Identifying the individual metabolic abnormities from a systemic perspective using whole-body PET imaging.

INTRODUCTION: Distinct physiological states arise from complex interactions among the various organs present in the human body. PET is a non-invasive modality with numerous successful applications in oncology, neurology, and cardiology. However, while PET imaging has been applied extensively in detecting focal lesions or diseases, its potential in detecting systemic abnormalities is seldom explored, mostly because total-body imaging was not possible until recently. METHODS: In this context, the present study proposes a framework capable of constructing an individual metabolic abnormality network using a subject's whole-body 18F-FDG SUV image and a normal control database. The developed framework was evaluated in the patients with lung cancer, the one discharged after suffering from Covid-19 disease, and the one that had gastrointestinal bleeding with the underlying cause unknown. RESULTS: The framework could successfully capture the deviation of these patients from healthy subjects at the level of both system and organ. The strength of the altered network edges revealed the abnormal metabolic connection between organs. The overall deviation of the network nodes was observed to be highly correlated to the organ SUV measures. Therefore, the molecular connectivity of glucose metabolism was characterized at a single subject level. CONCLUSION: The proposed framework represents a significant step toward the use of PET imaging for identifying metabolic dysfunction from a systemic perspective. A better understanding of the underlying biological mechanisms and the physiological interpretation of the interregional connections identified in the present study warrant further research.

Environmental sampling of the severe acute respiratory syndrome coronavirus 2 Delta variant in the inpatient wards of a hospital in Nanjing.

BACKGROUND: Little is known about severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant of concern (VOC)-contaminated environmental surfaces and air in hospital wards admitting COVID-19 cases. Our study was designed to identify high-risk areas of Delta VOC contamination in the hospital and provide suggestions to in-hospital infection control. We analyzed the SARS-CoV-2 Delta VOC contamination in the air and environmental surface samples collected from a hospital in Nanjing, China. METHODS: We collected data on clinical features, laboratory tests, swab tests, and hospital wards, identified the factors associated with environmental contamination, and analyzed patients' hygiene behaviors during hospitalization. RESULTS: A total of 283 environmental surface and air samples were collected from a hospital admitting 36 COVID-19 patients. Twelve swab samples from ten patients were positive. Toilet seats had the highest contamination rate (11.8%), followed by bedside tables (8.2%), garbage bins (5.9%), and bedrails (1.6%). The median time of symptom onset to surface sampling was shorter in the positive environment group than in the negative environment group (11 vs. 18 days; P=0.001). The results indicated that environmental surface contamination was associated with positive anal swabs [odds ratio (OR) 27.183; 95% CI: 2.359-226.063; P=0.003] and the time from symptom onset to surface sampling (OR 0.801; 95% CI: 0.501-0.990; P=0.046). The survey revealed that 33.3% of the patients never cleaned or disinfected their bedside tables or toilets, and 8.3% of them only cleaned their bedside tables or toilets. More than half of the patients often (25%) or always (30.6%) put the used masks on their bedside tables. Only 16.7% of the patients threw the masks into the specific garbage bin for used masks. CONCLUSIONS: The SARS-CoV-2 Delta VOC was detected on environmental surfaces, especially toilet seats and bedside tables, within a median time of 11 days after symptom onset. Our study provided potential predictors for environmental surface contamination, including positive anal swabs and the time from symptom onset to sampling. Disinfecting high-risk environmental surfaces should be emphasized in hospital wards, especially for patients in the early stage of COVID-19.

FaNet: fast assessment network for the novel coronavirus (COVID-19) pneumonia based on 3D CT imaging and clinical symptoms.

The novel coronavirus (COVID-19) pneumonia has become a serious health challenge in countries worldwide. Many radiological findings have shown that X-ray and CT imaging scans are an effective solution to assess disease severity during the early stage of COVID-19. Many artificial intelligence (AI)-assisted diagnosis works have rapidly been proposed to focus on solving this classification problem and determine whether a patient is infected with COVID-19. Most of these works have designed networks and applied a single CT image to perform classification; however, this approach ignores prior information such as the patient's clinical symptoms. Second, making a more specific diagnosis of clinical severity, such as slight or severe, is worthy of attention and is conducive to determining better follow-up treatments. In this paper, we propose a deep learning (DL) based dual-tasks network, named FaNet, that can perform rapid both diagnosis and severity assessments for COVID-19 based on the combination of 3D CT imaging and clinical symptoms. Generally, 3D CT image sequences provide more spatial information than do single CT images. In addition, the clinical symptoms can be considered as prior information to improve the assessment accuracy; these symptoms are typically quickly and easily accessible to radiologists. Therefore, we designed a network that considers both CT image information and existing clinical symptom information and conducted experiments on 416 patient data, including 207 normal chest CT cases and 209 COVID-19 confirmed ones. The experimental results demonstrate the effectiveness of the additional symptom prior information as well as the network architecture designing. The proposed FaNet achieved an accuracy of 98.28% on diagnosis assessment and 94.83% on severity assessment for test datasets. In the future, we will collect more covid-CT patient data and seek further improvement.