Cross contamination: spatters, aerosols, and biofilm in dental practices

2021 ◽  
Vol 128 (04) ◽  
pp. 221-227
Author(s):  
A.M.G.A. Laheij
Keyword(s):  
Cross Contamination ◽  
Dental Practices

A survey of intramural dental practices in United States Dental Schools

1973 ◽  
Vol 37 (1) ◽  
pp. 26-29
Author(s):  
EM Speed ◽  
A Wolff ◽  
JH Barr ◽  
JL Bomba ◽  
RW Rule
Keyword(s):  
United States ◽  
Dental Practices ◽  
Dental Schools

Restorative dental treatment in times of COVID-19

2020 ◽  
Vol 68 ◽  
Author(s):  
Robson de Lima GOMES ◽  
Marlus da Silva PEDROSA ◽  
Claudio Heliomar Vicente da SILVA
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Dental Treatment ◽  
Close Contact ◽  
Cross Contamination ◽  
Clinical Protocols ◽  
Know How ◽  
Dental Procedures ◽  
Dental Staff ◽  
Dental Professional ◽  
Restrictive Measures

ABSTRACT Since the outbreak of the Coronavirus Disease 2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), numerous restrictive measures have been adopted by governments of different countries. The return to elective dental care in Brazil is a reality even during the COVID-19 pandemic. During restorative dental procedures, the dental professional requires close contact with the patient, being exposed to contaminated saliva and fluids. In addition, transmission of COVID-19 by the generation of aerosol produced by dental handipieces may be possible. Thus, the dental staff must know how to act during restorative dental procedures, putting into practice the correct clinical protocols to avoid cross-contamination and COVID-19 spread. The purpose of this article is to review the literature on the biosafety practices especially in the context of restorative dental procedures in times of COVID-19.

Microbiological purity of syringes containing composites in the context of cross-infection prevention in dental practices

2020 ◽  
Vol 33 (2) ◽  
pp. 102-105
Author(s):  
Joanna Bialowska ◽  
Witold Bojar ◽  
Tomasz Zareba ◽  
Stefan Tyski ◽  
Barbara Tymczyna-Borowicz
Keyword(s):  
Infection Prevention ◽  
Dental Materials ◽  
Cross Infection ◽  
Aerobic Bacteria ◽  
Filling Material ◽  
Bodily Fluids ◽  
Dental Office ◽  
Dental Practices ◽  
Dental Offices ◽  
Aerobic And Anaerobic

AbstractCross-infection involves the transmission of microorganisms through secretions, bodily fluids and excreta, as well as undisinfected surfaces and medical equipment. In the dental office, diseases are transmitted via various routes, e.g. from patient to dentist or other member of dental team, from doctor or dental team member to patient, from patient to another patient, from dental office to community and from community to patient. The study was conducted to evaluate the effectiveness of infection control in dental practices based on the qualitative and quantitative assessment of microbiological contaminants detected on the surface of filling material packaging used in dental offices. The material for research were 9 packages containing dental materials during their use in 3 dental settings. The packages were placed in sterile flasks and rinsed to wash microorganisms from the surfaces. The washes were filtered through membrane filters and cultured under proper aerobic and anaerobic conditions, and at elevated CO2 concentration. Microbial growth on TIO and TSB media was observed. The contamination of most samples remained low as indicated by the growth from one to a maximum of five colonies on TSA. The contamination remained at the level of 10-50 CFU/package, i.e. <100 CFU/single package. The tests evaluating the contamination of dental package surfaces with aerobic bacteria confirmed high hygiene standards observed in dental offices from which the packages were brought.

Covid-19 and its financial effects on the dental fraternity and health-care workers: A literature review

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 628-631
Author(s):  
Devangi Agrawal ◽  
Namisha Khara ◽  
Bhushan Mundada ◽  
Nitin Bhola ◽  
Rajiv Borle
Keyword(s):  
Health Care ◽  
Health Care Providers ◽  
Health Care Workers ◽  
Care Providers ◽  
Dental Procedures ◽  
Care Workers ◽  
Dental Practices ◽  
Precautionary Measures ◽  
The Government ◽  
Set Up

In the wake of the current outbreak of novel Covid-19, which is now declared as a 'pandemic' by the WHO, people around the globe have been dealing with a lot of difficulties. This virus had come into light in December 2019 and since then has only grown exponentially. Amongst the most affected are the ones who have been working extremely hard to eradicate it, which includes the hospitals, dental fraternity and the health-care workers. These people are financially burdened due to limited practise. In the case of dentistry, to avoid the spread of the virus, only emergency treatments are being approved, and the rest of the standard procedures have been put on hold. In some cases, as the number of covid cases is rising, many countries are even trying to eliminate the emergency dental procedures to divert the finances towards the treatment of covid suffering patients. What we need to realise is that this is probably not the last time that we are facing such a situation. Instead of going down, we should set up guidelines with appropriate precautionary measures together with the use of standardised PPEs. The government should also establish specific policies to support dental practices and other health-care providers. Together, we can fight this pandemic and come out stronger.

An Effective SIMS Methodology for GOI Contamination Analysis

2013 ◽  
Author(s):  
Yanhua Huang ◽  
Lei Zhu ◽  
Kenny Ong ◽  
Hanwei Teo ◽  
Younan Hua
Keyword(s):  
Oxide Layer ◽  
Secondary Ion Mass Spectrometry ◽  
Gate Oxide ◽  
Sample Surface ◽  
Low Energy ◽  
Cross Contamination ◽  
Common Effect ◽  
Sims Analysis ◽  
Secondary Ion ◽  
Lower Background

Abstract Contamination in the gate oxide layer is the most common effect which cause the gate oxide integrate (GOI) issue. Dynamic Secondary Ion Mass Spectrometry (SIMS) is a mature tool for GOI contamination analysis. During the sample preparation, all metal and IDL layers above poly should be removed because the presence of these layers added complexity for the subsequent SIMS analysis. The normal delayering process is simply carried out by soaking the sample in the HF solution. However, the poly surface is inevitably contaminated by surroundings even though it is already a practice to clean with DI rinse and tape. In this article, TOFSIMS with low energy sputter gun is used to clean the sample surface after the normal delayering process. The residue signals also can be monitored by TOF SIMS during sputtering to confirm the cross contamination is cleared. After that, a much lower background desirable by dynamic SIMS. Thus an accurate depth profile in gate oxide layer can be achieved without the interference from surface.

An artificial intelligent platform for live cell identification and the detection of cross-contamination (Preprint)

2019 ◽  
Author(s):  
Ruixin Wang ◽  
Dongni Wang ◽  
Dekai Kang ◽  
Xusen Guo ◽  
Chong Guo ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Cell Morphology ◽  
Single Cell Level ◽  
Cross Contamination ◽  
Cell Level ◽  
Cell Identification ◽  
Pure Cell ◽  
Microscopy Images

BACKGROUND In vitro human cell line models have been widely used for biomedical research to predict clinical response, identify novel mechanisms and drug response. However, one-fifth to one-third of cell lines have been cross-contaminated, which can seriously result in invalidated experimental results, unusable therapeutic products and waste of research funding. Cell line misidentification and cross-contamination may occur at any time, but authenticating cell lines is infrequent performed because the recommended genetic approaches are usually require extensive expertise and may take a few days. Conversely, the observation of live-cell morphology is a direct and real-time technique. OBJECTIVE The purpose of this study was to construct a novel computer vision technology based on deep convolutional neural networks (CNN) for “cell face” recognition. This was aimed to improve cell identification efficiency and reduce the occurrence of cell-line cross contamination. METHODS Unstained optical microscopy images of cell lines were obtained for model training (about 334 thousand patch images), and testing (about 153 thousand patch images). The AI system first trained to recognize the pure cell morphology. In order to find the most appropriate CNN model，we explored the key image features in cell morphology classification tasks using the classical CNN model-Alexnet. After that, a preferred fine-grained recognition model BCNN was used for the cell type identification (seven classifications). Next, we simulated the situation of cell cross-contamination and mixed the cells in pairs at different ratios. The detection of the cross-contamination was divided into two levels, whether the cells are mixed and what the contaminating cell is. The specificity, sensitivity, and accuracy of the model were tested separately by external validation. Finally, the segmentation model DialedNet was used to present the classification results at the single cell level. RESULTS The cell texture and density were the influencing factors that can be better recognized by the bilinear convolutional neural network (BCNN) comparing to AlexNet. The BCNN achieved 99.5% accuracy in identifying seven pure cell lines and 86.3% accuracy for detecting cross-contamination (mixing two of the seven cell lines). DilatedNet was applied to the semantic segment for analyzing in single-cell level and achieved an accuracy of 98.2%. CONCLUSIONS This study successfully demonstrated that cell lines can be morphologically identified using deep learning models. Only light-microscopy images and no reagents are required, enabling most labs to routinely perform cell identification tests.

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