Plant-derived extracellular vesicles (PDEVs) are nanoscale extracellular vesicles secreted by plant cells, characterized by a lipid bilayer structure. These vesicles carry a variety of bioactive molecules, including proteins, nucleic acids, and lipids, and play essential roles in intercellular communication and physiological regulation in plants. Compared to animal-derived extracellular vesicles, PDEVs offer several advantages, such as a broad range of sources, high biocompatibility, low immunogenicity, and low production costs. Furthermore, PDEVs have demonstrated remarkable potential as natural nanocarriers for drug delivery, due to their ability to efficiently traverse biological barriers, such as the blood-brain barrier, making them promising candidates for drug delivery systems. This review systematically elaborates on the complex composition of PDEVs, which consists of lipids, proteins, and nucleic acids, the typical structural characteristics of their lipid bilayers ranging from 30 to 150 nm, and their versatile loading capabilities as drug carriers, efficiently encapsulating various types of therapeutic agents such as hydrophilic small molecules, hydrophobic drugs, nucleic acids, and proteins. We systematically summarize the recent advancements in strategies for enhancing the loading efficiency of PDEVs, which include methods such as co-incubation, ultrasound-assisted loading, electroporation, freeze-thaw cycles, and microfluidic technology. These techniques are evaluated based on their underlying principles, suitable drug types, and their respective advantages. In addition to loading strategies, we focus on the engineered approaches to achieve targeted delivery using PDEVs, such as genetic engineering modifications, chemical ligand conjugation, membrane fusion technology, and polyethylene glycol (PEG) modification. We discuss the mechanisms of these strategies in enhancing targeting efficiency, prolonging in vivo circulation time, and improving therapeutic efficacy. Further, this review highlights the application of PDEVs in various disease models, including tumor, skin inflammation, metabolic disorders, and neurodegenerative diseases, showcasing their therapeutic potential as multifunctional delivery platforms. The ability of PDEVs to encapsulate diverse therapeutic agents and target specific tissues or cells opens up new avenues for the treatment of complex diseases, offering advantages over conventional drug delivery systems. However, despite the promising applications of PDEVs, several challenges remain in their development and clinical translation. These challenges include variability in source materials, standardization of preparation processes, quality control, scalability of production, and the need for clinical validation. To overcome these obstacles, the integration of advanced technologies such as artificial intelligence-assisted design and multi-omics analysis is proposed as a way to facilitate the precise development of PDEVs. These emerging technologies hold the potential to further enhance the precision and effectiveness of plant-based drug delivery systems, ultimately advancing the field of precision medicine. In conclusion, the use of PDEVs as a platform for drug delivery represents a promising area of research with the potential to revolutionize therapeutic strategies. Their ability to encapsulate and deliver a wide variety of bioactive molecules, along with their inherent advantages in biocompatibility and versatility, makes them a valuable tool in the development of more efficient and targeted therapeutic interventions. Continued research and innovation in this field will pave the way for the clinical implementation of PDEVs in the treatment of various diseases, offering new hope for more effective and sustainable therapeutic options.

Objective To evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. Methods A retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. Results In the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, spicule sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), spicule sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians’ prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. Conclusion This AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.

To explore the role of the "Clinical Practice Guidelines" column and others in the Medical Joumnal of Peking Union Medical College Hospital (Med J PUMCH) in promoting diseiplinary development.

Objective:To integrate and analyze the trend of the disease burden of tracheal,bronchus,and lung cancer(TBL)attributable to secondhand smoke in China from 1990 to 2021 and to analyze future projections,aiming to provide data support for the prevention and treatment of TBL in China.Methods:Based on the global burden of disease(GBD)2021 database,TBL with ICD-10 disease classification C33,C34-C34.92 was studied.Using secondhand smoke as a risk factor,the data on TBL mortality and disability-adjusted life year(DALY)due to secondhand smoke in China from 1990 to 2021 were further age-standardized.Using Joinpoint 4.7.1 regression analysis model to calculate annual percentage change(APC)and average annual percentage change(AAPC),Hiplot software was used to plot disease burden data for different ages and genders,and R 4.3.1 software was used to construct a grey model GM(1,1)to predict the predicted value and trend of TBL disease burden attributed to secondhand smoke in China from 2022 to 2031.Results:From 1990 to 2021,the TBL mortality rate,age-standardized mortality rate,and DALY rate attributed to secondhand smoke in China increased from 1.76/100 000,2.63/100 000,and 49.43/100 000 to 4.08/100 000,2.80/100 000,and 95.57/100 000,respectively;the growth was 131.18%,6.45%,and 93.34%;the age-standardized DALY rate decreased from 65.04/100 000 to 63.32/100 000 with the reduction of 2.65%.The results of the Joinpoint regres-sion showed that the AAPC(95%CI)of mortality,age-standardized mortality rate,and DALY rate for TBL were 2.75(2.58-2.93)%,0.16(0.11-0.21)%,and 2.15(2.11-2.18)%,respectively,with an overall increasing trend;the AAPC(95%CI)of age-standardized DALY rate was-0.14(-0.40-0.12)%,with an overall fluctuating and unchanged trend and it was higher in males than in females.In both 1990 and 2021,the TBL mortality rate attributable to secondhand smoke in China gradually increased with age,and the DALY rate first increased and then slowed down with age.The main groups of the burden of disease were the elderly and males.The grey prediction model GM(1,1)showed that the age-standardized mortality rate of TBL attributable to secondhand smoke from 2022 to 2031 showed a slow increasing trend,and the predicted value in 2031 would increase to 2.95/100 000.The age-standardized DALY showed a slow decreasing trend,and the predicted value in 2031 would decrease to 63.83/100 000.Conclusions:From 1990 to 2021,the TBL mortality,age-standardized mortality,and DALY rates attributable to secondhand smoke in China increased,and the age-standardized DALY rate decreased.Men and the elderly are the main groups affected by TBL.Appropriate measures should be formulated to reduce exposure to and contact with secondhand smoke,tak-ing into account gender and age differences.Additionally,efforts should be made to strengthen secondhand smoke prevention and public health education.

High altitude heart disease(HAHD)is a chronic mountain sickness in which the body is exposed to high altitude(＞2 500 m)hypobaric hypoxia environment for a long time.HAHD has high morbidity and poor prognosis,and pulmonary hypertension is the main causative mechanism for its develop-ment.The phosphodiesterase-5 inhibitor sildenafil has become a hot drug for the treatment of pulmonary hypertension.This paper reviews the progress of HAHD and discusses the mechanism of action and effectiveness of sildenafil in the treatment of HAHD,with a view to providing a basis for the treatment of HAHD with sildenafil.

AIM To study the chemical constituents from Tetrastigma hemsleyanum Diels et Gilg and their antitumor activity in vitro.METHODS Silica gel,ODS,Sephadex LH-20 and semi-preparative HPLC were used for isolation and purification,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The antitumor activity in vitro was determined by MTT mothod.RESULTS Twenty-eight compounds were isolated and identified as triphyllin A(1),eruberin B(2),(2S,4R)-5,7-dihydroxy-4,4'-dimethyl-6,8-dimethyl-flavan-5-O-β-D-6-acetylglucopyranoside-7-O-β-D-glucopyranoside(3),eruberin A(4),abacopterin Ⅰ(5),matteucinol(6),homoerodictyol(7),(2S)-5,3',4'-trihydroxy-7-methoxy-flavanone(8),(2S)-5,2',5'-trihydroxy-7-methoxyflavanone(9),galinsonside B(10),quercetin-3-O-β-D-glucopyranoside(11),kaempferol 3-O-robinobioside(12),rutin(13),geniposide(14),jasminoside A(15),β-sitostenone(16),sitosterol palmitate(17),β-sitosterol(18),ursolic acid(19),hyptadienic acid(20),3,4-dihydroxybenzoic acid(21),3,4-dimethoxybenzoic acid(22),gallic acid(23),dibutylphthalate(24),bis-(2-ethylhexyl)phthalate(25),9-nonadecenoic acid(26),triacylglycerol(27),crocin Ⅰ(28).The IC50 values of compound 1 for human gastric adenocarcinoma cells BGC-823 and human colon cancer cells HCT-116 were(22.07±0.38),(20.67±0.11)μmol/L,respectively.The IC50 value of compound 9 for BGC-823 cells was(21.58±0.05)μmol/L,and the IC50 value of compound 4 for HCT-116 cells was(16.67±0.36)μmol/L.CONCLUSION Compounds 1-10,14-15 and 28 are first isolated from Tetrastigma genus.Compounds 1,4,9 have weak antitumor activity in vitro.

Objective:To integrate and analyze the trend of the disease burden of tracheal,bronchus,and lung cancer(TBL)attributable to secondhand smoke in China from 1990 to 2021 and to analyze future projections,aiming to provide data support for the prevention and treatment of TBL in China.Methods:Based on the global burden of disease(GBD)2021 database,TBL with ICD-10 disease classification C33,C34-C34.92 was studied.Using secondhand smoke as a risk factor,the data on TBL mortality and disability-adjusted life year(DALY)due to secondhand smoke in China from 1990 to 2021 were further age-standardized.Using Joinpoint 4.7.1 regression analysis model to calculate annual percentage change(APC)and average annual percentage change(AAPC),Hiplot software was used to plot disease burden data for different ages and genders,and R 4.3.1 software was used to construct a grey model GM(1,1)to predict the predicted value and trend of TBL disease burden attributed to secondhand smoke in China from 2022 to 2031.Results:From 1990 to 2021,the TBL mortality rate,age-standardized mortality rate,and DALY rate attributed to secondhand smoke in China increased from 1.76/100 000,2.63/100 000,and 49.43/100 000 to 4.08/100 000,2.80/100 000,and 95.57/100 000,respectively;the growth was 131.18%,6.45%,and 93.34%;the age-standardized DALY rate decreased from 65.04/100 000 to 63.32/100 000 with the reduction of 2.65%.The results of the Joinpoint regres-sion showed that the AAPC(95%CI)of mortality,age-standardized mortality rate,and DALY rate for TBL were 2.75(2.58-2.93)%,0.16(0.11-0.21)%,and 2.15(2.11-2.18)%,respectively,with an overall increasing trend;the AAPC(95%CI)of age-standardized DALY rate was-0.14(-0.40-0.12)%,with an overall fluctuating and unchanged trend and it was higher in males than in females.In both 1990 and 2021,the TBL mortality rate attributable to secondhand smoke in China gradually increased with age,and the DALY rate first increased and then slowed down with age.The main groups of the burden of disease were the elderly and males.The grey prediction model GM(1,1)showed that the age-standardized mortality rate of TBL attributable to secondhand smoke from 2022 to 2031 showed a slow increasing trend,and the predicted value in 2031 would increase to 2.95/100 000.The age-standardized DALY showed a slow decreasing trend,and the predicted value in 2031 would decrease to 63.83/100 000.Conclusions:From 1990 to 2021,the TBL mortality,age-standardized mortality,and DALY rates attributable to secondhand smoke in China increased,and the age-standardized DALY rate decreased.Men and the elderly are the main groups affected by TBL.Appropriate measures should be formulated to reduce exposure to and contact with secondhand smoke,tak-ing into account gender and age differences.Additionally,efforts should be made to strengthen secondhand smoke prevention and public health education.

Objective:To observe the clinical effect of aripiprazole combined with non-convulsive electroshock(MECT)for children and adolescents with first episode schizophrenia.Methods:94 children and adolescents with first episode schizophrenia who were admitted to Yiling Kangning Mental Hospital of Yichang from August 2022 to August 2024 were divided into control group(received aripiprazole,n=47)and study group(received aripiprazole combined with MECT,n=47)by using random number table method.The clinical efficacy,dysfunction scale[Positive and Negative Symptom Scale(PANSS),Montreal Cognitive Assessment Scale(MoCA),Wechsler Memory Scale(WMS)],glucose and lipid metabolism indexes[low density lipoprotein cholesterol(LDL-C),glycated hemoglobin(HbAlc),total cholesterol(TC),fasting blood glucose(FBG),High density lipoprotein cholesterol(HDL-C),inflammatory factor indexes[interleukin-1β(IL-1β),tumor necrosis factor-α(TNF-α),interleukin-6(IL-6)],neurocytokine levels[serum brain-derived neurotrophic factor(BDNF),s100βprotein(s100β),homocysteine(Hcy)]and the occurrence of adverse reactions were compared between the two groups.Results:Compared with the control group after treatment,the clinical total effective rate,MoCA,WMS score,HDL-C,BDNF were higher,PANSS score,HbAlc,FBG,TC,LDL-C,TNF-α,IL-1β,IL-6,s100β and Hcy were lower in the study group(P＜0.05).There was no difference in the incidence of adverse reactions between the two groups(P＞0.05).Conclusion:Aripiprazole combined with MECT in the treatment of in children and adolescents with first episode schizophrenia can reduce the degree of inflammation,regulate glucose and lipid metabolism,improve clinical symptoms and neurological function of children,with good safety.

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 To establish the cells co-expressing 5-HT2C re-ceptor(5-HT2C R)and enhanced green fluorescent protein(EG-FP)-tagged nuclear factor of activated T cells 2(NFAT2)in U2OS cells.Methods The 5-HT2CR stably expressed U2OS-EGFP-NFAT2-5-HT2CR cells were screened by hygromycin B af-ter 5-HT2C plasmid was transfected into U2OS-EGFP-NFAT2 cells.The mRNA and protein expression of 5-HT2CR in the se-lected U2OS-EGFP-NFAT2-5-HT2CR cells were detected by RT-qPCR and Western blot.The activation of U2OS-EGFP-NFAT2-5-HT2CR cells was verified by nuclear translocation level assay.The effects of 5-HT,LSD,DOM,DOI,psilocybin(PSI),lisuride(LIS)on the U2OS-EGFP-NFAT2-5-HT2CR cells were detected by the high throughout screening assay.Results Among these cells,No 56 had the highest nuclear translocation function.5-HT2CR mRNA and protein were stably expressed in U2OS-EG-FP-NFAT2-5-HT2CR transfected cell line for 1-15 generations by RT-qPCR and Western blot.Vabicaserin increased the EGFP-NFAT2 nuclear translocation in U2OS-EGFP-NFAT2-5-HT2C R during 1-15 generations.The 5-HT2CR antagonist SB242084 significantly decreased EGFP-NFAT2 nuclear translocation in-duced by Vabicaserin in U2OS-EGFP-NFAT2-5-HT2CR cells.5-HT,LIS,PSI slightly increased the EGFP-NFAT2 nuclear trans-location in U2OS-EGFP-NFAT2-5-HT2C R cells,whereas LSD,DOI,DOM had no effect.Conclusions U2OS-EGFP-NFAT2-5-HT2CR cells co-expressing 5-HT2CR and EGFP-NFAT2 are es-tablished,which can be used for high throughout screening of chemicals and the study on the mechanism of the5-HT2CR.