Deep Learning for Automated Contouring of Gross Tumor Volumes in Esophageal Cancer

PurposeThe aim of this study was to propose and evaluate a novel three-dimensional (3D) V-Net and two-dimensional (2D) U-Net mixed (VUMix-Net) architecture for a fully automatic and accurate gross tumor volume (GTV) in esophageal cancer (EC)–delineated contours.MethodsWe collected the computed tomography (CT) scans of 215 EC patients. 3D V-Net, 2D U-Net, and VUMix-Net were developed and further applied simultaneously to delineate GTVs. The Dice similarity coefficient (DSC) and 95th-percentile Hausdorff distance (95HD) were used as quantitative metrics to evaluate the performance of the three models in ECs from different segments. The CT data of 20 patients were randomly selected as the ground truth (GT) masks, and the corresponding delineation results were generated by artificial intelligence (AI). Score differences between the two groups (GT versus AI) and the evaluation consistency were compared.ResultsIn all patients, there was a significant difference in the 2D DSCs from U-Net, V-Net, and VUMix-Net (p=0.01). In addition, VUMix-Net showed achieved better 3D-DSC and 95HD values. There was a significant difference among the 3D-DSC (mean ± STD) and 95HD values for upper-, middle-, and lower-segment EC (p<0.001), and the middle EC values were the best. In middle-segment EC, VUMix-Net achieved the highest 2D-DSC values (p<0.001) and lowest 95HD values (p=0.044).ConclusionThe new model (VUMix-Net) showed certain advantages in delineating the GTVs of EC. Additionally, it can generate the GTVs of EC that meet clinical requirements and have the same quality as human-generated contours. The system demonstrated the best performance for the ECs of the middle segment

Sequential immunoaffinity-LC/MS assay for quantitation of a therapeutic protein in monkey plasma

Immunocapture-LC/MS has recently been used for quantitating therapeutic proteins/peptides and biomarkers in various matrices. The advantages of LC/MS quantitation include high speci­ficity and selectivity, wide dynamic range, and less susceptibility to interference from endogenous matrix components. We present a highly sensitive sequential immunoaffinity-LC/MS assay for quantitation of a biotherapeutic protein (39 kD) in monkey plasma. The first immunocapture utilized a biotinylat­ed mouse anti-drug antibody to capture the drug in plasma. After tryptic digestion, a unique peptide from the drug was then captured by the sec­ond immunocapture using a mouse anti-peptide antibody for further sample purification. Samples analysis was performed on a microLC-triple quadrupole mass spectrometry system (MS/MS). Both immunocapture procedures were carried out in 96-well plates using a magnetic beads handler. The LLOQ of the assay is 50 pg/mL, which was approximately 100x more sensitive than a corresponding single immunocapture-LC/MS assay either using the anti-drug or anti-peptide antibody

A Prediction Study on Archaeological Sites Based on Geographical Variables and Logistic Regression—A Case Study of the Neolithic Era and the Bronze Age of Xiangyang

Archaeological site predictive modeling is widely adopted in archaeological research and cultural resource management. It is conducive to archaeological excavation and reveals the progress of human social civilization. Xiangyang City is the focus of this paper. We selected eight geographical variables as the influencing variables, which are elevation, slope, aspect, micro-landform, slope position, plan curvature, profile curvature, and distance from water. With them, we randomly obtained 260 non-site points at the ratio of 1:1 between site points and non-site points based on the 260 excavated archaeological sites and constructed a sample set of geospatial data and the archaeological based on logistic regression (LR). Using 10-fold cross-validation, we trained and tested the model to select the best samples. Thus, the quantitative relationship between the archaeological sites and geographical variables was established. As a result, the Area Under the Curve (AUC) of the LR model is 0.797 and its accuracy is 0.897 in the study. A geographical detector unveils that the three influencing variables of Distance from water, elevation and Plan Curvature top the chart. The archaeological under LR is highly stable and accurate. The geographical variables constitute crucial variables in the archaeological

Regularized RKHS-Based Subspace Learning for Motor Imagery Classification

Brain–computer interface (BCI) technology allows people with disabilities to communicate with the physical environment. One of the most promising signals is the non-invasive electroencephalogram (EEG) signal. However, due to the non-stationary nature of EEGs, a subject’s signal may change over time, which poses a challenge for models that work across time. Recently, domain adaptive learning (DAL) has shown its superior performance in various classification tasks. In this paper, we propose a regularized reproducing kernel Hilbert space (RKHS) subspace learning algorithm with K-nearest neighbors (KNNs) as a classifier for the task of motion imagery signal classification. First, we reformulate the framework of RKHS subspace learning with a rigorous mathematical inference. Secondly, since the commonly used maximum mean difference (MMD) criterion measures the distribution variance based on the mean value only and ignores the local information of the distribution, a regularization term of source domain linear discriminant analysis (SLDA) is proposed for the first time, which reduces the variance of similar data and increases the variance of dissimilar data to optimize the distribution of source domain data. Finally, the RKHS subspace framework was constructed sparsely considering the sensitivity of the BCI data. We test the proposed algorithm in this paper, first on four standard datasets, and the experimental results show that the other baseline algorithms improve the average accuracy by 2–9% after adding SLDA. In the motion imagery classification experiments, the average accuracy of our algorithm is 3% higher than the other algorithms, demonstrating the adaptability and effectiveness of the proposed algorithm

A Novel Electrochemiluminescence Immunosensor Based on Resonance Energy Transfer between g-CN and NU-1000(Zr) for Ultrasensitive Detection of Ochratoxin A in Coffee

In this study, an electrochemiluminescence (ECL) immunosensor based on nanobody heptamer and resonance energy transfer (RET) between g-C3N4 (g-CN) and NU-1000(Zr) was proposed for ultrasensitive ochratoxin A (OTA) detection. First, OTA heptamer fusion protein was prepared by fusing OTA-specific nanometric (Nb28) with a c-terminal fragment of C4 binding protein (C4bpα) (Nb28-C4bpα). Then, Nb28-C4bpα heptamer with the high affinity used as a molecular recognition probe, of which plenty of binding sites were provided for OTA-Apt-NU-1000(Zr) nanocomposites, thereby improving the immunosensors’ sensitivity. In addition, the quantitative analysis of OTA can be achieved by using the signal quenching effect of NU-1000(Zr) on g-CN. As the concentration of OTA increases, the amount of OTA-Apt-NU-1000(Zr) fixed on the electrode surface decreases. RET between g-CN and NU-1000(Zr) is weakened leading to the increase of ECL signal. Thus, OTA content is indirectly proportional to ECL intensity. Based on the above principle, an ultra-sensitive and specific ECL immunosensor for OTA detection was constructed by using heptamer technology and RET between two nanomaterials, with a range from 0.1 pg/mL to 500 ng/mL, and the detection limit of only 33 fg/mL. The prepared ECL-RET immunosensor showed good performance and can be successfully used for the determination of OTA content in real coffee samples, suggesting that the nanobody polymerization strategy and the RET effect between NU-1000(Zr) and g-CN can provide an alternative for improving the sensitivity of important mycotoxin detection

Image1_Role of the TSPO–NOX4 axis in angiogenesis in glioblastoma.tif

Objective: Angiogenesis is a pathological feature of glioblastoma. Nicotinamide adenine dinucleotide phosphate oxidase 4 (NOX4) is a vital source of reactive oxygen species (ROS) related to angiogenesis. However, signaling pathways correlated with the isoform oxidase are unknown. The aim of this study was to elucidate the detailed mechanism of the role of NOX4 in angiogenesis in glioblastoma.Methods: Public datasets were searched for studies on immunohistochemistry and western blotting to evaluate NOX4 expression in glioma. The location of NOX4 expression was detected by immunofluorescence. We conducted conditional deletion of the translocator protein (TSPO) targeting the protein with the synthetic ligand XBD173 in the glioblastoma mouse model. NOX4 downregulation was conducted with the NOX4 inhibitor GLX351322, and ROS production and angiogenesis were detected in glioma tissues.Results: Clinical samples and public datasets showed that NOX4 was upregulated and associated with the prognosis. NOX4 is mainly expressed in endothelial cells of glioblastoma. Both TSPO and NOX4 promoted angiogenesis in an ROS-dependent manner, suggesting that TSPO triggered ROS production in glioblastoma via NOX4.Conclusion: These results showed that TSPO is an upstream target of NOX4-derived mitochondrial ROS, which is indispensable for NOX4-derived mitochondrial ROS-induced angiogenesis in glioblastoma. TSPO–NOX4 signaling could serve as a molecular target for therapeutic strategies for glioblastoma.</p

Evaluating the vaccination coverage: validity of household-hold vaccination booklet and caregiver’s recall

Background We compared results from household data sources to medical record sources by using data from a vaccination coverage survey. Methods Vaccination coverage (VC) was calculated based on parental recall, household vaccination booklet, and Zhejiang provincial immunization information system (ZJIIS). We evaluated the accuracy of VC based on household sources (vaccination booklet and recall) assuming the medical record was accurate. Concordance, sensitivity, specificity, positive predictive value, and negative predictive value were estimated as well as the Kappa statistic was also used to evaluate the agreement between data sources. Results Among the 1,800 children identified in the household survey, all were registered in ZJIIS. VC estimated using the vaccination booklet alone was substantially lower than that based on medical records (net bias 3.4–16.7% in different age groups). VC based on parental recall ranged from 2.5% below (among children aged 1 year) to 16.7% points above (among children aged 6 years) than those based on medical records. Concordance was lowest for card estimates (32.5–45.5%). Sensitivity was 75%, while negative predictive value was <50%, for all household sources. Kappa statistics generally indicated poor agreement between household and medical record sources. Conclusions Household-retained vaccination booklets and parental recall were insufficient sources for evaluating the VC. Our findings emphasized the importance of taking interventions to make the vaccination booklet more consistent with the records from medical resource