Objective To investigate the current status of family resilience of children with cancer and analyze its influencing factors,to provide a basis for medical staff to formulate intervention plans.Methods Using a convenient sampling method,children with cancer who were hospitalized in 2 tertiary hospitals in Henan Province from January to April 2024 were selected for the survey.A general information questionnaire,family resilience assessment scale,quality of life family version,ZBI caregiver burden interview,and social support rating scale were used to understand the current status of family resilience of children with cancer and to explore the related influencing factors by univariate analysis and multiple stepwise linear regression analysis.Results A total of 280 questionnaires were distributed and 265 valid questionnaires were recovered,with a valid questionnaire recovery rate of 94.64％.The total score of family resilience for primary caregivers of children with cancer was(185.63±30.66).The multiple stepwise linear regression analysis results showed that the children's self-care ability,caregiver's work status,family care burden,and social support level were the influencing factors for family resilience of children with cancer(P＜0.05),and the explanatory variance was 51.3％.Conclusion The family resilience of children with cancer is at a medium level.The worse the children's self-care ability and the heavier the family care burden,the worse the family resilience;the caregiver's work status and good social support are helpful for the family resilience of children with cancer.Healthcare workers should develop intervention programs to address these factors to enhance the family resilience of children with cancer.

Objective To investigate the current status of family resilience of children with cancer and analyze its influencing factors,to provide a basis for medical staff to formulate intervention plans.Methods Using a convenient sampling method,children with cancer who were hospitalized in 2 tertiary hospitals in Henan Province from January to April 2024 were selected for the survey.A general information questionnaire,family resilience assessment scale,quality of life family version,ZBI caregiver burden interview,and social support rating scale were used to understand the current status of family resilience of children with cancer and to explore the related influencing factors by univariate analysis and multiple stepwise linear regression analysis.Results A total of 280 questionnaires were distributed and 265 valid questionnaires were recovered,with a valid questionnaire recovery rate of 94.64％.The total score of family resilience for primary caregivers of children with cancer was(185.63±30.66).The multiple stepwise linear regression analysis results showed that the children's self-care ability,caregiver's work status,family care burden,and social support level were the influencing factors for family resilience of children with cancer(P＜0.05),and the explanatory variance was 51.3％.Conclusion The family resilience of children with cancer is at a medium level.The worse the children's self-care ability and the heavier the family care burden,the worse the family resilience;the caregiver's work status and good social support are helpful for the family resilience of children with cancer.Healthcare workers should develop intervention programs to address these factors to enhance the family resilience of children with cancer.

Objective:To investigate the efficacy of a domestically developed small esophageal cooling device in implementing targeted temperature management (TTM) after resuscitation and its impact on organ injury using a porcine model of cardiac arrest and resuscitation.Methods:Thirty healthy male domestic white pigs were randomly divided into four groups using a random number table: sham (S group, n=6), normothermia (NT group, n=8), surface cooling (SC group, n=8), and esophageal cooling (EC group, n=8). The S group underwent only surgical preparation, while the other groups were subjected to 12 minutes of ventricular fibrillation followed by 6 minutes of cardiopulmonary resuscitation to establish cardiac arrest. The S and NT groups maintained a core temperature of (37.5±0.5)°C using a surface blanket. In the SC and EC groups, therapeutic hypothermia was induced post-resuscitation via surface blanket or esophageal cooling catheter to achieve a target temperature of 34°C, maintained the target temperature (34±0.5)°C for 6 hours, followed by controlled rewarming at 0.5°C/h to 37°C. Core temperature was continuously monitored for 12 hours post-resuscitation. Hemodynamic parameters, including stroke volume (SV), global ejection fraction (GEF), extravascular lung water index (ELWI), and pulmonary vascular permeability index (PVPI), were assessed using pulse indicator continuous cardiac output (PiCCO) monitoring. Serum levels of cardiac troponin I (cTnI), neuron-specific enolase (NSE), creatinine (Cr), and intestinal fatty acid-binding protein (IFABP) were measured via ELISA at 2, 6, 12, and 24 hours post-resuscitation. Neurological outcomes were evaluated at 24 hours using the neurological deficit score (NDS) and cerebral performance category (CPC). Continuous variables were analyzed using one-way ANOVA. Results:During TTM, the EC group exhibited a faster cooling rate [(1.52±0.18)°C/h vs. (0.94±0.32)°C/h, P<0.05] and shorter time to target temperature [(2.32±0.43) h vs. (3.78±0.82) h, P<0.05] compared to the SC group, with comparable maintenance and rewarming ( P>0.05). Compared to the S group, the NT, SC, and EC groups demonstrated significant post-resuscitation multi-organ injury, characterized by reduced SV and GEF, elevated ELWI and PVPI, and increased serum cTnI, NSE, Cr, IFABP, NDS, and CPC scores (all P<0.05). Relative to the NT group, the SC and EC groups showed improved SV (at 1 h post-resuscitation), GEF (at 1, 2, 4, and 6 h), ELWI (at 12 h), and reduced cTnI and NSE (at 6 h), Cr and IFABP (at 2 h), and NDS and CPC (at 24 h) (all P<0.05). Compared to the SC group, the EC group exhibited lower PVPI (at 12 h), reduced cTnI, Cr, and IFABP (at 2 h), decreased NSE (at 2, 12, and 24 h), and improved NDS (at 24 h) (all P<0.05). Conclusions:In a porcine model of cardiac arrest and resuscitation, the domestic esophageal cooling device facilitated rapid induction, stable maintenance, and controlled rewarming during TTM, outperforming traditional surface cooling. This approach demonstrated superior organ protection, warranting further investigation.

The angiogenic response is essential for the repair of ischemic brain tissue. Integrin α6 (Itga6) expression has been shown to increase under hypoxic conditions and is expressed exclusively in vascular structures; however, its role in post-ischemic angiogenesis remains poorly understood. In this study, we demonstrate that mice with endothelial cell-specific knockout of Itga6 exhibit reduced neovascularization, reduced pericyte coverage on microvessels, and accelerated breakdown of microvascular integrity in the peri-infarct area. In vitro, endothelial cells with ITGA6 knockdown display reduced proliferation, migration, and tube-formation. Mechanistically, we demonstrated that ITGA6 regulates post-stroke angiogenesis through the PI3K/Akt-eNOS-VEGFA axis. Importantly, the specific overexpression of Itga6 in endothelial cells significantly enhanced neovascularization and enhanced the integrity of microvessels, leading to improved functional recovery. Our results suggest that endothelial cell Itga6 plays a crucial role in key steps of post-stroke angiogenesis, and may represent a promising therapeutic target for promoting recovery after stroke.
Animals ; Nitric Oxide Synthase Type III/metabolism* ; Mice ; Proto-Oncogene Proteins c-akt/metabolism* ; Integrin alpha6/genetics* ; Endothelial Cells/metabolism* ; Phosphatidylinositol 3-Kinases/metabolism* ; Stroke/pathology* ; Vascular Remodeling/physiology* ; Vascular Endothelial Growth Factor A/metabolism* ; Mice, Knockout ; Signal Transduction/physiology* ; Mice, Inbred C57BL ; Male ; Neovascularization, Physiologic/physiology*

Objective:To construct a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury (TBI) and evaluate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 1 120 TBI patients admitted to Changde Hospital Affiliated to Xiangya Medical College of Central South University from May 2019 to December 2024. The patients were divided into the training set ( n=784) and verification set ( n=336) at a ratio of 7∶3. Based on the Glasgow outcome scale-extended (GOS-E) at discharge, the training set was stratified into favorable prognosis group ( n=335, GOS-E 5-8 points) and poor prognosis group ( n=449, GOS-E 1-4 points). The two groups in the training set were compared in terms of general baseline indicators, TBI-related clinical indicators, and admission laboratory blood test results. Univariate analysis and Lasso regression analysis were employed to screen risk factors associated with postoperative poor in-hospital prognosis in TBI patients. Multivariate Logistic regression analysis was used to determine independent risk factors and construct a regression equation. The regression equation was presented using R language to create a visual nomogram for predicting postoperative poor in-hospital prognosis in TBI patients. In both the training set and verification set, the predictive performance of the model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC), plotting calibration curves, and performing decision curve analysis (DCA). Results:The results of the univariate analysis indicated that the age, Charlson complication index (CCI), time from trauma to admission, time from trauma to operation, cause of injury, abbreviated injury scale (AIS) (head and neck), injury severity score (ISS), admission Glasgow coma scale (GCS), admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraventricular hemorrhage, subarachnoid hemorrhage, decompressive craniotomy, intraoperative blood loss, intraoperative blood transfusion, traumatic cerebral infarction, postoperative delayed bleeding, epilepsy seizures, as well as the following admission tested results including red blood cell count, white blood cell count, platelet count, neutrophil percentage, percentage of lymphocytes, albumin, total bilirubin, urea nitrogen, thrombin time (TT), prothrombin time (PT), international standardized ratio (INR), glutamic aminotransferase, alanine aminotransferase, creatinine, and blood glucose were statistically different between the two groups in the training set ( P<0.05). Lasso regression analysis suggested 14 risk factors of age, CCI, cause of injury, head and neck AIS, ISS, admission GCS, admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraoperative blood loss, admission platelet count, admission albumin, admission blood glucose for postoperative poor in-hospital prognosis. The results of the multivariate Logistic regression analysis showed that age ( OR=1.02, 95% CI 1.00, 1.03, P<0.01), CCI ( OR=1.46, 95% CI 1.02, 2.09, P<0.05), head and neck AIS ( OR=1.43, 95% CI 1.11, 1.85, P<0.01), ISS ( OR=2.16, 95% CI 1.39, 3.35, P<0.01), admission GCS ( OR=1.59, 95% CI 1.19, 2.13, P<0.01), intracerebral hematoma ( OR=4.41, 95% CI 2.15, 9.44, P<0.01), intraoperative blood loss ( OR=1.05, 95% CI 1.00, 1.09, P<0.05), admission platelet count ( OR=0.98, 95% CI 0.97, 0.99, P<0.01), admission blood glucose ( OR=1.08, 95% CI 1.02, 1.15, P<0.05) could be the main risk factors to construct a prediction model for postoperative poor in-hospital prognosis in TBI patients. Meanwhile, a regression equation was constructed: Logit[ P/(1- P)]=-2.4+ 0.02×"age"+0.38×"CCI"+0.36×"head and neck AIS"+0.77×"ISS"+0.47×"admission GCS"+1.48×"intracerebral hematoma"+0.05×intraoperative blood loss-0.02×admission platelet count+0.08×admission blood glucose. In the training set, the predictive model for poor postoperative in-hospital prognosis in TBI patients achieved an AUC of 0.87 (95% CI 0.84, 0.89), with a Youden′s index of 0.57, sensitivity of 73.70%, and specificity of 83.00%. In the verification set, the model showed an AUC of 0.80 (95% CI 0.76, 0.85), with a Youden′s index of 0.63, sensitivity of 65.20%, and specificity of 77.90%. In the training set, the Brier score for the calibration curve was 0.14 (95% CI 0.13, 0.16). In the verification set, the Brier score for the calibration curve was 0.18 (95% CI 0.15, 0.20). The DCA diagram indicated that the nomogram prediction model provided high clinical net benefit for predicting postoperative poor in-hospital prognosis in TBI patients. Conclusion:The prediction model for postoperative poor in-hospital prognosis in TBI patients, constructed based on age, CCI, head and neck AIS, ISS, admission GCS, intracerebral hematoma, intraoperative blood loss, admission platelet count, and admission blood glucose, exhibits good predictive performance.

Vertebral refracture following percutaneous vertebral augmentation (PVA) is commonly seen in elderly patients with osteoporotic thoracolumbar compression fractures (OTLCF). It can lead to recurrent pain, loss of vertebral height, progression of kyphosis, and even neurological dysfunction, significantly impairing patients′ quality of life. Current diagnosis and treatment face multiple challenges, including high misdiagnosis rate, difficulty in choosing between surgical and non-surgical treatment options, lack of standardized surgical protocols, interference from intralesional bone cement during procedures, inadequate stability of internal fixation in osteoporotic bone, and suboptimal compliance of anti-osteoporotic therapy. Establishing a standardized diagnostic and therapeutic framework is urgently needed. To standardize the management process and improve outcomes for vertebral refractures after PVA in elderly OTLCF patients, Spinal Trauma Group of the Orthopedic Branch of Chinese Medical Doctor Association organized experts in the field to develop Guideline for the diagnosis and treatment of vertebral refracture after percutaneous vertebral augmentation in elderly patients with osteoporotic thoracolumbar compression fractures ( version 2025), based on current literature and clinical experience, and adhering to principles of scientific rigor and clinical applicability. A total of 11 recommendations were proposed, encompassing diagnosis, treatment, and rehabilitation of vertebral refracture after PVA in elderly patients with OTLCF, aiming to provide a foundation for a standardized management.

Objective:To construct a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury (TBI) and evaluate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 1 120 TBI patients admitted to Changde Hospital Affiliated to Xiangya Medical College of Central South University from May 2019 to December 2024. The patients were divided into the training set ( n=784) and verification set ( n=336) at a ratio of 7∶3. Based on the Glasgow outcome scale-extended (GOS-E) at discharge, the training set was stratified into favorable prognosis group ( n=335, GOS-E 5-8 points) and poor prognosis group ( n=449, GOS-E 1-4 points). The two groups in the training set were compared in terms of general baseline indicators, TBI-related clinical indicators, and admission laboratory blood test results. Univariate analysis and Lasso regression analysis were employed to screen risk factors associated with postoperative poor in-hospital prognosis in TBI patients. Multivariate Logistic regression analysis was used to determine independent risk factors and construct a regression equation. The regression equation was presented using R language to create a visual nomogram for predicting postoperative poor in-hospital prognosis in TBI patients. In both the training set and verification set, the predictive performance of the model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC), plotting calibration curves, and performing decision curve analysis (DCA). Results:The results of the univariate analysis indicated that the age, Charlson complication index (CCI), time from trauma to admission, time from trauma to operation, cause of injury, abbreviated injury scale (AIS) (head and neck), injury severity score (ISS), admission Glasgow coma scale (GCS), admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraventricular hemorrhage, subarachnoid hemorrhage, decompressive craniotomy, intraoperative blood loss, intraoperative blood transfusion, traumatic cerebral infarction, postoperative delayed bleeding, epilepsy seizures, as well as the following admission tested results including red blood cell count, white blood cell count, platelet count, neutrophil percentage, percentage of lymphocytes, albumin, total bilirubin, urea nitrogen, thrombin time (TT), prothrombin time (PT), international standardized ratio (INR), glutamic aminotransferase, alanine aminotransferase, creatinine, and blood glucose were statistically different between the two groups in the training set ( P<0.05). Lasso regression analysis suggested 14 risk factors of age, CCI, cause of injury, head and neck AIS, ISS, admission GCS, admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraoperative blood loss, admission platelet count, admission albumin, admission blood glucose for postoperative poor in-hospital prognosis. The results of the multivariate Logistic regression analysis showed that age ( OR=1.02, 95% CI 1.00, 1.03, P<0.01), CCI ( OR=1.46, 95% CI 1.02, 2.09, P<0.05), head and neck AIS ( OR=1.43, 95% CI 1.11, 1.85, P<0.01), ISS ( OR=2.16, 95% CI 1.39, 3.35, P<0.01), admission GCS ( OR=1.59, 95% CI 1.19, 2.13, P<0.01), intracerebral hematoma ( OR=4.41, 95% CI 2.15, 9.44, P<0.01), intraoperative blood loss ( OR=1.05, 95% CI 1.00, 1.09, P<0.05), admission platelet count ( OR=0.98, 95% CI 0.97, 0.99, P<0.01), admission blood glucose ( OR=1.08, 95% CI 1.02, 1.15, P<0.05) could be the main risk factors to construct a prediction model for postoperative poor in-hospital prognosis in TBI patients. Meanwhile, a regression equation was constructed: Logit[ P/(1- P)]=-2.4+ 0.02×"age"+0.38×"CCI"+0.36×"head and neck AIS"+0.77×"ISS"+0.47×"admission GCS"+1.48×"intracerebral hematoma"+0.05×intraoperative blood loss-0.02×admission platelet count+0.08×admission blood glucose. In the training set, the predictive model for poor postoperative in-hospital prognosis in TBI patients achieved an AUC of 0.87 (95% CI 0.84, 0.89), with a Youden′s index of 0.57, sensitivity of 73.70%, and specificity of 83.00%. In the verification set, the model showed an AUC of 0.80 (95% CI 0.76, 0.85), with a Youden′s index of 0.63, sensitivity of 65.20%, and specificity of 77.90%. In the training set, the Brier score for the calibration curve was 0.14 (95% CI 0.13, 0.16). In the verification set, the Brier score for the calibration curve was 0.18 (95% CI 0.15, 0.20). The DCA diagram indicated that the nomogram prediction model provided high clinical net benefit for predicting postoperative poor in-hospital prognosis in TBI patients. Conclusion:The prediction model for postoperative poor in-hospital prognosis in TBI patients, constructed based on age, CCI, head and neck AIS, ISS, admission GCS, intracerebral hematoma, intraoperative blood loss, admission platelet count, and admission blood glucose, exhibits good predictive performance.

OBJECTIVE: Although dietary preferences influence chronic diseases, few studies have linked dietary preferences to mortality risk, particularly in large cohorts. To investigate the relationship between dietary preferences and mortality risk (all-cause, cancer, and cardiovascular disease [CVD]) in a large adult cohort. METHODS: A cohort of 1,160,312 adults (mean age 62.48 ± 9.55) from the Shenzhen Healthcare Big Data Cohort (SHBDC) was analyzed. Hazard ratios ( HRs) for mortality were estimated using the Cox proportional hazards model. RESULTS: The study identified 12,308 all-cause deaths, of which 3,865 (31.4%) were cancer-related and 3,576 (29.1%) were attributed to CVD. Compared with a mixed diet of meat and vegetables, a mainly meat-based diet (hazard ratio [ HR] = 1.13; 95% confidence interval [ CI]: 1.02, 1.27) associated with a higher risk of all-cause mortality, while mainly vegetarian ( HR = 0.87; 95% CI: 0.78, 0.97) was linked to a reduced risk. Furthermore, there was a stronger correlation between mortality risk and dietary preference in the > 65 age range. CONCLUSION A meat-based diet was associated with an increased risk of all-cause mortality, whereas a mainly vegetarian diet was linked to a reduced risk.
Humans ; China/epidemiology* ; Middle Aged ; Male ; Female ; Prospective Studies ; Aged ; Cardiovascular Diseases/mortality* ; Diet/statistics & numerical data* ; Neoplasms/mortality* ; Adult ; Cause of Death ; Food Preferences ; Proportional Hazards Models ; Mortality ; Cohort Studies

OBJECTIVE: To investigate the relationship between physical activity and genetic risk and their combined effects on the risk of developing chronic obstructive pulmonary disease. METHODS: This prospective cohort study included 318,085 biobank participants from the UK. Physical activity was assessed using the short form of the International Physical Activity Questionnaire. The participants were stratified into low-, intermediate-, and high-genetic-risk groups based on their polygenic risk scores. Multivariate Cox regression models and multiplicative interaction analyses were used. RESULTS: During a median follow-up period of 13 years, 9,209 participants were diagnosed with chronic obstructive pulmonary disease. For low genetic risk, compared to low physical activity, the hazard ratios ( HRs) for moderate and high physical activity were 0.853 (95% confidence interval [ CI]: 0.748-0.972) and 0.831 (95% CI: 0.727-0.950), respectively. For intermediate genetic risk, the HRs were 0.829 (95% CI: 0.758-0.905) and 0.835 (95% CI: 0.764-0.914), respectively. For participants with high genetic risk, the HRs were 0.809 (95% CI: 0.746-0.877) and 0.818 (95% CI: 0.754-0.888), respectively. A significant interaction was observed between genetic risk and physical activity. CONCLUSION Moderate or high levels of physical activity were associated with a lower risk of developing chronic obstructive pulmonary disease across all genetic risk groups, highlighting the need to tailor activity interventions for genetically susceptible individuals.
Humans ; Pulmonary Disease, Chronic Obstructive/epidemiology* ; Exercise ; Male ; Female ; Middle Aged ; Prospective Studies ; Aged ; Genetic Predisposition to Disease ; Risk Factors ; United Kingdom/epidemiology* ; Incidence ; Adult

This study was conducted retrospectively on a cohort of 68 patients with steroid 5 α-reductase 2 (SRD5A2) deficiency and 46,XY disorders of sex development (DSD). Whole-exon sequencing revealed 28 variants of SRD5A2 , and further analysis identified seven novel mutants. The preponderance of variants was observed in exon 1 and exon 4, specifically within the nicotinamide adenine dinucleotide phosphate (NADPH)-binding region. Among the entire cohort, 53 patients underwent initial surgery at Sichuan Provincial People's Hospital (Chengdu, China). The external genitalia scores (EGS) of these participants varied from 2.0 to 11.0, with a mean of 6.8 (standard deviation [s.d.]: 2.5). Thirty patients consented to hormone testing. Their average testosterone-to-dihydrotestosterone (T/DHT) ratio was 49.3 (s.d.: 23.4). Genetic testing identified four patients with EGS scores between 6 and 9 as having this syndrome; and their T/DHT ratios were below the diagnostic threshold. Furthermore, assessments conducted using the crystal structure of human SRD5A2 have provided insights into the potential pathogenic mechanisms of these novel variants. These mechanisms include interference with NADPH binding (c.356G>C, c.365A>G, c.492C>G, and c.662T>G) and destabilization of the protein structure (c.727C>T). The c.446-1G>T and c.380delG variants were verified to result in large alterations in the transcripts. Seven novel variations were identified, and the variant database for the SRD5A2 gene was expanded. These findings contribute to the progress of diagnostic and therapeutic approaches for individuals with SRD5A2 deficiency.
Humans ; 3-Oxo-5-alpha-Steroid 4-Dehydrogenase/genetics* ; Disorder of Sex Development, 46,XY/blood* ; Male ; Membrane Proteins/genetics* ; Child, Preschool ; Child ; Retrospective Studies ; Adolescent ; Female ; Mutation ; Testosterone/blood* ; Infant ; Dihydrotestosterone/blood*