Background Patients with cancer may be at high risk of adverse outcomes from SARS-CoV-2 infection. We analyzed a cohort of patients with cancer and COVID-19 reported to the COVID-19 and Cancer Consortium (CCC19) to identify prognostic clinical factors, including laboratory measurements and anti-cancer therapies. Patients and Methods Patients with active or historical cancer and a laboratory-confirmed SARS-CoV-2 diagnosis recorded between March 17-November 18, 2020 were included. The primary outcome was COVID-19 severity measured on an ordinal scale (uncomplicated, hospitalized, admitted to intensive care unit, mechanically ventilated, died within 30 days). Multivariable regression models included demographics, cancer status, anti-cancer therapy and timing, COVID-19-directed therapies, and laboratory measurements (among hospitalized patients). Results 4,966 patients were included (median age 66 years, 51% female, 50% non-Hispanic white); 2,872 (58%) were hospitalized and 695 (14%) died; 61% had cancer that was present, diagnosed, or treated within the year prior to COVID-19 diagnosis. Older age, male sex, obesity, cardiovascular and pulmonary comorbidities, renal disease, diabetes mellitus, non-Hispanic Black race, Hispanic ethnicity, worse ECOG performance status, recent cytotoxic chemotherapy, and hematologic malignancy were associated with higher COVID-19 severity. Among hospitalized patients, low or high absolute lymphocyte count, high absolute neutrophil count, low platelet count, abnormal creatinine, troponin, LDH, and CRP were associated with higher COVID-19 severity. Patients diagnosed early in the COVID-19 pandemic (January-April 2020) had worse outcomes than those diagnosed later. Specific anti-cancer therapies (e.g. R-CHOP, platinum combined with etoposide, and DNA methyltransferase inhibitors) were associated with high 30-day all-cause mortality. Conclusions Clinical factors (e.g. older age, hematological malignancy, recent chemotherapy) and laboratory measurements were associated with poor outcomes among patients with cancer and COVID-19. Although further studies are needed, caution may be required in utilizing particular anti-cancer therapies.

No abstract

Texture synthesis is a necessary component of realistic scene generation. In particular, it is necessary for the simulation of image backgrounds for the testing of automatic target recognizers. We present a synthesis-by-analysis model for texture replication or simulation. This model can closely replicate a given textured image or produce another image which, although distinctly di erent from the original, has the same general visual characteristics and the same rst and second-order gray-level statistics as the original image. In e ect, such a synthetic image looks like a continuation of the original scene; as if another picture of the scene were taken adjacent to the original. The texture synthesis algorithm proposed herein contains three distinct components: A moving-average (MA) lter, a lter excitation function, and a gray-level histogram. The analysis portion of texture synthesis algorithm derives the three from a given image. The synthesis portion convolves the MA lter kernel with the excitation function, adds noise, and modi es the histogram of the result. The advantages of this texture model over others include conceptually and computationally simple and robust parameter estimation, inherent stability, parsimony in the number of parameters, and synthesis through convolution. We herein (1) describe a procedure for deriving the correct MA kernel using a signal enhancement algorithm; (2) demonstrate the e ectiveness of the model by using it to mimic several diverse textured images; (3) discuss its applicability to the problem of infrared background simulation; and (4) include detailed algorithms for the model's implementation.

No abstract

ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.