Elizabethkingia meningoseptica sepsis associated with COVID-19 infection: An emerging nosocomial pathogen

Elizabethkingia meningoseptica is a multi-drug resistant, aerobic, gram-negative bacteria known for causing nosocomial infections and high mortality in critically ill patients. A 68-year-old male with a past medical history significant for hypertension and stroke presented to the emergency department with worsening cough and shortness of breath ten days after being diagnosed with Coronavirus Disease-2019 (COVID-19) infection. He also endorsed fatigue, fever, loss of smell, and diarrhea. He denied any chest pain, nausea and vomiting. On examination, he was febrile with a temperature of 102.2-degree Fahrenheit, heart rate of 145 beats/minute, blood pressure of 120/90 mm of Hg, respiratory rate of 24 breaths/minute, and oxygen saturation of 85% while breathing ambient air. Laboratory data revealed a leukocytosis of 12,000/μL, elevated serum creatinine of 1.38 mg/dL, D-dimer of 4.36 mg/L, C-reactive protein of 18 mg/dL and markedly elevated ferritin of 2500 ng/mL. Chest radiograph showed patchy bilateral alveolar infiltrates. His clinical presentation was consistent with severe COVID-19 infection causing acute respiratory distress syndrome. The patient was initiated on bi-level positive pressure ventilation, but his respiratory status continued to worsen, requiring intubation and mechanical ventilation. He was managed with low tidal volume ventilation and ARDS-network protocol. Treatment with remdesivir, dexamethasone, and convalescent plasma was initiated. On day 10 of admission patient developed fever, increasing oxygen requirement, and hypotension concerning for sepsis. Empiric treatment with vancomycin and piperacillin-tazobactam was started after obtaining blood cultures, which grew Elizabethkingia meningoseptica resistant to all beta-lactam antibiotics (penicillins and cephalosporins). Intravenous trimethoprimsulfamethoxazole was started but later switched to clindamycin due to electrolyte abnormalities. Therapy was continued for two weeks, and repeat blood cultures were sterile. His hospital course was complicated by prolonged ventilator weaning, acute kidney injury, and hospital-acquired pneumonia. He was successfully extubated to a high-flow nasal cannula after twenty days and is currently being managed for delirium. Elizabethkingia meningoseptica causes neonatal meningitis and nosocomial sepsis in older adults with underlying chronic comorbidities or immunocompromised status like an organ transplant receiving immunosuppressive therapy, uncontrolled diabetes mellitus, and end-stage renal disease. Mortality is high and ranges from 30%- 50%. It is usually resistant to beta-lactam antibiotics, carbapenems, and aminoglycosides. Some isolates have shown varying susceptibility to fluoroquinolones, trimethoprim-sulfamethoxazole, minocycline, and tigecycline. With the increasing use of steroids and prolonged critical illness in patients with COVID-19 infection, this emerging pathogen is a paramount health concern during the pandemic.

SD-Measure: A Social Distancing Detector

The practice of social distancing is imperative to curbing the spread of contagious diseases and has been globally adopted as a non-pharmaceutical prevention measure during the COVID-19 pandemic. This work proposes a novel framework named SD-Measure for detecting social distancing from video footages. The proposed framework leverages the Mask R-CNN deep neural network to detect people in a video frame. To consistently identify whether social distancing is practiced during the interaction between people, a centroid tracking algorithm is utilised to track the subjects over the course of the footage. With the aid of authentic algorithms for approximating the distance of people from the camera and between themselves, we determine whether the social distancing guidelines are being adhered to. The framework attained a high accuracy value in conjunction with a low false alarm rate when tested on Custom Video Footage Dataset (CVFD) and Custom Personal Images Dataset (CPID), where it manifested its effectiveness in determining whether social distancing guidelines were practiced. © 2020 IEEE.

Deep Learning Framework to Detect Face Masks from Video Footage

The use of facial masks in public spaces has become a social obligation since the wake of the COVID-19 global pandemic and the identification of facial masks can be imperative to ensure public safety. Detection of facial masks in video footages is a challenging task primarily due to the fact that the masks themselves behave as occlusions to face detection algorithms due to the absence of facial landmarks in the masked regions. In this work, we propose an approach for detecting facial masks in videos using deep learning. The proposed framework capitalizes on the MTCNN face detection model to identify the faces and their corresponding facial landmarks present in the video frame. These facial images and cues are then processed by a neoteric classifier that utilises the MobileNetV2 architecture as an object detector for identifying masked regions. The proposed framework was tested on a dataset which is a collection of videos capturing the movement of people in public spaces while complying with COVID-19 safety protocols. The proposed methodology demonstrated its effectiveness in detecting facial masks by achieving high precision, recall, and accuracy. © 2020 IEEE.