1,372 research outputs found

    An open-source system for generating and computer grading traditional non-coding assignments

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    One of the most time-consuming activities in higher education is reviewing and grading student evaluations. Rapid and effective feedback of evaluations, along with an appropriate assessment strategy, can significantly improve students’ performance. Furthermore, academic dishonesty is a major issue in higher education that has been aggravated by the limitations derived from the COVID-19 pandemic. One of the possible ways to mitigate this issue is to give different evaluations to each student, with the negative cost of increasing reviewing time. In this work, an open-source system developed in Python to automatically create and correct evaluations is presented. Using Jupyter Notebook as the graphical user interface, the system allows the creation of individual student question sheets, with the same structure and different parameter values, to send them to students, grade them, and send the final score back to the students. The proposed system requires little programming knowledge for the instructors to use it. The system was applied in Civil Engineering and Geological Engineering programs at the Universidad Católica de la Santísima Concepción, drastically reducing grading time while improving students’ performance

    Endurance results of a refuels fleet test in a real application based on directly comparable truck test pairs

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    Synthetic fuels from a renewable base are an essential part of a greenhouse gas-neutral mobility, especially in transport sector. While scaling production of e-fuels (fuels based on electrolysis hydrogen) is ongoing, HVO called parafnic diesel fuels are already available. Their production is based on the hydrogenation of waste and residues and they are established as diesel substitute in several applications. With regard to the approval of these fuels in German regulation, the question repeatedly arises as to whether they can be used easily and what efects can be achieved. This article describes an application in a real logistics application, in which both the everyday use and the concrete comparability to a refueling with conventional gas station diesel were ensured. The use of several parallel active and diferent truck pairs has shown that the use of HVO in existing vehicles has achieved the desired CO2 reduction. A detailed analysis of the engine oil also showed that no undesirable efects could be observed here either. From the perspective of this project, HVO fuels are ready for use for a signifcant greenhouse gas reduction in logistics

    The Role of PKR/eIF2α Signaling Pathway in Prognosis of Non-Small Cell Lung Cancer

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    In this study, we investigated whether PKR protein expression is correlated with mRNA levels and also evaluated molecular biomarkers that are associated with PKR, such as phosphorylated PKR (p-PKR) and phosphorylated eIF2α (p-eIF2α).We determined the levels of PKR protein expression and mRNA in 36 fresh primary lung tumor tissues by using Western blot analysis and real-time reverse-transcriptase PCR (RT-PCR), respectively. We used tissue microarrays for immunohistochemical evaluation of the expression of p-PKR and p-eIF2α proteins. We demonstrated that PKR mRNA levels are significantly correlated with PKR protein levels (Spearman's rho = 0.55, p<0.001), suggesting that PKR protein levels in tumor samples are regulated by PKR mRNA. We also observed that the patients with high p-PKR or p-eIF2α expression had a significantly longer median survival than those with little or no p-PKR or p-eIF2α expression (p = 0.03 and p = 0.032, respectively). We further evaluated the prognostic effect of combined expression of p-PKR plus PKR and p-eIF2α plus PKR and found that both combinations were strong independent prognostic markers for overall patient survival on stage I and all stage patients.Our findings suggest that PKR protein expression may controlled by transcription level. Combined expression levels of PKR and p-PKR or p-eIF2α can be new markers for predicting the prognosis of patients with NSCLC

    Immune Cellular Patterns of Distribution Affect Outcomes of Patients With Non-Small Cell Lung Cancer

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    Studying the cellular geographic distribution in non-small cell lung cancer is essential to understand the roles of cell populations in this type of tumor. In this study, we characterize the spatial cellular distribution of immune cell populations using 23 makers placed in five multiplex immunofluorescence panels and their associations with clinicopathologic variables and outcomes. Our results demonstrate two cellular distribution patterns-an unmixed pattern mostly related to immunoprotective cells and a mixed pattern mostly related to immunosuppressive cells. Distance analysis shows that T-cells expressing immune checkpoints are closer to malignant cells than other cells. Combining the cellular distribution patterns with cellular distances, we can identify four groups related to inflamed and not-inflamed tumors. Cellular distribution patterns and distance are associated with survival in univariate and multivariable analyses. Spatial distribution is a tool to better understand the tumor microenvironment, predict outcomes, and may can help select therapeutic interventions

    A Phase 1b PK/PD Study to Demonstrate Antigen Elimination with RLYB212, a Monoclonal Anti-HPA-1a Antibody for FNAIT Prevention

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    Background - Fetal and neonatal alloimmune thrombocytopenia (FNAIT) is a rare bleeding disorder of the fetus/newborn caused by development of maternal alloantibodies against fetal human platelet antigens (HPAs), predominantly HPA-1a. Currently there are no treatments available to prevent maternal alloimmunization to HPAs or FNAIT. Methods - This proof-of-concept study (EudraCT Number: 2021-005380-49) was designed to assess the ability of subcutaneous (SC) RLYB212, a monoclonal anti-HPA-1a antibody, to eliminate HPA-1a-positive platelets in an antigen challenge model of a 30 mL fetal–maternal hemorrhage. Subjects were randomized to receive a single SC dose of RLYB212 or placebo on day 1 in a single-blinded manner, followed by transfusion of 10 × 109 HPA-1a-positive platelets on day 8. Results - Four subjects received 0.09 mg SC RLYB212, five received 0.29 mg SC RLYB212, and two received placebo. RLYB212 achieved rapid elimination of HPA-1a-positive platelets in a concentration-dependent manner, with concentrations as low as 3.57 ng/mL meeting the prespecified proof-of-concept criterion of ≥90% reduction in platelet elimination half-life versus placebo. Following HPA-1a-positive platelet transfusion, a rapid decline was observed in the concentration of RLYB212 over a period of 2 to 24 hours, corresponding to the time needed for RLYB212 to bind to ∼10% of HPA-1a on cell surfaces. RLYB212 was well tolerated with no reports of drug-related adverse events. Conclusion - The data from this study are consistent with preclinical efficacy data and support the potential use of RLYB212 as a prophylactic treatment for FNAIT that prevents maternal HPA-1a alloimmunization during at-risk pregnancies

    Histopathologic Response Criteria Predict Survival of Patients with Resected Lung Cancer After Neoadjuvant Chemotherapy

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    Introduction:We evaluated the ability of histopathologic response criteria to predict overall survival (OS) and disease-free survival (DFS) in patients with surgically resected non-small cell lung cancer (NSCLC) treated with or without neoadjuvant chemotherapy.Methods:Tissue specimens from 358 patients with NSCLC were evaluated by pathologists blinded to the patient treatment and outcome. The surgical specimens were reviewed for various histopathologic features in the tumor including percentage of residual viable tumor cells, necrosis, and fibrosis. The relationship between the histopathologic findings and OS was assessed.Results:The percentage of residual viable tumor cells and surgical pathologic stage were associated with OS and DFS in 192 patients with NSCLC receiving neoadjuvant chemotherapy in multivariate analysis (p = 0.005 and p = 0.01, respectively). There was no association of OS or DFS with percentage of viable tumor cells in 166 patients with NSCLC who did not receive neoadjuvant chemotherapy (p = 0.31 and p = 0.45, respectively). Long-term OS and DFS were significantly prolonged in patients who had ⩽10% viable tumor compared with patients with >10% viable tumor cells (5 years OS, 85% versus 40%, p < 0.0001 and 5 years DFS, 78% versus 35%, p < 0.001).Conclusion:The percentages of residual viable tumor cells predict OS and DFS in patients with resected NSCLC after neoadjuvant chemotherapy even when controlled for pathologic stage. Histopathologic assessment of resected specimens after neoadjuvant chemotherapy could potentially have a role in addition to pathologic stage in assessing prognosis, chemotherapy response, and the need for additional adjuvant therapies

    An HPA-1a-positive platelet-depleting agent for prevention of fetal and neonatal alloimmune thrombocytopenia: a randomized, single-blind, placebo-controlled, single-center, phase 1/2 proof-of-concept study

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    Background: Fetal/neonatal alloimmune thrombocytopenia (FNAIT) is a rare and potentially life-threatening bleeding disorder of the fetus/newborn. Antibodies against human platelet antigen 1a (HPA-1a) are associated with the most frequent FNAIT cases. There are no approved therapies for FNAIT prevention or treatment. RLYB211 is a polyclonal HPA-1a hyperimmune IgG being developed to prevent FNAIT. Objectives: To investigate whether a single dose of anti–HPA-1a (1000 IU) could markedly accelerate the elimination of HPA-1ab platelets transfused into healthy, HPA1a–negative participants as compared with placebo. Methods: This randomized, single-blind, placebo–controlled, single-center, phase 1/2 proof-of-concept study (EudraCT: 2019-003459-12) included HPA-1a– and HLA-A2– negative healthy men. Cohort 1 received intravenous RLYB211 or placebo 1 hour after transfusion of HPA-1ab platelets. Cohort 1B received RLYB211 or placebo, followed by platelet transfusion 1 week later. Primary endpoint was the half-life of transfused platelets in circulation after administration of RLYB211 or placebo, determined by flow cytometry. Proof of concept was ≥90% reduction of half-life relative to placebo. Results: Twelve participants were allocated to cohort 1 or 1B and randomized to receive RLYB211 (n = 9) or placebo (n = 3). RLYB211 markedly accelerated the elimination of HPA-1ab platelets in all participants vs placebo. In cohort 1B, this effect was observed 7 days after RLYB211 administration. Two treatment–emergent adverse events were possibly related to treatment, both in RLYB211–treated participants. No participants developed HPA-1a antibodies at 12 or 24 weeks. Conclusion: These data support the hypothesis that anti–HPA-1a could be used as prophylaxis in women at risk of having an FNAIT–affected pregnancy

    Circulating microRNA Panel for Prediction of Recurrence and Survival in Early-Stage Lung Adenocarcinoma

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    Early-stage lung adenocarcinoma (LUAD) patients remain at substantial risk for recurrence and disease-related death, highlighting the unmet need of biomarkers for the assessment and identification of those in an early stage who would likely benefit from adjuvant chemotherapy. To identify circulating miRNAs useful for predicting recurrence in early-stage LUAD, we performed miRNA microarray analysis with pools of pretreatment plasma samples from patients with stage I LUAD who developed recurrence or remained recurrence-free during the follow-up period. Subsequent validation in 85 patients with stage I LUAD resulted in the development of a circulating miRNA panel comprising miR-23a-3p, miR-320c, and miR-125b-5p and yielding an area under the curve (AUC) of 0.776 in predicting recurrence. Furthermore, the three-miRNA panel yielded an AUC of 0.804, with a sensitivity of 45.8% at 95% specificity in the independent test set of 57 stage I and II LUAD patients. The miRNA panel score was a significant and independent factor for predicting disease-free survival (p \u3c 0.001, hazard ratio [HR] = 1.64, 95% confidence interval [CI] = 1.51-4.22) and overall survival (p = 0.001, HR = 1.51, 95% CI = 1.17-1.94). This circulating miRNA panel is a useful noninvasive tool to stratify early-stage LUAD patients and determine an appropriate treatment plan with maximal efficacy

    Automated Cellular-Level Dual Global Fusion of Whole-Slide Imaging for Lung Adenocarcinoma Prognosis

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    Histopathologic whole-slide images (WSI) are generally considered the gold standard for cancer diagnosis and prognosis. Survival prediction based on WSI has recently attracted substantial attention. Nevertheless, it remains a central challenge owing to the inherent difficulties of predicting patient prognosis and effectively extracting informative survival-specific representations from WSI with highly compounded gigapixels. In this study, we present a fully automated cellular-level dual global fusion pipeline for survival prediction. Specifically, the proposed method first describes the composition of different cell populations on WSI. Then, it generates dimension-reduced WSI-embedded maps, allowing for efficient investigation of the tumor microenvironment. In addition, we introduce a novel dual global fusion network to incorporate global and inter-patch features of cell distribution, which enables the sufficient fusion of different types and locations of cells. We further validate the proposed pipeline using The Cancer Genome Atlas lung adenocarcinoma dataset. Our model achieves a C-index of 0.675 (±0.05) in the five-fold cross-validation setting and surpasses comparable methods. Further, we extensively analyze embedded map features and survival probabilities. These experimental results manifest the potential of our proposed pipeline for applications using WSI in lung adenocarcinoma and other malignancies

    Deep Learning-Based H-Score Quantification of Immunohistochemistry-Stained Images

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    Immunohistochemistry (IHC) is a well-established and commonly used staining method for clinical diagnosis and biomedical research. In most IHC images, the target protein is conjugated with a specific antibody and stained using diaminobenzidine (DAB), resulting in a brown coloration, whereas hematoxylin serves as a blue counterstain for cell nuclei. The protein expression level is quantified through the H-score, calculated from DAB staining intensity within the target cell region. Traditionally, this process requires evaluation by 2 expert pathologists, which is both time consuming and subjective. To enhance the efficiency and accuracy of this process, we have developed an automatic algorithm for quantifying the H-score of IHC images. To characterize protein expression in specific cell regions, a deep learning model for region recognition was trained based on hematoxylin staining only, achieving pixel accuracy for each class ranging from 0.92 to 0.99. Within the desired area, the algorithm categorizes DAB intensity of each pixel as negative, weak, moderate, or strong staining and calculates the final H-score based on the percentage of each intensity category. Overall, this algorithm takes an IHC image as input and directly outputs the H-score within a few seconds, significantly enhancing the speed of IHC image analysis. This automated tool provides H-score quantification with precision and consistency comparable to experienced pathologists but at a significantly reduced cost during IHC diagnostic workups. It holds significant potential to advance biomedical research reliant on IHC staining for protein expression quantification
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