Climate and land use changes may significantly impact the habitat distribution of Gastrodia elata, an endangered traditional medicinal plant. Accurately predicting its future potential suitable habitats is crucial for its conservation and sustainable development. This study integrates current distribution data of G. elata with 56 environmental variables and uses the MaxEnt model to predict changes in its suitable habitats under current climate conditions and four future climate scenarios(SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The results show that October precipitation and December minimum temperature are key environmental factors influencing its distribution. Under the current climate, optimal habitats for G. elata are concentrated in montane forest areas in Sichuan, Yunnan, Guizhou, and Hubei, which meet the species' requirements for understory growth. Across all future scenarios, the suitable habitat of G. elata consistently shows a stable northward shift, with a steady increase in suitable areas, extending to the middle and lower reaches of the Yangtze River and the Huang-Huai region, and even expanding into Liaoning, Jilin, and southern Heilongjiang. Land use analysis, taking into account the protection of arable land and the utilization of forest resources, indicates that by 2100, under future climate conditions, arable land in medium-to high-suitability areas is expected to increase by 30%-124%. While the conversion of non-suitable forest land into suitable habitats is projected to increase by 5%-52%, the growth of medium-to high-suitability areas within forests is relatively modest, ranging from 1% to 24%. These findings highlight the need to balance agricultural expansion with forest resource conservation to ensure the long-term sustainability of G. elata and provide scientific guidance for future suitable habitat management.
Ecosystem ; China ; Climate Change ; Gastrodia/growth & development* ; Conservation of Natural Resources ; Plants, Medicinal/growth & development*

Acute-on-chronic liver failure （ACLF） is a complex clinical syndrome， and early identification and accurate prognostic evaluation are of great importance for patient treatment and management. In recent years， with in-depth research on the pathogenesis of ACLF， multiple prognostic biomarkers have been proposed and used in clinical practice. This article systematically reviews the research advances in prognostic biomarkers for ACLF from the aspects of clinical predictive models， immunological biomarkers， metabolic biomarkers， genetic and epigenetic biomarkers， microbiome-related biomarkers， and emerging technologies such as artificial intelligence and multi-omics， and it also discusses the value and application prospects of these biomarkers in the prognostic evaluation of ACLF and proposes future research directions， in order to provide a scientific and comprehensive reference for clinicians， guide individualized treatment and management of ACLF patients， and finally improve the clinical outcomes of patients.

Objective To screen the risk factors for hypoglycemia in adult intensive care unit(ICU)patients,construct a risk prediction model,and validate its predictive effect.Methods A retrospective study was conducted on adult critically ill patients admitted to the general ICU of Hospital of Chengdu University of Traditional Chinese Medicine from December 2023 to September 2024.Patients admitted from December 2023 to June 2024 served as the modeling group,and those from July to September 2024 as the validation group.A total of 928 patients were included,with 650 in the modeling group and 278 in the validation group.After literature review and expert consultation,27 potential risk factors for hypoglycemia in ICU patients were initially screened,and data were collected including general information[gender,age,acute physiology and chronic health evaluation Ⅱ(APACHEⅡ)score,sequential organ failure assessment(SOFA)score,nutrition risk in critically ill(NUTRIC)score,mechanical ventilation status,hemodialysis status,enteral nutrition status],disease data(sepsis,liver disease history,kidney disease history,diabetes history,hypoglycemia history),blood glucose-related indicators[mean blood glucose,blood glucose coefficient of variation,insulin dosage,intravenous insulin titration use,inotropic drug use,insulin secretagogues(Sulfonylureas and Glinides),and combined use of hypoglycemic drugs(two or more)],and laboratory indicators[serum creatinine(SCr),blood urea nitrogen(BUN),serum albumin(Alb),alanine aminotransferase(ALT),aspartate aminotransferase(AST),total bilirubin(TBil),glomerular filtration rate(GFR)].The patients were divided into a hypoglycemia group and a non-hypoglycemia group based on the occurrence of hypoglycemia.Univariate analysis and binary Logistic regression analysis were used to identify influencing factors of hypoglycemia in adult ICU patients,and a nomogram prediction model was constructed.The area under the receiver operator characteristic curve(AUC)and calibration curves were employed to evaluate the discrimination and calibration of the model.Results The modeling cohort included 552 non-hypoglycemic patients and 98 hypoglycemic patients,with an ICU hypoglycemia incidence rate of 15.1％.Compared with the hypoglycemia group,the non-hypoglycemia group showed significantly lower proportions of patients with renal disease history,diabetes history,hypoglycemia history,undergoing hemodialysis,using intravenous insulin titration,and combined use of hypoglycemic drugs,as well as lower blood glucose coefficient of variation,lower APACHEⅡ scores,and significantly elevated GFR(all P<0.05).Binary Logistic regression analysis was performed using the 9 variables with statistically significant differences in univariate analysis as independent variables and hypoglycemia occurrence as the dependent variable.The results indicated that a history of diabetes,a history of hypoglycemia,APACHEⅡ score,GFR,blood glucose coefficient of variation,and combined use of hypoglycemic drugs were independent risk factors for hypoglycemia in ICU patients[odds ratios(OR)were 1.761,2.095,1.048,0.990,1.029,and 1.975,respectively,and 95％confidence intervals(95％CI)were 1.052-2.949,1.220-3.600,1.022-1.074,0.982-0.997,1.013-1.046,and 1.145-3.408,respectively.The corresponding Pvalues were 0.031,0.007,0.000,0.009,<0.001,0.014].A nomogram prediction model for hypoglycemia in ICU patients was constructed using six independent predictors selected through binary logistic regression analysis.The ROC curve AUC for the modeling group was 0.884(95％CI 0.826-0.941,P=0.250),with a maximum Youden index of 0.713,sensitivity of 92.1％,and specificity of 79.2％.The validation cohort included 38 patients with hypoglycemia and 240 patients without hypoglycemia.Compared with the hypoglycemia group,the non-hypoglycemia group showed significantly lower proportions of patients with a history of diabetes,a history of hypoglycemia,and combined use of hypoglycemic drugs,as well as lower APACHEⅡ scores and lower blood glucose coefficient of variation,with significantly increased GFR(all P<0.05).The ROC curve AUC for the validation cohort was 0.803(95％CI was 0.757-0.849,P=0.138),indicating high discriminatory ability.The predicted probability at the diagnostic cutoff point was P=0.138.The model's diagnostic threshold for predicted probability was P=0.138,while the optimal cut-off value based on the Youden index was 0.513,yielding a sensitivity of 76.5％and specificity of 74.8％,indicating predictive value for hypoglycemia in adult ICU patients.The mean absolute error(MAE)results for the modeling group and validation group were<0.05.The calibration curves of both the modeling and validation groups showed close alignment with the ideal curve,indicating excellent calibration performance of the model.Conclusion The constructed hypoglycemia risk prediction model for adult ICU patients has good predictive performance,which can quickly identify high-risk populations of hypoglycemia in ICU and provide reference for clinical preventive nursing.

Objective:To explore the effect of horizontal Baduanjin guided by interactive standard reaching theory(ISRT)on cardiac function and quality of life in patients with chronic heart failure(CHF).Methods:This random-ized controlled study enrolled 114 patients diagnosed with CHF in Bozhou People's Hospital between September 2024 and February 2025.Patients were randomly divided into control group(n=57,routine nursing care)and interven-tion group(n=57,horizontal Baduanjin under the guidance of interactive standard theory).Both groups were con-tinuously treated for 3 months.Cardiac function indexes,N-terminal pro-brain natriuretic peptide(NT-proB-NP),self-care ability,quality of life and incidence of major adverse cardiovascular events(MACE)were com-pared between the two groups.Results:Compared to patients in the control group,those in the intervention had significant higher left ventricular ejection fraction(LVEF)[(56.34±0.86)％vs.(46.69±0.91)％],6min walking distance(6MWD)[(477.16±6.62)m vs.(451.39±4.17)m]and the proportion of New York Heart Association(NYHA)class Ⅱ(70.18％vs.35.09％),and significant lower NT-proBNP[(237.76±6.41)pg/ml vs.(377.46±6.85)pg/ml],scores of the European heart failure self-care behavior scale 9(EHFSCB-9)[(12.61±1.81)points vs.(25.51±1.71)points],the physical domain[(3.96±0.91)points vs.(11.05±0.79)points],emotional domain[(4.00±0.85)points vs.(11.95±0.81)points]and other domain[(4.05±0.83)points vs.(13.47±1.15)points]of MLHFQ(P＜0.001 all).Compared to those in the control group,patients in intervention group had significant lower incidence of MACE(7.02％vs.22.81％,P=0.035).Conclusion:Under the guidance of the interactive standard theory,the horizontal Baduanjin exercise may enhance the cardiac function,improve self-care ability and quality of life in patients with chronic heart failure.

Acute kidney injury (AKI) has high morbidity and mortality, but effective clinical drugs and management are lacking. Previous studies have suggested that macrophages play a crucial role in the inflammatory response to AKI and may serve as potential therapeutic targets. Emerging evidence has highlighted the importance of forkhead box protein O1 (FoxO1) in mediating macrophage activation and polarization in various diseases, but the specific mechanisms by which FoxO1 regulates macrophages during AKI remain unclear. The present study aimed to investigate the role of FoxO1 in macrophages in the pathogenesis of AKI. We observed a significant upregulation of FoxO1 in kidney macrophages following ischemia-reperfusion (I/R) injury. Additionally, our findings demonstrated that the administration of FoxO1 inhibitor AS1842856-encapsulated liposome (AS-Lipo), mainly acting on macrophages, effectively mitigated renal injury induced by I/R injury in mice. By generating myeloid-specific FoxO1-knockout mice, we further observed that the deficiency of FoxO1 in myeloid cells protected against I/R injury-induced AKI. Furthermore, our study provided evidence of FoxO1's pivotal role in macrophage chemotaxis, inflammation, and migration. Moreover, the impact of FoxO1 on the regulation of macrophage migration was mediated through RhoA guanine nucleotide exchange factor 1 (ARHGEF1), indicating that ARHGEF1 may serve as a potential intermediary between FoxO1 and the activity of the RhoA pathway. Consequently, our findings propose that FoxO1 plays a crucial role as a mediator and biomarker in the context of AKI. Targeting macrophage FoxO1 pharmacologically could potentially offer a promising therapeutic approach for AKI.

Over the past few decades, tumor immunotherapy has revolutionized the landscape of cancer clinical treatment. There is a flourishing development of combination strategies to improve the anti-tumor efficacy of mono-immunotherapy. However, instead of a straightforward combination of multiple therapeutics, it is more preferable to pursue a synergistic effect by designing rational combinations as well as administration strategies, which are based on a comprehensive understanding of the physiological and pathological features. In this case, the timing and spatial distribution of the combination drugs become essential factors in achieving improved therapeutic outcomes. Therefore, the concept of Sequential Drug Delivery System (SDDS) is proposed to define the spatiotemporally programmed drug delivery/release through triggers of internal conditions and/or external interventions, thus complying with the dynamic disease evolution and the human immunity. This review summarizes the recent advancements in biomaterial-based SDDSs used for spatiotemporally-tuned combination tumor immunotherapy. Furthermore, the rationales behind various engineering strategies are discussed. Finally, an overview of potential synergistic mechanisms as well as their prospects for combination immunotherapy is presented.

Quality marker (Q-Marker) is an innovative concept and model for quality control of Traditional Chinese medicines (TCMs), which will navigate the new direction of quality development of TCMs. Yet, how to characterize the overall quality attributes of TCMs and their biological effects is still debating. In view of this key scientific issue, this paper proposes a research method based on mass spectrometry imaging (MSI) technology for the discovery and confirmation of TCMs Q-Marker. MSI is powerful in investigating the spatial distribution of molecules in a variety of samples, and visualizing the information obtained from MS. On this basis, combine with the five principles of TCMs Q-Marker validation, i.e., specificity, transmission and traceability, testability, prescription compatibility, and validity, were applied to confirm the finalized Q-Marker. It will lead the new direction of quality development of TCMs.

OBJECTIVE To compare the efficacy and safety of chemotherapy combined with tislelizumab or sintilimab in patients with advanced non-small cell lung cancer （NSCLC）， and to analyze the influential factors of prognostic. METHODS A retrospective study was conducted on 163 patients with advanced NSCLC who received chemotherapy combined with tislelizumab or sintilimab at the First People’s Hospital of Yunnan Province from September 1， 2021 to November 30， 2024. Among them， there were 90 patients in the tislelizumab group and 73 patients in the sintilimab group. The objective response rate （ORR）， disease control rate （DCR）， progression free survival （PFS）， and overall survival （OS） of two groups were observed， and the occurrence of adverse drug reactions in patients was evaluated. Kaplan-Meier method was used to plot PFS and OS survival curves， Log-rank test was applied for univariate analysis， and Cox regression model was used to evaluate the independent prognostic factors of PFS and OS. RESULTS The median PFS of patients in the tislelizumab group and the sintilimab group were 14.14 months （95%CI of 10.95-17.33） and 10.95 months （95%CI of 8.75-13.15）， respectively. The median OS was 25.89 months （95%CI of 22.67-29.11） and 24.25 months （95%CI of 19.34-29.16）， with ORR of 45.56% and 49.32%， DCR of 94.44% and 90.41%， and the incidence of adverse drug reactions of 84.44% and 79.45%， respectively， the differences were not statistically significant （P＞0.05）. Age ≥60 years （HR＝1.542， 95%CI of 1.044-2.278， P＝0.029） and systemic immune inflammatory nutritional index （SIINI）＞ 116.58 （HR＝1.541， 95%CI of 1.058-2.245， P＝0.024） were risk factors for PFS in NSCLC patients receiving immune checkpoint inhibitor therapy； the use of antibiotics may affect the overall survival of patients （P＝0.001）. CONCLUSIONS The efficacy and safety of chemotherapy combined with tislelizumab or sintilimab for advanced NSCLC are comparable； age≥60 years and SIINI ＞116.58 are risk factors for PFS in NSCLC patients， and the use of antibiotics may affect the patients’ OS.