scholarly journals Sensor-Embedded Face Masks for Detection of Volatiles in Breath: A Proof of Concept Study

Chemosensors ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 356
Author(s):  
Lorena Di Zazzo ◽  
Gabriele Magna ◽  
Martina Lucentini ◽  
Manuela Stefanelli ◽  
Roberto Paolesse ◽  
...  
Keyword(s):  
Volatile Compounds ◽  
Low Cost ◽  
Internal Surface ◽  
Breath Analysis ◽  
Proof Of Concept ◽  
Clinical Implementation ◽  
Complex Samples ◽  
Sensitivity Pattern ◽  
Optical Transducers ◽  
Bulky Mass

The correlation between breath volatilome and health is prompting a growing interest in the development of sensors optimized for breath analysis. On the other hand, the outbreak of COVID-19 evidenced that breath is a vehicle of infection; thus, the introduction of low-cost and disposable devices is becoming urgent for a clinical implementation of breath analysis. In this paper, a proof of concept about the functionalization of face masks is provided. Porphyrin-based sensors are among the most performant devices for breath analysis, but since porphyrins are scarcely conductive, they make use of costly and bulky mass or optical transducers. To overcome this drawback, we introduce here a hybrid material made of conducting polymer and porphyrins. The resulting material can be easily deposited on the internal surface of standard FFP face masks producing resistive sensors that retain the chemical sensitivity of porphyrins implementing their combinatorial selectivity for the identification of volatile compounds and the classification of complex samples. The sensitivity of sensors has been tested with respect to a set of seven volatile compounds representative of diverse chemical families. Sensors react to all compounds but with a different sensitivity pattern. Functionalized face masks have been tested in a proof-of-concept test aimed at identifying changes of breath due to the ingestion of beverages (coffee and wine) and solid food (banana- and mint-flavored candies). Results indicate that sensors can detect volatile compounds against the background of normal breath VOCs, suggesting the possibility to embed sensors in face masks for extensive breath analysis

scholarly journals Gut Microbiota and Host Metabolism: From Proof of Concept to Therapeutic Intervention

2021 ◽  
Vol 9 (6) ◽  
pp. 1302
Author(s):  
Patrice D. Cani ◽  
Emilie Moens de Hase ◽  
Matthias Van Hul
Keyword(s):  
Gut Microbiota ◽  
Research Field ◽  
Proof Of Concept ◽  
Clinical Implementation ◽  
Science Area ◽  
Microbiome Research ◽  
Fast Pace ◽  
Health And Disease ◽  
Underlying Mechanisms ◽  
Host Metabolism

The field of the gut microbiota is still a relatively young science area, yet many studies have already highlighted the translational potential of microbiome research in the context of human health and disease. However, like in many new fields, discoveries are occurring at a fast pace and have provided new hope for the development of novel clinical applications in many different medical conditions, not in the least in metabolic disorders. This rapid progress has left the field vulnerable to premature claims, misconceptions and criticism, both from within and outside the sector. Tackling these issues requires a broad collaborative effort within the research field and is only possible by acknowledging the difficulties and challenges that are faced and that are currently hindering clinical implementation. These issues include: the primarily descriptive nature of evidence, methodological concerns, disagreements in analysis techniques, lack of causality, and a rather limited molecular-based understanding of underlying mechanisms. In this review, we discuss various studies and models that helped identifying the microbiota as an attractive tool or target for developing various translational applications. We also discuss some of the limitations and try to clarify some common misconceptions that are still prevalent in the field.

scholarly journals From a classic Renault Twizy towards a low cost autonomous car prototype: a proof of concept

2020 ◽  
Vol 53 (2) ◽  
pp. 15161-15166
Author(s):  
Rodolfo Orjuela ◽  
Jean-Philippe Lauffenburger ◽  
Jonathan Ledy ◽  
Michel Basset ◽  
Joel Lambert ◽  
...  
Keyword(s):  
Low Cost ◽  
Proof Of Concept

scholarly journals A Low-Cost Assistive Robot for Children with Neurodevelopmental Disorders to Aid in Daily Living Activities

2021 ◽  
Vol 18 (8) ◽  
pp. 3974
Author(s):  
Roberto J. López-Sastre ◽  
Marcos Baptista-Ríos ◽  
Francisco Javier Acevedo-Rodríguez ◽  
Soraya Pacheco-da-Costa ◽  
Saturnino Maldonado-Bascón ◽  
...  
Keyword(s):  
Neurodevelopmental Disorders ◽  
Low Cost ◽  
Children And Youth ◽  
Proof Of Concept ◽  
Robotic Platform ◽  
Assistive Robot ◽  
Technical Details ◽  
The Impact ◽  
Near Future ◽  
Everyday Living

In this paper, we present a new low-cost robotic platform that has been explicitly developed to increase children with neurodevelopmental disorders’ involvement in the environment during everyday living activities. In order to support the children and youth with both the sequencing and learning of everyday living tasks, our robotic platform incorporates a sophisticated online action detection module that is capable of monitoring the acts performed by users. We explain all the technical details that allow many applications to be introduced to support individuals with functional diversity. We present this work as a proof of concept, which will enable an assessment of the impact that the developed technology may have on the collective of children and youth with neurodevelopmental disorders in the near future.

scholarly journals Low-Cost and Energy-Saving Wireless Sensor Network for Real-Time Urban Mobility Monitoring System

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Joyoung Lee ◽  
Zijia Zhong ◽  
Bo Du ◽  
Slobodan Gutesa ◽  
Kitae Kim
Keyword(s):  
Energy Saving ◽  
Fiber Optics ◽  
Monitoring System ◽  
Data Center ◽  
Low Cost ◽  
Wireless Sensor ◽  
Navigation Systems ◽  
Urban Mobility ◽  
Proof Of Concept ◽  
Sensor Unit

This paper presents a low-cost and energy-saving urban mobility monitoring system based on wireless sensor networks (WSNs). The primary components of the proposed sensor unit are a Bluetooth sensor and a Zigbee transceiver. Within the WSN, the Bluetooth sensor captures the MAC addresses of Bluetooth units equipped in mobile devices and car navigation systems. The Zigbee transceiver transmits the collected MAC addresses to a data center without any major communications infrastructures (e.g., fiber optics and 3G/4G network). A total of seven prototype sensor units have been deployed on roadway segments in Newark, New Jersey, for a proof of concept (POC) test. The results of the POC test show that the performance of the proposed sensor unit appears promising, resulting in 2% of data drop rates and an improved Bluetooth capturing rate.

Electromyography-controlled car: A proof of concept based on surface electromyography, Extreme Learning Machines and low-cost open hardware

2019 ◽  
Vol 73 ◽  
pp. 167-179 ◽  
Author(s):  
Rafaela C. de Freitas ◽  
Rodrigo Alves ◽  
Abel G. da Silva Filho ◽  
Ricardo E. de Souza ◽  
Byron L.D. Bezerra ◽  
...  
Keyword(s):  
Surface Electromyography ◽  
Low Cost ◽  
Proof Of Concept ◽  
Extreme Learning Machines ◽  
Open Hardware ◽  
Learning Machines

scholarly journals Development of an IoT System for Environmental Monitoring: Software

2021 ◽  
Author(s):  
Benjamin Secker
Keyword(s):  
Environmental Monitoring ◽  
Low Cost ◽  
Proof Of Concept ◽  
Embedded Devices ◽  
Data Loggers ◽  
Environmental Monitoring System ◽  
Use Of The Internet ◽  
High Level ◽  
Monitoring Software ◽  
The Internet Of Things

Use of the Internet of Things (IoT) is poised to be the next big advancement in environmental monitoring. We present the high-level software side of a proof-of-concept that demonstrates an end-to-end environmental monitoring system,<br><div>replacing Greater Wellington Regional Council’s expensive data loggers with low-cost, IoT centric embedded devices, and it’s supporting cloud platform. The proof-of-concept includes a Micropython-based software stack running on an ESP32 microcontroller. The device software includes a built-in webserver that hosts a responsive Web App for configuration of the device. Telemetry data is sent over Vodafone’s NB-IoT network and stored in Azure IoT Central, where it can be visualised and exported.</div><br>While future development is required for a production-ready system, the proof-of-concept justifies the use of modern IoT technologies for environmental monitoring. The open source nature of the project means that the knowledge gained can be re-used and modified to suit the use-cases for other organisations.

Detection of Volatile Compounds Correlated to Human Diseases Through Breath Analysis With Chemical Sensors

2001 ◽  
pp. 1746-1749
Author(s):  
Maximilian Fleischer ◽  
Elfriede Simon ◽  
Eva Rumpel ◽  
Heiko Ulmer ◽  
Mika Harbeck ◽  
...  
Keyword(s):  
Volatile Compounds ◽  
Chemical Sensors ◽  
Breath Analysis ◽  
Human Diseases

Clinical Implementation of a Parent Questionnaire to Identify Seizures in High-Risk Children

2020 ◽  
Vol 35 (7) ◽  
pp. 485-491
Author(s):  
Celia Greenlaw ◽  
Sarah Nuss ◽  
Cristina Camayd-Muñoz ◽  
Rinat Jonas ◽  
Julie Vanier Rollins ◽  
...  
Keyword(s):  
High Risk ◽  
Low Cost ◽  
Numerical Data ◽  
Primary Care Providers ◽  
Autism Spectrum ◽  
Categorical Variables ◽  
Care Providers ◽  
Clinical Implementation ◽  
Parent Questionnaire ◽  
High Risk Children

Background: This study evaluated the effectiveness of a parent-completed questionnaire for detecting seizures in high-risk children. Methods: A 2-part seizure screen for children up to 12 years of age with suspected autism spectrum disorder, developmental delay, or seizure, was implemented in 12 Massachusetts clinics serving populations with high health disparities. Primary care providers and developmental behavioral pediatricians administered part 1, a brief highly sensitive screen. If the result was positive, a research assistant administered part 2, a more detailed screen with higher specificity. Positive part 2 results prompted a specialized assessment by a pediatric neurologist. Screening data were evaluated for detection of seizures or other diagnoses, reason for conducting the screen, and appointment outcomes. Data analysis included chi-squared tests, percentages for categorical variables, and means for numerical data. Results: Of 207 administered seizure questionnaires, 78% of children screened positive on part 1. Of those, 94% of families completed part 2 by telephone, and 64 individuals screened positive. The screen helped to detect 15 new seizure diagnoses and 35 other neurologic diagnoses. Average time to first scheduled appointment was 23.8 days. The no-show rate was 7%. Conclusions: The seizure questionnaire effectively identified seizures and other disorders in a diverse population of high-risk children. Broader use of this low-cost screening tool could improve access to care for children with suspected seizures, increase seizure recognition, and help allocate resources more effectively.

scholarly journals Highly selective detection of methanol over ethanol by a handheld gas sensor

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
J. van den Broek ◽  
S. Abegg ◽  
S. E. Pratsinis ◽  
A. T. Güntner
Keyword(s):  
Gas Chromatography ◽  
Air Quality ◽  
Organ Failure ◽  
Gas Sensor ◽  
Chemical Sensors ◽  
Breath Analysis ◽  
Quality Monitoring ◽  
Selective Detection ◽  
Air Quality Monitoring ◽  
Proof Of Concept

Abstract Methanol poisoning causes blindness, organ failure or even death when recognized too late. Currently, there is no methanol detector for quick diagnosis by breath analysis or for screening of laced beverages. Typically, chemical sensors cannot distinguish methanol from the much higher ethanol background. Here, we present an inexpensive and handheld sensor for highly selective methanol detection. It consists of a separation column (Tenax) separating methanol from interferants like ethanol, acetone or hydrogen, as in gas chromatography, and a chemoresistive gas sensor (Pd-doped SnO2 nanoparticles) to quantify the methanol concentration. This way, methanol is measured within 2 min from 1 to 1000 ppm without interference of much higher ethanol levels (up to 62,000 ppm). As a proof-of-concept, we reliably measure methanol concentrations in spiked breath samples and liquor. This could enable the realization of highly selective sensors in emerging applications such as breath analysis or air quality monitoring.

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