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 establish a stable,reliable,and clinically relevant porcine model of endotoxin-induced acute respiratory distress syndrome(ARDS).Methods Ten 8-month-old male Bama minipigs were deeply sedated,followed by invasive mechanical ventilation and electrocardiographic monitoring.Lipopolysaccharide(LPS)was intravenously pumped at 600 μg/(kg·h)for 3 hours,then maintained at 15 μg/(kg·h)thereafter.Dynamic monitoring was performed at five time points after LPS injection(LPS 0,1,3,5,and 8 h),including arterial blood gas analysis and chest computed tomography(CT)scans.Pathological examination of lung tissues obtained via bronchoscopic biopsy(HE staining and transmission electron microscopy)was conducted.These indicators were comprehensively used to evaluate the success of the animal model.Results At 5 hours after LPS administration,8 minipigs developed symptoms such as skin cyanosis,elevated body temperature,and respiratory distress.The oxygenation index decreased to<300 mmHg.Chest CT scans showed diffuse pulmonary infiltrates.Histopathology revealed alveolar edema and hyaline membrane formation.Transmission electron microscopy demonstrated disruption of pulmonary blood-air barrier,depletion of lamellar bodies in type Ⅱ pneumocytes,inflammatory cell infiltration,and exudation of plasma proteins and fibrin.Compared with LPS 0 h,at LPS 8 h,the oxygenation index and arterial blood pH were significantly decreased(P<0.001),while blood lactic acid and serum potassium were significantly increased(P<0.05);serum calcium and base excess were significantly decreased(P<0.05),and the lung injury score based on HE-stained lung sections was significantly increased(P<0.01).Conclusion The porcine ARDS model established by continuous LPS injection can dynamically simulate the pathophysiological characteristics and typical pathological manifestations of clinical septic ARDS,making it an effective tool to study the pathogenesis,prevention,and treatment strategies of septic ARDS.

This study was aimed at determining the molecular characteristics of Yersinia pestis in the natural plague foci around Qinghai Lake through single nucleotide polymorphism technology,to lay a foundation for molecular epidemiological and source-tracing analysis of Y.pestis in this area.Using the whole genome sequencing technology,we obtained the whole genome sequences of 84 representative Y.pestis strains.Using the sequences of Y.pestis and Yersinia pseudotuberculosis IP32953 from the NCBI database as references,we compared and analyzed the 2 298 SNP loci of these strains.From 1957 to 2020,84 representative strains of Y.pestis from the natural plague foci around Qinghai Lake were divided into two clades:1.IN2 and 3.ANT1.The 1.IN2 clade was the characteristic population of Y.pestis throughout all epidemic years in this area.Additionally,analysis of the SNP distribution and hosts in the region indicated that the 1.IN2 clade was located in five counties except Wulan,whereas the 3.ANT1 clade was isolated from Himalayan marmot and dog in two counties.In conclusion,the population structure of SNP of Y.pestis in the natural plague foci around Qinghai Lake is relatively simple,and SNP analysis of Y.pestis provided a scientific basis for tracing plague epidemic sources and formulating plague prevention and control measures in this area.

This study was aimed at determining the molecular characteristics of Yersinia pestis in the natural plague foci around Qinghai Lake through single nucleotide polymorphism technology,to lay a foundation for molecular epidemiological and source-tracing analysis of Y.pestis in this area.Using the whole genome sequencing technology,we obtained the whole genome sequences of 84 representative Y.pestis strains.Using the sequences of Y.pestis and Yersinia pseudotuberculosis IP32953 from the NCBI database as references,we compared and analyzed the 2 298 SNP loci of these strains.From 1957 to 2020,84 representative strains of Y.pestis from the natural plague foci around Qinghai Lake were divided into two clades:1.IN2 and 3.ANT1.The 1.IN2 clade was the characteristic population of Y.pestis throughout all epidemic years in this area.Additionally,analysis of the SNP distribution and hosts in the region indicated that the 1.IN2 clade was located in five counties except Wulan,whereas the 3.ANT1 clade was isolated from Himalayan marmot and dog in two counties.In conclusion,the population structure of SNP of Y.pestis in the natural plague foci around Qinghai Lake is relatively simple,and SNP analysis of Y.pestis provided a scientific basis for tracing plague epidemic sources and formulating plague prevention and control measures in this area.

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.

The paper is to establish an UPLC-MS/MS method for the simultaneous determination of 19 components in Microctis Folium from different production areas. The 50% methanol was used as extraction solvent. The Agilent ZORBAX SB C18 (150 mm × 2.1 mm, 1.8 μm) column was used; mobile phase was acetonitrile - 0.1% acetic acid with gradient elution, flow rate was 0.3 mL·min^-1, colume temperature was 30 ℃, and the injection volume was 2 μL; electrospray ionizaton source was used and detected in negative ion mode. The results showed that the established UPLC-MS/MS method could well separate the 19 components, and the methodological investigation results of 19 components were good. By means of orthogonal partial least squares discriminant analysis (OPLS-DA), 28 batches of Microctis Folium samples from different production areas can be divided into three categories, Guangdong, Guangxi and Hainan are each classified into one category, and 10 signature compounds which affecting the quality differences of different production areas were screened out. The established method is accurate, reliable, sensitive and reproducible. It can provide a basis for the establishment of the quality standard of Microctis Folium, as well as for safety and quality research.

Rosuvastatin(RVS)is an excellent drug with anti-inflammatory and lipid-lowering properties in the aca-demic and medical fields.However,this drug faces a series of challenges when used to treat atherosclerosis caused by hyperhomocysteinemia(HHcy),including high oral dosage,poor targeting,and long-term toxic side effects.In this study,we applied nanotechnology to construct a biomimetic nano-delivery system,macrophage membrane(M?m)-coated RVS-loaded Prussian blue(PB)nanoparticles(MPR NPs),for improving the bioavailability and targeting capacity of RVS,specifically to the plaque lesions associated with HHcy-induced atherosclerosis.In vitro assays demonstrated that MPR NPs effectively inhibited the Toll-like receptor 4(TLR4)/hypoxia-inducible factor-1α(HIF-1α)/nucleotide-binding and oligomerization domain(NOD)-like receptor thermal protein domain associated protein 3(NLRP3)signaling pathways,reducing pyroptosis and inflammatory response in macrophages.Additionally,MPR NPs reversed the abnormal distribution of adenosine triphosphate(ATP)-binding cassette transporter A1(ABCA1)/ATP binding cassette transporter G1(ABCA1)/ATP binding cassette transporter G1(ABCG1)caused by HIF-1α,promoting cholesterol efflux and reducing lipid deposition.In vivo studies using apolipoprotein E knockout(ApoE-/-)mice confirmed the strong efficacy of MPR NPs in treating atherosclerosis with favorable bio-security,and the mechanism behind this efficacy is believed to involve the regulation of serum metabolism and the remodeling of gut microbes.These findings suggest that the synthesis of MPR NPs provides a promising nanosystem for the targeted therapy of HHcy-induced atherosclerosis.

Accurate receptor/ligand binding free energy calculations can greatly accelerate drug discovery by identifying highly potent ligands. By simulating the change from one compound structure to another, the relative binding free energy (RBFE) change can be calculated based on the theoretically rigorous free energy perturbation (FEP) method. However, existing FEP-RBFE approaches may face convergence challenges due to difficulties in simulating non-physical intermediate states, which can lead to increased computational costs to obtain the converged results. To fundamentally overcome these issues and accelerate drug discovery, a new combined-structure RBFE (CS-FEP) calculation strategy was proposed, which solved the existing issues by constructing a new alchemical pathway, smoothed the alchemical transformation, increased the phase-space overlap between adjacent states, and thus significantly increased the convergence and accelerated the relative binding free energy calculations. This method was extensively tested in a practical drug discovery effort by targeting phosphodiesterase-1 (PDE1). Starting from a PDE1 inhibitor (compound 9, IC₅₀ = 16.8 μmol/L), the CS-FEP guided hit-to-lead optimizations resulted in a promising lead (11b and its mesylate salt formulation 11b-Mesylate, IC₅₀ = 7.0 nmol/L), with ∼2400-fold improved inhibitory activity. Further experimental studies revealed that the lead showed reasonable metabolic stability and significant anti-fibrotic effects in vivo.

Abstract: Objective　To investigate the clustered regularly interspaced short palindromic repeats (CRISPR) genotypes and regional distribution of Yersinia pestis strains in the natural plague foci of Hainan Tibetan Autonomous Prefecture of Qinghai Province (referred to as "Hainan prefecture") and provide a scientific basis for plague prevention and control in this area. Methods　A total of 36 representative Yersinia pestis strains, which were isolated from different host animals and insect vectors from 1954 to 2009 in Hainan Prefecture, were selected as experimental subjects. The DNAs were extracted using the traditional sodium dodecyl sulfate decomposition and phenol-chloroform method. Three pairs of CRISPR primers (YPa, Ypb, YPc) were used for PCR amplification, sequencing and analysis of the DNA of the tested strains, respectively, as a means to identify the CRISPR genotypes of Yersinia pestis in Hainan Prefecture. Results　A total of 17 spacers were observed among 36 strains of Yersinia pestis, including 9 of YPa, 5 of YPb and 3 of YPc. All strains were divided into 5 CRISPR gene clusters (Cb2, Cb4 ', Ca7, Ca7 ', Ca35 ') and 6 genotypes (G1, G9, G22, G22-A1 ', G26-A1 ', G26-A1 'A4 -). The G26-a1 ' was the main genotype, which was distributed in Gonghe, Guide and Xinghai County, and the G22 is the second type, which was distributed in Gonghe and Guide County. Conclusions　The genetic polymorphism of CRISPR loci of Yersinia pestis strains in Hainan was high, and the regional distribution characteristics of Yersinia pestis strains with different genotypes were significant.

Abstract: Objective To investigate the current status of streptomycin resistance of Yersinia pestis caused by point mutations of rpsL gene in Qinghai, so as to provide theoretical basis for precise clinical medication and prevention of drug resistance of human plague outbreak in South area of Qinghai Province in the future. Methods A total of 104 representative strains of Yersinia pestis collected from plague patients, vector insects and intermediate hosts in South area of Qinghai Province from 1957 to 2009 were screened, isolated and cultured by Hiss agar plates. The DNA of representative Yersinia pestis was extracted by sodium dodecyl sulfate lysis and phenol-chloroform method. The primers forward primer and reverse primer and TaqMan-MGB probes probe1 [FAM] and probe2 [VIC] were designed for the rpsL gene of streptomycin resistance gene in China. Real-time PCR with TaqMan-MGB fluorescent probe was used to detect the mutations of rpsL gene in streptomycin resistance locus of 104 strains of Yersinia pestis in South area of Qinghai Province. Results The FAM test results of 104 strains in South area of Qinghai Province were positive, corresponding to the detection of rpsL (128 : A ), RFU peak >1 000,negative <200. VIC test results of all tested strains were negative, corresponding to the detection of rpsL (128:G), RFU peak <200, positive >1 000. That is, no strains with rpsL gene mutation related to streptomycin resistance were found in the 104 strains of Yersinia pestis in Qingnan Province. Conclusion This study provides basic data on the distribution of streptomycin resistance of Yersinia pestis in South area of Qinghai Province, and lays a foundation for preventing the occurrence of drug resistance and clinical treatment of Yersinia pestis in South area of Qinghai Province.