Advantages of pure platelet-rich plasma compared with leukocyte- and platelet-rich plasma in promoting repair of bone defects
Authors: Xiaolin Li
Wenjing Yin
Yuelei Zhang
Xuetao Xie
Jiagen Sheng
Changqing Zhang
Zhengliang Xu
Shicong Tao
Xin Qi
Journal: Journal of Translational Medicine (2016)
Discipline: CLINICAL MEDICINE
Keywords: Animal model; Bone regeneration; Leukocyte- and platelet-rich plasma; Nuclear factor Œ∫B; Platelet-rich plasma
Affiliations: Department of Orthopaedic Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China., Department of Orthopaedic Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China. .

Abstract:  Background: High levels of pro-inflammatory cytokines in leukocyte- and platelet-rich plasma (L-PRP) may activate the nuclear factor Œ∫B (NF-Œ∫B) pathway to counter the beneficial effect of the growth factors on bone regeneration. However, to date, no relevant studies have substantiated this. Methods: L-PRP and pure platelet-rich plasma (P-PRP) were isolated. The in vitro effects of L-PRP and P-PRP on the proliferation, viability and migration of human bone marrow-derived mesenchymal stem cells (HBMSCs) and EaHy926, tube formation of EaHy926, and osteogenic differentiation of HBMSCs were assessed by cell counting, flow cytometry, scratch assay, tube formation assay, and real-time quantitative polymerase chain reaction (RT-PCR), western blotting and Alizarin red staining, respectively. The in vitro effects of L-PRP and P-PRP on the nuclear translocation of NF-Œ∫B p65, mRNA expression of inducible nitric oxide synthase and cyclooxygenase-2, and production of prostaglandin E2 and nitric oxid were assessed by western blotting, RT-PCR, enzyme-linked immunosorbent assay and Griess reaction, respectively. The in vivo effects of L-PRP or P-PRP preprocessed Œ≤-tricalcium phosphate (Œ≤-TCP) on the calvarial defects in rats were assessed by histological and immunofluorescence examinations. Results: P-PRP, which had similar platelet and growth factors concentrations but significantly lower concentrations of leukocytes and pro-inflammatory cytokines compared with L-PRP, promoted the proliferation, viability and migration of HBMSCs and EaHy926, tube formation of EaHy926 and osteogenic differentiation of HBMSCs in vitro, compared with L-PRP. The implantation of P-PRP preprocessed Œ≤-TCP also yielded better histological results than the implantation of L-PRP preprocessed Œ≤-TCP in vivo. Moreover, L-PRP treatment resulted in the activation of the NF-Œ∫B pathway in HBMSCs and EaHy926 in vitro while the postoperative delivery of caffeic acid phenethyl ester, an inhibitor of NF-Œ∫B activation, enhanced the histological results of the implantation of L-PRP preprocessed Œ≤-TCP in vivo. Conclusions: Leukocytes in L-PRP may activate the NF-Œ∫B pathway via the increased pro-inflammatory cytokines to induce the inferior effects on bone regeneration of L-PRP compared with P-PRP. Hence, P-PRP may be more suitable for bone regeneration compared with L-PRP, and the combined use of P-PRP and Œ≤-TCP represents a safe, simple, and effective alternative option for autogenous bone graft in the treatment of bone defects. 

Retracted: No

PubPeer comments:
 There are similarities between the aforementioned paper (Published in *IEEE Sensors Journal* in 2019) and the following paper Title: ***A Comparison of Key Distribution Schemes Using Fuzzy Commitment and Fuzzy Vault within Wireless Body Area Networks*** Published in **IEEE 26th International Symposium on Personal, Indoor and Mobile Radio Communications u2013 (PIMRC): Services Applications and Business in 2015** by the following authors: *G.Zheng, G.Fang, M.A.Orgun* and *R.Shankaran* though apparently the 2015 paper was never cited in the 2019 paper. , 
 Keep in mind, one is a scholarly journal and the other is a conference proceeding. , 
 #2, FYI: https: journals.ieeeauthorcenter.ieee.org become-an-ieee-journal-author publishing-ethics publish-original-research  Publish Original Research When submitting your article for publication to IEEE, it should: contain original research that has not been published before; and not be submitted to any other publication while you await a peer review decision. IEEE recognizes that technical research is often published first as a conference article with preliminary findings. As those initial findings become fully developed, the conference article can evolve into a journal article which contains your more developed research and conclusions. IEEE supports this evolutionary publishing process provided that: Both the conference and journal articles undergo standard peer review. The journal article contains substantially more technical information than the conference article. The journal article cites the conference article and clearly indicates how the two articles differ. Source: IEEE Publication Services and Products Board Operations Manual, Section 8.1.7.E. , 
 Both papers were published by IEEE outlets and apparently self-plagiarism is against IEEE Publication Policy , 
 An overview of the similarities between the two papers are as follows: **AIMS OF THE TWO PAPERS:** Both papers compare two techniques called Fuzzy Commitment and Fuzzy Vaults for ECG Signal based Key Distribution. To do this, both the papers provide an introduction to the subject area and then provide models of the two techniques before comparing the two techniques. **Introduction:** The 2015 paper gives an introduction to security in Wireless Body Area Networks while the 2019 paper gives an introduction to security in Wearable and Implantable Devices. **Modelling:** Both the papers use mathematical models to depict the two techniques, Fuzzy Commitment and Fuzzy Vault. In the 2015 paper, Section II, titled **SYSTEM MODELLING** presents the mathematical models of Fuzzy Commitment and Fuzzy Vaults to process ECG Key distribution. In the 2019 paper, Section III, titled **MATHEMATICAL MODELS** presents mathematical models of the Fuzzy Commitment and Fuzzy Vaults to process ECG Key distribution. The Fuzzy Commitment is depicted in Fig-1 in the 2015 paper while it is depicted in Fig-2 in the 2019 paper. Fuzzy Vault is depicted in Fig-2 in the 2015 paper while it is depicted in Fig-3 in the 2019 paper. The common workflows are depicted in Fig-4 in both the papers. There seem to be no major differences in the models across the 2015 and 2019 papers. (though there is a bug, which may have occurred during transcribing, in the 2019 paper). **Compare the two techniques:** Section III of the 2015 paper compares the two techniques and Section IV of the 2019 paper compares the two techniques. The 2015 paper uses the term Common Workflows and the 2019 paper uses the term Common Process Flow in the respective sections. Fig-4 in both the papers depict the common workflow or common process flow. Other aspects such as obtaining ECG measurements and the process of using Error Correcting Codes (ECC) are covered in both the papers with the 2019 paper providing more details. The process of key hiding and subsequent key revealing is covered under various subsections in the respective sections (Section III of 2015 paper and Section IV of 2019 paper) In the course of their discussion, the 2015 paper states *. . . however, the fuzzy commitment scheme has to generate two random BSs with high matching performance at two ends respectively. . .* while the 2019 paper states *. . . it is still challenging to generate two random ECG BSes synchronously with a very high matching performance . . .* Both the papers say that the resources used by Fuzzy Commitment is lesser than the Fuzzy Vault. The 2015 paper while talking about FAR and FRR states this: *. . . the FAR decreases when the polynomial degree v increases. This is because a larger v requires more feature values in sets A and B overlapping with one another in order to successfully reveal the key K, thus the probability of a mismatch between two IPI data sets decreases. It also shows that the FRR increases when the polynomial degree v increases. This is due to the fact that, when more overlapped features in two ECG data sets are required, it is more likely for the fuzzy vault scheme to incorrectly reject the received vault . . .* while the 2019 paper says this: *. . . The FAR decrease dramatically when the polynomial order, v, increases. This is because a higher polynomial order requires more matching ECG IPIs in order to successfully reveal the key. On the other hand, the FRR increases when the the polynomial order increases. This is due to the fact that, when more matching IPIs in two ECG data sets are required, it is more likely for the fuzzy vault scheme to incorrectly reject the revealment of the key . . . .* In this context Table-1 in the 2015 paper may correspond to Fig-10 in the 2019 paper though there are more details in the 2019 paper. While explaining the complexity of Polynomial computations, the 2015 paper states *.....We also find from the simulation study that the polynomial calculation and reconstruction in the fuzzy vault scheme consumes resources. As discussed in [13], [14], the order of the polynomial can vary from $6$ to $12$. We take the lowest order polynomial to distribute a $56$-bit key for example. The equation of the polynomial, $p(x)$, is denoted by $p(x)$=$c_{6}x^6$ + $c_{5}x^5$ ...+ $c_{1}x$ + $c_{0}$ in which the coefficients are concatenated to form the 56-bit key $K$ = $c_{6}$|$c_{5}$|...|$c_{0}$. So, each coefficient, $c_{i}$, is $8$-bit long. For a normal sinus rhythm with a heart rate of 60-100 beats per minute, IPI values vary from 600ms to 1000ms. So, for the term with the highest degree, $c_{6}x^6$, its value varies from $2^{62}$ to $2^{68}$. Even if we use its variation part, from 0 to 400ms, the calculation result of $c_{6}x^6$ may be still up to $2^{62}$. Therefore, calculating the tpolynomial consumes resources and there may be a risk of a buffer overflow to the lightweight wireless sensors....* while the 2019 paper states *.... According to this experiment, the order of the polynomial, t$v$, can vary from $1$ to $15$. We construct a polynomial, $p(x)$, with a moderate order (e.g., v = 7) to distribute a 64-bit key as an example. The polynomial can be denoted by $p(x)$ = $c_{7}x^7$ + $c_{6}x^6$ + ... + $c_{1}x$ + $c_{0}$ in which the coefficients are concatenated to form the 64-bit key $K$ = $c_{7}$|$c_{6}$|...|$c_{0}$. So, each coefficient, $c_{i}$ , is 8-bit long. For a normal sinus rhythm with a heart rate of 60-100 beats per minute, IPI values vary from 600ms to 1000ms, that is, (29-210)ms. When evaluating $p(x)$, the variable, $x$, is substituted by the IPI values. So, the value range of the term with the highest degree in $p(x)$, $c_{7}x^7$, is $(2^{71}-2^{78})$, which is extremely out of the computation capability of current computing systems. Although some normalization process could be done to decrease these IPI values, its computation cost is still high. Therefore, the polynomial evaluation may have a risk of generating out-of-memory errors in lightweight WIMD nodes ...* , 
 There are similarities between the aforementioned paper (Published in *IEEE Sensors Journal* in 2019) and the following paper Title: ***A Comparison of Key Distribution Schemes Using Fuzzy Commitment and Fuzzy Vault within Wireless Body Area Networks*** Published in **IEEE 26th International Symposium on Personal, Indoor and Mobile Radio Communications u2013 (PIMRC): Services Applications and Business in 2015** by the following authors: *G.Zheng, G.Fang, M.A.Orgun* and *R.Shankaran* though apparently the 2015 paper was never cited in the 2019 paper. , 
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