ObjectiveTo investigate the modifiable areal unit problem （MAUP） in the spatial pattern of Pseudostellaria heterophylla production regions and reveal the impact of statistical scales on the spatial distribution characteristics of this medicinal plant species. MethodsUsing multi-source data （literature records， field surveys， and statistical data）， we systematically analyzed the spatial patterns across three administrative levels （provincial， prefectural， and county scales）. Spatial autocorrelation （Moran's I） analysis， high-low clustering （Getis-Ord General G）， and hot/cold spot analysis （Getis-Ord Gi*） were employed. ResultsThe literature-based analysis showed that the production regions of P. heterophylla presented random distribution on the provincial scale and significant aggregation on the prefectural scale. The field survey data showed that the production regions displayed random distribution on the provincial scale but significant aggregation on both prefectural and county scales. The statistical data revealed that the production regions lacked spatial autocorrelation on the provincial scale but demonstrated significant aggregation on prefectural and county scales. ConclusionMAUP effects have substantive implications for understanding and decision-making in the arrangement of medicinal plant production regions. The county scale proves to be the most sensitive and explanatory level for analyzing the spatial pattern of P. heterophylla production regions， providing a critical foundation for habitat modeling， suitability evaluation， and ecological cultivation planning of medicinal plants.

Objective To identify the independent predictors of programmed death-1 (PD-1) inhibitor-related endocrine adverse events and construct a clinically usable risk prediction model. Methods A total of 302 patients with solid tumors treated with PD-1 inhibitors were retrospectively enrolled. According to the presence or absence of endocrine immune-related adverse events (irAEs), the patients were divided into case group and control group. The clinical and laboratory indexes were compared between the two groups. Multivariable logistic regression was used to confirm independent predictors of endocrine irAEs. The nomogram was constructed, while the receiver operating characteristic (ROC) curve was used to test the prediction performance of the model. Results The overall incidence of endocrine irAEs was 21.9% (66/302), and the incidence of hypothyroidism was 19.5% (59/302). The age, PD-1 inhibitors, free thyroxine, thyroid peroxidase antibody (TPOAb), thyroglobulin, amylase, lymphocyte subset CD3 expression were statistically different between the two groups (P＜0.05). Multivariable logistic regression showed that higher expression of lymphocyte subset CD3 was a protective factor to prevent endocrine irAEs occurrence (P＝0.004), while age＜60 years, higher TPOAb and use of pembrolizumab were independent risk factors of endocrine irAEs (P＜0.05). The nomogram model thus constructed, and when the threshold probability of the model exceeded 0.1, its net benefit was higher. ROC curve showed that the AUC of the model to predict endocrine irAEs was 0.760. The prediction result of the model was highly consistent with the actual result. Conclusions The age, type of PD-1 inhibitor, baseline TPOAb level, and baseline CD3 expression can independently predict endocrine irAEs occurrence or not. The nomogram model based on this model has good predictive efficiency, which can provide reference for early identification of high-risk patients and immunotherapy management.

Lung transplantation is the ultimate therapeutic option for end-stage pulmonary diseases such as interstitial pneumonia, chronic obstructive pulmonary disease and pneumoconiosis. Currently, the shortage of allogeneic lung donors significantly limits the opportunity for end-stage lung disease patients to receive lung transplantation. In recent years, with the rapid development of biomedical engineering technologies, especially the major breakthroughs in genetic modification and cloning, xenogeneic lung transplantation has shown important potential for clinical translation. Among them, genetically modified pigs have become the most promising xenogeneic lung source due to the close similarity of organ size and physiological characteristics to humans, and the ability to perform targeted gene knockouts (such as α-Gal antigen knockout) to reduce the occurrence of hyperacute rejection. This article focuses on the research progress of porcine xenogeneic lung transplantation, systematically reviews the latest achievements and challenges in animal experiments and human trials, and introduces the technical experience accumulated by Shenzhen Third People's Hospital in the porcine-to-monkey xenogeneic lung transplantation model, in the hope of providing practical references for future research in this field.

This paper aims to contribute to guaranteeing the stable development and enhancing the understanding of ecological agriculture of Chinese materia medica so that the national strategy and industrial demand can be better served. It first introduces current traditional Chinese medicine(TCM)policy and industrial development status from five aspects, including policy guarantee, theoretical support, technological innovation, standardization system, and brand influence. Then, the paper analyzes the development dilemma of TCM agriculture in production and quality increase and ecological environment protection. It also proposes the development goals of ecological agriculture of Chinese materia medica that meet the current industrial development demand, which are reducing chemical fertilizers, pesticides, and carbon emissions, improving quality, increasing efficiency, and protecting ecological environment. In addition, the new development goals are interpreted through case studies. Finally, this paper proposes four development strategies for ecological agriculture of Chinese materia medica: conducting research on the pattern and spatial and temporal variations of nationwide TCM production areas; studying the internal and external ecological memories of medicinal plant growth from the perspectives of genetic variations and environmental adaptation variations and elucidating their contributions to the formation of quality; carrying out selection and breeding of stress-resistant varieties for ecological agriculture of Chinese materia medica, the optimization of key technologies for soil improvement and restoration and green prevention and control against diseases and pests, and the improvement of quality; carrying out research on the quality assurance and value realization of ecological products made from TCM. This research can provide guidance for policy formulation, theoretical development of the discipline, and the enhancement of industrial technology for ecological agriculture of Chinese materia medica.
Agriculture/methods* ; China ; Drugs, Chinese Herbal ; Plants, Medicinal/chemistry* ; Ecosystem ; Materia Medica ; Medicine, Chinese Traditional

OBJECTIVE: As an age-related neurodegenerative disease, the prevalence of mild cognitive impairment (MCI) increases with age. Within the framework of traditional Chinese medicine, spleen-kidney deficiency syndrome (SKDS) is recognized as the most frequent MCI subtype. Due to the covert and gradual onset of MCI, in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes. There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS (MCI-SKDS). METHODS: This investigation enrolled 312 elderly individuals diagnosed with MCI, who were randomly distributed into training and test datasets at a 3:1 ratio. Five machine learning methods, including logistic regression (LR), decision tree (DT), naive Bayes (NB), support vector machine (SVM), and gradient boosting (GB), were used to build a diagnostic prediction model for MCI-SKDS. Accuracy, sensitivity, specificity, precision, F1 score, and area under the curve were used to evaluate model performance. Furthermore, the clinical applicability of the model was evaluated through decision curve analysis (DCA). RESULTS: The accuracy, precision, specificity and F1 score of the DT model performed best in the training set (test set), with scores of 0.904 (0.845), 0.875 (0.795), 0.973 (0.875) and 0.973 (0.875). The sensitivity of the training set (test set) of the SVM model performed best among the five models with a score of 0.865 (0.821). The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset. The DCA of all models showed good clinical application value. The study identified ten indicators that were significant predictors of MCI-SKDS. CONCLUSION The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical; the model demonstrates good predictive value and clinical applicability, and the DT model had the best performance. Please cite this article as: Ai YT, Zhou S, Wang M, Zheng TY, Hu H, Wang YC, Li YC, Wang XT, Zhou PJ. Development of a machine learning-based risk prediction model for mild cognitive impairment with spleen-kidney deficiency syndrome in the elderly. J Integr Med. 2025; 23(4): 390-397.
Humans ; Cognitive Dysfunction/diagnosis* ; Aged ; Male ; Female ; Machine Learning ; Spleen ; Aged, 80 and over ; Kidney ; Medicine, Chinese Traditional

The introduction of non-vitamin K antagonist oral anticoagulant (NOAC) into clinical use heralds a new age for anticoagulation therapy in patients with atrial fibrillation (AF).However,anticoagulation-related bleeding is currently a major challenge in the anticoagulation process.Assessing the risk of anticoagulation-related bleeding is an important part for the management of patients with AF.Clinical risk factor scores have moderate ability to predict the risk of anticoagulation-related bleeding.To improve the anticoagulation safety of NOACs,additional clinical and biological markers and genetic polymorphisms should be considered to enhance the predictive capability for anticoagulation-related bleeding.This review summarizes the challenges in the management of anticoagulation therapy,with emphases on the bleeding risk scores,biomarkers,clinical indicators,and genetic loci currently used to guide the risk assessment of anticoagulation-related bleeding in AF patients.This review is expected to provide research insights and reference frameworks for predicting and evaluating the bleeding risk associated with NOACs.
Humans ; Atrial Fibrillation/drug therapy* ; Anticoagulants/therapeutic use* ; Hemorrhage/chemically induced* ; Risk Assessment ; Administration, Oral ; Risk Factors

Objective To explore the relationships of cognitive impairment with cardiovascular death and all-cause death in menopausal women with hypertension.Methods A total of 4 595 natural-menopausal women with hypertension screened in Wuyuan County of Jiangxi Province from July to August 2018 were selected as the research subjects,and a follow-up investigation of death information was completed from June to August 2022.According to the baseline mini-mental state examination(MMSE)score,all subjects were allocated into a normal cognitive function group and a cognitive impairment group.The basic characteristics and the cumulative risk of death evaluated by the Kaplan-Meier curve were compared between two groups.The multivariate Cox regression model was adopted to analyze the effect of cognitive function on death,and the relationship between MMSE score and death was fitted by the restricted cubic spline.Results A total of 4 595 subjects with the mean age of(65.1±8.4)years were included in this study,in which and 1 859(40.5%)patients with cognitive impairment were detected.During a mean follow-up period of(3.9±0.4)years,199 all-cause deaths were collected,including 102 cardiovascular deaths.The normal cognitive function group and the cognitive impairment group had the cumulative all-cause death rates of 2.6%and 6.9%and the cumulative cardiovascular death rates of 1.0%and 4.0%,respectively.The Kaplan-Meier curve showed that the cumulative risks of all-cause death(χ²=47.287,P<0.001)and cardiovascular death(χ²=45.169,P<0.001)in the cognitive impairment group were higher than those in the normal cognitive function group.The results of multivariate Cox regression analysis indicated that compared with the normal cognitive function group,the cognitive impairment group had increased risks of all-cause death(HR=1.75,95%CI=1.28-2.39,P<0.001)and cardiovascular death(HR=2.56,95%CI=1.61-4.09,P<0.001).The results of the restricted cubic spline curve fitting showed that the MMSE score had linearly negative correlations with the risk of all-cause death(P_all<0.001, P n o n - l i n e a r i t y=0.519)and cardiovascular death(P_all<0.001, P n o n - l i n e a r i t y=0.195).Conclusion Cognitive impairment is an independent risk factor for all-cause death and cardiovascular death in menopausal women with hypertension,and early identification of cognitive impairment in this population is essential for timely intervention.
Humans ; Female ; Cognitive Dysfunction ; Hypertension/complications* ; Aged ; Middle Aged ; Menopause ; Proportional Hazards Models ; Risk Factors ; Cardiovascular Diseases/mortality* ; Cause of Death ; Kaplan-Meier Estimate