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The increasing number of internal displaced people due to conflicts or natural disasters over the years represents the need to create structural solutions for first emergency shelter, which are currently supplied in tarpaulin and structures improvised by the locals. Innovative technologies like 4D printing could present an alternative, being possible to develop 3D structures with smart materials that react with environmental stimulus, such as water, light, heat and others. Unlike 3D printing, which is static, the factor ‘time’ adds a dimension to the 4D, making the structures self-assembling, multifunctional or self-repairing. Given this, the aim of this paper is to present research for the application of 4D-printed structures, for crises, to speed up and ease the assembly process, replacing the structures currently used with one that integrates the emergency kit. It also presents a review on emergency shelters, as well as on 3D and 4D printing processes, clarifying its definition and introducing a case study demonstrative of the process, by printing with shape memory filaments, specifically poly lactic acid and shape memory polymers. A concept was developed and validated, evaluating the possibility of applying 4D printing process in the development of structures that are easy to transport and assemble in emergencies, using the beneficial characteristics of shape memory polymer material. Therefore, the simulation of the concept and the improvement of material properties in terms of specific strength and toughness, which can be achieved using composite materials, are indicated as future recommendations. Moreover, the concept must be evaluated from a structural point of view.

Capsule endoscopy (CE) is an important tool in the management of patients with known or suspected inflammatory bowel disease. Ulcers and erosions of the enteric mucosa are prevalent findings in these patients. They frequently occur together, and their identification in CE is crucial for an accurate evaluation of disease severity. Nevertheless, reviewing CE images is a time-consuming task, and the risk of overlooking lesions is significant.Over the last decade, artificial intelligence (AI) has emerged as a means for overcoming these pitfalls. Of all AI methods, convolutional neural networks (CNN), due to their complex multilayer architecture present the best results in medical image analysis, particularly capsule endoscopy. Therefore, we aimed to develop a CNN for the automatic identification of ulcers and erosions in the small bowel mucosa. A total of 1483 CE exams (PillCam SB3®) performed at a single center between 2015 and 2020 were analysed. From these exams, a total of 6130 frames of the enteric mucosa were obtained, 4233 containing enteric ulcers and erosions, and the remaining containing normal mucosa or other findings. Ulcers and erosions were stratified according to Saurin's classification for bleeding potential: P1E-erosions with intermediate bleeding risk; P1U-ulcers with intermediate bleeding risk; P2U-ulcers with high bleeding risk. For automatic identification of these lesions, these images were inserted into a CNN model with transfer learning. The pool of images was divided for constitution of training and validation datasets, comprising 80% and 20% of the total number of images, respectively. The output provided by the CNN was compared to the classification provided by a consensus of specialists. After optimizing the neural architecture of the algorithm, our model was able to automatically detect and distinguish ulcers and erosions (any bleeding potential) in the small intestine mucosa with an accuracy of 95.6%, sensitivity of 90.8%, and a specificity of 97.1%. We believe that our study lays the foundation for the development and application of effective AI tools to CE. These techniques should improve diagnostic accuracy and reading efficiency. Schematic representation of the workflow and summary of the results.

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.

Chronic otitis media enables the appearance of a benign middle ear tumor, known as a cholesteatoma, that may compromise hearing. To evaluate the influence of a cholesteatoma growth on the hearing function, a computational middle ear model based on the finite element method was used and three different size of cholesteatoma were modeled. The cholesteatoma solidification and the consequent degradation of the ossicles were also simulated as two condition that commonly occurs during cholesteatoma evolution. A sound pressure level of 80 dB SPL was applied in the tympanic membrane and a steady state analysis was performed for frequencies from 100 Hz to 10 kHz. The displacements of both the tympanic membrane and the stapes footplate were measured. The results were compared with a healthy case and it was shown that the cholesteatoma development leads to a decrease in the umbo and stapes displacements. The ossicles degradation simulation showed the higher difference comparing with the cholesteatoma in an initial stage, with lower displacements in the stapes footplate mainly for high frequencies. The observed displacement differences are directly connected to hearing loss, being possible to conclude that cholesteatoma evolution in the middle ear will lead to hearing problems, mainly in an advanced stage.

Colon capsule endoscopy (CCE) is a minimally invasive alternative for patients unwilling to undergo conventional colonoscopy, or for whom the latter exam is contraindicated. This is particularly important in the setting of colorectal cancer screening. Nevertheless, these exams produce large numbers of images, and reading them is a monotonous and time-consuming task, with the risk of overlooking important lesions. The development of automated tools based on artificial intelligence (AI) technology may improve some of the drawbacks of this diagnostic instrument. A database of CCE images was used for development of a Convolutional Neural Network (CNN) model. This database included anonymized images of patients with protruding lesions in the colon or patients with normal colonic mucosa or with other pathologic findings. A total of 3,387,259 frames from 24 CCE exams were retrospectively reviewed. For CNN development, 3640 images (860 protruding lesions and 2780 with normal mucosa or other findings) were ultimately extracted. Training and validation datasets were constructed for the development and testing of the CNN. The CNN detected protruding lesions with a sensitivity, specificity, positive and negative predictive values of 90.7, 92.6, 79.2 and 96.9%, respectively. The area under the receiver operating characteristic curve for detection of protruding lesions was 0.97. The deep learning algorithm we developed is capable of accurately detecting protruding lesions. The application of AI technology to CCE may increase its diagnostic accuracy and acceptance for screening of colorectal neoplasia.

The cervical spine is a common site of injury in the vertebral column, with severe injuries often associated with damage to the spinal cord. Several studies have been performed to better understand the mechanisms of such situations and develop ways to treat or even prevent them. Among the most advantageous and most widely used methods are computational models, as they offer unique features such as providing information on strains and stresses that would otherwise be difficult to obtain. Therefore, the main objective of this work is to help better understand the mechanics of the neck by creating a new finite element model of the human cervical spine that accurately represents most of its components. The initial geometry of the cervical spine was obtained using the computer tomography scans of a 46-year-old female. The complete model was then sectioned, and a functional spinal unit consisting of the C6–C7 segment was simulated to initiate the validation process. The reduced model was validated against experimental data obtained from in vitro tests that evaluated the range of motion of various cervical segments in terms of flexion–extension, axial rotation, and lateral bending.

During the last decades, metal additive manufacturing (AM) technology has transitioned from rapid prototyping application to industrial adoption owing to its flexibility in product design, tooling, and process planning. Thus, understanding the behavior, interaction, and influence of the involved processing parameters on the overall AM production system in order to obtain high-quality parts and stabilized manufacturing process is crucial. Despite many advantages of the AM technologies, difficulties arise due to modelling the complex nature of the process-structure-property relations, which prevents its wide utilization in various industrial sectors. It is known that many of the most important defects in direct energy deposition (DED) are associated with the volume and timescales of the evolving melt pool. Thus, the development of methodologies for monitoring, and controlling the melt pool is critical. In this study, an adaptive numerical transient solution is developed, which is fed from the set of experiments for single-track scanning of super-alloy Inconel 625 on the hot-tempered steel type 42CrMo4. An established exponential formula based on the response surface methodology (RSM) that quantifies the influence of process parameters and geometries of deposited layers from experiments are considered to activate the volume fraction of passive elements in the finite element discretization. By resorting to the FORTRAN language framework capabilities, commercial finite element method software ABAQUS has been steered in order to control unfavorable defects induced by localized rapid heating and cooling, and unstable volume of the melt pool. A thermodynamic consistent phase-field model is coupled with a transient thermal simulation to track the material history. A Lagrangian description for the spatial and time discretization is used. The goal is to present a closed-loop approach to track the melt pool morphology and temperature to a reference deposition volume profile which is established based on deep reinforcement learning (RL) architecture aiming to avoid instabilities, defects and anomalies by controlling the laser power density adaptability. Despite the small number of iterations during RL model training, the agent was able to learn the desired behaviour and two different reward functions were evaluated. This approach allows us to show the possibility of using RL with openAI Gym for process control and its interconnection with ABAQUS framework to train a model first in a simulation environment, and thus take advantage of RL capabilities without creating waste or machine time in real-world.

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.

The last decades have been driven by significant progress in the computational capacity, which have been supporting the development of increasingly realistic and detailed simulations. However, despite these improvements, several problems still do not have an effective solution, due to their numerical complexity. As a result, the answer to these problems can be improved by using techniques that enable the description of phenomena with less resolution, but with lower computational costs, which is the case of the reduced order models. The main objective of this article is the presentation of a new approach for reduced order model development and application in the design and optimization of structural parts. The selected method is the artificial neural networks. Artificial neural networks allow the prediction of certain variables based on a given dataset. Two typical case studies are addressed: the first is a fixed plate subjected to uniformly distributed pressure and the second is a reinforced panel also subjected to internal pressure, with regular reinforcements to improve the specific strength. With this method, a substantial reduction in the simulation time is observed, being, approximately, 40 times faster than the solution obtained with Ansys. The developed neural network has a relative average difference of about 20 %, which is considered satisfactory given the complexity of the problem and considering it is a first application of these networks in this domain. In conclusion, this research made it possible to highlight the potential of reduced order model: including the shorter response time, the less computational resources, and the simplification of problems in detriment of less resolution in the description of structural behaviour. Given these advantages, it is expected that these models will play a key role in future applications, as in digital twins.

Background and aims The use of capsule endoscopy (CE) is paramount for the detection of small bowel ulcers and erosions. These lesions are responsible for a significant part of obscure gastrointestinal bleeding cases. The interpretation of CE exams is time-consuming and susceptible to errors. This study aims to develop a convolutional neural network (CNN) model for identification and differentiation of ulcers and erosion with distinct hemorrhagic potential in CE images. Methods A CNN based on CE images was developed. This database included images of normal small intestinal mucosa, mucosal erosions, and ulcers with distinct bleeding potential. The hemorrhagic risk was assessed by the Saurin's classification. For CNN development, 23,720 images were ultimately extracted (18,045 normal mucosa, 1765 mucosal erosions, 1300 images of ulcers with uncertain bleeding potential—P1 ulcers; and 2610 ulcers with high bleeding potential—P2 ulcers. Two image datasets were created for CNN training and validation. Results Overall, the network had a sensitivity of 86.6% and a specificity of 95.9% for detection of ulcers and erosions. Mucosal erosions were detected with a sensitivity and specificity of 73.1% and 96.1%, respectively. P1 ulcers were identified with a sensitivity of 71.5%, and a specificity of 97.8%. P2 ulcers were detected with a sensitivity and specificity of 91.4% and 98.8%, respectively. Conclusion Our algorithm is the first deep learning-based model to accurately detect and distinguish enteric mucosal breaks with different hemorrhagic risk. CNN-assisted CE reading may improve the diagnostic of these lesions and overall CE efficiency.

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.

Bonded tubular joints have found use in various engineering and industrial applications, providing an efficient and durable method for joining tubular substrates while ensuring good sealing propert...

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.

Introduction It is not possible to recover from chronic diseases; however, a healthy lifestyle and correct adherence to therapy can avoid complications and co-morbidities. The aim of this study was the cross-sectional evaluation, by means of a questionnaire, of real-world data on the prevalence of non-adherence to metformin and atorvastatin oral therapies in a sample of patients that attend community pharmacies in the Piedmont Region. The secondary aim was to evaluate the presence of correlations between non-adherence and a number of variables detected by the questionnaire. Materials and methods Data were gathered from face-to-face interviews in six community pharmacies in Piedmont. The questionnaire was divided into two sections: the first included the Morisky, Green and Levine Medication Adherence Questionnaire (MAQ) (to assess therapy adherence); the second included questions on gender, age, level of education and the pharmacy in which the questionnaire was administered. Comparisons between proportions and mean values were performed using the χ2 test. Modified Poisson regression with robust standard errors was used for multivariate analysis. The level of significance was fixed at 0.05, CI at 95%. Results The sample analysed was composed of 408 subjects (receiving either metformin or atorvastatin). According to MAQ, 62 patients were non-adherent (15% of the total cohort). Crude and multivariate analysis did not show any statistically significant correlation between gender, age, level of education and non-adherence. It emerged that there was a correlation between non-adherence and being a customer of two of the pharmacies involved [PR = 3.31 (p=0.028) and PR = 3.11 (p=0.027)]. Conclusions Community pharmacies can be an appropriate setting to identify non-adherent patients. Therefore, healthcare professionals could realize an integrated and structured intervention to improve adherence. However, MAQ could underestimate the number of non-adherent patients. Further studies to test the association between non-adherence prevalence and being the customer of a specific pharmacy should be performed.

Developing a low-cost medical device requires numerous stages of prototyping, where using traditional manufacturing technologies can increase development costs. The current study presents the development of a critical component for a low-cost microscope whose goal is to confer an inexpensive solution for automated analysis of microscopic smears. A novel design methodology was developed to optimize the achieved solution. During the exploration of this methodology, it is asked: how can an additive-manufactured prototype be cost-effective for accurate examination of cervical cytology smears? To understand the effect on cost and tensile strength that infill density and perimeter wall count, tensile tests were conducted. These results combined with the developed methodology achieved the most cost-effective solution. To achieve this, topology optimization was used to improve the stiffness-per-weight ratio of different parts. Finally, design for additive manufacturing and topology optimization was proven as an effective design tool.

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.

Meniere's disease is an inner ear disorder, associated with episodes of vertigo, fluctuant hearing loss, tinnitus, and aural fullness. Meniere'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 Meniere'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 Meniere'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.

ObjectiveCapsule endoscopy (CE) is pivotal for evaluation of small bowel disease. Obscure gastrointestinal bleeding most often originates from the small bowel. CE frequently identifies a wide range of lesions with different bleeding potentials in these patients. However, reading CE examinations is a time-consuming task. Convolutional neural networks (CNNs) are highly efficient artificial intelligence tools for image analysis. This study aims to develop a CNN-based model for identification and differentiation of multiple small bowel lesions with distinct haemorrhagic potential using CE images.DesignWe developed, trained, and validated a denary CNN based on CE images. Each frame was labelled according to the type of lesion (lymphangiectasia, xanthomas, ulcers, erosions, vascular lesions, protruding lesions, and blood). The haemorrhagic potential was assessed by Saurin’s classification. The entire dataset was divided into training and validation sets. The performance of the CNN was measured by the area under the receiving operating characteristic curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).ResultsA total of 53 555 CE images were included. The model had an overall accuracy of 99%, a sensitivity of 88%, a specificity of 99%, a PPV of 87%, and an NPV of 99% for detection of multiple small bowel abnormalities and respective classification of bleeding potential.ConclusionWe developed and tested a CNN-based model for automatic detection of multiple types of small bowel lesions and classification of the respective bleeding potential. This system may improve the diagnostic yield of CE for these lesions and overall CE efficiency.

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.

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.

Background and Aims Capsule endoscopy is a central element in the management of patients with suspected or known Crohn’s disease. In 2017, PillCam™ Crohn’s Capsule was introduced and demonstrated to have greater accuracy in the evaluation of extension of disease in these patients. Artificial intelligence [AI] is expected to enhance the diagnostic accuracy of capsule endoscopy. This study aimed to develop an AI algorithm for the automatic detection of ulcers and erosions of the small intestine and colon in PillCam™ Crohn’s Capsule images. Methods A total of 8085 PillCam™ Crohn’s Capsule images were extracted between 2017 and 2020, comprising 2855 images of ulcers and 1975 erosions; the remaining images showed normal enteric and colonic mucosa. This pool of images was subsequently split into training and validation datasets. The performance of the network was subsequently assessed in an independent test set. Results The model had an overall sensitivity and specificity of 90.0% and 96.0%, respectively. The precision and accuracy of this model were 97.1% and 92.4%, respectively. In particular, the algorithm detected ulcers with a sensitivity of 83% and specificity of 98%, and erosions with sensitivity and specificity of 91% and 93%, respectively. Conclusion A deep learning model capable of automatically detecting ulcers and erosions in PillCam™ Crohn’s Capsule images was developed for the first time. These findings pave the way for the development of automatic systems for detection of clinically significant lesions, optimizing the diagnostic performance and efficiency of monitoring Crohn’s disease activity.

Background Pelvic organ prolapse, various types of incontinence (urinary incontinence, defecatory dysfunction), chronic cystourethritis, and sexual dysfunctions remain between the most common disorders in urogynecology. Currently, it is believed that the nature and number of births plays a major role in their development. Moreover, after these events, pelvic floor tissues may not recover to their original statuses. The close anatomical relationship among the vaginal wall, bladder and rectum often contribute to the emergence of anatomical-functional failure of adjacent organs and systems. Basic procedures The aim of this study was to investigate the effect of consecutive pregnancies on pelvic floor soft tissues, conducting biomechanical and histological analysis. Fifteen Swifter ewes: virgins, parous and pregnant were used. Samples, for uniaxial tension tests and histological analysis, were cut out from fresh tissue. A description of the mechanical properties of native tissue was obtained from the stress-strain curve. Histological samples were stained with Miller’s Elastica staining and analyzed using ImageJ software. Collagen, elastin, and smooth muscle contents (%) were analyzed along the full wall thickness of the selected organs. The links between mechanical properties of the soft tissues and histological parameters were analyzed. Main findings Mechanically, vaginal wall tissue and cervix of pregnant sheep were more compliant. In contrast, bladder and rectum became stiffer and had the highest total collagen content. Parous sheep rectum and bladder were stiffer, compared to virgin sheep. Principal conclusions Tensile strength appears to be linked to total collagen content. Elastin and smooth muscle show a direct influence on tissue compliance.

Injuries sustained by the pelvic floor muscles during childbirth are one of the major risk factors for the development of pelvic floor dysfunctions. The ability to predict the loss of the tissue integrity and the most affected regions prior to the childbirth would represent a compelling difference in choosing the appropriate management of labour. Previous biomechanical studies, using the finite element method, were able to simulate a vaginal delivery and analyse the mechanical effects on the pelvic floor muscles during the passage of the foetus. Complementing these studies, the aim of this work is to improve the characterization of the pelvic floor muscles, by using an anisotropic visco-hyperelastic constitutive model, including a continuum mechanics damage model. Viscoelasticity is a key feature to obtain more realistic results since biological tissues present relaxation effects that allow larger deformations without damage. This work analyses the reaction forces and the loss of tissue integrity sustained by the pelvic floor and evaluates the effects of different durations of labour. A delaying pushing technique of rest and descend is also studied in this work. The results obtained showed that the reaction forces vary with the duration of labour, with higher force levels associated with higher stretch rates. The pubovisceral muscle is the most affected of the levator ani, presenting an affected region of approximately 30%. The relaxation properties of the tissue contribute to diminish the damage levels, supporting the theory of delayed pushing applied in the second stage of labour.

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.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.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).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.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 deFoi 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.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).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.

Background and aims Diagnosis and characterization of biliary strictures is challenging. The introduction of digital single-operator cholangioscopy (DSOC) allowing direct visual inspection of the lesion and targeted biopsies significantly improved the diagnostic yield in patients with indeterminate biliary strictures. 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 biliary strictures in DSOC images. Methods We developed, trained, and validated a CNN based on DSOC images. Each frame was labeled as normal/benign findings or as a malignant lesion if histopathological 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, positive, and negative predictive values (PPV and NPV, respectively). Results A total of 11855 images from 85 patients were included (9695 of malignant strictures and 2160 of benign findings). The model had an overall accuracy of 94.9%, a sensitivity of 94.7%, a specificity of 92.1% and an AUC of 0.988 in cross-validation analysis. The image processing speed of the CNN was 7 ms/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.

Hearing impairment is one of the most common health disorders, affecting individuals of all ages, reducing considerably their quality of life. At present, it is known that during an acoustic stimulation a travelling wave is developed inside the cochlea. Existing mathematical and numerical models available in the literature try to describe the shape of this travelling wave, the majority of them present a set of approaches based on some limitations either or both of the mechanical properties used and the geometrical description of the realistic representation. The present numerical study highlights the distinctions of using a spiral model of the cochlea, by comparing the obtained results with a straight, or simplified model. The influence of the implantation of transversely isotropic mechanical models was also studied, by comparing the basilar membrane with isotropic and transversely isotropic mechanical properties. Values of the root mean square error calculated for all models show a greater proximity of the cochlear mapping to the Greenwood function when the basilar membrane is assumed with transversely isotropic mechanical properties for both straight and spiral model. The root-mean square errors calculated were: 2.05, 1.70, 2.72, 2.08 mm, for the straight-isotropic, straight-transversely isotropic, spiral-isotropic and spiral-transversely isotropic model, respectively.

Colon capsule endoscopy (CCE) represents a landmark in minimally invasive exploration of the colonic mucosa for patients with contraindications for a conventional colonoscopy, or for whom the latter exam is unwanted or unfeasible. Colorectal neoplasia is the most common lesion found in CCE. The widespread acceptance of CCE as a non-invasive diagnostic method is particularly important in the setting of colorectal cancer screening. However, reviewing these exams is a time-consuming process as they generate a large number of frames, with the risk of overlooking important lesions. We aimed to develop an artificial intelligence (AI) algorithm using a convolutional neural network (CNN) architecture for the automatic detection of colonic protruding lesions. A CNN was constructed using an anonymized database of CCE images collected from a total of 124 patients. This database included images of patients with colonic protruding lesions or patients with normal colonic mucosa or with other pathologic findings. A total of 5715 images (2410 protruding lesions, 3305 normal mucosa or other findings) were extracted for CNN development. Two image datasets were created and used for training and validation of the CNN. The performance of the CNN was measured by calculating the area under the receiving operating characteristic curve (AUROC), sensitivity, specificity, positive and negative predictive values (PPV and NPV, respectively). The AUROC for detection of protruding lesions was 0.99. The sensitivity, specificity, PPV and NPV were 90.0%, 99.1%, 98.6% and 93.2%, respectively. The overall accuracy of the network was 95.3%. The developed deep learning algorithm accurately detected protruding lesions in CCE images. The introduction of AI technology to CCE may increase its diagnostic accuracy and acceptance for the screening of colorectal neoplasia.

Introduction Type 2 diabetes is an important public health issue, yet adherence to drugs and regular clinical follow-up is still suboptimal. This study aims to evaluate a community pharmacy programme for monitoring and enhancing adherence to prescribed pharmacological therapies and recommended examinations among patients with confirmed diabetes. Methods The intervention was conducted in different Italian areas between April 2017 and January 2018. All adult patients who entered a pharmacy with a personal prescription for any antidiabetic drug and agreed to participate, were interviewed. Those found to be non-adherent received counselling from the pharmacists. All patients were invited for a follow-up interview after 3 months. Results Overall, 930 patients were enrolled and completed the baseline interview. We found low rates of non-adherence, ranging from 8% to 13% for prescribed pharmacological therapies, and 11–29% for the recommended clinical examinations. Non-adherence to oral therapies was higher among younger and recently diagnosed patients; that to clinical examinations was higher in men, those with an intermediate duration of diabetes and less educated patients. Large geographical differences persisted after the adjustment for individual factors. Only 306 patients (32.9%) returned for the follow-up interview, most of whom were already adherent at baseline. Conclusions Poor adherence to drugs or clinical examinations is not easy to identify in the usual operating setting of community pharmacies. Furthermore, the majority of patients did not return for follow-up, making it impossible to evaluate the efficacy of the pharmacists’ counselling. It might be more effective to plan interventions addressed to specific subgroups of patients or areas.

Background Capsule endoscopy (CE) is the first line for evaluation of patients with obscure gastrointestinal bleeding. A wide range of small intestinal vascular lesions with different hemorrhagic potential are frequently found in these patients. Nevertheless, reading CE exams is time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence tools with high performance levels in image analysis. This study aimed to develop a CNN-based model for identification and differentiation of vascular lesions with distinct hemorrhagic potential in CE images. Methods The development of the CNN was based on a database of CE images. This database included images of normal small intestinal mucosa, red spots, and angiectasia/varices. The hemorrhagic risk was assessed by Saurin's classification. For CNN development, 11,588 images (9525 normal mucosa, 1026 red spots, and 1037 angiectasia/varices) were ultimately extracted. Two image datasets were created for CNN training and testing. Results The network was 91.8% sensitive and 95.9% specific for detection of vascular lesions, providing accurate predictions in 94.4% of cases. In particular, the CNN had a sensitivity and specificity of 97.1% and 95.3%, respectively, for detection of red spots. Detection of angiectasia/varices occurred with a sensitivity of 94.1% and a specificity of 95.1%. The CNN had a frame reading rate of 145 frames/sec. Conclusions The developed algorithm is the first CNN-based model to accurately detect and distinguish enteric vascular lesions with different hemorrhagic risk. CNN-assisted CE reading may improve the diagnosis of these lesions and overall CE efficiency.

BACKGROUND AND AIMS Capsule endoscopy (CE) revolutionized the study of the small intestine. Nevertheless, reviewing CE images is a 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. We aimed to develop a CNN-based algorithm for 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 development of the model. The performance of the network was evaluated using an independent dataset. The performance of the network was evaluated 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%. Our CNN had a sensitivity and specificity of 96.8%, 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 automatic detection of enteric protruding lesions with a wide range of morphologies. The development of these tools may enhance the diagnostic efficiency of CE.

The female pelvic cavity involves muscles, ligaments, endopelvic fasciae and multiple organs where different pathologies may occur, namely the pelvic organ prolapse (POP). The synthetic implants are used for the reconstructive surgery of POP, but severe complications associated with their use have been reported, mainly related to their mechanical properties (e.g., implant stiffness) and microstructure. In this study, we mimicked a transvaginal reconstructive surgery to repair the apical ligaments (uterosacral ligaments (USLs) and cardinal ligaments (CLs)), by modeling, their impairment (90% and 50%) and/or total rupture. The implants to reinforce/replace these ligaments were built based on literature specifications and their mechanical properties were obtained through uniaxial tensile tests. The main aim of this study was to simulate the effect of mesh anchoring technique (simple stich and continuous stitch), and compare the displacement magnitude of the pelvic tissues, during Valsalva maneuver. The absence/presence of the synthetic implant was simulated when total rupture of the CLs and USLs occurs, causing a variation of the vaginal displacement (9% for the CLs and 27% for the USLs). Additionally, the simulations showed that there was a variation of the supero-inferior displacement of the vaginal wall between different anchoring techniques (simple stich and continuous stitch) being approximately of 10% for the simulation USLs and CLs implant. The computational simulation was able to mimic the biomechanical behavior of the USLs and CLs, in response to different anchoring techniques, which can be help improving the outcomes of the prolapse surgery.

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.