Go to Top

i-needling

Theory

I-needling stands for intelligent needling. It is meant as a research tool to discover the ‘biochemical signaling pathway’ that the mechanical rotation of an acupuncture needle sets in motion.

Needle

The i-needle is in development, but a nanobiochip in the tip of the needle has to collect the necessary data.

Read more about this complicated and challenging needle in the article below.

Authors: Christine Nardini, Sandro Carrara, Yuanhua Liu, Valentina Devescovi, Youtao Lu, Xiaoyuan Zhou

From: The Art and Science of Traditional  Medicine. Part 1 A case for integration.

 

i-Needle: Detecting the biological mechanisms of acupuncture
A long standing obstacle to the (full) integration and acceptance of acupuncture in conventional medicine lies in the difficulty
of reconciling traditionally defined categories (acupoints, meridians, and energy flow or qi) with anatomical structures and biochemical pathways. Additionally, a unified scientific theory to explain the diverse effects of acupuncture (from pain control to immunomodulation) is lacking, despite important advances in the association of purinergic signaling with the effects of acupuncture
on pain control. As new technologies simultaneously offer enhanced capacities to explore breadth (using ‘omics) and depth (using nanobiochips) of biochemical events, we propose the innovative conjugation of these approaches into an intelligent needle (i-needle) as a means to overcome the abovementioned limitations. Acupuncture is being widely debated in the medical community as a potential alternative or complementary treatment for many diseases (1). There are numerous challenges to achieving a consensus over the use of acupuncture in a medical environment, including: filling the gap in knowledge about the underlying molecular mechanisms of acupuncture, and (re)interpreting traditional categories (such as acupoints, meridians, and qi) and therapeutic indications within an evidencebased medicine framework. Important questions aimed at increasing our understanding of the molecular effects of needle stimulation have been posed, mostly regarding pain control(2), functional recovery of tissue (3), and immunomodulation(4), with remarkable work done as to the correlation of pain control with purinergic signaling (5, 6). Using ‘omics-based technology and network representations, researchers have successfully mapped the molecular underpinnings of traditional categories (7). More generally, the holistic method used in acupuncture, which has long been difficult to reconcile with the scientific reductionist viewpoint, has recently been found to be compatible with a systems biology approach (8). ‘Omics-based techniques are diverse and allow for thescreening of targets from nucleic acids (DNA-sequencing, RNA-sequencing) to proteins and metabolites (mass spectrometry/
liquid chromatography, nuclear magnetic resonance) and their heterogeneous interactions (chromatin immunoprecipitation-
sequencing), to name just the major technologies. Recently, whole new areas of exploration have been opened
with metagenomics and metatranscriptomics where the hostmicrobiome relationship can be analyzed systemically and in
situ. Further, rapidly decreasing costs are permitting researchers to prefigure relatively high spatial (different body regions
and tissues) and temporal resolution. Here, we propose to integrate such highly resolved molecular, temporal, and spatial data to reveal the molecular signaling pathways that flow from the tip of the needle to the disease/injury site. Understanding the biochemical signaling pathway that the mechanical rotation of an acupuncture needle sets into motion (9) is an important starting point. Mechanosensing and mechanotransduction are widespread in biology with well-assessed relevance in embryonic development, i.e., type1 epithelial-mesenchymal transition (EMT) (10). Their roles, however, have not been well explored under the broader
definition of EMT (11)—which includes events such as wound healing (type 2 EMT) and cancer (type 3 EMT)—despite promising
therapeutic results when mechanical stimulation is locally applied (12). Acupuncture needle stimulation (9) and low level laser therapy (13) are among the triggers that have been shown to initiate a series of synergistic events, including calcium waves, ATP fluxes (purinergic signaling), and changes in reactive oxygen and nitrogen species concentration, known to initiate healing (14, 15). The homeostatic effects of type 2 EMT include local changes in purinergic signaling, inflammation control, regeneration, and remodeling at the site of injury. By contrast, acupuncture is recommended for systemic diseases like rheumatoid arthritis (1) and is thought to act in a more global fashion. Using the framework we propose here, we can investigate the long range, systemic effects of mechanotransduction by building on what has already been reported about the wound healing process, including the presence of peripheral markers of EMT (16).
To explore the long range effects of acupuncture, multiomic analysis of molecular events—occurring proximally (acupoint),
distally from the stimulation point (target organ), and systemically (blood and gastrointestinal microbiome)—can be used to construct a spatial analysis (17). This information can then be enriched with data about the temporal onset of early gene expression, in addition to later time points (Figure 1A. See in link above!) to construct a systems biology view (network) of the biochemical events.
To build such networks and identify new targets for diagnosis and therapy, computational analysis must bring together
the different ‘omics approaches (Figure 1B), coupled with the requisite temporal and spatial resolution of the data (19). This
type of network approach can identify the most important molecules from the thousands to tens-of-thousands of interactions
and hundreds-to-thousands of molecules analyzed, also taking into account distal factors that might play a role in causing or
modulating the pathologies. Furthermore, the identification of additional markers is made possible with a complementary approach to the high throughput and low sensitivity of these ‘omics analyses. This can be imagined in the form of a nanobiochip that is the size
and shape of an acupuncture needle (hence, an “intelligent” needle or i-needle) (Figure 1C).
Toward this end, we recently created a proof-of-principle miniaturized platform, integrating revolutionary carbon nanotubes and nanographite petals, which can monitor five endogenous human metabolites using highly sensitive and selective nanobiosensors (20). The electronics needed to acquire and transfer the detected signal have already been sufficiently
miniaturized (21) and can be powered by ultrathin polymerbased batteries (22) currently available on the market and able to meet the energy demands of the proposed i-needle (~80–130 μAh). The challenge for the realization of the i-needle has already
moved from the miniaturization to the integration step (23). Progress has already been made, based on recent reports of
the measurement and transmission of temperature, pH, and endogenous metabolite data using single-platform enzymecarbon
nanotube hybrid sensors (24, 25) .

Conclusions
Overall, it is our hope that this research can provide a more unified approach to understanding the complex nature of patient responses to acupuncture—including effects as diverse as the control of pain, degeneration, and inflammation—and to addressing fundamental issues in acupuncture treatment, such as the frequency of delivery, developing more precise therapeutic indications, and establishing proper “dosage” guidelines. These steps will undoubted encourage acceptance of acupuncture as a complementary and/or alternative personalized treatment, with important application in a wide variety of areas including pain control, and degenerative and chronic inflammatory diseases, among others.
References
1. WHO, Acupuncture: review and analysis of reports on controlled
clinical trials. (WHO, Geneva, Switzerland, 2002), http://apps.
who.int/iris/handle/10665/42414.
2. G. Burnstock, Med. Hypotheses 73, 470 (2009).
3. Y. Ding et al., BMC Neurosci. 10, 35 (2009).
4. Y. Liu et al., PLOS ONE 8, e51573 (2013).
5. N. Azorin et al., Exp. Dermatol. 20, 401 (2011).
6. W. Z. Tu et al., Neurochem. Int. 60, 379 (2012).
7. H. van Wietmarschen et al., J. Clin. Rheumatol. 15, 330 (2009).
8. J. Jia et al., J. Ethnopharmacol. 140, 594 (2012).
9. H. M. Langevin, N. A. Bouffard, G. J. Badger, D. L. Churchill, A. K.
Howe, J. Cell. Physiol. 207, 767 (2006).
10. A. Mammoto, T. Mammoto, D. E. Ingber, J. Cell Sci. 125, 3061 (2012).
11. R. Kalluri, R. A. Weinberg, J. Clin. Invest. 119, 1420 (2009).
12. V. W. Wong, S. Akaishi, M. T. Longaker, G. C. Gurtner, J. Invest.
Dermatol. 131, 2186 (2011).
13. E. Takai, M. Tsukimoto, H. Harada, S. Kojima, Radiat. Res. 175,
358 (2011).
14. J. V. Cordeiro, A. Jacinto, Nat. Rev. Mol. Cell. Biol. 14, 249 (2014).
15. G. C. Gurtner, S. Werner, Y. Barrandon, M. T. Longaker, Nature
453, 314 (2008).
16. M. Mildner et al., PLOS ONE 8, e60103 (2013).
17. S. E. Winter, C. A. Lopez, A. J. Baumler, EMBO Rep. 14, 319 (2013).
18. P. Tieri, X. Zhou, L. Zhu, C. Nardini, Front. Cell Dev. Biol., 2, Epub
before print (2014), doi: 10.3389/fcell.2014.00059.
19. B. Kholodenko, M. B. Yaffe, W. Kolch, Sci. Signal. 5, re1 (2012).
20. I. Taurino et al., Nano Letters 14, 6 (2014).
21. J. Yoo, L. Yan, S. Lee, Y. Kim, H.-J. Yoo, IEEE Journal of Solid-State
Circuits 45, 178-188 (2010).
22. H. Nishide, K. Oyaizu, Science 319, 737 (2008).
23. T. E. Kazior, Philosophical Transactions of the Royal Society A:
Mathematical, Physical and Engineering Sciences 372, 20130105
(2014).
24. A. Cavallini, C. Baj-Rossi, S. Ghoreishizadeh, G. De Micheli, S.
Carrara, Biomedical Circuits and Systems Conference (BioCAS),
2012 IEEE, pp. 124 (2012).
25. S. Carrara, A. Cavallini, S. Ghoreishizadeh, J. Olivo, G. De
Micheli, in IEEE Sensors conference, (2012), pp. 28–31.
Autonomic