Effect of Aegle marmelos leaf extract on N-methyl N-nitrosourea-induced hepatocarcinogensis in Balb/c mice
Authors: Okezie I Aruoma
Theeshan Bahorun
Arvind Kumar
Shalini Verma
Ranjan Kumar Singh
Journal: Pharmaceutical Biology (2013)
Discipline: PHARMACOLOGY & TOXICOLOGY
Keywords: Tobacco smoke; N-methyl N-nitrosourea; Aegle marmelos; hepatoprotective potential; MNU-induced hepatocarcinogenesis
Affiliations: School of Biotechnology, Faculty of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India.

Abstract:  Context and objective: Tobacco smoke and nitrostable foods containing N-methyl N-nitrosourea (MNU) are among the primary causes of liver cancer. To substantiate the beneficial claims ascribed to Aegle marmelos (L.) Corrêa (Rutaceae), the hepatoprotective potential of its leaf extract was studied using an MNU-induced hepatocarcinogenesis model in Balb/c mice. Materials and methods: After dose selection, 40 mice were randomly assigned to 4 groups: I (control), II (intraperitoneally (i.p.) primed with 50 mg/kg MNU), III (100 mg/kg A. marmelos hydroalcoholic extract (HEAM) i.p.) and IV (MNU + HEAM, i.p.). Inflammatory (IL-1β, IL-6), anti-inflammatory (IL-4) cytokine expression, apoptosis (Bcl-2) and tumor-related (p53, c-jun) genes were assessed at mRNA level. HEAM effects on hematological parameters were examined. Results and discussion: HEAM treatment decreased IL-1β, IL-6, Bcl-2 and c-jun respectively expressions by 90, 25, 53 and 30%, respectively. p53 and IL-4 expression was up-regulated by 1.5- and 2-fold. MNU decreased hemoglobin concentration (25%), lymphocyte count (42%) and increased leukocyte (100%), platelet (4-fold), neutrophil (43%), monocyte (10-fold) and eosinophil (10-fold) counts in Group II mice while HEAM modulated the same parameters by -7%, -21%, +24%, +3-fold, +12%, +3-fold and +4-fold, respectively, in MNU-induced mice compared to control. HEAM protective effect was confirmed by Raman spectroscopy where the MNU-induced peak at 1252 cm(-1) was normalized. DNA fragmentation data suggest apoptosis as one of the protective mechanisms of HEAM. Conclusion: The hepatoprotective, anti-carcinogenic and immunomodulatory effects of A. marmelos extract indicate potential beneficial effects in cancer therapy. 

Retracted: No

PubPeer comments:
 ## The mosaic in Fig. 6 appears to be a duplicate of a mosaic from another publication, both stated to have been obtained using different methods The bladder sequence mosaic shown in Fig. 6 of this publication (in IPTA, 2014, [pdf on research gate] ) is unexpectedly similar to the mosaic shown in Fig. 14(b) in [Anisotropic motion estimation on edge preserving Riesz wavelets for robust video mosaicing by Sharib Ali et al. in Pattern Recognition (2016)] (the link leads to the PubPeer discussion page of this publication). Each of these publications states to have used a different approach to mosaic this sequence. These two mosaics are shown together in 3 ways to appreciate their extreme similarity: 1. The two mosaics placed side by side: ![file] 2. The two mosaics are superimposed at the same zoom level in a GIF and shown alternatively at about 1s interval. This facilitates visual comparison. It can be observed that there is barely any difference between the two (very small differences, especially at borders, can be attributed to different printings). Out of dozens of images from which these mosaics are built, not a single image appears to be aligned differently despite the authors reported use of different image alignment approaches. ![file] 3. A difference image of the two mosaics at the same zoom level, which shows the matching regions in grey-level and dissimilar regions in magenta or green. ![file] The correspondence with the authors in the initial comments on the PubPeer page of [Anisotropic motion estimation on edge preserving Riesz wavelets for robust video mosaicing by Sharib Ali et al. in Pattern Recognition (2016)] illustrates why this extreme similarity is unexpected. The apparent lack of difference between these mosaics is even more surprising since the authors fervidly emphasize the singular attributes and performance of each of the approaches for obtaining these mosaics in the face of various challenges. I have highlighted below some such descriptions regarding these mosaics in some excerpts from these publications: ![file] In both cases, the authors have stated that there is some visible misalignment. How is it that this misalignment appears to be the same in the two mosaics that are supposed to have been obtained using different methods? Furthermore, the processing time per frame for the mosaic of the Pattern Recognition article is given as 0.4 s in the article and given as 1 s on page 127 of Sharib Ali s dissertation ([Total variational optical flow for robust and accurate bladder image mosaicing] , [pdf](http: docnum.univ-lorraine.fr public DDOC_T_2016_0006_ALI.pdf)) for the same method (AOFW method is linked to [Ali et al., 2015b] on page 93 of the dissertation and this reference is indeed the Pattern Recognition article evoked here). , 
 ## The mosaic in Fig. 5 of this publication appears to be a duplicate of another mosaic from another publication. The approaches stated to have been used to obtain them have some differences. The other mosaic in question is from Figure 5 (a) [Robust bladder image registration by redefining data-term in total variational approach by Ali et al. in SPIE (2015)] ([pdf] ). One notable difference between the approaches stated to have been used in the two publications is that this publication (from IPTA, [pdf] ) introduces the deactivation of the regularization term at the contours of the blood vessels (to preserve supposed flow field discontinuities at these locations). This curious aspect is absent in the approach of SPIE. It is astounding that the two approaches would result in strikingly similar mosaics despite their differences that fundamentally influence the flowfield computation and, consequently, the point correspondences. As with the previous comments about numerous apparent duplicates in the work of this group, I ll present three ways to appreciate the extreme similarity of these two mosaics: i) The two mosaics placed together: ![file] ![file] ii) The two mosaics superimposed in a GIF, at the same zoom level, where they are shown alternatively at 1 s interval (upon playing the GIF, one does not even notice the mosaic switching between the two frames of the GIF due to extreme similarity of the two mosaics): ![file] iii) An overlay of the two mosaics at the same zoom level. The gray colors represent the same intensities in the two mosaics and any differences are shown in magenta or green (practically no difference is noticed). ![file] At this point, it is extremely repetitive to state why these remarkable similarities are unexpected. See #1 for some of the reasons. Some related discussions are found in the initial comments at this PubPeer thread on another publication from this group: https: pubpeer.com publications 52F2E174A4BDA55F91D5B44AC466BD , 
 ## Do the authors use Chambolle and Pock s 2011 work to solve for something different than what is already solved in Chambolle and Pock s work? The authors state in this publication:  *Classical TV-L1 has been used to formulate the model. This energy formulation is solved using primal-dual model proposed in [13].* The reference [13] is: [A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging by Antonin Chambolle and Thomas Pock in JMIV (2011)] . Since Chambolle and Pock already have presented a solution for an optical flow model in their 2011 publication in question, can the authors specify what exactly was solved for their work in this publication that Chambolle and Pock s solution fails to offer? A consultation of this group s prior publications indicates that they are aware of Chambolle and Pock s existing solution (see https: pubpeer.com publications ECDD9A4E7AC9E09197325E8D60128B#1 ). Another publication from this group presents an optimization procedure and solution that is strikingly like Chambolle and Pock s optimization scheme and solution (not referenced in that publication). This is pointed out in this comment on this other publication s PubPeer page: https: pubpeer.com publications BD8300AE697D7574A34F74DEC40371#6. , 
 ## The approach used for obtaining the structure image in this publication appears to be the same as the blood vessel enhancement approach proposed by Frangi et al. A comparison of Eq. (7) in this publication with Eq. (15) of Alejandro F. Frangi et al. s approach in [Multiscale vessel enhancement filtering (MICCAI 1998)] indicates that the two approaches are the same. I have given below the extracts of the corresponding parts from the two publications. A detailed comparison is given in the 2nd comment on another of Ali et al. s publications (in SPIE, 2015): https: pubpeer.com publications BD8300AE697D7574A34F74DEC40371#2. ![file] ![file] Ali et al. have stated in this publication (from IPTA) *after* explaining the ideas leading up to the final stage of the scheme:  *A similar approach was proposed by Frangi et. al.* Did the authors discover Frangi et al. s approach only after they had independently developed their method for the structure estimation? Also, what makes Frangi et al. s approach only *similar* and not the same as theirs? It is very considerate of the authors to mention Frangi et al. s work, that they find *similar* to theirs, in this publication (from IPTA). Any reason they did not mention it in their [2015 SPIE publication] ? The conditional setting of the structure term to 0 for $lambda_2 gt 0$ or $lambda_2 lt 0$ in Frangi et al. s work corresponds to choosing either the bright or the dark structures in comparison to the background (as the text highlighted in yellow in the extract from Frangi et al. indicates). Ali et al. have not indicated this aspect in their scheme. However, the results in their publications do indicate this discrimination. I have given below two results for Ali et al. s structure estimation for the same image, one from this very publication (from IPTA) and the other from Ali s thesis ([Total variational optical flow for robust and accurate bladder image mosaicing, 2016] ). ![file] It is pretty much obvious that the result from IPTA extracts dark curvilinear shapes (including thin shadows). The result from Ali s thesis, on the other hand, extracts bright curvilinear shapes. Considering this, could the authors explain in what aspect is their work exactly different than Frangi et al. s? , 
 ## The *structure constancy* proposed in this publication appears to be influenced by shadows and reflections (and raises some other concerns) ### 1. Key observations about the structure constancy term Ali et al. have introduced a modified Hessian constancy assumption (which is referred to as structure constancy assumption , in some of their other publications) to complement the brightness constancy assumption as can be seen in the following extract: ![file] The examples of the structure image $S$ from this publication are shown in the following excerpts: ![file] Although the above figure from IPTA shows the extraction of the dark structures, the following figure from Ali s thesis ([Total variational optical flow for robust and accurate bladder image mosaicing, 2016] ) shows the structure estimate for the bright shapes, that may include reflections: ![file] As can be seen in the above images, the structure image contains curvilinear shapes, without any discrimination between the solid objects obstacles (like the lizard) and the on-surface information (like the printing on the cloth). These shapes can be shadows, reflections, creases, etc. **Since shadows and reflections are not likely to be constant between two frames, on what grounds do the authors suppose this structure constancy?** Furthermore, the authors are not consistent about what exactly are the structures; they are dark curvilinear shapes in one instance and bright in another. Also, how can such sparse information be helpful over the entire image (also see the implementation concerns mentioned at the end of this post)? The discussion in comment #4 above indicates that the approach used to obtain the structure estimate is the same as Frangi et al. s approach for extracting the blood-vessel structure (that can be used to extract either the dark or the bright curvilinear shapes). It is already curious why would the authors consider this approach to be of particular importance in the optical flow computation, especially since at least one of the authors is aware of its specific application for blood-vessel enhancement and has already used it for this purpose (see the note below). **Note:** One of the authors (Sharib Ali) has used Frangi et al. s approach for blood vessel enhancement in retinal images in some prior publications: [Statistical atlas based exudate segmentation by Ali et al. (2013)] ([pdf] ) and [Exudate segmentation on retinal atlas space by Ali et al. (2013)] ([pdf] ). ### 2. Examination of authors supporting concepts for the structure constancy term The above concerns (about shadows and reflections not being constant) alone makes the efficacy of the structure constancy term very dubious. However, out of curiosity, I will examine some of the other concepts given by Ali et al. in support of this term. #### 2.1. Possible association with Wedel et al. s use of structure-texture decomposition Ali et al. do not provide any explicit support from the literature for their idea of the structure constancy. The closest I can find is the evocation of Wedel et al. s 2009 work involving structure-texture decomposition ([An Improved Algorithm for TV-L1 Optical Flow] ). Wedel et al. s work is evoked when introducing the structure constancy in Ali s thesis. I have given below the excerpt of the corresponding section and highlighted the phrases centered around the idea of structure. I will note that Ali et al. have not stated that they use the concept of structure in the same way as Wedel et al. However, it is still interesting to compare these concepts in the two works, especially so since Wedel et al. s work is evoked right before the description of Ali et al.u2019s structure constancy term. ![file] The structure part in Wedel et al. s work contains large objects along with shadows and reflections (see the parts highlighted in blue in excerpts from Wedel et al.). They use the texture part, that contains fine details, to avoid the influence of shadows and reflections (see parts highlighted in green). The structure estimation scheme used by Ali et al. also extracts information corresponding to objects (inclined towards curvilinear shapes) along with possible reflections and shadows (as is pointed out in the images above). **However, contrary to Wedel et al., Ali et al. seek to *increase* the impact of their structure estimates by adding them to the data term.** Whether or not Ali et al. mean to use structures the same way as Wedel et al., they are adding non-constant information (shadows and reflections) in their constancy assumption. This term is even more curious considering that the practical application of Ali et al. s work is in mosaicing of the cystoscopic sequences. ![file] #### 2.2. Association with some sort of Hessian constancy In this IPTA publication, Ali et al. associate the use of structures with Hessian constancy (see the excerpt below) that they equate with the 2nd order derivatives of the image (to be precise, the Hessian would be a matrix of the second-order partial derivatives). The authors have formulated a Hessian constancy constraint. However, instead of using it directly, they proceed to describe a structure estimation scheme, which appears to be the same Frangi et al. s approach for vessel extraction (see comment #4). **Perhaps the authors can explain what they mean by the optimal exploitation of the Hessian matrices. What do they mean by  the most significant structure information ? Is this significant information the dark curvilinear shapes or the bright ones (see the structure images at the beginning of this post)?** ![file] #### 2.3. Association with shape priors The authors also associate the structure information with the shape priors, as highlighted in the excerpts below. The shape prior is a concept generally used in image segmentation. How do the authors use it to  improve the orientation of the flow vectors ? ![file] ### 3. Implementation considerations I could not find any support, in Ali et al. s work or any other literature, about this strange structure constancy that implies that the curvilinear shadows and reflections across different frames remain constant. Apart from the concerns raised above, there is also the issue of incorporating this term into an optimization scheme. **Since the structure image is quite sparse, it raises the question of incorporating it into a continuous framework.** Ali et al. do not appear to have taken any particular step in this regard. Furthermore, their incorporation of this term appears to have created some discrepancies, as pointed out here: https: pubpeer.com publications BD8300AE697D7574A34F74DEC40371#3. , 
 ## Why should there be any motion discontinuities across the in-plane on-surface edges? ### 1. Key observations Ali et al. have proposed an approach for preserving supposed motion discontinuities in the flow field at the edges of all the texture in the image, regardless of whether they represent a solid object or an in-plane on-surface shape. I ll first reproduce a related image from Ali s thesis ([Total variational optical flow for robust and accurate bladder image mosaicing, 2016] ), that shows the edges where the authors seek to preserve the supposed discontinuities in the flow field (the IPTA paper under discussion does not give this illustration): ![file] I have reproduced below some excerpts from Ali et al. s work that propose to conserve the supposed motion discontinuities at all the texture edges, without any discrimination. The text highlighted in blue describes an edge discriminator that distinguishes between the edge and non-edge pixels. The text highlighted in green points towards the edges shown in Figure 2.11 (c), which is reproduced above. The text highlighted in red shows the mechanism for preserving the supposed motion discontinuities at the edge pixels, which is by turning off or toning down the regularization at all the edges in the images. ![file] ![file] **It is evident that the overwhelming majority of the edges shown in Figure 2.11 (c) do not represent the boundaries of the solid objects but rather on-surface texture (like the text on the cloth). Why would the authors expect there to be any motion discontinuities at these edges?** This gets worse on the contour map of the bladder image (Figure 2.11 (f)), where all the edges are the contours of the on-surface blood vessels. Deactivating or toning down the regularizer at these edges is very likely to disturb the correct flow field computation. Also, how would this sort of regularization result in a faster convergence (as stated in the text highlighted in violet in the above excerpt)? **Note**: There appears to be some difference between the IPTA paper and Ali s thesis about how the authors compute the edge discriminator. Regardless of this discrepancy, they are not discriminating between solid objects obstacles and on-surface texture. Also, Lorenz et al. s work, that is referred to for the edge discriminator, is closely related to Frangi et al. s work on blood vessel enhancement, an approach that is used by Ali et al. for their structure estimates (see comment #5 above for some concerns about Ali et al. s use of these structures). A search of the keywords like edge, discontinuities, texture, structure, smooth, etc. in Ali et al. s publications related to optical flow estimation shows an overwhelming focus on the preservation of flow field discontinuities. Since their application involves mosaicing of patient data, like cystoscopic sequences, it is surprising why would they consider this aspect in the first place. **A close reading of their work shows that they not only do not discriminate between solid objects and on-surface texture but explicitly treat them the same way, as can be seen in the highlighted phrase in the following excerpt from page 43 of Ali s thesis:** ![file] ### 2. Counterexamples from the literature: The above excerpt might explain the authors attempt to preserve supposed flow-field discontinuities at the on-surface texture edges. The idea of motion discontinuities occurring across on-surface edges without occlusions or creases etc. is so fundamentally against the concept of the motion field that it won t be easy to find its direct disapproval in the existing literature, let alone support for it. Some illustrations of a few cases where flow filed discontinuities arise can be found at [this link] . The smoothness of the displacement field in the registration of creaseless surfaces like that of bladder is implicit, as indicated in the following excerpts from [EasyFlow: Increasing the Convergence Basin of Variational Image Matching with a Feature-based Cost by Jim Braux-Zin, Romain Dupont, **Adrien Bartoli** and Mohamed Tamaazousti in IET Computer Vision (2017)] ([pdf](http: igt.ip.uca.fr encov publications pubfiles 2016_Braux-Zin_etal_CV_flow.pdf)): ![file] The above extract also shows that the identification of the motion boundaries is a more involved process than computing the image gradients. This excerpt also shows the need for specific considerations for computing the flow field from sparse correspondences (see section 4 in comment #5 above for concerns over Ali et al. s use of a sparse term). ### 3. Recurring emphasis on the preservation of (supposed) motion discontinuities in related publications It is interesting to note that one of the authors of the IET publication evoked above is [Adrien Bartoli](http: igt.ip.uca.fr ~ab ), who was the president of the committee that approved Sharib Ali s doctoral thesis (as indicated on [Adrien Baroli s CV](http: igt.ip.uca.fr ~ab CV-Bartoli.pdf)). Perhaps Adrien Bartoli could illustrate how he reconciles the apparent contradiction between the ideas in his work and the work he approved. Along with the notion of a strange structure constancy (addressed in #5), the idea of preserving the (supposed) flow field discontinuities is of one the key (if not the principle) themes of Ali s thesis ([Total variational optical flow for robust and accurate bladder image mosaicing, 2016] ) and is emphasized in its very abstract, as shown in the text highlighted in blue below: ![file] In the above extract, I have highlighted the principal applications of this work in yellow. It is already curious why should the concept of motion discontinuities be given central importance for these applications? This concept is widely present in this thesis and related publications. I have given below links to the PubPeer discussion pages of some of these publications against the **corresponding chapters in Sharib Ali s thesis:** **Chapter 2** of Ali s thesis corresponds to this IPTA publication under discussion. Along with a curious structure constancy assumption(see comment #5), the preservation of the supposed motion discontinuities at all the texture edges is the principal theme of this chapter. Both of these propositions appear to be incorporated in an existing optical flow estimation scheme (from Chambolle and Pock s 2011 publication) by a slight alteration of a couple of its terms, as pointed out in [the sixth comment of the PubPeer page of another publication (in SPIE 2015 by Ali et al.)] . The thesis or related publications do not appear to show any result comparison with Chambolle and Pock s unmodified approach. **Chapter 3** of Ali s thesis corresponds to [this publication in Pattern Recognition] . As is pointed out on in [the 11th comment on its PubPeer page] , this publication s overwhelming focus is on the preservation of supposed motion discontinuities and their preservation is sought through a scheme that resembles the approach discussed above. This publication or its reproduction in the thesis does not provide any solution of the proposed energy formulation, which involves a 2nd order div-curl regularization. In the thesis, the reader is referred to the solution presented in chapter 2, which is for a simple gradient-based regularizer and hence not applicable to the problem involving a 2nd order div-curl regularizer. **Chapter 4** of Ali s thesis corresponds to [this publication in CVIU] . The optical flow estimation solution presented in this publication seems to be obtainable by swapping one term of an existing solution with a term from another exiting work, as pointed out in [the 11th comment on its PubPeer page] . This publication too puts an excessive focus on the preservation of the motion discontinuities, which should not be of concern in mosaicing of the bladder sequences. This publication does not introduce any new approach for preserving the motion discontinuities. Perhaps the authors or the examiners of this work could explain this counter-intuitive use of the concept of motion discontinuities. Why would the motion be interrupted across the edges of the on-surface in-plane texture? ,