Abstract In China, the onset of puberty in children has been occurring increasingly earlier, coinciding with a rising prevalence of hypertension among adolescents. The association between early pubertal timing and hypertension has become a focal point of research. The study systematically examines the relationship between earlier pubertal timing of children and hypertension during both childhood and adulthood. It explores the underlying mechanisms from multiple perspectives, including sex hormone regulation, metabolic disorders, and psychological and behavioral factors. Furthermore, based on the principles of three level prevention, relevant intervention measures have been proposed to provide new evidence for advancing the prevention and control of hypertension starting in childhood.

Objective:To evaluate the long-term effectiveness of lingual mucosal graft ureteroplasty (LMGU) for managing long-segment (≥5 cm) ureteral strictures in a multi-institutional cohort of patients.Methods:A multi-center retrospective case series study was conducted on clinical data from 42 patients undergoing LMGU for long-segment ureteral strictures (≥5 cm) across five institutions between February 2017 and June 2024. The cohort comprised 31 males and 11 females, with an age of (43.4±12.0) years (range: 15 to 64 years) and a body mass index of (24.6±2.6) kg/m2 (range: 16.0 to 30.0 kg/m2). Strictures involved the left ureter in 24 cases and right ureter in 18 cases, demonstrating a stricture length of (6.4±1.5) cm (range: 5.0 to 11.5 cm). Surgical interventions included either onlay ureteroplasty or augmented anastomotic ureteroplasty, selected according to intraoperative findings. Intraoperative parameters, postoperative complications, and follow-up outcomes were analyzed.Results:Laparoscopic surgery was performed in 22 cases and robot-assisted surgery in 20 cases. Among the 42 patients, 22 underwent onlay ureteroplasty while 20 received augmented anastomotic ureteroplasty. The graft length was (5.9±1.8) cm (range: 3.0 to 12.0 cm), operative time (191.5±55.6) minutes (range: 105.0 to 350.0 minutes), and intraoperative estimated blood loss (86.7±73.6) ml (range: 10.0 to 400.0 ml). All procedures were successfully completed without conversion to open surgery. The postoperative hospital stay was (7.6±2.0) days (range: 4.0 to 15.0 days), with double-J stent removal at 6 to 8 weeks postoperatively. During a follow-up of (49.1±25.0) months (range: 12.0 to 99.0 months), no stricture recurrence was observed in any patient.Conclusion:LMGU is a safe, feasible, and effective long-term technique for managing long-segment (≥5 cm) ureteral strictures.

Objective:To analyze the influencing factors of knee joint stiffness in patients with tibial plateau fractures after arthroscopy and construct a predictive model.Methods:The clinical data of 154 patients with tibial plateau fractures from June 2020 to May 2023 in Xi'an Daxing Hospital were retrospectively analyzed. All patients were treated with arthroscopic assisted internal fixation surgery. The gender, age, body weight, fracture cause, Schatzker classification, osteoporosis, meniscus and ligament structure injury, lower limb malalignment, bone graft, knee extension device injury, internal fixation method, external fixation time, heterotopic ossification, Tscherne classification, drainage tube placement, debridement times, operation opportunity and anesthesia method were recorded. The patients were followed up for 1 year, and the occurrence of knee joint stiffness was recorded. The patients were divided into knee joint stiffness group and normal knee joint group. Multivariate Logistic regression was used to analyze the independent risk factors of knee joint stiffness after arthroscopy in patients with tibial plateau fractures. R language software package was used to construct the nomogram model for predicting the knee joint stiffness after arthroscopy in patients with tibial plateau fractures, calibration curve was drawn, and the Bootstrap method was used to verify the model discrimination. The predictive value of model was evaluated by the receiver operating characteristics (ROC) curve.Results:Among 154 patients with tibial plateau fractures, 28 patients developed knee joint stiffness after arthroscopy (knee joint stiffness group), with an incidence rate of 18.18%; 126 patients had normal knee joints (normal knee joint group). The proportions of obesity, meniscus and ligament structure injury, knee extension device injury, lower limb malalignment and heterotopic ossification in knee joint stiffness group were significantly higher than those in normal knee joint group, and there were statistical differences ( P<0.05 or <0.01); there were no statistical differences in gender composition, age, fracture cause, osteoporosis, Schatzker classification, bone graft, internal fixation method, external fixation time, Tscherne classification, drainage tube placement, debridement times, operation opportunity and anesthesia method between the two groups ( P>0.05). Multivariate Logistic regression analysis result showed that obesity, meniscus and ligament structure injury, knee extension device injury, lower limb malalignment and heterotopic ossification were independent risk factors for knee joint stiffness after arthroscopy in patients with tibial plateau fractures ( OR = 5.387, 4.613, 3.308, 3.178 and 4.579; 95% CI 1.207 to 24.034, 1.447 to 14.709, 1.063 to 10.291, 1.155 to 8.745 and 1.540 to 13.613; P<0.05 or <0.01). The body weight, meniscus and ligament structure injury, knee extension device injury, lower limb malalignment and heterotopic ossification were used as predictors to construct a nomogram model for predicting the knee joint stiffness after arthroscopy in patients with tibial plateau fractures. The calibration curve analysis result showed that the theoretical curve was basically consistent with the actual curve trend trajectory ( C- index = 0.861). The ROC curve analysis result showed that the model for predicting the knee joint stiffness after arthroscopy in patients with tibial plateau fractures had good accuracy (area under curve was 0.861), with the sensitivity of 92.9% and specificity of 73.0%. Conclusions:The patients with tibial plateau fractures have the risk of knee joint stiffness after arthroscopy, which may be related to patient obesity, meniscus and ligament structure injury, knee extension device injury, lower limb malalignment and heterotopic ossification. Based on these risk factors, a column chart risk prediction model can be constructed to visualize the risk and have certain predictive value for knee joint stiffness within 1 year after surgery.

To summarize the emergency nursing experience of 1 patient with neck suicide complicated with airway obstruction and hemorrhagic shock.Nursing points:to ensure airway safety and establish artificial airway;to optimize airway management and implement early sedation and wake-up;to control injury bleeding and limited fluid resuscitation;to establish a micro blood station in the emergency department and start a high-dose blood transfusion procedure;to carry out the refined target body temperature management to reduce complications;to optimize speech communication strategies and implement early psychological care.After 7 days of careful treatment and nursing,the patient's condition improved and recovered.After 2 months of follow-up,the patient recovered well.

There are no reports on the application of perirenal fat covering technique in lingual mucosal graft ureteroplasty（LMGU），and its safety and effectiveness need to be observed. This study retrospectively analyzed clinical data of 7 patients who underwent lingual mucosa ureteroplasty with intraoperative perirenal fat covering technique. The median stricture length was 5.5（range 4.0 to 6.0）cm，operative duration was 170（range 120 to 230）min，estimated blood loss was 92（range 15 to 110）ml. No conversions to open surgery occurred. During a median follow-up of 46（range 8 to 86）months，all procedures achieved clinical success. The application of perirenal fat covering technique in LMGU is safe and effective.

This report summarizes the emergency care and nursing interventions for a Parkinson's disease patient with severe hypothermia complicated by ventricular arrhythmia.Key nursing points included continuous core temperature monitoring with goal-directed,phased,progressive rewarming;proactive identification and rapid response to arrhythmias;precise fluid management and skin protection to dynamically optimize blood perfusion and pressure redistribution;vigilant prevention and early intervention of hypothermia-related complications.With multidisciplinary treatment and meticulous nursing care,the patient was successfully discharged after 17 days of hospitalization with a favorable recovery.At the three-month follow-up after discharge,the patient had recovered well with no recurrence of similar episodes.

Objective To construct a regional collaborative cloud-based treatment model treatment model for patients with severe road traffic injuries,and to preliminarily evaluate the differences in nursing timeliness indicators and outcomes.Methods The regional collaborative cloud-based treatment platform includes 4 ports,including public security traffic police,pre-hospital emergency center,regional trauma center triage,and regional trauma center resuscitation unit.This forms a standardized real-time interactive treatment process between regional medical services and police for patients with severe road traffic injuries.Using a concurrent control study design,241 patients with severe road traffic injuries admitted to the emergency department of a regional trauma center in Zhejiang Province from May 2022 to May 2024 were selected as the study subjects.Among them,120 patients treated with the regional real-time collaborative cloud-based treatment model were designated as an experimental group,while 121 patients treated with the original trauma care process were designated as a control group.The differences in timeliness indicators and outcomes between the 2 groups were compared.Results The study included 241 patients with severe trauma.After the application of the regional collaborative cloud-based treatment model,the time from the scene of the accident to the hospital,the proportion of information early waming,completion time of pre-examination and triage,waiting time of the trauma team,the time of the first CT,the length of multidisciplinary consultation,and the time for completing hospitalization procedures in the experimental group were shorter than those in the control group(P＜0.05),the proportion of information early waming in the experimental group was 100％(120/120),and the proportion of information early waming in the control group was 52.1％(63/121).The difference between the two groups was statistically significant(P＜0.001).The survival rate of the experimental group was 90.8％(109/120),and that of the control group was 86.0％(104/121).There was no significant difference between the two groups(x2=1.399,P=0.237).Conclusion The regional collaborative cloud-based treatment model improves the timeliness and standardization of the treatment of patients with severe road traffic injury,which has certain reference significance and promotion value.

Objective To construct a regional collaborative cloud-based treatment model treatment model for patients with severe road traffic injuries,and to preliminarily evaluate the differences in nursing timeliness indicators and outcomes.Methods The regional collaborative cloud-based treatment platform includes 4 ports,including public security traffic police,pre-hospital emergency center,regional trauma center triage,and regional trauma center resuscitation unit.This forms a standardized real-time interactive treatment process between regional medical services and police for patients with severe road traffic injuries.Using a concurrent control study design,241 patients with severe road traffic injuries admitted to the emergency department of a regional trauma center in Zhejiang Province from May 2022 to May 2024 were selected as the study subjects.Among them,120 patients treated with the regional real-time collaborative cloud-based treatment model were designated as an experimental group,while 121 patients treated with the original trauma care process were designated as a control group.The differences in timeliness indicators and outcomes between the 2 groups were compared.Results The study included 241 patients with severe trauma.After the application of the regional collaborative cloud-based treatment model,the time from the scene of the accident to the hospital,the proportion of information early waming,completion time of pre-examination and triage,waiting time of the trauma team,the time of the first CT,the length of multidisciplinary consultation,and the time for completing hospitalization procedures in the experimental group were shorter than those in the control group(P＜0.05),the proportion of information early waming in the experimental group was 100％(120/120),and the proportion of information early waming in the control group was 52.1％(63/121).The difference between the two groups was statistically significant(P＜0.001).The survival rate of the experimental group was 90.8％(109/120),and that of the control group was 86.0％(104/121).There was no significant difference between the two groups(x2=1.399,P=0.237).Conclusion The regional collaborative cloud-based treatment model improves the timeliness and standardization of the treatment of patients with severe road traffic injury,which has certain reference significance and promotion value.

This report summarizes the emergency care and nursing interventions for a Parkinson's disease patient with severe hypothermia complicated by ventricular arrhythmia.Key nursing points included continuous core temperature monitoring with goal-directed,phased,progressive rewarming;proactive identification and rapid response to arrhythmias;precise fluid management and skin protection to dynamically optimize blood perfusion and pressure redistribution;vigilant prevention and early intervention of hypothermia-related complications.With multidisciplinary treatment and meticulous nursing care,the patient was successfully discharged after 17 days of hospitalization with a favorable recovery.At the three-month follow-up after discharge,the patient had recovered well with no recurrence of similar episodes.

Objective:To analyze the influencing factors of knee joint stiffness in patients with tibial plateau fractures after arthroscopy and construct a predictive model.Methods:The clinical data of 154 patients with tibial plateau fractures from June 2020 to May 2023 in Xi'an Daxing Hospital were retrospectively analyzed. All patients were treated with arthroscopic assisted internal fixation surgery. The gender, age, body weight, fracture cause, Schatzker classification, osteoporosis, meniscus and ligament structure injury, lower limb malalignment, bone graft, knee extension device injury, internal fixation method, external fixation time, heterotopic ossification, Tscherne classification, drainage tube placement, debridement times, operation opportunity and anesthesia method were recorded. The patients were followed up for 1 year, and the occurrence of knee joint stiffness was recorded. The patients were divided into knee joint stiffness group and normal knee joint group. Multivariate Logistic regression was used to analyze the independent risk factors of knee joint stiffness after arthroscopy in patients with tibial plateau fractures. R language software package was used to construct the nomogram model for predicting the knee joint stiffness after arthroscopy in patients with tibial plateau fractures, calibration curve was drawn, and the Bootstrap method was used to verify the model discrimination. The predictive value of model was evaluated by the receiver operating characteristics (ROC) curve.Results:Among 154 patients with tibial plateau fractures, 28 patients developed knee joint stiffness after arthroscopy (knee joint stiffness group), with an incidence rate of 18.18%; 126 patients had normal knee joints (normal knee joint group). The proportions of obesity, meniscus and ligament structure injury, knee extension device injury, lower limb malalignment and heterotopic ossification in knee joint stiffness group were significantly higher than those in normal knee joint group, and there were statistical differences ( P<0.05 or <0.01); there were no statistical differences in gender composition, age, fracture cause, osteoporosis, Schatzker classification, bone graft, internal fixation method, external fixation time, Tscherne classification, drainage tube placement, debridement times, operation opportunity and anesthesia method between the two groups ( P>0.05). Multivariate Logistic regression analysis result showed that obesity, meniscus and ligament structure injury, knee extension device injury, lower limb malalignment and heterotopic ossification were independent risk factors for knee joint stiffness after arthroscopy in patients with tibial plateau fractures ( OR = 5.387, 4.613, 3.308, 3.178 and 4.579; 95% CI 1.207 to 24.034, 1.447 to 14.709, 1.063 to 10.291, 1.155 to 8.745 and 1.540 to 13.613; P<0.05 or <0.01). The body weight, meniscus and ligament structure injury, knee extension device injury, lower limb malalignment and heterotopic ossification were used as predictors to construct a nomogram model for predicting the knee joint stiffness after arthroscopy in patients with tibial plateau fractures. The calibration curve analysis result showed that the theoretical curve was basically consistent with the actual curve trend trajectory ( C- index = 0.861). The ROC curve analysis result showed that the model for predicting the knee joint stiffness after arthroscopy in patients with tibial plateau fractures had good accuracy (area under curve was 0.861), with the sensitivity of 92.9% and specificity of 73.0%. Conclusions:The patients with tibial plateau fractures have the risk of knee joint stiffness after arthroscopy, which may be related to patient obesity, meniscus and ligament structure injury, knee extension device injury, lower limb malalignment and heterotopic ossification. Based on these risk factors, a column chart risk prediction model can be constructed to visualize the risk and have certain predictive value for knee joint stiffness within 1 year after surgery.