Abstract
Introduction
Chronic constipation (CC) is a highly prevalent disease in Western society. Chronic constipation can have a different etiology in patients who underwent a cesarean section and ...result from postoperative stress and metabolic response to trauma, analgesic agents, immobilization, and dietary restrictions. Chronic constipation may also occur due to puerperium-related psychological changes and to the stretching and weakening of the perineal and abdominal muscles after childbirth.
Objectives
The present study analyzes intestinal transit restoration after a cesarean section and the influence of osmotic laxative agents.
Methods
The present prospective, nonrandomized sample study used the ROME III questionnaire and the Bristol stool scale in adult women who underwent a cesarean section. We divided the subjects into 2 groups, each with 30 patients, to compare the effect of the prophylactic administration of an osmotic laxative.
Results
We evaluated 60 randomly-chosen pregnant women from the Obstetrics ward of Hospital Santa Marcelina, São Paulo, SP, Brazil, from October 2019 to March 2020. Their mean age was 26.8 years old, and the mean gestation time was 37.95 weeks. Ten patients (16.7%) presented with constipation before the cesarean section, and 38 (63.3%) had a bowel movement after the procedure. However, in 84.2% of these patients, the usual stool consistency worsened. After the cesarean section, 46.7% of the women who did not receive laxative agents had a bowel movement, compared with 80% of those who did (
p
= 0.0074).
Conclusion
Some factors, including those related to the procedure, may hamper intestinal transit restoration after a cesarean section. Osmotic laxative agents can facilitate transit restoration with no negative effects in this group of patients.
Resumo Com o objetivo de analisar as relações interprofissionais produzidas a partir da alta responsável, na perspectiva e no agir da enfermagem durante o a pandemia de COVID-19, realizou-se um ...estudo qualitativo, tipo estudo de caso. A técnica de pesquisa foi a observação participante de uma enfermaria de hospital de grande porte do SUS na cidade de São Paulo. A produção de narrativas e a análise micropolítica das relações fez emergir dois planos de visibilidade: para além da alta responsável - a antropofagia dos arranjos tecnológicos do cuidado e a ambivalência da enfermagem na produção das relações interprofissionais; e alta médico-centrada e alta negociada - o entrecruzamento com outros profissionais, com as famílias e com a vida “como ela é”. A pandemia de COVID interrompeu as visitas multiprofissionais e foi um analisador das relações interprofissionais. A partir de uma inteligência astuciosa, a enfermagem negocia a alta com os médicos, que detêm este poder, e aciona a equipe, a partir de uma autonomia elástica. A alta responsável por si só não foi capaz de produzir um plano comum de ação interprofissional, de modificar os papéis instituídos no hospital, situação que recrudesceu durante a pandemia mas abriu condições para o aumento da profissionalização da equipe.
Abstract A qualitative-case study was carried out aimed at analyzing the interprofessional relationships generated by the planned discharge from the nursing actions’ perspective during the COVID-19 pandemic. The study method was the participating observation by one nurse who works in a large SUS hospital in the city of São Paulo. The production of narratives and the micropolitics analysis resulted in two diverse visibility plans: beyond the planned discharge the anthropophagy of the technological arrangements for care and the ambivalence of the nursing staff in the production of interprofessional relationships; and the medical discharge and negotiated discharge: the intersecting with other professionals, with the families and with “real” life. The pandemic interrupted the multiprofessional visits and it was an analyzer of the interprofessional relationships. Wittingly, the nursing staff negotiates the discharges with physicians, who retain this power, and sets the team in motion using an elastic autonomy. The planned discharge alone was not able to guarantee a common interprofessional action plan, was not able to modify the constituted roles in the hospital, a situation that increased during the pandemic, but allowed the right setting aimed to increase the team’s professionalism.
A qualitative-case study was carried out aimed at analyzing the interprofessional relationships generated by the planned discharge from the nursing actions' perspective during the COVID-19 pandemic. ...The study method was the participating observation by one nurse who works in a large SUS hospital in the city of Sao Paulo. The production of narratives and the micropoliti-cs analysis resulted in two diverse visibility plans: beyond the planned discharge the anthropophagy of the technological arrangements for care and the ambivalence of the nursing staff in the production of interprofessional relationships; and the medical discharge and negotiated discharge: the intersecting with other professionals, with the families and with "real" life. The pandemic interrupted the multiprofessional visits and it was an analyzer of the interprofessional relationships. Wittingly, the nursing staff negotiates the discharges with physicians, who retain this power, and sets the team in motion using an elastic autonomy. The planned discharge alone was not able to guarantee a common interprofessional action plan, was not able to modify the constituted roles in the hospital, a situation that increased during the pandemic, but allowed the right setting aimed to increase the team's professionalism.
A qualitative-case study was carried out aimed at analyzing the interprofessional relationships generated by the planned discharge from the nursing actions' perspective during the COVID-19 pandemic. ...The study method was the participating observation by one nurse who works in a large SUS hospital in the city of São Paulo. The production of narratives and the micropolitics analysis resulted in two diverse visibility plans: beyond the planned discharge the anthropophagy of the technological arrangements for care and the ambivalence of the nursing staff in the production of interprofessional relationships; and the medical discharge and negotiated discharge: the intersecting with other professionals, with the families and with "real" life. The pandemic interrupted the multiprofessional visits and it was an analyzer of the interprofessional relationships. Wittingly, the nursing staff negotiates the discharges with physicians, who retain this power, and sets the team in motion using an elastic autonomy. The planned discharge alone was not able to guarantee a common interprofessional action plan, was not able to modify the constituted roles in the hospital, a situation that increased during the pandemic, but allowed the right setting aimed to increase the team's professionalism.
Convolutional Neural Networks (CNNs) achieve excellent computer-assisted diagnosis with sufficient annotated training data. However, most medical imaging datasets are small and fragmented. In this ...context, Generative Adversarial Networks (GANs) can synthesize realistic/diverse additional training images to fill the data lack in the real image distribution; researchers have improved classification by augmenting data with noise-to-image (e.g., random noise samples to diverse pathological images) or image-to-image GANs (e.g., a benign image to a malignant one). Yet, no research has reported results combining noise-to-image and image-to-image GANs for further performance boost. Therefore, to maximize the DA effect with the GAN combinations, we propose a two-step GAN-based DA that generates and refines brain Magnetic Resonance (MR) images with/without tumors separately: (i) Progressive Growing of GANs (PGGANs), multi-stage noise-to-image GAN for high-resolution MR image generation, first generates realistic/diverse 256×256 images; (ii) Multimodal UNsupervised Image-to-image Translation (MUNIT) that combines GANs/Variational AutoEncoders or SimGAN that uses a DA-focused GAN loss, further refines the texture/shape of the PGGAN-generated images similarly to the real ones. We thoroughly investigate CNN-based tumor classification results, also considering the influence of pre-training on ImageNet and discarding weird-looking GAN-generated images. The results show that, when combined with classic DA, our two-step GAN-based DA can significantly outperform the classic DA alone, in tumor detection (i.e., boosting sensitivity 93.67% to 97.48%) and also in other medical imaging tasks.
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•Use of lignin oligomers in the synthesis of a bio-based pre-polymer via ring-opening of caprolactone.•Synthesis of a lignin-based polyurethane (LBPU) via urethane bonding of the ...pre-polymer.•Tuning the reaction parameters to modulate properties and degradation of the proposed LBPUs.
Bio-based and degradable materials were proposed to challenge the major problem of plastic disposal in the environment. In this context, polyurethane production was re-evaluated, encouraging the search for replacing both petroleum components and highly toxic species. A novel synthesis route is explored in this work, aimed to produce degradable lignin-based polyurethanes. Oligomers from steam-exploded lignin were extracted and used with ε-caprolactone (ε-CL) to generate a fully bio-based pre-polymer (oligo-grafted-poly(ε-CL)), exploiting ring-opening polymerization. We have demonstrated that tuning the main reaction parameters, such as ε-CL:oligomer and catalyst:ε-CL mass ratios, and reaction time, it is possible to obtain different pre-polymers enabling the synthesis of bio-based polyurethanes with variable physicochemical properties. In particular, the oligomeric content modulates the thermal and mechanical properties of the polymer (melting point range: 54–62 °C; Young modulus range: 3–7 kPa) and enhances the degradability (up to 13 % wt, in acid environment), highlighting the potential of the material for possible applications.
•A novel medical image enhancement method based on Genetic Algorithms is proposed.•MedGA enhances images characterized by nearly bimodal gray level histograms.•The fitness function strengthens the ...two underlying intensity distributions.•MedGA considerably outperforms the classical image enhancement techniques.•MedGA achieves excellent results in terms of signal and perceived image quality.
Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram equalization, bi-histogram equalization, encoding and decoding Gamma transformations, and sigmoid transformations. We show that MedGA considerably outperforms the other approaches in terms of signal and perceived image quality, while preserving the input mean brightness. MedGA may have a significant impact in real healthcare environments, representing an intelligent solution for Clinical Decision Support Systems in radiology practice for image enhancement, to visually assist physicians during their interactive decision-making tasks, as well as for the improvement of downstream automated processing pipelines in clinically useful measurements.
•We propose USE-Net that incorporates Squeeze-and-Excitation blocks into U-Net.•It achieves accurate prostate zonal segmentation results on multiple MRI datasets.•Training on multiple datasets ...provides excellent intra/cross-dataset generalization.•USE-Net remarkably outperforms related methods when trained/tested on all datasets.•Feature recalibration may be a valuable solution in multi-institutional scenarios.
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Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, the capability to differentiate between the blurry boundary of the Central Gland (CG) and Peripheral Zone (PZ) can lead to differential diagnosis, since the frequency and severity of tumors differ in these regions. To tackle the prostate zonal segmentation task, we propose a novel Convolutional Neural Network (CNN), called USE-Net, which incorporates Squeeze-and-Excitation (SE) blocks into U-Net, i.e., one of the most effective CNNs in biomedical image segmentation. Especially, the SE blocks are added after every Encoder (Enc USE-Net) or Encoder-Decoder block (Enc-Dec USE-Net). This study evaluates the generalization ability of CNN-based architectures on three T2-weighted MRI datasets, each one consisting of a different number of patients and heterogeneous image characteristics, collected by different institutions. The following mixed scheme is used for training/testing: (i) training on either each individual dataset or multiple prostate MRI datasets and (ii) testing on all three datasets with all possible training/testing combinations. USE-Net is compared against three state-of-the-art CNN-based architectures (i.e., U-Net, pix2pix, and Mixed-Scale Dense Network), along with a semi-automatic continuous max-flow model. The results show that training on the union of the datasets generally outperforms training on each dataset separately, allowing for both intra-/cross-dataset generalization. Enc USE-Net shows good overall generalization under any training condition, while Enc-Dec USE-Net remarkably outperforms the other methods when trained on all datasets. These findings reveal that the SE blocks’ adaptive feature recalibration provides excellent cross-dataset generalization when testing is performed on samples of the datasets used during training. Therefore, we should consider multi-dataset training and SE blocks together as mutually indispensable methods to draw out each other’s full potential. In conclusion, adaptive mechanisms (e.g., feature recalibration) may be a valuable solution in medical imaging applications involving multi-institutional settings.
Computational Intelligence methods, which include Evolutionary Computation and Swarm Intelligence, can efficiently and effectively identify optimal solutions to complex optimization problems by ...exploiting the cooperative and competitive interplay among their individuals. The exploration and exploitation capabilities of these meta-heuristics are typically assessed by considering well-known suites of benchmark functions, specifically designed for numerical global optimization purposes. However, their performances could drastically change in the case of real-world optimization problems. In this paper, we investigate this issue by considering the Parameter Estimation (PE) of biochemical systems, a common computational problem in the field of Systems Biology. In order to evaluate the effectiveness of various meta-heuristics in solving the PE problem, we compare their performance by considering a set of benchmark functions and a set of synthetic biochemical models characterized by a search space with an increasing number of dimensions. Our results show that some state-of-the-art optimization methods – able to largely outperform the other meta-heuristics on benchmark functions – are characterized by considerably poor performances when applied to the PE problem. We also show that a limiting factor of these optimization methods concerns the representation of the solutions: indeed, by means of a simple semantic transformation, it is possible to turn these algorithms into competitive alternatives. We corroborate this finding by performing the PE of a model of metabolic pathways in red blood cells. Overall, in this work we state that classic benchmark functions cannot be fully representative of all the features that make real-world optimization problems hard to solve. This is the case, in particular, of the PE of biochemical systems. We also show that optimization problems must be carefully analyzed to select an appropriate representation, in order to actually obtain the performance promised by benchmark results.
•We compare state-of-the-art algorithms on benchmark functions and real-world problems.•We consider the parameter estimation of biochemical systems as real-world problem.•Algorithms performing well on benchmarks are inappropriate for parameter estimation and vice versa.•Benchmark functions do not capture the complexity of real-world problems.
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