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Purpose: Anorectal and urogenital dysfunctions are common after rectal surgery and have a significant impact on quality of life. Intraoperative pelvic autonomic nerve monitoring (pIONM) has been proposed as a tool to identify patients at risk of these functional sequelae. This systematic review aims to evaluate the diagnostic accuracy of pIONM in detecting anorectal and urogenital dysfunction following rectal surgery., Methods: A systematic review of articles published since 1990 was conducted using MEDLINE, Embase, CINAHL, Google Scholar, Scopus, and Web of Science. Studies describing pIONM for rectal surgery and reporting anorectal or urogenital functional outcomes were included. The risk of bias was assessed using the QUADS-2 tool. The diagnostic accuracy of pIONM was established with pooled sensitivity and specificity alongside summary receiver-operating characteristic curves., Results: Twenty studies including 686 patients undergoing pIONM were identified, with seven of these studies including a control group. There was heterogeneity in the pIONM technique and reported outcome measures used. Results from five studies indicate pIONM may be able to predict postoperative anorectal (sensitivity 1.00 [95% CI 0.03-1.00], specificity 0.98 [0.91-0.99]) and urinary (sensitivity 1.00 [95% CI 0.03-1.00], specificity 0.99 [0.92-0.99]) dysfunction., Conclusions: This review identifies the diagnostic accuracy of pIONM in detecting postoperative anorectal and urogenital dysfunction following rectal surgery. Further research is necessary before pIONM can be routinely used in clinical practice., Prospero Registration Details: CRD42022313934., Competing Interests: Declarations. Conflict of interest: The authors declare no competing interests. Ethical Statement: This systematic review does not require ethical approval as it is not considered research after consultation with the United Kingdom HRA research decision tool. Consent Statement: Informed consent was not required for this systematic review., (© 2024. The Author(s).)

This work shows the design and construction of a servomechanism of a rotator-type joint based on NiTi Shape Memory Alloys (SMA) with an angular position measurement based on a potentiometer sensor and digital electronic position control. The expected application of this prototype is for the use of small charges that emulate the movement of the human being, being bio-inspired and activated by artificial muscles, their potential applications they will be in medical and humanoid robotics. Computer Aided Design (CAD) allows evaluating and validating the most convenient parameters for construction of servomechanism, experimental results validate allowed us to obtain the values of the range of motion ± 20 ° and a maximum torque of 1.01 kg-cm exerted on the axis of rotation for the prototype., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

The data presented in this article is generated by a steady-state simulation for performing a techno-economic assessment for comparing three electrolysis technologies in the PtM context. The data is focused on two aspects. First, the description of the steady-state simulation of six PtM systems modeled using Aspen Custom Modeler (ACM) and Aspen Plus (AP). Second, an economic assessment is carried out for each of the mentioned PtM systems to compare the feasibility, the profitability and performance of these systems on a larger scale to produce synthetic natural gas, power generation and carbon utilization given in the main research article. Three electrolysis technologies (namely Alkaline Electrolysis - AE, Proton Exchange Membrane Electrolysis - PEME and Solid Oxide Electrolysis - SOE) were modeled having in mind two methane applications: a combined cycle for power generation and the syngas generation. In addition, on each PtM system is carried out an economic evaluation by calculating fixed capital investment (FCI) and manufacturing costs (MC)., (© 2024 The Authors.)

Background: It is unknown whether vegetarian diets (VDs) may improve outcomes in people with overweight and obesity., Objective: To systematically assess the effects of VDs vs. omnivore diets on anthropometric, metabolic, and blood pressure outcomes in people with overweight and obesity., Methods: We searched for randomized controlled trials (RCTs) in EMBASE, PubMed, Web of Science, and Scopus until February 2, 2022. Primary outcomes were anthropometric risk factors (weight, body mass index [BMI], waist circumference [WC], hip circumference [HC], and body fat percentage). Secondary outcomes were metabolic risk factors (fasting serum glucose, HbA1c, insulin levels) and blood pressure (systolic blood pressure [SBP], diastolic blood pressure [DBP]). Random-effects meta-analyses were performed and effects were expressed as mean difference (MD) and their 95% confidence intervals (CI). The quality of evidence was assessed using GRADE methods., Results: Nine RCTs (n = 1628) were included. VDs decreased weight (MD -3.60 kg, 95%CI -4.75 to -2.46) and glucose (MD -10.64 mg/dL, 95%CI -15.77 to -5.51), but did not decrease WC (MD -3.00 cm, 95%CI -6.20 to 0.20), BMI (MD -0.87 kg/m2, 95%CI -1.80 to 0.06), or HC (MD: -0.86 cm, 95%CI -3.46 to 1.74). VDs did not decrease HbA1c (MD -0.40%, 95%CI -0.89 to 0.10), insulin (MD -3.83 mU/L, 95%CI -8.06 to 0.40), SBP (MD -0.25 mmHg, 95%CI -2.58 to 2.07), or DBP (MD -1.57 mmHg, 95%CI -3.93 to 0.78). Subgroup analyses by type of VD (four RCTs evaluated lacto-ovo-vegetarian diets and five RCTs vegan diets) showed similar results to the main analyses. QoE was very low for most of the outcomes., Conclusions: In comparison to an omnivorous diet, VDs may reduce weight and glucose, but not blood pressure or other metabolic or anthropometric outcomes. However, the QoE was mostly very low. Larger RCTs are still needed to evaluate the effects of VD on anthropometric, metabolic factors, and blood pressure in people with overweight and obesity., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Some statistical analysis techniques may require complete data matrices, but a frequent problem in the construction of databases is the incomplete collection of information for different reasons. One option to tackle the problem is to estimate and impute the missing data. This paper describes a form of imputation that mixes regression with lower rank approximations. To improve the quality of the imputations, a generalisation is proposed that replaces the singular value decomposition (SVD) of the matrix with a regularised SVD in which the regularisation parameter is estimated by cross-validation. To evaluate the performance of the proposal, ten sets of real data from multienvironment trials were used. Missing values were created in each set at four percentages of missing not at random, and three criteria were then considered to investigate the effectiveness of the proposal. The results show that the regularised method proves very competitive when compared to the original method, beating it in several of the considered scenarios. As it is a very general system, its application can be extended to all multivariate data matrices. •The imputation method is modified through the inclusion of a stable and efficient computational algorithm that replaces the classical SVD least squares criterion by a penalised criterion. This penalty produces smoothed eigenvectors and eigenvalues that avoid overfitting problems, improving the performance of the method when the penalty is necessary. The size of the penalty can be determined by minimising one of the following criteria: the prediction errors, the Procrustes similarity statistic or the critical angles between subspaces of principal components., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

Xanthogranulomatous endometritis (XGE) or histiocytic endometritis is a chronic inflammatory pathology of rare presentation, characterized by an exaggerated inflammatory infiltrate that can mimic an endometrial carcinoma. We report two cases of this disease, one of them with a classic presentation of endometritis and the other one with a severe compromise in which the clinical presentation and imaging findings suggested a possible endometrial carcinoma. Knowledge of this unusual and rare pathology, including its etiopathogenesis, is important since it can be included in the differential diagnosis of endometrial carcinoma and, therefore, whenever it is found, to avoid excessive treatment., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Silva-Rengifo et al.)

Meiotic recombination is a crucial cellular process, being one of the major drivers of evolution and adaptation of species. In plant breeding, crossing is used to introduce genetic variation among individuals and populations. While different approaches to predict recombination rates for different species have been developed, they fail to estimate the outcome of crossings between two specific accessions. This paper builds on the hypothesis that chromosomal recombination correlates positively to a measure of sequence identity. It presents a model that uses sequence identity, combined with other features derived from a genome alignment (including the number of variants, inversions, absent bases, and CentO sequences) to predict local chromosomal recombination in rice. Model performance is validated in an inter-subspecific indica x japonica cross, using 212 recombinant inbred lines. Across chromosomes, an average correlation of about 0.8 between experimental and prediction rates is achieved. The proposed model, a characterization of the variation of the recombination rates along the chromosomes, can enable breeding programs to increase the chances of creating novel allele combinations and, more generally, to introduce new varieties with a collection of desirable traits. It can be part of a modern panel of tools that breeders can use to reduce costs and execution times of crossing experiments., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Peñuela et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Epilepsy is the most common neurological disorder that affects ~1-2% of the global population, leading to presentation in the emergency room. The neuroimaging modalities have an important application in diagnosing new onset unprovoked seizures and epilepsy. This article discusses the various neuroimaging modalities for diagnosing seizures and epilepsy and addresses that the MRI is the investigation of choice, and urgent imaging is more commonly done by computed tomography in patients with new-onset seizures. The goal of the article was to diagnose seizures and epilepsy for early intervention to prevent complications or damage to the brain. MRI detects even small cortical epileptogenic lesions, whereas computed tomography is used in screening, diagnosis, evaluation, and monitoring of the prognosis of seizures in children. Magnetic resonance spectroscopy provides biochemical measurements of reduced N-acetyl aspartate and increased creatinine and choline in dysfunctioning epileptic zones. Volumetric MRI is very sensitive and specific in determining seizures originating in extratemporal and extrahippocampal sites. Even though diffusion tensor magnetic resonance imaging has a limited role, it is used in specific pediatric patient groups with temporal lobe epilepsy. Functional radionuclide imaging modalities (positron emission tomography and single-photon emission computerized tomography) are increasingly significant for the identification of the epileptic region. Furthermore, the authors recommend the use of artificial intelligence and further research on imaging modalities for early diagnosis of seizures and epilepsy., Competing Interests: The authors report no conflicts of interest., (© 2023 the Author(s). Published by Wolters Kluwer Health, Inc.)

Functional enrichment analysis is a cornerstone in bioinformatics as it makes possible to identify functional information by using a gene list as source. Different tools are available to compare gene ontology (GO) terms, based on a directed acyclic graph structure or content-based algorithms which are time-consuming and require a priori information of GO terms. Nevertheless, quantitative procedures to compare GO terms among gene lists and species are not available. Here we present a computational procedure, implemented in R, to infer functional information derived from comparative strategies. GOCompare provides a framework for functional comparative genomics starting from comparable lists from GO terms. The program uses functional enrichment analysis (FEA) results and implement graph theory to identify statistically relevant GO terms for both, GO categories and analyzed species. Thus, GOCompare allows finding new functional information complementing current FEA approaches and extending their use to a comparative perspective. To test our approach GO terms were obtained for a list of aluminum tolerance-associated genes in Oryza sativa subsp. japonica and their orthologues in Arabidopsis thaliana. GOCompare was able to detect functional similarities for reactive oxygen species and ion binding capabilities which are common in plants as molecular mechanisms to tolerate aluminum toxicity. Consequently, the R package exhibited a good performance when implemented in complex datasets, allowing to establish hypothesis that might explain a biological process from a functional perspective, and narrowing down the possible landscapes to design wet lab experiments., Competing Interests: Declaration of Competing Interest All authors declare no conflicts of interest., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

This paper proposes the transition times of Petri net models of human gait as training features for multiclass random forests (RFs) and classification trees (CTs). These models are designed to support screening for neurodegenerative diseases. The proposed Petri net describes gait in terms of nine cyclic phases and the timing of the nine events that mark the transition between phases. Since the transition times between strides vary, each is represented as a random variable characterized by its mean and standard deviation. These transition times are calculated using the PhysioNet database of vertical ground reaction forces (VGRFs) generated by feet-ground contact. This database comprises the VGRFs of four groups: amyotrophic lateral sclerosis, the control group, Huntington's disease, and Parkinson disease. The RF produced an overall classification accuracy of 91%, and the specificities and sensitivities for each class were between 80% and 100%. However, despite this high performance, the RF-generated models demonstrated lack of interpretability prompted the training of a CT using identical features. The obtained tree comprised only four features and required a maximum of three comparisons. However, this simplification dramatically reduced the overall accuracy from 90.6% to 62.3%. The proposed set features were compared with those included in PhysioNet database of VGRFs. In terms of both the RF and CT, more accurate models were established using our features than those of the PhysioNet., (© 2022 IOP Publishing Ltd.)

Background: This paper proposes a workflow to identify genes that respond to specific treatments in plants. The workflow takes as input the RNA sequencing read counts and phenotypical data of different genotypes, measured under control and treatment conditions. It outputs a reduced group of genes marked as relevant for treatment response. Technically, the proposed approach is both a generalization and an extension of WGCNA. It aims to identify specific modules of overlapping communities underlying the co-expression network of genes. Module detection is achieved by using Hierarchical Link Clustering. The overlapping nature of the systems' regulatory domains that generate co-expression can be identified by such modules. LASSO regression is employed to analyze phenotypic responses of modules to treatment., Results: The workflow is applied to rice (Oryza sativa), a major food source known to be highly sensitive to salt stress. The workflow identifies 19 rice genes that seem relevant in the response to salt stress. They are distributed across 6 modules: 3 modules, each grouping together 3 genes, are associated to shoot K content; 2 modules of 3 genes are associated to shoot biomass; and 1 module of 4 genes is associated to root biomass. These genes represent target genes for the improvement of salinity tolerance in rice., Conclusions: A more effective framework to reduce the search-space for target genes that respond to a specific treatment is introduced. It facilitates experimental validation by restraining efforts to a smaller subset of genes of high potential relevance., (© 2021. The Author(s).)

Synthetic mesh is often utilised for reinforcement in pelvic organ prolapse surgery. Mesh erosion to surrounding structures is a recognised complication following sacrocolpopexy, but translocation to the vagina is more common. We report an unusual case of delayed asymptomatic erosion of the mesh into the rectum 12 years after sacrocolpopexy.

Background: COVID-19 can be asymptomatic in a substantial proportion of patients. The assessment and management of these patients constitute a key element to stop dissemination., Aim: To describe the assessment and treatment of asymptomatic infection in patients with a confirmed diagnosis of COVID-19., Methods: We searched five databases and search engines for preprints/preproofs, up to August 22, 2020. We included cohort, cross-sectional, and case series studies, reporting the assessment and management of asymptomatic individuals. We extracted data on total discharges with negative PCR, length of hospitalization, treatment, and number of patients who remained asymptomatic. A random-effects model with inverse variance method was used to calculate the pooled prevalence., Results: 41 studies (nine cross-sectional studies, five retrospective studies and 27 reports/case series; 647 asymptomatic individuals), were included, of which 47% were male (233/501). The age of patients was between 1month and 73 years. In patients who became symptomatic, length of hospitalization mean was 13.6 days (SD 6.4). Studies used lopinavir/ritonavir, hydroxychloroquine plus ritonavir/lopinavir, hydroxychloroquine with and without azithromycin, ribavirin plus interferon and interferon alfa. The proportion of individuals who remained asymptomatic was 91% (463/588 patients; 95%CI: 78.3%-98.7%); and asymptomatic individuals discharged with negative PCR was 86% (102/124 individuals; 95%CI: 58.4%-100%)., Conclusions: There is no standard treatment for asymptomatic COVID-19 individuals. There are no studies of adequate design to make this decision. It has been shown that most asymptomatic individuals who were followed have recovered, but this cannot be attributed to standard treatment., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

In this contribution, an open-source computational toolbox composed of FEniCS and complementary packages is introduced to the chemical and process engineering field by addressing two case studies. First, the oxidation of o-xylene to phthalic anhydride is modelled and used as a FEniCS' proof-of-concept based on a comparison with the software Aspen Custom Modeler (ACM). The results show a maximum absolute error of 2% and thus a good FEniCS/ACM agreement. Second, synthetic natural gas (SNG) production through CO 2 methanation is covered in further detail. In this instance, a parametric study is performed for a tube bundle fixed-bed reactor employing a two-dimensional and transient pseudo-homogeneous model. An operating window for critical variables is evaluated, discussed, and successfully contrasted with the literature. Therefore, the computational toolbox methodology and the consistency of the results are validated, strengthening FEniCS and complements as an interesting alternative to solve mathematical models concerning chemical reaction engineering., Competing Interests: The authors declare no conflict of interest., (© 2020 Published by Elsevier Ltd.)

Breast cancer currently affects 1 in 8 women in the UK during their lifetime. Common sites for breast cancer metastasis include the axillary lymph nodes, bones, lung, liver, brain, soft tissue and adrenal glands. There is well documented evidence detailing breast metastasis to the gastrointestinal tract but anal metastasis is exceptionally rare. We present the case of a 78-year-old woman with an anal metastasis as the sentinel and isolated presentation of an invasive ductal breast carcinoma. As advances in the treatment of breast cancer improve, and with an ageing and expanding population, there will be an increasing number of cancer survivors, and more of these unusual presentations may be encountered in the future.

In this paper, a new neuromusculoskeletal simulation strategy is proposed. It is based on a cascade control approach with an inner muscular-force control loop and an outer joint-position control loop. The originality of the work is located in the optimization criterion used to distribute forces between synergistic and antagonistic muscles. The cost function and the inequality constraints depend on an estimation of the muscle fiber length and its time derivative. The advantages of a such criterion are exposed by theoretical analysis and numerical tests. The simulation model used in the numerical tests consists in an anthropomorphic arm model composed by two joints and six muscles. Each muscle is modeled as a second-order dynamical system including activation and contraction dynamics. Contraction dynamics is represented using a classical Hill's model.