Numerical Design of a Thread-Optimized Gripping System for Lap Joint Testing in a Split Hopkinson Apparatus.

Currently, few experimental methods exist that enable the mechanical characterization of adhesives under high strain rates. One such method is the Split Hopkinson Bar (SHB) test. The mechanical characterization of adhesives is performed using different specimen configurations, such as Single Lap Joint (SLJ) specimens. A gripping system, attached to the bars through threading, was conceived to enable the testing of SLJs. An optimization study for selecting the best thread was performed, analyzing the thread type, the nominal diameter, and the thread pitch. Afterwards, the gripping system geometry was numerically evaluated. The optimal threaded connection for the specimen consists of a trapezoidal thread with a 14 mm diameter and a 2 mm thread pitch. To validate the gripping system, the load-displacement (P-δ) curve of an SLJ, which was simulated as if it were tested on the SHB apparatus, was compared with an analogous curve from a validated drop-weight test numerical model.

Artificial intelligence for automatic diagnosis of biliary stricture malignancy status in single-operator cholangioscopy: a pilot study.

BACKGROUND AND AIMS: The diagnosis and characterization of biliary strictures (BSs) is challenging. The introduction of digital single-operator cholangioscopy (DSOC) that allows direct visual inspection of the lesion and targeted biopsy sampling significantly improved the diagnostic yield in patients with indeterminate BSs. However, the diagnostic efficiency of DSOC remains suboptimal. Convolutional neural networks (CNNs) have shown great potential for the interpretation of medical images. We aimed to develop a CNN-based system for automatic detection of malignant BSs in DSOC images. METHODS: We developed, trained, and validated a CNN-based on DSOC images. Each frame was labeled as a normal/benign finding or as a malignant lesion if histopathologic evidence of biliary malignancy was available. The entire dataset was split for 5-fold cross-validation. In addition, the image dataset was split for constitution of training and validation datasets. The performance of the CNN was measured by calculating the area under the receiving operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values. RESULTS: A total of 11,855 images from 85 patients were included (9695 malignant strictures and 2160 benign findings). The model had an overall accuracy of 94.9%, sensitivity of 94.7%, specificity of 92.1%, and AUC of .988 in cross-validation analysis. The image processing speed of the CNN was 7 ms per frame. CONCLUSIONS: The developed deep learning algorithm accurately detected and differentiated malignant strictures from benign biliary conditions. The introduction of artificial intelligence algorithms to DSOC systems may significantly increase its diagnostic yield for malignant strictures.

On the management of maternal pushing during the second stage of labor: a biomechanical study considering passive tissue fatigue damage accumulation.

BACKGROUND: During the second stage of labor, the maternal pelvic floor muscles undergo repetitive stretch loading as uterine contractions and strenuous maternal pushes combined to expel the fetus, and it is not uncommon that these muscles sustain a partial or complete rupture. It has recently been demonstrated that soft tissues, including the anterior cruciate ligament and connective tissue in sheep pelvic floor muscle, can accumulate damage under repetitive physiological (submaximal) loads. It is well known to material scientists that this damage accumulation can not only decrease tissue resistance to stretch but also result in a partial or complete structural failure. Thus, we wondered whether certain maternal pushing patterns (in terms of frequency and duration of each push) could increase the risk of excessive damage accumulation in the pelvic floor tissue, thereby inadvertently contributing to the development of pelvic floor muscle injury. OBJECTIVE: This study aimed to determine which labor management practices (spontaneous vs directed pushing) are less prone to accumulate damage in the pelvic floor muscles during the second stage of labor and find the optimum approach in terms of minimizing the risk of pelvic floor muscle injury. STUDY DESIGN: We developed a biomechanical model for the expulsive phase of the second stage of labor that includes the ability to measure the damage accumulation because of repetitive physiological submaximal loads. We performed 4 simulations of the second stage of labor, reflecting a directed pushing technique and 3 alternatives for spontaneous pushing. RESULTS: The finite element model predicted that the origin of the pubovisceral muscle accumulates the most damage and so it is the most likely place for a tear to develop. This result was independent of the pushing pattern. Performing 3 maternal pushes per contraction, with each push lasting 5 seconds, caused less damage and seemed the best approach. The directed pushing technique (3 pushes per contraction, with each push lasting 10 seconds) did not reduce the duration of the second stage of labor and caused higher damage accumulation. CONCLUSION: The frequency and duration of the maternal pushes influenced the damage accumulation in the passive tissues of the pelvic floor muscles, indicating that it can influence the prevalence of pelvic floor muscle injuries. Our results suggested that the maternal pushes should not last longer than 5 seconds and that the duration of active pushing is a better measurement than the total duration of the second stage of labor. Hopefully, this research will help to shed new light on the best practices needed to improve the experience of labor for women.

Pelvic floor muscle injury during a difficult labor. Can tissue fatigue damage play a role?

Pubovisceral muscle (PVM) injury during a difficult vaginal delivery leads to pelvic organ prolapse later in life. If one could address how and why the muscle injury originates, one might be able to better prevent these injuries in the future. In a recent review we concluded that many atraumatic injuries of the muscle-tendon unit are consistent with it being weakened by an accumulation of passive tissue damage during repetitive loading. While the PVM can tear due to a single overstretch at the end of the second stage of labor we hypothesize that it can also be weakened by an accumulation of microdamage and then tear after a series of submaximal loading cycles. We conclude that there is strong indirect evidence that low cycle fatigue of PVM passive tissue is a possible mechanism of its proximal failure. This has implications for finding new ways to better prevent PVM injury in the future.

Patient-specific surrogate model to predict pelvic floor dynamics during vaginal delivery.

Childbirth is a challenging event that can lead to long-term consequences such as prolapse or incontinence. While computational models are widely used to mimic vaginal delivery, their integration into clinical practice is hindered by time constraints. The primary goal of this study is to introduce an artificial intelligence pipeline that leverages patient-specific surrogate modeling to predict pelvic floor injuries during vaginal delivery. A finite element-based machine learning approach was implemented to generate a dataset with information from finite element simulations. Thousands of childbirth simulations were conducted, varying the dimensions of the pelvic floor muscles and the mechanical properties used for their characterization. Additionally, a mesh morphing algorithm was developed to obtain patient-specific models. Machine learning models, specifically tree-based algorithms such as Random Forest (RF) and Extreme Gradient Boosting, as well as Artificial Neural Networks, were trained to predict the nodal coordinates of nodes within the pelvic floor, aiming to predict the muscle stretch during a critical interval. The results indicate that the RF model performs best, with a mean absolute error (MAE) of 0.086 mm and a mean absolute percentage error of 0.38%. Overall, more than 80% of the nodes have an error smaller than 0.1 mm. The MAE for the calculated stretch is equal to 0.0011. The implemented pipeline allows loading the trained model and making predictions in less than 11 s. This work demonstrates the feasibility of implementing a machine learning framework in clinical practice to predict potential maternal injuries and assist in medical-decision making.

Pregnancy state before the onset of labor: a holistic mechanical perspective.

Successful pregnancy highly depends on the complex interaction between the uterine body, cervix, and fetal membrane. This interaction is synchronized, usually following a specific sequence in normal vaginal deliveries: (1) cervical ripening, (2) uterine contractions, and (3) rupture of fetal membrane. The complex interaction between the cervix, fetal membrane, and uterine contractions before the onset of labor is investigated using a complete third-trimester gravid model of the uterus, cervix, fetal membrane, and abdomen. Through a series of numerical simulations, we investigate the mechanical impact of (i) initial cervical shape, (ii) cervical stiffness, (iii) cervical contractions, and (iv) intrauterine pressure. The findings of this work reveal several key observations: (i) maximum principal stress values in the cervix decrease in more dilated, shorter, and softer cervices; (ii) reduced cervical stiffness produces increased cervical dilation, larger cervical opening, and decreased cervical length; (iii) the initial cervical shape impacts final cervical dimensions; (iv) cervical contractions increase the maximum principal stress values and change the stress distributions; (v) cervical contractions potentiate cervical shortening and dilation; (vi) larger intrauterine pressure (IUP) causes considerably larger stress values and cervical opening, larger dilation, and smaller cervical length; and (vii) the biaxial strength of the fetal membrane is only surpassed in the cases of the (1) shortest and most dilated initial cervical geometry and (2) larger IUP.

Development of a multilayer fetal membrane material model calibrated using bulge inflation mechanical tests.

The fetal membranes are an essential mechanical structure for pregnancy, protecting the developing fetus in an amniotic fluid environment and rupturing before birth. In cooperation with the cervix and the uterus, the fetal membranes support the mechanical loads of pregnancy. Structurally, the fetal membranes comprise two main layers: the amnion and the chorion. The mechanical characterization of each layer is crucial to understanding how each layer contributes to the structural performance of the whole membrane. The in-vivo mechanical loading of the fetal membranes and the amount of tissue stress generated in each layer throughout gestation remains poorly understood, as it is difficult to perform direct measurements on pregnant patients. Finite element analysis of pregnancy offers a computational method to explore how anatomical and tissue remodeling factors influence the load-sharing of the uterus, cervix, and fetal membranes. To aid in the formulation of such computational models of pregnancy, this work develops a fiber-based multilayer fetal membrane model that captures its response to previously published bulge inflation loading data. First, material models for the amnion, chorion, and maternal decidua are formulated, informed, and validated by published data. Then, the behavior of the fetal membrane as a layered structure was analyzed, focusing on the respective stress distribution and thickness variation in each layer. The layered computational model captures the overall behavior of the fetal membranes, with the amnion being the mechanically dominant layer. The inclusion of fibers in the amnion material model is an important factor in obtaining reliable fetal membrane behavior according to the experimental dataset. These results highlight the potential of this layered model to be integrated into larger biomechanical models of the gravid uterus and cervix to study the mechanical mechanisms of preterm birth.

Improvement and validation of a female finite element model of the cervical spine.

Although the cervical spine supports and controls the kinematics of the head, it is vulnerable to injuries during mechanical loading. Severe injuries often result in damage to the spinal cord, leading to significant ramifications. The role of gender in determining the outcome of such injuries has been established as significant. In order to better understand the essential mechanics and develop treatments or preventative measures, various forms of research have been conducted. Computational modelling is one of the most useful and extensively utilised methods, as it provides information that would otherwise be difficult to obtain. As such, the primary goal of this research is to create a new finite element of the female cervical spine that will more accurately represent the group most affected by such injuries. This work is a continuation of a previous study where a model was created from the computer tomography scans of a 46-year-old female. A functioning spinal unit consisting of the C6-C7 segment was simulated as a validation procedure. The experimental data obtained from cadaveric specimens, that assessed the range of motion of different cervical segments in flexion-extension, axial rotation, and lateral bending, was used to validate the reduced model.

A finite element model to predict the consequences of endolymphatic hydrops in the basilar membrane.

Ménière's disease is an inner ear disorder, associated with episodes of vertigo, fluctuant hearing loss, tinnitus, and aural fullness. Ménière's disease is associated with endolymphatic hydrops. Clinical evidences show that this disease is often incapacitating, negatively affecting the patients' everyday life. The pathogenesis of Ménière's disease is still not fully understood and remains unclear. Previous numerical studies available in the literature related with endolymphatic hydrops, are very scarce. The present work applies the finite element method to investigate the consequences of endolymphatic hydrops in the normal hearing, associated with the Ménière's disease. The obtained results for the steady state dynamics analysis are in accordance with clinical evidences. The results show that the basilar membrane is not affected in the same intensity along its length and that the lower frequencies are more affected by the endolymphatic hydrops. From a clinical point of view, this work shows the relationship between the increasing of the endolymphatic pressure and the development of hearing loss.

Biomechanical characterization of the small intestine to simulate gastrointestinal tract chyme propulsion.

Regular intestinal motility is essential to guarantee complete digestive function. The coordinative action and integrity of the smooth muscle layers in the small intestine's wall are critical for mixing and propelling the luminal content. However, some patients present gastrointestinal limitations which may negatively impact the normal motility of the intestine. These patients have altered mechanical and muscle properties that likely impact chyme propulsion and may pose a daily scenario for long-term complications. To better understand how mechanics affect chyme propulsion, the propulsive capability of the small intestine was examined during a peristaltic wave along the distal direction of the tract. It was assumed that such a wave works as an activation signal, inducing peristaltic contractions in a transversely isotropic hyperelastic model. In this work, the effect on the propulsion mechanics, from an impairment on the muscle contractile ability, typical from patients with systemic sclerosis, and the presence of sores resultant from ulcers was evaluated. The passive properties of the constitutive model were obtained from uniaxial tensile tests from a porcine small intestine, along with both longitudinal and circumferential directions. Our experiments show decreased stiffness in the circumferential direction. Our simulations show decreased propulsion forces in patients in systemic sclerosis and ulcer patients. As these patients may likely need medical intervention, establishing action concerning the impaired propulsion can help to ease the evaluation and treatment of future complications.

Artificial Intelligence and Capsule Endoscopy: Automatic Detection of Small Bowel Blood Content Using a Convolutional Neural Network.

Introduction: Capsule endoscopy has revolutionized the management of patients with obscure gastrointestinal bleeding. Nevertheless, reading capsule endoscopy images is time-consuming and prone to overlooking significant lesions, thus limiting its diagnostic yield. We aimed to create a deep learning algorithm for automatic detection of blood and hematic residues in the enteric lumen in capsule endoscopy exams. Methods: A convolutional neural network was developed based on a total pool of 22,095 capsule endoscopy images (13,510 images containing luminal blood and 8,585 of normal mucosa or other findings). A training dataset comprising 80% of the total pool of images was defined. The performance of the network was compared to a consensus classification provided by 2 specialists in capsule endoscopy. Subsequently, we evaluated the performance of the network using an independent validation dataset (20% of total image pool), calculating its sensitivity, specificity, accuracy, and precision. Results: Our convolutional neural network detected blood and hematic residues in the small bowel lumen with an accuracy and precision of 98.5 and 98.7%, respectively. The sensitivity and specificity were 98.6 and 98.9%, respectively. The analysis of the testing dataset was completed in 24 s (approximately 184 frames/s). Discussion/Conclusion: We have developed an artificial intelligence tool capable of effectively detecting luminal blood. The development of these tools may enhance the diagnostic accuracy of capsule endoscopy when evaluating patients presenting with obscure small bowel bleeding.

Introdução: A endoscopia por cápsula revolucionou a abordagem a doentes com hemorragia digestiva obscura. No entanto, a leitura de imagens de endoscopia por cápsula é morosa, havendo suscetibilidade para a perda de lesões significativas, limitando desta forma a sua eficácia diagnóstica. Este estudo visou a criação de um algoritmo de deep learning para deteção automática de sangue e resíduos hemáticos no lúmen entérico usando imagens de endoscopia por cápsula. Métodos: Foi desenvolvida uma rede neural convolucional com base num conjunto de 22,095 imagens de endoscopia de cápsula (13,510 imagens contendo sangue e 8,585 mucosa normal ou outros achados). Foi construído um grupo de imagens para treino, compreendendo 80% do total de imagens. O desempenho da rede foi comparado com a classificação consenso de dois especialistas em endoscopia por cápsula. Posteriormente, o desempenho da rede foi avaliado usando os restantes 20% de imagens. Foi calculada a sua sensibilidade, especificidade, exatidão e precisão. Resultados: O algoritmo detetou sangue e resíduos hemáticos no lúmen do intestino delgado com uma exatidão e precisão de 98.5% e 98.7%, respetivamente. A sensibilidade e especificidade foram 98.6% e 98.9%, respetivamente. A análise do conjunto de usado para teste da rede foi concluída em 24 segundos (aproximadamente 184 frames/s). Discussão/Conclusão: Foi desenvolvida uma ferramenta de inteligência artificial capaz de detetar efetivamente o sangue luminal. O desenvolvimento dessas ferramentas pode aumentar a precisão do diagnóstico da endoscopia por cápsula ao avaliar pacientes que apresentam sangramento obscuro do intestino delgado.

Deep learning and colon capsule endoscopy: automatic detection of blood and colonic mucosal lesions using a convolutional neural network.

Background and study aims Colon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. However, CCE produces long videos, making its analysis time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence (AI) algorithms with high performance levels in image analysis. We aimed to develop a deep learning model for automatic identification and differentiation of significant colonic mucosal lesions and blood in CCE images. Patients and methods A retrospective multicenter study including 124 CCE examinations was conducted for development of a CNN model, using a database of CCE images including anonymized images of patients with normal colon mucosa, several mucosal lesions (erosions, ulcers, vascular lesions and protruding lesions) and luminal blood. For CNN development, 9005 images (3,075 normal mucosa, 3,115 blood and 2,815 mucosal lesions) were ultimately extracted. Two image datasets were created and used for CNN training and validation. Results The mean (standard deviation) sensitivity and specificity of the CNN were 96.3 % (3.9 %) and 98.2 % (1.8 %) Mucosal lesions were detected with a sensitivity of 92.0 % and a specificity of 98.5 %. Blood was detected with a sensitivity and specificity of 97.2 % and 99.9 %, respectively. The algorithm was 99.2 % sensitive and 99.6 % specific in distinguishing blood from mucosal lesions. The CNN processed 65 frames per second. Conclusions This is the first CNN-based algorithm to accurately detect and distinguish colonic mucosal lesions and luminal blood in CCE images. AI may improve diagnostic and time efficiency of CCE exams, thus facilitating CCE adoption to routine clinical practice.

Automatic detection of tumor vessels in indeterminate biliary strictures in digital single-operator cholangioscopy.

Background and study aims Indeterminate biliary strictures pose a significative clinical challenge. Dilated, irregular, and tortuous vessels, often described as tumor vessels, are frequently reported in biliary strictures with high malignancy potential during digital single-operator cholangioscopy (D-SOC). In recent years, the development of artificial intelligence (AI) algorithms for application to endoscopic practice has been intensely studied. We aimed to develop an AI algorithm for automatic detection of tumor vessels (TVs) in D-SOC images. Patients and methods A convolutional neural network (CNN) was developed. A total of 6475 images from 85 patients who underwent D-SOC (Spyglass, Boston Scientific, Marlborough, Massachusetts, United States) were included. Each frame was evaluated for the presence of TVs. The performance of the CNN was measured by calculating the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values. Results The sensitivity, specificity, positive predictive value, and negative predictive value were 99.3 %, 99.4 %, 99.6% and 98.7 %, respectively. The AUC was 1.00. Conclusions Our CNN was able to detect TVs with high accuracy. Development of AI algorithms may enhance the detection of macroscopic characteristics associated with high probability of biliary malignancy, thus optimizing the diagnostic workup of patients with indeterminate biliary strictures.

Injuries in Muscle-Tendon-Bone Units: A Systematic Review Considering the Role of Passive Tissue Fatigue.

BACKGROUND: Low-cycle fatigue damage accumulating to the point of structural failure has been recently reported at the origin of the human anterior cruciate ligament under strenuous repetitive loading. If this can occur in a ligament, low-cycle fatigue damage may also occur in the connective tissue of muscle-tendon units. To this end, we reviewed what is known about how, when, and where injuries of muscle-tendon units occur throughout the body. PURPOSE: To systematically review injuries in the muscle-tendon-bone complex; assess the site of injury (muscle belly, musculotendinous junction [MTJ], tendon/aponeurosis, tendon/aponeurosis-bone junction, and tendon/aponeurosis avulsion), incidence, muscles and tendons involved, mechanism of injury, and main symptoms; and consider the hypothesis that injury may often be consistent with the accumulation of multiscale material fatigue damage during repetitive submaximal loading regimens. METHODS: PubMed, Web of Science, Scopus, and ProQuest were searched on July 24, 2019. Quality assessment was undertaken using ARRIVE, STROBE, and CARE (Animal Research: Reporting In Vivo Experiments, Strengthening the Reporting of Observational Studies in Epidemiology, and the Case Report Statement and Checklist, respectively). RESULTS: Overall, 131 studies met the inclusion criteria, including 799 specimens and 2,823 patients who sustained 3,246 injuries. Laboratory studies showed a preponderance of failures at the MTJ, a viscoelastic behavior of muscle-tendon units, and damage accumulation at the MTJ with repetitive loading. Observational studies showed that 35% of injuries occurred in the tendon midsubstance; 28%, at the MTJ; 18%, at the tendon-bone junction; 13%, within the muscle belly and that 6% were tendon avulsions including a bone fragment. The biceps femoris was the most injured muscle (25%), followed by the supraspinatus (12%) and the Achilles tendon (9%). The most common symptoms were hematoma and/or swelling, tenderness, edema and muscle/tendon retraction. The onset of injury was consistent with tissue fatigue at all injury sites except for tendon avulsions, where 63% of the injuries were caused by an evident trauma. CONCLUSION: Excluding traumatic tendon avulsions, most injuries were consistent with the hypothesis that material fatigue damage accumulated during repetitive submaximal loading regimens. If supported by data from better imaging modalities, this has implications for improving injury detection, prevention, and training regimens.

Automatic Identification of Papillary Projections in Indeterminate Biliary Strictures Using Digital Single-Operator Cholangioscopy.

INTRODUCTION: Characterization of biliary strictures is challenging. Papillary projections (PP) are often reported in biliary strictures with high malignancy potential during digital single-operator cholangioscopy. In recent years, the development of artificial intelligence (AI) algorithms for application to endoscopic practice has been intensely studied. We aimed to develop an AI algorithm for automatic detection of PP in digital single-operator cholangioscopy images. METHODS: A convolutional neural network (CNN) was developed. Each frame was evaluated for the presence of PP. The CNN's performance was measured by the area under the curve, sensitivity, specificity, and positive and negative predictive values. RESULTS: A total of 3,920 images from 85 patients were included. Our model had a sensitivity and specificity 99.7% and 97.1%, respectively. The area under the curve was 1.00. DISCUSSION: Our CNN was able to detect PP with high accuracy. Future development of AI tools may optimize the macroscopic characterization of biliary strictures.

Artificial intelligence and colon capsule endoscopy: automatic detection of blood in colon capsule endoscopy using a convolutional neural network.

Colon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. Most studies on CCE focus on colorectal neoplasia detection. The development of automated tools may address some of the limitations of this diagnostic tool and widen its indications for different clinical settings. We developed an artificial intelligence model based on a convolutional neural network (CNN) for the automatic detection of blood content in CCE images. Training and validation datasets were constructed for the development and testing of the CNN. The CNN detected blood with a sensitivity, specificity, and positive and negative predictive values of 99.8 %, 93.2 %, 93.8 %, and 99.8 %, respectively. The area under the receiver operating characteristic curve for blood detection was 1.00. We developed a deep learning algorithm capable of accurately detecting blood or hematic residues within the lumen of the colon based on colon CCE images.

A numerical study on fetal head molding during labor.

During vaginal delivery, the fetal head molds into an elongated shape to adapt to the birth canal, a process known as fetal head molding. However, excessive molding can occur due to prolonged labor or strong contractions, leading to several disorders on the fetal head. This work aims to perform a numerical study on the biomechanics of fetal head molding by measuring specific diameters and the corresponding molding index. A finite element model of the pelvic floor muscles and the fetal body was used. The fetal head is composed of the skin and soft tissues, the skull with sutures and fontanelles, and the brain. The sutures and fontanelles were modeled with membrane elements and characterized by a visco-hyperelastic constitutive model adapted to a plane stress state. Simulations were performed to replicate the second stage of labor in the vertex presentation and occipito-anterior position. With the introduction of viscoelasticity to assess a time-dependent response, a prolonged second stage of labor resulted in higher molding. The pressure exerted by the birth canal and surrounding structures, along with the presence of the pelvic floor muscles, led to a percentage of molding of 9.1%. Regarding the pelvic floor muscles, a 19.4% reduction on the reaction forces and a decrease of 2.58% in muscle stretching was reported, which indicates that sufficient molding may lead to fewer injuries. The present study demonstrates the importance of focusing on the fetus injuries with non-invasive methods that can allow to anticipate complications during labor.

On the mechanical response of the actomyosin cortex during cell indentations.

A mechanical model is presented to analyze the mechanics and dynamics of the cell cortex during indentation. We investigate the impact of active contraction on the cross-linked actin network for different probe sizes and indentation rates. The essential molecular mechanisms of filament stretching, cross-linking and motor activity, are represented by an active and viscous mechanical continuum. The filaments behave as worm-like chains linked either by passive rigid linkers or by myosin motors. In the first example, the effects of probe size and loading rate are evaluated using the model for an idealized rounded cell shape in which properties are based on the results of parallel-plate rheometry available in the literature. Extreme cases of probe size and indentation rate are taken into account. Afterward, AFM experiments were done by engaging smooth muscle cells with both sharp and spherical probes. By inverse analysis with finite element software, our simulations mimicking the experimental conditions show the model is capable of fitting the AFM data. The results provide spatiotemporal dependence on the size and rate of the mechanical stimuli. The model captures the general features of the cell response. It characterizes the actomyosin cortex as an active solid at short timescales and as a fluid at longer timescales by showing (1) higher levels of contraction in the zones of high curvature; (2) larger indentation forces as the probe size increases; and (3) increase in the apparent modulus with the indentation depth but no dependence on the rate of the mechanical stimuli. The methodology presented in this work can be used to address and predict microstructural dependence on the force generation of living cells, which can contribute to understanding the broad spectrum of results in cell experiments.

Predicting the mechanical response of the vaginal wall in ball burst tests based on histology.

A histologically motivated (HM) coefficient that establishes a link between tissue's microstructure and material model through histological data is used in the prediction of the mechanical properties of vaginal tissue that is subjected to multiaxial loading conditions. Therefore, the material parameters were based on an HM coefficient obtained from tensile testing and histological data of comparable tissues. Uniaxial tensile test data and histological data were collected from three groups of sheep at different time points in their life cycle, including virgins, pregnant, and parous ewes. From this data, a correlation between material parameters and histological data was obtained. Spherical indentation (ball burst [BB]) tests were then performed in specimens with similar tissue structure. The histological data of these samples were used in conjunction with the correlations already established for the uniaxial samples data, to define the material parameters of the BB samples. Mechanical properties of the BB specimens were predicted through basic histology and using finite element modeling (FEM) simulations, without direct mechanical measurements. The predicted force and displacement values of the FEM simulation displayed a good correlation with the experimental (BB) testing results. No fitting of the BB results was performed. In this way, the use of uniaxial tests coupled with useful histological information offers a promising approach to predicting macroscopic material behavior under multiaxial loading conditions in biomechanics.

The management of episiotomy technique and its effect on pelvic floor muscles during a malposition childbirth.

Vaginal childbirth is the leading cause of pelvic floor muscles injury, which contributes to pelvic floor dysfunction, being enhanced by fetal malposition. Therefore, the aim of the present study is to verify the influence of mediolateral episiotomies in the mechanics of the pelvic floor with the fetus in occiput posterior position when compared to the occiput anterior position. Numerical simulations of vaginal deliveries, with and without episiotomy, are performed based on the Finite Element Method. The biomechanical model includes the pelvic floor muscles, a surface to delimit the anterior region of the birth canal and a fetus. Fetal malposition induces greater extension of the muscle compared to the normal position, leading to increases of stretch. The faster enlargement may be responsible for a prolonged second stage of labor. Regarding the force required to achieve delivery, the difference between the analyzed cases are 35 N, which might justify the increased need of surgical interventions. Furthermore, episiotomy is essential in reducing the damage to values near the ones obtained with normal position, making the fetal position irrelevant. These biomechanical models have become extremely useful tools to provide some understanding of pelvic floor function during delivery helping in the development of preventative strategies.