The AP2/ERF transcription factor family is a class of transcription factors widely present in plants, playing a crucial role in regulating flowering, flower development, flower opening, and flower senescence. Based on transcriptome data from flower, leaf, and stem samples of two Lonicera macranthoides varieties, 117 L. macranthoides AP2/ERF family members were identified, including 14 AP2 subfamily members, 61 ERF subfamily members, 40 DREB subfamily members, and 2 RAV subfamily members. Bioinformatics and differential gene expression analyses were performed using NCBI, ExPASy, SOMPA, and other platforms, and the expression patterns of L. macranthoides AP2/ERF transcription factors were validated via qRT-PCR. The results indicated that the 117 LmAP2/ERF members exhibited both similarities and variations in protein physicochemical properties, AP2 domains, family evolution, and protein functions. Differential gene expression analysis revealed that AP2/ERF transcription factors were primarily differentially expressed in the flowers of the two L. macranthoides varieties, with the differentially expressed genes mainly belonging to the ERF and DREB subfamilies. Further analysis identified three AP2 subfamily genes and two ERF subfamily genes as potential regulators of flower development, two ERF subfamily genes involved in flower opening, and two ERF subfamily genes along with one DREB subfamily gene involved in flower senescence. Based on family evolution and expression analyses, it is speculated that AP2/ERF transcription factors can regulate flower development, opening, and senescence in L. macranthoides, with ERF subfamily genes potentially serving as key regulators of flowering duration. These findings provide a theoretical foundation for further research into the specific functions of the AP2/ERF transcription factor family in L. macranthoides and offer important theoretical insights into the molecular mechanisms underlying floral phenotypic differences among its varieties.
Plant Proteins/chemistry* ; Gene Expression Regulation, Plant ; Transcription Factors/chemistry* ; Lonicera/classification* ; Flowers/metabolism* ; Phylogeny ; Gene Expression Profiling ; Multigene Family

OBJECTIVES: To explore the mediating role of sleep duration in the relationship between depression symptoms and myopia among middle school students. METHODS: This study was a cross-sectional research conducted using a stratified cluster random sampling method. A total of 1 728 middle school students were selected from two junior high schools and two senior high schools in certain urban areas and farms of the Xinjiang Production and Construction Corps. Questionnaire surveys and vision tests were conducted among the students. Spearman analysis was used to analyze the correlation between depression symptoms, sleep duration, and myopia. The Bootstrap method was employed to investigate the mediating effect of sleep duration between depression symptoms and myopia. RESULTS: The prevalence of myopia in the overall population was 74.02% (1 279/1 728), with an average sleep duration of (7.6±1.0) hours. The rate of insufficient sleep was 83.62% (1 445/1 728), and the proportion of students exhibiting depression symptoms was 25.29% (437/1 728). Correlation analysis showed significant negative correlations between visual acuity in both eyes and sleep duration with depressive emotions as measured by the Center for Epidemiologic Studies Depression Scale (with correlation coefficients of -0.064, -0.084, and -0.199 respectively; P<0.01), as well as with somatic symptoms and activities (with correlation coefficients of -0.104, -0.124, and -0.233 respectively; P<0.01) and interpersonal relationships (with correlation coefficients of -0.052, -0.059, and -0.071 respectively; P<0.05). The correlation coefficients for left and right eye visual acuity and sleep duration were 0.206 and 0.211 respectively (P<0.001). Sleep duration exhibited a mediating effect between depression symptoms and myopia (indirect effect=0.056, 95%CI: 0.029-0.088), with the mediating effect value for females (indirect effect=0.066, 95%CI: 0.024-0.119) being higher than that for males (indirect effect=0.042, 95%CI: 0.011-0.081). CONCLUSIONS Sleep duration serves as a partial mediator between depression symptoms and myopia in middle school students.
Humans ; Myopia/etiology* ; Male ; Female ; Depression/physiopathology* ; Cross-Sectional Studies ; Sleep ; Adolescent ; Students ; Child ; Time Factors ; Sleep Duration

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Adenosine triphosphate(ATP),as the core energy metabolism molecule in living systems,has dynamic changes closely related to fundamental physiological processes.To meet the urgent demand for spatiotemporal ATP detection in vivo and in situ,the development of highly sensitive multifunctional synchronous sensing fluorescent probes has become a recent research focus.These dual-function probes achieve fluorescence detection of dual targets by designing recognition sites for ATP alongside biological factors or microenvironment parameters such as reactive oxygen/nitrogen/sulfur species,metal ions,and enzymes,enabling physiological/pathological state correlation analysis through bioimaging.This paper systematically reviews recent advances in fluorescent probes for the collaborative detection of ATP and key biomolecules.It specifically examines probe construction strategies based on specific molecular recognition mechanisms(e.g.,metal coordination competition,electrostatic interactions,and host-guest recognition),multi-modal optical signal transduction mechanisms(ratiometric fluorescence,fluorescence lifetime,and photodynamic therapy),and their applications in pathological models such as oxidative stress monitoring,metal homeostasis imbalance,and enzyme activity co-detection.Finally,from the perspective of molecular probe engineering,current challenges and future research directions are proposed to provide methodological support for precise analysis of ATP-related life process regulation networks.

Numerous studies have shown that depression is main-ly associated with the abnormal expression of connexin 43(Cx43)in astrocytes(Astro)and its mediated dysfunction of gap junction(GJ).However,the molecular mechanism of post-translational modifications targeting Cx43 to regulate neuroin-flammation-associated depression is still unclear.Post-transla-tional modifications of Cx43 mainly include phosphorylation of specific amino acid sites by PKC,PKA,PKG,MAPK and PTK,and protein degradation of Cx43 through the K48/K63 polyubiq-uitylation and deubiquitination pathways,which ultimately lead to protein degradation through K48/K63 polyubiquitination and deubiquitination.These modifications are ultimately involved in the regulation of neuroinflammatory responses through the associ-ation of GJ function.In this paper,we systematically review the role of Cx43 post-translational modifications in neuroinflamma-tion,with the aim of further exploring the potential application of targeting these modifications to modulate the inflammatory re-sponse mechanism in improving depressive symptoms.

Numerous studies have shown that depression is main-ly associated with the abnormal expression of connexin 43(Cx43)in astrocytes(Astro)and its mediated dysfunction of gap junction(GJ).However,the molecular mechanism of post-translational modifications targeting Cx43 to regulate neuroin-flammation-associated depression is still unclear.Post-transla-tional modifications of Cx43 mainly include phosphorylation of specific amino acid sites by PKC,PKA,PKG,MAPK and PTK,and protein degradation of Cx43 through the K48/K63 polyubiq-uitylation and deubiquitination pathways,which ultimately lead to protein degradation through K48/K63 polyubiquitination and deubiquitination.These modifications are ultimately involved in the regulation of neuroinflammatory responses through the associ-ation of GJ function.In this paper,we systematically review the role of Cx43 post-translational modifications in neuroinflamma-tion,with the aim of further exploring the potential application of targeting these modifications to modulate the inflammatory re-sponse mechanism in improving depressive symptoms.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To construct and validate a clinical model combining CYP11B2 gene polymorphisms with clinical parameters to predict complete postoperative hypertension remission in primary aldosteronism patients.Methods:The clinical data of a total of 116 patients with primary aldosteronism who underwent unilateral adrenalectomy from April 2018 to August 2024 were retrospectively included. There were 63 males and 53 females，with a body mass index（BMI）of（25.50 ± 2.03）kg/m 2. Genomic DNA was extracted from venous blood leukocytes before surgery，and polymerase chain reaction-restriction fragment length polymorphisms（PCR-RFLP）were used to detect CYP11B2（rs1799998）promoter region 344（C > T）base substitution. The follow-up duration was more than 6 months，with the following parameters recorded at the last follow-up：plasma aldosterone，renin，serum potassium，and sodium levels. Blood pressure progression and antihypertensive medication usage were also assessed. The postoperative outcome was determined according to the Primary Aldosteronism Surgical Outcome score（PASO）for primary aldosteronism，and the specific criteria were as follows. ① Clinical complete remission：the patient's blood pressure returned to normal（< 140/90 mmHg，1 mmHg = 0.133 kPa）and all antihypertensive drugs were discontinued；②Partial clinical remission：blood pressure returns to normal，and the number or dose of antihypertensive drugs is reduced compared with before；③Clinical non-remission：blood pressure does not drop and antihypertensive drugs do not change or increase compared with before surgery. Patients were divided into complete and incomplete remission groups. The chi-square test was used for univariate analysis，followed by binary logistic forward conditional regression for multivariate analysis，and a variety of machine learning algorithms such as random forest，logistic regression，support vector machine and gradient lifter were integrated，and the results of multivariate analysis were included to construct a postoperative blood pressure outcome model，and the predictive performance of the model was evaluated by using receiver operating characteristic（ROC）curve，calibration curve and clinical decision curve. Results:The PCR-RFLP detection results of 116 cases showed the genotype distribution of CYP11B2（344C > T）（rs1799998）as follows：CC type in 50 cases（43.1%），CT type in 46 cases（39.7%），and TT type in 20 cases（17.3%）. There were 74 cases in the complete remission group and 42 cases in the incomplete remission group，and the rate of complete remission with hypertension at the end of the operation was 63.8%. Univariate analysis showed that the the differences between complete remission group and incomplete remission group in body mass index［（24.27 ± 2.90）kg/m 2 vs.（26.98 ± 3.17）kg/m 2， P<0.001］，preoperative hypertension grade（grade 1/2/3：29/29/16 cases vs. 9/13/20 cases， P = 0.012），preoperative antihypertensive drugs（0/1/≥ 2：25/32/17 cases vs. 7/15/20 cases， P = 0.016），and CYP11B2（344C > T）（CC/TT + CT：39/35 cases vs. 11/31 cases， P = 0.006）were statistically significant. Multivariate analysis showed that the type of preoperative antihypertensive drugs［≥ 2： OR = 5.26（95% CI 1.12?24.61， P = 0.016；1： OR = 4.55（95% CI 1.23?22.47）， P = 0.025］was the strongest independent predictor，followed by CYP11B2（344C > T）［ OR = 4.02（95% CI 1.16?13.82）， P = 0.028］and BMI［ OR = 3.96（95% CI 2.26?6.92）， P < 0.001］. Comparing the receiver operating feature（ROC）curves of the four types of machine learning models，the best model was the support vector machine model with an area under the curve（AUC）of 0.88（95% CI 0.82?0.95），followed by the gradient elevator model of 0.83（95% CI 0.76?0.91），the logistic regression model of 0.78（95% CI 0.68?0.88），and the random forest model of 0.77（95% CI 0.68?0.86）. The optimal threshold of the Yoden index of the support vector machine model was 0.588，with a sensitivity of 78.5% and a specificity of 86.5%. The clinical decision curve and calibration curve show that the support vector machine model has a higher net benefit and acceptable stability and reliability. Conclusions:The support vector machine model incorporating CYP11B2 gene polymorphisms，BMI，and types of preoperative antihypertensive medications could effectively predict postoperative hypertension remission in primary aldosteronism patients，providing new evidence for personalized treatment strategies

Objective:To construct and validate a clinical model combining CYP11B2 gene polymorphisms with clinical parameters to predict complete postoperative hypertension remission in primary aldosteronism patients.Methods:The clinical data of a total of 116 patients with primary aldosteronism who underwent unilateral adrenalectomy from April 2018 to August 2024 were retrospectively included. There were 63 males and 53 females，with a body mass index（BMI）of（25.50 ± 2.03）kg/m 2. Genomic DNA was extracted from venous blood leukocytes before surgery，and polymerase chain reaction-restriction fragment length polymorphisms（PCR-RFLP）were used to detect CYP11B2（rs1799998）promoter region 344（C > T）base substitution. The follow-up duration was more than 6 months，with the following parameters recorded at the last follow-up：plasma aldosterone，renin，serum potassium，and sodium levels. Blood pressure progression and antihypertensive medication usage were also assessed. The postoperative outcome was determined according to the Primary Aldosteronism Surgical Outcome score（PASO）for primary aldosteronism，and the specific criteria were as follows. ① Clinical complete remission：the patient's blood pressure returned to normal（< 140/90 mmHg，1 mmHg = 0.133 kPa）and all antihypertensive drugs were discontinued；②Partial clinical remission：blood pressure returns to normal，and the number or dose of antihypertensive drugs is reduced compared with before；③Clinical non-remission：blood pressure does not drop and antihypertensive drugs do not change or increase compared with before surgery. Patients were divided into complete and incomplete remission groups. The chi-square test was used for univariate analysis，followed by binary logistic forward conditional regression for multivariate analysis，and a variety of machine learning algorithms such as random forest，logistic regression，support vector machine and gradient lifter were integrated，and the results of multivariate analysis were included to construct a postoperative blood pressure outcome model，and the predictive performance of the model was evaluated by using receiver operating characteristic（ROC）curve，calibration curve and clinical decision curve. Results:The PCR-RFLP detection results of 116 cases showed the genotype distribution of CYP11B2（344C > T）（rs1799998）as follows：CC type in 50 cases（43.1%），CT type in 46 cases（39.7%），and TT type in 20 cases（17.3%）. There were 74 cases in the complete remission group and 42 cases in the incomplete remission group，and the rate of complete remission with hypertension at the end of the operation was 63.8%. Univariate analysis showed that the the differences between complete remission group and incomplete remission group in body mass index［（24.27 ± 2.90）kg/m 2 vs.（26.98 ± 3.17）kg/m 2， P<0.001］，preoperative hypertension grade（grade 1/2/3：29/29/16 cases vs. 9/13/20 cases， P = 0.012），preoperative antihypertensive drugs（0/1/≥ 2：25/32/17 cases vs. 7/15/20 cases， P = 0.016），and CYP11B2（344C > T）（CC/TT + CT：39/35 cases vs. 11/31 cases， P = 0.006）were statistically significant. Multivariate analysis showed that the type of preoperative antihypertensive drugs［≥ 2： OR = 5.26（95% CI 1.12?24.61， P = 0.016；1： OR = 4.55（95% CI 1.23?22.47）， P = 0.025］was the strongest independent predictor，followed by CYP11B2（344C > T）［ OR = 4.02（95% CI 1.16?13.82）， P = 0.028］and BMI［ OR = 3.96（95% CI 2.26?6.92）， P < 0.001］. Comparing the receiver operating feature（ROC）curves of the four types of machine learning models，the best model was the support vector machine model with an area under the curve（AUC）of 0.88（95% CI 0.82?0.95），followed by the gradient elevator model of 0.83（95% CI 0.76?0.91），the logistic regression model of 0.78（95% CI 0.68?0.88），and the random forest model of 0.77（95% CI 0.68?0.86）. The optimal threshold of the Yoden index of the support vector machine model was 0.588，with a sensitivity of 78.5% and a specificity of 86.5%. The clinical decision curve and calibration curve show that the support vector machine model has a higher net benefit and acceptable stability and reliability. Conclusions:The support vector machine model incorporating CYP11B2 gene polymorphisms，BMI，and types of preoperative antihypertensive medications could effectively predict postoperative hypertension remission in primary aldosteronism patients，providing new evidence for personalized treatment strategies