Molecular Radar: The Future of Rapid Noninvasive Diagnostics

All matter is made up of elementary particles, which have a spin. This implies all matter would have an associated Electromagnetic Field (EMF) signal that may exhibit unique resonance phenomenon that can be measured with highly sensitive instruments in a sufficiently shielded environment. Recent viral outbreaks (e.g., SARS-CoV-2) highlight the need for rapid identification of viruses and other pathogens, which could be realized by exploiting current theoretical work on EMF signal resonance [1-3]. In addition, such EMF sensing devices could be particularly useful for differentiating or resolving viral samples and perhaps even nucleic acid sequence information non-invasively.
Several papers have already been published claiming to experimentally demonstrate that organic molecules (such as DNA/RNA) have an intrinsic electromagnetic signature which carries the structural information and possible function of the molecule. A summary of such work can be found in [4,5]. However, much of this research makes strong claims without careful adherence to the scientific method. Although the current claims are promising, a proper mathematical formalism and setup rigorous experiments need to be conducted. As stated by Romanenko et al. [5]:
One of the most disputed statements [regarding the interaction of EMF and biomolecules] is a resonant-like effect at certain frequencies. Nevertheless, those findings are consistent to some extent with the original Frohlich theoretical conclusions about the role and importance of mmW [millimetre waves] and terahertz electromagnetic oscillations in biology. If such statements are true, then we hypothesize that there may exist a unique signal for a given biomolecule and its corresponding sequence.

Examining pathogens and sequences via electromagnetic spectrum

Since the human body itself can act as an antenna array [6], we hypothesize this may also hold true for biomolecules/sequences under various experimental/engineered states. Assuming the measured signal can be obtained, from a theoretical perspective, the data represents a resonance phenomenon characterized by waveform scattering. Modulating spatial, temporal, frequency and temperature parameters of the sensor antenna array, we conjecture it is possible to resolve signal information of the resonance structures thereby decoding the sequence data. Furthermore, recent advances in sensor design [7,8] suggest that this is possible to further improve precision by exploiting quantum entanglement of sensor networks. As stated in [8]:
The entanglement shared by different sensors reduces errors and boosts the sensitivity of extracting global information of the object under investigation [where] a typical method to minimize the cost function involves the use of the stochastic gradient descent algorithm...This opens new avenues for ultrasensitive measurements in biological, thermal, and mechanical systems...In optical biosensors, the evanescent wave of the probe light interacts with the sample, and the induced phase shift serves as a means to identify the density and species of the biomolecule.
If the EMF biomolecule data has been collected, we can construct a database and AI system (similar to Google Translate) that will predict the profile of a sequence or pathogen. More specifically, we could develop a Waveform Genetic Translation Engine (WGTE), i.e., a database of molecular waveform signatures, mapping the sequence-to-sequence data to uniquely identify a genetic sample. To construct the training dataset, many independent separate measurements for various different pathogens over a large range of frequencies for each viral stock are required. Systems such as AIFeynman (neural network based symbolic regression), can be used to aid in the development of analytical models that complement the statistical methods [9]. Once the most important frequency range is determined for different types of biomolecules (e.g., viruses, bacteria, fungi, etc.), a less expensive device can be developed that uses the WGTE as its backend to identify a sample. An example of where a similar technique has been used can be found in [6], which is a paper that outlines how WiFi can be used to track human movement through walls, known as WiVi by training a network on input from a hybrid mode sensor fusion network.
The frequency of vibrational resonances of biological molecules are typically within the 0.5-2THz range [4,10]. However, there remain significant challenges when using THz sensing systems. Firstly, resonance-based sensors are material and case specific as each biomolecule has its own resonance. This means that a general THz sensing system for biomolecule identification would be very expensive due to the number of case specific fabrications that would be required. Moreover, current technology does not yet have optimal capabilities to deal with the noise floor of such systems, which typically are on the order of -50dBm. If lower frequency measurement systems could instead be used for distinguishing various biomolecules, this would be ideal since they are less expensive, and have more adequate accuracy and precision because the noise floor is on the order of -150dBm [SSN]. However, it is possible that microwave sensing techniques could be effectively used for sample identification [11-13]. The authors are currently investigating such sensing methods to create a “molecular radar” device for medical diagnostics [14].

In summary, if sufficiently precise and accurate measurements of EMF signatures of biomolecules can be performed, we conjecture that a waveform-based sensor could be rapidly deployed to enable real-time pandemic diagnostics. If our proposed method is feasible, it has the potential to revolutionize the medical industry, specifically for non-invasive rapid identification of pathogens. Such technologies would allow healthcare professionals and epidemiologists to track the emergence of viral variants in real time. In turn, this could help prevent future outbreaks which could possibly be more destructive than the current pandemic.

1. Adair RK (2002) Vibrational resonances in biological systems at microwave frequencies. Biophys J 82(3): 1147-1152.
2. Ikeda M, Nakazato K, Mizuta H, Green M, Hasko D, et al. (2003) Frequency-dependent electrical characteristics of DNA using molecular dynamics simulation. Nanotechnology 14(2): 123.
3. Mobin M, Majid G (2020) A mathematical model for vibration behavior analysis of DNA and using a resonant frequency of DNA for genome engineering. Scientific Reports 10(1): 3439.
4. Tang BQ, Li T, Bai X, Zhao M, Wang B, et al. (2019) Rate limiting factors for DNA transduction induced by weak electromagnetic field. Electromagnetic Biology and Medicine 38(1): 55-65.
5. Romanenko S, Begley R, Harvey AR, Hool L, Wallace VP (2017) The interaction between electromagnetic fields at megahertz, gigahertz and terahertz frequencies with cells, tissues and organisms: risks and potential. Journal of The Royal Society Interface 14(137): 20170585.
6. Adib F, Katabi D (2013) See through walls with WiFi!. In: Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM, New York, USA, pp: 75-86.
7. Taylor MA, Janousek J, Daria V, Knittel J, Hage B, et al. (2013) Biological measurement beyond the quantum limit. Nature Photonics 7(3): 229-233.
8. Zhuang Q, Zhang Z (2019) Physical-layer supervised learning assisted by an entangled sensor network. Quantum Physics 9(4): 041023.
9. Udrescu SM, Tegmark M (2020) AI Feynman: A physics-inspired method for symbolic regression. Science Advances 6(16): eaay2631.
10. Markelz AG, Roitberg A, Heilweil EJ (2000) Pulsed terahertz spectroscopy of DNA, bovine serum albumin and collagen between 0.1 and 2.0 THz. Chemical Physics Letters 320(1-2): 42-48.
11. Ansari MAH, Jha AK, Akhtar MJ (2015) Design and application of the CSRR-based planar sensor for noninvasive measurement of complex permittivity. IEEE Sensors Journal 15(12): 7181-7189.
12. Ebrahimi A, Scott J, Ghorbani K (2018) Differential sensors using microstrip lines loaded with two split-ring resonators. IEEE Sensors Journal 18(14): 5786-5793.
13. Lee CS, Yang CL (2015) Single-compound complementary split-ring resonator for simultaneously measuring the permittivity and thickness of dual-layer dielectric materials. IEEE Transactions on Microwave Theory and Techniques 63(6): 2010-2023.
14. Safavi Naeini S (2021) Personal conversation, University of Waterloo, Canada.