Objective To investigate the expression of carcinoembryonic antigen-related cell adhesion molecule 6 (CEACAM6) in esophageal squamous cell carcinoma (ESCC) and analyze its correlation with immune cell infiltration and patient prognosis. Methods Three ESCC datasets (GSE161533, GSE26886, and GSE23400) from the GEO database were analyzed to identify differentially expressed genes. CEACAM6 was identified as a key gene through survival analysis. Its expression, prognostic value, and relationship with immune cell infiltration were further explored using databases, such as TIMER. Tissue samples were collected from 162 patients with ESCC. Immunohistochemistry was performed to detect the expression of CEACAM6, immune cell markers (CD4, CD8, CD20, and CD56), and immune checkpoint molecules (HHLA2 and CD40LG). Correlations between CEACAM6 expression and clinicopathological features, immune cell infiltration, and immune checkpoints were analyzed. Results Bioinformatic analysis and clinical sample validation confirmed that CEACAM6 expression was significantly upregulated in ESCC tissues compared with adjacent nontumor tissues (P<0.05). High CEACAM6 expression was closely associated with advanced clinical stage (AJCC Ⅲ-Ⅳ), high T stage (T3-T4), lymph node metastasis, nonulcerative type, and poor prognosis. Furthermore, CEACAM6 expression levels were positively correlated with the infiltration density of CD8⁺ T cells, CD4⁺ T cells, and CD20⁺ B cells within the tumor microenvironment and with the expression of the immune checkpoint molecules HHLA2 and CD40LG (all P<0.05). Conclusion CEACAM6 serves as an independent poor prognostic factor for ESCC. Its high expression is implicated in the modulation of the tumor immune microenvironment by correlating with specific immune cell infiltration and immune checkpoint molecules, suggesting its potential as a novel prognostic biomarker and immunotherapeutic target for ESCC.

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.

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.

Clinical prediction models, which utilize clinical data and statistical methods, aim to enhance the accuracy and efficiency of medical decision-making and improve patient health outcomes. These models play a crucial role in optimizing healthcare decisions and tailoring treatments to individual needs. However, many studies currently face systemic challenges during the development process, including unclear model design objectives, redundant model construction, lack of clinical relevance in variable selection, and irregular data preprocessing. These issues finally lead to reduced model performance and limited clinical applicability. To address these challenges, this study systematically reviews relevant literature, including articles from the BMJ, and draws on practical research experience to propose a structured preparation process. This process aims to provide a scientific guiding framework for model development, ensuring the efficiency of subsequent model construction and the accuracy of predictions, thus laying a foundation for the application and advancement of clinical prediction models.

Clinical prediction models are statistical tools that incorporate multiple variables to predict the likelihood of specific outcomes, by which the accuracy and efficiency of medical decision-making can be facilitated and patient health outcomes can be improved. However, many current studies face problems, such as model construction and reporting irregularities, as well as questionable reliability, which limit their clinical application of clinical prediction model. Therefore, this study systematically reviews relevant literatures, including publications from journals like BMJ, and outline the steps involved in constructing clinical prediction models based on practical research experience. It also provides an in-depth comparison of commonly used methods during the construction process and proposes a comprehensive guiding framework to help researchers in the field to better understand and master the core concepts and practical skills of clinical prediction models for the purpose of improving their professional capabilities in the development, validation, and application of clinical prediction models.

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.