A Comprehensive Look at In Vitro Angiogenesis Image Analysis Software.

One of the complex challenges faced presently by tissue engineering (TE) is the development of vascularized constructs that accurately mimic the extracellular matrix (ECM) of native tissue in which they are inserted to promote vessel growth and, consequently, wound healing and tissue regeneration. TE technique is characterized by several stages, starting from the choice of cell culture and the more appropriate scaffold material that can adequately support and supply them with the necessary biological cues for microvessel development. The next step is to analyze the attained microvasculature, which is reliant on the available labeling and microscopy techniques to visualize the network, as well as metrics employed to characterize it. These are usually attained with the use of software, which has been cited in several works, although no clear standard procedure has been observed to promote the reproduction of the cell response analysis. The present review analyzes not only the various steps previously described in terms of the current standards for evaluation, but also surveys some of the available metrics and software used to quantify networks, along with the detection of analysis limitations and future improvements that could lead to considerable progress for angiogenesis evaluation and application in TE research.

Artificial intelligence and capsule endoscopy: automatic detection of enteric protruding lesions using a convolutional neural network.

BACKGROUND AND AIMS: capsule endoscopy (CE) revolutionized the study of the small intestine. Nevertheless, reviewing CE images is time-consuming and prone to error. Artificial intelligence algorithms, particularly convolutional neural networks (CNN), are expected to overcome these drawbacks. Protruding lesions of the small intestine exhibit enormous morphological diversity in CE images. This study aimed to develop a CNN-based algorithm for the automatic detection small bowel protruding lesions. METHODS: a CNN was developed using a pool of CE images containing protruding lesions or normal mucosa from 1,229 patients. A training dataset was used for the development of the model. The performance of the network was evaluated using an independent dataset, by calculating its sensitivity, specificity, accuracy, positive and negative predictive values. RESULTS: a total of 18,625 CE images (2,830 showing protruding lesions and 15,795 normal mucosa) were included. Training and validation datasets were built with an 80 %/20 % distribution, respectively. After optimizing the architecture of the network, our model automatically detected small-bowel protruding lesions with an accuracy of 92.5 %. CNN had a sensitivity and specificity of 96.8 % and 96.5 %, respectively. The CNN analyzed the validation dataset in 53 seconds, at a rate of approximately 70 frames per second. CONCLUSIONS: we developed an accurate CNN for the automatic detection of enteric protruding lesions with a wide range of morphologies. The development of these tools may enhance the diagnostic efficiency of CE.

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.

Characterizing the Biomechanical Properties of the Pubovisceralis Muscle Using a Genetic Algorithm and the Finite Element Method.

To better understand the disorders in the pelvic cavity associated with the pelvic floor muscles (PFM) using computational models, it is fundamental to identify the biomechanical properties of these muscles. For this purpose, we implemented an optimization scheme, involving a genetic algorithm (GA) and an inverse finite element analysis (FEA), in order to estimate the material properties of the pubovisceralis muscle (PVM). The datasets of five women were included in this noninvasive analysis. The numerical models of the PVM were built from static axial magnetic resonance (MR) images, and the hyperplastic Mooney-Rivlin constitutive model was used. The material parameters obtained were compared with the ones established through a similar optimization scheme, using Powell's algorithm. To validate the values of the material parameters that characterize the passive behavior of the PVM, the displacements obtained via the numerical models with both methods were compared with dynamic MR images acquired during Valsalva maneuver. The material parameters (c1 and c2) were higher for the GA than for Powell's algorithm, but when comparing the magnitude of the displacements in millimeter of the PVM, there was only a 5% difference, and 4% for the principal logarithmic strain. The GA allowed estimating the in vivo biomechanical properties of the PVM of different subjects, requiring a lower number of simulations when compared to Powell's algorithm.

Modelling skin wound healing angiogenesis: A review.

The occurrence of wounds is a main health concern in Western society due to their high frequency and treatment cost. During wound healing, the formation of a functional blood vessel network through angiogenesis is an essential process. Angiogenesis allows the reestablishment of the normal blood flow, the sufficient exchange of oxygen and nutrients and the removal of metabolic waste, necessary for cell proliferation and viability. Mathematical and computational models provide new tools to improve the healing process. In fact, over the last thirty years, in silico models have been continuously formulated to describe the effect of several biological and mechanical factors in angiogenesis during wound healing. Additionally, with different levels of complexity, these models allow coupling the human skin structure, to distinct cell types and growth factors, to study extracellular matrix composition and to understand its deformation. This paper discusses how in silico models, which are more economical and less time-consuming comparatively to laboratory methodologies, can help test new strategies to promote/optimize angiogenesis. The continuum, cell-based and hybrid mathematical models of wound healing angiogenesis are reviewed in the present paper, in order to identify possible improvements. Accordingly, the development of higher dimension models incorporating multiscale analysis at molecular, cellular and tissue level remains a challenge that future models should consider.

Performing high-level sport is strongly associated with urinary incontinence in elite athletes: a comparative study of 372 elite female athletes and 372 controls.

OBJECTIVE: To evaluate the prevalence of urinary incontinence (UI) in female elite athletes compared with controls and to investigate potential risk factors for UI among elite athletes. METHODS: This cross-sectional study included 372 elite athletes (athletes group, AG) and 372 age-matched controls (control group, CG). The median age was low (19 years) and the vast majority were nulliparous. Potential risk factors, including clinical, demographic and sports practice characteristics, were collected by using a questionnaire. The International Consultation on Urinary Incontinence Questionnaire-Urinary Incontinence-Short Form was applied to estimate the prevalence of UI. OR with 95% CIs were used to estimate the association with UI. The final model was adjusted for constipation, family history of UI and history of urinary infection. RESULTS: The prevalence of UI was 29.6% and 13.4% in AG and CG, respectively (p<0.001). The following prevalences were obtained: AG: 19.6% and CG: 3.5% (p<0.001) for stress UI, AG: 3.8% and CG: 5.4% (p=0.292) for urgency UI and AG: 5.9% and CG: 0.8% (p<0.001) for mixed UI. After adjustment, performing high-level sport (adjusted (adj) OR=3.31; 95% CI 2.20 to 4.97), family history of UI (adj OR=1.54; 95% CI 1.04 to 2.29), history of urinary infection (adj OR=1.53; 95% CI 1.05 to 2.23) and constipation (adj OR=1.79; 95% CI 1.07 to 2.98) were associated with UI. CONCLUSION: The prevalence of UI among Portuguese female elite athletes is high and the odds of UI were three times higher than in controls. Also, constipation, family history of UI and history of urinary infections were significantly associated with UI.

Association Between Physical Activity Level and Pelvic Floor Muscle Variables in Women.

In order to investigate the potential impact of physical activity (PA) on pelvic floor muscle (PFM) function, a cross-sectional study was made to analyse the association between PA level and vaginal resting pressure (VRP) and PFM strength and endurance. Thirty-eight continent women and 20 women with stress urinary incontinence (SUI) aged 19 to 49 years were enrolled in the study. PFM variables were assessed by manometry. The PA level was assessed through the International Physical Activity Questionnaire - Short Form. The International Consultation on Incontinence Questionnaire-Urinary Incontinence - Short Form was applied to identify SUI. Pearson's correlation coefficients were applied to estimate the association between PA and PFM variables. Incontinent women were classified as having a high PA level compared to the continent ones (65.0% vs 34.2%, respectively; p=0.030). There was a positive weak association between PA and VRP in continent (r=0.377) and an inverse association in incontinent women (r=-0.458). No associations were found between PA and PFM strength and endurance. Further studies are needed in order to identify a causal association between PA and SUI.

On the Stiffness of the Mesh and Urethral Mobility: A Finite Element Analysis.

Midurethral slings are used to correct urethral hypermobility in female stress urinary incontinence (SUI), defined as the complaint of involuntary urine leakage when the intra-abdominal pressure (IAP) is increased. Structural and thermal features influence their mechanical properties, which may explain postoperative complications, e.g., erosion and urethral obstruction. We studied the effect of the mesh stiffness on urethral mobility at Valsalva maneuver, under impairment of the supporting structures (levator ani and/or ligaments), by using a numerical model. For that purpose, we modeled a sling with "lower" versus "higher" stiffness and evaluated the mobility of the bladder and urethra, that of the urethrovesical junction (the α-angle), and the force exerted at the fixation of the sling. The effect of impaired levator ani or pubourethral ligaments (PUL) alone on the organs displacement and α-angle opening was similar, showing their important role together on urethral stabilization. When the levator ani and all the ligaments were simulated as impaired, the descent of the bladder and urethra went up to 25.02 mm, that of the bladder neck was 14.57 mm, and the α-angle was 129.7 deg, in the range of what was found in women with SUI. Both meshes allowed returning to normal positioning, although at the cost of higher force exerted by the mesh with higher stiffness (3.4 N against 2.3 N), which can relate to tissue erosion. This finite element analysis allowed mimicking the biomechanical response of the pelvic structures in response to changing a material property of the midurethral synthetic mesh.

Urinary Incontinence in Physically Active Young Women: Prevalence and Related Factors.

This cross-sectional survey aims to (1) verify the prevalence of urinary incontinence and its impact on the quality of life among nulliparous fit women, and to (2) analyze whether urinary incontinence is influenced by the intensity of the sport (high- vs. low-impact) or by the volume of physical activity (minutes per week) performed. Two hundred forty-five nulliparous women (18-40 years) completed the International Consultation on Incontinence Questionnaire-Short Form, the Kings Health Questionnaire and a questionnaire regarding demographic and training variables. Overall 22.9% of the participants self-reported urinary incontinence, and among them, 60.7% had stress urinary incontinence. Incontinent women demonstrated worse quality of life than continent females (p=0.000). Women practicing high-impact sports presented higher frequency in loss of urine than those practicing low-impact sports (p=0.004). Regardless the intensity of the sport, the volume of exercise showed positive association with the frequency of loss of urine (p=0.005, r=0.475). In conclusion, almost one fourth of the women enrolled in this study reported symptoms of urinary incontinence and worse quality of life than those who were continent. Women who practice high-impact sports or who have higher volume of training should be aware of the symptoms associated with pelvic floor dysfunction, since they seem to predispose to urine leakage.

Volume of training and the ranking level are associated with the leakage of urine in young female trampolinists.

OBJECTIVES: The aim of this study was to investigate in young nulliparous female trampolinists the hypothetical associations between the level of athletic performance and the volume of training with urine leakage. DESIGN: Cross-sectional cohort study. SETTING: Professional trampolinists. PARTICIPANTS: Twenty-two female athletes participating in a trampoline national championship were included in this study. ASSESSMENT OF RISK FACTORS: The associations for player-related performance variables were evaluated using Spearman rank correlation and Kruskal-Wallis H test verified the differences between the tertiles. MAIN OUTCOME MEASURES: Rate of urinary incontinence (UI) among young athletes and the relationship with the athletic performance. Screening was performed through a questionnaire. This included the demographic sample characterization, the assessment of training volume, and the athletes' ranking in the championship. Additionally, it included the International Consultation on Incontinence Questionnaire Short-Form (ICIQ-UI-SF) to assess the leakage. RESULTS: About 72.7% of the participants reported that they experienced urine leakage during trampoline practice and described that the episodes of leakage started after the beginning of the trampoline. Significant associations between the athletic performance and the training volume were observed with the severity of incontinence. Dividing the sample in tertiles of training volume, it was observed that the third tertile was the one with greatest impact of incontinence on their quality of life (ICIQ score). CONCLUSIONS: The results confirm a high frequency of UI in young trampolinists and reveal a clear identification of the athletes' ranking and the training volume as risk factors to develop and worsen urine loss. CLINICAL RELEVANCE: Special attention from team physicians and trainers is required for this athletes' problem that is often hidden by shame. In addition, female athletes practicing high-impact sports should be informed about the risk to develop pelvic floor muscles dysfunction and should be encouraged to seek help from health professionals to minimize or even eliminate the urine incontinence.

Transcriptome analysis highlights changes in the leaves of maize plants cultivated in acidic soil containing toxic levels of Al(3+).

Soil acidity limits crop yields worldwide and is a common result of aluminum (Al) phytotoxicity, which is known to inhibit root growth. Here, we compared the transcriptome of leaves from maize seedlings grown under control conditions (soil without free Al) and under acidic soil containing toxic levels of Al. This study reports, for the first time, the complex transcriptional changes that occur in the leaves of maize plants grown in acidic soil with phytotoxic levels of Al. Our data indicate that 668 genes were differentially expressed in the leaves of plants grown in acidic soil, which is significantly greater than that observed in our previous work with roots. Genes encoding TCA cycle enzymes were upregulated, although no specific transporter of organic acids was differentially expressed in leaves. We also provide evidence for positive roles for auxin and brassinosteroids in Al tolerance, whereas gibberellin and jasmonate may have negative roles. Our data indicate that plant responses to acidic soil with high Al content are not restricted to the root; tolerance mechanisms are also displayed in the aerial parts of the plant, thus indicating that the entire plant responds to stress.

Effects of preparation conditions of poly(lactide-co-glycolide) nanoparticles loaded with amphiphilic porphyrins and their photoactivities.

Three porphyrins, (5,10,15,20-tetra(3-hydroxyphenyl)porphyrin, 5-hexyl-10,20-bis(3-hydroxyphenyl)-porphyrin and 5-hexyl-10,15,20-tris(3-hydroxyphenyl)porphyrin), with different amphiphilicities and equal singlet oxygen quantum yields in ethanol, were encapsulated into 50:50 poly(lactide-co-glycolide), nanoparticles prepared by the emulsion/evaporation technique. A 22 factorial design was utilized to evaluate the influence of the porphyrin/polymer mass ratio and the percentage of ethanol in the aqueous phase on the size and zeta potential of the nanoparticles. Increasing both the amount of ethanol and the porphyrin/polymer ratio decreases the size and increases zeta potential for the photosensitizers studied, except for 5-hexyl-10,15,20-tri(3-hydroxyphenyl)porphyrin. Entrapment efficiency depended on the individual m-hydroxyphenylporphyrin and ranged from 69 to 97%. After 1.5 h incubation with m-hydroxyphenylporphyrin-loaded nanoparticles the percentages of intracellular uptake were the same for all porphyrins since the molecules are confined in the nanoparticles, hampering the interaction of the amphiphilic photosensitizers with the cellular membrane. All encapsulated porphyrins caused the same decrease of cell viability and always localized in the perinuclear region of the cells. Results show that these m-hydroxyphenylporphyrins, although with different amphiphilicities, have equal photodynamic efficacies.

Computational Insights into the Interplay of Mechanical Forces in Angiogenesis.

This study employs a meshless computational model to investigate the impacts of compression and traction on angiogenesis, exploring their effects on vascular endothelial growth factor (VEGF) diffusion and subsequent capillary network formation. Three distinct initial domain geometries were defined to simulate variations in endothelial cell sprouting and VEGF release. Compression and traction were applied, and the ensuing effects on VEGF diffusion coefficients were analysed. Compression promoted angiogenesis, increasing capillary network density. The reduction in the VEGF diffusion coefficient under compression altered VEGF concentration, impacting endothelial cell migration patterns. The findings were consistent across diverse simulation scenarios, demonstrating the robust influence of compression on angiogenesis. This computational study enhances our understanding of the intricate interplay between mechanical forces and angiogenesis. Compression emerges as an effective mediator of angiogenesis, influencing VEGF diffusion and vascular pattern. These insights may contribute to innovative therapeutic strategies for angiogenesis-related disorders, fostering tissue regeneration and addressing diseases where angiogenesis is crucial.

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.