ObjectiveTo investigate the novel post-translational modifications (PTMs) of SnRK2.6, a central component in the abscisic acid (ABA) signaling pathway, such as SUMOylation, and to establish a foundation for revealing the physiological functions and molecular mechanisms of SnRK2.6 regulated by these new modifications. MethodsThe interaction between SnRK2.6 and the SUMO E3 ligase SIZ1, as well as members of the SUMO protease family, was examined using yeast two-hybrid and in vitro pull-down assays. An in vitro SUMOylation system in Escherichia coli was utilized to determine whether SnRK2.6 undergoes SUMOylation. Mass spectrometry, combined with site-directed mutagenesis of candidate lysine residues, was employed to identify potential SUMOylation sites on SnRK2.6. In vitro de-SUMOylation assays were performed to assess whether SUMO proteases interacting with SnRK2.6 could catalyze the removal of SUMO moieties from modified SnRK2.6. The protein stability of SnRK2.6 was assessed in a cell-free degradation assay using bacterial-purified SnRK2.6 incubated with total protein extracts from Col and siz1 mutant seedlings. To dissect the genetic relationship between SnRK2.6 and SIZ1, stomatal aperture assays were performed under ABA treatment using snrk2.6, siz1, and snrk2.6 siz1 double mutant plants. ResultsSnRK2.6 physically interacts with SIZ1 and the SUMO protease ESD4, with the binding domains localized to the C-terminal region of SIZ1 and the N-terminal region of ESD4, respectively. SnRK2.6 was found to be SUMOylated, exhibiting two distinct high-molecular-mass bands ranging from 70 to 100 ku, indicative of modified forms. Bioinformatics analysis predicted four putative SUMOylation sites on lysine residues K57, K63, K142, and K190. Mass spectrometry identified three SUMOylation sites on K63, K142, and K174. However, individual or combinatorial point mutations on these sites had minimal impact on the pattern or intensity of SUMOylation signals, suggesting that these residues may not be responsible for the SUMOylation on SnRK2.6. Instead, such mutations only weaken the protein stability or accelerate the protein mobility of SnRK2.6. Therefore, the exact SUMOylation sites on SnRK2.6 remain unidentified. In de-SUMOylation experiments, incubation of GST-ESD4 with SUMOylated SnRK2.6 for 1-2 h led to the near-complete disappearance of both SUMOylated bands. In contrast, neither the GST control nor the catalytically inactive mutant GST-ESD4^C448S exhibited any de-SUMOylation activity. In protein turnover experiments, SnRK2.6 exhibited markedly enhanced half-life in siz1 compared with Col, indicating that SIZ1-dependent SUMOylation promotes SnRK2.6 turnover. Phenotypically, snrk2.6 mutants were completely insensitive to ABA-induced stomatal closure; siz1 mutants displayed pronounced hypersensitivity; and the snrk2.6 siz1 double mutant phenocopied snrk2.6—showing no significant response to ABA beyond that of the snrk2.6 mutant. These data indicate that SIZ1 acts as a negative regulator of ABA-triggered stomatal closure and SnRK2.6 functions as a positive regulator, and the inhibitory activity of SIZ1 is strictly dependent on SnRK2.6, placing SnRK2.6 genetically upstream of SIZ1 in the ABA signaling pathway. ConclusionSnRK2.6 undergoes SUMOylation, although the specific SUMOylation sites have not been defined. SnRK2.6 is dynamically regulated by reversible SUMOylation—catalyzed by SIZ1 and reversed by ESD4—which controls its protein stability. SUMOylation acts as a destabilizing signal for SnRK2.6, and SIZ1 exerts its negative effect on ABA-triggered stomatal closure probably through promoting SnRK2.6 degradation via SUMOylation. These findings uncover SUMOylation as a critical regulatory layer fine-tuning SnRK2.6 abundance in ABA signaling.

OBJECTIVE: To investigate the effect of zedoarondiol on neovascularization of atherosclerotic (AS) plaque by exosomes experiment. METHODS: ApoE^-/- mice were fed with high-fat diet to establish AS model and treated with high- and low-dose (10, 5 mg/kg daily) of zedoarondiol, respectively. After 14 weeks, the expressions of anti-angiogenic protein thrombospondin 1 (THBS-1) and its receptor CD36 in plaques, as well as platelet activation rate and exosome-derived miR-let-7a were detected. Then, zedoarondiol was used to intervene in platelets in vitro, and miR-let-7a was detected in platelet-derived exosomes (Pexo). Finally, human umbilical vein endothelial cells (HUVECs) were transfected with miR-let-7a mimics and treated with Pexo to observe the effect of miR-let-7a in Pexo on tube formation. RESULTS: Animal experiments showed that after treating with zedoarondiol, the neovascularization density in plaques of AS mice was significantly reduced, THBS-1 and CD36 increased, the platelet activation rate was markedly reduced, and the miR-let-7a level in Pexo was reduced (P<0.01). In vitro experiments, the platelet activation rate and miR-let-7a levels in Pexo were significantly reduced after zedoarondiol's intervention. Cell experiments showed that after Pexo's intervention, the tube length increased, and the transfection of miR-let-7a minics further increased the tube length of cells, while reducing the expressions of THBS-1 and CD36. CONCLUSION Zedoarondiol has the effect of inhibiting neovascularization within plaque in AS mice, and its mechanism may be potentially related to inhibiting platelet activation and reducing the Pexo-derived miRNA-let-7a level.
Animals ; MicroRNAs/genetics* ; Exosomes/drug effects* ; Plaque, Atherosclerotic/genetics* ; Neovascularization, Pathologic/genetics* ; Human Umbilical Vein Endothelial Cells/metabolism* ; Humans ; Blood Platelets/drug effects* ; Apolipoproteins E/deficiency* ; Thrombospondin 1/metabolism* ; CD36 Antigens/metabolism* ; Platelet Activation/drug effects* ; Male ; Mice ; Mice, Inbred C57BL

PURPOSE: Deep vein thrombosis (DVT) of the left and right lower extremities was treated in the same way, but the left and right extremities received different levels of attention. This study aimed to investigate the differences between the right and left lower extremity deep vein thrombosis (LEDVT). METHODS: Clinical characteristics of LEDVT patients from July 2020 to June 2022 were retrospectively analyzed to compare the incidence of LEDVT on different limbs, demographics, predisposing factors, and anatomical characteristics. The exclusion criteria were bilateral LEDVT and recurrent thrombosis. Measured data was analyzed using independent samples t-test or Mann-Whitney test. Count data were analyzed by Chi-square test. A p < 0.05 was considered a statistically significant difference. RESULTS: There were 478 patients included in this study and the ratio of left to right LEDVT on the left and right limbs was 3.16:1 (363:115). Left LEDVT predominantly affected female, with the major aged > 50 years (50 - 60 years: 16.80%; > 60 years: 57.30%). The primary predisposing factor was iliac vein compression syndrome, with iliofemoral thrombosis being the main type. Male patients with LEDVT on the right limb were predominant and the age of onset was usually ≤ 60 years (52.17%). The main predisposing factor was recent surgery or trauma (< 30 days) and femoropopliteal thrombosis was the main type. In more detail, the left iliac vein was compressed mainly in the proximal segment, and the right iliac vein was compressed mainly in the intermediate and distal segments. Recent surgery or trauma to the locomotor system and genitourinary system often induced LEDVT. CONCLUSION The incidence of LEDVT on the left is significantly higher than that on the right. LEDVT on different sides has different characteristics, which is crucial for prevention and diagnosis in the relevant population so there are also differences in treatment of the affected limbs.
Humans ; Venous Thrombosis/etiology* ; Male ; Female ; Middle Aged ; Lower Extremity/blood supply* ; Retrospective Studies ; Adult ; Aged ; Age Factors ; Sex Factors ; Risk Factors ; Acute Disease ; Incidence ; Aged, 80 and over ; Young Adult ; Adolescent

Objective To investigate an automated pretreatment technology for detecting levels of free silica in workplace dust. Methods An fully automated pretreatment workstation for detecting free silica levels in workplace dust was developed by integrating graphite-controlled digestion temperature, online-controlled dilution of digestion solutions, and filtration endpoint recognition based on monitoring technology, combined with multi-channel synchronous measurements. Results The fully automatic pretreatment workstation was used to digest and filter 14 standard samples of free silica produced by three institutions, and then detected by pyrophosphate method. The result range of high-, medium-, and low-level free silica standard samples detection was 66.5%-84.8%, 40.0%-44.5%, and 2.1%-24.8%, respectively. The mean relative standard deviations were 3.9%, 1.4% and 1.5%. Conclusion The fully automated pretreatment workstation produced results that met relevant requirements. It can effectively replace the manual digestion and filtration steps of the pyrophosphate method to measure free silica levels in workplace dust and enable rapid detection of free silica in dust samples.

Recent studies have shown that the physiological homeostasis of oral mucosal cells is regulated by the circadian clock.Dis-ruption or dysfunction of the circadian clock is closely associated with the development of oral squamous cell carcinoma(OSCC).Research based on the circadian clock offers a novel perspective on the pathogenesis and therapeutic strategies for OSCC.However,there is current-ly limited research on this topic,and people generally have insufficient understanding and recognition of the circadian clock.Given the complexity and challenges of circadian clock which is the fourth dimension of medical research,we organize relevant experts based on summarizing the current research results of circadian clock in the pathogenesis and precision diagnosis and treatment of OSCC,combining the scientific principles of the circadian clock's role and their long-term research experience,then summarizes and recommends the con-sensus opinions for the research of circadian clock in the pathogenesis mechanism and precision diagnosis and treatment of human OSCC,with the hope of providing guidance for the basic research and clinical application of circadian clock or circadian rhythm in the pathogene-sis mechanism and precision diagnosis and treatment of oral and maxillofacial squamous cell carcinoma.

Objective:To explore the predictive value of atherogenic index of plasma(AIP)combined with neutro-phil/HDL-C ratio(NHR),lymphocyte/HDL-C ratio(LHR)and monocyte/HDL-C ratio(MHR)for major adverse cardiovascular events(MACE)in patients with acute coronary syndrome(ACS).Methods:This retrospec-tive study enrolled 320 patients underwent coronary angiography in Department of Cardiovascular Medicine,Harri-son International Peace Hospital between January 2021 and December 2022,including 215 ACS patients and 105 pa-tients without ACS;according to Gensini score,ACS patients were divided into low risk group(n=50),medium risk group(n=70)and high risk group(n=95).According to the presence of MACE within 1 year,patients were divided into MACE group(n=55)and no MACE group(n=160).After admission,blood routine,blood lipids,C-reactive protein(CRP)and endothelin-1(ET-1)levels were measured,then NHR,LHR,MHR and AIP were calculated.Spearman method was used to analyze the association of above-mentioned indexes with Gensini score;stepwise Logistic regression was used to analyze the influencing factors of MACE within 1 year in ACS patients;and ROC curve was used to analyze the predictive value of single and combined detection of above indexes for MACE within 1 year in ACS patients.Results:Compared to patients in no ACS group,those in ACS group had significantly higher BMI[(23.59±0.85)kg/m2 vs.(21.57±1.16)kg/m2],proportions of hypertension(57.67％vs.13.33％),diabetes(23.26％vs.8.57％),NHR[(12.09±3.46)vs.(3.81±1.29)],MHR[(0.70±0.18)vs.(0.33±0.10)],LHR[(0.79±0.21)vs.(0.40±0.09)],AIP[(0.21±0.06)vs.(0.11±0.02)],CRP[(9.82±3.09)mg/L vs.(2.20±0.58)mg/L],ET-1[(31.25±10.34)μg/L vs.(10.60±1.96)μg/L](P＜0.001 all).Compared to those in no MACE group,patients in MACE group had significantly higher NHR[(17.33±3.87)vs.(10.12±2.68)],MHR[(0.93±0.24)vs.(0.61±0.13)],LHR[(1.05±0.27)vs.(0.71±0.16)],AIP[(0.28±0.04)vs.(0.17±0.02)],CRP[(15.52±3.83)mg/L vs.(7.69±2.40)mg/L],ET-1[(46.68±9.51)μg/L vs.(25.47±4.66)μg/L]levels(P＜0.001 all).Spearman correlation analysis showed that NHR,MHR,LHR and AIP were significant positively correlated with Gensini score(r=0.837～0.868,P＜0.001 all).Stepwise Logistic regression analysis showed that NHR(OR=1.225,95％CI 1.016～1.550,P=0.035),AIP(OR=2.632,95％CI 1.055～6.566,P=0.038)were independent risk factors for MACE within 1 year in ACS patients.ROC curve shows that AUC of AIP combined with NHR,MHR and LHR predicting MACE within 1 year in ACS patients was 0.786(95％CI 0.725～0.839),which was significantly higher than those of NHR(AUC 0.768,95％CI 0.706～0.823),LHR(AUC 0.749,95％CI 0.686～0.806)and AIP(AUC 0.764,95％CI 0.701～0.819)alone(Z=2.597,2.687,1.965,P＜0.05 or＜0.01).Conclusion:AIP combined with NHR,MHR and LHR have certain predictive value for MACE within 1 year in ACS patients.

Objective:To explore the predictive value of atherogenic index of plasma(AIP)combined with neutro-phil/HDL-C ratio(NHR),lymphocyte/HDL-C ratio(LHR)and monocyte/HDL-C ratio(MHR)for major adverse cardiovascular events(MACE)in patients with acute coronary syndrome(ACS).Methods:This retrospec-tive study enrolled 320 patients underwent coronary angiography in Department of Cardiovascular Medicine,Harri-son International Peace Hospital between January 2021 and December 2022,including 215 ACS patients and 105 pa-tients without ACS;according to Gensini score,ACS patients were divided into low risk group(n=50),medium risk group(n=70)and high risk group(n=95).According to the presence of MACE within 1 year,patients were divided into MACE group(n=55)and no MACE group(n=160).After admission,blood routine,blood lipids,C-reactive protein(CRP)and endothelin-1(ET-1)levels were measured,then NHR,LHR,MHR and AIP were calculated.Spearman method was used to analyze the association of above-mentioned indexes with Gensini score;stepwise Logistic regression was used to analyze the influencing factors of MACE within 1 year in ACS patients;and ROC curve was used to analyze the predictive value of single and combined detection of above indexes for MACE within 1 year in ACS patients.Results:Compared to patients in no ACS group,those in ACS group had significantly higher BMI[(23.59±0.85)kg/m2 vs.(21.57±1.16)kg/m2],proportions of hypertension(57.67％vs.13.33％),diabetes(23.26％vs.8.57％),NHR[(12.09±3.46)vs.(3.81±1.29)],MHR[(0.70±0.18)vs.(0.33±0.10)],LHR[(0.79±0.21)vs.(0.40±0.09)],AIP[(0.21±0.06)vs.(0.11±0.02)],CRP[(9.82±3.09)mg/L vs.(2.20±0.58)mg/L],ET-1[(31.25±10.34)μg/L vs.(10.60±1.96)μg/L](P＜0.001 all).Compared to those in no MACE group,patients in MACE group had significantly higher NHR[(17.33±3.87)vs.(10.12±2.68)],MHR[(0.93±0.24)vs.(0.61±0.13)],LHR[(1.05±0.27)vs.(0.71±0.16)],AIP[(0.28±0.04)vs.(0.17±0.02)],CRP[(15.52±3.83)mg/L vs.(7.69±2.40)mg/L],ET-1[(46.68±9.51)μg/L vs.(25.47±4.66)μg/L]levels(P＜0.001 all).Spearman correlation analysis showed that NHR,MHR,LHR and AIP were significant positively correlated with Gensini score(r=0.837～0.868,P＜0.001 all).Stepwise Logistic regression analysis showed that NHR(OR=1.225,95％CI 1.016～1.550,P=0.035),AIP(OR=2.632,95％CI 1.055～6.566,P=0.038)were independent risk factors for MACE within 1 year in ACS patients.ROC curve shows that AUC of AIP combined with NHR,MHR and LHR predicting MACE within 1 year in ACS patients was 0.786(95％CI 0.725～0.839),which was significantly higher than those of NHR(AUC 0.768,95％CI 0.706～0.823),LHR(AUC 0.749,95％CI 0.686～0.806)and AIP(AUC 0.764,95％CI 0.701～0.819)alone(Z=2.597,2.687,1.965,P＜0.05 or＜0.01).Conclusion:AIP combined with NHR,MHR and LHR have certain predictive value for MACE within 1 year in ACS patients.

Objective:To investigate the predictive efficacy of Delta radiomics for the patholo-gical complete remission (pCR) of pancreatic cancer after total neoadjuvant therapy (TNT).Methods:The retrospective cohort study was conducted. The clinicopathological data of 263 patients with pancreatic cancer who were admitted to Henan Provincial People′s Hospital (Zhengzhou University People's Hspital) from January 2019 to September 2024 were collected. There were 166 males and 97 females, aged (56±12)years. All patients underwent TNT. The 263 patients were randomly divided into a training set of 184 cases and a test set of 79 cases using a 7∶3 random seed count. The training set was used to construct the prediction model, and the test set was used to validate the performance of the prediction model. Observation indicators: (1) postoperative and follow-up condi-tions; (2) imaging feature selection and model construction; (3) evaluation of predictive efficacy of different radiomic models. Comparison of measurement data with normal distribution between groups was conducted using the t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. The Kaplan-Meier method was used to calculate the survival rate and draw survival curve. The Log-rank test was used for survival analysis. The perfor-mance of the prediction model for pCR after TNT was evaluated using the receiver operator charac-teristic (ROC) curve, precision-recall (P-R) curve and Bootstrap method, along with the calculation of area under the curve (AUC), precision rate, recall rate, F1-score. Results:(1) Postoperative and follow-up conditions. All 263 patients underwent surgery after TNT, with pathological examination revealing 124 cases of pCR (86 cases in the training set, 38 cases in the test set) and 139 cases of non-pCR (98 cases in the training set, 41 cases in the test set), respectively. All 263 patients were followed up for 6(range, 3-12) months after surgery, of which 15 cases (4 cases of pCR and 11 cases of non-pCR) were lost to follow-up or died due to non-tumor reasons within 6 months after surgery. The postoperative 6-month recurrence-free survival rate of 124 pCR patients and 139 non-pCR patients were 80% and 50%, respectively, showing a significant difference between the two groups of patients ( χ2=22.84, P<0.05). (2) Imaging feature selection and model construction. Construction of the traditional radiology model: based on the response evaluation criteria in solid tumors 1.1, the Logistic regression model was constructed using the relative shrinkage (D%) as a predictive variable. The AUC of traditional radiology model was 0.72 [95% confidence interval ( CI) as 0.63?0.81] in the training set and 0.75 (95% CI as 0.66?0.84) in the test set, respectively. Construction of the Delta radiomics model: 10 non-zero coefficient features were selected. The Delta radiomics models were constructed by using the regularized Logistic regression, random forest, gradient boosting machine, and support vector machine algorithms through using selected features as input variables. (3) Evaluation of predictive efficacy of different radiomic models. The AUC of Delta radiomics model constructed by regularized Logistic regression algorithm in the test set for predicting pCR in pancreatic cancer after TNT was 0.90, higher than that of the random forest algorithm, gradient boosting machine algorithm, support vector machine algorithm (AUC as 0.81, 0.81, 0.83), and higher than that of the traditional radiology model (AUC as 0.72). Results of Bootstrap method revealed significant differences in the predictive efficacy of Delta radiomics model constructed by regularized Logistic regression algorithm compared to the Delta radiomics model constructed by random forest algorithm, gradient boosting machine algorithm, support vector machine algorithm and the tradi-tional radiology model (95% CI as 0.03?0.16, 0.03?0.16, 0.03?0.13, 0.08?0.29, P<0.05). The regularized Logistic regression algorithm within the Delta radiomics model demonstrated the best overall performance among the above models evaluated. Conclusion:Compared to the traditional radiology model, the Delta radiomics model offers superior efficacy in predicting pCR of pancreatic cancer after TNT, in which the regularized Logistic regression algorithm demonstrates the best overall performance metrics.

Objective:To investigate the predictive efficacy of Delta radiomics for the patholo-gical complete remission (pCR) of pancreatic cancer after total neoadjuvant therapy (TNT).Methods:The retrospective cohort study was conducted. The clinicopathological data of 263 patients with pancreatic cancer who were admitted to Henan Provincial People′s Hospital (Zhengzhou University People's Hspital) from January 2019 to September 2024 were collected. There were 166 males and 97 females, aged (56±12)years. All patients underwent TNT. The 263 patients were randomly divided into a training set of 184 cases and a test set of 79 cases using a 7∶3 random seed count. The training set was used to construct the prediction model, and the test set was used to validate the performance of the prediction model. Observation indicators: (1) postoperative and follow-up condi-tions; (2) imaging feature selection and model construction; (3) evaluation of predictive efficacy of different radiomic models. Comparison of measurement data with normal distribution between groups was conducted using the t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. The Kaplan-Meier method was used to calculate the survival rate and draw survival curve. The Log-rank test was used for survival analysis. The perfor-mance of the prediction model for pCR after TNT was evaluated using the receiver operator charac-teristic (ROC) curve, precision-recall (P-R) curve and Bootstrap method, along with the calculation of area under the curve (AUC), precision rate, recall rate, F1-score. Results:(1) Postoperative and follow-up conditions. All 263 patients underwent surgery after TNT, with pathological examination revealing 124 cases of pCR (86 cases in the training set, 38 cases in the test set) and 139 cases of non-pCR (98 cases in the training set, 41 cases in the test set), respectively. All 263 patients were followed up for 6(range, 3-12) months after surgery, of which 15 cases (4 cases of pCR and 11 cases of non-pCR) were lost to follow-up or died due to non-tumor reasons within 6 months after surgery. The postoperative 6-month recurrence-free survival rate of 124 pCR patients and 139 non-pCR patients were 80% and 50%, respectively, showing a significant difference between the two groups of patients ( χ2=22.84, P<0.05). (2) Imaging feature selection and model construction. Construction of the traditional radiology model: based on the response evaluation criteria in solid tumors 1.1, the Logistic regression model was constructed using the relative shrinkage (D%) as a predictive variable. The AUC of traditional radiology model was 0.72 [95% confidence interval ( CI) as 0.63?0.81] in the training set and 0.75 (95% CI as 0.66?0.84) in the test set, respectively. Construction of the Delta radiomics model: 10 non-zero coefficient features were selected. The Delta radiomics models were constructed by using the regularized Logistic regression, random forest, gradient boosting machine, and support vector machine algorithms through using selected features as input variables. (3) Evaluation of predictive efficacy of different radiomic models. The AUC of Delta radiomics model constructed by regularized Logistic regression algorithm in the test set for predicting pCR in pancreatic cancer after TNT was 0.90, higher than that of the random forest algorithm, gradient boosting machine algorithm, support vector machine algorithm (AUC as 0.81, 0.81, 0.83), and higher than that of the traditional radiology model (AUC as 0.72). Results of Bootstrap method revealed significant differences in the predictive efficacy of Delta radiomics model constructed by regularized Logistic regression algorithm compared to the Delta radiomics model constructed by random forest algorithm, gradient boosting machine algorithm, support vector machine algorithm and the tradi-tional radiology model (95% CI as 0.03?0.16, 0.03?0.16, 0.03?0.13, 0.08?0.29, P<0.05). The regularized Logistic regression algorithm within the Delta radiomics model demonstrated the best overall performance among the above models evaluated. Conclusion:Compared to the traditional radiology model, the Delta radiomics model offers superior efficacy in predicting pCR of pancreatic cancer after TNT, in which the regularized Logistic regression algorithm demonstrates the best overall performance metrics.

Recent studies have shown that the physiological homeostasis of oral mucosal cells is regulated by the circadian clock.Dis-ruption or dysfunction of the circadian clock is closely associated with the development of oral squamous cell carcinoma(OSCC).Research based on the circadian clock offers a novel perspective on the pathogenesis and therapeutic strategies for OSCC.However,there is current-ly limited research on this topic,and people generally have insufficient understanding and recognition of the circadian clock.Given the complexity and challenges of circadian clock which is the fourth dimension of medical research,we organize relevant experts based on summarizing the current research results of circadian clock in the pathogenesis and precision diagnosis and treatment of OSCC,combining the scientific principles of the circadian clock's role and their long-term research experience,then summarizes and recommends the con-sensus opinions for the research of circadian clock in the pathogenesis mechanism and precision diagnosis and treatment of human OSCC,with the hope of providing guidance for the basic research and clinical application of circadian clock or circadian rhythm in the pathogene-sis mechanism and precision diagnosis and treatment of oral and maxillofacial squamous cell carcinoma.