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.

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:Bidirectional causal associations of 41 cytokines with atopic dermatitis(AD)were explored based on a Mendelian randomization(MR)approach.Methods:Pooled data from genome wide association study(GWAS)of 41 cytokines and AD were utilized for instrumental variable(IV)screening,and single nucleotide polymorphism(SNP)affecting the results of MR analyses was excluded by the MR-PRESSO outlier test as well as by the MR Steiger filtering method.Two-sample bidirectional MR analyses were performed using inverse variance weighting(IVW),MR-Egger regression,and weighted median methods(WM).MR-Egger intercept term test and Cochran's Q test were performed to test the pleiotropy and heterogeneity of IV,and MR results were visu-alized by scatterplots,funnel plots,and leave-one-out plots.Results:Forward MR analysis showed that MIG(IVW:OR=0.89;95%CI:0.81～0.97;P=0.006)reduced the risk of AD development.In contrast,IL-5(IVW:OR=1.17;95%CI:1.01～1.36;P=0.042)and IL-18(MR Egger:OR=1.17;95%CI:1.03～1.33;P=0.030)increased the risk of AD development.Inverse MR analysis showed a potential causal association between AD and increased MIG(IVW:Beta=0.10;95%CI:0.02～0.17;P=0.014).None of the sensitivity analyses indicated pleiotropy and heterogeneity of the included IV.Conclusion:MIG may be an important marker in the progression of AD with a potential bidirectional causal association with risk of morbidity.IL-5 and IL-18 have a potential positive causal association for AD.

Objective To compare trunk muscle activation,muscle co-contraction levels and plantar pressure distribution between patients with chronic low back pain(cLBP)and healthy subjects during static standing.Methods From July,2021 to July,2022,17 healthy subjects(healthy control group)and 18 cLBP patients(cLBP group)were recruited from the Department of Rehabilitation Medicine,the First Affiliated Hospital of Sun Yat-sen Uni-versity.Demographic information was collected for both groups,as well as the pain intensity,functional disabili-ty,and center of pressure displacement parameters of the cLBP group in static standing.A plantar pressure plate was used to record plantar pressure distribution in static standing task.Simultaneously,a wireless surface electro-myography(sEMG)system was used to record the activity of the transverse abdominis(TrA),erector spinae(ES)and lumbar multifidus(LM).The activation levels of each muscle and the co-contraction index(CCI)between trunk muscle pairs were analyzed.Results Compared with the healthy control group,the cLBP group showed higher activation levels of the bilateral LM(∣t∣>2.479,P<0.05)and higher lumbar-abdominal muscle CCI of left LM-left TrA(t=2.212,P=0.042),left LM-right TrA(t=2.868,P=0.008)and right LM-right TrA(U=75.000,P=0.009).In the cLBP group,pres-sure decreased in the right big toe region(t=2.825,P=0.009),and increased in the right medial and left lateral heel regions(U<77.000,P<0.05).Conclusions During static standing,patients with cLBP require higher level of trunk muscle activation and co-contrac-tion,with a posterior shift in plantar pressure distribution,which might be due to the adoption of different postur-al compensation strategies to maintain postural stability.

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:Bidirectional causal associations of 41 cytokines with atopic dermatitis(AD)were explored based on a Mendelian randomization(MR)approach.Methods:Pooled data from genome wide association study(GWAS)of 41 cytokines and AD were utilized for instrumental variable(IV)screening,and single nucleotide polymorphism(SNP)affecting the results of MR analyses was excluded by the MR-PRESSO outlier test as well as by the MR Steiger filtering method.Two-sample bidirectional MR analyses were performed using inverse variance weighting(IVW),MR-Egger regression,and weighted median methods(WM).MR-Egger intercept term test and Cochran's Q test were performed to test the pleiotropy and heterogeneity of IV,and MR results were visu-alized by scatterplots,funnel plots,and leave-one-out plots.Results:Forward MR analysis showed that MIG(IVW:OR=0.89;95%CI:0.81～0.97;P=0.006)reduced the risk of AD development.In contrast,IL-5(IVW:OR=1.17;95%CI:1.01～1.36;P=0.042)and IL-18(MR Egger:OR=1.17;95%CI:1.03～1.33;P=0.030)increased the risk of AD development.Inverse MR analysis showed a potential causal association between AD and increased MIG(IVW:Beta=0.10;95%CI:0.02～0.17;P=0.014).None of the sensitivity analyses indicated pleiotropy and heterogeneity of the included IV.Conclusion:MIG may be an important marker in the progression of AD with a potential bidirectional causal association with risk of morbidity.IL-5 and IL-18 have a potential positive causal association for AD.

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 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.