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Journal of Investigative Medicine ; 70(2):672-673, 2022.
Article in English | EMBASE | ID: covidwho-1705898


Purpose of Study The majority of documented SARS-CoV2 infections in children have been mild illnesses. The highest frequency of infection is documented in children between the ages of 5 -17 years;with the incidence of SARS-CoV2 being the highest in adolescents aged 12-17 years. Severe respiratory complications and a multi-system inflammatory syndrome (MIS-C) have been documented in pediatrics. There is very limited information about pediatric hematology and oncology patients in the United States, actively undergoing therapy, and how SARS-CoV2 affects them. Louisiana was an early 'hotspot' for SARS-CoV2 with its first documented infection on March 9, 2020. We present our institutional experience with SARS-CoV2 and pediatric hematology-oncology patients. Methods Used A retrospective chart review was performed on all pediatric hematology-oncology patients who were actively being treated at Children's Hospital of New Orleans between March 9, 2020, through December 15, 2020. Any patient who had a positive SARS-CoV2 test was included in the chart review. Information including demographics, signs, and symptoms at the time of testing, hospitalization, medications, diagnosis, and treatment was obtained. The institutional review board at Louisiana State University Health Sciences Center and Children's Hospital of New Orleans approved this study. Summary of Results Between March 9, 2020 and December 15, 2020, 15,404 patients were tested for SARS-CoV2 at Children's Hospital of New Orleans;628 children tested positive. Ten of those children had a pediatric hematological or oncological diagnosis. The mean age of the pediatric hematology- oncology patients was 7.9 years, and 80% were female. Ten percent of the patients identified as Hispanic. Forty percent were African American. Of the 10, four children (40%) had a diagnosis of acute lymphoblastic leukemia, and all were actively undergoing chemotherapy. One of the ten total children had undergone a bone marrow transplant. Five (50%) were hospitalized;2 (20%) with severe infections requiring PICU admission and 3 (30%) patients were treated for MIS-C with SARS-CoV2 specific therapy including Remdesivir, steroids, and Tocilizumab. One of our patients died from SARSCoV2 related complications. Conclusions Pediatric hematology-oncology patients are a heterogeneous group of patients, and little was known about how SARS-CoV2 would affect these patients. Of the 15,404 patients tested for SARS-CoV2 at CHNOLA, there were 628 that tested positive between March 9, 2020, and December 15, 2020. 1.6% of those had an oncology or hematology diagnosis. Most of our pediatric hematology oncology patients did not require hospitalization and did not require treatment. There was one patient who died of SARS-CoV2 related complications.

Remote Sensing for Agriculture, Ecosystems, and Hydrology Xxii ; 11528, 2020.
Article in English | Web of Science | ID: covidwho-1242187


Fast and reliable tests for the new coronavirus are urgently needed. Current Polymerase Chain Reaction based virus detection approaches are typically time-consuming and expensive. Technologies capable of providing a fast, real-time and non-contact detection of virus contamination and real-time virus classification are not yet available. Here, we demonstrate the potential of a fluorescence detection technique along with machine-learning based classification for virus detection. The ultraviolet (UV) light irradiated virus emits a fluorescent signal with a characteristic spectrum, which is regarded as a fingerprint for the virus. We analyzed eight virus samples including a heat-inactivated SARS-CoV-2 (virus causing COVID-19) and collected a number of emission spectra. Machine learning techniques are applied to discriminate among the candidate viruses via classifying a number of spectra data collected. First, Principle Component Analysis (PCA) was applied to reduce spectra data dimensionality. Then support vector machine (SVM) with various kernel functions (kernel-SVM), k-nearest-neighbor (k-NN) and Artificial Neural Networks (ANN) methods were used to classify these viruses with dimension-reduced data from PCA. We found that dimension-reduced data in 3 principal components (PCs) space performs better than that in 2 PCs space in the machine learning algorithms mentioned above. Variance ratio analysis is able to explain nearly 95% of variance which allows nearly 100% accuracy of predictions for 25% data test set randomly chosen from the whole dataset. Finally, cross validation (CV) analysis is applied to kernel-SVM and k-NN methods.