Increasing evidence shows that the early lesions of Parkinson's disease (PD) originate from gut, and correction of microbiota dysbiosis is a promising therapy for PD. FLZ is a neuroprotective agent on PD, which has been validated capable of alleviating microbiota dysbiosis in PD mice. However, the detailed mechanisms still need elucidated. Through metabolomics and 16S rRNA analysis, we identified glycoursodeoxycholic acid (GUDCA) was the most affected differential microbial metabolite by FLZ treatment, which was specially and negatively regulated by Clostridium innocuum, a differential microbiota with the strongest correlation to GUDCA production, through inhibiting bile salt hydrolase (BSH) enzyme. The protection of GUDCA on colon and brain were also clarified in PD models, showing that it could activate Nrf2 pathway, further validating that FLZ protected dopaminergic neurons through promoting GUDCA production. Our study uncovered that FLZ improved PD through microbiota-gut-brain axis, and also gave insights into modulation of microbial metabolites may serve as an important strategy for treating PD.

Although enteric glial cell (EGC) abnormal activation is reported to be involved in the pathogenesis of Parkinson's disease (PD), and inhibition of EGC gliosis alleviated gut and dopaminergic neuronal dysfunction was verified in our previous study, the potential role of gut microbiota on EGC function in PD still need to be addressed. In the present study, fecal microbiota transplantation revealed that EGC function was regulated by gut microbiota. By employing 16S rRNA and metabolomic analysis, we identified that 3-indolepropionic acid (IPA) was the most affected differential microbial metabolite that regulated EGC gliosis. The protective effects of IPA on PD were validated in rotenone-stimulated EGCs and rotenone (30 mg/kg i.g. for 4 weeks)-induced PD mice, as indicated by decreased inflammation, improved intestinal and brain barrier as well as dopaminergic neuronal function. Mechanistic study showed that IPA targeted pregnane X receptor (PXR) in EGCs, and inhibition of IL-13Rα1 involved cytokine-cytokine receptor interaction pathway, leading to inactivation of downstream JAK1-STAT6 pathway. Our data not only provided evidence that EGC gliosis was critical in spreading intestinal damage to brain, but also highlighted the potential role of microbial metabolite IPA in alleviating PD pathological damages through gut-brain axis.

[This corrects the article DOI: 10.1016/j.apsb.2025.02.029.].

Objective:To describe the positive rates of high-risk human papillomavirus (HR-HPV) DNA and serum-neutralizing antibody in cervical intraepithelial neoplasia (CIN) tissues of rural women in Xiangyuan County, Shanxi Province, and evaluate the association of HR-HPV DNA and neutralizing antibody positive status with the occurrence of CIN.Methods:In a cohort of 1 897 women aged 35-45 years established by the Shanxi Province Cervical Cancer Screening StudyⅠ, DNA typing (SPF10 PCR-DEIA-LiPA25) was performed by using tissue samples of women with positive HR-HPV test results [Hybrid CaptureⅡ(HC2)] or abnormal cytological or pathological results. Serum HR-HPV neutralizing antibody detection was conducted with multicolor pseudovirion-based neutralization assay. Cochran-Armitage trend test was used to analyze the changing trend of the positive rate of HR-HPV DNA and neutralizing antibody with the progression of CIN. Multivariate logistic regression models were used to evaluate the influence and multiplicative interaction of HR-HPV DNA and neutralizing antibody positive status on the occurrence of CIN. The relative excess risk ( RERI), attributable proportion of interaction ( AP), and the synergy index ( SI) of the interaction were calculated to evaluate the additive interaction of HR-HPV DNA and neutralizing antibody on the occurrence of CIN. Results:The positive rate of any type of HR-HPV DNA (HPV16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 68) in 479 women who were HC2 positive or had abnormal cytological or pathological detection results was 37.16%. In normal, CIN1, CIN2, and CIN3+ groups, the HR-HPV DNA positive rates were 18.03%, 49.53%, 90.24% and 94.59%, respectively. The positive rate of any type of HR-HPV neutralizing antibody was 63.88%. In normal, CIN1, CIN2, and CIN3+ groups, the positive rates of HR-HPV neutralizing antibody were 63.95%, 57.94%, 70.73%, and 72.97%, respectively. The positive rate of any type of HR-HPV neutralizing antibody was 53.31% in 1 418 women who were HC2 negative and had normal cytopathology, and the most common types were HPV51 (27.36%) and HPV39 (24.96%). Multivariate logistic regression analysis showed that any type of HR-HPV DNA positive status ( OR=9.15, 95% CI: 5.99-14.20, P<0.001) was the independent factor for the occurrence of CIN, HR-HPV neutralizing antibody positive status was not associated with the occurrence of CIN ( OR=0.95, 95% CI: 0.61-1.48, P=0.815). The OR value of the multiplication of HR-HPV DNA and neutralizing antibody positive status of the occurrence of CIN was 1.63 (95% CI: 0.67-3.95), P=0.283. Quantitative analysis of interaction showed that RERI was 1.65 (95% CI:-3.56-6.86), SI was 1.28 (95% CI: 0.58-2.82), and AP was 0.19 (95% CI:-0.36-0.75). Conclusions:HR-HPV DNA positive status was a risk factor for the occurrence of CIN, but neutralizing antibody positive status was not associated with the occurrence of CIN. They had no significant multiplicative or additive interaction with the occurrence of CIN.

ObjectiveTo investigate the application value of a multimodal model integrating radiomics features， deep learning features， and clinical structured data in predicting severe acute pancreatitis （SAP）， and to provide more accurate tools for the early identification of SAP in clinical practice. MethodsThe patients with acute pancreatitis （AP） who attended The First Affiliated Hospital of Soochow University， Jintan Hospital Affiliated to Jiangsu University， and Suzhou Yongding Hospital from January 1， 2017 to December 31， 2023 were included. Related data were collected， including demographic information， previous medical history， etiology， laboratory test data， and systemic inflammatory response syndrome （SIRS） within 24 hours after admission， as well as imaging data within 72 hours after admission， while related scores were calculated， including Ranson score， modified CT severity index （MCTSI）， bedside index for severity in acute pancreatitis （BISAP）， and systemic inflammatory response syndrome， albumin， blood urea nitrogen and pleural effusion （SABP） score. The model was constructed in the following process： （1） three-dimensional CT images were used to extract and identify radiomics features， and a radiomics classification model was established based on the extreme gradient Boost （XGBoost） algorithm； （2） U-Net is used to perform semantic segmentation of three-dimensional CT images， and then the results of segmentation were imported into 3D ResNet50 to construct a deep learning classification model； （3） the predicted values of the above two models were integrated with clinical structured data to establish a multimodal model based on the XGBoost algorithm. The variable importance plot and local interpretability plot were used to perform visual interpretation of the model. The independent samples t-test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups； the chi-square test or Fisher’s exact test was used for comparison of categorical data between groups. The receiver operating characteristic （ROC） curve was plotted for each model and existing scoring systems， and the area under the ROC curve （AUC） was calculated to assess their performance； the Delong test was used for comparison of AUC. ResultsA total of 609 patients who met the criteria were included， among whom 114 （18.7%） developed SAP. In this study， the data of 426 patients from The First Affiliated Hospital of Soochow University was used as the training set， and the data of 183 patients from Jintan Hospital Affiliated to Jiangsu University and Suzhou Yongding Hospital were used as the independent test set. The multimodal model had an AUC of 0.914 in the test set， which was significantly higher than the AUC of traditional scoring systems such as MCTSI （AUC=0.827）， Ranson score （AUC=0.675）， BISAP （AUC=0.791）， and SABP score （AUC=0.648）； in addition， the multimodal model showed a significant improvement in performance compared with the radiomics classification model （AUC=0.739） and the deep learning classification model （AUC=0.685） （the Delong test： Z=-3.23， -4.83， -3.48， -4.92， -4.31， and -4.59， all P <0.01）. The top 10 variables in terms of importance in the multimodal model were pleural effusion， predicted value of the deep learning model， predicted value of the radiomics model， triglycerides， calcium ions， SIRS， white blood cell count， age， platelets， and C-reactive protein， suggesting that the above variables had significant contributions to the performance of the model in predicting SAP. ConclusionBased on structured data， radiomic features， and deep learning features， this study constructs a multicenter prediction model for SAP based on the XGBoost algorithm， which has a better predictive performance than existing traditional scoring systems and unimodal models.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

Objective:To describe the positive rates of high-risk human papillomavirus (HR-HPV) DNA and serum-neutralizing antibody in cervical intraepithelial neoplasia (CIN) tissues of rural women in Xiangyuan County, Shanxi Province, and evaluate the association of HR-HPV DNA and neutralizing antibody positive status with the occurrence of CIN.Methods:In a cohort of 1 897 women aged 35-45 years established by the Shanxi Province Cervical Cancer Screening StudyⅠ, DNA typing (SPF10 PCR-DEIA-LiPA25) was performed by using tissue samples of women with positive HR-HPV test results [Hybrid CaptureⅡ(HC2)] or abnormal cytological or pathological results. Serum HR-HPV neutralizing antibody detection was conducted with multicolor pseudovirion-based neutralization assay. Cochran-Armitage trend test was used to analyze the changing trend of the positive rate of HR-HPV DNA and neutralizing antibody with the progression of CIN. Multivariate logistic regression models were used to evaluate the influence and multiplicative interaction of HR-HPV DNA and neutralizing antibody positive status on the occurrence of CIN. The relative excess risk ( RERI), attributable proportion of interaction ( AP), and the synergy index ( SI) of the interaction were calculated to evaluate the additive interaction of HR-HPV DNA and neutralizing antibody on the occurrence of CIN. Results:The positive rate of any type of HR-HPV DNA (HPV16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 68) in 479 women who were HC2 positive or had abnormal cytological or pathological detection results was 37.16%. In normal, CIN1, CIN2, and CIN3+ groups, the HR-HPV DNA positive rates were 18.03%, 49.53%, 90.24% and 94.59%, respectively. The positive rate of any type of HR-HPV neutralizing antibody was 63.88%. In normal, CIN1, CIN2, and CIN3+ groups, the positive rates of HR-HPV neutralizing antibody were 63.95%, 57.94%, 70.73%, and 72.97%, respectively. The positive rate of any type of HR-HPV neutralizing antibody was 53.31% in 1 418 women who were HC2 negative and had normal cytopathology, and the most common types were HPV51 (27.36%) and HPV39 (24.96%). Multivariate logistic regression analysis showed that any type of HR-HPV DNA positive status ( OR=9.15, 95% CI: 5.99-14.20, P<0.001) was the independent factor for the occurrence of CIN, HR-HPV neutralizing antibody positive status was not associated with the occurrence of CIN ( OR=0.95, 95% CI: 0.61-1.48, P=0.815). The OR value of the multiplication of HR-HPV DNA and neutralizing antibody positive status of the occurrence of CIN was 1.63 (95% CI: 0.67-3.95), P=0.283. Quantitative analysis of interaction showed that RERI was 1.65 (95% CI:-3.56-6.86), SI was 1.28 (95% CI: 0.58-2.82), and AP was 0.19 (95% CI:-0.36-0.75). Conclusions:HR-HPV DNA positive status was a risk factor for the occurrence of CIN, but neutralizing antibody positive status was not associated with the occurrence of CIN. They had no significant multiplicative or additive interaction with the occurrence of CIN.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

Objective:To validate the morphological changes of the parieto-occipital sulcus on the transcalvarial axial plane between 20 and 32 weeks of gestation, simplify grade for assessing fetal parieto-occipital sulcus development, and confirm its clinical feasibility.Methods:This was a cross-sectional study analysis that included 550 cases of normal singleton fetuses between 20 and 32 weeks of gestation, who underwent routine ultrasound examinations at Shenzhen Maternity and Child Healthcare Hospital from September 2019 to June 2022. The morphological changes of the bilateral parieto-occipital sulci on the transcalvarial axial plane were observed. The development of the parieto-occipital sulcus was classified into 6 grades based on the developmental features of angulation, progressive closure, and curvilinear growth: straight or shallow arcuate (Grade 0), shallow and wide V-shaped (Grade 1), deep and narrow V-shaped (Grade 2), Y-shaped (Grade 3), I-shaped (Grade 4), and curvilinear (Grade 5). The gestational age at examination and pregnancy outcomes were recorded. The distribution of gestational weeks for fetuses with different grades of parieto-occipital sulci on the left and right sides was analyzed. The symmetry between bilateral parieto-occipital sulcus gradings within individuals, as well as the inter-observer and intra-observer reliability were assessed using the Weighted Kappa coefficient. The gender differences in asymmetry of parieto-occipital sulci grades between the left and right sides was analyzed. Moreover, a model for predicting the grade of the parieto-occipital sulcus based on gestational week was established.Results:Grade for the left parieto-occipital sulcus was obtained for 549 fetuses, while grade for the right was obtained for 550 fetuses. From 20 to 32 weeks of gestation, the morphology of the fetal parieto-occipital sulcus was divided into Grade 0-5, progressing from low to high with gestational development. Grade 0 showed that the sulcus was not visible or only had a slight arcuate indentation, occurring at 20-22 weeks; Grade 1 presented as a shallow and wide "V" shape with an obtuse angle at the top, appearing from 20 to 27 weeks; Grade 2 was a deep and narrow "V" shape with an acute angle at the top, appearing from 24 to 29 weeks; Grade 3 appeared as a "Y" shape with the top part partially closed and the bottom still open, occurring between 26 to 30 weeks; Grade 4 was a fully closed "I" shape, appearing at 29-32 weeks; Grade 5 presented as a curved shape, indicating the parieto-occipital sulcus was approaching maturity, appearing from 31 to 32 weeks. There was no statistically significant difference in the distribution of gestational weeks for bilateral parieto-occipital sulcus developmental grade ( P>0.05). Bilateral parieto-occipital sulcus grade could be assessed in 549 fetuses, of which 43 cases (7.83%) exhibited grade asymmetry with a one-grade difference between sides; such asymmetry showed no significant difference between male and female genders ( P=0.647). The weighted kappa coefficient analysis results indicated a strong consistency in the development of the parieto-occipital sulci on both sides within individuals, generally demonstrating symmetrical development ( P<0.001). The intra-observer and inter-observer weighted kappa coefficients were 0.92 and 0.75, respectively, with good consistency. Conclusions:Prenatal ultrasound via the transcalvarial axial plane enables a preliminary and rapid assessment of the development of bilateral parieto-occipital sulci, facilitating early evaluation of fetal cortical maturation.

Analyzing the distribution characteristics of allergen sIgE in the serum of patients with respiratory and skin mucosal diseases in Shanghai City, and to provide epidemiological characteristics and diagnostic basis for the prevention and treatment of allergic respiratory and dermo-mucous diseases in Shanghai City. Adopting cross-sectional research, a total of 3 822 patients who received treatment in Tongren Hospital, Shanghai Jiao Tong University School of Medicine from July 2022 to July 2023 due to respiratory diseases or skin and dermo-mucous symptoms were included. Among them, there were 1 456 males and 2 366 females, with an age range of 1-97 years old. The median age (interquartile range) was 33 (27, 44) years old. The sIgE was detected by using immunoblotting. Statistical analysis was conducted using SPSS 22.0 software, and the comparison of count data (rates) between groups was conducted using χ 2 test. The results showed that a total of 3 377 (88.4%) cases among 3 822 patients were at least one allergen sIgE positive, and 72.9% (2 788/3 822) of them were multiple allergies sIgE positive. The top five allergen sIgE positive rates were Dermatophagoides pteronyssinus (37.9%, 1 447/3 822), Dermatophagoides farinae (32.1%, 1 225/3 822), milk (31.7%, 1 211/3 822), fungi (28.3%, 1 080/3 822), and Blomia tropicdis (23.8%, 909/3 822), with only milk was a kind of food allergen. The highest positive rates within the respiratory system disease group or dermo-mucous disease group were also these five allergens, without any difference in disease categories. The positive rates of cat dandruff, Humulusscandens, and juniper/birch in the respiratory system disease group were significantly higher than those in the skin and mucous membrane disease group, while the positive rates of shrimp/crab were relatively low (11.3% vs 14.9%, χ 2=9.616, P=0.002). Whether in the respiratory system disease group or the dermo-mucous disease group, the positive rates of Dermatophagoides pteronyssinus in male patients were significantly higher than those of females(42.6% vs 35.7%,41.0% vs 34.4%), with statistical significance ( χ 2=12.515, P<0.001; χ 2=5.143, P=0.023), And the three allergens, Dermatophagoides farinae, cat dander, and egg white allergens are also characterized by this feature.In addition, the positive rates of milk(33.8% vs 30.1%, χ 2=3.911, P=0.048), shrimp/crab(13.2% vs 10.0%, χ 2=6.423, P=0.014) in the respiratory system disease group were higher in males than in females, while in the dermo-mucous disease group, dog dander(20.5% vs 14.6%, χ 2=6.726, P=0.010) and peanuts/soybeans(10.5% vs 6.9%, χ 2=4.698, P=0.030) showed this phenomenon. In both the respiratory system disease group and the dermo-mucous disease group, there were 6 types of inhaled allergens (Dermatophagoides pteronyssinus, Dermatophagoides farinae, Blomia tropicdis, cat dandruff, dog dander, fungi) and 4 types of food allergens (egg yolks, egg white allergens, milk, shrimp/crab). However, the positivity rate of Aspergillus fumigatus (7.2% vs 9.3% vs 10.5% vs 15.7%, χ 2=10.996, P=0.012)in the respiratory disease group and cockroaches(4.2% vs 11.3% vs 9.6% vs 16.4%, χ 2=10.237, P=0.017) in the skin and mucosal disease group was the lowest in the underage group. There are seasonal differences in the positivity rates of allergens, with most allergens having significantly higher positivity rates in summer and autumn. In conclusion, the most common allergens sIgE positive in patients with respiratory and dermo-mucous diseases in Shanghai City are Dermatophagoides pteronyssinus, Dermatophagoides farinae, milk, fungi, and Blomia tropicdis. The trend of allergen sIgE prevalence in the two major categories of diseases is basically consistent. Allergen sIgE distribution varies among patient populations of different gender, age or season, and clinical prevention and treatment can be based on the results of serum allergen testing.