Objective To understand mosquito density, species composition, and seasonal variations around the Beijing Capital International Airport(BCIA)and provide scientific evidence for mosquito-borne disease prevention and control. Methods Meteorological data were collected from 2016 to 2022, and mosquito density was monitored at seven surveillance sites around the BCIA from May to October each year using CO2-baited mosquito traps to analyze the relationship between mosquito density and meteorological factors. Results In total,68 518 female mosquitoes were captured, with a mosquito density of 10.20 per light · hour. The dominant species was Culex pipiens pallens(73.90%), followed by Aedes albopictus(9.71%)and Ae. vexans(9.29%);the mosquito density was highest in 2016(14.8 per light·hour)and lowest in 2022(4.46 per light·hour). Significant statistical differences were observed in mosquito density among different species(F=18.118, P<0.05). The density of Cx. pipiens pallens and other mosquito species showed a mutually exclusive trend; the peak of mosquito density varied based on years and habitats, with significant statistical differences in mosquito density among different habitats(F=8.504, P<0.05). The highest mosquito density was observed near the Wenyu River(21.50 per light·hour), whereas the lowest was recorded at the BCIA construction site(2.44 per light· hour). The monthly average temperature(r=0.595), monthly average highest temperature(r=0.575), and monthly average lowest temperature(r=0.624)showed moderate positive correlations, whereas the monthly average air pressure(r=-0.484)showed a moderate negative correlation. The monthly average minimum temperature was included in the regression model(F=25.575, P<0.000), and the equation Y=1.029X4-8.181 was used. The monthly average air pressure 2 months prior(b=-2.418, β=-1.619, P<0.05)and the monthly average relative humidity(b=-0.739, β=-1.201, P<0.05)significantly negatively predicted the mosquito density. Notably, the regression equation used was y=2526.170+(-2.418)X9+(-0.739)X13. The exposure-response analysis revealed that the density of mosquitoes was not linearly related to the average monthly air pressure, rainfall, and duration of sunshine. Conclusions The dominant mosquito species around the BCIA was Cx. pipiens pallens, Ae. albopictus, and Ae. vexans. During the control of Cx. pipiens pallens, the impact on other mosquito species must also be considered. Environmental management, breeding sites, and scientific use of pesticides should be prioritized based on the activity periods of mosquitoes. Further studies on mosquitoes and meteorological factors should be conducted to provide references and novel avenues for mosquito control.

Owing to its inherent advantages—such as being non-destructive, rapid, highly molecule-specific, and minimally interfered with by moisture—Raman spectroscopy has been widely adopted in the fields of skin barrier function assessment, monitoring the transdermal penetration of active cosmetic ingredients, and the identification and quality control of cosmetic products. Despite these strengths, the practical application of this technique faces a significant bottleneck: the strong fluorescence background generated by endogenous skin components and exogenous cosmetic additives. Endogenous skin substances, such as structural proteins (e.g., collagen and elastin), metabolic coenzymes (e.g., nicotinamide adenine dinucleotide), and pigments (e.g., melanin), together with exogenous cosmetic constituents like organic colorants, chemical sunscreens, and fragrances, often possess strong absorption and emission characteristics. When excited by lasers, these components produce a fluorescence background that can be 10⁶ to 10⁸ times stronger than the Raman scattering signals, effectively masking the inherently weak vibrational fingerprint information. In recent years, driven by the rapid development of optoelectronic hardware and artificial intelligence algorithms, fluorescence suppression strategies have evolved from isolated, single-method approaches into comprehensive, multi-level synergistic systems. These systems are categorized into three distinct tiers: sample preparation, signal acquisition, and data processing. At the sample preparation level, techniques such as photobleaching and surface-enhanced Raman spectroscopy (SERS) are employed to eliminate or bypass the generation of fluorescence at the source. At the signal acquisition level, instrumental improvements—including the use of long-wavelength near-infrared excitation (typically 785 nm or 1 064 nm), confocal spatial filtering, and shifted excitation Raman difference spectroscopy (SERDS)— are utilized to physically isolate Raman signals from the fluorescence background. Furthermore, at the data processing level, numerical baseline correction methods such as polynomial fitting, penalized least squares (e.g., airPLS, arPLS), wavelet transform, and derivative algorithms are increasingly integrated into the analytical pipeline to extract Raman spectral features from mixed signals without increasing hardware costs or acquisition time. This review provides a systematic categorization and critical evaluation of these fluorescence suppression methods, detailing their underlying principles, technical advantages, and inherent limitations in diverse experimental setups. By focusing on critical application scenarios—including skin barrier assessment, percutaneous absorption monitoring, the routine quality control of cosmetics, and the emerging field of portable on-site detection—this paper explores the current state of technique selection and optimization. Finally, the article discusses future development trends, emphasizing the necessity of constructing adaptive, tiered suppression strategies, developing intelligent and automated data processing algorithms, and promoting the integration of portable, multi-modal diagnostic devices. The objective of this review is to provide a comprehensive technical reference to facilitate the transition of Raman spectroscopy from a specialized laboratory tool into a routine, robust analytical platform for advancements in skin science and cosmetic research.

Objective To explore the value of phase contrast(PC)MRI intracranial hemodynamic parameters for predicting acute mountain sickness(AMS).Methods Totally 72 healthy young volunteers were prospectively recruited.Intracranial hemodynamic parameters of internal carotid artery(ICA)and internal jugular vein(IJV)were measured using PC MRI under normal breathing,as well as mild,moderate and severe Valsalva maneuvers(VM)in plain area.The subjects were divided into AMS group(n=9)and non-AMS group(n=63)according to results of Lake Louise score(LLS)10 h after a rapid ascent to plateau area with altitude of 4 411 m.Univariate and multivariate logistic regression analyses were performed to screen independent predictors of AMS under different states and then construct single and combined VM states prediction models.Receiver operating characteristic curves were plotted,and the area under the curve(AUC)was calculated to evaluate the predictive efficacy of each model.Results ICA pulsatility index(PIICA)under mild VM,IJV cross-sectional area(SIJV)under moderate VM and IJV resistance index(RIIJV)under severe VM were all independent predictors of AMS(all P＜0.05).The efficacy of combined VM states model(AUC=0.869)for predicting AMS was higher than each single VM state model(AUC=0.698-0.738).Conclusion The model constructed based on PIICA under mild VM,SIJV under moderate VM and RIIJV under severe VM could be used to effectively predict AMS.

Objective:To analyze the preferences of medical staff in TCM hospitals in Beijing regarding the achievements transformation of hospital traditional Chinese medicine preparations,and to provide a reference for formulating incentive policies.Methods:233 medical staff from five TCM hospitals in Beijing were taken as the research objects,and surveyed with a questionnaire designed based on the discrete choice experiment(DCE).Mixed logit models and latent class models were then used to analyze their transformation preferences.Results:The mixed Logit model revealed that seven key attributes significantly influenced medical staff's preferences for the achievements transformation of traditional Chinese medicine preparations(P<0.05).Latent class analysis identified three distinct preference groups among respondents:an organization-dependent group(27.0%),a pro-transformation group(61.4%),and a conservative group(11.6%).Conclusions:Medical Staff preferred transformation conditions that increased monthly income;utilized"human use+re-experimentation";involved the hospital's achievements transformation department as the entity;were funded by the hospital;offered a 70%profit share;enabled promotion three years earlier,and assigned patents to the hospital.The study recommends implementing diverse incentive measures and developing differentiated strategies tailored to the distinct Medical Staff categories to facilitate the transformation of hospital traditional Chinese medicine preparations into new drugs.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Aim Mouse model of myocardial hypertro-phy was established via intraperitoneal injection of iso-proterenol(ISO)in mice.This approach allows for an in-depth investigation into the pharmacological effects and mechanisms of action of emodin,offering novel in-sights and directions for the improvement of myocardial hypertrophy.Methods The mice were randomly di-vided into the following groups:control group(CON),emodin group(EMO),MAPK activator control group(EMO+Ani),model group(ISO),treatment group(ISO+EMO),and activator intervention group(ISO+EMO+Ani).After treatment with emodin and inter-vention with MAPK activator,the heart weight ratio and cardiac size of each group were observed.Hematoxy-lin-eosin(HE)staining was used to observe the patho-logical changes in cardiac tissue,and kits were utilized to measure the levels of GSH,LDH,and MDA in the serum.Western blot was employed to detect the protein expression levels of inflammatory and oxidative factors,as well as p-p38,p-ERK,p38,and ERK in cardiac tis-sue.Results Emodin can significantly inhibit the production of myocardial inflammatory and oxidative factors induced by ISO,thereby effectively alleviating the degree of myocardial hypertrophy and fibrosis.Af-ter the p38/ERK signaling pathway was specifically ac-tivated by farnesol,the improvement effect of emodin on myocardial hypertrophy was weakened.Further comparison revealed that,compared with the myocardi-al hypertrophy pathological model group,the pathologi-cal protein expression levels in the farnesol-treated group showed no significant difference,and were even higher in some indicators.Conclusion Emodin can effectively inhibit the release of inflammatory factors and improve the state of oxidative stress by modulating the p38/ERK signaling pathway,thereby exerting an ameliorative effect on myocardial hypertrophy.

Mesenchymal stem cells(MSCs)play a crucial role in tissue repair and regeneration,and the extracellular vesicle(EV)released by them holds great promise for applications in clinical biomarkers,vaccines,and drug delivery.However,MSCs-derived EV(MSCs-EV)face challenges such as low pro-duction yield,poor retention,and targeted delivery issues.There-fore,engineering MSCs-EV to enhance their performance and en-able visual research has become a hot topic.Curcumin(CUR),an active component in traditional chinese medicine,exhibits pharmacological effects but has limited bioavailability.Using MSCs-EV as a carrier for CUR delivery can address its solubility and bioavailability challenges.This article reviews the drug loading methods,engineering strategies of MSCs-EV,and their important applications in the delivery and treatment of CUR for cartilage injury diseases.It provides a basis for the clinical ap-plication of engineered MSCs-EV in CUR delivery for cartilage repair,offering potential solutions to the challenges in cartilage tissue repair.

Objective To analyze the habitat suitability status of Rhododendron anthopogonoides Maxim.in Gansu and Qinghai Provinces;To predict its potential suitable areas in different climate scenarios;To provide references for the protection and rational utilization of Rhododendron anthopogonoides Maxim.Methods The MaxEnt model,combined with ArcGIS 10.3,was used to simulate and analyze the potential habitat suitability and spatial distribution of Rhododendron anthopogonoides Maxim.under current and future climate conditions,using species distribution data and environmental factors.Additionally,the habitat suitability was classified,and key environmental factors influencing the distribution of Rhododendron anthopogonoides Maxim.under current climate conditions were identified through a comprehensive evaluation of factor contribution rates and validation using the Jackknife method.Results The predicting results showed that under the current climate,the total area of potential suitable habitat for Rhododendron anthopogonoides Maxim.was 21.27×104 km2,primarily distributed in Lanzhou,Linxia Hui Autonomous Prefecture and Gannan Tibetan Autonomous Prefecture in Gansu Province,and in Xining,Haidong,Huangnan Tibetan Autonomous Prefecture and Hainan Tibetan Autonomous Prefecture in Qinghai Province.Under different future climate scenarios,the potential suitable habitat area was projected to increase by 4.51%to 21.91%,and migrating to high altitude areas.Elevation,precipitation in the driest month,annual precipitation and the average temperature in the driest quarter were identified as the main environmental factors influencing the distribution of Rhododendron anthopogonoides Maxim.Conclusion Under different climate scenarios in the future,the habitat area of Rhododendron anthopogonoides Maxim.in Gansu and Qinghai Provinces is showing an increasing trend,which can provide reference for the protection and rational utilization of Rhododendron anthopogonoides Maxim.resources under climate change.

Objective To formulate an expert consensus on the puncture of difficult arteriovenous fistula under real-time guidance of ultrasound(hereinafter referred to as the consensus).To standardize the clinical practice of ultrasound-guided puncture of difficult autologous arteriovenous fistula.Methods We conducted a comprehensive literature review of domestic and international publications,integrated clinical nursing experts' practical experience,and followed evidence-based nursing principles to identify the best available evidence.Through expert panel discussions and 3 rounds of Delphi expert consultation,we systematically revised,refined and improved the initial draft of the consensus,ultimately developing the finalized version.Results The response rates of the 3 rounds of Delphi expert correspondence inquiries were all 100％;the authority coefficient of expert correspondence inquiries was 0.97;the mean importance assignment of the 3 rounds of correspondence inquiries was more than 3.50.The Kendall coordination coefficients of the 3 rounds of expert consultation were 0.127,0.120,and 0.201,respectively(P＜0.05),and the degree of coordination of expert opinions was good and the expert opinions were consistent.The consensus summarized 5 aspects,including relevant terms and definitions,indications for real-time ultrasound-guided arteriovenous fistula puncture,personnel qualifications and training,difficult arteriovenous fistula puncture procedures under real-time ultrasound-guided,and nursing quality control.Conclusion The consensus is scientific,which can provide a basis for hemodialysis practitioners to practice the puncture of difficult arteriovenous fistula under real-time guidance of ultrasound.

Objective To explore the value of phase contrast(PC)MRI intracranial hemodynamic parameters for predicting acute mountain sickness(AMS).Methods Totally 72 healthy young volunteers were prospectively recruited.Intracranial hemodynamic parameters of internal carotid artery(ICA)and internal jugular vein(IJV)were measured using PC MRI under normal breathing,as well as mild,moderate and severe Valsalva maneuvers(VM)in plain area.The subjects were divided into AMS group(n=9)and non-AMS group(n=63)according to results of Lake Louise score(LLS)10 h after a rapid ascent to plateau area with altitude of 4 411 m.Univariate and multivariate logistic regression analyses were performed to screen independent predictors of AMS under different states and then construct single and combined VM states prediction models.Receiver operating characteristic curves were plotted,and the area under the curve(AUC)was calculated to evaluate the predictive efficacy of each model.Results ICA pulsatility index(PIICA)under mild VM,IJV cross-sectional area(SIJV)under moderate VM and IJV resistance index(RIIJV)under severe VM were all independent predictors of AMS(all P＜0.05).The efficacy of combined VM states model(AUC=0.869)for predicting AMS was higher than each single VM state model(AUC=0.698-0.738).Conclusion The model constructed based on PIICA under mild VM,SIJV under moderate VM and RIIJV under severe VM could be used to effectively predict AMS.