false
Catalog
AUGS/IUGA Scientific Meeting 2019
Long Oral Session 1 - Basic Science
Long Oral Session 1 - Basic Science
Back to course
[Please upgrade your browser to play this video content]
Video Transcription
Thank you for allowing me to present today. I'm presenting biomechanical transformations of the female pelvic floor after prolapse surgeries. So as a background, we all know there's a better need to assess in vivo properties of our patients at baseline and then also following our interventions to look at individual tissue properties that could clinically guide both our counseling and our treatment options. One of the really big challenges in looking at functional in vivo mechanical properties is to longitudinally follow those patients out. So how do you measure your longer term outcomes? This can be a quite difficult prospect logistically, costly, can require significant training and can be quite time consuming for both patients and clinicians. Vaginal tactile imaging is an imaging modality looking at some mechanical properties of the pelvic floor. In previous studies, we've looked at just the parameters, identifying specific .31 parameters to look at the differences between normal state and prolapse states and found there were significant correlations. Here's an example of some of the imagery looking at both tissue properties on the left side of the screen at the anterior and posterior compartment, measuring a pressure map for tissue deformation, both static and dynamic, trying to have inferences of rigidity as well as correlations to elasticity. On the right hand side, you can see more of the muscle contribution and that's been measured both with a patient contracting their muscles as well as a response to valsalva or cough. So the objective of this study was to really explore some biomechanical transformations with pre and post-operative outcomes. Patients were looked at pre-surgery and then four or more months after surgery. For each patient, they were not following a surgical algorithm. This was up to the individual clinician as to the appropriate interventions. Tactile imaging data was acquired for manually deflected patterns along a pressure probe that's shown on the image here. Fifty-two parameters were automatically generated for each study, pre and post, and included a number of maneuvers using the probe. So probe insertion, elevation or canting of the probe, rotation, asking the patient to valsalva with the probe in place, cough, and contract the pelvic floor muscles both as just a quick release and then sustained contraction. The pre and the post data were analyzed to look for changes. The data was then grouped into whether there was a positive change or a negative change in the parameter. So a positive parameter change was seen as an increase in pressure value at the same tissue deformation along the same site of the vagina, an increased pressure gradient value, which is a stress-strain ratio to compromise a value closer to elasticity, an increase of contraction pressure from the patient, a decrease in muscle relaxation, so a sustained contraction ability, an increase of mobility, you can see on that bottom dynamic picture there where the muscle is contracting with a cough. We also looked at two different groups to see are these groups different at all, the group one with a positive parameter change and group two, a negative parameter change regardless of the surgery they underwent. Initially 119 subjects were enrolled, but 35 cases were excluded, allowing for 78 cases pre and post to be included in this analysis. The average patient age was 59 years old with a parity median of 2.4. There were a significant variety in surgical approaches, as you can imagine. These are the procedures that were performed, of course, in combination in most patients. About one-third of the patients were native tissue repairs and two-thirds were mesh repairs, and it was about 50-50 whether it was an abdominal or a vaginal approach taken, so quite a variety of surgical approaches. When we categorized them, and again, these are really not comparing apples to apples, but these are looking at just general categories, sacral colpexy, sacrospinous ligament, and utero-sacral, so comparing apical supports. These are the average number of parameters changed per each category, so the green being a positive change and the red being a negative change. Remembering that the tactile imaging is looking at both tissue factors as well as functional factors, the muscle contribution, when you separate them out you can see slightly different results, with a negative impact more notable in the utero-sacral and sacrospinous ligament groups. Notably, when you look at the data of these groups, the utero-sacral suspension was often an isolated apical repair with a hysterectomy utero-sacral suspension and did not always, actually did not often include an anterior-posterior colporaphy, and did not include a graft material. When we look at anterior and posterior colporaphy compared to an enterocele repair, again, the enterocele repair was typically an apical only repair. We saw a greater improvement as far as tissue properties with an anterior and posterior colporaphy comparatively. Looking at just supra-cervical versus total hysterectomy, this is a group where supra-cervical hysterectomies were all sacral colpopexies, and a total hysterectomy was typically a native tissue approach with a utero-sacral suspension, so you can see comparable differences. Looking at Stage 2 and Stage 3 prolapse, there seemed to be a greater improvement in the tissue factors with Stage 2 compared to Stage 3, as well as a negative impact on the functional properties with Stage 3. Stage 2 prolapse was often more of a vaginal repair, and Stage 3 was often the apical predominant repair. The secondary analysis was to look at whether presurgical conditions could influence their changes after surgery, so looking at two different groups, not the individual surgery types, but looking at whether the patients had a positive parameter change overall or a negative parameter change after surgery. Looking at those two groups, Group 1 being the positive change that's represented by the blue colors, and it's a little hard to see, but those three sections, the leftmost is anterior vaginal wall pressure elasticity calculations, the middle is the posterior vaginal wall, and the right-sided is muscle contraction, a reflex contraction with a cough, so just looking at three basic groups for tissue properties and functional properties. Group 1 was the positive parameter change, and we saw the greatest improvement in those. You also saw that those were also the weakest at baseline, so you can see that best on the top graph. So Group 1 had weaker tissue with the largest impact as far as changing biomechanics in surgery. To see that, sometimes it may be a little bit obvious that the weaker the tissue, the more the improvement, but a lot of that's really unexplained, so could it be that we're really restoring the 3D anatomy better in those patients that they started from a different perspective from where the forces are traveling through the pelvic floor, the muscles were unable to contract appropriately in those scenarios? Or is there more to the story, and more to the story is, did the surgeons select more aggressive procedures and patients they perceived had weaker tissue? And that's really one of the X factors that we all know and we all see every day when we look at our POPQ measurements. That tissue, you may decide on a different, more aggressive or less aggressive surgical approach based on something you can't measure. So the idea is maybe we can, maybe we can take a look at the weakened tissue and try to understand a little bit better and more standardize our outcomes. So the observed changes in tissue elasticity, underlying support and function collected before and after pelvic surgery demonstrate that distinct pelvic surgeries do affect the biomechanics of the pelvic floor and that the proposed biomechanical mapping could be used to further characterize our surgical outcomes and patient selection for specific procedures. It may assist us to predict the changes resulting from our surgeries and, again, better tailor our individual approaches. Thank you. Any other questions? We only have time for one question. Dr. Downing. Dr. Downing, New York. Have you followed some of these patients long enough to see failures versus successes and begin to discern whether there are differences from their pre- to post-elasticity change and functional changes? Yeah. So I hope to see you next year and tell you a little more information. We don't have long-term results yet on this, on, you know, these kinds of things, but we hope to see you next year. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you for your time and I welcome your questions. Good morning. I'm going to change the gear a little bit. So now I'm going to look at the pelvic floor from an engineering point of view instead of looking at the biology of it. I have no disclosure. Okay. When you look at the sailboat, the wing strength and the sail size will determine the cable tension. Is that similar underlying mechanics is associated with the development of the assistive field? We think so. That's why we developed this, you know, theoretical model. So in our theoretical model, we speculated with the increasing intra-dominant pressure and increased the hiatus, we exposed part of the vagina into the pressure differential and that increased the tension in the apex and driving the apex down. However, due to the difficulty to measure the tension in the women, so far it's still speculating. And here now, we developed like this new generation of the computational tool called the POP thing and the objective of today is to using this simulation tool to answer the question, interaction between the increasing intra-dominant pressure with the hiatus size, exposed vaginal length and apical tension. And more importantly, I also want to look at the effect of increasing resting hiatus size on the exposed vaginal length and apical tension at the maximum valsalva. So what is POP thing? POP thing is like, you know, new generation computational model I have been developing. So for the base model, it's similar to our like previous model, based on real women's MRI geometry. We simplify the, you know, volumetric model and the, you know, develop this finite element model with the simplified geometry. The model including the key elements of all support structure, including anterior vaginal wall, levator anus muscle, cardinal uterine sacral ligament, and all like pre-neon membrane, pre-neon body, level three support. So what's different with this new generation model is now we develop the tools and algorithm so allow the customization of this model. That is, if you put the input of your measurement into the model, you don't need to like repeat this process all over again. So we be able to, you know, pretty much customize like all the three level of the support structure, including geometry and the material property in this model. But in this talk, we're going to focusing on just the hiatus and apical tension. So we measured the hiatus on MRI from the tip of the pubic bone to pre-neon body in women with and without prolapse, so we can get their hiatus variation and range. So we customized like, you know, the pub spin to generate an array of the model represented the typical hiatus size within, you know, for women with and without prolapse. And all the models, we assign the same material property taken from literature, and we loaded the model with the physiologic loading. Here I loaded with like 100 centimeter from water. And then we solve these models using the, you know, abacus. So first, we're going to look at how the increasing intradominal pressure will affect the exposed vaginal length and the apical tension. So these two dots demonstrate the distance of the hiatus. And for each intradominal pressure during the simulation, we will like calculate the exposed vaginal length, will show as a bar graph. And then we also calculate the apical tension, will show as a line graph. And so now you just kind of like, you know, see this is one simulation with the increasing intradominal pressure. The hiatus is start to open, and the exposed vaginal wall start to develop. And then it's only after the exposed vaginal wall develop, you have the significant increase in the apical tension. So now I'm going to like answer the second question. What's the effect of enlarged resting hiatus on the exposed vaginal wall length and apical tension at the maximum valsalva? So remember the arrays of the model I established? And then we all simulate at the 100 centimeter of water, and then we will analyze the exposed vaginal length and the apical tension at this stage. So now the x-axis is different. Now x-axis is the resting hiatus length. The y-axis is still exposed vaginal length and apical tension. So notice if I hold everything else constant, if I just change the hiatus, there's almost a linear relationship between the apical tension and the hiatus size, the resting hiatus size. So here it maps out the clinical measure based on MRI, the resting hiatus size, and exposed vaginal length. You can see our simulation model, the range of it sit right there in the middle. Don't have much of a variation because we controlled the other variable. In conclusion, we noticed there's a threshold like pressure effect there. Before that, no exposed vaginal length developed, little apical tension was applied on apical ligament. It is after that, after the exposed vaginal wall start to develop, we see the significant increase in apical tension. More importantly, we got the evidence for the increase in resting hiatus size. So for every one centimeter increase in resting hiatus size, we have 3.5 centimeter longer exposed vaginal length and about two newton of apical tension increase. That's about half pound with the 100 centimeter of loading. So the strength of this study is a new generation of a customizable model. And the resting hiatus size is measured on MRI for women with and without prolapse. And the range of our simulation model consists of MRI measurements. And the limitation, of course, including simplified geometry, material property, and the current least simulation is holding the other parameters constant. More complicated sensitivity analysis will be carried out. So what? So this is like direct evidence we showed increasing resting hiatus will cause the ligament tension increase and have a longer exposed vaginal length. So this is suggesting restoring normal hiatus can reduce the apical tension. So that's the call for the new treatment target or strategy that targeted on restoring the hiatus. Because the current posture repair only restore the hiatus to normal range in less than half of the women have that procedure. I welcome any questions and comments. Thank you. Question for Dr. Chin. Yes. Pam O'Reilly, University of Pittsburgh. Really nice talk. As with these models, I mean, because we struggle with this as well, what you put into it is, you know, that's how good it is. And I saw that, you know, some of the parameters you used for skeletal muscle and connective tissue, but for your perineal membrane, which is obviously hugely important in how much tension you're going to put on that apex, what did you model it after for your material parameters? I was using similar material property as more dense sacral ligaments. So you modeled it after connective tissue with no muscle. Yes. Thank you. Thank you, Dr. Chin. Thank you. Hi, everybody. First of all, I would like to thank the organizers. The organizers of this meeting for the chance to share my vision of the challenges and breakthrough in basic science research on female pelvic floor disorders. The topic was suggested by the organizers, and I happily agreed to talk about this. And also to share our recent data produced by our group in Mount Sinai Hospital, Toronto. So I would like to start saying that, as you all know, each year pelvic floor disorders affects about half a million of North American women so severely that they require surgery. And almost 30% of these operations actually are re-operations. So the success rate is pretty low. And what we also know is that if we talk about stress-related incontinence, which is a second from the common pelvic floor disorders, it's also a major problem. And it happens apparently in 200 millions of men and women worldwide. However, it still remains the main issue. It happens two to three times more often in women than in men. And altogether, all this basically will bring us to the point where we know that there is a 20% of probability for each woman to have a pelvic floor surgery by the age of 80 years. Which is very scary because you know that 65-year-old women is the most fast-growing segment in the population of US and in Canada. And by 2050, it will be 43 millions of pelvic floor surgeries done in US and up to 1 million in Canada, which actually poses a huge burden on the health care system. So what is known about the risk factors for pelvic organ prolapse and for stress-related incontinence? Despite the high incidence, basically what we know is that there is a whole group of different factors that are categorized in inciting, which is a parity of vaginal delivery, predisposing, promoting, and decompensating, which is age and menopause. There is no doubt that vaginal delivery is one of the most important risk factors. However, we also all agree that pelvic organ prolapse is a connective tissue disorder because it is very well known that women who have some specific disease of connective tissue will develop this much more often. So this picture, I wanted to show you this picture. It represents a transverse section showing the anatomy of a non-pregnant female pelvic. And this is just to support the idea that still the major cause of pelvic floor disorder is the vaginal delivery. Because if you look at this image, which actually represents the pelvic floor of the pregnant female during vaginal delivery, you can appreciate that vaginal childbirth is accompanied by significant, significant injury of different pelvic floor structures. The most important is muscle injury of external urethral sphincter and pudental nerve, which only these two injuries, along with other injuries, actually correlate with the later development of stress-related incontinence. And it is also known that injury of pelvic floor structure also caused the development of POP, because in some percent of women, there's a failure of repair process, again based on the failure of supportive tissue. So what are options? You know better than me that basically one of the most probably commonly used is vaginal meshes for pelvic organ prolapse. However, again, it's now very well published that there is a lot of irreversible complications like mesh erosion, protrusion, pain, chronic pain. And this is all now just bring us to the point when we need to think about some other options. The same with SUI. There is definitely some pharmacological and behavioral options. However, for women who fail this, the gold standard is surgical, only surgical option, which are actually available and pretty important to use. So however, the risk of perioperative and postoperative complications have increased the interest to regenerative medicine approach. And application of stem cells for tissue regeneration in the area of pelvic floor disorder is now became very popular. Stem cell therapy are emerging as a next major development in medicine. And they can treat spectrum of degenerative disease and injuries, including stress-related incontinence and pelvic organ prolapse. So I believe that one of the first challenges that every researcher that will want to go to the area of cell-based therapy experience is just to find a suitable cell type. Because there is a large number of different stem cells derived from different sources that could be used to treat pelvic floor disorders. Only a few names there, mesenchymal stem cells, adipose-derived, muscle-derived cells, and also urine-derived cells. I'm not even mentioning some cells that could be derived from amniotic fluid that we know are from endometrium, which is not available for every patient. So something that is basically common is presented here. You know that a stem cell could be derived from bone marrow, from fat tissue, from urine. However, the second question that has to be answered when talking about a stress-related incontinence and cell therapy is what exact structure we want to repair. Because as you can, again, looking at this anatomy of urethral female urethra, you can see that it's basically, it has different components. It's a combination of smooth muscle, striatum muscle, and connective tissue, and some mucosa. So we should choose what type of cells we want to inject if we wanted to repair urethral sphincter. And also, anatomically, sphincter could be divided into two different, the skeletal muscle, basically sphincter, abdosphincter, and smooth muscle. Next challenge for all researchers that are working in this area is to choose the right animal model. Many, many models, animal models of pelvic floor disorders have been described in detail during the last, I think, 20 years. And most studies have been performed using small animals, such as rodents and rabbits. I just put some pictures of a red urethra to show that this is a male urethra. To show that anatomy of red urethra is pretty similar. They also have smooth muscle component and skeletal muscle component, skeletal muscle, this external sphincter, which is on immunohistochemical pictures. You can see it in green. However, there is also some disadvantage of these models. Because all these models are acute, so they use acute preclinical models to explore the potential therapy of stress-related incontinence. There is a lot of advantage also, because we can use older animals. We can produce the model of menopause by doing variectomization of these mice. Dr. Margot Damasser was a pioneer who developed the model of vaginal distension and pudental nerve crash model, which are very, very popular. However, recently models, large model, large animal models were also developed. People are using pigs, dogs, and a few very good publications. It came literally a few years ago, 2018, and this year, 2019. First author is Dr. Williams, describing a non-human primate model. I think it's very, very important to use this model, even though there is a lot of ethical and economical limitations. However, they developed a model when they used premenopausal female monkeys. You know that monkey, they have a lot of similarity with humans. They have age and hormone-related health problems similar to humans. They have pre and post menopause. They have upright posture, pelvic location of bladder and urethra. They developed models that basically reminds just some situation with older females. However, as I said, there is a lot of limitations, and as was mentioned in one of the review, lack of interactive communication with monkeys. So going back to choice of cells. So mesenchymal stem cells are actually the most popular because they have, they cannot induce immune reaction after allogenic transplantation. However, the invasive procurement of the cells basically prevent them from being used very largely. However, there are many, many publications, multiple publications using mesenchymal stem cells to treat stress-related incontinence, but mostly using ARET models. Similar to mesenchymal cells, adipose-derived cells are very popular. You can get much more cells from, basically from adipose tissue, almost 10,000 times more than from bone marrow. However, also it is pretty invasive way to get cells, and one of the most popular, I think it was studies with muscle-derived cells. First, we're just, the pioneer in this study is Dr. Michael Chancellor. So during last 20 years, it was multiple publications using muscle-derived cells to treat any models with stress-related incontinence induced by vaginal distension, and it was shown that muscle-derived cell can restore sphincter function, and this is why it was moved, the studies were moved to clinical trials. This is just an example of one of clinical trials, clinical studies, but I found publications, so basically currently at least six different human clinical trials results were published. They all have a weak point, so basically there is not, the follow-up is mostly 12 to 24 months, the latest was six years, and the number of patients are pretty low, so less than 200 majorly in all these clinical trials. There are some good results, however, for example, this clinical trial, which is good, it's double-blind, randomized, placebo-controlled, but they reported a super high placebo response rate, up to 90%. So going back to choice of cells, so in our group, when we just looked at all these factors, we decided to go with the less invasive source of cells, and we decided to go with urine-derived cells. Also, I think the point of inclusivity, urine-derived cells could be basically in any age, in any stage, female or male, we can all get urine-derived cells. So this is not a new idea, it was first proposed around 10 years ago, and yeah, we can get cells from urine, and this is multipotent dull stem cells that could be expanded in the culture and put back in the patient. However, when we start looking more close to this, we found that there are two different type of cells in urine. It's, yeah, we can basically collect some of multipotent urine stem cells, but it's in very low number. However, there is a lot of renal tubular cells that are just adult cells without any stem cell activity. So this is why we came to the idea to transform the cells to induce pre-reported stem cells, which could be just differentiated to any cells that we need, and we need skeletal muscles, smooth muscle, and fibroblasts. And we can, by doing this, we can produce a very big number of cells, and they will be patient-specific, and we can differentiate them to cell types that we need. So this is why we used epizomal reprogramming of the urine-derived cells in collaboration with Dr. Ian Rogers in our institute, and he is expert in this, and he already provided some reports. So we're able to produce stable, transient free lines from human umbilical cord blood, from corp tissue, from fibroblasts. So we use the same AP1 system, and we were able to show that we can successfully derive cells from urine of pre- and post-menopausal patients with stress-related incontinence. These cells were fully characterized, and you can see the proof of this by PCR, by immunocytochemistry. I want to just highlight one thing. All these experiments are done in our lab. They are done using GMP standards. So there is only a xenofree culture condition, so we are not using any animal-derived products. There is a chemically-defined culture condition, so there is no human serum or human blood product, so all chemical compounds are known. We are doing this so in anticipation that in one day we will be able to use these cells for clinical trials. So on the next stage of this project, these urine-derived stem cells were transformed to fibroblasts. All these studies were done under direct supervision by Dr. Mark Kip Schull, who was presenting all the studies last three – in 2017, he presented fibroblast differentiation. As you can see, again, these cells were fully characterized. In 2018, last year, these cells were differentiated to skeletal muscle cells. It takes a lot of time. As you can see, only after four and a half months, you can actually detect multinucleated myotubes that are positive for dysmynthetine and fast skeletal muscle troponin, which proves that they are skeletal muscle cells. So this year, we will be presenting a new stage of this project. So now these cells basically are sorted using a fox to – and purified to be able to increase their number, because in a culture, usually it's up to 10 percent of cells that are differentiated. We re-cultured them, and using Molde-Ionrodimine B, which is just ultra-small super-paramagnetic ion-oxide particles, we labeled the cells, and we injected them back to immunocompromised RNU rats, specifically in periuretral area. We injected 2 million of cells, and we were able to trace these labeled cells in these rats using, as you can see in this immunogistology, that first of all, you can easily trace this with a Prussian blue, and also just to prove that these are human cells in a rat tissue, we used two different markers of human – so it's human mitochondria and human nuclear protein. Similarly, it was done by injecting of fibroblasts and myocytes, and I – my last slide, I just wanted to say that we here provide a new workflow. It's a way to generate a specific cell type for autologous cell treatment in future therapies for pelvic floor disorders, both SUI and pelvic organ prolapse. We believe that this basic science research funding that I described will be the foundation for the development of future personalized intervention strategies with – which will hopefully translate to improve clinical care. And I want to thank all my colleagues that helped with this project. Thank you. Thank you, Dr. Oksana, for accepting and giving this brilliant presentation.
Video Summary
Dr. Oksana presented on the challenges and breakthroughs in basic science research on female pelvic floor disorders. She discussed the prevalence of pelvic floor disorders and the need for better treatment options. One option that has gained interest is stem cell therapy, which can treat degenerative diseases and injuries. Dr. Oksana highlighted the need to choose the right cell type for therapy, such as mesenchymal stem cells, adipose-derived cells, or muscle-derived cells. She also discussed the importance of selecting a suitable animal model for studying pelvic floor disorders, including small animal models like rodents and rabbits, as well as larger animal models like pigs, dogs, and non-human primates. Dr. Oksana focused on the use of urine-derived cells, which can be obtained less invasively and can be transformed into pluripotent stem cells for differentiation into various cell types needed for pelvic floor repair. She presented her research on transforming urine-derived cells into fibroblasts and skeletal muscle cells, as well as injecting these cells into immunocompromised rats, showing their presence in the periurethral area. Overall, Dr. Oksana's presentation highlighted the potential of stem cell therapy for improving the treatment of female pelvic floor disorders.
Asset Caption
Amanda M. Artsen, MD, Pamela Duran, BS, Indira U. Mysorekar, PhD, Luyun Chen, PhD, Heather van Raalte, MD, Bing Xie, MD, Oksana Shynlova, PhD
Keywords
female pelvic floor disorders
stem cell therapy
cell type
animal model
urine-derived cells
pluripotent stem cells
pelvic floor repair
fibroblasts
skeletal muscle cells
×
Please select your language
1
English