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 investigate the current status of life stress and hypertension among couples of childbearing age across diverse economic regions in China, and to explore relevant influencing factors.Methods:This study was a cross-sectional study, with subjects from the “Research on the standardized system of comprehensive prevention and control of birth defects based on preconception-prenatal-postnatal whole chain”. From February to May 2021, urban and rural couples of childbearing age (18-49 years old) from Beijing, Henan, and Gansu provinces were enrolled, representing the eastern, central, and western regions of China, respectively. The detection rate, cognition and control of hypertension in the general population, as well as the detection rate of hypertension in different genders and regions were analyzed. Subjects were divided into hypertensive group and non-hypertensive group based on whether their blood pressure was≥140/90 mmHg (1 mmHg=0.133 kPa), and the general clinical data of the two groups were compared. Subjects were also divided into prehypertension and hypertension group and normal blood pressure group based on whether their blood pressure was≥130/80 mmHg, and the general clinical data of the two groups were compared, with subgroup analyses conducted by gender. Multifactorial logistic regression model was applied to identify factors associated with prehypertension and hypertension in both males and females.Results:A total of 1 942 couples of childbearing age, comprising 3 884 individuals, aged (29.8±5.2) years were enrolled, with 1 942 males (50.0%). The overall hypertension detection rate was 6.3% (246/3 884), with a detection rate of 10.5% (203/1 942) in males and 2.2% (43/1 942) in females. The hypertension detection rates in Beijing, Henan, and Gansu were 6.2% (92/1 482), 11.6% (139/1 200), and 1.2% (15/1 202), respectively. The overall detection rate of prehypertension and hypertension was 40.5% (1 574/3 884). Multifactorial logistic regression analysis showed that life pressure factors had no effect on female blood pressure levels ( P>0.05), while a significant or high level of life/work pressure was a risk factor for prehypertension and hypertension in males ( OR=2.30, 95% CI 1.06-4.99, P<0.05). Conclusion:The detection rate of prehypertension and hypertension among young couples of childbearing age in China is high, with poor awareness and control of hypertension. There are sex differences in the relationship between life pressure and blood pressure levels. Comprehensive consideration of individual living environments and mental health factors is crucial in blood pressure management. Measures to reduce life stress and enhance mental resilience should be implemented to address this public health issue.

Objective:To investigate the prevalence and severity of symptom burden among long-term survivors of oropharyngeal cancer after radiotherapy, to identify core symptom clusters, and to explore their correlation with quality of life.Methods:A previous retrospective study was conducted by the Cancer Hospital, Chinese Academy of Medical Sciences on patients with oropharyngeal cancer who underwent radiotherapy between January 2010 and December 2020. Patients who were still alive as of December 2023 were further followed and analyzed. From December 2023 to August 2024, symptom burden and quality of life were assessed using the Chinese version of the MD Anderson Symptom Inventory–Head and Neck Module (MDASI-HN) and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ). Exploratory factor analysis (principal component analysis with Promax rotation) were used to identify symptom clusters. Spearman correlation analysis was performed to explore the relationship between total symptom cluster scores and standardized domain scores of quality of life. Multivariate linear regression analysis was further employed to determine the relationship between identified symptom clusters and overall quality of life.Results:A total of 273 patients were included, with a median follow-up duration of 6.2 years (range: 3.5-14.5 years) and a median age of 61 years (range: 27-88 years) at follow-up. The top 5 incidence rates of symptom reported by patients were mucus problems in the mouth or throat (147 cases, 53.8%), dental or gum issues (143 cases, 52.4%), xerostomia (140 cases, 51.3%), difficulty swallowing or chewing (95 cases, 34.8%), and taste disturbance (79 cases, 28.9%). Among them, xerostomia was the most serious symptom. The most frequently reported interference was impact on work (including household chores) (55 cases, 20.1%). Exploratory factor analysis identified 3 symptom clusters: fatigue-nausea cluster, eating-voice cluster, and xerostomia-sleep cluster, all of which were significantly correlated with lower overall quality of life of patients (all P<0.001). Conclusion:Long-term survivors of oropharyngeal cancer after radiotherapy experience substantial symptom burden. The fatigue-nausea, eating-voice, and xerostomia-sleep clusters are the core symptom clusters impacting quality of life.

Objective To investigate whether the application of automated software for computed tomography angiography(CTA)and computed tomography perfusion imaging(CTP)can improve in-hospital workflow for endovascular treatment(EVT)in acute ischemic stroke patients.Methods We included patients with acute ischemic stroke who received CTA and CTP evaluation followed by EVT through the stroke emergency pathway at the Affiliated Hospital of Southwest Medical University between January 1,2020 and December 30,2022.The patients were divided into two groups:control group and artificial intelligence(Al)group based on whether automated software was used for assessment.The control group consisted of patients who underwent manual post-processing of multimodal imaging before June 2021,while the AI group was composed of patients whose imaging was processed with automated software from July 2021 onwards.The primary outcome was door-to-puncture time(DPT),and the secondary outcome was the 90-day modified Rankin Scale(mRS)score.Results A total of 312 patients were included,with 145 in the control group and 167 in the AI group.The median age of all the patients was 68 years(range:58-74 years),and 55.4％(173 patients)were male.The median National Institutes of Health Stroke Scale(NIHSS)score at presentation was 16 scores(range:12-19 scores).The median DPT was reduced from 110 min(range:80-150 min)before the use of automated software to 95 min(range:65-125 min)after its implementation(P＜0.001).However,there was no significant difference in the proportion of patients achieving functional independence(mRS score of 0-2)between the two groups(39.3％vs.41.3％,P=0.719).Conclusion The application of multimodal CT automated software improves the in-hospital workflow for acute ischemic stroke patients by reducing the time to EVT.However,the software did not significantly impact neurological functional outcomes as measured by the mRS.

Migratory birds are found worldwide.These birds are natural carriers of pathogens,because of their long-distance mi-grations across different climatic and geographic regions.The pathogens carried by migratory birds can enter ecosystems along migra-tory routes through direct and indirect contact between animal hosts,thereby potentially threatening the health of local poultry and even humans.Herein,we systematically analyzed evidence of pathogen spillover between migratory birds and poultry,and summa-rized the three modes of long-distance pathogen transmission by migratory birds(internal carriage,surface carriage,and environmen-tal contamination),as well as the two pathways of pathogen spillover between migratory birds and poultry(direct contact and indirect contact).Furthermore,this study proposes a multi-tiered mitigation strategy,grounded in the One Health framework,for targeting animal-environment-human interfaces to decrease the pathogen transmission risks associated with migratory birds.Key interventions include establishing an early-warning surveillance system for migratory flyways,enhancing biosecurity protocols in poultry production systems,and developing cross-regional risk assessment models for pathogen spread.Our findings provide critical theoretical founda-tions and empirical evidence for preventing zoonotic disease emergence and safeguarding sustainable poultry production.This inte-grated approach advances the coordinated development of biosafety measures and global health security.

This study analyzed the genetic evolutionary characteristics of sandflies and their effects on the spread of kala-azar in various environments in endemic provinces in China,to provide a scientific basis for kala-azar disease prevention and control.Sand-flies were collected in kala-azar endemic areas such as southern Xinjiang,the large hilly areas of southern Gansu,the northern Sich-uan and Taihang Mountains,and surrounding small hills.The cytochrome c oxidase subunit I and cytochrome b gene fragments of mito-chondrial DNA were amplified to identify sandfly species.The COI and Cytb gene sequences of sandflies from southern Xinjiang and Si-chuan recorded in NCBI were also collected.The intraspecific and interspecific genetic differences of sandflies were calculated in MEGA11.0,and a phylogenetic tree was constructed through the neighbor-joining method,for analysis of the genetic and evolutionary characteristics of sandfly populations and their effects on disease transmission.A total of 155 sandflies were collected from nine sam-pling sites in seven provinces of China;the species included Phlebotomus chinensis,Phlebotomus wui,and Sergentomyia squamirostris.Five sandfly species belonging to two genera were collected:P.chinensis,P.wui,and Phlebotomus alexandri in the genus Phleboto-mus,and S.squamirostris in the genus Sergentomyia.Genetic evolution analysis based on COI and Cytb gene sequences indicated intra-specific genetic distances of 0-0.062 and 0-0.056,respectively,and interspecific genetic distances of 0.126-0.176 and 0.110-0.171,respectively.The phylogenetic tree indicated that P.wui,P.alexandri,Phlebotomus longiductus,and S.squamirostris clus-tered into one branch.The sequences of P.chinensis in the large and small hilly areas clustered into two geographical clades.In the small hilly areas,the sequences of P.chinensis aggregates showed small genetic differences,the pathogen infection was consistent,and the cases showed an epidemic spread trend.Large genetic differences at the molecular level were observed among sandflies in dif-ferent ecological regions,thus indicating key effects on leishmaniasis transmission.On the basis of these findings,prevention and con-trol strategies should be adapted to local conditions,and precise and effective prevention and control measures should be formulated according to the genetic evolution characteristics of sandflies in different regions,to better control the transmission of Kala-azar.

Objective To investigate the influencing factors of autism spectrum disorder(ASD)among the children in Bijie City.Methods A total of 195 children diagnosed as ASD in Bijie City from June 2022 to August 2023 were selected as the case group and 201 healthy children extracted from the urban and rural kin-dergartens by the simple random sampling survey method served as the control group.The guardians of the children were surveyed by using a self-designed questionnaire.The logistic regression was adopted to analyze the influencing factors of childhood ASD onset.Results The univariate analysis results revealed that the differences in the sex,age,residential environment,maternal education level,maternal occupation,maternal age at pregnancy,maternal prenatal depression,health status at birth,feeding mode,early health status after birth,primary caregiver before 1 year old,left-behind experience before 3 years old,parenting attitude towards discipline and parental disciplinary severity between the two groups had statistical significance(P＜0.05).The multivariate logistic regression analysis showed that the child's sex,age,residential environment,mater-nal education level,maternal occupation,annual household income,maternal age at pregnancy,health status in neonatal period,primary caregiver beforel year old and left-behind experience before 3 years old were the inde-pendent influencing factors for ASD occurrence(P＜0.05).The specific manifestations included the boys,aged 5-6 years old,living in urban areas,mothers with a junior high school education or below,mothers as full-time housewives,family average annual income＜50 000 Yuan,mother pregnancy age＜20 years old,dyspnea or suffocation at birth,mainly taking care by others before 1 year old,and experience of being a left-behind child before 3 years old,which were more likely to develop autism.Conclusion The onset of childhood ASD is the results of multiple factors concurrent action.

A centrifugation-free,single-reaction colorimetric method for detection of glucose,utilizing a dual nanozyme cascade system based on gold nanoparticles(AuNPs)and iron-doped carbon dots(FeCDs),was developed in this work.The AuNPs exhibited glucose oxidase-like activity to catalyze glucose oxidation for generation of H2O2,while the FeCDs demonstrated peroxidase-like activity to subsequently catalyze the H2O2-mediated oxidation of 3,3',5,5'-tetramethylbenzidine(TMB).To prevent interference from the blue signal generated by self-aggregation of AuNPs in subsequent quantitative detection,the reaction system was terminated with HCl,converting oxTMB into a stable yellow product.Based on changes in the absorbance at 450 nm of this yellow solution,a quantitative relationship was established between glucose concentration and absorbance at 450 nm(A450).Experimental results demonstrated that this sensor achieved a linear detection range of 44 μmol/L to 11.11 mmol/L(R2=0.993)with a detection limit of 30.68 μmol/L and spiked recoveries of 97.9%-104.7%.By integrating smartphone-based color recognition capabilities,a rapid visual detection platform was established for quantification of glucose through RGB analysis.The validation experimental results using commercial glucose injection samples further confirmed the practical application potential of this methodology.

A portable molecularly imprinted(MIP)surface-enhanced Raman scattering(SERS)chip was fabricated via a green electrochemical approach for highly selective detection of bisphenol A(BPA).This MIP-AuNP/UIO-66/SPE sensor was fabricated through a single-step co-deposition process.The process involved electropolymerization onto a UIO-66 modified screen-printed electrode(SPE),by usingo-phenylenediamine(OPD)as functional monomer and BPA as template,and simultaneously electro-reduction generated gold nanoparticles(AuNPs),which served as the SERS-active substrate.Ultimately,this one-step method formed a three-dimensional porous architecture on the electrode surface.Under 785 nm laser excitation,the sensing chip exhibited a highly sensitive SERS response towards BPA.The intensity of its characteristic peak at 850 cm-1 showed a good linear relationship with logarithm of BPA concentration in the range of 1.0×10-10 to 1.0×10-6 mol/L,with a detection limit of 1.0×10-12 mol/L.More importantly,the fabricated chips maintained highly selective binding affinity for BPA in water samples even in the presence of structural analogs bisphenol F(BPF)and bisphenol S(BPS).When the chip was applied to detection of BPA in water samples from plastic bottle and paper cup,the recovery rates ranged from 94.0%to 103.0%with relative standard deviations(RSD)less than 4.7%.The developed chip offered a highly sensitive and selective solution for detection of trace BPA in complex water samples.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.