Maloneyslater2077
Among all possible factors, field scale solution viscosity and injection rate had a statistically significant effect on nZVI travel distance in both the horizontal and vertical directions, as well as, on the attached mass. Additionally, the lag period between injections had a statistically significant effect on the attached mass, but not the travel distance. These results suggest that having a long injection period followed by a short lag phase during field deployment may result in less nZVI attachment. Lastly, aquifer heterogeneity impacted the nZVI spread while the impact of intrinsic groundwater velocity and injection concentration was found not to be statistically significant. Results from this numerical study can aid in field-scale CMC-nZVI injection by identifying key factors for remediation optimization.We propose a simulation-optimization (SO) model based on a novel two-step strategy for the optimal design of groundwater remediation systems. The SO models are developed by coupling simulation models directly or through the extreme learning machine (ELM) with evolutionary hunting strategy based metaheuristics (EHSMs). In the first step, EHSMs with a combinatorial optimization technique are used to obtain optimal pumping locations by minimizing the percentage of contaminant mass that remained in the aquifer while keeping the pumping strategy as constant. In the second step, the optimal pumping locations are directly used as input, and a composite function is employed to minimize the sum of the water extraction rates and the percentage of extracted contaminant mass by constraining hydraulic heads and contaminant concentrations. The performance of the two-step strategy is found to be slightly better and computationally more efficient than the alternate approach. Moreover, various statistical measures suggest the superiority of EHSMs over other metaheuristics for groundwater remediation.Sentinel lymph node (SLN) imaging and biopsy has been advocated as an important technique to evaluate the metastatic status of regional lymph nodes and determine subsequent surgical procedure for many cancers, yet there is no reliable means to provide accurate and rapid diagnosis of metastatic SLN during surgery. CX-5461 in vitro Here we develop a new approach, named "Ratiometric Raman dual-nanotag strategy", that using folic acid functionalized targeted and nontargeted gap-enhanced Raman tags (FA-GERTs and Nt-GERTs) to detect metastatic SLN based on Raman imaging combined with classical least square data processing methods. By using this strategy, with built-in self-calibration for signal correction, rather than absolute intensity-dependent signal readout, we realize the visualization and prompt intraoperative diagnosis of metastatic SLN with a high accuracy of 87.5 % when the cut-off value of ratio (FA-GERTs/Nt-GERTs) set at 1.255. This approach may outperform the existing histopathological assessment in diagnosing SLN metastasis and is promising for guiding surgical procedure in the future.Adenovirus (Ad) has been extensively developed as a gene delivery vector, but the potential side effect caused by systematic immunization remains one major obstacle for its clinical application. Needle-free mucosal immunization with Ad-based vaccine shows advantages but still faces poor mucosal responses. We herein report that the chemical engineering of single live viral-based vaccine effectively modulated the location and pattern of the subsequently elicited immunity. Through precisely assembly of functional materials onto single live Ad particle, the modified virus entered host cell in a phagocytosis-dependent manner, which is completely distinct from the receptor-mediated entry of native Ad. RNA-Seq data further demonstrated that the modified Ad-induced innate immunity was sharply reshaped via phagocytosis-related pathway, therefore promoting the activation and mature of antigen presentation cells (APC). Moreover, the functional shell enabled the modified Ad-based vector with enhanced muco-adhesion to nasal tissues in mice, and then prolonged resident time onto mucosal surface, leading to the robust mucosal IgA production and T cell immunity at local and even remote mucosal-associated lymphoid tissues. This study demonstrated that vaccine-induced immunity can be well modulated by chemistry engineering, and this method provides the rational design for needle-free mucosa-targeting vaccine against a variety of emerging infectious diseases.
To determine the influence of high-efficacy disease modifying therapy (DMT) on the development of IgG SARS-CoV-2 antibody response in COVID-19 convalescent people with multiple sclerosis (pwMS).
Seventy-four pwMS taking high-efficacy DMTs (specifically natalizumab, fingolimod, alemtuzumab, ocrelizumab, cladribine and ublituximab) and diagnosed with COVID-19 and 44 healthy persons (HC) were enrolled. SARS-CoV2 antibodies were tested with Elecsys® Anti-SARSCoV-2S assay.
pwMS taking high-efficacy DMTs had a significantly higher chance of having negative titer of SARS-CoV2 antibodies compared to healthy controls (33 negative pwMS [44.6%] compared to one negative HC [2.3%], p<0.001). pwMS taking B-cell depleting therapy (ocrelizumab and ublituximab) had a significantly higher chance of having negative titer of SARS-CoV2 antibodies compared to pwMS on all other DMTs (29 negative pwMS on B-cell therapy [64.4%] compared to four negative pwMS on all other DMTs [13.8%], p<0.001). Out of other DMTs, two (33.3%) pwMS taking fingolimod and two (16.7%) pwMS taking cladribine failed to develop IgG SARS-COV-2 antibodies. B-cell depleting therapy independently predicted negative titer of IgG SARS-CoV-2 antibody (Exp[B] =0.014, 95%CI 0.002-0.110, p<0.001).
A significant proportion of convalescent COVID-19 pwMS on high-efficacy DMTs will not develop IgG SARS-CoV-2 antibodies. B-cell depleting therapies independently predict negative and low titer of IgG SARS-CoV-2 antibody.
A significant proportion of convalescent COVID-19 pwMS on high-efficacy DMTs will not develop IgG SARS-CoV-2 antibodies. B-cell depleting therapies independently predict negative and low titer of IgG SARS-CoV-2 antibody.
Chronic Kidney Disease (CKD) is a condition characterized by a progressive loss of kidney function over time caused by many diseases. The most effective weapons against CKD are early diagnosis and treatment, which in most of the cases can only postpone the onset of complete kidney failure. The CKD grading system is classified based on the estimated Glomerular Filtration Rate (eGFR), and it helps to stratify patients for risk, follow up and management planning. This study aims to effectively predict how soon a CKD patient will need to be dialyzed, thus allowing personalized care and strategic planning of treatment.
To accurately predict the time frame within which a CKD patient will necessarily have to be dialyzed, a computational model based on a supervised machine learning approach is developed. Many techniques, regarding both information extraction and model training phases, are compared in order to understand which approaches are most effective. The different models compared are trained on the data exta great support in predicting the patient's clinical pathway. The model promising results, coupled with the knowledge and experience of the clinicians, can effectively lead to better personalized care and strategic planning of both patient's needs and hospital resources.
In this study, we tried to create a machine-learning method that detects disease lesions from chest X-ray (CXR) images using a data set annotated with extracted CXR reports information. We set the nodule as the target disease lesion. Manually annotating nodules is costly in terms of time. Therefore, we used the report information to automatically produce training data for the object detection task.
First, we use semantic segmentation model PSP-Net to recognize lung fields described in the CXR reports. Next, a classification model ResNeSt-50 is used to discriminate the nodule in segmented right and left field. It also can provide attention map by Grad-Cam. If the attention region corresponds to the location of the nodule in the CXR reports, an attention bounding box is generated. Finally, object detection model Faster-RCNN was performed using generated attention bounding box. The bounding boxes predicted by Faster-RCNN were filtered to satisfy the location extracted from CXR reports.
For lung field segmetes bounding boxes with disease lesions on chest X-ray (CXR) images using the positional information extracted from CXR reports. We set the nodule as the target lesion. First, we use PSP-Net to segment the lung field according to the CXR reports. Next, a classification model ResNeSt-50 was used to discriminate the nodule in segmented lung field. We also created an attention map using the Grad-Cam algorithm. If the area of attention matched the area annotated by the CXR report, the coordinate of the bounding box was considered as a possible nodule area. Finally, we used the attention information obtained from the nodule classification model and let the object detection model trained by all of the generated bounding boxes. Through object detection model, the precision of the bounding boxes to detect nodule is improved.
Mechanics and biology may be interconnected and amplify each other during disc degeneration. It remains unknown the influence of pre-existing disc degeneration and its impact on adjacent segment degeneration (ASD) after anterior cervical discectomy and fusion (ACDF). This study aimed to discuss the necessity of including degenerated adjacent segments in single-level ACDF surgery from a biomechanical view.
A poroelastic C2-T1 finite element model was created and validated. A C5-C6 ACDF model was developed based on this normal model. Moderate C4-C5 disc degeneration was created by appropriately modifying the morphology and tissue material properties in this fusion model. Degenerative morphology was modeled based on Thompson's grading system and Walraevens's scoring system for cervical spine, including disc height, whole disc area, nucleus pulposus (NP) area, endplate sclerosis and curvature. Stresses in disc and endplate and loads in facet joint were computed under moment loads in the fusion models with norerated disc experience increased biomechanical changes in adjacent segment after single-level ACDF. It may pose a long-term cumulative problem related to biomechanics in cervical spine after fusion. Before surgery, surgeons should be careful about selecting the fusion level.In the present work the temperature response of the constitutive S1 segment of the SARS-CoV-2 Spike Glycoprotein (GPS) has been studied. The intensity of the Raman bands remained almost constant before reaching a temperature of 133 °C. At this temperature a significant reduction of peak intensities was observed. Above 144 °C the spectra ceased to show any recognizable feature as that of the GPS S1, indicating that it had transformed after the denaturation process that it was subjected. The GPS S1 change is irreversible. Hence, Raman Spectroscopy (RS) provides a precision method to determine the denaturation temperature (TD) of dry powder GPS S1. The ability of RS was calibrated through the reproduction of TD of other well studied proteins as well as those of the decomposition temperature of some amino acids (AA). Through this study we established a TD of 139 ± 3 °C for powder GPS S1 of SARS-CoV-2.
