transform of the EEG signals acquired by conventional disc electrodes to indirectly estimate the LEEG. It can be mainly classified into local methods, global methods, and realistic Laplacian methods. The global method is the most commonly used approach in CSD, which can achieve more accurate estimation compared with the local method, and it does not require additional imaging equipment compared with the realistic Laplacian method. CRE employs new concentric ring electrodes instead of the traditional disc electrodes, and measures the LEEG directly by differential acquisition of the multi-ring signals. Depending on the structure, it can be divided into bipolar CRE, quasi-bipolar CRE, tripolar CRE, and multi-pole CRE. The tripolar CRE is widely used due to its optimal detection performance. While ensuring the quality of signal acquisition, the complexity of its preamplifier is relatively acceptable. Here, this paper introduces the study of the SL technique in resting rhythms, visual-related potentials, movement-related potentials, and sensorimotor rhythms. These studies demonstrate that SL technology can improve signal quality and enhance signal characteristics, confirming its potential applications in neuroscientific research, disease diagnosis, visual pathway detection, and brain-computer interfaces. CSD is frequently utilized in applications such as neuroscientific research and disease detection, where high-precision estimation of LEEG is required. And CRE tends to be used in brain-computer interfaces, that have stringent requirements for real-time data processing. Finally, this paper summarizes the strengths and weaknesses of SL technology and envisages its future development. SL technology boasts advantages such as reference independence, high spatial resolution, high temporal resolution, enhanced source connectivity analysis, and noise suppression. However, it also has shortcomings that can be further improved. Theoretically, simulation experiments should be conducted to investigate the theoretical characteristics of SL technology. For CSD methods, the algorithm needs to be optimized to improve the precision of LEEG estimation, reduce dependence on the number of channels, and decrease computational complexity and time consumption. For CRE methods, the electrodes need to be designed with appropriate structures and sizes, and the low-noise, high common-mode rejection ratio preamplifier should be developed. We hope that this paper can promote the in-depth research and wide application of SL technology.

ObjectiveTo optimize Tusizi prescription for ovarian reserve function based on the uniform design method combined with in vitro experiments and explore the underlying mechanisms of this prescription. MethodsThe uniform design method was adopted to design a 5-factor 11-level experiment on the water extract of Tusizi prescription. The cell-counting kit-8 （CCK-8） assay was employed to measure the viability of human ovarian granulosa cells （KGN cells） treated with Tusizi prescription extracts 1-11， and multivariate regression analysis was performed to determine the optimal herb ratio in this prescription. The potential targets of active ingredients in the prescription were retrieved from traditional Chinese medicine systems pharmacology database and analysis platform （TCMSP） and encyclopedia of traditional Chinese medicine （ETCM）. The common targets shared by Tusizi prescription and diminished ovarian reserve （DOR） were selected and imported into search tool for the retrieval of interacting genes/proteins （STRING） to construct a protein-protein interaction （PPI） network and into gene function annotation database （DAVID） for gene ontology （GO） analysis. The CCK-8 assay was used to measure the viability of ovarian germline stem cells treated with hyperoside. The CCK-8 assay， 5-ethynyl-2'-deoxyuridine （EdU） staining， terminal-deoxynucleoitidyl transferase mediated nick-end labeling （TUNEL）， and enzyme-linked immunosorbent assay （ELISA） were employed to examine the proliferation， apoptosis， and estradiol （E₂） secretion of KGN cells treated with the water extract 11 of Tusizi prescription （Cuscutae Semen-Lycii Fructus-Dioscoreae Rhizoma-Poria-Nelumbinis Semen 4∶4∶2∶1∶1） and the optimal prescription screened by uniform design. On this basis， the optimal prescription composition for maximizing the effect on ovarian reserve function was determined and preliminary insights into the underlying mechanisms of this prescription were gained. ResultsA total of 147 common targets were obtained from 278 targets of Tusizi prescription and 1 721 targets of DOR. GO analysis revealed 194 biological processes， primarily involving cellular responses to exogenous compound stimuli， negative regulation of apoptotic process， and positive regulation of cell proliferation. It identified 84 cellular components， including cell membrane， mitochondria， and neuronal cell body， as well as 144 molecular functions such as enzyme binding， estrogen response element binding， and nuclear estrogen receptor binding. The multivariate regression analysis revealed that when Tusizi prescription was composed of Cuscutae Semen， Lycii Fructus， Dioscoreae Rhizoma， Poria， and Nelumbinis Semen in a ratio of 27∶30∶17∶12∶14， the water extract of Tusizi prescription had the best effect of enhancing the viability of KGN cells. CCK-8 results showed that compared with the normal group， the hyperoside group demonstrated increased viability of ovarian germline stem cells （P<0.01）. The CCK-8， EdU， and ELISA results showed that compared with the normal group， the optimal prescription screened by uniform design and the water extract 11 of Tusizi prescription increased the proliferation and reduced the apoptosis of KGN cells （P<0.05， P<0.01）. ELISA results showed that compared with the normal group， the water extract 11 of Tusizi prescription promoted the E₂ secretion of KGN cells （P<0.05）， while the optimal prescription screened by uniform design had no significant effect on the E₂ secretion. ConclusionBoth the water extract 11 of Tusizi prescription （Cuscutae Semen-Lycii Fructus-Dioscoreae Rhizoma-Poria-Nelumbinis Semen 4∶4∶2∶1∶1） and the optimal prescription screened by uniform design （Cuscutae Semen-Lycii Fructus-Dioscoreae Rhizoma-Poria-Nelumbinis Semen 27∶30∶17∶12∶14） can improve the ovarian reserve function， and the former has better effect. Tusizi prescription can modulate biological processes （such as cell proliferation and apoptosis） and molecular functions （such as enzyme binding and estrogen response element binding） through active components like hyperoside to promote the proliferation and E₂ secretion and inhibit the apoptosis of KGN cells， thereby protecting the ovarian reserve function.

ObjectiveTo optimize Tusizi prescription for ovarian reserve function based on the uniform design method combined with in vitro experiments and explore the underlying mechanisms of this prescription. MethodsThe uniform design method was adopted to design a 5-factor 11-level experiment on the water extract of Tusizi prescription. The cell-counting kit-8 （CCK-8） assay was employed to measure the viability of human ovarian granulosa cells （KGN cells） treated with Tusizi prescription extracts 1-11， and multivariate regression analysis was performed to determine the optimal herb ratio in this prescription. The potential targets of active ingredients in the prescription were retrieved from traditional Chinese medicine systems pharmacology database and analysis platform （TCMSP） and encyclopedia of traditional Chinese medicine （ETCM）. The common targets shared by Tusizi prescription and diminished ovarian reserve （DOR） were selected and imported into search tool for the retrieval of interacting genes/proteins （STRING） to construct a protein-protein interaction （PPI） network and into gene function annotation database （DAVID） for gene ontology （GO） analysis. The CCK-8 assay was used to measure the viability of ovarian germline stem cells treated with hyperoside. The CCK-8 assay， 5-ethynyl-2'-deoxyuridine （EdU） staining， terminal-deoxynucleoitidyl transferase mediated nick-end labeling （TUNEL）， and enzyme-linked immunosorbent assay （ELISA） were employed to examine the proliferation， apoptosis， and estradiol （E₂） secretion of KGN cells treated with the water extract 11 of Tusizi prescription （Cuscutae Semen-Lycii Fructus-Dioscoreae Rhizoma-Poria-Nelumbinis Semen 4∶4∶2∶1∶1） and the optimal prescription screened by uniform design. On this basis， the optimal prescription composition for maximizing the effect on ovarian reserve function was determined and preliminary insights into the underlying mechanisms of this prescription were gained. ResultsA total of 147 common targets were obtained from 278 targets of Tusizi prescription and 1 721 targets of DOR. GO analysis revealed 194 biological processes， primarily involving cellular responses to exogenous compound stimuli， negative regulation of apoptotic process， and positive regulation of cell proliferation. It identified 84 cellular components， including cell membrane， mitochondria， and neuronal cell body， as well as 144 molecular functions such as enzyme binding， estrogen response element binding， and nuclear estrogen receptor binding. The multivariate regression analysis revealed that when Tusizi prescription was composed of Cuscutae Semen， Lycii Fructus， Dioscoreae Rhizoma， Poria， and Nelumbinis Semen in a ratio of 27∶30∶17∶12∶14， the water extract of Tusizi prescription had the best effect of enhancing the viability of KGN cells. CCK-8 results showed that compared with the normal group， the hyperoside group demonstrated increased viability of ovarian germline stem cells （P<0.01）. The CCK-8， EdU， and ELISA results showed that compared with the normal group， the optimal prescription screened by uniform design and the water extract 11 of Tusizi prescription increased the proliferation and reduced the apoptosis of KGN cells （P<0.05， P<0.01）. ELISA results showed that compared with the normal group， the water extract 11 of Tusizi prescription promoted the E₂ secretion of KGN cells （P<0.05）， while the optimal prescription screened by uniform design had no significant effect on the E₂ secretion. ConclusionBoth the water extract 11 of Tusizi prescription （Cuscutae Semen-Lycii Fructus-Dioscoreae Rhizoma-Poria-Nelumbinis Semen 4∶4∶2∶1∶1） and the optimal prescription screened by uniform design （Cuscutae Semen-Lycii Fructus-Dioscoreae Rhizoma-Poria-Nelumbinis Semen 27∶30∶17∶12∶14） can improve the ovarian reserve function， and the former has better effect. Tusizi prescription can modulate biological processes （such as cell proliferation and apoptosis） and molecular functions （such as enzyme binding and estrogen response element binding） through active components like hyperoside to promote the proliferation and E₂ secretion and inhibit the apoptosis of KGN cells， thereby protecting the ovarian reserve function.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Objective To explore the relationship between IgA nephropathy(IgAN)and intestinal flora and mucosal immune response disorder based on the theory of"intestinal-kidney axis",and to summarize and enumerate the measures of Chinese and Western medicine in the treatment of IgAN by regulating intestinal flora,mucosal immune response and improving intestinal microecology.Methods Through literature search,the changes of intestinal flora between healthy people and IgAN patients and their effects on intestinal mucosal immunity were compared and analyzed,and then the relationship between different intestinal flora and IgAN was summarized.Results This paper summarizes that different floras including Firmicutes,Escherichia coli,Bifidobacterium and Enterococcus faecalis are closely related to the occurrence and development of IgAN.Conclusions Intestinal flora imbalance can negatively regulate IgAN,regulate intestinal flora and improve intestinal microecological environment,which may become a new target for the treatment of IgAN.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To summarize the changing prevalence of carbapenem resistance in Enterobacterales based on the data of CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021 for improving antimicrobial treatment in clinical practice.Methods Antimicrobial susceptibility testing was performed using a commercial automated susceptibility testing system according to the unified CHINET protocol.The results were interpreted according to the breakpoints of the Clinical & Laboratory Standards Institute(CLSI)M100 31st ed in 2021.Results Over the seven-year period(2015-2021),the overall prevalence of carbapenem-resistant Enterobacterales(CRE)was 9.43％(62 342/661 235).The prevalence of CRE strains in Klebsiella pneumoniae,Citrobacter freundii,and Enterobacter cloacae was 22.38％,9.73％,and 8.47％,respectively.The prevalence of CRE strains in Escherichia coli was 1.99％.A few CRE strains were also identified in Salmonella and Shigella.The CRE strains were mainly isolated from respiratory specimens(44.23±2.80)％,followed by blood(20.88±3.40)％and urine(18.40±3.45)％.Intensive care units(ICUs)were the major source of the CRE strains(27.43±5.20)％.CRE strains were resistant to all the β-lactam antibiotics tested and most non-β-lactam antimicrobial agents.The CRE strains were relatively susceptible to tigecycline and polymyxins with low resistance rates.Conclusions The prevalence of CRE strains was increasing from 2015 to 2021.CRE strains were highly resistant to most of the antibacterial drugs used in clinical practice.Clinicians should prescribe antimicrobial agents rationally.Hospitals should strengthen antibiotic stewardship in key clinical settings such as ICUs,and take effective infection control measures to curb CRE outbreak and epidemic in hospitals.