Objective To evaluate the predictive value of different machine learning models constructed in a multicenter environment for potential organ donors and verify their clinical application feasibility. Methods The study included 2 000 inpatients admitted to five domestic tertiary hospitals from January 2020 to December 2023, who met the criteria for potential organ donation assessment. They were randomly divided into a training set and an internal validation set (7∶3). Another 300 similar patients admitted to the First Affiliated Hospital of Harbin Medical University from January 2024 to April 2025 were included as an external validation set. The area under the curve (AUC), sensitivity, specificity, accuracy and F1-score of three models were compared, and the consistency of the potential organ donor determination process was tested. Multivariate logistic regression analysis was used to identify predictive factors of potential organ donors. Decision curve analysis (DCA) was employed to verify the resource efficiency of each model, and the threshold interval and intervention balance point were assessed. Results Apart from age, there were no significant differences in other basic characteristics among the centers (all P>0.05). The consistency of the potential organ donor determination process among researchers in each center was good [all 95% confidence interval (CI) lower limits >0]. In the internal validation set, the XGBoost model had the best predictive performance (AUC=0.92, 95% CI 0.89-0.94) and the best calibration (P=0.441, Brier score 0.099). In the external validation set, the XGBoost model also had the best predictive performance (AUC=0.91, 95% CI 0.88-0.94), outperforming logistic regression and random forest models. Multivariate logistic regression showed that mechanical ventilation had the greatest impact (odds ratio=2.06, 95% CI 1.54-2.76, P<0.001). DCA indicated that the XGBoost model had the highest net benefit in the threshold interval of 0.2-0.6. The “treat all” strategy only had a slight advantage at extremely low thresholds. The recommended threshold interval, which balances intervention costs and clinical benefits, considers ≥50% positive predictive value (PPV) and ≤50 referrals per 100 high-risk patients. Conclusions The XGBoost model established in a multicenter environment is accurate and well-calibrated in predicting potential organ donors. Combined with DCA, it may effectively guide the timing of clinical interventions and resource allocation, providing new ideas for the assessment and management of organ donation after brain death.

Objective: To investigate the basic situation of pre-analytical quality management in blood station laboratories in North China, and to provide baseline data for promoting the homogenization and standardization of these pre-analytical processes in each blood station laboratory. Methods: A cross-sectional status survey was designed based on the quality management regulations of blood stations, ISO15189 standards and relevant quality management requirements. This survey covering various aspects including laboratory general situation, sample collection and temporary storage, transportation, reception, and quality continuous improvement situations. Data analysis was performed on the survey results of each laboratory. Results: All the 38 blood station laboratories in North China had established a pre-analytical quality management system framework and implemented basic pre-analytical quality control activities; however, there were differences in implementation. 1) Among the 12 basic quality items, 3 items were monitored by all the investigated laboratories (100%), 6 items were monitored by the vast majority of laboratories (about 90%), and 3 items were monitored by a portion of laboratories (about 60%). There were no significant differences in the monitoring index among the three regions and among different types of laboratories (P>0.05). 2) Among the total of 26 items in the three key processes before testing (sample collection and storage, transportation, reception and processing), 12 items were monitored by all laboratories (100%), 11 items were monitored by the vast majority of laboratories (about 90%), and 3 items were monitored by a portion of laboratories (about 75%). There were no significant differences in monitoring index among different regions and types of laboratories (P>0.05). Conclusion: This survey provides a reference and basis for the gap analysis of the pre-analytical process quality management in 38 blood station laboratories across North China. It facilitates laboratories in identifying pre-analytical quality problems, resolving problems, preventing errors, and ensuring that the quality of blood samples before testing meets the established requirements. It lays a foundation for the homogenization of pre-analytical quality management in regional blood stations.

Objective: To determine the frequency and main reasons of unqualified samples by analyzing the quality of pre-analytical samples in blood stations in North China, thereby providing a reference and basis for gap analysis in the implementation of pre-analytical process quality management for participating laboratories and ensuring that only high-standard and high-quality blood samples proceed to testing. Methods: Data on the quality of pre-analytical samples from blood station laboratories in North China was collected via questionnaire. Statistical analysis were performed on: 1) the basic information of samples quality monitoring in the laboratories; 2) the distribution of the overall pre-analytical unqualified rate of samples and the pre-analytical unqualified rate of samples in each laboratory; 3) the distribution of reasons for sample disqualification. Results: 1) The overall pre-analytical unqualified rate of samples in blood station laboratories in North China was 4.55, with a total sigma level of 5.39σ. The 25th, 50th and 75th percentiles (P25, P50, P75) for the total unqualified rate were 0.00, 1.10 and 5.96, respectively. The corresponding percentiles for the Sigma level were 5.34σ, 5.71σ, and 6.00σ, respectively. The pre-analytical unqualified rate of serological and nucleic acid samples (4.89 vs 4.22) showed a significant difference (χ =9.575, P<0.05). 2) The average unqualified rate of samples in region A, B and C was 1.71, 9.50 and 12.64 (χ =1 590.721, P<0.05), and the sigma level was 5.66σ, 5.21σ and 5.16σ, respectively. 3) The main reasons for unqualified serological samples were chylous blood (72.65%), hemolysis (17.39%), abnormal hematocrit (5.80%), and insufficient volume (3.50%). The main reasons for the unqualified nucleic acid samples were chylous blood (78.26%), hemolysis (8.84%), failure to centrifuge as required (5.01%), abnormal hematocrit (4.66%), and insufficient volume (1.92%). Conclusion: In North China, the quality indicators for the pre-analytical processes in blood station laboratories are generally well-managed. Laboratories in region A outperformed the national average in pre-analytical specimen quality control. However, participating laboratories exhibit gaps in implementing pre-analytical quality management. Through effective analysis of pre-analytical process quality metrics and inter-laboratory comparisons, laboratories can identify discrepancies and address shortcomings. By establishing clear quality objectives, they can achieve continuous improvement and ensure the validity of test results.

Objective: To investigate the assessment criteria and subsequent handling practices of hemolytic and lipemic blood samples before testing in blood screening laboratories in North China, and to provide data to support the standardization of their management in blood station laboratories. Methods: Data on the preanalytical management of hemolytic and lipemic samples from 38 laboratories were collected. The details of management on the criteria and verificatioon for assessment, the assessment methods, and subsequent handling procedures of hemolytic and lipemic samples in blood station laboratories were analyzed. Results: 1) All 38 blood station laboratories monitored serological and nucleic acid samples for hemolysis and lipemia in pre-analytical phase. 2) The criteria and methods for assessing hemolytic and lipemic samples varied among the laboratories of the 38 blood stations. 15 laboratories (39.47%) followed manufacturer's instructions, 9 laboratories (23.68%) formulated their own criteria, and 14 laboratories (36.84%) referred to the criteria of other laboratories. 16 laboratories (42.11%) verified the criteria for assessing hemolytic and lipemic samples, with significant variations in verification rate across laboratories from different regions (P<0.05). For the assessment methods, visual inspection was used by 28 laboratories (73.68%) for hemolytic samples and by 27 laboratories (71.05%) for lipemic samples; the colorimetric card method was used by 10 laboratories (26.32%) for assessing both hemolytic and lipemic samples; the instrumental method was used by 1 laboratory (2.63%) for assessing lipemic samples.3) The handling procedures for hemolytic and lipemic samples varied significantly and followed a gradient distribution pattern among 38 laboratories (including accepting samples for testing, accepting samples for concession testing, re-collecting samples, and rejecting samples and halting testing). With increasing severity of hemolysis and lipemia, more laboratories halted testing, and relatively fewer laboratories accepted samples for normal testing. 5 laboratories (13.16%) applied different handling procedures on serological and nucleic acid samples. Conclusion: This survey provides a reference and basis for analyzing gaps in the management of hemolytic and lipemic samples during the preanalyical phase in blood station laboratories in North China. It enables laboratories to identify the problems and deficiencies in the management of hemolytic and lipemic samples, to ensure preanalytical samples quality meets the established requirements, and to lay a foundation for promoting the homogenization and standardization of the regional sample quality management mode.

Objective:To analyze and identify the expression profiles of oxidative stress-related genes and circular RNAs(circRNAs)in ricin toxin(RT)-induced pyroptosis of mouse mononuclear macrophages(RAW264.7)using transcriptome sequencing and bioinformatics technology,and to preliminarily analyze their potential functions.Methods:The macrophages(RAW264.7 cells)were treated with RT to establish a cell pyroptosis model and divided into control group,40 μg·L-1 RT group,and 80 ng/mL RT group.Transmission electron microscope(TEM)was used to observe the morphology of the RAW264.7 cells in various groups;Western blotting method was used to detect the expression levels of the pyroptosis pathway-related proteins in the cells in various groups;80 μg·L-1 RT was selected for subsequent experiments.Transcriptome sequencing(RNA-Seq)was performed to obtain the circRNA and mRNA expression profiles in the RAW264.7 cells in control group and RT-treated group,followed by bioinformatics analysis.Results:Compared with control group,the cells in 40 and 80 μg·L-1 RT groups underwent morphological changes;the cells in RT groups showed obvious pyroptosis-like morphological changes,characterized by significant swelling of dying cells and the appearance of characteristic large bubbles on the plasma membrane.Compared with control group,the expression level of gasdermin DN-terminal fragment(GSDMD-N)protein in 40 and 80 μg·L-1 RT groups was increased(P<0.05);compared with 40 μg·L-1 RT group,the expression level of GSDMD-N protein in the cells in 80 μg·L-1 RT group was increased(P<0.05);therefore,the subsequent experiments used the RT concentration of 80 μg·L-1.A total of 2930 differentially expressed messenger RNAs(mRNAs)and 24 differentially expressed circRNAs were identified.The constructed circRNA-microRNA(miRNA)-mRNA competing endogenous RNA(ceRNA)regulatory network consisted of 7 circRNAs,12 miRNAs and 13 mRNAs.Gene Ontology(GO)functional enrichment analysis showed that in biological process(BP),the functions regulated by differentially expressed genes mainly included immune response and oxidative stress response;in cellular component(CC),differentially expressed genes were mainly localized to the external side of plasma membrane and cell leading edge;in molecular function(MF),they were mainly involved in transporter transmembrane activity and hormone receptor binding.Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis showed that differentially expressed genes were mainly enriched in Toll-like receptor signaling pathway,Forkhead box O(FoxO)signaling pathway and nuclear factor kappa-light-chain-enhancer of activated B cells(NF-κB)signaling pathway.In the protein-protein interaction(PPI)network,the top 10 hub genes with the highest connectivity were screened by CytoHubba,including matrix metalloproteinase 9(MMP9),superoxide dismutase 2(SOD2),and v-src sarcoma viral oncogene homolog(Src).Conclusion:The expression profiles of oxidative stress-related genes and circRNAs in RAW264.7 cells are altered after RT treatment.The screened differentially expressed circRNAs and mRNAs may serve as potential targets to regulate RT-induced pyroptosis in RAW264.7 cells through oxidative stress pathways.

Objective:To investigate the external quality control assessment results of iodine deficiency disorders (IDD) laboratories at all levels in Yunnan Province, so as to provide technical support for IDD monitoring.Methods:In 2023, all IDD laboratories at provincial, city (prefecture) and county level in Yunnan Province tested the assessment samples randomly provided by the National IDD Reference Laboratory based on their participation in external quality control assessment projects (urinary iodine, salt iodine, water iodine). For laboratories that do not use fully automatic iodine detectors, urinary iodine detection was carried out using the "Determination of Iodine in Urine - Part 1: As 3+-Ce 4+ Catalytic Spectrophotometry" (WS/T 107.1-2016), salt iodine detection was carried out using the direct titration method in the "General Test Methods in Salt Industry-Determination of Iodine"(GB/T 13025.7-2012), and water iodine detection was carried out using the cerium sulfate catalytic spectrophotometry. The test results were evaluated using standard value ± uncertainty. Results:A total of 146 IDD laboratories in Yunnan Province participated in the external quality control assessment, including 1 at the provincial level, 16 at the city (prefecture) level, and 129 at the county level. The feedback rate and qualification rate of urinary iodine, salt iodine in the provincial, city (prefecture) and county level IDD laboratories were all 100% (146/146). The feedback rate and the qualified rate of urinary iodine, salt iodine and water iodine in county level IDD laboratories were all 100%(17/17).Conclusion:In 2023, all external quality control assessment results of IDD laboratories in Yunnan Province are qualified, with stable testing quality, providing reliable laboratory technical support for IDD monitoring.

Schizophrenia is a debilitating mental disorder with complex etiology. Early identification of high-risk individuals and early intervention for early-stage patients can help improve prognosis, but effective identification methods are lacking. In recent years, research on proteomic techniques based on peripheral blood has achieved certain advancements in exploring the etiology of schizophrenia, as well as in early recognition and diagnostic modeling. This paper provides a review of the research methodologies of peripheral blood-based proteomics and the candidate biomarkers identified through these method.

Objective To establish a ultra-high performance liquid chromatography-tandem mass spectrometer(UPLC-MS/MS)method for determining the concentration of sotagliflozin in rat plasma and apply it to pharmacokinetic studies in rats.Methods Electrospray negative ion multi-reaction ion detection was used.Chromatographic column:EXT-C18(2.1 mm × 100.0 mm,2.7 μm);column temperature:45 ℃;mobile phase:5 mmol·L-1 ammonium acetate aqueous solution-acetonitrile;flow rate:0.35 mL·min-1;ion pairs:sotagliflozin m/z 483.3→315.1,dapagliflozin m/z 467.4→329.2;injection volume:6 μL,plasma samples were processed using methyl tert-butyl ether liquid-liquid extraction.Six male SD rats were administered a single oral dose of sogliflozin at 40 mg·kg-1,and detected the concentration of sogliflozin in plasma.Pharmacokinetic parameters were calculated using Drug And Statistics(DAS)2.1.1.Results Sotagliflozin showed good linearity within the range of 5-2 000 ng·mL-1,with intra-day and inter-day precision both less than 15％.The recovery rate,matrix effect,and stability were all within the specified range.Pharmacokinetic parameters:Cmax was(3 716.67±568.28)ng·mL-1,tmax was(1.00±0.32)h,t1/2 was(2.28±0.45)h,AUC0-t was(1.70 × 104±2 075.87)ng·mL-1·h.Conclusion This study established a method for determining the concentration of sotagliflozin in rat plasma,which is characterized by high sensitivity,rapid detection,and good repeatability.It is suitable for the determination of sotagliflozin concentration in plasma and pharmacokinetic studies.

Objective:This study aims to construct a machine learning model to predict depression symptoms in the elderly and analyze the key influencing factors of depression in the elderly using the shapley additive interpretation (SHAP) method.Methods:Based on entries from the 2018 China Health and Retirement Longitudinal Study database, a sample of 5 954 elderly individuals was selected. Feature selection using Support Vector Machine Recursive Feature Elimination, Extreme Gradient Boosting (XGBoost) - Recursive Feature Elimination (RFE), and the Lasso algorithm, which was combined with five classifiers-logistic regression, decision trees, random forests, support vector machines, and XGBoost-to explore the classification effectiveness for depressive symptoms in the elderly. Finally, the SHAP method was used to interpret the analysis of the model with the highest receiver operating characteristic curve areas under the curve (AUC).Results:The accuracy of 15 prediction models ranged from 0.702 to 0.743, with AUC between 0.730 and 0.795. Sensitivity was reported at 0.546 to 0.588, while specificity ranges from 0.783 to 0.865. The model XGBoost-RFE-XGBoost presented the highest AUC. Based on SHAP values, the top four factors influencing depressive symptoms in older adults were life satisfaction, duration of nighttime sleep, disability status, and self-rated health.Conclusion:This study developed a highly efficient and interpretable risk prediction model for depressive symptoms in older adults, which could help identify high-risk older adults and give personalized interventions.

Objective:To explore the differences in early clinical features between Kawasaki disease shock syndrome (KDSS) and septic shock (SS).Methods:A retrospective case-control study was conducted. Clinical data was collected from 64 children who were diagnosed with KDSS or SS and admitted to the Department of Critical Care Medicine of Capital Center for Children′s Health, Capital Medical University from January 2018 to February 2025. Mann-Whitney U test, χ2 test, or Fisher′s exact test were used to compare the differences in clinical features, treatment, and outcomes between children with KDSS and SS. Lasso regression was applied to screen predictive variables, and multivariable logistic regression analysis was performed to identify factors associated with KDSS. Receiver operating characteristic (ROC) curve was used to evaluate the predictive value of parameters for KDSS. Results:Among the 64 children (30 males and 34 females), the age was 3.6 (1.2, 6.5) years. There were 51 cases in the SS group and 13 cases in the KDSS group. Compared to children with SS, children with KDSS had a longer pre-shock fever duration, lower lactate levels and serum albumin levels, and higher soluble interleukin-2 receptor (sIL-2R) levels (all P<0.05). Additionally, they exhibited a higher incidence of coronary involvement, pericardial effusion, and ascites, a higher utilization rate of intravenous immunoglobulin, and a lower utilization rate of invasive mechanical ventilation (all P<0.05). There was no significant difference in in-hospital mortality between KDSS and SS ( P=0.574). Multivariate logistic regression analysis identified pre-shock fever duration and sIL-2R as independent factors associated with KDSS ( OR=1.52 and 1.54 per 1 000 U increase, 95% CI 1.12-2.05 and 1.06-2.24, respectively; both P<0.05). ROC curve analysis showed that the areas under the curve for pre-shock fever duration and sIL-2R in identifying KDSS were 0.83 (95% CI 0.73-0.94, P=0.001) and 0.70 (95% CI 0.53-0.87, P=0.042), respectively. The optimal cutoff values were 3.5 d and 3.8×10 6 U/L, with sensitivities of 0.91 and 0.82, and specificities of 0.71 and 0.62, respectively. Conclusions:Children with KDSS have higher incidences of coronary involvement, pericardial effusion, and ascites compared to those with SS. Pre-shock fever duration and sIL-2R may serve as potential early indicators for distinguishing KDSS from SS.