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.

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.

Objective: To explore the network structure of eating behaviors with anxiety, depression and perceived stress in adolescents, so as to provide a basis for effective prevention and intervention of eating behavior problems and negative emotions in adolescents. Methods: Based on the Psychology and Behavior Investigation of Chinese Residents (2021) database, the study was conducted among 3 087 adolescents. Sakata Eating Behavior Scale Short From(EBS-SF) was used to investigate their eating behaviors. The Patient Health Questionnaire-9(PHQ-9), Generalized Anxiety Disorder Scale-7 Item(GAD-7), and Perceived Stress Questionnaire-3 Item (PSQ-3) were used to evaluate their depression, anxiety and perceived stress. Network analysis method was applied to construct a network of eating behaviors and negative emotional symptoms among adolescents, so as to evaluate the centrality, bridge strength, stability and accuracy of each item. Results: The total scores of eating behaviors, depression,anxiety and stress perception in adolescents were 17.41±4.53,6.95±6.08,4.86±5.03,9.34±3.80,respectively. The symptom with the highest intensity and expected impact was "I am only satisfied when I buy more food than I need", with a node intensity and expected impact value of 4.37. The nodes Depression and Anxiety were the most closely connected(weight=0.87). There were no statistically significant differences in the network structure( M =0.13,0.11) and network connection strength(female and male:4.16,4.06, s =0.10;urban and rural areas:4.08,4.07, s =0.01) between different sexes and residents ( P >0.05). Conclusion The negative impact of comorbidities such as anxiety, depression, perceived stress and eating behaviors among adolescents can be reduced through targeted prevention and intervention of core symptoms and bridging symptoms.

Objective: To investigate the subclinical eating disorders among college students and to analyze associated factors, so as to provide a basis for the prevention and treatment of eating disorders among adolescents. Methods: From November to December 2023, a total of 5 201 college students were selected by stratified random cluster sampling from one undergraduate college and one specialized college in Yangzhou City, Jiangsu Province. Data on general information, subclinical eating disorders, body image perception, depressive symptoms, anxiety symptoms, and mental health literacy were collected using questionnaires. The Chisquare test was used to compare the detection rates of subclinical eating disorders between groups, and binary Logistic regression was employed to analyze associated factors. Results: The detection rate of subclinical eating disorders among college students was 16.0%. Binary Logistic regression analysis showed that the prevalence of subclinical eating disorders among college students was higher in the following categories:being in a relationship (OR=1.22, 95%CI=1.04-1.44), being overweight and obese (OR=2.75, 3.82, 95%CI=2.24-3.38, 2.89-5.06), overestimation of body shape (OR=2.04, 95%CI=1.68-2.49), being in a depressive state (OR=2.53, 95%CI=1.99-3.21), experiencing anxiety (OR=2.63, 95%CI=2.16-3.20), and having substandard mental health literacy (OR=1.37, 95%CI=1.11-1.70). Conversely, low body weight (OR=0.15, 95%CI=0.10-0.22) and underestimation of body shape (OR=0.37, 95%CI=0.27-0.51) were associated with a lower risk (P<0.05). Conclusions The detection rate of subclinical eating disorders among college students is high, and it is associated with relationship status, body mass index classification, body shape perception, depressive and anxiety symptoms, and mental health literacy. Comprehensive interventions should be implemented to improve the subclinical eating disorders and promote the physical and mental health of college students.