To guide clinical blood transfusion practices for pediatric patients, the National Health Commission has issued the health standard "Guideline for pediatric transfusion" (WS/T 795-2022). Blood transfusion is one of the most commonly used supportive treatments for children with hematological diseases. This guideline provides guidance and recommendations for blood transfusions in children with aplastic anemia, thalassemia, autoimmune hemolytic anemia, glucose-6-phosphate dehydrogenase deficiency, acute leukemia, myelodysplastic syndromes, immune thrombocytopenic purpura, and thrombotic thrombocytopenic purpura. This article presents the evidence and interpretation of the blood transfusion provisions for children with hematological diseases in the "Guideline for pediatric transfusion", aiming to assist in the understanding and implementing the blood transfusion section of this guideline.
Humans ; Child ; Hematologic Diseases/therapy* ; Blood Transfusion/standards* ; Practice Guidelines as Topic

To guide clinical blood transfusion practices for pediatric patients, the National Health Commission has issued the health standard "Guideline for pediatric transfusion" (WS/T 795-2022). Blood transfusion for children undergoing hematopoietic stem cell transplantation is highly complex and challenging. This guideline provides recommendations on transfusion thresholds and the selection of blood components for these children. This article presents the evidence and interpretation of the transfusion provisions for children undergoing hematopoietic stem cell transplantation, with the aim of enhancing the understanding and implementation of the "Guideline for pediatric transfusion".
Humans ; Hematopoietic Stem Cell Transplantation ; Child ; Blood Transfusion/standards* ; Practice Guidelines as Topic

To guide clinical blood transfusion practices for pediatric patients, the National Health Commission has issued the health standard "Guideline for pediatric transfusion" (WS/T 795-2022). Critically ill children often present with anemia and have a higher demand for transfusions compared to other pediatric patients. This guideline provides guidance and recommendations for blood transfusions in cases of general critical illness, septic shock, acute brain injury, extracorporeal membrane oxygenation, non-life-threatening bleeding, and hemorrhagic shock. This article interprets the background and evidence of the blood transfusion provisions for critically ill and severely bleeding children in the "Guideline for pediatric transfusion", aiming to enhance understanding and implementation of this aspect of the guidelines. Citation:Chinese Journal of Contemporary Pediatrics, 2025, 27(4): 395-403.
Humans ; Critical Illness ; Blood Transfusion/standards* ; Child ; Hemorrhage/therapy* ; Practice Guidelines as Topic

To guide clinical blood transfusion practices in pediatric patients, the National Health Commission has issued the health standard "Guideline for pediatric transfusion" (WS/T 795-2022). Children undergoing cardiac surgery are at high risk of bleeding, and the causes of perioperative anemia and coagulation disorders in neonates and children are complex and varied, often necessitating the transfusion of allogeneic blood components. This guideline provides direction and recommendations for specific measures in blood management for children undergoing cardiac surgery before, during, and after surgery. This article interprets the background and evidence for the formulation of the blood transfusion provisions for children undergoing cardiac surgery, hoping to facilitate the understanding and implementation of this guideline.
Humans ; Cardiac Surgical Procedures ; Blood Transfusion/standards* ; Child ; Practice Guidelines as Topic

OBJECTIVE: To investigate the effect and mechanism of Shuangshi Tonglin Capsules (SSTL) in the treatment of prostate fibrosis (PF). METHODS: Human prostate stromal cells (WPMY-1) were used for in vitro experiments to establish PF cell models induced with estradiol (E₂). The cell proliferation, migration and clonogenic capacity were determined by cell counting kit-8, scratch assay, and crystal violet staining, respectively. Sprague-Dawley rats were used for in vivo experiments. The changes in histomorphology and organ index of rat prostate by SSTL were determined. Pathologic changes and collagen deposition changes in rat prostate were observed by haematoxylin and eosin (HE) and Masson staining. Enzyme-linked immunosorbent assay kits were used to determine changes in rat PF markers fibroblast growth factor-23 (FGF-23), E₂ and prostate specific antigen (PSA). Mechanistically, changes in oxidative stress indicators by SSTL were determined in WPMY-1 cells and PF rats. Then the expressions of nuclear factor erythroid 2-related factor 2 (Nrf2)/heme oxygenase-1 (HO-1) and transforming growth factor-β1 (TGF-β1)/Smad pathway-related proteins as well as Nrf2 and TGF-β1 mRNA were further detected by Western blot or quantitative real-time polymerase chain reaction both in vivo and in vitro. RESULTS: In the efficacy study, SSTL significantly reduced the proliferation, migration, and clonogenic ability of cells, improved the morphology of the glandular tissue, significantly reduced the prostate index, reduced glandular fibrous tissue and collagen deposition, and resulted in a significant decrease in the levels of FGF-23, E₂ and PSA (P<0.01 or P<0.05). In the mechanistic study, SSTL ameliorated oxidative stress by significantly increasing superoxide dismutase and glutathione peroxidase levels and decreasing malondialdehyde level in WPMY-1 cells and rats (P<0.01 or P<0.05). SSTL significantly elevated the expressions of Nrf2, HO-1, NAD(P)H quinone oxidoreductase 1 (NQO-1), and Smad7 proteins in both cells and rats, and significantly decreased the expressions of TGF-β1, collagen I, α-smooth muscle actin and Smad4 proteins (P<0.01 or P<0.05). SSTL also elevated the content of Nrf2 mRNA and decreased the content of TGF-β1 mRNA in cells and rats (P<0.01 or P<0.05). The Nrf2 inhibitor ML385 was added in in vitro experiments to further validate the pathway relevance. CONCLUSION SSTL was effective in improving PF in vivo and in vitro, and its mechanism of action may function through the Nrf2/TGF-β1 signaling pathway.
Male ; NF-E2-Related Factor 2/metabolism* ; Animals ; Drugs, Chinese Herbal/therapeutic use* ; Signal Transduction/drug effects* ; Transforming Growth Factor beta1/metabolism* ; Rats, Sprague-Dawley ; Humans ; Fibrosis ; Prostate/drug effects* ; Cell Proliferation/drug effects* ; Capsules ; Cell Movement/drug effects* ; Oxidative Stress/drug effects* ; Rats

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To understand the changing composition and antibiotic resistance of bacterial species in the clinical isolates from outpatient and emergency department(hereinafter referred to as outpatients)and inpatient children over time in various hospitals,and to provide laboratory evidence for rational antibiotic use.Methods The data on clinically isolated pathogenic bacteria and antimicrobial susceptibility of isolates from outpatients and inpatient children in the CHINET program from 2015 to 2021 were collected and analyzed.Results A total of 278 471 isolates were isolated from pediatric patients in the CHINET program from 2015 to 2021.About 17.1％of the strains were isolated from outpatients,primarily group A β-hemolytic Streptococcus,Escherichia coli,and Staphylococcus aureus.Most of the strains(82.9％)were isolated from inpatients,mainly SS.aureus,E.coli,and H.influenzae.The prevalence of methicillin-resistant S.aureus(MRSA)in outpatients(24.5％)was lower than that in inpatient children(31.5％).The MRSA isolates from outpatients showed lower resistance rates to the antibiotics tested than the strains isolated from inpatient children.The prevalence of vancomycin-resistant Enterococcus faecalis or E.faecium and penicillin-resistant S.pneumoniae was low in either outpatients or inpatient children.S.pneumoniae,β-hemolytic Streptococcus and S.viridans showed high resistance rates to erythromycin.The prevalence of erythromycin-resistant group A β-hemolytic Streptococcus was higher in outpatients than that in inpatient children.The prevalence of β-lactamase-producing H.influenzae showed an overall upward trend in children,but lower in outpatients(45.1％)than in inpatient children(59.4％).The prevalence of carbapenem-resistant Klebsiella pneumoniae(CRKpn),carbapenem-resistant Pseudomonas aeruginosa(CRPae)and carbapenem-resistant Acinetobacter baumannii(CRAba)was 14％,11.7％,47.8％in outpatients,but 24.2％,20.6％,and 52.8％in inpatient children,respectively.The prevalence of multidrug-resistant E.coli,K.pneumoniae,Proteus mirabilis,P.aeruginosa and A.baumannii strains was lower in outpatients than in inpatient children.The prevalence of fluoroquinolone-resistant E.coli,ESBLs-producing K.pneumoniae,ESBLs-producing P.mirabilis,carbapenem-resistant E.coli(CREco),CRKpn,and CRPae was lower in children in outpatients than in inpatient children,but the prevalence of CRAba in 2021 was higher than in inpatient children.Conclusions The distribution of clinical isolates from children is different between outpatients and inpatients.The prevalence of MRSA,ESBL,and CRO was higher in inpatient children than in outpatients.Antibiotics should be used rationally in clinical practice based on etiological diagnosis and antimicrobial susceptibility test results.Ongoing antimicrobial resistance surveillance and prevention and control of hospital infections are crucial to curbing bacterial resistance.

Objective To investigate the changing antibiotic resistance profiles of E.coli isolated from patients in the 52 hospitals participating in the CHINET program from 2015 to 2021.Methods Antimicrobial susceptibility was tested for clinical isolates of E.coli according to the unified protocol of CHINET program.WHONET 5.6 and SPSS 20.0 software were used for data analysis.Results Atotal of 289 760 nonduplicate clinical strains ofE.coli were isolated from 2015 to 2021,mainly from urine samples(44.7±3.2)％.The proportion of E.coli strains isolated from urine samples was higher in females than in males(59.0％vs 29.5％).The proportion of E.coli strains isolated from respiratory tract and cerebrospinal fluid samples was significantly higher in children than in adults(16.7％vs 7.8％,0.8％vs 0.1％,both P＜0.05).The isolates from internal medicine department accounted for the largest proportion(28.9±2.8)％with an increasing trend over years.Overall,the prevalence of ESBLs-producing E.coli and carbapenem resistant E.coli(CREco)was 55.9％and 1.8％,respectively during the 7-year period.The prevalence of ESBLs-producing E.coli was the highest in tertiary hospitals each year from 2015 to 2021 compared to secondary hospitals.The prevalence of CREco was higher in children's hospitals compared to secondary and tertiary hospitals each year from 2015 to 2021.The prevalence of ESBLs-producing E.coli in tertiary hospitals and children's hospitals and the prevalence of CREco in children's hospitals showed a decreasing trend over the 7-year period.The prevalence of CREco in secondary and tertiary hospitals increased slowly.Antibiotic resistance rates changed slowly from 2015 to 2021.Carbapenem drugs(imipenem,meropenem)were the most active drugs amongβ-lactams against E.coli(resistance rate≤2.1％).The resistance rates of E.coli to β-lactam/β-lactam inhibitor combinations(piperacillin-tazobactam,cefoperazone-sulbactam),aminoglycosides(amikacin),nitrofurantoin and fosfomycin(for urinary isolates only)were all less than 10％.The resistance rate of E.coli strains to antibiotics varied with the level of hospitals and the departments where the strains were isolated,especially for cefazolin and ciprofloxacin,to which the resistance rate of E.coli strains from children in non-ICU departments was significantly lower than that of the strains isolated from other departments(P＜0.05).The E.coli isolates from ICU showed higher resistance rate to most antimicrobial agents tested(excluding tigecycline)than the strains isolated from other departments.The E.coli strains isolated from tertiary hospitals showed higher resistance rates to the antimicrobial agents tested(excluding tigecycline,polymyxin B,cefepime and carbapenems)than the strains from secondary hospitals and children's hospitals.Conclusions E.coli is an important pathogen causing clinical infection.More than half of the clinical isolates produced ESBL.The prevalence of CREco is increasing in secondary and tertiary hospitals over the 7-year period even though the overall prevalence is still low.This is an issue of concern.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.