A microchannel-based electrochemiluminescence(ECL)sensor was developed for detection of tetracycline(TC)utilizing luminol/H2O2 as ECL reporting system.The low excitation potential of luminol/H2O2 effectively mitigated the impact of clamping voltage,thereby enhancing the detection performance of the microchannel-based ECL sensor.The microchannel modified with TC aptamer selectively recognized and captured target TC.The positively charged TC reduced the surface charge density within the microchannel,thereby increasing the ionic current in the microchannel,leading to change of ECL signal of system.The experimental conditions such as electrolyte concentration,TC-aptamer concentration,and reaction time between TC and TC-aptamer were optimized.Under optimal conditions,the difference of ECL signal in the absence and presence of TC(?ECL)exhibited a good linear relationship with TC concentration in the range from 1.00 ng/mL to 200 ng/mL,with a detection limit as low as 0.69 ng/mL.The sensor had good selectivity and was successfully used in detection of TC in milk samples.

Objective To investigate the feasibility of developing a grading diagnostic model for schistosomiasis-induced liver fibrosis based on B-mode ultrasonographic images and clinical laboratory indicators. Methods Ultrasound images and clinical laboratory testing data were captured from schistosomiasis patients admitted to the Second People’s Hospital of Duchang County, Jiangxi Province from 2018 to 2022. Patients with grade I schistosomiasis-induced liver fibrosis were enrolled in Group 1, and patients with grade II and III schistosomiasis-induced liver fibrosis were enrolled in Group 2. The machine learning binary classification tasks were created based on patients’radiomics and clinical laboratory data from 2018 to 2021 as the training set, and patients’radiomics and clinical laboratory data in 2022 as the validation set. The features of ultrasonographic images were labeled with the ITK-SNAP software, and the features of ultrasonographic images were extracted using the Python 3.7 package and PyRadiomics toolkit. The difference in the features of ultrasonographic images was compared between groups with t test or Mann-Whitney U test, and the key imaging features were selected with the least absolute shrinkage and selection operator (LASSO) regression algorithm. Four machine learning models were created using the Scikit-learn repository, including the support vector machine (SVM), random forest (RF), linear regression (LR) and extreme gradient boosting (XGBoost). The optimal machine learning model was screened with the receiver operating characteristic curve (ROC), and features with the greatest contributions to the differentiation features of ultrasound images in machine learning models with the SHapley Additive exPlanations (SHAP) method. Results The ultrasonographic imaging data and clinical laboratory testing data from 491 schistosomiasis patients from 2019 to 2022 were included in the study, and a total of 851 radiomics features and 54 clinical laboratory indicators were captured. Following statistical tests (t = −5.98 to 4.80, U = 6 550 to 20 994, all P values < 0.05) and screening of key features with LASSO regression, 44 features or indicators were included for the subsequent modeling. The areas under ROC curve (AUCs) were 0.763 and 0.611 for the training and validation sets of the SVM model based on clinical laboratory indicators, 0.951 and 0.892 for the training and validation sets of the SVM model based on radiomics, and 0.960 and 0.913 for the training and validation sets of the multimodal SVM model. The 10 greatest contributing features or indicators in machine learning models included 2 clinical laboratory indicators and 8 radiomics features. Conclusions The multimodal machine learning models created based on ultrasound-based radiomics and clinical laboratory indicators are feasible for intelligent identification of schistosomiasis-induced liver fibrosis, and are effective to improve the classification effect of one-class data models.

ObjectiveTo investigate the effect of mucin 1 (MUC1) on the proliferation and apoptosis of nasopharyngeal carcinoma (NPC) and its regulatory mechanism. MethodsThe 60 NPC and paired para-cancer normal tissues were collected from October 2020 to July 2021 in Quanzhou First Hospital. The expression of MUC1 was measured by real-time quantitative PCR (qPCR) in the patients with PNC. The 5-8F and HNE1 cells were transfected with siRNA control (si-control) or siRNA targeting MUC1 (si-MUC1). Cell proliferation was analyzed by cell counting kit-8 and colony formation assay, and apoptosis was analyzed by flow cytometry analysis in the 5-8F and HNE1 cells. The qPCR and ELISA were executed to analyze the levels of TNF-α and IL-6. Western blot was performed to measure the expression of MUC1, NF-кB and apoptosis-related proteins (Bax and Bcl-2). ResultsThe expression of MUC1 was up-regulated in the NPC tissues, and NPC patients with the high MUC1 expression were inclined to EBV infection, growth and metastasis of NPC. Loss of MUC1 restrained malignant features, including the proliferation and apoptosis, downregulated the expression of p-IкB、p-P65 and Bcl-2 and upregulated the expression of Bax in the NPC cells. ConclusionDownregulation of MUC1 restrained biological characteristics of malignancy, including cell proliferation and apoptosis, by inactivating NF-κB signaling pathway in NPC.

Multisystem inflammatory syndrome in children(MIS-C)is a complex syndrome characterized by multi-organ involvement that has emerged in the context of the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)outbreak.The clinical presentation of MIS-C is similar to Kawasaki disease but predominantly presents with fever and gastrointestinal symptoms,and severe cases can involve toxic shock and cardiac dysfunction.Epidemiological findings indicate that the majority of MIS-C patients test positive for SARS-CoV-2 antibodies.The pathogenesis and pathophysiology of MIS-C remain unclear,though immune dysregulation following SARS-CoV-2 infection is considered a major contributing factor.Current treatment approaches for MIS-C primarily involve intravenous immunoglobulin therapy and symptomatic supportive care.This review article provides a comprehensive overview of the definition,epidemiology,pathogenesis,clinical presentation,diagnosis,treatment,and prognosis of MIS-C.

Objective: To investigate and elucidate the clinicopathological and prognostic characteristics of SMARCA4-deficient non-small cell lung cancer. Methods: The clinicopathological and prognostic data were collected in 127 patients with SMARCA4-deficient non-small cell lung cancer diagnosed in Shanghai Pulmonary Hospital, Shanghai, China from January 2020 to March 2022. The variation and expression of biomarkers related to treatment were retrospectively reviewed. Results: One hundred and twenty-seven patients were eligible for enrollment. Among them 120 patients (94.5%) were male and 7 cases (5.5%) were female, while the average age was 63 years (range 42-80 years). There were 41 cases (32.3%) of stage Ⅰ cancer, 23 cases (18.1%) of stage Ⅱ, 31 cases (24.4%) of stage Ⅲ and 32 cases (25.2%) of stage Ⅳ. SMARCA4 expression detected by immunohistochemistry was completely absent in 117 cases (92.1%) and partially absent in 10 cases (7.9%). PD-L1 immunohistochemical analyses were performed on 107 cases. PD-L1 was negative, weakly positive and strongly positive in 49.5% (53/107), 26.2% (28/107) and 24.3% (26/107) of the cases, respectively. Twenty-one cases showed gene alterations (21/104, 20.2%). The KRAS gene alternation (n=10) was most common. Mutant-type SMARCA4-deficient non-small cell lung cancer was more commonly detected in females, and was associated with positive lymph nodes and advanced clinical stage (P<0.01). Univariate survival analysis showed that advanced clinical stage was a poor prognosis factor, and vascular invasion was a poor predictor of progression-free survival in patients with surgical resection. Conclusions: SMARCA4-deficient non-small cell lung cancer is a rare tumor with poor prognosis, and often occurs in elderly male patients. However, SMARCA4-deficient non-small cell lung cancers with gene mutations are often seen in female patients. Vascular invasion is a prognostic factor for disease progression or recurrence in patients with resectable tumor. Early detection and access to treatment are important for improving patient survivals.
Humans ; Male ; Female ; Aged ; Adult ; Middle Aged ; Aged, 80 and over ; Carcinoma, Non-Small-Cell Lung/pathology* ; B7-H1 Antigen/metabolism* ; Lung Neoplasms/pathology* ; Retrospective Studies ; China ; Prognosis ; Biomarkers, Tumor/analysis* ; DNA Helicases/genetics* ; Nuclear Proteins/genetics* ; Transcription Factors/genetics*

Objective:To investigate the influence and critical period of pregnancy-related anxiety during pregnancy on the neurobehavioral development of infants.Methods:The subjects of this study were derived from the Ma′anshan Birth Corhot. From May 2013 to September 2014, a total of 3 474 pregnant women who registered in Ma ′anshan Maternal and Child Health Care Center were enrolled in the study. A total of 2 242 mother-infant pairs who completed three times assessments of maternal anxiety and at least once assessment of infants′ neurobehavioral development were included in the final analysis. Maternal pregnancy-related anxiety was assessed by the Pregnancy-Related Anxiety Questionnaire during the first, second and third trimesters of pregnancy. When their children were at 6 and 18 months, their neurobehavioral development was evaluated using the Ages & Stages Questionnaire-China. The influence of maternal pregnancy-related anxiety on the neurobehavioral development of infants was analyzed by bi-nominal logistic regression.Results:The age of 2 242 pregnant women was (26.62±3.65) years, and the proportion of boys, low birth weight and exclusive breastfeeding for 6 months was 50% (1 120/2 242), 1.7% (38/2 242) and 11.5% (252/2 191), respectively. The detection rates of pregnancy-related anxiety during the first, second and third trimester were 24.9% (558), 28.6% (642) and 30.3% (674), respectively. After controlling confounding variables and other two trimester′s anxiety, only pregnancy-related anxiety during the third trimester (not first or second trimester) significantly increased the risk of developmental delay in the domain of communication (relative risk, RR = 3.52, 95% confidence interval, CI: 1.89-6.58) and personal-social ( RR=2.46, 95% CI: 1.10-5.49) at the 6 months of age, as well as in the domain of fine motor ( RR=2.07, 95% CI: 1.11-3.85), problem-solving domains ( RR=2.31, 95% CI: 1.24-4.31). Conclusion:Maternal pregnancy-related anxiety was associated with the risk of neurobehavioral development of infants, and the third trimester may be the critical period.

Gene co-expression network (GCN) mining identifies gene modules with highly correlated expression profiles across samples/conditions. It enables researchers to discover latent gene/molecule interactions, identify novel gene functions, and extract molecular features from certain disease/condition groups, thus helping to identify disease bio-markers. However, there lacks an easy-to-use tool package for users to mine GCN modules that are relatively small in size with tightly connected genes that can be convenient for downstream gene set enrichment analysis, as well as modules that may share common members. To address this need, we developed an online GCN mining tool package:TSUNAMI (Tools SUite for Network Analysis and MIning). TSUNAMI incorporates our state-of-the-art lmQCM algorithm to mine GCN modules for both public and user-input data (microarray, RNA-seq, or any other numerical omics data), and then performs downstream gene set enrichment analysis for the identified modules. It has several features and advantages:1) a user-friendly interface and real-time co-expression network mining through a web server;2) direct access and search of NCBI Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, as well as user-input gene ex-pression matrices for GCN module mining; 3) multiple co-expression analysis tools to choose from, all of which are highly flexible in regards to parameter selection options;4) identified GCN modules are summarized to eigengenes, which are convenient for users to check their correlation with other clinical traits;5) integrated downstream Enrichr enrichment analysis and links to other gene set enrichment tools;and 6) visualization of gene loci by Circos plot in any step of the process. The web service is freely accessible through URL:https://biolearns.medicine.iu.edu/. Source code is available at https://github.com/huangzhii/TSUNAMI/.

Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression. Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment. Here, we propose BrcaSeg, an image analysis pipeline based on a convolutional neural network (CNN) model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin (H&E) stained histopathological images. The CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas (TCGA) Program. BrcaSeg achieves a classification accuracy of 91.02％, which outperforms other state-of-the-art methods. Using this model, we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression data. We subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue ratios. Gene Ontology (GO) enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes, whereas each subtype also has its own idiosyncratic biological processes governing the development of these tissues. Taken all together, our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid tumors. BrcaSeg can be accessed at https://github.com/Serian1992/ImgBio.

Objective:To investigate the influence and critical period of pregnancy-related anxiety during pregnancy on the neurobehavioral development of infants.Methods:The subjects of this study were derived from the Ma′anshan Birth Corhot. From May 2013 to September 2014, a total of 3 474 pregnant women who registered in Ma ′anshan Maternal and Child Health Care Center were enrolled in the study. A total of 2 242 mother-infant pairs who completed three times assessments of maternal anxiety and at least once assessment of infants′ neurobehavioral development were included in the final analysis. Maternal pregnancy-related anxiety was assessed by the Pregnancy-Related Anxiety Questionnaire during the first, second and third trimesters of pregnancy. When their children were at 6 and 18 months, their neurobehavioral development was evaluated using the Ages & Stages Questionnaire-China. The influence of maternal pregnancy-related anxiety on the neurobehavioral development of infants was analyzed by bi-nominal logistic regression.Results:The age of 2 242 pregnant women was (26.62±3.65) years, and the proportion of boys, low birth weight and exclusive breastfeeding for 6 months was 50% (1 120/2 242), 1.7% (38/2 242) and 11.5% (252/2 191), respectively. The detection rates of pregnancy-related anxiety during the first, second and third trimester were 24.9% (558), 28.6% (642) and 30.3% (674), respectively. After controlling confounding variables and other two trimester′s anxiety, only pregnancy-related anxiety during the third trimester (not first or second trimester) significantly increased the risk of developmental delay in the domain of communication (relative risk, RR = 3.52, 95% confidence interval, CI: 1.89-6.58) and personal-social ( RR=2.46, 95% CI: 1.10-5.49) at the 6 months of age, as well as in the domain of fine motor ( RR=2.07, 95% CI: 1.11-3.85), problem-solving domains ( RR=2.31, 95% CI: 1.24-4.31). Conclusion:Maternal pregnancy-related anxiety was associated with the risk of neurobehavioral development of infants, and the third trimester may be the critical period.