Dataset Information

Transformation of personal computers and mobile phones into genetic diagnostic systems.

ABSTRACT: Molecular diagnostics based on the polymerase chain reaction (PCR) offer rapid and sensitive means for detecting infectious disease, but prohibitive costs have impeded their use in resource-limited settings where such diseases are endemic. In this work, we report an innovative method for transforming a desktop computer and a mobile camera phone--devices that have become readily accessible in developing countries--into a highly sensitive DNA detection system. This transformation was achieved by converting a desktop computer into a de facto thermal cycler with software that controls the temperature of the central processing unit (CPU), allowing for highly efficient PCR. Next, we reconfigured the mobile phone into a fluorescence imager by adding a low-cost filter, which enabled us to quantitatively measure the resulting PCR amplicons. Our system is highly sensitive, achieving quantitative detection of as little as 9.6 attograms of target DNA, and we show that its performance is comparable to advanced laboratory instruments at approximately 1/500th of the cost. Finally, in order to demonstrate clinical utility, we have used our platform for the successful detection of genomic DNA from the parasite that causes Chagas disease, Trypanosoma cruzi, directly in whole, unprocessed human blood at concentrations 4-fold below the clinical titer of the parasite.

SUBMITTER: Walker FM

PROVIDER: S-EPMC4165218 | biostudies-other | 2014 Sep

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Transformation of personal computers and mobile phones into genetic diagnostic systems.

Walker Faye M FM Ahmad Kareem M KM Eisenstein Michael M Soh H Tom HT

Analytical chemistry 20140831 18

Molecular diagnostics based on the polymerase chain reaction (PCR) offer rapid and sensitive means for detecting infectious disease, but prohibitive costs have impeded their use in resource-limited settings where such diseases are endemic. In this work, we report an innovative method for transforming a desktop computer and a mobile camera phone--devices that have become readily accessible in developing countries--into a highly sensitive DNA detection system. This transformation was achieved by c ...[more]

PMID: 25223929

Similar Datasets

Project description:Mobile phones have a large spectrum of applications, aiding in risk prevention and improving health and wellbeing of their owners. So far, however, they have not been used for direct assessment of personal exposure to air pollution. In this study, we comprehensively evaluated the first, and the only available, mobile phone-BROAD Life-equipped with air pollution sensors (PM2.5 and VOC), to answer the question whether this technology is a viable option in the quest of reducing the burden of disease to air pollution. We tested its performance, applicability and suitability for the purpose by subjecting it to varied concentrations of different types of aerosol particles (cigarette smoke, petrol exhaust and concrete dust) and formaldehyde under controlled laboratory conditions, as well as to ambient particles during field measurements. Six reference instruments were used in the study: AEROTRAK Optical Particle Counter (OPC model number 9306), DustTrak, Aerodynamic Particle Counter (APS), Scanning Mobility Particle Sizer (SMPS), Tapered Element Oscillating Microbalance (TEOM) and Formaldehyde Analyser. Overall, we found that the phone's response was linear at higher particle number concentrations in the chamber, above 5 and 10 ?g m-3, for combustion and concrete dust particles, respectively, and for higher formaldehyde concentrations, making it potentially suitable for applications in polluted environments. At lower ambient concentrations of particles around 10 ug m-3 and 20 ?g m-3 for PM2.5 and PM10, respectively, the phone's response was below its noise level, suggesting that it is not suitable for ambient monitoring under relatively clean urban conditions. This mobile phone has a number of limitations that may hinder its use in personal exposure and for continuous monitoring. Despite these limitations, it may be used for comparative assessments, for example when comparing outcomes of intervention measures or local impacts of air pollution sources. It should be kept in mind, however, that a mobile phone measuring air quality alone cannot as such 'reduce the burden of disease to air pollution, as knowing ambient concentrations is only one of the building block in this quest. As long as individuals cannot avoid exposure e.g. in urban areas, knowing concentrations is not sufficient to reduce potential adverse effects. Yet, there are many situations and microenvironments, which individuals could avoid knowing the concentrations and also being aware of the risk caused by exposure to them. This includes for example to proximity to vehicle emissions, either for social purposes (e.g. street cafes) or exercising (e.g. walking or jogging along busy roads)or indoor environments affected by combustion emissions (smoking, candle burning, open fire).

Project description:BACKGROUND:No study has comprehensively investigated the association between the usage of typical screen-based electronic media devices and sleep quality in a Chinese population with individuals in a wide range of ages. OBJECTIVE:This study aimed to understand the characteristics of television (TV) viewing, computer usage, and mobile phone usage in a representative Chinese population in Macau and to examine their roles in predicting the variations in sleep quality. METHODS:This cross-sectional study was an analysis of 1500 Macau residents aged 15 to 90 years based on a community-based health needs assessment study entitled, "Healthy Living, Longer Lives." Data collection was conducted in 7 districts of Macau from 2017 to 2018 through face-to-face interviews. The durations of daily TV viewing, computer usage, and mobile phone usage were recorded in a self-administered questionnaire. The Chinese version of the Pittsburgh Sleep Quality Index (PSQI) was used to assess the sleep quality. RESULTS:The prevalence of TV, computer, and mobile phone usage was 78.4% (1176/1500), 51.6% (769/1490), and 85.5% (1276/1492), respectively. The average daily hours of usage were 1.75 (1.62), 1.53 (2.26), and 2.85 (2.47) hours, respectively. Females spent more time watching TV (P=.03) and using mobile phones (P=.02) and less time on the computer (P=.04) as compared to males. Older adults were more likely to watch TV while young people spent more time using the computer and mobile phones (P for all trends<.001). The mean PSQI global score was 4.79 (2.80) among the participants. Females exhibited significantly higher PSQI scores than males (5.04 vs 4.49, respectively; P<.001). No linear association was observed between the PSQI score and the amount of time spent on the 3 electronic devices (P=.58 for PSQI-TV, P=.05 for PSQI-computer, and P=.52 for PSQI-mobile phone). Curve estimation showed significant quadratic curvilinear associations in PSQI-TV (P=.003) and PSQI-computer (P<.001) among all the participants and in PSQI-mobile phone among youths (age, 15-24 years; P=.04). After adjustment of the gender, age, body mass index, demographics, and lifestyle factors, more than 3 hours of TV viewing and 4 hours of computer usage or mobile phone usage was associated with 85% (95% CI 1.04-1.87; P=.008), 72% (95% CI 1.01-2.92; P=.045), and 53% (95% CI 1.06-2.22; P=.03) greater odds of having poor sleep quality (PSQI score>5), respectively. CONCLUSIONS:The mobile phone was the most popular screen-based electronic device used in the Macau population, especially among young people. "J" shape associations were observed between sleep quality and the duration of TV viewing, computer usage, and mobile phone usage, indicating that the extreme use of screen-based electronic devices predicted poorer sleep status, whereas moderate use would be acceptable.

Project description:BACKGROUND: Given the ubiquity of mobile phones, their use to support healthcare in the Indian context is inevitable. It is however necessary to assess end-user perceptions regarding mobile health interventions especially in the rural Indian context prior to its use in healthcare. This would contextualize the use of mobile phone communication for health to 70% of the country's population that resides in rural India. OBJECTIVES: To explore the acceptability of delivering healthcare interventions through mobile phones among users in a village in rural Bangalore. METHODS: This was an exploratory study of 488 mobile phone users, residing in a village, near Bangalore city, Karnataka, South India. A pretested, translated, interviewer-administered questionnaire was used to obtain data on mobile phone usage patterns and acceptability of the mobile phone, as a tool for health-related communication. The data is described using basic statistical measures. RESULTS: The primary use of mobile phones was to make or receive phone calls (100%). Text messaging (SMS) was used by only 70 (14%) of the respondents. Most of the respondents, 484 (99%), were willing to receive health-related information on their mobile phones and did not consider receiving such information, an intrusion into their personal life. While receiving reminders for drug adherence was acceptable to most 479 (98%) of our respondents, 424 (89%) preferred voice calls alone to other forms of communication. Nearly all were willing to use their mobile phones to communicate with health personnel in emergencies and 367 (75%) were willing to consult a doctor via the phone in an acute illness. Factors such as sex, English literacy, employment status, and presence of chronic disease affected preferences regarding mode and content of communication. CONCLUSION: The mobile phone, as a tool for receiving health information and supporting healthcare through mHealth interventions was acceptable in the rural Indian context.

Dataset Information

Transformation of personal computers and mobile phones into genetic diagnostic systems.

Publications

Transformation of personal computers and mobile phones into genetic diagnostic systems.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets