This study aims to innovate a homogeneous outpatient service management system across multiple hospital campuses to enhance service quality.Based on the practical experience of a tertiary general hospital in Guangdong Province and in accordance with the"Interim Regulations on Outpatient Quality Management in Healthcare Institutions,"we constructed a"6+1"homogeneous outpatient service management system.This system includes:① a multi-stakeholder co-governance outpa-tient management system,②a vertical and cross-hierarchical management network,③ a democratic-centralized clinical coordina-tion strategy,④ a guidance-encouragement performance evaluation standard,⑤a collaborative dynamic supervision mechanism,⑥a spiral retrospective evaluation and improvement method,and ⑦ an integrated outpatient diagnosis and treatment system.Af-ter over two years of implementation,the hospital's outpatient volume has grown by an average of over 15％annually,patient waiting time after appointment has been reduced to 20 minutes,and patient satisfaction in the tertiary public hospital performance evaluation achieved full marks.The electronic medical record system functionality reached Level 6,significantly improving healthcare service efficiency and quality while enhancing homogeneous management across campuses.

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.

This study aims to innovate a homogeneous outpatient service management system across multiple hospital campuses to enhance service quality.Based on the practical experience of a tertiary general hospital in Guangdong Province and in accordance with the"Interim Regulations on Outpatient Quality Management in Healthcare Institutions,"we constructed a"6+1"homogeneous outpatient service management system.This system includes:① a multi-stakeholder co-governance outpa-tient management system,②a vertical and cross-hierarchical management network,③ a democratic-centralized clinical coordina-tion strategy,④ a guidance-encouragement performance evaluation standard,⑤a collaborative dynamic supervision mechanism,⑥a spiral retrospective evaluation and improvement method,and ⑦ an integrated outpatient diagnosis and treatment system.Af-ter over two years of implementation,the hospital's outpatient volume has grown by an average of over 15％annually,patient waiting time after appointment has been reduced to 20 minutes,and patient satisfaction in the tertiary public hospital performance evaluation achieved full marks.The electronic medical record system functionality reached Level 6,significantly improving healthcare service efficiency and quality while enhancing homogeneous management across campuses.

Objective:To analyze the effect of precipitation on road traffic injuries (RTI) in Zhejiang Province.Methods:The RTI surveillance and meteorological data from 2009 to 2022 in Zhejiang Province were collected. Based on the time-stratified case-crossover design, the precipitation of case day and control day was compared, and the distributed lag nonlinear model was applied to analyze the correlation of precipitation and RTI. Stratified analyses were conducted to analyze the effect modification of gender, age, injury location, and temperature. An attributable fraction was used to assess the burden of RTI caused by precipitation.Results:A total of 239 970 RTIs were monitored in Zhejiang Province from 2009 to 2022, averaging 46 daily cases. The distributed lag nonlinear model showed that compared with no rain, the risk of RTI increased first and then decreased with the increase of precipitation. The risk of RTI was the highest when the precipitation was 30.99 mm ( OR=1.08, 95% CI: 1.05-1.11). The adverse effects on RTI mainly occurred on the day of precipitation, and it showed insignificant or protective effects with the extension of lag days. 1.34%(95% CI: 1.31%-1.36%) of RTI could be attributed to precipitation. Stratified analysis showed that gender, age, injury location, and temperature may modify the effect of precipitation on RTI. Precipitation caused a heavier burden on RTI in subgroups aged 18-64, females, and occurring on roads and in low temperatures. Conclusions:Precipitation can increase the risk of RTI. People aged 18-64 or females are the key groups for RTI prevention, and prevention and control efforts of precipitation-related RTI should be increased in road and low-temperature environments.

Objective:To analyze the effect of precipitation on road traffic injuries (RTI) in Zhejiang Province.Methods:The RTI surveillance and meteorological data from 2009 to 2022 in Zhejiang Province were collected. Based on the time-stratified case-crossover design, the precipitation of case day and control day was compared, and the distributed lag nonlinear model was applied to analyze the correlation of precipitation and RTI. Stratified analyses were conducted to analyze the effect modification of gender, age, injury location, and temperature. An attributable fraction was used to assess the burden of RTI caused by precipitation.Results:A total of 239 970 RTIs were monitored in Zhejiang Province from 2009 to 2022, averaging 46 daily cases. The distributed lag nonlinear model showed that compared with no rain, the risk of RTI increased first and then decreased with the increase of precipitation. The risk of RTI was the highest when the precipitation was 30.99 mm ( OR=1.08, 95% CI: 1.05-1.11). The adverse effects on RTI mainly occurred on the day of precipitation, and it showed insignificant or protective effects with the extension of lag days. 1.34%(95% CI: 1.31%-1.36%) of RTI could be attributed to precipitation. Stratified analysis showed that gender, age, injury location, and temperature may modify the effect of precipitation on RTI. Precipitation caused a heavier burden on RTI in subgroups aged 18-64, females, and occurring on roads and in low temperatures. Conclusions:Precipitation can increase the risk of RTI. People aged 18-64 or females are the key groups for RTI prevention, and prevention and control efforts of precipitation-related RTI should be increased in road and low-temperature environments.

Objective To investigate the potential mechanism of action of polygonatum odoratum in treating Alzheimer's disease through the utilization of network pharmacology and molecular docking techniques.Methods The methods employed include target screening,Gene Ontology(GO)function and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis,and molecular docking simulations to assess the binding interactions between the active compounds in polygonatum odoratum(POD)and the key target proteins associated with Alzheimer's disease.Subsequently,lipopolysaccharide(LPS)was used to induce an inflammatory cell model in BV2 microglial cells.After treating the cell model with POD extract for 24 hours,the cells were collected,and the expression of the target genes were detected by Real-time PCR.Results Eight active ingredients and 172 targets of POD were screened.The biological processes such as protein phosphorylation and signal transduction,protein binding and ATP binding were obtained by GO functional analysis.KEGG enrichment yielded PI3K/Akt,cAMP and other signaling pathways.The molecular docking result showed that the active ingredient of POD had well binding activity with epidermal growth factor receptor(EGFR),proto-oncogene tyrosine-protein kinase Src(SRC),tumor necrosis factor(TNF),STAT3.Through Real-time PCR experiments,the gene expressions of inducible nitric oxide synthase(iNOS),prostaglandin G/H synthase 2(PTGS2),interleukin(IL)-6,and IL-1β in the LPS-induced inflammatory cell model were significantly upregulated.After treating the inflammatory model with POD extract for 24 hours,the expression of TNF-α was significantly reduced,the expression of STAT3 was upregulated,there were no significant changes in the expressions of SRC and EGFR.Conclusion Network pharmacology suggests polygonatum odoratum's potential anti-Alzheimer's effects may be mediated through its interaction with targets such as EGFR,TNF,SRC,and STAT3.The experimental results suggest that polygonatum odoratum exerts an anti-inflammatory effect by acting on TNF-α,which may further alleviate the symptoms of Alzheimer's disease.

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.

Di-(2-ethylhexyl) phthalate (DEHP) is currently one of the most widely used plasticizers, widely found in all kinds of items, such as children’s toys and food packaging materials, but also added to wallpaper, cable protective agents and other building decoration materials. DEHP is toxic and absorbed by the human body through respiratory tract, digestive tract and skin contact, which can cause damage to multiple systems, especially the male reproductive system, and testis is an important target organ. Oxidative stress injury is the core mechanism of spermatogenesis disorder caused by DEHP. DEHP exposure can cause oxidative stress or reactive oxygen species (ROS) increase in germ cells, and on this basis, promote cell apoptosis or cause excessive autophagy. The toxicity of DEHP to Leydig cells is mainly to interfere with the synthesis of steroid hormones. For Sertoli cells, ferroptosis and destruction of the blood-testis barrier are common injury mechanisms. In addition, gene methylation caused by DEHP not only affects the spermatogenic process, but also has epigenetic effects on offspring. In this paper, we reviewed the pathological damage, germ cell toxicity and epigenetic effects of DEHP on testis, and focused on the damage and molecular mechanism on testicular spermatogenic cells, Leydig cells and Sertoli cells. Future research is required to elucidate the body’s clearance mechanism and treatment plan after exposure to DEHP and whether DEHP will damage the function of myoid cells. It is hoped that this can provide new ideas for prevention and treatment of male reproductive disorders resulting from long-term exposure to plastic products.

As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence* ; Humans ; Consensus ; Computer Security/standards* ; Confidentiality/ethics* ; Informed Consent/ethics*

MADS-box protein family are important transcriptional regulatory factors in plant growth and development. The AGAMOUS 12 (AGL12) subfamily is believed to play an important regulatory role in the process of plant flowering transition. To explore the potential mechanism of AGL12 subfamily involved in regulating the flower development of Lonicera macranthoides, quantitative real-time polymerase chain reaction (qRT-PCR), prokaryotic expression and yeast two-hybrid techniques were used to analyze the expression pattern, protein expression, and protein-protein interaction pattern of LmAGL12 based on transcriptome data. The results showed that the LmAGL12 gene contains a 603 bp open reading frame (ORF), encoding 200 amino acids, and the encoded protein was stable and hydrophilic without a transmembrane region and signal peptide. Through homologous sequence alignment and phylogenetic analysis, it was confirmed that LmAGL12 protein belongs to the MADS-box protein family and is closely related to the AGL12 protein of Heracleum sosnowskyi and Daucus carota subsp. sativus. The LmAGL12 gene was cloned into prokaryotic expression vector pET-28a and the recombinant constructs were transformed into Escherichia coli BL21 (DE3), which inducing the target protein successfully. The yeast two-hybrid results showed that LmAGL12 protein interacts with LmSVP protein, LmSOC1 protein and LmAP1 protein, respectively. The qRT-PCR results showed that LmAGL12 gene were differentially expressed in different development stages of flower bud, stem, and leaves of 'Longhua' and 'Baiyun' in L. macranthoides. The LmAGL12 gene showed the highest expression level in the middle stage of the 'Longhua' floral bud; For the 'Baiyun' variety, the relative expression level of LmAGL12 gene is the highest in the stem. This study cloned the LmAGL12 gene in L. macranthoides and analyzed it expression for the first time, enriching the research on flower organ development and providing a research basis for further exploring the molecular mechanisms of long bud stage and non-unfolded corolla in L. macranthoides, as well as for variety improvement.