Objective To investigate the changes in eosinophil counts and the activation of microglial cells in the brain tissues of mice at different stages of Angiostrongylus cantonensis infection, and to examine the role of microglia in regulating the progression of angiostrongyliasis and unravel the possible molecular mechanisms. Methods Fifty BALB/c mice were randomly divided into the control group and the 7-d, 14-d, 21-day and 25-d infection groups, of 10 mice in each group. All mice in infection groups were infected with 30 stage III A. cantonensis larvae by gavage, and animals in the control group was given an equal amount of physiological saline. Five mice were collected from each of infection groups on days 7, 14, 21 d and 25 d post-infection, and 5 mice were collected from the control group on the day of oral gavage. The general and focal functional impairment was scored using the Clark scoring method to assess the degree of mouse neurological impairment. Five mice from each of infection groups were sacrificed on days 7, 14, 21 d and 25 d post-infection, and 5 mice from the control group were sacrificed on the day of oral gavage. Mouse brain tissues were sampled, and the pathological changes of brain tissues were dynamically observed using hematoxylin and eosin (HE) staining. Immunofluorescence staining with eosinophilic cationic protein (ECP) and ionized calcium binding adaptor molecule 1 (Iba1) was used to assess the degree of eosinophil infiltration and the counts of microglial cells in mouse brain tissues in each group, and the morphological parameters of microglial cells (skeleton analysis and fractal analysis) were quantified by using Image J software to determine the morphological changes of microglial cells. In addition, the expression of M1 microglia markers Fcγ receptor III (Fcgr3), Fcγ receptor IIb (Fcgr2b) and CD86 antigen (Cd86), M2 microglia markers Arginase 1 (Arg1), macrophage mannose receptor C-type 1 (Mrc1), chitinase-like 3 (Chil3), and phagocytosis genes myeloid cell triggering receptor expressed on myeloid cells 2 (Trem2), CD68 antigen (Cd68), and apolipoprotein E (Apoe) was quantified using real-time quantitative reverse transcription PCR (RT-qPCR) assay in the mouse cerebral cortex of mice post-infection. Results A large number of A. cantonensis larvae were seen on the mouse meninges surface post-infection, and many neuronal nuclei were crumpled and deeply stained, with a large number of bleeding points in the meninges. The median Clark scores of mouse general functional impairment were 0 (interquartile range, 0), 0 (interquartile range, 0.5), 6 (interquartile range, 1.0), 14 (interquartile range, 8.5) points and 20 (interquartile range, 9.0) points in the control group and the 7-d, 14-d, 21-d and 25-d groups, respectively (H = 22.45, P < 0.01), and the median Clark scores of mouse focal functional impairment were 0 (interquartile range, 0), 2 (interquartile range, 2.5), 7 (interquartile range, 3.0), 18 (interquartile range, 5.0) points and 25 (interquartile range, 6.5) points in the control group and the 7-d, 14-d, 21-d and 25-d groups, respectively (H = 22.72, P < 0.01). The mean scores of mice general and focal functional impairment were all higher in the infection groups than in the control group (all P values < 0.05). Immunofluorescence staining showed a significant difference in the eosinophil counts in mouse brain tissues among the five groups (F = 40.05, P < 0.000 1), and the eosinophil counts were significantly higher in mouse brain tissues in the 14-d (3.08 ± 0.78) and 21-d infection groups (5.97 ± 1.37) than in the control group (1.00 ± 0.28) (both P values < 0.05). Semi-quantitative analysis of microglia immunofluorescence showed a significant difference in the counts of microglial cells among the five groups (F = 17.66, P < 0.000 1), and higher Iba1 levels were detected in mouse brain tissues in 14-d (5.75 ± 1.28), 21-d (6.23 ± 1.89) and 25-d infection groups (3.70 ± 1.30) than in the control group (1.00 ± 0.30) (all P values < 0.05). Skeleton and fractal analyses showed that the branch length [(162.04 ± 34.10) μm vs. (395.37 ± 64.11) μm; t = 5.566, P < 0.05] and fractal dimension of microglial cells (1.30 ± 0.01 vs. 1.41 ± 0.03; t = 5.266, P < 0.05) were reduced in mouse brain tissues in the 21-d infection group relative to the control group. In addition, there were significant differences among the 5 groups in terms of M1 and M2 microglia markers Fcgr3 (F = 48.34, P < 0.05), Fcgr2b (F = 55.46, P < 0.05), Cd86 (F = 24.44, P < 0.05), Arg1 (F = 31.18, P < 0.05), Mrc1 (F = 15.42, P < 0.05) and Chil3 (F = 24.41, P < 0.05), as well as phagocytosis markers Trem2 (F = 21.19, P < 0.05), Cd68 (F = 43.95, P < 0.05) and Apoe (F = 7.12, P < 0.05) in mice brain tissues. Conclusions A. cantonensis infections may induce severe pathological injuries in mouse brain tissues that are characterized by massive eosinophil infiltration and persistent activation of microglia cells, thereby resulting in progressive deterioration of neurological functions.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

ObjectiveBased on modern analytical techniques and a depressed mouse model established by chronic unpredictable mild stress（CUMS）， to evaluate the quality marker（Q-Marker） and pharmacodynamic difference of Baihe Dihuangtang prepared by different processes. MethodsHigh performance liquid chromatography（HPLC） was used to establish the characteristic profiles of Baihe Dihuangtang， and determine the content of Q-Marker in the samples prepared by ancient and modern processes. Seventy C57BL/6J mice were randomly divided into the normal group， model group， fluoxetine group（3 mg·kg^-1）， low and high dose groups of ancient process（6.5， 26 g·kg^-1）， and low and high dose groups of modern process（6.5， 26 g·kg^-1）， with 10 mice in each group. Except for the normal group， CUMS was used to induce depression in mice from the other groups for 28 d. After successful modeling， administration groups were given the corresponding drugs by gavage every day， and the normal and model groups were given an equal volume of pure water by gavage for 21 consecutive days. Change in body mass of mice was recorded， tail suspension test and open field test were used to evaluate the depressive behavior of mice， and enzyme-linked immunosorbent assay（ELISA） was employed to determine the contents of tumor necrosis factor-α（TNF-α）， interleukin（IL）-1β and IL-6 in serum. ResultsCharacteristic profiles of Baihe Dihuangtang prepared by the two processes were established， the similarity between the two was 0.951， and 8 characteristic peaks were recognized with the reference peak of regaloside A. The results of quantitative analysis showed that the Q-Marker content was similar in Baihe Dihuangtang prepared by ancient and modern processes. The results of pharmacodynamics showed that， compared with the normal group， the model group showed increased immobility time in the tail suspension test， reduced total movement distance in the open field test， and elevated IL-1β， IL-6 and TNF-α levels in the serum（P<0.01）. Compared with the model group， the behavioral indicators of mice in the Baihe Dihuangtang treatment group were significantly improved in terms of tail suspension time and open field exercise， and the levels of IL-1β， IL-6 and TNF-α in serum were significantly reduced（P<0.05， P<0.01）. Baihe Dihuangtang prepared by the two processes both had antidepressant effects， and the difference between the two was not statistically significant in improving depressive symptoms. ConclusionQ-Marker of Baihe Dihuangtang prepared by modern and ancient methods are equivalent in content， and the pharmacological effects are consistent， indicating that dried Lilii Bulbus can replace fresh products in the preparation of Baihe Dihuangtang. This study provides a scientific basis for the development of new drugs of Baihe Dihuangtang and a reference for its rational application and clinical use.

ObjectiveBased on modern analytical techniques and a depressed mouse model established by chronic unpredictable mild stress（CUMS）， to evaluate the quality marker（Q-Marker） and pharmacodynamic difference of Baihe Dihuangtang prepared by different processes. MethodsHigh performance liquid chromatography（HPLC） was used to establish the characteristic profiles of Baihe Dihuangtang， and determine the content of Q-Marker in the samples prepared by ancient and modern processes. Seventy C57BL/6J mice were randomly divided into the normal group， model group， fluoxetine group（3 mg·kg^-1）， low and high dose groups of ancient process（6.5， 26 g·kg^-1）， and low and high dose groups of modern process（6.5， 26 g·kg^-1）， with 10 mice in each group. Except for the normal group， CUMS was used to induce depression in mice from the other groups for 28 d. After successful modeling， administration groups were given the corresponding drugs by gavage every day， and the normal and model groups were given an equal volume of pure water by gavage for 21 consecutive days. Change in body mass of mice was recorded， tail suspension test and open field test were used to evaluate the depressive behavior of mice， and enzyme-linked immunosorbent assay（ELISA） was employed to determine the contents of tumor necrosis factor-α（TNF-α）， interleukin（IL）-1β and IL-6 in serum. ResultsCharacteristic profiles of Baihe Dihuangtang prepared by the two processes were established， the similarity between the two was 0.951， and 8 characteristic peaks were recognized with the reference peak of regaloside A. The results of quantitative analysis showed that the Q-Marker content was similar in Baihe Dihuangtang prepared by ancient and modern processes. The results of pharmacodynamics showed that， compared with the normal group， the model group showed increased immobility time in the tail suspension test， reduced total movement distance in the open field test， and elevated IL-1β， IL-6 and TNF-α levels in the serum（P<0.01）. Compared with the model group， the behavioral indicators of mice in the Baihe Dihuangtang treatment group were significantly improved in terms of tail suspension time and open field exercise， and the levels of IL-1β， IL-6 and TNF-α in serum were significantly reduced（P<0.05， P<0.01）. Baihe Dihuangtang prepared by the two processes both had antidepressant effects， and the difference between the two was not statistically significant in improving depressive symptoms. ConclusionQ-Marker of Baihe Dihuangtang prepared by modern and ancient methods are equivalent in content， and the pharmacological effects are consistent， indicating that dried Lilii Bulbus can replace fresh products in the preparation of Baihe Dihuangtang. This study provides a scientific basis for the development of new drugs of Baihe Dihuangtang and a reference for its rational application and clinical use.

Chronic liver disease(CLD)tends to have a high incidence rate and impose a serious burden on society and families.Studies have shown that metal ion metabolism is closely associated with CLD,and some Chinese herbal medicines can play a role in the prevention and treatment of CLD by regulating metal ion metabolism.At present,the synthetic drugs currently used for the treatment of CLD fail to achieve a satisfactory effect,and therefore,a variety of Chinese herbal medicines are being used as supplementary and alternative therapies for CLD.This article introduces the role of metal ion metabolism in CLD and the regulatory effect of Chinese herbal medicines and their active components on CLD,and the analysis shows that metal ion metabolism is expected to provide new ideas for the research on CLD and a theoretical basis for the clinical treatment of CLD.For the role of metal ion metabolism in the treatment of CLD,more prospective clinical study data are needed in the future to provide effective and safe treatment regimens for patients with CLD.

The geographical origin of forensic evidence provides important information for crime investigation and solving cases,and is one of the key elements of criminal cases.Previous studies have shown significant differences in the distribution of microorganisms in different regions.Detecting environmental DNA samples and inferring the geographical and spatial sources can provide clues and evidence for case handling.However,due to the diversity of criminal environments and the trace amount of frequently encountered exhibits,stable and reliable technical methods for inferring geographical origin from environmental DNA are not yet available.This article summarizes the sample collection and DNA extraction methods for four types of environmental samples:dust,soil,water,and air.It compares the differences between amplicon sequencing and metagenomic sequencing in studying environmental biological populations,outlines the full process of high-throughput sequencing-based data analysis,and focuses on reviewing the research progress in inferring geographical sources of environmental samples based on bacteria,fungi,and other eukaryotes,to provide references for establishing sequencing and analysis methods for environmental DNA in forensic DNA laboratories and exploring environmental DNA information for forensic applications.