Objective To investigate the impact of red blood cell suspension infusion across various perioperative periods on patients with valvular heart disease. Methods The patients with valvular heart disease admitted to Tianjin Chest Hospital from 2018 to 2020 were selected. Based on the timing of perioperative red cell suspension infusion, patients were categorized into three groups: a group 1 receiving intraoperative red cell suspension infusion, a group 2 receiving red cell suspension infusion within 24 hours after entering the ICU, and a group 3 receiving red cell suspension infusion at both time points. The laboratory results, perioperative blood component infusion volume, and other relevant parameters were retrospectively analyzed. After propensity score matching, the differences in different variables among the three groups were compared. Results After propensity score matching, 102 patients were enrolled, including 52 males and 50 females, with an average age of (61.74±10.58) years. There were 34 patients in each group. The preoperative hemoglobin (Hb) value of the group 2 was significantly higher than that of the group 1 and the group 3, and the amount of red cell suspension and autoblood transfusion was the lowest (P<0.05). Group 1 had the highest postoperative Hb, as well as the highest Hb and hematocrit (HCT) levels within 24 hours post-surgery (P<0.05). The group 1 had the lowest plasma, platelet and cryoprecipitate infusion volumes, and the shortest cardiopulmonary bypass time, aortic occlusion time, postoperative ICU stay and hospital stay, and the least blood loss and total drainage volume (P<0.05). The difference between postoperative and preoperative Hb (△Hb1) was highest in group 1 (P<0.05). Conclusion For patients with valvular heart disease, intraoperative-only infusion of red blood cell suspension is associated with a better prognosis at discharge and during follow-up.

Diabetic kidney disease（DKD） is a chronic kidney structural and functional disorder caused by diabetes. With the global prevalence of diabetes continuing to rise， DKD has gradually become a major cause of chronic kidney disease and end-stage renal disease（ESRD）， posing a serious threat to patients' quality of life and long-term health outcomes. Studies have shown that apoptosis plays a pivotal role in the development and progression of DKD， with its mechanisms involving abnormal activation of multiple signaling pathways such as Toll-like receptor 4（TLR4）/nuclear transcription factor-κB（NF-κB）/B-cell lymphoma-2（Bcl-2）/cysteinyl aspartate-specific proteinase（Caspase）-3， protein kinase R-like endoplasmic reticulum kinase（PERK）/eukaryotic initiation factor 2α（eIF2α）/activating transcript factor 4（ATF4）/CCAAT enhancer-binding protein homologous protein（CHOP）， phosphatidylinositol 3-kinase（PI3K）/protein kinase B（Akt）/glycogen synthase kinase-3β（GSK-3β）， Janus kinase 2（JAK2）/signal transducer and activator of transcription 3（STAT3）， adenosine monophosphate-activated protein kinase（AMPK）/mammalian target of rapamycin（mTOR） and silent information regulator 1（SIRT1）/tumor suppressor protein 53（p53）， thereby accelerating renal pathological damage in DKD. Extensive evidence-based medical studies have confirmed that traditional Chinese medicine（TCM）， leveraging its unique therapeutic advantages of multi-target， multi-component and multi-pathway approaches， has demonstrated remarkable efficacy and favorable safety profiles in treating DKD. Recent studies have demonstrated that active components of TCM can specifically target and modulate key effectors in apoptotic signaling pathways. Meanwhile， traditional compound formulations exert synergistic effects through multiple approaches such as replenishing deficiency and activating blood circulation， detoxifying and dredging collaterals， tonifying kidney essence， and removing stasis and purging turbidity， thereby comprehensively regulating critical pathological processes including endoplasmic reticulum stress and mitochondrial apoptosis pathways. This combined therapeutic approach of molecular targeting and holistic regulation provides novel strategies for delaying the progression of DKD. Based on this， this paper provides an in-depth analysis of key apoptotic signaling pathways and their regulatory mechanisms， while systematically summarizing recent research advances regarding the therapeutic effects of TCM active components， compound formulations， and proprietary Chinese medicines on DKD through modulation of these pathways， with particular emphasis on their underlying molecular mechanisms. These findings not only elucidate the modern scientific connotation and theoretical basis of TCM in treating DKD but also establish a solid theoretical and practical foundation for promoting the wider clinical application and further research of TCM in the field of DKD treatment.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

This study aimed to investigate the association between secondhand smoke exposure in different places and overweight/obesity among children and adolescents. Children and adolescents aged 7 to 18 years old in Hunan Province were recruited for questionnaire surveys and physical examinations using a multi-stage stratified cluster sampling method. Secondhand smoke exposure was evaluated according to the answer to the question, "Has someone smoked in front of you in the last 7 days?". Overweight and obesity were determined using BMI-for-age and BMI-for-sex according to the national standard WS/T586-2018. Multivariate logistic regression was used to explore the association between secondhand smoke exposure in different places and overweight/obesity among children and adolescents. A total of 187 863 participants were included in this study, with a prevalence of overweight and obesity of 28.4%. The prevalence of secondhand smoke exposure at home, school and other public places was 25.5%, 12.6% and 32.3%, respectively. Children and adolescents with secondhand smoke exposure at home, school and other public places had a higher prevalence of overweight and obesity than those without exposure. After adjusting for confounding factors, secondhand smoke exposure was positively associated with overweight and obesity among children and adolescents, and exposure at home showed the closest association ( OR=1.091; 95% CI: 1.066-1.117). In conclusion, secondhand smoke exposure, especially at home, significantly increases the risk of overweight and obesity among children and adolescents. Comprehensive strategies should be implemented to avoid secondhand smoke exposure and protect children and adolescents from overweight and obesity.

Objective:To explore the clinical symptoms, imaging characteristics, cerebrospinal fluid (CSF) features, as well as the treatment and prognosis of autoimmune glial fibrillary acidic protein (GFAP) astrocytopathy.Methods:Sixty-one patients with anti-GFAP astrocyte antibody (GFAP-IgG) single-positive autoimmune encephalitis who were treated at the Third Affiliated Hospital, Sun Yat-sen University between January 2017 and September 2023 were retrospectively collected. The demographic characteristics (age at onset, sex), clinical symptoms (core symptoms, neurological deficits, psychiatric behavioral abnormalities, and autonomic dysfunction), imaging features [brain/spinal cord/optic nerve magnetic resonance imaging (MRI) lesion distribution and enhancement patterns], and CSF parameters were analyzed. Acute-phase treatments, including methylprednisolone pulse therapy, intravenous immunoglobulin (IVIG), etc, along with the follow-up outcomes [modified Rankin Scale (mRS) score] were recorded.Results:The onset age was 40 (30, 55) years, and 68.9% (42/61) of the patients were male. The most common clinical manifestations were fever (65.6%, 40/61), headache (60.7%, 37/61), and urinary/defecatory abnormalities (45.9%, 28/61). Brain MRI revealed lesions predominantly in the cerebral cortex and subcortical white matter (57.4%, 35/61), periventricular white matter (50.8%, 31/61), and basal ganglia (36.1%, 22/61). Periventricular linear-radiating enhancement was the predominant MRI enhancement pattern (55.7%, 34/61). Spinal MRI showed lesions mainly in the cervical (42.6%, 26/61) and thoracic spinal cord (32.8%, 30/61), with leptomeningeal enhancement (31.1%, 19/61) and scattered punctate/patchy enhancements (21.3%, 13/61). Optic neuropathy was observed in 6 cases (9.8%). CSF analysis demonstrated a pressure of 180 (133, 240) mmH 2O (1 mmH 2O=0.009 8 kPa), white blood cell count of 29 (4, 156)×10?/L, and protein level of 0.72 (0.40, 1.44) g/L. Nineteen patients (31.1%) experienced rapid progression of meningoencephalitis or myelitis within 3 days of admission. All patients received methylprednisolone pulse therapy, with 47.5% (29/61) additionally treated with IVIG. At a follow-up of 12 (3, 28) months, 12 cases (19.7%) relapsed, and 75.4% (46/61) had favorable outcomes (mRS score 0-2). Poor prognosis (mRS score>2) was observed in 4 cases, including 3 with cervical spinal cord involvement and status epilepticus, 1 elderly patient with lung cancer. Conclusions:GFAP astrocytopathy predominantly affects young adults, with a male predominance. Spinal cord involvement is common, manifesting as myelitis and myelopathy. Rapid progression of meningoencephalitis or myelitis may occur early in the disease course. Periventricular linear-radiating enhancement on brain MRI is a key diagnostic clue. Leukocyte and protein levels in the cerebrospinal fluid are generally mildly to moderately elevated. Most patients respond well to corticosteroids and immunotherapy, with favorable outcomes. However, advanced age and cervical spinal cord involvement are associated with poor prognosis.

The in-depth research of artificial intelligence in the medical field has greatly improved the workflow and diagnostic ability of diagnostic radiology.This article focuses on artificial intelligence technology in the field of interventional medicine,and enumerates its potential application scenarios,including improving image analysis capabilities to assist diagnosis and predict treatment response.It also describes the challenges that need to be overcome for practical application.Finally,with the continuous development of artificial intelligence in interventional medicine,artificial intelligence will further optimize the channels of interventional medicine and bring revolutionary changes to the clinical practice of interventional medicine.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

This study aimed to investigate the association between secondhand smoke exposure in different places and overweight/obesity among children and adolescents. Children and adolescents aged 7 to 18 years old in Hunan Province were recruited for questionnaire surveys and physical examinations using a multi-stage stratified cluster sampling method. Secondhand smoke exposure was evaluated according to the answer to the question, "Has someone smoked in front of you in the last 7 days?". Overweight and obesity were determined using BMI-for-age and BMI-for-sex according to the national standard WS/T586-2018. Multivariate logistic regression was used to explore the association between secondhand smoke exposure in different places and overweight/obesity among children and adolescents. A total of 187 863 participants were included in this study, with a prevalence of overweight and obesity of 28.4%. The prevalence of secondhand smoke exposure at home, school and other public places was 25.5%, 12.6% and 32.3%, respectively. Children and adolescents with secondhand smoke exposure at home, school and other public places had a higher prevalence of overweight and obesity than those without exposure. After adjusting for confounding factors, secondhand smoke exposure was positively associated with overweight and obesity among children and adolescents, and exposure at home showed the closest association ( OR=1.091; 95% CI: 1.066-1.117). In conclusion, secondhand smoke exposure, especially at home, significantly increases the risk of overweight and obesity among children and adolescents. Comprehensive strategies should be implemented to avoid secondhand smoke exposure and protect children and adolescents from overweight and obesity.

Objective:To explore the clinical symptoms, imaging characteristics, cerebrospinal fluid (CSF) features, as well as the treatment and prognosis of autoimmune glial fibrillary acidic protein (GFAP) astrocytopathy.Methods:Sixty-one patients with anti-GFAP astrocyte antibody (GFAP-IgG) single-positive autoimmune encephalitis who were treated at the Third Affiliated Hospital, Sun Yat-sen University between January 2017 and September 2023 were retrospectively collected. The demographic characteristics (age at onset, sex), clinical symptoms (core symptoms, neurological deficits, psychiatric behavioral abnormalities, and autonomic dysfunction), imaging features [brain/spinal cord/optic nerve magnetic resonance imaging (MRI) lesion distribution and enhancement patterns], and CSF parameters were analyzed. Acute-phase treatments, including methylprednisolone pulse therapy, intravenous immunoglobulin (IVIG), etc, along with the follow-up outcomes [modified Rankin Scale (mRS) score] were recorded.Results:The onset age was 40 (30, 55) years, and 68.9% (42/61) of the patients were male. The most common clinical manifestations were fever (65.6%, 40/61), headache (60.7%, 37/61), and urinary/defecatory abnormalities (45.9%, 28/61). Brain MRI revealed lesions predominantly in the cerebral cortex and subcortical white matter (57.4%, 35/61), periventricular white matter (50.8%, 31/61), and basal ganglia (36.1%, 22/61). Periventricular linear-radiating enhancement was the predominant MRI enhancement pattern (55.7%, 34/61). Spinal MRI showed lesions mainly in the cervical (42.6%, 26/61) and thoracic spinal cord (32.8%, 30/61), with leptomeningeal enhancement (31.1%, 19/61) and scattered punctate/patchy enhancements (21.3%, 13/61). Optic neuropathy was observed in 6 cases (9.8%). CSF analysis demonstrated a pressure of 180 (133, 240) mmH 2O (1 mmH 2O=0.009 8 kPa), white blood cell count of 29 (4, 156)×10?/L, and protein level of 0.72 (0.40, 1.44) g/L. Nineteen patients (31.1%) experienced rapid progression of meningoencephalitis or myelitis within 3 days of admission. All patients received methylprednisolone pulse therapy, with 47.5% (29/61) additionally treated with IVIG. At a follow-up of 12 (3, 28) months, 12 cases (19.7%) relapsed, and 75.4% (46/61) had favorable outcomes (mRS score 0-2). Poor prognosis (mRS score>2) was observed in 4 cases, including 3 with cervical spinal cord involvement and status epilepticus, 1 elderly patient with lung cancer. Conclusions:GFAP astrocytopathy predominantly affects young adults, with a male predominance. Spinal cord involvement is common, manifesting as myelitis and myelopathy. Rapid progression of meningoencephalitis or myelitis may occur early in the disease course. Periventricular linear-radiating enhancement on brain MRI is a key diagnostic clue. Leukocyte and protein levels in the cerebrospinal fluid are generally mildly to moderately elevated. Most patients respond well to corticosteroids and immunotherapy, with favorable outcomes. However, advanced age and cervical spinal cord involvement are associated with poor prognosis.

Spine fracture and dislocation are common traumatic spinal conditions that often require surgical intervention due to compromised spinal stability. Surgical approaches include anterior, posterior, and combined anterior-posterior spinal procedures. According to the specific surgical requirements, patients may be placed in the prone position or repositioned between prone and supine positions during surgery. Intraoperative repositioning has become an essential step in patient positioning. However, during repositioning, patients with spinal fracture and dislocation are at increased risk for complications such as hemodynamic instability, nerve injury, and pressure injuries to the skin and soft tissue. Notably, due to the instability of the spinal cord, even minor manipulations can further exacerbate the damage, potentially leading to severe outcomes like paraplegia. Although the current clinical guidelines provide instructive recommendations for standard position, there remains no specific protocols for intraoperative repositioning in patients with spine fracture and dislocation. With a concern for the lack of clinical studies on positioning techniques, risk prevention, and operational norms for special patients, no applicable guidelines or standards are available. A consensus was required to provide clinical reference, meet the requirements of surgical treatment, and minimize the safety risks of patients caused by improper placement of positions. Professional Committee of Operating Room Nursing of Shaanxi Nursing Association organized experts in nursing management and operating room nursing from major hospitals across China to formulate Expert consensus on intraoperative repositioning for patients with spinal fracture and dislocation ( version 2025). The consensus provides 11 recommendations covering pre-repositioning preparation, intraoperative maneuvers, and post-repositioning observation, aiming to provide references for clinical standardization of the intraoperative repositioning process and protection of patients′ safety.