Advancements in Research on Duck Tembusu Virus Infections.

Duck Tembusu Virus (DTMUV) is a pathogen of the Flaviviridae family that causes infections in poultry, leading to significant economic losses in the duck farming industry in recent years. Ducks infected with this virus exhibit clinical symptoms such as decreased egg production and neurological disorders, along with serious consequences such as ovarian hemorrhage, organ enlargement, and necrosis. Variations in morbidity and mortality rates exist across different age groups of ducks. It is worth noting that DTMUV is not limited to ducks alone; it can also spread to other poultry such as chickens and geese, and antibodies related to DTMUV have even been found in duck farm workers, suggesting a potential risk of zoonotic transmission. This article provides a detailed overview of DTMUV research, delving into its genomic characteristics, vaccines, and the interplay with host immune responses. These in-depth research findings contribute to a more comprehensive understanding of the virus's transmission mechanism and pathogenic process, offering crucial scientific support for epidemic prevention and control.

Efficacy of fosaprepitant combined with tropisetron plus dexamethasone in preventing nausea and emesis during fractionated radiotherapy with weekly cisplatin chemotherapy: interim analysis of a randomized, prospective, clinical trial using competing risk analysis.

PURPOSE: There are no well-recognized guidelines for antiemesis during concurrent chemoradiotherapy (CCRT) for cervical cancer (CC) and nasopharyngeal cancer (NPC) until now. The study was designed to assess the efficacy and safety of fosaprepitant combined with tropisetron and dexamethasone in preventing nausea and vomiting during 5 weeks of fractionated radiotherapy and concomitant weekly low-dose cisplatin chemotherapy in patients with CC or NPC. METHODS: Patients with CC or NPC were scheduled to receive fractionated radiotherapy and weekly cisplatin (25-40 mg/m2) chemotherapy for at least 5 weeks. Patients stratified by tumor type and induction chemotherapy were 1:1 randomly assigned to receive fosaprepitant, tropisetron, and dexamethasone or tropisetron plus dexamethasone as an antiemetic regimen. Efficacy was assessed primarily by the cumulative incidence of emesis after 5 weeks of treatment, and safety by adverse events (AEs). RESULTS: Between July 2020 and July 2022, 116 patients consented to the study of whom 103 were included in this interim analysis (fosaprepitant group [N = 52] vs control group [N = 51]). The cumulative incidence of emesis at 5 weeks (competing risk analysis) was 25% (95% CI 14.2-37.4) for the fosaprepitant group compared with 59% (95% CI 43.9-71.0) for the control group. There was a significantly lower cumulative risk of emesis in the fosaprepitant group (HR 0.35 [95% CI 0.19-0.64]; p < 0.001). Fosaprepitant was well tolerated as the incidences of adverse events in the two groups were comparable. CONCLUSION: The addition of fosaprepitant to tropisetron plus dexamethasone significantly reduced the risk of nausea and vomiting during 5 weeks of CCRT in patients with CC or NPC, and fosaprepitant was well tolerated. TRIAL REGISTRATION: The trial was registered with ClinicalTrials.gov on October 3, 2022, number NCT05564286.

Bimetallic UiO-66-NH2(Zr-Hf) synergistic photocatalytic and piezoelectric effects for the degradation of rhodamine B.

A series of bimetallic UiO-66-NH2(Zr-Hf) metal-organic frameworks (MOFs) were prepared by a green hydrothermal method and tested for their photocatalytic and piezo-catalytic properties. Among them, UN(0.75Zr) (metal node: 0.75Zr : 0.25Hf) shows the most superior piezo-photocatalytic activity. The degradation rate for 40 mg L-1 rhodamine B (Rh B) could reach 96.78% within 30 min, which was ∼4.66 and ∼3.30 times higher than that of photocatalysis and piezo-catalysis, respectively. Moreover, the main reactive oxidizing substances (ROS), including vacancies (h+) and hydroxyl radicals (˙OH), were identified by free radical scavenging experiments. In addition, the intermediates generated during Rh B degradation were analyzed by HPLC-MS, while a reasonable degradation pathway was presented. Our work utilizes a green and environmentally friendly method to prepare bimetallic MOFs, providing a novel solution for the rapid degradation of highly concentrated dye wastewater.

Radiomics models based on CT at different phases predicting lymph node metastasis of esophageal squamous cell carcinoma (GASTO-1089).

Purpose: To investigate the value of radiomics models based on CT at different phases (non-contrast-enhanced and contrast-enhanced images) in predicting lymph node (LN) metastasis in esophageal squamous cell carcinoma (ESCC). Methods and materials: Two hundred and seventy-four eligible patients with ESCC were divided into a training set (n =193) and a validation set (n =81). The least absolute shrinkage and selection operator algorithm (LASSO) was used to select radiomics features. The predictive models were constructed with radiomics features and clinical factors through multivariate logistic regression analysis. The predictive performance and clinical application value of the models were evaluated by area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The Delong Test was used to evaluate the differences in AUC among models. Results: Sixteen and eighteen features were respectively selected from non-contrast-enhanced CT (NECT) and contrast-enhanced CT (CECT) images. The model established using only clinical factors (Model 1) has an AUC value of 0.655 (95%CI 0.552-0.759) with a sensitivity of 0.585, a specificity of 0.725 and an accuracy of 0.654. The models contained clinical factors with radiomics features of NECT or/and CECT (Model 2,3,4) have significantly improved prediction performance. The values of AUC of Model 2,3,4 were 0.766, 0.811 and 0.809, respectively. It also achieved a great AUC of 0.800 in the model built with only radiomics features derived from NECT and CECT (Model 5). DCA suggested the potential clinical benefit of model prediction of LN metastasis of ESCC. A comparison of the receiver operating characteristic (ROC) curves using the Delong test indicated that Models 2, 3, 4, and 5 were superior to Model 1(P< 0.05), and no difference was found among Model 2, 3, 4 and Model 5(P > 0.05). Conclusion: Radiomics models based on CT at different phases could accurately predict the lymph node metastasis in patients with ESCC, and their predictive efficiency was better than the clinical model based on tumor size criteria. NECT-based radiomics model could be a reasonable option for ESCC patients due to its lower price and availability for renal failure or allergic patients.

A Combined Model to Improve the Prediction of Local Control for Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy Based on Radiomic Signature Plus Clinical and Dosimetric Parameters.

PURPOSE: Stereotactic body radiotherapy (SBRT) is an important treatment modality for lung cancer patients, however, tumor local recurrence rate remains some challenge and there is no reliable prediction tool. This study aims to develop a prediction model of local control for lung cancer patients undergoing SBRT based on radiomics signature combining with clinical and dosimetric parameters. METHODS: The radiomics model, clinical model and combined model were developed by radiomics features, incorporating clinical and dosimetric parameters and radiomics signatures plus clinical and dosimetric parameters, respectively. Three models were established by logistic regression (LR), decision tree (DT) or support vector machine (SVM). The performance of models was assessed by receiver operating characteristic curve (ROC) and DeLong test. Furthermore, a nomogram was built and was assessed by calibration curve, Hosmer-Lemeshow and decision curve. RESULTS: The LR method was selected for model establishment. The radiomics model, clinical model and combined model showed favorite performance and calibration (Area under the ROC curve (AUC) 0.811, 0.845 and 0.911 in the training group, 0.702, 0.786 and 0.818 in the validation group, respectively). The performance of combined model was significantly superior than the other two models. In addition, Calibration curve and Hosmer-Lemeshow (training group: P = 0.898, validation group: P = 0.891) showed good calibration of combined nomogram and decision curve proved its clinical utility. CONCLUSIONS: The combined model based on radiomics features plus clinical and dosimetric parameters can improve the prediction of 1-year local control for lung cancer patients undergoing SBRT.

Efficient Toluene Adsorption on Metal Salt-Activated Porous Carbons Derived from Low-Cost Biomass: A Discussion of Mechanism.

Porous carbons (PCS) derived from sodium lignin sulfonate were activated by four common metal salts. The samples exhibit distinct characteristics of irregular, sunflower-like, interconnected sheet, and tine block morphologies under the impact of NaCl, CaCl2, ZnCl2, and FeCl3, respectively (PCS-MCl x ). Surprisingly, the maximum and minimum specific surface areas are 1524 and 44 m2/g corresponding to PCS-ZnCl2 and PCS-NaCl. All of the samples have plentiful functional groups; herein, PCS-NaCl and PCS-FeCl3 are detected with the highest O and S contents (11.85, 1.08%), respectively, which signifies sufficient active sites for adsorption. These porous materials were applied in toluene adsorption from paraffin liquid and matched the Langmuir isotherm models well. Thus, the activation mechanism was discussed in detail. PCS-MCl x has a completely different pyrolysis behavior according to thermogravimetry/derivative thermogravimetry (TG/DTG) analysis. It is speculated that H[ZnCl2(OH)] would have an etching effect on the carbon structure of PCS-ZnCl2, and HCl or H2SO4, resulting from FeCl3 hydrolysis and a reduction reaction, would be corrosive to the sodium lignin sulfonate (SLS) surface. Each metal salt plays a different role in activation. The devised method for the synthesis of porous carbons is green and economical, which is suited to mass production.

Deep Learning Method Based on Gated Recurrent Unit and Variational Mode Decomposition for Short-Term Wind Power Interval Prediction.

Wind power interval prediction (WPIP) plays an increasingly important role in evaluations of the uncertainty of wind power and becomes necessary for managing and planning power systems. However, the intermittent and fluctuating characteristics of wind power mean that high-quality prediction intervals (PIs) production is a challenging problem. In this article, we propose a novel hybrid model for the WPIP based on the gated recurrent unit (GRU) neural networks and variational mode decomposition (VMD). In the hybrid model, VMD is employed to decompose complex wind power data into simplified modes. Basic GRU prediction models, comprising a GRU input layer, multiple fully connected layers, and a rank-ordered terminal layer, are then trained for each mode to produce PIs, which are combined to obtain final PIs. In addition, an adaptive optimization method based on constructed intervals (CIs) is proposed to build high-quality training labels for supervised learning with the hybrid model. Several numerical experiments were implemented to validate the effectiveness of the proposed method. The results indicate that the proposed method performs better than the traditional interval prediction models with much higher quality PIs, and it requires less training time.