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 investigate the association of the interaction and combined effect of renal function and body mass index (BMI) with the risk for all-cause death in patients with type 2 diabetes mellitus (T2DM) in communities of Jiangsu Province.Methods:The study subjects were from the Comprehensive Research Project of Diabetes Prevention and Control conducted in Jiangsu from December 2013 to January 2014, and follow up was conducted for them until September 30, 2023. A total of 20 025 subjects were included in the study. Cox proportional hazards regression model was used to analyze the association of renal function with risk for death in T2DM patients, and the association of interaction between renal function and BMI and their combined effect with all-cause death risk in T2DM patients.Results:In the follow up for 198 370 person-years, a total of 4 459 deaths were recorded. Cox proportional hazards regression model analysis showed that renal dysfunction was associated with 71% risk of all-cause mortality in all T2DM patients [hazard ratio ( HR) =1.71, 95% CI: 1.59-1.84], as well as in all BMI subgroups. Likelihood ratio test indicated an interaction between renal function and BMI (interaction for P=0.030). Compared with patients with normal renal function and normal BMI, those with normal renal function and over weight or obesity had a lower risk of all-cause mortality, and those with renal dysfunction and low weight had the highest risk for death ( HR=2.78, 95% CI: 1.87-4.14). Conclusions:There is association of interaction between renal function and BMI with all-cause mortality in T2DM patients. T2DM patients with renal dysfunction and low body weight had significant higher risk for death.

Objective:Analyze the risk factors for acute lung injury of postoperative acute lung injury(ALI)in patients with Stanford type A aortic dissection(STAAD),and construct a nomogram predictive model.Methods:A retrospective cohort study design was adopted.A total of 112 patients with STAAD who underwent surgical treatment in our hopital from January 2021 to August 2024 were included.They were divided into two groups according to the occurrence of ALI after the surgical:non-ALI group(73 cases)and ALI group(39 cases).Clinical data were collected from both groups of patients.The influencing factors of postoperative ALI in patients with STAAD were analyzed by multivariate logistic regression.Established nomogram prediction model based on influencing factors and validated.Results:Among 112 patients with STAAD who underwent surgical treatment,39 case postoperative ALI occurred,with an incidence rate of 34.82％.Age,preoperative creatinine,body mass index(BMI),preoperative white blood cell count,preoperative lactate and other aspects compared,The difference were statistically significant(P＜0.05).The length of stay in the intensive care unit(ICU)of the ALI group was longer than that of the non ALI group(P＜0.05).The intraoperative red blood cell transfusion volume and extracorporeal circulation time in the ALI group were higher than those in the non ALI group(P＜0.05).Long intraoperative cardiopulmonary bypass time,high BMI,high intraoperative red blood cell transfusion volume and high preoperative white blood cell count were risk factors for postoperative ALI(P＜0.05).The receiver operating characteristic(ROC)curve analysis results show that,the Area under the curve(AUC)of the nomogram prediction model was 0.871.When the optimal critical value was 0.472,its sensitivity and specificity wew 0.887 and 0.776,respectively.The internal validation results of Bootstrap show that,the C-index of the column chart prediction model was 0.862,with an absolute error of 0.032.The calibration curve is close to the ideal curve and the original curve,with a slope close to 1.Conclusions:Long intraoperative cardiopulmonary bypass time,high BMI,high intraoperative red blood cell transfusion volume and high preoperative white blood cell count are independent risk factors for postoperative ALI in patients with STAAD.The nomogram model constructed based on the above risk factors can effectively evaluate the risk of postoperative ALI in patients with STAAD.

OBJECTIVE: To explore the incidence, clinical features, genetic variant characteristics and prognosis of Glutaric acidemia type I (GA1) among neonates from Henan Province. METHODS: A total of 814 625 neonates undergoing screening for inherited metabolic diseases by tandem mass spectrometry (MS/MS) at the Third Affiliated Hospital of Zhengzhou University from January 2016 to December 2022 were selected as the study subjects. A retrospective method was adopted to collect the clinical data of the patients. Whole exome sequencing was carried out to detect GCDH gene variants in individuals with positive results by GA1 newborn screening, and Sanger sequencing was used to verify the candidate variants. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), the pathogenicity of candidate variants was rated. This study was approved by the Medical Ethics Committee of the Hospital (Ethics Number: 2019 Medical Ethics Review No. 67). RESULTS: Eight cases of GA1 were diagnosed among the 814 625 neonates. Blood glutaryl carnitine (C5DC) and urine glutaric acid (GA) levels of the 8 children were higher than the normal reference values. In total 12 variants were detected, all of which were missense variants. c.1064G>A (p.Arg355His) was the most common one, accounting for 21.4% (3/14). Three GCDH gene variants, including 1297G>C (p.Ala433Pro), c.467G>A (p.Gly156Asp) and c.1125T>G (p.Cys375Trp), were previously unreported. REVEL software analysis predicted that all of the three variants were harmful. 3D protein structure modeling indicated that the three variants may cause amino acid residue alterations, and c.1297G>C (p.Ala433Pro) and c.1125T>G (p.Cys375Trp) may result in increase in hydrogen bonds and affect the function of GCDH protein. By December 2023, one of the eight children had deceased, and another child had severe clinical symptoms with poor prognosis. Six children had a good prognosis, of which two had mild motor development delay and four had normal development without clinical symptoms. CONCLUSION The incidence of GA1 in newborns screened by MS/MS in Henan Province is 1/101 828, and the carrier rate of pathogenic GCDH variants is 1/160. The c.1064G>A (p.Arg355His) may be the hotspot variant of the GCDH gene among children with GA1 in Henan. Discovery of the three novel variants has enriched the mutational spectrum of the GCDH gene and provide a basis for the early diagnosis, treatment, prognosis and genetic counseling of this disease.
Humans ; Amino Acid Metabolism, Inborn Errors/epidemiology* ; Glutaryl-CoA Dehydrogenase/chemistry* ; Infant, Newborn ; Female ; Neonatal Screening/methods* ; Male ; Brain Diseases, Metabolic/epidemiology* ; China/epidemiology* ; Retrospective Studies ; Mutation ; Genetic Variation ; Glutarates

Objective:To explore the incidence, clinical features, genetic variant characteristics and prognosis of Glutaric acidemia type I (GA1) among neonates from Henan Province.Methods:A total of 814 625 neonates undergoing screening for inherited metabolic diseases by tandem mass spectrometry (MS/MS) at the Third Affiliated Hospital of Zhengzhou University from January 2016 to December 2022 were selected as the study subjects. A retrospective method was adopted to collect the clinical data of the patients. Whole exome sequencing was carried out to detect GCDH gene variants in individuals with positive results by GA1 newborn screening, and Sanger sequencing was used to verify the candidate variants. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), the pathogenicity of candidate variants was rated. This study was approved by the Medical Ethics Committee of the Hospital (Approval Number: 2019 Medical Ethics Review No. 67). Results:Eight cases of GA1 were diagnosed among the 814 625 neonates. Blood glutaryl carnitine (C5DC) and urine glutaric acid (GA) levels of the 8 children were higher than the normal reference values. In total 12 variants were detected, all of which were missense variants. c. 1064G>A (p.Arg355His) was the most common one, accounting for 21.4% (3/14). Three GCDH gene variants, including 1297G>C (p.Ala433Pro), c. 467G>A (p.Gly156Asp) and c. 1125T>G (p.Cys375Trp), were previously unreported. REVEL software analysis predicted that all of the three variants were harmful. 3D protein structure modeling indicated that the three variants may cause amino acid residue alterations, and c. 1297G>C (p.Ala433Pro) and c. 1125T>G (p.Cys375Trp) may result in increase in hydrogen bonds and may affect the function of GCDH protein. By December 2023, one of the eight children had deceased, and another child had severe clinical symptoms with poor prognosis. Six children had a good prognosis, of which two had mild motor development delay and four had normal development without clinical symptoms. Conclusion:The incidence of GA1 in newborns screened by MS/MS in Henan Province is 1/101 828, and the carrier rate of pathogenic GCDH variants is 1/160. The c. 1064G>A (p.Arg355His) may be the hotspot variant of the GCDH gene among children with GA1 in Henan. Discovery of the three novel variants has enriched the mutational spectrum of the GCDH gene and provide a basis for the early diagnosis, treatment, prognosis and genetic counseling of this disease.

OBJECTIVE: To propose a machine learning-based method for predicting the trajectories during manual acupuncture manipulation (MAM), aiming to improve the precision and consistency of acupuncture practitioner' operation and provide the real-time suggestions on MAM error correction. METHODS: Computer vision technology was used to analyze the hand micromotion when holding needle during acupuncture, and provide a three-dimensional coordinate description method of the index finger joints of the holding hand. Focusing on the 4 typical motions of MAM, a machine learning-based MAM trajectory predictive model was designed. By integrating the changes of phalangeal joint angle and hand skeletal information of acupuncture practitioner, the motion trajectory of the index finger joint was predicted accurately. Besides, the roles of machine learning-based MAM trajectory predictive model in the skill transmission of acupuncture manipulation were verified by stratified randomized controlled trial. RESULTS: The performance of MAM trajectory predictive model, based on the long short-term memory network (LSTM), obtained the highest stability and precision, up to 98%. The learning effect was improved when the model applied to the skill transmission of acupuncture manipulation. CONCLUSION The machine learning-based MAM predictive model provides acupuncture practitioner with precise action prediction and feedback. It is valuable and significant for the inheritance and error correction of manual operation of acupuncture.
Humans ; Acupuncture Therapy/instrumentation* ; Machine Learning ; Adult ; Male ; Female

OBJECTIVE: To investigate the effect of manual acupuncture manipulations (MAMs) on subcutaneous muscle tissue, by developing quantitative models of "lifting and thrusting" and "twisting and rotating", based on machine learning techniques. METHODS: A depth camera was used to capture the acupuncture operator's hand movements during "lifting and thrusting" and "twisting and rotating" of needle. Simultaneously, the ultrasound imaging was employed to record the muscle tissue responses of the participants. Amplitude and angular features were extracted from the movement data of operators, and muscle fascicle slope features were derived from the data of ultrasound images. The dynamic time warping barycenter averaging algorithm was adopted to align the dual-source data. Various machine learning techniques were applied to build quantitative models, and the performance of each model was compared. The most optimal model was further analyzed for its interpretability. RESULTS: Among the quantitative models built for the two types of MAMs, the random forest model demonstrated the best performance. For the quantitative model of the "lifting and thrusting" technique, the coefficient of determination (R²) was 0.825. For the "twisting and rotating" technique, R² reached 0.872. CONCLUSION Machine learning can be used to effectively develop the models and quantify the effects of MAMs on subcutaneous muscle tissue. It provides a new perspective to understand the mechanism of acupuncture therapy and lays a foundation for optimizing acupuncture technology and designing personalized treatment regimen in the future.
Humans ; Acupuncture Therapy/methods* ; Machine Learning ; Male ; Adult ; Female ; Subcutaneous Tissue/diagnostic imaging* ; Young Adult

Objective To establish autoverification rules for six routine coagulation assays(PT,APTT,TT,Fib,DD,and FDP)based on the stratification of outpatients and inpatients,in accordance with CLSI AUTO-10A,AUTO-15,and WS/T 616-2018 guide-lines,and to validate the feasibility of this stratified strategy with clinical data while optimizing verification efficiency.Methods A to-tal of 323 451 coagulation test results from Zhongshan Hospital,Fudan University in 2022 were retrospectively analyzed to define auto-verification rules involving critical values,instrument flags,logical rules,historical comparison,and numerical ranges.A stratified au-toverification system was established by applying distinct rules for outpatient and inpatient populations.Subsequently,the rules were op-timized using 87 830 coagulation test results from January to March 2024,and the consistency between autoverification and manual veri-fication was prospectively evaluated using 33 968 consecutive coagulation specimens collected in April 2024.Results A stratified au-toverification system was successfully developed,comprising a total of 53 rules.The pass rate of overall verification was 77.16％(26 210/33 968),with a true-positive rate of 19.64％(6 672/33 968),a false-positive rate of 3.20％(1 086/33 968),a true-nega-tive rate of 77.16％(26 210/33 968),and no false negatives were detected.Conclusion The proposed autoverification system signifi-cantly improved verification efficiency.The stratified design based on outpatient and inpatient populations effectively minimized the risk of false negatives,and may provide a novel approach for the further development and optimization of coagulation test autoverification.

Objective:Analyze the risk factors for acute lung injury of postoperative acute lung injury(ALI)in patients with Stanford type A aortic dissection(STAAD),and construct a nomogram predictive model.Methods:A retrospective cohort study design was adopted.A total of 112 patients with STAAD who underwent surgical treatment in our hopital from January 2021 to August 2024 were included.They were divided into two groups according to the occurrence of ALI after the surgical:non-ALI group(73 cases)and ALI group(39 cases).Clinical data were collected from both groups of patients.The influencing factors of postoperative ALI in patients with STAAD were analyzed by multivariate logistic regression.Established nomogram prediction model based on influencing factors and validated.Results:Among 112 patients with STAAD who underwent surgical treatment,39 case postoperative ALI occurred,with an incidence rate of 34.82％.Age,preoperative creatinine,body mass index(BMI),preoperative white blood cell count,preoperative lactate and other aspects compared,The difference were statistically significant(P＜0.05).The length of stay in the intensive care unit(ICU)of the ALI group was longer than that of the non ALI group(P＜0.05).The intraoperative red blood cell transfusion volume and extracorporeal circulation time in the ALI group were higher than those in the non ALI group(P＜0.05).Long intraoperative cardiopulmonary bypass time,high BMI,high intraoperative red blood cell transfusion volume and high preoperative white blood cell count were risk factors for postoperative ALI(P＜0.05).The receiver operating characteristic(ROC)curve analysis results show that,the Area under the curve(AUC)of the nomogram prediction model was 0.871.When the optimal critical value was 0.472,its sensitivity and specificity wew 0.887 and 0.776,respectively.The internal validation results of Bootstrap show that,the C-index of the column chart prediction model was 0.862,with an absolute error of 0.032.The calibration curve is close to the ideal curve and the original curve,with a slope close to 1.Conclusions:Long intraoperative cardiopulmonary bypass time,high BMI,high intraoperative red blood cell transfusion volume and high preoperative white blood cell count are independent risk factors for postoperative ALI in patients with STAAD.The nomogram model constructed based on the above risk factors can effectively evaluate the risk of postoperative ALI in patients with STAAD.

Objective:To investigate the relationship between lipid levels and cognitive impairment in the elderly Chinese population using prospective cohort data.Methods:Based on the China-PAR (Prediction for Atherosclerotic Cardiovascular Disease Risk in China) cohort, this study included 24 380 individuals aged ≥60 years who participated in the cognitive function follow-up survey from 2018 to 2019. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), with cognitive impairment defined according to different educational levels: MMSE ≤17 for illiterate individuals, MMSE ≤20 for those with primary education and MMSE ≤24 for those with secondary education or above. Multivariable linear regression and logistic regression models were employed to examine the associations between six baseline lipid indicators and cognitive scores, as well as cognitive impairment. Additionally, restricted cubic splines were used to explore the exposure-dose relationship between lipid levels and cognitive function.Results:The study population had a median follow-up time of 11.6 years, with a baseline age of (59.7±6.8) years. Among the participants, 9 510 (39.0%) were males, and the mean MMSE score was 24.7±6.8. A total of 3 887 individuals (15.9%) were identified as cognitively impaired. The results of multivariable linear regression and logistic regression indicated that total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C) levels were not only significantly positively associated with cognitive scores but also significantly associated with a lower risk of cognitive impairment. Each 1 mmol/L increase in these lipid levels corresponded to β values (95% CI) of 0.267 (0.173-0.361), 0.385(0.271-0.499) and 0.331(0.231-0.431), respectively. Each 1 mmol/L increase in these lipid levels corresponded to odds ratio ( OR) (95% CI) values of 0.915 (0.876-0.956), 0.875 (0.830-0.923) and 0.886 (0.848-0.927), respectively. The dose-response curve demonstrated that the negative association was primarily observed within the guideline-recommended optimal lipid level range. Specifically, when LDL-C was less than 3.4 mmol/L and non-HDL-C was less than 4.1 mmol/L, the corresponding OR (95% CI) values were 0.859 (0.796-0.926) and 0.876 (0.818-0.939). Conclusion:Lipid levels exhibit a certain linear negative association with cognitive impairment in elderly Chinese adults, with LDL-C and non-HDL-C demonstrating a stronger effect, particularly within the guideline-recommended optimal range.