The European Society of Cardiology (ESC) released the "2024 ESC guidelines for the management of elevated blood pressure and hypertension" on August 30, 2024. This guideline updates the 2018 "Guidelines for the management of arterial hypertension." One notable update is the introduction of the concept of "elevated blood pressure" (120-139/70-89 mm Hg). Additionally, a new systolic blood pressure target range of 120-129 mm Hg has been proposed for most patients receiving antihypertensive treatment. The guideline also includes numerous additions or revisions in areas such as non-pharmacological interventions and device-based treatments for hypertension. This article interprets the guideline's recommendations on definition and classification of elevated blood pressure and hypertension, and cardiovascular disease risk assessment, diagnosing hypertension and investigating underlying causes, preventing and treating elevated blood pressure and hypertension. We provide a comparison interpretation with the 2018 "Guidelines for the management of arterial hypertension" and the "2017 ACC/AHA guideline on the prevention, detection, evaluation, and management of high blood pressure in adults."

[Objective] To explore the relationship between neutrophil activation under cardiopulmonary bypass (CPB) and the incidence of cardiac surgery-associated acute kidney injury (CS-AKI). [Methods] This prospective cohort study enrolled adult patients who scheduled for cardiac surgery under CPB at West China Hospital between May 1, 2022 and March 31, 2023. The primary outcome was acute kidney injury (AKI). Blood samples (5 mL) were obtained from the central vein before surgery, at rewarming, at the end of CPB, and 24 hours after surgery. Neutrophils were labeled with CD11b, CD54 and other markers. To assess the effect of neutrophils activation on AKI, propensity score matching (PSM) was employed to equilibrate covariates between the groups. [Results] A total of 120 patients included into the study, and 17 (14.2%) developed AKI. Both CD11b⁺ and CD54⁺ neutrophils significantly increased during the rewarming phase and the increases were kept until 24 hours after surgery. During rewarming, the numbers of CD11b⁺ neutrophils were significantly higher in AKI compared to non-AKI (4.71×10⁹/L vs 3.31×10⁹/L, Z=-2.14, P<0.05). Similarly, the CD54⁺ neutrophils counts were also significantly higher in AKI than in non-AKI before surgery (2.75×10⁹/L vs 1.79×10⁹/L, Z=-2.99, P<0.05), during rewarming (3.12×10⁹/L vs 1.62×10⁹/L, Z=-4.34, P<0.05), and at the end of CPB (4.28×10⁹/L vs 2.14×10⁹/L, Z=-3.91, P<0.05). An analysis of 32 matched patients (16 in each group) revealed that CD11b⁺ and CD54⁺ neutrophil levels of AKI were 1.74 folds (4.83×10⁹/L vs 2.77×10⁹/L, Z=-2.72, P<0.05) and 2.34 folds (3.32×10⁹/L vs 1.42×10⁹/L, Z=-4.12, P<0.05), respectively, of non-AKI at rewarming phase. [Conclusion] Neutrophils are activated during CPB, and they can be identified by CD11b/CD54 markers. The activated neutrophils of AKI patients are approximately 2 folds of non-AKI during the rewarming phase, with disparity reached peak between groups during rewarming. These findings suggest the removal of 50% of activated neutrophils during the rewarming phase may be effective to reduce the risk of AKI.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

BACKGROUND: The main cause of restenosis after percutaneous transluminal angioplasty (PTA) is the excessive proliferation and migration of vascular smooth muscle cells (VSMCs). Lin28a has been reported to play critical regulatory roles in this process. However, whether CCAAT/enhancer-binding proteins β (C/EBPβ) binds to the Lin28a promoter and drives the progression of restenosis has not been clarified. Therefore, in the present study, we aim to clarify the role of C/EBPβ-Lin28a axis in restenosis. METHODS: Restenosis and atherosclerosis rat models of type 2 diabetes ( n   =  20, for each group) were established by subjecting to PTA. Subsequently, the difference in DNA methylation status and expression of C/EBPβ between the two groups were assessed. EdU, Transwell, and rescue assays were performed to assess the effect of C/EBPβ on the proliferation and migration of VSMCs. DNA methylation status was further assessed using Methyltarget sequencing. The interaction between Lin28a and ten-eleven translocation 1 (TET1) was analysed using co-immunoprecipitation (Co-IP) assay. Student's t -test and one-way analysis of variance were used for statistical analysis. RESULTS: C/EBPβ expression was upregulated and accompanied by hypomethylation of its promoter in restenosis when compared with atherosclerosis. In vitroC/EBPβ overexpression facilitated the proliferation and migration of VSMCs and was associated with increased Lin28a expression. Conversely, C/EBPβ knockdown resulted in the opposite effects. Chromatin immunoprecipitation assays further demonstrated that C/EBPβ could directly bind to Lin28a promoter. Increased C/EBPβ expression and enhanced proliferation and migration of VSMCs were observed after decitabine treatment. Further, mechanical stretch promoted C/EBPβ and Lin28a expression accompanied by C/EBPβ hypomethylation. Additionally, Lin28a overexpression reduced C/EBPβ methylation via recruiting TET1 and enhanced C/EBPβ-mediated proliferation and migration of VSMCs. The opposite was noted in Lin28a knockdown cells. CONCLUSION Our findings suggest that the C/EBPβ-Lin28a axis is a driver of restenosis progression, and presents a promising therapeutic target for restenosis.
Animals ; Cell Proliferation/genetics* ; Cell Movement/genetics* ; Muscle, Smooth, Vascular/metabolism* ; Rats ; DNA Methylation/physiology* ; CCAAT-Enhancer-Binding Protein-beta/genetics* ; Male ; Myocytes, Smooth Muscle/cytology* ; Rats, Sprague-Dawley ; RNA-Binding Proteins/genetics* ; Cells, Cultured ; Coronary Restenosis/metabolism*

BACKGROUND: Increasing evidence indicates that oxidative stress and interferon γ (IFNγ)-driven cellular immune responses are responsible for the pathogenesis of vitiligo. However, the connection between oxidative stress and the local production of IFNγ in early vitiligo remains unexplored. The aim of this study was to identify the mechanism underlying the production of IFNγ by mast cells and its impact on vitiligo pathogenesis. METHODS: Skin specimens from the central, marginal, and perilesional skin areas of active vitiligo lesions were collected to characterize changes of mast cells, CD8 + T cells, and IFNγ-producing cells. Cell supernatants from hydrogen peroxide (H 2 O 2 )-treated keratinocytes (KCs) were harvested to measure levels of soluble stem cell factor (sSCF) and matrix metalloproteinase (MMP)-9. A murine vitiligo model was established using Mas-related G protein-coupled receptor-B2 (MrgB2, mouse ortholog of human MrgX2) conditional knockout (MrgB2 -/- ) mice to investigate IFNγ production and inflammatory cell infiltrations in tail skin following the challenge with tyrosinase-related protein (Tyrp)-2 180 peptide. Potential interactions between the Tyrp-2 180 peptide and MrgX2 were predicted using molecular docking. The siRNAs targeting MrgX2 and the calcineurin inhibitor FK506 were also used to examine the signaling pathways involved in mast cell activation. RESULTS: IFNγ-producing mast cells were closely aligned with the recruitment of CD8 + T cells in the early phase of vitiligo skin. sSCF released by KCs through stress-enhanced MMP9-dependent proteolytic cleavage recruited mast cells into sites of inflamed skin (Perilesion vs . lesion, 13.00 ± 4.00/high-power fields [HPF] vs . 26.60 ± 5.72/HPF, P <0.05). Moreover, IFNγ-producing mast cells were also observed in mouse tail skin following challenge with Tyrp-2 180 (0 h vs . 48 h post-recall, 0/HPF vs . 3.80 ± 1.92/HPF, P <0.05). The IFNγ + mast cell and CD8 + T cell counts were lower in the skin of MrgB2 -/- mice than in those of wild-type mice (WT vs . KO 48 h post-recall, 4.20 ± 0.84/HPF vs . 0.80 ± 0.84/HPF, P <0.05). CONCLUSION Mast cells activated by MrgX2 serve as a local IFNγ producer that bridges between innate and adaptive immune responses against MCs in early vitiligo. Targeting MrgX2-mediated mast cell activation may represent a new strategy for treating vitiligo.
Vitiligo/metabolism* ; Mast Cells/immunology* ; Animals ; Interferon-gamma/metabolism* ; Mice ; Humans ; Melanocytes/metabolism* ; Receptors, G-Protein-Coupled/genetics* ; Mice, Knockout ; Mice, Inbred C57BL ; Male ; Female ; Matrix Metalloproteinase 9/metabolism* ; Stem Cell Factor/metabolism*

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Objective To analyze the proportion and clinicopathological characteristics of HER2-positive breast cancer patients with BRCA1/2 pathogenic variants, and their response to neoadjuvant anti-HER2 targeted therapy. Methods The clinicopathological data of 531 breast cancer patients with germline BRCA1/2 pathogenic variants (201 with BRCA1 variants and 330 with BRCA2 variants) were analyzed. Results Among the 201 BRCA1 and 330 BRCA2 variants, 17 (8.5%) and 42 (12.7%) HER2-positive breast cancer cases were identified, respectively, accounting for 11.1% of all BRCA1/2-mutated breast cancers. Compared with BRCA1/2-mutated HR-positive/HER2-negative patients, HER2-positive patients did not present any significant differences in clinicopathological features; however, compared with triple-negative breast cancer patients, HER2-positive patients had a later onset age and lower tumor grade. Among the 17 patients who received neoadjuvant anti-HER2 targeted therapy, 10 cases achieved pCR (58.8%), whereas 7 cases did not (41.2%). Conclusion HER2-positive breast cancer accounts for more than 10% of BRCA1/2-mutated patients. Approximately 40% of these patients fail to achieve pCR after neoadjuvant targeted therapy. This phenomenon highlights the possibility of combining anti-HER2 targeted agents with poly (adenosine diphosphate-ribose) polymerase inhibitors.