OBJECTIVE To investigate the mechanism by which Qiaobai cold compress solution （QBCS） improves acne vulgaris （AV） based on transcriptomics and animal experiments. METHODS Rats were randomly divided into a blank control group （ n ＝6） and a modeling group （ n ＝30）. AV models were established in the modeling group by topical application of oleic acid to the inner surface of both ears， combined with subcutaneous injection of Cutibacterium acnes suspension into the auricle. Successfully modeled rats were further divided into the model group， positive control group （Tretinoin cream， 0.045 g/kg）， and QBCS low-， medium-， high-dose groups [3.55， 7.11， 14.22 g/kg （calculated by the amount of crude drug） ] ， with 6 rats in each group. Rats in each d rug group were treated with the corresponding drugs once daily for 14 consecutive days. After the final administration， changes in the appearance of the ears and histopathological changes in the ear tissues were observed， and serum levels of inflammatory factors， including tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6） and IL-1β， were measured. Auricular tissues from the blank control group， model group and QBCS medium-dose group were collected for transcriptome sequencing. Differential expressed genes （DEGs） were screened and subjected to Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis， followed by validation using real-time quantitative polymerase chain reaction and Western blot assay. RESULTS Compared with the model group， rats in all QBCS groups showed alleviated auricular acne symptoms， with reduced epidermal thickening， sebaceous gland hyperplasia， and inflammatory cell infiltration. Serum levels of TNF-α （except for the QBCS low-dose group）， IL-6 （except for the QBCS low-dose group） and IL-1β were significantly decreased （ P ＜0.05）. A total of 590 DEGs were identified （blank control group vs. model group）， and 596 DEGs were identified （model group vs. QBCS medium-dose group）. Above DEGs （blank control group vs. model group） were mainly enriched in Toll-like receptor （TLR） and nuclear factor-kappa B （NF-κB） signaling pathways， etc. Validation experiments showed that， compared with model group， low-， medium- and high-dose of QBCS reduced， to varying degrees， the mRNA expression of TNF-α， TLR2， interferon-γ and CXC chemokine ligand 8 in the auricular tissues of AV rats， increased the mRNA expression of peroxisome-proliferator-activated receptor gamma and tumor protein 53， and inhibited the phosphorylation of NF-κB p65 protein as well as the expressions of TLR2 and myeloid differentiation primary response protein 88（MyD88） （ P ＜0.05）. CONCLUSIONS QBCS can alleviate auricular inflammation and skin lesions in AV rats. This effect may be related to inhibition of the TLR/MyD88/NF-κB signaling pathway， thereby suppressing the expression of downstream inflammatory factors such as TNF-α.

Objective:To establish and validate a prediction model based on least absolute shrinkage and selection operator（LASSO）regression and classification and regression tree（CART）algorithm for acute kidney injury（AKI）in PICU.Methods:The prospective derivation cohort consisted of 350 critically ill children admitted to the PICU of Children′s Hospital of Soochow University from September 2020 to January 2021.The external data set consisting of 866 critically ill children admitted to the PICU of Children′s Hospital of Soochow University from February 2021 to February 2022 was employed for the external validation.Clinical data was obtained from the electronic medical record system，including demographic characteristics，laboratory data and the pediatric risk of mortality Ⅲ（PRISM Ⅲ）score.The variables associated with AKI were identified using LASSO regression.Subsequently，a decision tree prediction model was built using the CART algorithm.The predictive value of decision tree prediction model was evaluated using the receiver operating characteristic（ROC）curve，calibration curve，and decision curve analysis.Results:Among the 350 children in the derivation cohort，107（30.6%）developed AKI during the PICU stay；and of 866 children in the external validation cohort，165（19.1%）developed AKI during the PICU stay.The LASSO regression screened 16 candidate variables for further analysis，and the decision tree model ultimately identified 4 variables more closely associated with AKI，including fold change in serum creatinine from baseline，urine volume，PRISM Ⅲ，and C-reactive protein.The decision tree model exhibited high accuracy with AUC of 0.92，0.88，and 0.86 in the training，internal validation，and external validation cohorts，respectively.The model demonstrated good calibration and clinical applicability based on the calibration curve and decision curve analysis.Conclusion:The decision tree model based on the 4 identified clinical indicators，including fold change in serum creatinine from baseline，urine volume，PRISM Ⅲ，and C-reactive protein，is effective for the early prediction of AKI.

Objective To develop a three-dimensional(3D)deep neural network based preoperative classification model for coronary computed tomography angiography(CTA)in atrial fibrillation patients,and to explore the effects of transfer learning on the performance of medical image classification models,thereby providing preoperative decision support for catheter ablation to advance atrial fibrillation treatment toward precision and personalization.Methods Utilizing 3D ConvNet and 3D ResNet as backbone network,the three-dimensional classification features were extracted from coronary CTA sequences.The publicly available pre-trained weights were used for transfer learning.The model performance was evaluated through metrics such as confusion matrix,classification accuracy,and area under the curve(AUC).A comparative analysis was also conducted to evaluate the performance differences between the transfer learning model and the initialized training model.Results Transfer learning yielded significant performance improvements over the initialized training models,attaining AUC improvement of 9.1%-16.7%and accuracy enhancement of 6.2%-23.5%.Among all models,3D-ResNet18 model with MedicalNet pre-training weights performed the best,achieving an AUC of 0.77 and an accuracy of 0.71.Conclusion The proposed three-dimensional deep network enhanced by transfer learning can effectively identify atrial fibrillation patients requiring additional ablation besides pulmonary vein isolation through preoperative coronary CTA,which will assist clinicians in optimizing surgical strategies and improving treatment outcomes,thereby reducing long-term postoperative recurrence rates.

Objective This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.

Objective This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.

Objective:To establish and validate a prediction model based on least absolute shrinkage and selection operator（LASSO）regression and classification and regression tree（CART）algorithm for acute kidney injury（AKI）in PICU.Methods:The prospective derivation cohort consisted of 350 critically ill children admitted to the PICU of Children′s Hospital of Soochow University from September 2020 to January 2021.The external data set consisting of 866 critically ill children admitted to the PICU of Children′s Hospital of Soochow University from February 2021 to February 2022 was employed for the external validation.Clinical data was obtained from the electronic medical record system，including demographic characteristics，laboratory data and the pediatric risk of mortality Ⅲ（PRISM Ⅲ）score.The variables associated with AKI were identified using LASSO regression.Subsequently，a decision tree prediction model was built using the CART algorithm.The predictive value of decision tree prediction model was evaluated using the receiver operating characteristic（ROC）curve，calibration curve，and decision curve analysis.Results:Among the 350 children in the derivation cohort，107（30.6%）developed AKI during the PICU stay；and of 866 children in the external validation cohort，165（19.1%）developed AKI during the PICU stay.The LASSO regression screened 16 candidate variables for further analysis，and the decision tree model ultimately identified 4 variables more closely associated with AKI，including fold change in serum creatinine from baseline，urine volume，PRISM Ⅲ，and C-reactive protein.The decision tree model exhibited high accuracy with AUC of 0.92，0.88，and 0.86 in the training，internal validation，and external validation cohorts，respectively.The model demonstrated good calibration and clinical applicability based on the calibration curve and decision curve analysis.Conclusion:The decision tree model based on the 4 identified clinical indicators，including fold change in serum creatinine from baseline，urine volume，PRISM Ⅲ，and C-reactive protein，is effective for the early prediction of AKI.

Objective To develop a three-dimensional(3D)deep neural network based preoperative classification model for coronary computed tomography angiography(CTA)in atrial fibrillation patients,and to explore the effects of transfer learning on the performance of medical image classification models,thereby providing preoperative decision support for catheter ablation to advance atrial fibrillation treatment toward precision and personalization.Methods Utilizing 3D ConvNet and 3D ResNet as backbone network,the three-dimensional classification features were extracted from coronary CTA sequences.The publicly available pre-trained weights were used for transfer learning.The model performance was evaluated through metrics such as confusion matrix,classification accuracy,and area under the curve(AUC).A comparative analysis was also conducted to evaluate the performance differences between the transfer learning model and the initialized training model.Results Transfer learning yielded significant performance improvements over the initialized training models,attaining AUC improvement of 9.1%-16.7%and accuracy enhancement of 6.2%-23.5%.Among all models,3D-ResNet18 model with MedicalNet pre-training weights performed the best,achieving an AUC of 0.77 and an accuracy of 0.71.Conclusion The proposed three-dimensional deep network enhanced by transfer learning can effectively identify atrial fibrillation patients requiring additional ablation besides pulmonary vein isolation through preoperative coronary CTA,which will assist clinicians in optimizing surgical strategies and improving treatment outcomes,thereby reducing long-term postoperative recurrence rates.

Objective: To investigate the protective effects and underlying mechanisms of sodium pyruvate (SP) on RBC storage lesions using an oxidative damage model. Methods: Six units of leukocyte-depleted suspended RBCs (discarded for non-infectious reasons within three days post-collection) were randomly assigned to four groups: negative control (NS), positive control (PS), experimental group 1 (SP1), and experimental group 2 (SP2). Oxidative stress was induced in the PS group by the addition of hydrogen peroxide (H O ), while SP1 and SP2 received SP supplementation at different concentrations (25 mM and 50 mM, respectively) in the presence of H O . After 1 hour of incubation, RBC morphology was assessed microscopically, and biochemical indicators including glutathione (GSH), malondialdehyde (MDA), methemoglobin (MetHb), adenosine triphosphate (ATP), and Na /K -ATPase activity were measured. Results: RBCs in the PS group exhibited pronounced morphological damage, including cell shrinkage and echinocyte formation, whereas both SP-treated groups showed significantly reduced structural injury. SP treatment led to elevated GSH levels and decreased concentrations of MDA and MetHb, suggesting attenuation of oxidative stress. Additionally, SP enhanced intracellular ATP levels and Na /K -ATPase activity, thereby contributing to membrane stability. Notably, the SP2 group (50 mM) demonstrated superior protective effects compared to SP1 (25 mM). Conclusion: Sodium pyruvate effectively attenuates oxidative storage lesions in RBCs, primarily through its antioxidant properties, energy metabolism supporting ability, and celluar membrane stabilizing function. These findings suggest SP as a promising additive for enhancing the quality and safety of stored RBCs.

In-depth study of the components of polymyxins is the key to controlling the quality of this class of antibiotics. Similarities and variations of components present significant analytical challenges. A two-dimensional (2D) liquid chromatography-mass spectrometr (LC-MS) method was established for screening and comprehensive profiling of compositions of the antibiotic colistimethate sodium (CMS). A high concentration of phosphate buffer mobile phase was used in the first-dimensional LC system to get the components well separated. For efficient and high-accuracy screening of CMS, a targeted method based on a self-constructed high resolution (HR) mass spectrum database of CMS components was established. The database was built based on the commercial MassHunter Personal Compound Database and Library (PCDL) software and its accuracy of the compound matching result was verified with six known components before being applied to genuine sample screening. On this basis, the unknown peaks in the CMS chromatograms were deduced and assigned. The molecular formula, group composition, and origins of a total of 99 compounds, of which the combined area percentage accounted for more than 95% of CMS components, were deduced by this 2D-LC-MS method combined with the MassHunter PCDL. This profiling method was highly efficient and could distinguish hundreds of components within 3 h, providing reliable results for quality control of this kind of complex drugs.