OBJECTIVES: To identify potential plasma lipidomic biomarkers that distinguish non-metastatic medulloblastoma (nmMB) from metastatic medulloblastoma (mMB) in children. METHODS: In this prospective study, 17 children with mMB and 20 matched children with nmMB were enrolled. Plasma samples were analyzed using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. Lipid metabolites were evaluated for their associations and diagnostic performance. RESULTS: Orthogonal partial least squares discriminant analysis based on lipid profiles clearly separated nmMB from mMB, and 14 differential lipids were identified, including DG(18:2/20:4/0:0) and SM(d18:1/20:0). Receiver operating characteristic analysis showed nine metabolites with area under the curve greater than 0.7. Differential lipids were enriched in sphingolipid, glycerophospholipid, and arachidonic acid metabolism, suggesting an association with the metastatic phenotype. CONCLUSIONS Plasma lipidomics provides a new approach to identify mMB, and the identified lipid metabolites may support early diagnosis and treatment, prognostic assessment, and selection of therapeutic targets for metastatic medulloblastoma.
Humans ; Medulloblastoma/diagnosis* ; Lipidomics ; Child ; Male ; Female ; Child, Preschool ; Cerebellar Neoplasms/blood* ; Biomarkers, Tumor/blood* ; Neoplasm Metastasis ; Prospective Studies ; Adolescent ; Lipids/blood*

ObjectiveTo investigate the role of 4-week high-intensity interval training (HIIT) in modulating the metabolic homeostasis of the pyruvate-lactate axis in the hippocampus of rats with chronic unpredictable mild stress (CUMS) to improve their depressive-like behavior. MethodsForty-eight SPF-grade 8-week-old male SD rats were randomly divided into 4 groups: the normal quiet group (C), the CUMS quiet group (M), the normal exercise group (HC), and the CUMS exercise group (HM). The M and HM groups received 8 weeks of CUMS modeling, while the HC and HM groups were exposed to 4 weeks of HIIT starting from the 5th week (3 min (85%-90%) S_max+1 min (50%-55%) S_max, 3-5 cycles, S_max is the maximum movement speed). A lactate analyzer was used to detect the blood lactate concentration in the quiet state of rats in the HC and HM groups at week 4 and in the 0, 2, 4, 8, 12, and 24 h after exercise, as well as in the quiet state of rats in each group at week 8. Behavioral indexes such as sucrose preference rate, number of times of uprightness and number of traversing frames in the absenteeism experiment, and other behavioral indexes were used to assess the depressive-like behavior of the rats at week 4 and week 8. The rats were anesthetized on the next day after the behavioral test in week 8, and hippocampal tissues were taken for assay. LC-MS non-targeted metabolomics, target quantification, ELISA and Western blot were used to detect the changes in metabolite content, lactate and pyruvate concentration, the content of key metabolic enzymes in the pyruvate-lactate axis, and the protein expression levels of monocarboxylate transporters (MCTs). Results4-week HIIT intervention significantly increased the sucrose preference rate, the number of uprights and the number of traversed frames in the absent field experiment in CUMS rats; non-targeted metabolomics assay found that 21 metabolites were significantly changed in group M compared to group C, and 14 and 11 differential metabolites were significantly dialed back in the HC and HM groups, respectively, after the 4-week HIIT intervention; the quantitative results of the targeting showed that, compared to group C, lactate concentration in the hippocampal tissues of M group, compared with group C, lactate concentration in hippocampal tissue was significantly reduced and pyruvate concentration was significantly increased, and 4-week HIIT intervention significantly increased the concentration of lactate and pyruvate in hippocampal tissue of HM group; the trend of changes in blood lactate concentration was consistent with the change in lactate concentration in hippocampal tissue; compared with group C, the LDHB content of group M was significantly increased, the content of PKM2 and PDH, as well as the protein expression level of MCT2 and MCT4 were significantly reduced. The 4-week HIIT intervention upregulated the PKM2 and PDH content as well as the protein expression levels of MCT2 and MCT4 in the HM group. ConclusionThe 4-week HIIT intervention upregulated blood lactate concentration and PKM2 and PDH metabolizing enzymes in hippocampal tissues of CUMS rats, and upregulated the expression of MCT2 and MCT4 transport carrier proteins to promote central lactate uptake and utilization, which regulated metabolic homeostasis of the pyruvate-lactate axis and improved depressive-like behaviors.

ObjectiveThis study aimed to investigate the effects of 4-week high-intensity interval training (HIIT) on synaptic plasticity in the prefrontal cortex (PFC) of rats exposed to chronic unpredictable mild stress (CUMS), and to explore its potential mechanisms. MethodsA total of 48 male Sprague-Dawley rats were randomly divided into 4 groups: control (C), model (M), control plus HIIT (HC), and model plus HIIT (HM). Rats in groups M and HM underwent 8 weeks of CUMS to establish depression-like behaviors, while groups HC and HM received HIIT intervention beginning from the 5th week for 4 consecutive weeks. The HIIT protocol consisted of repeated intervals of 3 min at high speed (85%-90% maximal training speed, S_max) alternated with one minute at low speed (50%-55% S_max), with 3 to 5 sets per session, conducted 5 d per week. Behavioral assessments and tail-vein blood lactate levels were measured at the end of the 4th and 8th weeks. After the intervention, rat PFC tissues were collected for Golgi staining to analyze synaptic morphology. Enzyme-linked immunosorbent assays (ELISA) were employed to detect brain-derived neurotrophic factor (BDNF), monocarboxylate transporter 1 (MCT1), lactate, and glutamate levels in the PFC, as well as serotonin (5-HT) levels in serum. Additionally, Western blot analysis was conducted to quantify the expression of synaptic plasticity-related proteins, including c-Fos, activity-regulated cytoskeleton-associated protein (Arc), and N-methyl-D-aspartate receptor 1 (NMDAR1). ResultsCompared to the control group (C), the CUMS-exposed rats (group M) exhibited significant reductions in sucrose preference rates, number of grid crossings, frequency of upright postures, and entries into and duration spent in open arms of the elevated plus maze, indicating marked depressive-like behaviors. Additionally, the group M showed significantly reduced dendritic spine density in the PFC, along with elevated levels of c-Fos, Arc, NMDAR1 protein expression, and increased concentrations of lactate and glutamate. Conversely, BDNF and MCT1 contents in the PFC and 5-HT levels in serum were significantly decreased. Following HIIT intervention, rats in the group HM displayed considerable improvement in behavioral indicators compared with the group M, accompanied by significant elevations in PFC MCT1 and lactate concentrations. Furthermore, HIIT notably normalized the expression levels of c-Fos, Arc, NMDAR1, as well as glutamate and BDNF contents in the PFC. Synaptic spine density also exhibited significant recovery. ConclusionFour weeks of HIIT intervention may alleviate depressive-like behaviors in CUMS rats by increasing lactate levels and reducing glutamate concentration in the PFC, thereby downregulating the overexpression of NMDAR, attenuating excitotoxicity, and enhancing synaptic plasticity.

Objective To propose a novel identification algorithm based on ensemble learning for assessing the severity of acute respiratory distress syndrome(ARDS)to achieve continuous monitoring of the disease severity.Methods Firstly,leve-raging the open-source MIMIC-Ⅳ database,a variety of non-invasive physiological parameters of patients were extracted and subjected to preliminary preprocessing.A multivariate feature selection algorithm was employed to rank these parameters and calculate feature importance scores through weighted computation.Secondly,based on the feature importance scores,a subset search algorithm was utilized to identify the subset of features that could yield optimal performance across four machine learning algorithms:neural networks,logistic regression,AdaBoost and XGBoost.Finally,a soft voting ensemble method was designed using a generalized linear regression model to integrate the results of each single machine learning algorithm,and a multivariate ensemble learning algorithm was proposed by combining the optimal feature subsets.The algorithm proposed when used to identify the severity of ADRS was evaluated with MIMIC-Ⅳ database,and compared with the traditional algorithms.Results The sensitivity,specificity,accuracy and AUC of the algorithm were 87.15％,89.23％,88.34％and 0.923 4,respectively,all of which outperformed those of the traditional algorithms.Conclusion The ARDS severity identification algorithm based on ensemble learning is capable of achieving continuous and real-time monitoring of the severity of ARDS,thereby offering robust support for the early identification and warning of ARDS in patients.[Chinese Medical Equipment Journal,2025,46(2):1-9]

Objective:To evaluate the cost-effectiveness and impact on mortality of health management services for the elderly aged 65 years and older in national essential public health service project.Methods:Based on the data of county-level medical institutions in Henan Province from 2019 to 2024,the Random Forest Method was used to construct a counterfactual framework to predict the hospitalization expenses under the unmanaged scenario,and then the cost-benefit ratio(BCR)and net income were calculated.Time-dependent Cox proportional hazards model was used to evaluate the effect of health management on all-cause mortality and cardiovascular and cerebrovascular disease mortality in the elderly.Results:A total of 962 955 elderly patients were included,451 119(46.85%)were included in the management group.The average hospitalization cost of the management group was significantly lower than that of the non-management group(P<0.05).Except for 2020-2021,BCRS in 2019 and 2022-2024 were 6.34,2.05,4.45 and 6.60,respectively.The risk of all-cause death was reduced by 76.96%,and the risk of cardiovascular and cerebrovascular death was reduced by 75.57%in the elderly patients included in the management group compared with those not included in the management group.Suggestions:It is necessary to establish a health outcomes-based evaluation system and promote the transformation and upgrading of the service model from single chronic disease management to"integrated health services with multi-disease management".

Objective To propose a novel identification algorithm based on ensemble learning for assessing the severity of acute respiratory distress syndrome(ARDS)to achieve continuous monitoring of the disease severity.Methods Firstly,leve-raging the open-source MIMIC-Ⅳ database,a variety of non-invasive physiological parameters of patients were extracted and subjected to preliminary preprocessing.A multivariate feature selection algorithm was employed to rank these parameters and calculate feature importance scores through weighted computation.Secondly,based on the feature importance scores,a subset search algorithm was utilized to identify the subset of features that could yield optimal performance across four machine learning algorithms:neural networks,logistic regression,AdaBoost and XGBoost.Finally,a soft voting ensemble method was designed using a generalized linear regression model to integrate the results of each single machine learning algorithm,and a multivariate ensemble learning algorithm was proposed by combining the optimal feature subsets.The algorithm proposed when used to identify the severity of ADRS was evaluated with MIMIC-Ⅳ database,and compared with the traditional algorithms.Results The sensitivity,specificity,accuracy and AUC of the algorithm were 87.15％,89.23％,88.34％and 0.923 4,respectively,all of which outperformed those of the traditional algorithms.Conclusion The ARDS severity identification algorithm based on ensemble learning is capable of achieving continuous and real-time monitoring of the severity of ARDS,thereby offering robust support for the early identification and warning of ARDS in patients.[Chinese Medical Equipment Journal,2025,46(2):1-9]

Objective:To evaluate the cost-effectiveness and impact on mortality of health management services for the elderly aged 65 years and older in national essential public health service project.Methods:Based on the data of county-level medical institutions in Henan Province from 2019 to 2024,the Random Forest Method was used to construct a counterfactual framework to predict the hospitalization expenses under the unmanaged scenario,and then the cost-benefit ratio(BCR)and net income were calculated.Time-dependent Cox proportional hazards model was used to evaluate the effect of health management on all-cause mortality and cardiovascular and cerebrovascular disease mortality in the elderly.Results:A total of 962 955 elderly patients were included,451 119(46.85%)were included in the management group.The average hospitalization cost of the management group was significantly lower than that of the non-management group(P<0.05).Except for 2020-2021,BCRS in 2019 and 2022-2024 were 6.34,2.05,4.45 and 6.60,respectively.The risk of all-cause death was reduced by 76.96%,and the risk of cardiovascular and cerebrovascular death was reduced by 75.57%in the elderly patients included in the management group compared with those not included in the management group.Suggestions:It is necessary to establish a health outcomes-based evaluation system and promote the transformation and upgrading of the service model from single chronic disease management to"integrated health services with multi-disease management".

Objective:To understand the whole genome and genetic evolution characteristics of the first epidemic influenza A (H3N2) viruses in Wuxi from 2022-2023.Methods:Real time fluorescence quantitative RT-PCR method was used to perform typing on respiratory samples of influenza cases. Virus isolation was performed on samples with positive nucleic acid of subtype A H3N2 influenza virus detected. After cell culture, nucleic acid was extracted from strains with red blood cell agglutination test (HA) ≥ 1∶8, whole genome sequence was amplified, library was constructed, and computer sequencing was performed using MiSeq sequencer. Using NC_007366.1 as reference strain, the data were analyzed using CLC Genomics Workbench (Version 23) software. The phylogenetic tree was constructed using MEGA 7.0 software, and the N-glycosylation sites were predicted by NetNGlyc 1.0 Server software.Results:The nucleotide homology and amino acid homology among 35 strains of influenza A H3N2 virus from 2022 to 2023 were 96.4%-100% and 95.2%-100%, respectively. The 16 epidemic strains in 2022 belong to the 3C.2a1b.2a.1a evolutionary branch, while the 19 epidemic strains in 2023 belong to the 3C.2a1b.2a.2a.3a.1 evolutionary branch. There are 7 differences in the nucleotide sequence of the HA gene between the 2022 epidemic strain and the corresponding vaccine strain, sharing 15 mutation sites; There are 28 differences in the nucleotide sequence of the HA gene between the 2023 epidemic strain and the corresponding vaccine strain, sharing 17 mutation sites. The HA genes of 35 epidemic strains all lack N-glycosylation site 61: NSS, while in 2023, the HA genes of 19 epidemic strains added N-glycosylation site 110: NSS.Conclusions:The HA and NA genes of influenza A H3N2 virus in 2022 and 2023 belong to two evolutionary branches, respectively, and both show specific amino acid site changes compared to the corresponding vaccine strains. The antigen matching between the 2022 epidemic strain and the vaccine strain is relatively good, while there is a risk of low antigen matching between the 2023 epidemic strain and the vaccine strain.

Attenuated Salmonella typhimurium VNP20009 is a widely used natural oncolytic bacterium, which has great application potential given its unique characteristics, including clinical safety, tumor targeting specificity, and explicit genome sequence. Here, we show that tumor progression can be effectively reduced by intraperitoneal administration with VNP20009 in a mouse model of melanoma (all animal experiments were conducted in accordance with the Animal Ethics Committee of China Pharmaceutical University); co-culture experiment in vitro demonstrated that VNP20009 can induce the polarization of macrophage M1, accompanied by expression of inflammation-related factors; flow cytometry analysis showed that VNP20009 induced the increase of immune cell infiltration in tumor. Further analysis showed that T cells infiltration in tumor-draining lymph node (TDLN) increased, and VNP20009 induced the activation of CD4⁺ T cells and CD8⁺ T cells in tumor. Our results demonstrate that VNP20009 treatment significantly inhibited melanoma tumors by remodeling tumor-associated macrophages to an M1-like phenotype, as well as recruiting and activating cytotoxic T cells, combined with its own antigenic activity to exert anti-tumor immunity.

OBJECTIVE: To evaluate the associations of lipid indicators and mortality in Beijing Elderly Comprehensive Health Cohort Study. METHODS: A prospective cohort was conducted based on Beijing Elderly Comprehensive Health Cohort Study with 4499 community older adults. After the baseline survey, the last follow-up was March 31, 2021 with an average 8.13 years of follow-up. Cox proportional hazard model was used to estimate the hazard ratios (HR) with 95% CI for cardiovascular disease (CVD) death and all-cause death in associations with baseline lipid indicators. RESULTS: A total of 4499 participants were recruited, and the mean levels of uric acid, body mass index, systolic blood pressure, diastolic blood pressure, fasting plasma glucose, total cholesterol (TC), triglyceride, and low-density lipoprotein cholesterol (LDL-C) showed an upward trend with the increasing remnant cholesterol (RC) quarters (P_trend < 0.05), while the downward trend was found in high-density lipoprotein cholesterol (HDL-C). During the total 36,596 person-years follow-up, the CVD mortality and all-cause mortality during an average 8.13 years of follow-up was 3.87% (95% CI: 3.30%-4.43%) and 14.83% (95% CI: 13.79%-15.86%) with 174 CVD death participants and 667 all-cause death participants. After adjusting for confounders, the higher level of TC (HR = 0.854, 95% CI: 0.730-0.997), LDL-C (HR = 0.817, 95% CI: 0.680-0.982) and HDL-C (HR = 0.443, 95% CI: 0.271-0.724) were associated with lower risk of CVD death, and the higher level of HDL-C (HR = 0.637, 95% CI: 0.501-0.810) were associated with lower risk of all-cause death. The higher level of RC (HR = 1.276, 95% CI: 1.010-1.613) increase the risk of CVD death. Compared with the normal lipid group, TC ≥ 6.20 mmol/L group and LDL-C ≥ 4.10 mmol/L group were no longer associated with lower risk of CVD death, while RC ≥ 0.80 mmol/L group was still associated with higher risk of CVD death. In normal lipid group, the higher levels of TC, LDL-C and HDL-C were related with lower CVD death. CONCLUSIONS In community older adults, higher levels of TC and HDL-C were associated with lower CVD mortality in normal lipid reference range. Higher RC was associated with higher CVD mortality, which may be a better lipid indicator for estimating the CVD death risk in older adults.