Abstract:Food and waterborne diseases pose considerable public health threats even in highly industrialized parts of the world. Examples of these pathogens in food can be Escherichia coli O157: H7, Salmonella sp., and Listeria monocytogenes. Rapid, reliable detection of pathogens mitigates serious health problems and economic losses due to outbreaks and robust tests safeguard the food supply. In this study, a smartphone-based apparatus was employed to demonstrate quantitative detection of E. coli. To validate the appli… Show more
“…The app can be used intuitively, requiring minimal input from an untrained user. The app algorithm for fluorescent signal measurement involves capturing, storing and processing the image of the sample 23 . Color information from the image is extracted and matched with the Red, Green, Blue (RGB) values to assist the user in selecting the target color.…”
Section: Methodsmentioning
confidence: 99%
“…Once an image is taken, the application allows the user to interact with the selected image and allows for the touched pixel of the image to determine the RGB value. Further details are found in Reference 23 . Figure 3 Fluorescent image of a sample captured by a smart phone application.…”
Food associated diseases pose significant public health threat in the United States. Health risks associated with food-borne pathogens drive the need for constant monitoring of food products. An efficient method that can diagnose food-borne pathogens rapidly will be invaluable and in high demand. In this study, we showed the feasibility of a novel rapid detection platform based on fluorescence imaging/detection that combines a user-friendly, portable loop mediated isothermal amplification (LAMP) reaction device and a smartphone-based detection system. The proposed platform was used to detect Staphylococcus aureus which is one of the most important food-borne pathogen especially dairy products. The complete protocol is quicker; the reaction is performed under isothermal conditions and completed in 1 h or less. Experimental results show that LAMP assays were ten-fold more sensitive than PCR-based detection. The proposed smartphone detection system was able to detect and quantify LAMP assay samples containing three different concentrations of S. aureus from 109 CFU/mL down to 103 CFU/mL. The present proof-of-concept study demonstrated that this platform offers a portable, easy to use method for measuring target pathogens with LAMP amplification.
“…The app can be used intuitively, requiring minimal input from an untrained user. The app algorithm for fluorescent signal measurement involves capturing, storing and processing the image of the sample 23 . Color information from the image is extracted and matched with the Red, Green, Blue (RGB) values to assist the user in selecting the target color.…”
Section: Methodsmentioning
confidence: 99%
“…Once an image is taken, the application allows the user to interact with the selected image and allows for the touched pixel of the image to determine the RGB value. Further details are found in Reference 23 . Figure 3 Fluorescent image of a sample captured by a smart phone application.…”
Food associated diseases pose significant public health threat in the United States. Health risks associated with food-borne pathogens drive the need for constant monitoring of food products. An efficient method that can diagnose food-borne pathogens rapidly will be invaluable and in high demand. In this study, we showed the feasibility of a novel rapid detection platform based on fluorescence imaging/detection that combines a user-friendly, portable loop mediated isothermal amplification (LAMP) reaction device and a smartphone-based detection system. The proposed platform was used to detect Staphylococcus aureus which is one of the most important food-borne pathogen especially dairy products. The complete protocol is quicker; the reaction is performed under isothermal conditions and completed in 1 h or less. Experimental results show that LAMP assays were ten-fold more sensitive than PCR-based detection. The proposed smartphone detection system was able to detect and quantify LAMP assay samples containing three different concentrations of S. aureus from 109 CFU/mL down to 103 CFU/mL. The present proof-of-concept study demonstrated that this platform offers a portable, easy to use method for measuring target pathogens with LAMP amplification.
“…When equipped with the appropriate accessories, fluorescence microscopy imaging is possible on a smartphone platform. This would provide more diagnostically-oriented biomedical observation [ 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 ]. A typical smartphone platform-based fluorescence microscope consists of an excitation light source (LED or laser diode), an emission filter, and an objective lens.…”
In bio-medical mobile workstations, e.g., the prevention of epidemic viruses/bacteria, outdoor field medical treatment and bio-chemical pollution monitoring, the conventional bench-top microscopic imaging equipment is limited. The comprehensive multi-mode (bright/dark field imaging, fluorescence excitation imaging, polarized light imaging, and differential interference microscopy imaging, etc.) biomedical microscopy imaging systems are generally large in size and expensive. They also require professional operation, which means high labor-cost, money-cost and time-cost. These characteristics prevent them from being applied in bio-medical mobile workstations. The bio-medical mobile workstations need microscopy systems which are inexpensive and able to handle fast, timely and large-scale deployment. The development of lightweight, low-cost and portable microscopic imaging devices can meet these demands. Presently, for the increasing needs of point-of-care-test and tele-diagnosis, high-performance computational portable microscopes are widely developed. Bluetooth modules, WLAN modules and 3G/4G/5G modules generally feature very small sizes and low prices. And industrial imaging lens, microscopy objective lens, and CMOS/CCD photoelectric image sensors are also available in small sizes and at low prices. Here we review and discuss these typical computational, portable and low-cost microscopes by refined specifications and schematics, from the aspect of optics, electronic, algorithms principle and typical bio-medical applications.
Historically, aerial surveys have been used widely to monitor abundance of large mammals in the western United States. In California, such surveys have typically served as minimum count indices rather than true abundance estimates. Here, we evaluated the utility of aerial multiple covariate distance sampling (MCDS) to estimate abundance of three populations of tule elk (Cervus canadensis nannodes) in northern California. We also compared estimates and costs with published results from a concurrent fecal DNA spatial capture-recapture (SCR) survey. During December 2018 and 2019, we flew line transects for distance sampling of tule elk in Colusa and Lake counties. We modeled detection functions and evaluated effects of group size, canopy cover, and survey year. We averaged the top models comprising ≥0.95 of Akaike Model Weight and estimated abundance of both total and discrete populations. Detection probability increased with increasing group size and decreasing canopy cover. We estimated a two-year average total population size of N̂ = 674 elk (90% CI = 501–907) in our survey area which was similar to N̂ = 653 elk (90% CI = 573–745) from SCR estimates. Overall precision was greater (CV = 0.08; range = 0.11–0.30 by population) for SCR than for MCDS (CV = 0.18; range = 0.22–0.43 by population). Although estimates differed somewhat between methods for the individual populations, the combined estimate across the study region compared favorably. Total cost of SCR and MCDS surveys was $98,326 and $147,324, respectively. While SCR efforts were more precise and less expensive overall, our MCDS approach reduced staff time by 64% (587 person-hours) and the number of survey days by 87% (64 days). Our results suggest MCDS methods can produce reliable abundance estimates across a gradient of canopy cover, particularly when observations can be pooled across populations to decrease variance. We recommend future research to assess use of hybrid models, such as mark-recapture distance sampling or hierarchical distance sampling, to improve precision and estimation of detection probability.
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