Lung transplantation is the optimal treatment for end-stage lung diseases and can significantly improve prognosis of the patients. However, postoperative complications such as infection, rejection, ischemia-reperfusion injury, and other challenges (like shortage of donor lungs) , limit the practical application of lung transplantation in clinical practice. Chinese research teams have been making continuous efforts and have achieved breakthroughs in basic research on lung transplantation by integrating emerging technologies and cutting-edge achievements from interdisciplinary fields, which has strongly propelled the development of this field. This article will comprehensively review the academic progress made by Chinese research teams in the field of lung transplantation in 2024, with a focus on the achievements of Chinese teams in basic research on lung transplantation. It aims to provide innovative ideas and strategies for key issues in the basic field of lung transplantation and to help China's lung transplantation cause reach a higher level.

With the growing awareness of public health, the value and importance of traditional Chinese medicine(TCM) resources have become increasingly prominent. Despite the undeniable significance of TCM in medical treatment and healthcare, the protection, development, and utilization of TCM resources still face numerous challenges. Under the traditional model, the development and utilization of TCM resources heavily rely on manual labor and empirical decision-making, which not only leads to inefficiencies and high costs but also causes serious issues such as unstable drug quality and imbalances in market supply and demand. In the current era of rapid advancements in artificial intelligence(AI) and technology, AI has emerged as a new engine to address many challenges and difficulties throughout the entire TCM resource industry chain. By leveraging AI technology, intelligent management, precise production, and optimized utilization of TCM resources can be achieved, thereby improving efficiency, reducing costs, ensuring stable quality, and balancing market supply and demand. This article primarily explores the application of AI technology in the entire TCM resource industry chain from different perspectives and provides an in-depth analysis of the future development of AI in the TCM industry. It holds significant importance and value in promoting the intelligent development of the TCM sector and facilitating the healthy development of the entire TCM resource industry chain.
Artificial Intelligence ; Medicine, Chinese Traditional/economics* ; Humans ; Drugs, Chinese Herbal/economics* ; Drug Industry

To determine the optimal cultivation duration and harvest period for cultivated Notopterygium incisum and promote its industrial development, this study established a characteristic chromatographic profile of cultivated N. incisum and employed chemometrics combined with entropy-weighted grey correlation analysis to assess differences in agronomic traits and quality indicators across different cultivation years and harvest periods. By comparing with reference substances, ten common peaks were identified, including chlorogenic acid, p-coumaric acid, ferulic acid, marmesinin, nodakenin, isochlorogenic acid B, notopterol, phenethyl ferulate, isoimperatorin, and falcarindiol. The similarity between the characteristic chromatographic profiles of N. incisum at different cultivation years and the reference profile was all above 0.932. Principal component analysis(PCA) and orthogonal partial least squares discriminant analysis(OPLS-DA) revealed that the quality of 1-to 3-year-old cultivated N. incisum was highly dispersed and unstable, whereas the quality of 4-year-old cultivated N. incisum remained relatively stable across different harvest periods. This suggests that the accumulation of relevant compounds in the medicinal material had reached a plateau, confirming that the optimal cultivation period for N. incisum is four years. Entropy-weighted grey correlation analysis indicated that the quality of 4-year-old cultivated N. incisum across different harvest periods ranked from highest to lowest as follows: November, December, October, August, July, and September, demonstrating that November is the optimal harvest time. The findings of this study establish the suitable cultivation duration and optimal harvest period for N. incisum, providing a scientific basis for cultivation guidance and quality standardization.
Chromatography, High Pressure Liquid/methods* ; Apiaceae/chemistry* ; Entropy ; Chemometrics/methods* ; Drugs, Chinese Herbal/chemistry* ; Principal Component Analysis ; Quality Control

OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index

A rapid analytical method for simultaneous determination of 32 kinds of multi-residue veterinary drugs in eggs was developed using a modified QuEChERS technique based on a reduced graphene oxide-coated melamine sponge(r-GO@MeS)by ultra-high performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS).The influences of graphene oxide(GO)concentrations,sponge dosages,and purification modes on drug recoveries were investigated during the purification process.The optimal purification conditions involved using a GO concentration of 0.5 mg/mL,a sponge dosage of 6.0 cm3/mL,and a dynamic purification mode of 5 extrusion cycles.Separation was achieved using an Agilent Eclipse Plus C18 RRHD column(100 mm×2.1 mm,1.8 μm),and quantitative analysis was performed by the external standard method using an electrospray ionization source(ESI)in multiple reaction monitoring(MRM)mode.The results showed that all 32 kinds of veterinary drugs exhibited good linear correlation with coefficients greater than 0.999,and matrix effects(MEs)ranging from?7.8%to 18.9%.The limits of detection(LODs)and quantification(LOQs)ranged from 0.2 to 10.2 μg/kg and from 0.6 to 28.0 μg/kg,respectively.The recoveries for the three spiked levels were in the range of 66.5%?117.5%,with intra-day and inter-day precision(Relative standard deviation)below 13.3%and 16.3%,respectively.The synthetic r-GO@MeS exhibited efficient matrix purification without the need of high-speed centrifugation or strong magnetic field assistance.This significantly shorted the sample pretreatment time and improved the convenience of the matrix purification process.Combined with UPLC-MS/MS,the method was suitable for the rapid determination of multi-residue veterinary drugs in eggs.

Objective:To analyze the clinical characteristics of patients undergoing extracorporeal cardiopulmonary resuscitation (ECPR), and to explore the risk factors leading to poor prognosis.Methods:The clinical data of 95 patients with ECPR admitted to the First Affiliated Hospital of Zhengzhou University from January 2020 to May 2023 were retrospectively analyzed. According to the survival status at the time of discharge, the patients were divided into the survival group and death group. The difference of clinical data between the two groups was compared to explore the risk factors related to death and poor prognosis. Risk factors associated with death were identified by Binary Logistic regression analysis. Results:A total of 95 patients with ECPR were included in this study, 62 (65.3%) died and 33 (34.7%) survived at discharge. Patients in the death group had longer low blood flow time [40 (30, 52.5) min vs. 30 (24.5, 40) min ] and total cardiac arrest time[40 (30, 52.5) min vs. 30(24.5, 40) min], shorter total hospital stay [3 (2, 7.25) d vs. 19 (13.5, 31) d] and extracorporeal membrane oxygenation (ECMO) assisted time [26.5 (17, 50) h vs. 62 (44, 80.5) h], and more IHCA patients (56.5% vs. 33.3%) and less had spontaneous rhythm recovery before ECMO (37.1% vs. 84.8%). Initial lactate value [(14.008 ± 5.188) mmol/L vs.(11.23 ± 4.718) mmol/L], APACHEⅡ score [(30.10 ± 7.45) vs. (25.88 ± 7.68)] and SOFA score [12 (10.75, 16) vs. 10 (9.5, 13)] were higher ( P< 0.05). Conclusions:No spontaneous rhythm recovery before ECMO, high initial lactic acid and high SOFA score are independent risk factors for poor prognosis in ECPR patients.