OBJECTIVES: To explore the relationship between white matter injury (WMI) and cerebral perfusion in preterm infants using arterial spin labeling (ASL). METHODS: A total of 293 preterm infants (gestational age <34 weeks) hospitalized at the Third Affiliated Hospital of Zhengzhou University between June 2022 and June 2024 were included. After achieving clinical stability, the infants underwent brain magnetic resonance imaging (MRI) and ASL. Based on MRI findings, infants were classified into WMI (n=66) and non-WMI (n=227) groups. Cerebral perfusion parameters were compared between groups, and the association between WMI and perfusion alterations was evaluated. RESULTS: The WMI group showed a higher incidence of mild intraventricular hemorrhage (IVH) than the non-WMI group (P<0.05). Significantly lower cerebral perfusion was observed in the WMI group across bilateral frontal, temporal, parietal, and occipital lobes, as well as the basal ganglia and thalamus (P<0.05). After adjusting for gestational age, corrected gestational age at ASL scan, and mild IVH, WMI remained significantly associated with reduced regional perfusion (P<0.05). CONCLUSIONS WMI in preterm infants correlates with localized cerebral hypoperfusion. ASL-detected perfusion abnormalities may provide novel insights into WMI pathogenesis.
Humans ; White Matter/blood supply* ; Infant, Newborn ; Spin Labels ; Infant, Premature ; Female ; Male ; Cerebrovascular Circulation ; Magnetic Resonance Imaging

OBJECTIVE: To investigate the relationship between serum levels of fibroblast growth factor-23 (FGF-23), heparanase (HPSE) and early renal impairment (RI) in patients with multiple myeloma (MM). METHODS: A retrospective analysis was conducted on the clinical data of 125 MM patients who were initially diagnosed in the Department of Hematology of the First Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology from June 2020 to June 2023. The patients were divided into RI group (>176.80 μmol/L) and non-RI group (≤176.80 μmol/L) based on their serum creatinine levels when diagnosed. The baseline data and laboratory indexes of the two groups were compared. The relationship between serum FGF-23, HPSE and early RI in MM patients was analyzed. RESULTS: Among 125 newly diagnosed MM patients, 33 cases developed early RI, accounting for 26.40%. The proportion of light chain type, blood urea nitrogen (BUN), blood uric acid, lactate dehydrogenase, FGF-23, and HPSE levels in RI group were higher than those in non-RI group (all P <0.05). There was no statistical significant difference in other data between the two groups (P >0.05). Multivariate logistic regression analysis showed that BUN, FGF-23 and HPSE were associated with early RI in MM patients (all P <0.05). The serum FGF-23 level was divided into Q1-Q4 groups by quartile, and the serum HPSE level was divided into q1-q4 groups. The correlation analysis showed that with the increase of serum FGF-23 and HPSE levels, the incidence of early RI increased (r =0.668, 0.592). Furthermore, logistic regression analysis showed that after controlling for confounding factors, elevated levels of serum FGF-23 and HPSE were still influencing factors for early RI in MM patients (OR>1, P <0.05). According to Pearson's linear correlation test, there was a positive correlation between serum FGF-23 level and HPSE level (r =0.373). CONCLUSION There is a certain correlation between serum levels of FGF-23, HPSE and early RI in MM patients, and the incidence of early RI is higher in patients with abnormally high levels of both.
Humans ; Multiple Myeloma/complications* ; Fibroblast Growth Factor-23 ; Retrospective Studies ; Fibroblast Growth Factors/blood* ; Glucuronidase/blood* ; Male ; Female ; Middle Aged ; Renal Insufficiency/blood* ; Aged

OBJECTIVE: To investigate chronic hepatitis C virus (HCV) infection's effect on gestational liver function, pregnancy and delivery complications, and neonatal development. METHODS: A total of 157 HCV antibody-positive (anti-HCV[+]) and HCV RNA(+) patients (Group C) and 121 anti-HCV(+) and HCV RNA(-) patients (Group B) were included as study participants, while 142 anti-HCV(-) and HCV RNA(-) patients (Group A) were the control group. Data on biochemical indices during pregnancy, pregnancy complications, delivery-related information, and neonatal complications were also collected. RESULTS: Elevated alanine aminotransferase (ALT) rates in Group C during early, middle, and late pregnancy were 59.87%, 43.95%, and 42.04%, respectively-significantly higher than Groups B (26.45%, 15.70%, 10.74%) and A (23.94%, 19.01%, 6.34%) ( P < 0.05). Median ALT levels in Group C were significantly higher than in Groups A and B at all pregnancy stages ( P < 0.05). No significant differences were found in neonatal malformation rates across groups ( P > 0.05). However, neonatal jaundice incidence was significantly greater in Group C (75.16%) compared to Groups A (42.25%) and B (57.02%) ( χ ² = 33.552, P < 0.001). HCV RNA positivity during pregnancy was an independent risk factor for neonatal jaundice ( OR = 2.111, 95% CI 1.242-3.588, P = 0.006). CONCLUSIONS Chronic HCV infection can affect the liver function of pregnant women, but does not increase the pregnancy or delivery complication risks. HCV RNA(+) is an independent risk factor for neonatal jaundice.
Humans ; Female ; Pregnancy ; Adult ; Pregnancy Complications, Infectious/epidemiology* ; Retrospective Studies ; Pregnancy Outcome ; Infant, Newborn ; Viremia/virology* ; Hepatitis C ; Hepacivirus/physiology* ; Hepatitis C, Chronic/virology* ; Young Adult ; Alanine Transaminase/blood*

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.

To explore the absorption mechanism of APS-Ⅱ in vivo by establishing M cell model. First, Astragalus polysaccharides (APS) was divided into two different molecular weight polysaccharides APS-I ( > 2 000 kDa) and APS-II (10 kDa) by ultrafiltration, and APS-II (10 kDa) was prepared and fluorescently labeled. Meanwhile, M cell model was constructed by Caco-2 cells and Raji cells. The M cell model was treated with transport inhibitors to explore the transport of APS-Ⅱ on M cells. The results show that FITC has been successfully labeled to the end of APS-Ⅱ, and the M cell model was successfully constructed, which found that APS-Ⅱ could be transported by M cells, and four transport inhibitors of 5-(N-ethyl-N-isopropyl) amiloride (EIPA), genistein, dynasore and nocodazole indicated that APS-II may enter cells through clathrin and caveolin-mediated endocytosis.

Objective The aim of this study was to assess the impact of bisphenol A (BPA) and its substitute, bisphenol F (BPF), on the colonic fecal community structure and function of mice.Methods We exposed 6-8-week-old male C57BL/6 mice to 5 mg/(kg·day) and 50 μg/(kg·day) of BPA or BPF for 14 days. Fecal samples from the colon were analyzed using 16S rRNA sequencing. Results Gut microbiome community richness and diversity, species composition, and function were significantly altered in mice exposed to BPA or BPF. This change was characterized by elevated levels of Ruminococcaceae UCG-010 and Oscillibacter and decreased levels of Prevotella 9 and Streptococcus. Additionally, pathways related to carbohydrate and amino acid metabolism showed substantial enrichment. Conclusion Mice exposed to different BP analogs exhibited distinct gut bacterial community richness, composition, and related metabolic pathways. Considering the essential role of gut bacteria in maintaining intestinal homeostasis, our study highlights the intestinal toxicity of BPs in vertebrates.

This study analyzed and compared the epidemiological characteristics of foodborne listeriosis in the United States and China,to provide evidence for optimizing the listeriosis surveillance program in China.Descriptive statistical analysis was performed on the listeriosis monitoring data from 2009 to 2021 registered in the FDOSS system and the attribution estimates of Listeria monocytogenes(L.monocytogenes)from 2013 to 2021 published by IFSAC.Sporadic and outbreak data on listeriosis in China from the CNKI,Wanfang Medical,and CQVIP databases were collected.From 2009 to 2021,a total of 1 037 listeriosis cases were reported in the United States,including 902 hospitalizations and 165 deaths.The peak of cases caused by Lm con-taminated food was in July.The number of cases,hospitalizations,and deaths accounted for 18.4％(191/1 037),20.5％(185/902),and 22.4％(37/165)of the total,respectively.Most listeriosis outbreaks were attributed to three food groups:dairy products,vegetable crops,and fruits,with attribution percentages ranging from 73.8％to 89.6％.The overall incidence of list-eriosis in China was not high:619 cases were reported from 2009 to 2021,and only 177 cases were recorded in detailed inci-dence years;the maximum number of cases in 2018 was 26.A total of 220 cases were reported with detailed onset months;the highest number of cases in April was 30.Data on listeriosis cases in China are incomplete and sporadic,and only seven cases have been successfully traced to food.Listeriosis surveillance systems in the United States are relatively complete,and there are more foodborne outbreaks.Dairy products,vegetable row crops,and fruits are the most likely causes of disease outbreaks.Although only sporadic cases have been reported in China,China should take actions such as gradually improving multi-department coop-eration mechanisms,achieving data sharing and deepening data mining,and accelerating progress in the detection technology of food-borne pathogenic microorganisms,to ensure food safety and public health.

The identification of the components absorbed in serum of platycosides in total saponins fraction of Platycodonis Radix is great significance, but there are still great challenges. In this study, 8 types of 44 primordial components from Platycodon saponins were firstly identified using the ultra-performance liquid chromatography-linear ion trap electrostatic field orbitrap high resolution mass spectrometry (UPLC-LTQ-Orbitrap-MS) equipped with the software of Compound Discoverer 3.2 and Trace finder 2.1. Then, the platycosides and their deglycosylated metabolites were used as the template molecules to construct the intestinal microbiota mediated method to identify the primary components and the prototypes in serum. As results, 57 components originating from 44 prototypes of platycosides were identified from drug-containing plasma. Those compounds consist of 12 prototypes of platycosides and 45 metabolites, while the prototypes in serum are also deglycosylated metabolites of other platycosides. The results showed that the intestinal microbiota mediated method could be applied to identify the metabolites of platycosides in total saponins fraction of Platycodonis Radix; and reveal the potential existing forms of prototypes of platycosides in plasma; In addition, it could clearly illustrate the one to many and many to one network of the prototypes and metabolites of platycosides. All animal protocols were approved by the Animal Ethics Committee of Jiangxi University of Traditional Chinese Medicine (No.JZLLSC-202100322).

Stable isotope tracer metabolomics tracks and analyzes the whole metabolic process of the body through the tracer atoms, which belongs to the frontier technology in the field of biomedicine. This technology is of great significance and value for explaining the pathogenesis of diseases, finding biomarkers of diseases and drug action targets. Taking the mechanism of glucose catabolism disorder in depression as an example, this paper systematically expounds the stable isotope tracer metabolomics technology and its application. The research idea of stable isotope tracer metabolomics based on unmarked metabolomics was put forward, and the research strategy of biological significance interpretation from four dimensions of metabolite isotope abundance, key metabolic enzymes, metabolic flow direction and metabolite flow was given, which broke through the bottleneck of stable isotope tracer metabolomics research technology based on overall animal experiment, and provided scientific basis for the promotion and application of this technology.