Objective:To explore the independent risk factors for uremic encephalopathy(UE)in maintenance hemodialysis(MHD)patients and provide evidence for early clinical warning and intervention.Methods:A case-control study was conducted,enrolling 67 MHD patients diagnosed with UE(UE group)at Laibin People's Hospital from January 2010 to December 2024,and 67 non-UE patients during the same period(control group).Demographic characteristics,dialysis parameters,laboratory indicators,and infection events were collected.Univariate and multivariate logistic regression analyses were used to identify independent risk factors for UE.Results:The UE group had significantly higher rates of infection(58.2％vs.29.9％),serum creatinine(789 vs.702 μmol/L),and iPTH levels(568 vs.385 pg/mL)compared to the control group(P＜0.05).Multivariate analysis revealed that concurrent infection(OR=3.022,95％CI:1.312-6.958),elevated serum creatinine(OR=1.004,95％CI:1.000-1.008),and elevated iPTH(OR=1.002,95％CI:1.001-1.003)were independent risk factors for UE(P＜0.05).The combined prediction model achieved an AUC of 0.878(95％CI:0.822-0.934),with 82.1％sensitivity and 80.6％specificity.Conclusion:Infection,elevated serum creatinine,and elevated iPTH significantly increase the risk of UE in MHD patients.Clinical management should emphasize infection prevention,toxin clearance optimization,and parathyroid function regulation to reduce UE incidence.

AIM To evaluate the quality consistency of Tongmai Granules,Tongmai Tablets,Tongmai Capsules and Tongmai Oral Liquid.METHODS The HPLC fingerprints were established,after which the contents of danshensu,protocatechuic aldehyde,3'-hydroxy puerarin,puerarin,puerarin apioside,daidzin,ferulic acid,salvianolic acid B and salvianolic acid A were determined,and cluster analysis and principal component analysis were adopted in the quality analysis from the perspective of daily intake.RESULTS There were 21 common peaks in the fingerprints for 39 batches of samples with the similarities of 0.765-0.997.Various batches of samples were clustered into 5 categories,2 principal components demonstrated the accumulative variance contribution rate of 83.53％ .The daily intakes of various constituents in different dosage forms exhibited obvious differences,especially for that of salvianolic acid B,which were low in tablets and capsules,and their heterogeneities existed among the same dosage forms.CONCLUSION This simple and accurate method can provide a reference for the quality evaluation of Tongmai preparations from different manufacturers.

Objective To explore the predictive efficacy of several multimodal MRI-based machine learning models for the promoter methylation states of O6-methylguanine-DNA methyltransferase(MGMT)of glioblastoma muliforme(GBM)patients in terms of the GBM heterogeneity and the complexity of the tumor microenvironment.Methods Firstly,the multimodal MRI images of 317 GBM patients from The University of Pennsylvania Glioblastoma(UPENN-GBM)dataset were pre-processed,with four sequences involved in including T1-weighted imaging(T1WI)sequence,T1-weighted contrast-enhanced imaging(T1CE)sequence,T2-weighted imaging(T2WI)sequence and fluid-attenuated inversion recovery(FLAIR)sequence,and the radiomics features were extracted for two regions of interest(ROIs)such as the tumor core region and the tumor edema region.Secondly,the data of the 317 GBM patients were randomly divided into a training set(254 cases)and a test set(63 cases),which underwent normalization with Z-scores and feature selection and dimensionality reduction with Lasso regression.Finally,three models were established respectively with particle swarm optimization-support vector machine(PSO-SVM),C-support vector classification(C-SVC)and adaptive boosting(adaptive boosting(Adaboost)algorithms,and the predictive efficacy of the three models for glioblastoma multiforme MGMT promoter methylation states were evaluated in terms of accuracy and AUC.Results The Adaboost model based on T2WI sequence and radiomics features of the tumor core region had the highest predictive efficacy with accuracy and AUC values of 67％and 0.74,respectively,higher than those of other combinations of sequences,models and regions of interest.Conclusion The multimodal MRI-based machine learning models can be used for the prediction of glioblastoma multiforme MGMT promoter methylation states,which provides powerful support for personalized treatment and prognostic assessment of GBM.[Chinese Medical Equipment Journal,2025,46(6):7-13]

Objective To explore the predictive efficacy of several multimodal MRI-based machine learning models for the promoter methylation states of O6-methylguanine-DNA methyltransferase(MGMT)of glioblastoma muliforme(GBM)patients in terms of the GBM heterogeneity and the complexity of the tumor microenvironment.Methods Firstly,the multimodal MRI images of 317 GBM patients from The University of Pennsylvania Glioblastoma(UPENN-GBM)dataset were pre-processed,with four sequences involved in including T1-weighted imaging(T1WI)sequence,T1-weighted contrast-enhanced imaging(T1CE)sequence,T2-weighted imaging(T2WI)sequence and fluid-attenuated inversion recovery(FLAIR)sequence,and the radiomics features were extracted for two regions of interest(ROIs)such as the tumor core region and the tumor edema region.Secondly,the data of the 317 GBM patients were randomly divided into a training set(254 cases)and a test set(63 cases),which underwent normalization with Z-scores and feature selection and dimensionality reduction with Lasso regression.Finally,three models were established respectively with particle swarm optimization-support vector machine(PSO-SVM),C-support vector classification(C-SVC)and adaptive boosting(adaptive boosting(Adaboost)algorithms,and the predictive efficacy of the three models for glioblastoma multiforme MGMT promoter methylation states were evaluated in terms of accuracy and AUC.Results The Adaboost model based on T2WI sequence and radiomics features of the tumor core region had the highest predictive efficacy with accuracy and AUC values of 67％and 0.74,respectively,higher than those of other combinations of sequences,models and regions of interest.Conclusion The multimodal MRI-based machine learning models can be used for the prediction of glioblastoma multiforme MGMT promoter methylation states,which provides powerful support for personalized treatment and prognostic assessment of GBM.[Chinese Medical Equipment Journal,2025,46(6):7-13]

Objective:To explore the independent risk factors for uremic encephalopathy(UE)in maintenance hemodialysis(MHD)patients and provide evidence for early clinical warning and intervention.Methods:A case-control study was conducted,enrolling 67 MHD patients diagnosed with UE(UE group)at Laibin People's Hospital from January 2010 to December 2024,and 67 non-UE patients during the same period(control group).Demographic characteristics,dialysis parameters,laboratory indicators,and infection events were collected.Univariate and multivariate logistic regression analyses were used to identify independent risk factors for UE.Results:The UE group had significantly higher rates of infection(58.2％vs.29.9％),serum creatinine(789 vs.702 μmol/L),and iPTH levels(568 vs.385 pg/mL)compared to the control group(P＜0.05).Multivariate analysis revealed that concurrent infection(OR=3.022,95％CI:1.312-6.958),elevated serum creatinine(OR=1.004,95％CI:1.000-1.008),and elevated iPTH(OR=1.002,95％CI:1.001-1.003)were independent risk factors for UE(P＜0.05).The combined prediction model achieved an AUC of 0.878(95％CI:0.822-0.934),with 82.1％sensitivity and 80.6％specificity.Conclusion:Infection,elevated serum creatinine,and elevated iPTH significantly increase the risk of UE in MHD patients.Clinical management should emphasize infection prevention,toxin clearance optimization,and parathyroid function regulation to reduce UE incidence.

Objective:To investigate the impact of fibroblast growth factor(FGF)on recurrence following segmen-tal mastectomy in patients with plasma cell mastitis.Methods:A total of 162 female patients diagnosed with plasma cell mastitis(PCM)were selected from our hospital from October 2021 to May 2023.All patients underwent segmental mastectomy.They were divided into recurrence group(n=28)and non-recurrence group(n=134)according to the follow-up survey on recurrence.Conduct a univariate analysis on the factors influencing recurrence in patients with PCM who undergo segmental mastectomy.After correcting for confounding factors,conduct a multiple linear regression analysis.Using a multivariate logistic regression model to explore the independent risk factors for recurrence in patients undergo-ing segmental mastectomy for PCM.Utilizing logistic regression analysis to explore the independent,multiplicative,or additive interaction between FGF and angiogenic factor in the management of recurrence in PCM patients undergoing segmental mastectomy.The Local Weighted Regression Scatter Method(LOWESS)is used to analyze the two-dimensional curve relationship of continuous variables.Evaluate the predictive efficacy of FGF for PCM recurrence fol-lowing segmental mastectomy using Receiver Operating Characteristic(ROC)curves.Results:The results of univariate analysis showed that the body mass index(BMI),estradiol,prolactin levels,nipple depression,and sinus phase propor-tion of patients in the recurrent group were significantly higher than those in the non recurrent group,and the differ-ences were statistically significant(P＜0.05).Before surgery and 1 and 3 months after surgery,the levels of FGF,vascu-lar endothelial growth factor(VEGF),endostatin(ES),and VEGF/ES in the recurrent group were higher than those in the non recurrent group,with statistically significant differences between the groups(P＜0.05).The intra group comparison results showed that compared with before surgery,all indicators in both groups of patients were significantly reduced at 1 month after surgery(P＜0.05),while in the recurrent group,all indicators were significantly increased at 3 months after surgery(P＜0.001).Logistic regression analysis showed that patients with elevated FGF had a higher risk of recurrence in PCM(P＜0.05).LOWESS analysis found that there is a certain non-linear relationship between PCM recurrence rate and FGF.FGF has good predictive performance for PCM recurrence.After further adjusting for various confounding fac-tors such as BMI,it was found that the angiogenic factor is related to FGF.The interaction results show that there is an additive or multiplicative interaction between FGF and VEGF/ES.Conclusion:FGF elevation increases the risk of re-currence after segmental mastectomy for PCM.FGF and VEGF/ES exhibit additive or multiplicative interactions.FGF has good predictive performance for PCM recurrence.

Brain’s neural activities encompass both periodic rhythmic oscillations and aperiodic neural fluctuations. Rhythmic oscillations manifest as spectral peaks of neural signals, directly reflecting the synchronized activities of neural populations and closely tied to cognitive and behavioral states. In contrast, aperiodic fluctuations exhibit a power-law decaying spectral trend, revealing the multiscale dynamics of brain neural activity. In recent years, researchers have made notable progress in studying brain aperiodic dynamics. These studies demonstrate that aperiodic activity holds significant physiological relevance, correlating with various physiological states such as external stimuli, drug induction, sleep states, and aging. Aperiodic activity serves as a reflection of the brain’s sensory capacity, consciousness level, and cognitive ability. In clinical research, the aperiodic exponent has emerged as a significant potential biomarker, capable of reflecting the progression and trends of brain diseases while being intricately intertwined with the excitation-inhibition balance of neural system. The physiological mechanisms underlying aperiodic dynamics span multiple neural scales, with activities at the levels of individual neurons, neuronal ensembles, and neural networks collectively influencing the frequency, oscillatory patterns, and spatiotemporal characteristics of aperiodic signals. Aperiodic dynamics currently boasts broad application prospects. It not only provides a novel perspective for investigating brain neural dynamics but also holds immense potential as a neural marker in neuromodulation or brain-computer interface technologies. This paper summarizes methods for extracting characteristic parameters of aperiodic activity, analyzes its physiological relevance and potential as a biomarker in brain diseases, summarizes its physiological mechanisms, and based on these findings, elaborates on the research prospects of aperiodic dynamics.

This study aims to investigate the effect of Chaijin Jieyu Anshen Formula on the neuroinflammation of rats with insomnia complicated with depression through the regulation of triggering receptor expressed on myeloid cells 2(TREM2)/complement protein C1q signaling pathway. Rats were randomly divided into a normal group, a model group, a positive drug group, as well as a high, medium, and low-dose groups of Chaijin Jieyu Anshen Formula, with 10 rats in each group. Except for the normal group, the other groups were injected with p-chlorophenylalanine and exposed to chronic unpredictable mild stress to establish the rat model of insomnia complicated with depression. The sucrose preference experiment, open field experiment, and water maze test were performed to evaluate the depression in rats. Enzyme-linked immunosorbent assay was employed to detect serum 5-hydroxytryptamine(5-HT), dopamine(DA), and norepinephrine(NE) levels. Hematoxylin and eosin staining and Nissl staining were used to observe the damage in nucleus accumbens neurons. Western blot and immunofluorescence were performed to detect TREM2, C1q, postsynaptic density 95(PSD-95), and synaptophysin 1(SYN1) expressions in rat nucleus accumbens, respectively. Golgi-Cox staining was utilized to observe the synaptic spine density of nucleus accumbens neurons. The results show that, compared with the model group, Chaijin Jieyu Anshen Formula can significantly increase the sucrose preference as well as the distance and number of voluntary activities, shorten the immobility time in forced swimming test and the successful incubation period of positioning navigation, and prolong the stay time of space exploration in the target quadrant test. The serum 5-HT, DA, and NE contents in the model group are significantly lower than those in the normal group, with the above contents significantly increased after the intervention of Chaijin Jieyu Anshen Formula. In addition, Chaijin Jieyu Anshen Formula can alleviate pathological damages such as swelling and loose arrangement of tissue cells in the nucleus accumbens, while increasing the Nissl body numbers. Chaijin Jieyu Anshen Formula can improve synaptic damage in the nucleus accumbens and increase the synaptic spine density. Compared to the normal group, the expression of C1q protein was significantly higher in the model group, while the expression of TREM2 protein was significantly lower. Compared to the model group, the intervention with Chaijin Jieyu Anshen Formula significantly downregulated the expression of C1q protein and significantly upregulated the expression of TREM2. Compared with the model group, the PSD-95 and SYN1 fluorescence intensity is significantly increased in the groups receiving different doses of Chaijin Jieyu Anshen Formula. In summary, Chaijin Jieyu Anshen Formula can reduce the C1q protein expression, relieve the TREM2 inhibition, and promote the synapse-related proteins PSD-95 and SNY1 expression. Chaijin Jieyu Anshen Formula improves synaptic injury of the nucleus accumbens neurons, thereby treating insomnia complicated with depression.
Animals ; Male ; Rats ; Nucleus Accumbens/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Depression/complications* ; Membrane Glycoproteins/genetics* ; Rats, Sprague-Dawley ; Sleep Initiation and Maintenance Disorders/complications* ; Neurons/metabolism* ; Receptors, Immunologic/genetics* ; Signal Transduction/drug effects* ; Synapses/metabolism*

The differences in chemical quality characteristics between Xinjiang Rehmannia glutinosa and R. glutinosa were analyzed to provide a theoretical basis for the introduction and quality control of R. glutinosa. In this study, the high performance liquid chromatography(HPLC) fingerprints of 6 batches of Xinjiang R. glutinosa and 10 batches of R. glutinosa samples were established. The content of iridoid glycosides, phenylethanoid glycosides, monosaccharides, oligosaccharides, and polysaccharides in Xinjiang R. glutinosa and R. glutinosa was determined by high performance liquid chromatography-diode array detection(HPLC-DAD), high performance liquid chromatography-evaporative light scattering detection(HPLC-ELSD), and ultraviolet-visible spectroscopy(UV-Vis). The determination results were analyzed with by chemical pattern recognition and entropy weight TOPSIS method. The results showed that there were 19 common peaks in the HPLC fingerprints of the 16 batches of R. glutinosa, and catalpol, aucubin, rehmannioside D, rehmannioside A, hydroxytyrosol, leonuride, salidroside, cistanoside A, and verbascoside were identified. Hierarchical cluster analysis(HCA) and principal component analysis(PCA) showed that Qinyang R. glutinosa, Mengzhou R. glutinosa, and Xinjiang R. glutinosa were grouped into three different categories, and eight common components causing the chemical quality difference between Xinjiang R. glutinosa and R. glutinosa in Mengzhou and Qinyang of Henan province were screened out by orthogonal partial least squares discriminant analysis(OPLS-DA). The results of content determination showed that there were glucose, sucrose, raffinose, stachyose, polysaccharides, and nine glycosides in Xinjiang R. glutinosa and R. glutinosa samples, and the content of catalpol, rehmannioside A, leonuride, cistanoside A, verbascoside, sucrose, and glucose was significantly different between Xinjiang R. glutinosa and R. glutinosa. The analysis with entropy weight TOPSIS method showed that the comprehensive quality of R. glutinosa in Mengzhou and Qinyang of Henan province was better than that of Xinjiang R. glutinosa. In conclusion, the types of main chemical components of R. glutinosa and Xinjiang R. glutinosa were the same, but their content was different. The chemical quality of R. glutinosa was better than Xinjiang R. glutinosa, and other components in R. glutinosa from two producing areas and their effects need further study.
Rehmannia/classification* ; Drugs, Chinese Herbal/chemistry* ; Chromatography, High Pressure Liquid/methods* ; Quality Control

Objective:To investigate the impact of fibroblast growth factor(FGF)on recurrence following segmen-tal mastectomy in patients with plasma cell mastitis.Methods:A total of 162 female patients diagnosed with plasma cell mastitis(PCM)were selected from our hospital from October 2021 to May 2023.All patients underwent segmental mastectomy.They were divided into recurrence group(n=28)and non-recurrence group(n=134)according to the follow-up survey on recurrence.Conduct a univariate analysis on the factors influencing recurrence in patients with PCM who undergo segmental mastectomy.After correcting for confounding factors,conduct a multiple linear regression analysis.Using a multivariate logistic regression model to explore the independent risk factors for recurrence in patients undergo-ing segmental mastectomy for PCM.Utilizing logistic regression analysis to explore the independent,multiplicative,or additive interaction between FGF and angiogenic factor in the management of recurrence in PCM patients undergoing segmental mastectomy.The Local Weighted Regression Scatter Method(LOWESS)is used to analyze the two-dimensional curve relationship of continuous variables.Evaluate the predictive efficacy of FGF for PCM recurrence fol-lowing segmental mastectomy using Receiver Operating Characteristic(ROC)curves.Results:The results of univariate analysis showed that the body mass index(BMI),estradiol,prolactin levels,nipple depression,and sinus phase propor-tion of patients in the recurrent group were significantly higher than those in the non recurrent group,and the differ-ences were statistically significant(P＜0.05).Before surgery and 1 and 3 months after surgery,the levels of FGF,vascu-lar endothelial growth factor(VEGF),endostatin(ES),and VEGF/ES in the recurrent group were higher than those in the non recurrent group,with statistically significant differences between the groups(P＜0.05).The intra group comparison results showed that compared with before surgery,all indicators in both groups of patients were significantly reduced at 1 month after surgery(P＜0.05),while in the recurrent group,all indicators were significantly increased at 3 months after surgery(P＜0.001).Logistic regression analysis showed that patients with elevated FGF had a higher risk of recurrence in PCM(P＜0.05).LOWESS analysis found that there is a certain non-linear relationship between PCM recurrence rate and FGF.FGF has good predictive performance for PCM recurrence.After further adjusting for various confounding fac-tors such as BMI,it was found that the angiogenic factor is related to FGF.The interaction results show that there is an additive or multiplicative interaction between FGF and VEGF/ES.Conclusion:FGF elevation increases the risk of re-currence after segmental mastectomy for PCM.FGF and VEGF/ES exhibit additive or multiplicative interactions.FGF has good predictive performance for PCM recurrence.