Early in the COVID-19 pandemic, it was recognized that infection with SARS-CoV-2 is associated with increased morbidity and mortality in patients with cancer; therefore, preventive vaccination in ...cancer survivors is expected to be particularly impactful. Heterogeneity in how a neoplastic disease diagnosis and treatment interferes with humoral and cellular immunity, however, poses a number of challenges in vaccination strategies. Herein, the available literature on the effectiveness of COVID-19 vaccines among patients with cancer is critically appraised under the lens of anti-neoplastic treatment optimization. The objective of this review is to highlight areas of uncertainty, where more research could inform future SARS-CoV-2 immunization programs and maximize benefits in the high-risk cancer survivor population, and also minimize cancer treatment deviations from standard practices.
Usage of Unmanned Aerial Vehicles (UAVs) is growing rapidly in a wide range of consumer applications, as they prove to be both autonomous and flexible in a variety of environments and tasks. However, ...this versatility and ease of use also brings a rapid evolution of threats by malicious actors that can use UAVs for criminal activities, converting them to passive or active threats. The need to protect critical infrastructures and important events from such threats has brought advances in counter UAV (c-UAV) applications. Nowadays, c-UAV applications offer systems that comprise a multi-sensory arsenal often including electro-optical, thermal, acoustic, radar and radio frequency sensors, whose information can be fused to increase the confidence of threat's identification. Nevertheless, real-time surveillance is a cumbersome process, but it is absolutely essential to detect promptly the occurrence of adverse events or conditions. To that end, many challenging tasks arise such as object detection, classification, multi-object tracking and multi-sensor information fusion. In recent years, researchers have utilized deep learning based methodologies to tackle these tasks for generic objects and made noteworthy progress, yet applying deep learning for UAV detection and classification is considered a novel concept. Therefore, the need to present a complete overview of deep learning technologies applied to c-UAV related tasks on multi-sensor data has emerged. The aim of this paper is to describe deep learning advances on c-UAV related tasks when applied to data originating from many different sensors as well as multi-sensor information fusion. This survey may help in making recommendations and improvements of c-UAV applications for the future.
Adopting effective techniques to automatically detect and identify small drones is a very compelling need for a number of different stakeholders in both the public and private sectors. This work ...presents three different original approaches that competed in a grand challenge on the "Drone vs. Bird" detection problem. The goal is to detect one or more drones appearing at some time point in video sequences where birds and other distractor objects may be also present, together with motion in background or foreground. Algorithms should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds, nor being confused by the rest of the scene. In particular, three original approaches based on different deep learning strategies are proposed and compared on a real-world dataset provided by a consortium of universities and research centers, under the 2020 edition of the Drone vs. Bird Detection Challenge. Results show that there is a range in difficulty among different test sequences, depending on the size and the shape visibility of the drone in the sequence, while sequences recorded by a moving camera and very distant drones are the most challenging ones. The performance comparison reveals that the different approaches perform somewhat complementary, in terms of correct detection rate, false alarm rate, and average precision.
There are differences between African-American and white patients with colorectal cancer, concerning their characteristics before and after diagnosis. Whites are more likely to adhere to screening ...guidelines. This is also the case among people with positive family history. Colorectal cancer is more frequent in Blacks. Studies have shown that that since 1985, colon cancer rates have dipped 20% to 25% for Whites, while rates have gone up for African-American men and stayed the same for African-American women. Overall, African-Americans are 38% to 43% more likely to die from colon cancer than are Whites. Furthermore, it seems that there is an African-American predominance in right-sited tumors. African Americans tend to be diagnosed at a later stage, to suffer from better differentiated tumors, and to have worse prognosis when compared with Whites. Moreover, less black patients receive adjuvant chemotherapy for resectable colorectal cancer or radiation therapy for rectal cancer. Caucasians seem to respond better to standard chemotherapy regimens than AfricanAmericans. Concerning toxicity, it appears that patients of African-American descent are more likely to develop 5-FU toxicity than Whites, possibly because of their different dihydropyridine dehydrogenase status. Last but not least, screening surveillance seems to be higher among white than among black long-term colorectal cancer survivors. Socioeconomic and educational status account for most of these differences whereas little evidence exists for a genetic contribution in racial disparity. Understanding the nature of racial differences in colorectal cancer allows tailoring of screening and treatment interventions.
HLA evolutionary divergence reflects the ability to recognize diverse neoantigens as non-self, and as a biomarker is conceptually distinct from programmed cell death ligand 1 expression and tumor ...mutation burden. HLA-based assays to predict benefit from immunotherapy in lung cancer require prospective validation. See related article by Jiang et al., p. 4830.
In this paper, two novel and practical regularizing methods are proposed to improve existing neural network architectures for monocular optical flow estimation. The proposed methods aim to alleviate ...deficiencies of current methods, such as flow leakage across objects and motion consistency within rigid objects, by exploiting contextual information. More specifically, the first regularization method utilizes semantic information during the training process to explicitly regularize the produced optical flow field. The novelty of this method lies in the use of semantic segmentation masks to teach the network to implicitly identify the semantic edges of an object and better reason on the local motion flow. A novel loss function is introduced that takes into account the objects’ boundaries as derived from the semantic segmentation mask to selectively penalize motion inconsistency within an object. The method is architecture agnostic and can be integrated into any neural network without modifying or adding complexity at inference. The second regularization method adds spatial awareness to the input data of the network in order to improve training stability and efficiency. The coordinates of each pixel are used as an additional feature, breaking the invariance properties of the neural network architecture. The additional features are shown to implicitly regularize the optical flow estimation enforcing a consistent flow, while improving both the performance and the convergence time. Finally, the combination of both regularization methods further improves the performance of existing cutting edge architectures in a complementary way, both quantitatively and qualitatively, on popular flow estimation benchmark datasets.
Immunotherapy options for patients with cancer have emerged following decades of research on immune responses against tumors. Most treatments in this category harness T cells with specificity for ...tumor associated antigens, neoantigens, and cancer-testis antigens. GSK3β is a serine-threonine kinase with the highest number of substrates and multifaceted roles in cell function including immune cells. Importantly, inhibitors of GSK3β are available for clinical and research use. Here, we review the possible role of GSK3β in the immune tumor microenvironment, with goal to guide future research that tests GSK3β inhibition as an immunotherapy adjunct.
Despite ocular adverse events from immune checkpoint inhibitors being uncommon, they are still important complications to be aware of. We present the case of metastatic melanoma on ...ipilimumab/nivolumab in a patient who developed immunotherapy complications with delayed diagnosis because the only presenting symptom was unilateral ptosis. We reviewed the literature for relevant and important ocular and neurological complications of immune checkpoint inhibitors.
MET is a receptor present in the membrane of NSCLC cells and is known to promote cell proliferation, survival and migration. MET gene copy number is a common genetic alteration and inhibition o MET ...emerges as a promising targeted therapy in NSCLC. Here we aim to combine in a meta-analysis, data on the effect of high MET gene copy number on the overall survival of patients with resected NSCLC.
Two independent investigators applied parallel search strategies with the terms "MET AND lung cancer", "MET AND NSCLC", "MET gene copy number AND prognosis" in PubMed through January 2014. We selected the studies that investigated the association of MET gene copy number with survival, in patients who received surgery.
Among 1096 titles that were identified in the initial search, we retrieved 9 studies on retrospective cohorts with adequate retrievable data regarding the prognostic impact of MET gene copy number on the survival of patients with NSCLC. Out of those, 6 used FISH and the remaining 3 used RT PCR to assess the MET gene copy number in the primary tumor. We calculated the I2 statistic to assess heterogeneity (I2 = 72%). MET gene copy number predicted worse overall survival when all studies were combined in a random effects model (HR = 1.78, 95% CI 1.22-2.60). When only the studies that had at least 50% of adenocarcinoma patients in their populations were included, the effect was significant (five studies, HR 1.55, 95% CI 1.23-1.94). This was not true when we included only the studies with no more than 50% of the patients having adenocarcinoma histology (four studies HR 2.18, 95% CI 0.97-4.90).
Higher MET gene copy number in the primary tumor at the time of diagnosis predicts worse outcome in patients with NSCLC. This prognostic impact may be adenocarcinoma histology specific.
Pancreatic cancer has historically proven resistant to anticancer agents. On the one hand, drugs might be more efficient if higher levels could be achieved at the tumor site rather than the normal ...tissues. On the other hand, the thick stroma and the relative absence of abundant vessels may account at least partially for the failure of successive clinical trials to demonstrate effective treatments in this type of malignancy. In this context, the development and testing in clinical trials of treatment strategies that aim to optimize drug delivery is an important target in improving the prognosis of patients with pancreatic cancer.
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