Clinical Manifestations and Outcomes of Critically Ill Children and Adolescents with Coronavirus Disease 2019 in New York City.

OBJECTIVES: To describe the clinical manifestations and outcomes of critically ill children with coronavirus disease-19 (COVID-19) in New York City. STUDY DESIGN: Retrospective observational study of children 1 month to 21 years admitted March 14 to May 2, 2020, to 9 New York City pediatric intensive care units (PICUs) with severe acute respiratory syndrome coronavirus 2 infection. RESULTS: Of 70 children admitted to PICUs, median age was 15 (IQR 9, 19) years; 61.4% male; 38.6% Hispanic; 32.9% black; and 74.3% with comorbidities. Fever (72.9%) and cough (71.4%) were the common presenting symptoms. Twelve patients (17%) met severe sepsis criteria; 14 (20%) required vasopressor support; 21 (30%) developed acute respiratory distress syndrome (ARDS); 9 (12.9%) met acute kidney injury criteria; 1 (1.4%) required renal-replacement therapy, and 2 (2.8%) had cardiac arrest. For treatment, 27 (38.6%) patients received hydroxychloroquine; 13 (18.6%) remdesivir; 23 (32.9%) corticosteroids; 3 (4.3%) tocilizumab; and 1 (1.4%) anakinra; no patient was given immunoglobulin or convalescent plasma. Forty-nine (70%) patients required respiratory support: 14 (20.0%) noninvasive mechanical ventilation, 20 (28.6%) invasive mechanical ventilation (IMV), 7 (10%) prone position, 2 (2.8%) inhaled nitric oxide, and 1 (1.4%) extracorporeal membrane oxygenation. Nine (45%) of the 20 patients requiring IMV were extubated by day 14 with median IMV duration of 218 (IQR 79, 310.4) hours. Presence of ARDS was significantly associated with duration of PICU and hospital stay, and lower probability of PICU and hospital discharge at hospital day 14 (P < .05 for all). CONCLUSIONS: Critically ill children with COVID-19 predominantly are adolescents, have comorbidities, and require some form of respiratory support. The presence of ARDS is significantly associated with prolonged PICU and hospital stay.

Jewish and non-Jewish World War II child and adolescent survivors at 60 years after war: effects of parental loss and age at exposure on well-being.

The study investigated the effects of World War II (WWII) on psychological and social functioning of Jewish and non-Jewish survivors 60 years after the war. The authors hypothesized that the level of posttraumatic symptoms, depression, and social isolation of survivors who were at least 5 years old (but younger than 18) in the last year of WWII would be predicted by the extent of traumatic loss, (i.e., death of parent[s]) and age at the end of WWII. Data were collected from 211 individuals living in Poland, ages 66-80; 30% were Jewish Holocaust survivors. Current posttraumatic stress disorder was almost 2 times higher for Jewish (55.6%) than for non-Jewish survivors (30.9%), whereas no differences were found for depression and social isolation. Parental loss during the war predicted a global decrement of well-being (across measured outcome indices). For certain subgroups (e.g., Jewish survivors who had not lost their parents during WWII), war trauma may have less profound effects if most of the trauma exposure occurred during an earlier age (i.e., <5 years).

Deep learning approach to bacterial colony classification.

In microbiology it is diagnostically useful to recognize various genera and species of bacteria. It can be achieved using computer-aided methods, which make the recognition processes more automatic and thus significantly reduce the time necessary for the classification. Moreover, in case of diagnostic uncertainty (the misleading similarity in shape or structure of bacterial cells), such methods can minimize the risk of incorrect recognition. In this article, we apply the state of the art method for texture analysis to classify genera and species of bacteria. This method uses deep Convolutional Neural Networks to obtain image descriptors, which are then encoded and classified with Support Vector Machine or Random Forest. To evaluate this approach and to make it comparable with other approaches, we provide a new dataset of images. DIBaS dataset (Digital Image of Bacterial Species) contains 660 images with 33 different genera and species of bacteria.