Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder worldwide, causing dementia and affecting millions of individuals. One prominent characteristic in the brains of AD patients is glucose hypometabolism. In the context of galactose metabolism, intracellular glucose levels are heightened. Galactose mutarotase (GALM) plays a crucial role in maintaining normal galactose metabolism by catalyzing the conversion of β-D-galactose into α-D-galactose (α-D-G). The latter is then converted into glucose-6-phosphate, improving glucose metabolism levels. However, the involvement of GALM in AD progression is still unclear. In the present study, we found that the expression of GALM was significantly increased in AD patients and model mice. Genetic knockdown of GALM using adeno-associated virus did not change the expression of amyloid precursor protein (APP) and APP-cleaving enzymes including a disintegrin and metalloprotease 10 (ADAM10), β-site APP-cleaving enzyme 1 (BACE1), and presenilin-1 (PS1). Interestingly, genetic overexpression of GALM reduced APP and Aβ deposition by increasing the maturation of ADAM10, although it did not alter the expression of BACE1 and PS1. Further electrophysiological and behavioral experiments showed that GALM overexpression significantly ameliorated the deficits in hippocampal CA1 long-term potentiation (LTP) and spatial learning and memory in AD model mice. Importantly, direct α-D-G (20 mg/kg, i.p.) also inhibited Aβ deposition by increasing the maturation of ADAM10, thereby improving hippocampal CA1 LTP and spatial learning and memory in AD model mice. Taken together, our results indicate that GALM shifts APP processing towards α-cleavage, preventing Aβ generation by increasing the level of mature ADAM10. These findings indicate that GALM may be a potential therapeutic target for AD, and α-D-G has the potential to be used as a dietary supplement for the prevention and treatment of AD.
Animals ; ADAM10 Protein/metabolism* ; Alzheimer Disease/pathology* ; Amyloid Precursor Protein Secretases/metabolism* ; Disease Models, Animal ; Humans ; Mice ; Amyloid beta-Peptides/metabolism* ; Male ; Mice, Transgenic ; Membrane Proteins/metabolism* ; Cognitive Dysfunction/pathology* ; Mice, Inbred C57BL ; Amyloid beta-Protein Precursor/metabolism* ; Female ; Hippocampus/metabolism* ; Long-Term Potentiation/physiology*

Objective:To systematically evaluate the predictive value of the reverse shock index multiplied by the Glasgow coma scale score (rSIG) for mortality of trauma patients.Methods:A comprehensive literature search was conducted to identify studies on the predictive value of rSIG for mortality of trauma patients in the following databases from inception to April 2025, including CNKI, Wanfang Data, SinoMed, PubMed, Cochrane Library, Web of Science, and Embase. Two investigators independently screened the literature, extracted data, and assessed study quality according to predefined inclusion and exclusion criteria. The Quality assessment of diagnostic accuracy studies-2 (QUADAS-2) tool was used to evaluate the risk of bias in the included studies. Meta analysis was performed using Stata 17.0 software with a bivariate mixed-effects model. The following metrics were used to assess the predictive value of rSIG for mortality in trauma patients, including sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the summary receiver operating characteristic (SROC) curve (AUC). The influence of various factors on the predictive performance of rSIG was examined, including injury type, study design, region, sample size, cut-off value, rSIG measurement time, and outcome measures. Additionally, sensitivity analysis, Fagan′s nomogram, and Deeks′ funnel plot were employed to assess the robustness of the findings, clinical applicability, and publication bias.Results:A total of 15 studies involving 710 612 trauma patients were included, 26 105 of whom were deceased. Meta analysis results showed that rSIG had a pooled sensitivity of 0.78(95% CI 0.71, 0.84), a pooled specificity of 0.78(95% CI 0.68, 0.86), a pooled PLR of 3.60(95% CI 2.46, 5.27), a pooled NLR of 0.28(95% CI 0.22, 0.36), a pooled DOR of 12.70(95% CI 8.10, 19.91), and an AUC of 0.85(95% CI 0.81, 0.87) for predicting mortality of trauma patients. Subgroup analysis identified injury type as one of the major sources of heterogeneity, and the predictive specificity of rSIG was significantly higher in patients with multiple trauma (0.82) than in those with isolated traumatic brain injury (0.65) ( P<0.05). Sensitivity analysis indicated that the findings were robust and stable. Fagan′s nomogram showed that when the pre-test probability was 7%, the post-test probability of death increased to 21% in patients with low rSIG and decreased to 2% in those with high rSIG. Deeks′ funnel plots suggested no significant publication bias among the included studies ( P>0.05). Conclusion:Low rSIG has good predictive performance for mortality of trauma patients and can serve as an effective tool for early and rapid prognosis assessment with superior predictive performance in patients with multiple trauma compared to those with traumatic brain injury.

Objective:To develop radiomics score (RS) based on 18F-FDG PET/CT, and construct the machine learning model combining clinical and other relevant factors for personalized prediction of 2-year event-free survival (2-EFS) in patients with diffuse large B-cell lymphoma (DLBCL), and to perform interpretability analysis of the model. Methods:A total of 91 patients (49 males, 42 females; age (57.8±12.8) years) with pathologically confirmed DLBCL from December 2017 to December 2020 at the First Hospital of Shanxi Medical University were retrospectively analyzed. According to the ratio of 7∶3, patients were randomly divided into training set ( n=63) and test set ( n=28), and divided into non-progression group and progression group according to the follow-up results. The whole-body PET semi-quantitative parameters were calculated from the PET/CT images before treatment, and 328 radiomics features were extracted from the largest target lesions of patients. The least absolute shrinkage and selection operator (LASSO) was used to develop the RS. Clinical and PET characteristic difference analysis was performed through χ2 test and Mann-Whitney U test. Extreme gradient boosting (XGBoost) models were constructed based on clinical, PET radiomics features and RS, and the prediction efficiency of each model was evaluated by ROC AUC. The model interpretability was analyzed by using shapely additive explanation (SHAP). Results:Of all patients, 32 had disease progression and 59 did not. There were no significant differences in baseline characteristics between the training set and the test set ( χ2 values: 0.06-1.84, U values: 665.00-763.00, all P>0.05). The comparison between the progression group and non-progression group in the training set showed statistical differences in the international prognostic index (IPI) score ( χ2=4.87, P=0.027), myelocytomatosis viral oncogene (MYC) protein expression ( χ2=4.29, P=0.038), and metabolic tumor volume (MTV; U=307.00, P=0.038). Seven radiomics features were screened by LASSO. Among XGBoost models with different feature combinations, IPI score, MYC protein expression, MTV combined with RS had the highest predictive efficiency (training set: AUC=0.73; test set: AUC=0.70). Through SHAP analysis, RS was the most predictive feature in the optimal model. Conclusion:The machine learning integrated model of IPI score, MYC protein expression and MTV combined with RS can effectively predict the prognosis of DLBCL patients, and baseline 18F-FDG PET/CT radiomics can be used as a potential means to evaluate the prognosis of DLBCL patients.

Objective:To investigate the clinical value of 18F-NaF PET/CT coronary plague imaging in evaluating the long-term prognosis of patients with coronary artery disease (CAD). Methods:A retrospective cohort study was conducted among 54 patients (37 males and 17 females, aged (57.2±9.8) years) diagnosed with CAD from a multicenter study between September 2015 and October 2022. All patients underwent 18F-NaF PET/CT and coronary angiography (CAG) within 1 week, and the PET/CT imaging was performed at the First Hospital of Shanxi Medical University. Major adverse cardiovascular events (MACE) were followed up. ROC curves were established to obtain the optimal thresholds of SUV max and accumulated SUV max of all lesions of main coronary artery branches (S-SUV max) for predicting MACE. Cox proportional risk model and Kaplan-Meier method (log-rank test) were used to analyze the predictive value of PET parameters for MACE. Differences in metabolic parameters between 2 groups were compared by Mann-Whitney U test. Results:The median follow-up time of the 54 patients was 6.0(1.8, 6.6) years, and 13(24.1%) patients developed MACE, including 7 deaths, 5 myocardial infarction and 1 severe arrhythmia. S-SUV max in MACE group was significantly higher than that in the non-MACE group (2.64(2.08, 4.49) vs 1.83(0.95, 2.90); Z=-2.04, P=0.041). ROC curve showed that the optimal threshold of S-SUV max for MACE prediction was 2.05 (AUC=0.690). Multivariate Cox analysis showed that S-SUV max was a strong predictor of MACE (hazard ratio ( HR)=2.434(95% CI: 1.547-3.828), P<0.001). ROC curve showed that the optimal threshold of SUV max to predict MACE was 0.55 (AUC=0.659), and univariate Cox analysis showed that SUV max was a factor to predict MACE ( HR=10.192 (95% CI: 2.667-38.953), P=0.001). In 25 patients with incomplete revascularization (ICR), Kaplan-Meier analysis showed that the incidence of MACE in patients with positive 18F-NaF uptake (single medium stenosis (40%-70%) with SUV max≥0.55) was significantly higher than that in patients with negative 18F-NaF uptake (5/14 vs 0/11; χ2=6.07, P=0.014). Conclusions:18F-NaF PET/CT can be used as an independent predictor of MACE in patients with CAD and can quantitatively assess the long-term progression of moderate coronary artery stenosis. In the future, it is expected to be a new non-invasive way to guide the revascularization treatment decision of multi-vessel CAD.

Objective:To investigate the effects of acute sleep deprivation (ASD) on hippocampal glucose metabolism and neuroinflammation in rat models.Methods:Twenty SD rats (10 males and 10 females) were divided into four groups (five in each group) by random sampling method: female ASD group, male ASD group, female control group, and male control group. Among them, the ASD group constructed the ASD model. After 72h sleep deprivation, all rats underwent 18F-FDG and N, N-diethyl-2-(2-(4-(2- 18F-fluoroethoxy)phenyl)-5, 7-dimethylpyrazolo[1, 5-a]pyrimidin-3-yl)acetamide ( 18F-DPA-714) microPET/CT brain imaging in 2d to compare the changes of 18F-FDG and 18F-DPA-714 SUV mean in the hippocampus of rats. Brain histopathology, immunohistochemistry and immunofluorescence staining were detected in rats. Independent-sample t test was used to analyze the data. Results:18F-FDG imaging showed the hippocampal SUV mean between ASD group and control group (female: 4.11±0.35 vs 1.89±0.28; male: 3.43±0.47 vs 2.02±0.54) were statistically significant ( t values: 9.65, 3.92, P values: <0.001, 0.002). 18F-DPA-714 imaging showed the hippocampal SUV mean between ASD group and control group (females: 0.28±0.01 vs 0.28±0.02; male: 0.26±0.02 vs 0.31±0.04) were not statistically significant ( t values: -0.18, -2.24, P values: 0.859, 0.056). The 18×10 3 translocator protein (TSPO) immunohistochemistry showed the expression in the hippocampal region of the brain between ASD group and control group (female: 0.19±0.02 vs 0.19±0.01; male: 0.21±0.01 vs 0.20±0.01) were not statistically different ( t values: -0.48, -1.67, P values: 0.651, 0.139). Immunofluorescence staining showed that microglial cytosol in the hippocampal region of the brain decreased after 72h of ASD, and the protrusion points and surrounding branches were significantly reduced. Conclusion:Increased hippocampal glucose metabolism in rats is observed after 72 h of ASD without significant neuroinflammation.

ObjectiveTo investigate the level of hepatitis B surface antibody (anti-HBs) among preschool children (aged 3‒6 years) and primary and secondary school students in Tonglu County, Zhejiang Province, to evaluate the effectiveness of hepatitis B vaccination, and to provide a basis for hepatitis B prevention and control in the region. MethodsAs part of the 2023 Tonglu County Urban and Rural Residents Health Examination Program, blood samples were collected during health check-ups. Fingertip blood samples were obtained from preschool children, while venous blood samples were collected from primary and secondary school children. The anti-HBs levels in blood (positive + / negative -) were qualitatively tested using hepatitis B surface antibody test kits (latex method). The differences in anti-HBs positivity rates among different age groups were analyzed. ResultsBetween April 1, 2023 and June 30, 2023, a total of 52 919 individuals were surveyed, including 11 973 preschool children and 40 946 primary and secondary school students. The overall anti-HBs positivity rate was 39.74%, with the highest positivity rate observed among preschool children (60.20%). Age was negatively correlated with the anti-HBs positivity rate (P<0.001). No significant gender differences in anti-HBs positivity rates were observed. The anti-HBs positive rate in rural areas was higher than that in urban areas, with statistically significant differences across school grade groups (primary grades 1‒3, grades 4‒6, middle school, and high school) (P<0.001). ConclusionThe anti-HBs positivity rate among preschool and school-age children in Tonglu County decreases with age and remains relatively low. It is recommended to strengthen the monitoring of hepatitis B antibody levels and promote health education among preschool and school-age children. Children who have not completed the full hepatitis B vaccination should receive timely catch-up vaccination.

Objective To investigate the effect of SerpinA5 on the malignant biological behavior of esophageal squamous cell carcinoma (ESCC) and its molecular mechanism. Methods The expression levels of the SerpinA5 gene in various tumors and adjacent normal tissues were analyzed by using the TIMER2.0 database. The expression levels of SerpinA5 in the ESCC cell line and esophageal epithelial cells were detected through Western blot analysis. Stably transfected KYSE150 cell line with overexpression of SerpinA5 was constructed through lentiviral transfection, and overexpression efficiency was detected via Western blot analysis. The effects of SerpinA5 overexpression on the proliferation, apoptosis, migration, and invasion of ESCC cells were detected by employing the CCK8, plate cloning, flow cytometry, wound healing, and Transwell invasion assays. The nude mice subcutaneous xenograft model with SerpinA5 overexpression was constructed. Tumor growth was observed, and tumor volume and mass were measured. The cell proliferation level of the subcutaneous xenograft tumors in nude mice was detected via immunohistochemistry (IHC). Coimmunoprecipitation (Co-IP) was employed to determine the interaction between SerpinA5 and Fn. Western blot analysis was applied to detect the expression levels of proteins (Fn, Integrin-β1, FAK, and p-FAK) related to the Fn/Integrin-β1 signaling pathway in transplanted tumors. Results SerpinA5 was expressed at low levels in ESCC tissues and cell lines. In ESCC cells, SerpinA5 overexpression can considerably inhibit cell proliferation, migration, and invasion and promote cell apoptosis. In the subcutaneous xenograft experiment on nude mice, the tumor volume and weight of the SerpinA5 overexpression group were lower than those of the negative control group. IHC results demonstrated that SerpinA5 overexpression significantly inhibited the proliferation of ESCC cells in tumor tissues. Co-IP confirmed the interaction between SerpinA5 and Fn. Western blot analysis results showed that the expression levels of Fn, Integrin-β1, and p-FAK in the Fn/Integrin-β1 signaling pathway of ESCC cells in the subcutaneous xenograft tumors of nude mice significantly decreased after SerpinA5 overexpression. Conclusion Serpin A5 may inhibit proliferation, migration, and invasion and promote apoptosis of ESCC cells by regulating the Fn/Integrin-β1 signaling pathway.

Objective To investigate the effects of electroacupuncture at Neiguan(PC6)combined with Buyang Huanwu Decoction on myocardial edema-related proteins and its cardioprotective role in mice with acute high-altitude hypoxic myocardial injury,and to explore the potential mechanisms by which this combined therapy ameliorates acute hypoxic myocardial damage.Methods Mice were randomly divided into control,hypoxia model,electroacupuncture at Neiguan,Buyang Huanwu Decoction,and electroacupuncture at Neiguan+Buyang Huanwu Decoction groups.Except for the normal control group,all other groups were subjected to the establishment of an acute high-altitude hypoxia-induced myocardial injury model.Four days before entering the low-pressure hypoxia animal simulation chamber,the electroacupuncture at Neiguan group was treated with bilateral electroacupuncture at Neiguan,the Buyang Huanwu Decoction group was treated with Buyang Huanwu Decoction by gavage,and the electroacupuncture at Neiguan+Buyang Huanwu Decoction group was treated with a combination of electroacupuncture at Neiguan and Buyang Huanwu Decoction.The intervention lasted for 7 days.The normal control group and the hypoxia model group were handled normally without any other treatment.Myocardial pathology and ultrastructure were evaluated using HE staining and transmission electron microscopy.Serum levels of creatine kinase-MB(CK-MB)and cardiac troponin I(cTn-I)were measured by ELISA.Western blot was performed to quantify β1-AR,cAMP,PKA,and AQP1 protein expression.Results Compared with normal control group,the hypoxia model group exhibited significant myocardial damage,elevated cardiac biomarkers,and upregulated β1-AR/cAMP/PKA pathway proteins with increased AQP1 expression(all P<0.01).The electroacupuncture at Neiguan+Buyang Huanwu Decoction group demonstrated attenuated myocardial injury,reduced biomarker levels,and downregulated target proteins(all P<0.01)versus the hypoxia model group.Conclusion Electroacupuncture at Neiguan combined with Buyang Huanwu Decoction alleviates myocardial edema and injury in acute hypobaric hypoxia by reducing vascular permeability,potentially via suppression of the β1-AR/cAMP/PKA pathway and subsequent inhibition of AQP1 expression.

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms

Objective To investigate the differential expression genes(DEGs)related to angiogenesis in rosacea(RA)by utilizing bioinformatics analysis in order to screen the key genes and verify their mRNA expression levels.Methods The gene microarray dataset GSE65914 was retrieved from the Gene Expression Omnibus(GEO)repository.Analyzed by R programming,the dataset was refined to identify DEGs related to RA,and then cross-referenced with angiogenesis-related genes from the GeneCards database to get a subset specific to RA angiogenesis.The process of identifying key genes was augmented by employing protein-protein interaction(PPI)network analysis and Cytoscape-based computational algorithms.The mRNA expression levels of the aforementioned pivotal genes were detected by real-time fluorescent quantitative reverse transcription PCR(RT-qPCR).Results A total of 947 RA-associated DEGs were identified from GEO dataset,and then 202 genes related to RA angiogenesis were further delineated.PPI network analysis and Cytoscape algorithm finally identified 3 key genes,that is,CXCL8,IL-1B,and STAT1.The results of RT-qPCR showed that the mRNA expression levels of MIP-2,GCP-2,IL-1B and STAT1 in RA lesions were significantly higher than those in normal controls(P<0.05).Conclusion With aid of bioinformatics analysis,our study has screened and validated key genes associated with angiogenesis in RA,namely CXCL8,IL-1B,and STAT1,which providing a theoretical basis for elucidating the potential mechanisms underlying RA-induced angiogenesis and developing targeted therapeutic strategies.