Severity of COVID-19 in patients with lung cancer: evidence and challenges.

Cancer patients are highly vulnerable to SARS-CoV-2 infections due to frequent contacts with the healthcare system, immunocompromised state from cancer or its therapies, supportive medications such as steroids and most importantly their advanced age and comorbidities. Patients with lung cancer have consistently been reported to suffer from an increased risk of death compared with other cancers. This is possibly due to the combination of specific pathophysiological aspects, including underlying pulmonary compromise due to smoking history and the increased specific pressures on respiratory healthcare services caused by the related pandemic. Rationally and safely treating patients with lung cancer during the pandemic has become a continuous challenge over the last year. Deciding whether to offer, modify, postpone or even cancel treatments for this particular patient's population has become the crucial recurrent dilemma for lung cancer professionals. Chemotherapy, immunotherapy and targeted agents represent distinct risks factors in the context of COVID-19 that should be balanced with the short-term and long-term consequences of delaying cancer care. Despite the rapid and persistent trend of the pandemic, declared by WHO on March 11, 2020, and still ongoing at the time of writing (January 2021), various efforts were made by oncologists worldwide to understand the impact of COVID-19 on patients with cancer. Adapted recommendations of our evidence-based practice guidelines have been developed for all stakeholders. Different small and large-scale registries, such as the COVID-19 and Cancer Consortium (CCC19) and Thoracic Cancers International COVID-19 Collaboration quickly collected data, supporting cancer care decisions under the challenging circumstance created by the COVID-19 pandemic. Several recommendations were developed as guidance for prioritizing the various aspects of lung cancer care in order to mitigate the adverse effects of the COVID-19 healthcare crisis, potentially reducing the morbidity and mortality of our patients from COVID-19 and from cancer. These recommendations helped inform decisions about treatment of established disease, continuation of clinical research and lung cancer screening. In this review, we summarize available evidence regarding the direct and indirect impact of the COVID-19 pandemic on lung cancer care and patients

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

681598

Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation

745918

Proceedings of SPIE - The International Society for Optical Engineering

90880

A variable block insertion heuristic for permutation flowshops with makespan criterion

This paper proposes a populated variable block insertion heuristic (PVBIH) algorithm for solving the permutation flowshop scheduling problem with the makespan criterion. The PVBIH algorithm starts with a minimum block size being equal to one. It removes a block from the current solution and inserts it into the partial solution randomly with a predetermined move size. A local search is applied to the solution found after several block moves. If the new solution generated after the local search is better than the current solution, it replaces the current solution. It retains the same block size as long as it improves. Otherwise, the block size is incremented by one and a simulated annealing-type of acceptance criterion is used to accept the new solution. This process is repeated until the block size reaches at the maximum block size. In addition, we present a randomized profile fitting heuristic with excellent results. Extensive computational results on the Taillard's well-known benchmark suite show that the proposed PVBIH algorithm substantially outperforms the differential evolution algorithm (NS-SGDE) recently proposed in the literature. © 2017 IEEE

Mind-Body Therapies in Childhood Cancer

Purpose of Review: Advances in the field of Pediatric Oncology have led to increased survival rates in children with cancer, and addressing the emotional well-being and quality of life of this specific population is a critical component of care. Mind-body therapies (MBTs) are an adjuvant modality of treatment that appears to have a positive impact on patient quality of life, patient mental health, and family perceptions toward illness. In this review, we describe several evidence-based MBTs, such as art therapy, meditation, prayer, music therapy, hypnosis and relaxation techniques, their use, and our personal experience with MBT in our institution. Recent Findings: Current data suggests that MBTs have been effective in decreasing symptoms related to oncologic pathology in children. Based on experience in our institution, the administration of these therapies can be expanded with the use of technology and also foster family inclusion in care, which can lead to improved quality of life for the patient and family. Further studies are warranted to ascertain the effects of MBTs in childhood cancer. Summary: MBTs are increasingly important in the care of youth with oncologic disease. It is necessary to increase the quantity and quality of research for the selection and inclusion of MBT in this population. © 2018, Springer Science+Business Media, LLC, part of Springer Nature

An approach for printing the maximum possible number of images

This paper considers a real life application of a printing process in a lithographic company that manufactures food packaging bags. The focus area of this problem is to print a variety of 2D images of bags on a surface area for maximum press layouts, with a constraint that the orientation of all bags is fixed and they should be placed parallel to the edges of the impression material. The problem under study is NP-hard and is an extension of the classical knapsack problem. An algorithm to maximize the number of rectangular images of bags that can be placed within the printing material is proposed. The results were compared with a Particle Swarm Optimization (PSO) metaheuristic. Several numerical experiments show that our procedure outperforms the PSO algorithm and improves the lithography's performance