Inhibitory effect of vanillin on RANKL-induced osteoclast formation and function through activating mitochondrial-dependent apoptosis signaling pathway
Authors: Tao Yu
Lan Zhou
Yueqi Chen
Xiaolong Yin
Jianmei Li
Mengmeng Liang
Yufeng Zhao
Jin Yi
Fei Kang
Wei Luo
Ruohui Tang
Ce Dou
Shiwu Dong
Journal: Life Sciences (2018)
Discipline: BIOLOGY & BIOCHEMISTRY
Keywords: Apoptosis; Bone resorption; Formation; Osteoclast; Vanillin.
Affiliations: Department of Biomedical Materials Science, School of Biomedical Engineering, Third Military Medical University (Army Medical University), Chongqing 400038, China; Battalion Five of Basic Medical Sciences, Third Military Medical University (Army Medical University), Chongqing 400038, China., Department of Biomedical Materials Science, School of Biomedical Engineering, Third Military Medical University (Army Medical University), Chongqing 400038, China., Center of Trauma, Daping Hospital, Third Military Medical University (Army Medical University), Chongqing 400042, China. Electronic address: ., Department of Biomedical Materials Science, School of Biomedical Engineering, Third Military Medical University (Army Medical University), Chongqing 400038, China; State Key Laboratory of Trauma, Burns and Combined Injury, Third Military Medical University (Army Medical University), Chongqing 400038, China. Electronic address: .

Abstract:  Bone matrix homeostasis associated diseases such as osteoporosis and erosive arthritis were caused by the imbalance of osteoclast-mediated bone-resorption and osteoblast-mediated bone-formation. Suppressing the fusion and differentiation of osteoclast from osteoclast precursors are an essential way to maintain the dynamic balance of resorption and formation. Recently, some natural products were discovered to inhibit osteoclast formation and function for potential treatment of osteoporosis. Vanillin was previously reported to have anti-tumor and anti-oxidant activities; however, its effect on bone health has not been elucidated. In this study, we found that the inhibitory effect of vanillin on RANKL-induced multinucleated osteoclast formation and bone resorption (concentration of 0.25 mM-2.5 mM). Morphologically, the number of mature osteoclasts was decreased after treating with vanillin in a dose-dependent manner, which was evaluated by TRAP staining and FAK staining. Vanillin could significantly inhibit bone resorption and promote the early apoptosis rate during RANKL-induced osteoclastogenesis. Furthermore, vanillin could activate the mitochondrial-dependent apoptosis via inducing the expression of cytochrome c, cleaved caspease-3, BAX and Apaf-1 both on mRNA and protein level. Otherwise, the expression of Bcl-2 was inhibited. Thereby, these data provide the clue that vanillin could be a candidate to treat bone matrix metabolic diseases. 

Retracted: No

PubPeer comments:
 The paragraph below is quoted for reference purpose, which from Prof. Liao, one of the authors of this paper, commented himself on https: pubpeer.com publications CF3471D0BE439BE323364A399498A9#2 *u201dWe would like to state that the PLTS is a new information representation form proposed by our group. It is just at its beginning stage as a new theory. Many scholars have already tried to improve and perfect this theory in the past years after it was proposed. Some of the debates mentioned in the comments have already been discussed and solved publicly by many scholars. As we know, in the development of the majority of disciplines, there are always many debates from different research teams. It is these debates that promote the development of research. It is nearly impossible to get a perfect theory in any research community, especially at the beginning of a new theory. We would like to thank you very much for your comments. They are a great help for us to further enhance this theory. Should you still have any question, please feel free to let us know.u201d* This 2019 paper are authored by J. Li, **L. Luo**, X. Wu, C. Liao, **H. Liao**, and W. Shen and belongs to a series of paper on inpatient admission in the West China Hospital. Another paper on this series, authored by **H. Liao**, C. Zhang, **L. Luo**, Z. Xu, J. Yang, and D. Xu, is Liao, H. et al. (2020). Distance-based intuitionistic multiplicative multiple criteria decision-making methods for healthcare management in West China Hospital. Expert Systems. 37: e12479. Doi: 10.1111 exsy.12479 This paper was commented on https: pubpeer.com publications 95CB34C28C43C1DBBB6F10FBE9A446 Another similar paper in this series, authored by T. Zhu, **L. Luo**, **H. Liao**, X. Zhang, W. Shen, is Zhu, T. et al. (2019). A hybrid multi-criteria decision making model for elective admission control in a Chinese public hospital. Knowledge-Based Systems. Volume 173, Pages 37-51. Doi: 10.1016 j.knosys.2019.02.020 This paper was commented on https: pubpeer.com publications 4B5683F29DC5135F86013819F4F3E4 **These three papers, with overlying authors, are not cited by each other.** Other than the issues on the patient number and the methods used, **the most puzzling part is the decision criteria and the expert teams used in these three papers.** In this 2019 paper in Applied Soft Computing Journal, the authors claimed in Section 3.1 that **u201d We firstly use u2018u2018priorityu2019u2019, u2018u2018patientsu2019u2019, u2018u2018waiting listu2019u2019, and u2018u2018criteriau2019u2019 as keywords to search in Wiley Inter Science, Web of Science, Scoops Pub Med, The Cochrane Library, Science Direct, and Springer databases (Time is up to December 31, 2017), and sort out 17 criteria. In addition, we consult domestic experts in related fields to add other 12 criteria. These 29 criteria for patientsu2019 prioritization can be further divided into five dimensions from the perspectives of disease, hospital, patient and society. These five dimensions include clinical and functional disorders ($C_1 $), expected outcomes ($C_2 $ ), social factors ($C_3 $ ), patient basic information ($C_4 $ ), and subject value and research development ($C_5 $ ), shown in Table 1.u201d** The authors also claimed in Section 3.2 for the expert teams that **u201dWe invite 51 authoritative and experienced experts (25 experts are from the health management field, 20 experts from clinical medicine, and 6 experts from other fields) from WCH to conduct questionnaire surveys on the indicators. We adopt the Delphi method to construct evaluation indicators for patientsu2019 admission prioritization (for more details, please refer to Shen et al. [46]). The questionnaire surveys with the above 51 experts are conducted in three rounds u2026 u201d** In the 2019 paper in Knowledge-Based Systems, the authors claimed for a team of 13 experts that **u201cA hospital administrator (the Dean who is in charge of WCH), the chief bed manager (who is in charge of the ASC of WCH), 8 bed schedulers (who are senior elective admission controllers of the ASC of WCH), one clinical manager of specialty care units (who is in charge of the urology specialty care unit we studied), and 2 senior nurses (who are nursing staff in the urology specialty care unit we studied).u201d** The authors also explained the reason for such an expert term **u201d(1) the hospital administrator can provide the top-level design of the goal for elective admission control from the perspective of the whole hospital, (2) the chief bed manager knows the contradiction between supply and demand of hospital, doctor, and patient most who can provide the medium-level design of the goal for elective admission control from the perspective of ASC, (3) 8 bed schedulers are the direct operators of elective admission control who know the control process and influencing criteria most, (4) the clinical manager of the urology specialty care unit can provide the bottom level design of the goal for elective admission control from the clinical perspective, and (5) 2 senior nurses of the urology specialty care unit know the disease characteristics of all awaiting elective patients from the nursing perspective.u201d** Then the authors described the way to choose the decision criteria by **a 30-min conversation with each expert, 10 criteria were set for their patient scheduling method** to rank the prioritization of inpatients, namely, **u201dSeverity Level (SL), Waiting Time (WT), Dual Referral (DR), Disease Type (DT), Insurance Location (IL), Insurance Type (IT), Case Mix Index (CMI), Research Value (RV), DRGs Cost (DRGC), and Average LoS (ALoS).u201d** In the 2020 paper in Expert Systems, the authors simply stated that the decision criteria were set by consulting experts and staff in the admission center and referring to the previous literatures. ![file] ![file] Can the authors comment on the three methods proposed separately in these three papers? Which one is best or would there be a more effective and reliable method? , 
 #1 Thanks for your interest in our work and sharing your concerns. It seems that you did not read the whole paper carefully. We would like to first state that your concerns are neither related to the theoretical model nor the main contributions of this paper. Thus, there is no any issue about the research integrity. Regarding your first concern, we were confused why you want us to cite our own papers. It was strange to have self-citations, no matter to say that these papers were published in different years. The idea of this paper comes from a practical problem of a Hospital. Initially, around 2015, the author Ting Zhu interviewed the hospital administrators, Then, they proposed an indicator system and used the modified 2-tuple DEMATEL to determine the relative weights of the evaluation criteria and the fuzzy VIKOR method to assess the alternatives (waiting patients) over the criteria. But the review process of their article (doi: 10.1016 j.knosys.2019.02.020) is relatively slow. It was finally accepted for publication in 2019. The last coauthor of our paper is a hospital administrator. He conducted a large-scale questionnaire survey in 2018. From this, a general Patient admission evaluation criteria system was obtained and the system in our paper was an expansion based on the previous article. From the perspective of patient relevance, this paper proposed to use the HFL-ORESTE method to rank patients. The HFL-ORESTE method is suitable for decision-makers to screen and rank patients whose situations are even similar. If you read any book or survey on MCDM (here we just recommend one famous book to you: Ku00f6ksalan, M., Wallenius, J., and Zionts, S. (2011). Multiple Criteria Decision Making: From Early History to the 21st Century. Singapore: World Scientific.), you will find that there are more than 20 MCDM methods. It is not wisdom to say which method is the best. Different methods have different advantages and applied scenarios. After giving the framework, the hospital can choose the appropriate screening method based on its own characteristics and actual situation. Hope you are now clear for your concerns. ,