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* Samueli Institute for Information Biology, Alexandria, Virginia, 22314, USA
Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA;
Walter Reed Army Institute of Research, Washington, D.C., USA;
University of Maryland, College Park, Maryland, USA;
|| George Mason University, Fairfax, Virginia, USA;
¶ American University, Washington, D.C., USA; and
** Defense Advance Research Projects Agency, Arlington, Virginia, USA
2Correspondence: Samueli Institute, 1700 Diagonal Rd., Suite 400, Alexandria, VA 22314, USA. E-mail: wjonas{at}siib.org
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ABSTRACT |
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 TOP ABSTRACT BACKGROUNDMANAGING SOCIAL CONFLICTSOCIAL CONFLICT IN MEDICAL...INSERTION OF SOCIAL MANAGEMENT...THE DIGITAL BIOLOGY EXPERIMENTSAPPLICATION OF THE SOCIAL...SUMMARY AND CONCLUSIONSREFERENCES |
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Key Words: social management of science • controversial science claims • electromagnetic signals • digital biology • thrombin • fibrinogen • coagulation
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BACKGROUND |
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TOPABSTRACTBACKGROUNDMANAGING SOCIAL CONFLICTSOCIAL CONFLICT IN MEDICAL...INSERTION OF SOCIAL MANAGEMENT...THE DIGITAL BIOLOGY EXPERIMENTSAPPLICATION OF THE SOCIAL...SUMMARY AND CONCLUSIONSREFERENCES |
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. The article was accompanied by an editorial expressing disbelief in the phenomenon (2)
. In an unorthodox and controversial move, Naturebypassed the normal scientific process of waiting for independent replication and sent a team to investigate the claim in Benveniste’s laboratory. Under emotionally charged conditions, they performed and subsequently published their results (3)
. Charge and counter-charge ensued (mostly in the press) (4)
while subsequent attempts to scientifically investigate the claim produced negative (5)
, equivocal (6
, 7
), and positive (8
, 9
) reports. While the topic largely disappeared from the scientific literature, it continues to be discussed and evaluated by the public in prominent media places such as the BBC and ABC’s 20/20 (http://www.abcnews.go.com/sections/2020/GiveMeABreak/GMAB_homeopathy_040130-1.html).
During the ensuing years Prof. Benveniste has pursued possible mechanisms for the claimed effect. He reported in The FASEB Journal and in other journals that he could mimic the electromagnetic nature of molecule-receptor interaction with digital signals to which biological systems respond in a specific manner (10
, 11
) (www.digibioSA.com). While it is known that molecular interactions in biological systems can be influenced by low-level electromagnetic fields (12)
, the ability to specify and deliver signals through electromagnetic mechanisms has not otherwise been reported. The implications for digitally mimicked biological signals led the Defense Advance Research Projects Agency (DARPA), a branch of the U.S. Department of Defense, to solicit us for an independent replication of Prof. Benveniste’s reports.
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MANAGING SOCIAL CONFLICT |
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TOPABSTRACTBACKGROUNDMANAGING SOCIAL CONFLICTSOCIAL CONFLICT IN MEDICAL...INSERTION OF SOCIAL MANAGEMENT...THE DIGITAL BIOLOGY EXPERIMENTSAPPLICATION OF THE SOCIAL...SUMMARY AND CONCLUSIONSREFERENCES |
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, 14
). The process is an adaptation from interactional communication theory and identifies four conflict discourse "frames" that focus communication among parties around interactional "triggers" of conflict escalation and de-escalation (15
–17
). These four frames and their corresponding behaviors revolve around 1) substantive disagreements around the goals and methods of the research effort; 2) attunement issues (e.g., degree of trust among members of the research team, acceptability of different levels of power and influence, demonstrated consideration for one another); 3) face concerns (e.g., degree to which member of the research team perceive their reputation or professional identity is being attacked or honored); and 4) emotional discord (e.g., degree to which negative emotions arise that increase mistrust and inhibit accomplishment of research objectives). |
SOCIAL CONFLICT IN MEDICAL RESEARCH |
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TOPABSTRACTBACKGROUNDMANAGING SOCIAL CONFLICTSOCIAL CONFLICT IN MEDICAL...INSERTION OF SOCIAL MANAGEMENT...THE DIGITAL BIOLOGY EXPERIMENTSAPPLICATION OF THE SOCIAL...SUMMARY AND CONCLUSIONSREFERENCES |
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, 19
); 2) a one-day meeting that focused on the design and implementation of a process for managing conflict dynamics interpretation and judgment processes of OAM sponsored research (20)
; and 3) a 3-day meeting and follow-up activities that involved 80 cancer researchers and practitioners from the complementary and alternative cancer therapy community and researchers from the NCI. The focus of this effort was on the development of conflict management processes to be integrated into a practice outcomes monitoring and evaluation system (POMES) designed to address research questions related to controversial cancer therapies (21)
.
These findings indicated that difficulties negatively affecting the conduct of research efforts on controversial topics not only reflect differences of opinion on substantive issues (e.g., research protocols, appropriate statistical analysis) but, more important, reflect social conflict among the parties. This leads to interpretive and judgment biases that result in selective filtering and recall of data, dismissal of nonconforming data, increased mistrust of the motives of the other party, reduced ability to empathize with the other, simplified thinking, and over-reliance on prior expectations and experiences (18
, 19
).
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INSERTION OF SOCIAL MANAGEMENT INTO A RESEARCH PROJECT |
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TOPABSTRACTBACKGROUNDMANAGING SOCIAL CONFLICTSOCIAL CONFLICT IN MEDICAL...INSERTION OF SOCIAL MANAGEMENT...THE DIGITAL BIOLOGY EXPERIMENTSAPPLICATION OF THE SOCIAL...SUMMARY AND CONCLUSIONSREFERENCES |
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THE DIGITAL BIOLOGY EXPERIMENTS |
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TOPABSTRACTBACKGROUNDMANAGING SOCIAL CONFLICTSOCIAL CONFLICT IN MEDICAL...INSERTION OF SOCIAL MANAGEMENT...THE DIGITAL BIOLOGY EXPERIMENTSAPPLICATION OF THE SOCIAL...SUMMARY AND CONCLUSIONSREFERENCES |
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The pre-pilot phase was conducted in our laboratory July 14–21, 2001 by Didier Guillonnet and Jamal Aissa from Benveniste’s laboratory. They set up an Automated Bio-Analyzer (ABA) machine that delivers the digital signals and automatically runs the experiments (Fig. 1
A). A number of modifications to the ABA program and protocol were implemented and verified at that time, including blinding and randomization procedures. These same two individuals, along with Prof. Benveniste, returned on October 30 through November 3, 2001 to run the first pilot phase under the modified protocol. The test phase was then conducted by our research team using the same protocol, but independent of Prof. Benveniste’s team. Between the pre-pilot and pilot experiments, a series of blank runs were made on the ABA with water signals to estimate sample sizes required to observe a 20% effect difference between groups with a P value set at 0.05 for hypothesis determination.
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According to Prof. Benveniste, a biological system that can be used to detect digital signals is the inhibition of fibrinogen-thrombin coagulation by a digitized thrombin inhibitor (DTI). Our working hypothesis was that there would be no difference in coagulation rates between groups exposed to a digitized thrombin inhibitor (DTI), a digitized water signal (WAT), and no signal (NS), as measured by optical density (OD) curves of samples exposed to these signals.
To maximize objectivity and reliability, the DigiBio research team set up an ABA robot that performs the entire protocol, including sample pipetting and delivery of the signal, with minimal human intervention (Fig. 1A, B
). The protocol can be summarized as follows. Thrombin dissolved in water (0.3 µg/mL) is exposed to the computer recording of either DTI or control solutions consisting of either no electromagnetic (EM) signal or water signal. After exposure, the samples are mixed with fibrinogen (24 mg/mL in 0.1% sodium azide, to prevent bacterial growth) and distributed in 96-well plates. Coagulation was assessed by spectrophotometry and expressed as OD. The entire procedure was automated except for data analysis, stock preparation of fibrinogen and thrombin, and their loading into ABA reservoirs.
The basic ABA procedure is as follows. After placement of fibrinogen and thrombin into appropriate reservoirs, the computer program is started. The machine distributes the thrombin into Eppendorf tubes and then places the tube into an EM coil inside of a grounded copper tube open at the top. The computer "informs" the water through the EM coil with the digital signal for 10 min and moves the tube back to the holding rack. It then proceeds in the same way for the other tubes. The recordings (DTI signal, WAT signal, NS) are "played" in a random order determined by an Excel program on start-up of the computer. The "informed" thrombin is then mixed with fibrinogen and 2 aliquots of the thrombin/fibrinogen mixture are distributed side-by-side into the wells of a 96-well microplate where coagulation starts (Fig. 1
A).The latter is read every 60 s for 1 h using a spectrophotometer and the program saves the data to an Excel spreadsheet. Analysis of this data was done by hand to determine coagulation kinetics.
It was agreed at the start of the study that only pilot phase and test phase data would be formally analyzed. However, in the pre-pilot phase, DTI, WAT, and NS samples each produced apparent delayed coagulation (decreased rate of OD increase) 25% of the time, indicating some digital effect might be occurring.
Experiments done between the pre-pilot and pilot phases were designed to determine variance and estimate a sample size sufficient to detect a 20% difference between groups. These data were obtained with NS and WAT treated samples only. The ABA machine produced a variance of < 1% standard error, suggesting that four experiments would be sufficient to detect a 20% difference between samples. No digital effects were seen in 17 experiments with two investigators during this pre-pilot phase. (Fig. 2
A)
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During the pilot phase (October 30–November 3, 2001) Prof. Benveniste and his team conducted 16 experiments using the three-group design. While these experiments were randomized it was possible to determine when no signal (NS) was the experimental condition by observing a volt/ohm meter placed in parallel with the signal playing from the computer to coil. However, it was not possible to determine when the DTI or WAT signals were being played. Coagulation differences between WAT and NS were < 1%. However, in 7 of the experiments a 21–28% decrease in mean coagulation rate was observed for DTI (Fig. 2B
).
A subgroup analysis of pilot phase data showed that all DTI effects occurred when experiments were conducted by one member of Benveniste’s team (Jamal Aissa) and that this usually occurred when using a split sample technique in which he interrupted the operation of the ABA machine to do manual plating followed by the automated plating. Two of the 16 experiments done only by the ABA machine (no interruption) showed effects when Jamal was present. In three instances Jamal set up experiments and then left for the day. None of these showed DTI effects. An example curve of a "successful" experiment is shown in Fig. 2B
.
After November 3, 2001, the independent test phase was conducted. This consisted of 29 formal experiments run by six different investigators in two different laboratories using the same three group design conducted in the pilot phase. Mean coagulation differences between groups was 1–2% and nonsignificant (ANOVA F=0.04, P=0.96; Kruskal-Wallis Chi square=0.06, P=0.97). The mean optical densities at 40 min were 1.08 ± 0.1 (95% CI: 1.05–1.12), 1.08 ± 0.09 (95% CI: 1.05–1.11), and 1.09 ± 0.08 (95% CI: 1.06–1.12) for DTI, water, and blank, respectively. An example curve is seen in Fig. 2C
.
After the test phase was completed, we conducted several experiments using varying concentrations of thrombin and fibrinogen with and without digital signals in an attempt to reproduce the curves illustrated in Fig. 2B
with known chemicals. No combination of sample splitting or concentration change exactly reproduced the curves of the Benveniste team. However, adding between 100 and 200 µL water or between 2 and 4 ng/mL of the thrombin inhibitor Lepirudin per well resulted in a decreased coagulation rate (Fig. 2D
). Statistical results similar to the pilot phase experiments can be produced with these techniques, although the coagulation curve is flatter and more linear than those produced in the pilot phase experiments. In addition, the volume differences required to produce such effects are easily distinguished by visual inspection. There was no evidence of volume or concentration irregularities seen during any of the pilot phase experiments.
A detailed analysis of the digital signal conducted by our electrical engineer found no differences between DTI and WAT signals. A simple voltmeter (tester in Fig. 1B
) was used to measure the average signal strength being applied to the coils. This was used to verify that a signal was in fact being presented to the samples. The same voltage, and thus signal intensity, was observed when each of these signals was presented to the samples.
Verification of procedural protocols was provided by our consultants in statistics and hematology.
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APPLICATION OF THE SOCIAL MANAGEMENT PROCESS |
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TOPABSTRACTBACKGROUNDMANAGING SOCIAL CONFLICTSOCIAL CONFLICT IN MEDICAL...INSERTION OF SOCIAL MANAGEMENT...THE DIGITAL BIOLOGY EXPERIMENTSAPPLICATION OF THE SOCIAL...SUMMARY AND CONCLUSIONSREFERENCES |
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Results from these interviews revealed strong agreement among all interviewed that the study was progressing well and certain challenges were identified and were thoroughly discussed to everyone’s satisfaction. Prof. Benveniste’s group felt their visit and set-up of the experiment was very successful. To question #4, concerns were expressed by Prof. Benveniste’s group concerning the kinds of interpretations one might give to findings of no significance or inconsistent findings. These concerns were directly addressed, again to everyone’s satisfaction, in an explicit statement by the research team regarding these issues in a follow-up memo of October 31, 2001 (not shown). Responses to the remaining two questions indicated lack of conflict or disagreements in these areas. The overall dynamics of the American research team’s interaction with the Digibio members as well as among themselves was respectful of differences of opinion and collaborative in decision-making.
A quantitative assessment of the degree of group consensus within the American research team was achieved. A 21-item Consensus Scale was administered following the conclusion of the research effort to the five members of the American research team. Responses were obtained from three members. This measure, which obtained reliability coefficients of 0.89 in previous administrations, assessed the degree of agreement of respondents to five dimensions of group consensus: effectiveness of decision-making process, individual agreement with the group decision, group members’ relations, individual effectiveness and opportunity to participate. Results indicated a mean scale score of 4.53/5.00 (4.61, 4.33, and 4.71), suggesting a high degree of overall group consensus among research team members (22)
. Overall ratings among the three respondents on each of the five subscales of the social consensus measure were similarly high. Overall, all three respondents were in high agreement with one another concerning the strong level of social consensus obtained among the members. The social and communication process was characterized as open in providing opportunities for improvement in communication, establishing fair and consistent protocols for the study, and respectful and direct resolution of differences of opinion during the experiment. Thus, there was an avoidance of the perils of "groupthink phenomena" (23
, 24
).
In addition, a set of guiding principles was established that aided the research team in addressing some important changes/concerns expressed by Prof. Benveniste and his associates at different times during the study as well as facilitating agreement on the interpretation and judgment of the obtained findings by the research team. Prof. Benveniste provided comments on these test results and suggestions but no editing or authorship on the final report for DARPA. For example, one guideline stated, "differing interpretations of the meaning and implications of the data are possible and can be reflected in individual research team member reports." This guideline permitted alternative voices to be heard if full agreement on specific issues or findings was not obtained.
Overall, a fair and effective communicative process was established and maintained around research conducted on a highly controversial topic. Thus, the social management process allowed for the project to be completed in a fair and collegial manner in a highly controversial area of science.
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SUMMARY AND CONCLUSIONS |
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TOPABSTRACTBACKGROUNDMANAGING SOCIAL CONFLICTSOCIAL CONFLICT IN MEDICAL...INSERTION OF SOCIAL MANAGEMENT...THE DIGITAL BIOLOGY EXPERIMENTSAPPLICATION OF THE SOCIAL...SUMMARY AND CONCLUSIONSREFERENCES |
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Prof. Benveniste died on October 3, 2004. Before he passed away, however, he posited unknown interactions with digital signals that produce these effects and states that he observed similar experimenter variability in his laboratory (personal communication). He stated that certain individuals consistently get digital effects and other individuals get no effects or block those effects. While it is possible that other, unknown "experimenter" factors, such as the influence of chemical residues, energetic emanations or intentionality from individual experimenters could be an explanation for these findings, we did not test these hypotheses nor developed a framework that would control for such factors. Without such a framework, continued research on this approach to digital biology would be at worst an endless pursuit without likely conclusion, or at best premature (25)
.
In addition, we feel that this first incorporation of a systematic social management process into research on a controversial issue has proven useful and is a better skeptical approach to such claims than "armchair" criticism. Future research teams exploring claims that lie outside the normal paradigms of science should consider incorporating such procedures into their investigations.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Received for publication March 1, 2005. Accepted for publication September 23, 2005.
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REFERENCES |
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  A. R. Khuda-Bukhsh Laboratory research in homeopathy: pro. Integr Cancer Ther, December 1, 2006; 5(4): 320 - 332. [Abstract] [PDF]
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= water (sample not exposed to any digital signal); 
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= water signal (sample exposed to signal produced from plain water); •, 
= samples exposed to digitized signal produced from thrombin inhibitor (DTI). A) Example run produced by automated biological analyzer (ABA) with no signals running. Variation between samples was < 1% over 10 runs completed on 5 different days. B) Example run from "successful" experiment conducted by Jamal Aissa. Delayed coagulation was observed in samples exposed to DTI in 11 of 23 samples analyzed, average inhibition 24% (P<0.0001). C) Example curve selected from over 40 runs conducted by seven independent investigators on the digital effect. Variation between samples < 2% throughout replication attempts (P=0.96). D) Example coagulation inhibition differences produced by adding 5 concentrations of the thrombin inhibitor Lepirudin (a hirudin derivative) to test samples compared with digitized water and plain water demonstrating ability of ABA to produce a coagulation inhibition effect with a standard chemical thrombin inhibitor. • = 4 ng/mL;