Dataset Information

Clinical Efficacy of Novel Patient-Covering Negative-Pressure Box for Shielding Virus Transmission during Esophagogastroduodenoscopy: A Prospective Observational Study.

ABSTRACT: Esophagogastroduodenoscopy (EGD) has a high risk of virus transmission during the current coronavirus disease 2019 era, and preventive measures are under investigation. We investigated the effectiveness of a newly developed patient-covering negative-pressure box system (Endo barrier^®) (EB) for EGD. Eighty consecutive unsedated patients who underwent screening EGD with EB use were prospectively enrolled. To examine the aerosol ratio before, during, and after EGD, 0.3- and 0.5-μm aerosols were measured every 60 s using an optical counter. Moreover, the degree of contamination of the examiners' goggles and vinyl gowns was assessed before and after EGD using a rapid adenosine triphosphate (ATP) test for simulated droplets. Data were available in 73 patients and showed that 0.3- and 0.5-μm particles did not increase in 95.8% (70/73) and 94.5% (69/73) of patients during EGD under EB. There were no significant differences in the total 0.3- or 0.5-μm particle counts before versus after EGD. The difference in the ATP levels before and after EGD was -0.6 ± 16.6 relative light units (RLU) on goggles and 1.59 ± 19.9 RLU on gowns (both within the cutoff value). EB use during EGD may provide a certain preventive effect against aerosols and droplets, decreasing examiners' exposure to viruses.

SUBMITTER: Fujihara S

PROVIDER: S-EPMC8470820 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Introduction: The use of negative pressure dressings for open abdominal therapy has made a great impact on strategies for open abdominal treatment. Observed intestinal damage and developement of fistula formation raises questions about safety of commonly used systems (AB-Thera). The most common used system uses foils for shielding intestines directly from negative pressure. As an alternative a system with open pore dressing in double layer film was introduced (Suprasorb CNP) and proved to safe in animal studies. We compared the effects of this two systems on patients requiring open abdominal treatment. Materials and methods: Patients with secondary peritonitis in at least two abdominal quadrants were included in this randomized study. Inclusion criteria were secondary peritonitis (ACS), abdominal compartment syndrome, and abdominal trauma combined with ACS and/or contaminated abdomen. Patients with active bleeding and pancreatitis were not included. We examined Mannheim peritonitis Index (MPI), bloodcount, PCT, amount of fluid collected, and morphological changes on the bowel. Data were collected on day 2, 4, 7, 14, 21, and 28. Primary end point was fascial closure. Examination was terminated in case of death and damage to the abdominal organs. Groups were compared using Mann Whitney U-test and chi square test. Trend evaluation was evaluated using an one way repeated measure analysis of variance. P-values below 0.05 was considered significat. Results: Thirty four patients were included between August 2010 and September 2012. There were no significant difference between two groups in MPI, age, and gender. Mean duration of treatment, WBC, CRP, and abdominal closure rate were not significantly different between groups. Suprasorb CNP System collected twice more fluid than AB-Thera and decreased PCT on significantly faster rate than AB-Thera. Four patients died (11%) and four patients developed enteric fistula (11%). Closure rate was achieved in 27 out of 34 Patients (79.5%). Closure rate was not significantly different between groups. Conclusion: The use of both systems proved to be efficient and safe. The application of well-dosed, moderate negative pressure on contaminated areas of the abdomen seems to have a lot of potential and it is worth directing greater research potential in this direction.

Project description:BACKGROUND:In silico analyses are increasingly being used to support mode-of-action investigations; however many such approaches do not utilise the large amounts of inactive data held in chemogenomic repositories. The objective of this work is concerned with the integration of such bioactivity data in the target prediction of orphan compounds to produce the probability of activity and inactivity for a range of targets. To this end, a novel human bioactivity data set was constructed through the assimilation of over 195 million bioactivity data points deposited in the ChEMBL and PubChem repositories, and the subsequent application of a sphere-exclusion selection algorithm to oversample presumed inactive compounds. RESULTS:A Bernoulli Naïve Bayes algorithm was trained using the data and evaluated using fivefold cross-validation, achieving a mean recall and precision of 67.7 and 63.8 % for active compounds and 99.6 and 99.7 % for inactive compounds, respectively. We show the performances of the models are considerably influenced by the underlying intraclass training similarity, the size of a given class of compounds, and the degree of additional oversampling. The method was also validated using compounds extracted from WOMBAT producing average precision-recall AUC and BEDROC scores of 0.56 and 0.85, respectively. Inactive data points used for this test are based on presumed inactivity, producing an approximated indication of the true extrapolative ability of the models. A distance-based applicability domain analysis was also conducted; indicating an average Tanimoto Coefficient distance of 0.3 or greater between a test and training set can be used to give a global measure of confidence in model predictions. A final comparison to a method trained solely on active data from ChEMBL performed with precision-recall AUC and BEDROC scores of 0.45 and 0.76. CONCLUSIONS:The inclusion of inactive data for model training produces models with superior AUC and improved early recognition capabilities, although the results from internal and external validation of the models show differing performance between the breadth of models. The realised target prediction protocol is available at https://github.com/lhm30/PIDGIN.Graphical abstractThe inclusion of large scale negative training data for in silico target prediction improves the precision and recall AUC and BEDROC scores for target models.

Dataset Information

Clinical Efficacy of Novel Patient-Covering Negative-Pressure Box for Shielding Virus Transmission during Esophagogastroduodenoscopy: A Prospective Observational Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure