The Effect of Bidet Use on Severity of Constipation and Quality of Life Among Pregnant Women

Background: Constipation is a common problem in pregnancy. This study aims to elucidate the efficacy of using a bidet before defecation to reduce the severity of constipation and improve the quality of life in pregnancy. Methods: The sample consisted of an experimental group (n = 30) and a control group (n = 30), a total of 60 pregnant women. Randomization was performed using the pitch-and-toss method from simple probability randomization methods. The research data were collected using the Personal Information Form, the Constipation Assessment Scale for Pregnancy, and the Constipation Quality of Life Scale. Results: There was a statistically significant difference between the pregnant women’s mean scores on the Constipation Assessment Scale for Pregnancy due to the intervention of bidet before defecation. Although the members of the intervention group had severe constipation at first, they reported only “some problems” on defecation after the intervention. In addition, statistically significant improvements were observed in the intervention group via all subscales of the Constipation Quality of Life Scale except the satisfaction subscale. Conclusion: Providing pregnant women with training on constipation and information about how to control constipation using a bidet is very important in terms of reducing the severity of constipation, enabling them to feel better and continue their daily activities, and thus to improve their quality of life

The Use of Oxytocin by Healthcare Professionals During Labor

Background: Oxytocin is widely used in perinatal medicine, but it can cause serious side effects. Health professionals should be familiar with the pharmacokinetics, dosing regimen, and fetal effects of oxytocin. This study aims to explore the use of oxytocin by healthcare professionals during labor. Methods: This study was conducted in one medical faculty, one training and research hospital, one maternity hospital, and one private hospital in Adana, Turkey. The sample group included 107 participants. The data were gathered using a survey prepared in line with the literature. The survey was comprised of 30 questions. These questions concern the social demographic information of the participants, the knowledge and actual oxytocin use, and the views of the participants. The data were analyzed using descriptive statistics. Results: The average age of the participants was 36.76 ± 8.70 years, the mean of working experience in the delivery room was 7.79 ± 7.73 years. 85.6% of the participants who answered the question of possible effects of oxytocin as contraction, 57.9% of the possible side effects as fetal distress. 69.2% of the participants stated that they applied oxytocin after dilution in a fluid while 47% stated that they applied it after dilution in fluid with 5% Dextrose. While 40% of the participants responded that they sometimes forgot to administer medication, 39.2% stated that they did not register medication in their survey responses. Conclusion: It was determined that most of the participants answered the questions about the effect of oxytocin correctly, but they could not respond to all the side effects of oxytocin. It was found that most of the participants could not answer the storage conditions that are important for the effectiveness of the drug correctly. In addition, the importance level given to the principles of drug administration by the participants was generally found to be high

Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial Late Gadolinium Enhanced MRI Images

Accurate assessment of left atrial fibrosis in patients with atrial fibrillation relies on high-quality 3D late gadolinium enhancement (LGE) MRI images. However, obtaining such images is challenging due to patient motion, changing breathing patterns, or sub-optimal choice of pulse sequence parameters. Automated assessment of LGE-MRI image diagnostic quality is clinically significant as it would enhance diagnostic accuracy, improve efficiency, ensure standardization, and contributes to better patient outcomes by providing reliable and high-quality LGE-MRI scans for fibrosis quantification and treatment planning. To address this, we propose a two-stage deep-learning approach for automated LGE-MRI image diagnostic quality assessment. The method includes a left atrium detector to focus on relevant regions and a deep network to evaluate diagnostic quality. We explore two training strategies, multi-task learning, and pretraining using contrastive learning, to overcome limited annotated data in medical imaging. Contrastive Learning result shows about $4\%$, and $9\%$ improvement in F1-Score and Specificity compared to Multi-Task learning when there's limited data.Comment: Accepted to STACOM 2023. 11 pages, 3 figure

Selective mechanical transfer deposition of Langmuir graphene films for high-performance silver nanowire hybrid electrodes

In this work we present silver nanowire hybrid electrodes, prepared through the addition of small quantities of pristine graphene by mechanical transfer deposition from surface-assembled Langmuir films. This technique is a fast, efficient, and facile method for modifying the opto-electronic performance of AgNW films. We demonstrate that it is possible to use this technique to perform two-step device production by selective patterning of the stamp used, leading to controlled variation in the local sheet resistance across a device. This is particularly attractive for producing extremely low-cost sensors on arbitrarily large scales. Our aim is to address some of the concerns surrounding the use of AgNW films as replacements for indium tin oxide (ITO); namely the use of scarce materials and poor stability of AgNWs against flexural and environmental degradation

Demographic Inference and Representative Population Estimates from Multilingual Social Media Data

Social media provide access to behavioural data at an unprecedented scale and granularity. However, using these data to understand phenomena in a broader population is difficult due to their non-representativeness and the bias of statistical inference tools towards dominant languages and groups. While demographic attribute inference could be used to mitigate such bias, current techniques are almost entirely monolingual and fail to work in a global environment. We address these challenges by combining multilingual demographic inference with post-stratification to create a more representative population sample. To learn demographic attributes, we create a new multimodal deep neural architecture for joint classification of age, gender, and organization-status of social media users that operates in 32 languages. This method substantially outperforms current state of the art while also reducing algorithmic bias. To correct for sampling biases, we propose fully interpretable multilevel regression methods that estimate inclusion probabilities from inferred joint population counts and ground-truth population counts. In a large experiment over multilingual heterogeneous European regions, we show that our demographic inference and bias correction together allow for more accurate estimates of populations and make a significant step towards representative social sensing in downstream applications with multilingual social media.Comment: 12 pages, 10 figures, Proceedings of the 2019 World Wide Web Conference (WWW '19

Risk of COVID-19 hospital admission and COVID-19 mortality during the first COVID-19 wave with a special emphasis on ethnic minorities: an observational study of a single, deprived, multiethnic UK health economy

© 2021 The Authors. Published by BMJ. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: http://dx.doi.org/10.1136/bmjopen-2020-046556Objectives The objective of this study was to describe variations in COVID-19 outcomes in relation to local risks within a well-defined but diverse single-city area. Design Observational study of COVID-19 outcomes using quality-assured integrated data from a single UK hospital contextualised to its feeder population and associated factors (comorbidities, ethnicity, age, deprivation). Setting/participants Single-city hospital with a feeder population of 228 632 adults in Wolverhampton. Main outcome measures Hospital admissions (defined as COVID-19 admissions (CA) or non-COVID-19 admissions (NCA)) and mortality (defined as COVID-19 deaths or non-COVID-19 deaths). Results Of the 5558 patients admitted, 686 died (556 in hospital); 930 were CA, of which 270 were hospital COVID-19 deaths, 47 non-COVID-19 deaths and 36 deaths after discharge; of the 4628 NCA, there were 239 in-hospital deaths (2 COVID-19) and 94 deaths after discharge. Of the 223 074 adults not admitted, 407 died. Age, gender, multimorbidity and black ethnicity (OR 2.1 (95% CI 1.5 to 3.2), p<0.001, compared with white ethnicity, absolute excess risk of <1/1000) were associated with CA and mortality. The South Asian cohort had lower CA and NCA, lower mortality compared with the white group (CA, 0.5 (0.3 to 0.8), p<0.01; NCA, 0.4 (0.3 to 0.6), p<0.001) and community deaths (0.5 (0.3 to 0.7), p<0.001). Despite many common risk factors for CA and NCA, ethnic groups had different admission rates and within-group differing association of risk factors. Deprivation impacted only the white ethnicity, in the oldest age bracket and in a lesser (not most) deprived quintile. Conclusions Wolverhampton’s results, reflecting high ethnic diversity and deprivation, are similar to other studies of black ethnicity, age and comorbidity risk in COVID-19 but strikingly different in South Asians and for deprivation. Sequentially considering population and then hospital-based NCA and CA outcomes, we present a complete single health economy picture. Risk factors may differ within ethnic groups; our data may be more representative of communities with high Black, Asian and minority ethnic populations, highlighting the need for locally focused public health strategies. We emphasise the need for a more comprehensible and nuanced conveyance of risk

Somatic Variants in SVIL in Cerebral Aneurysms

Publisher Copyright: © American Academy of Neurology.Background and ObjectivesWhile somatic mutations have been well-studied in cancer, their roles in other complex traits are much less understood. Our goal is to identify somatic variants that may contribute to the formation of saccular cerebral aneurysms.MethodsWe performed whole-exome sequencing on aneurysm tissues and paired peripheral blood. RNA sequencing and the CRISPR/Cas9 system were then used to perform functional validation of our results.ResultsSomatic variants involved in supervillin (SVIL) or its regulation were found in 17% of aneurysm tissues. In the presence of a mutation in the SVIL gene, the expression level of SVIL was downregulated in the aneurysm tissue compared with normal control vessels. Downstream signaling pathways that were induced by knockdown of SVIL via the CRISPR/Cas9 system in vascular smooth muscle cells (vSMCs) were determined by evaluating changes in gene expression and protein kinase phosphorylation. We found that SVIL regulated the phenotypic modulation of vSMCs to the synthetic phenotype via Krüppel-like factor 4 and platelet-derived growth factor and affected cell migration of vSMCs via the RhoA/ROCK pathway.DiscussionWe propose that somatic variants form a novel mechanism for the development of cerebral aneurysms. Specifically, somatic variants in SVIL result in the phenotypic modulation of vSMCs, which increases the susceptibility to aneurysm formation. This finding suggests a new avenue for the therapeutic intervention and prevention of cerebral aneurysms.Peer reviewe

Optimal management of asymptomatic carotid stenosis in 2021: the jury is still out. An International, multispecialty, expert review and position statement

Objectives: The recommendations of international guidelines for the management of asymptomatic carotid stenosis (ACS) often vary considerably and extend from a conservative approach with risk factor modification and best medical treatment (BMT) alone, to a more aggressive approach with a carotid intervention plus BMT. The aim of the current multispecialty position statement is to reconcile the conflicting views on the topic. Materials and methods: A literature review was performed with a focus on data from recent studies. Results: Several clinical and imaging high-risk features have been identified that are associated with an increased long-term ipsilateral ischemic stroke risk in patients with ACS. Such high-risk clinical/imaging features include intraplaque hemorrhage, impaired cerebrovascular reserve, carotid plaque echolucency/ulceration/ neovascularization, a lipid-rich necrotic core, a thin or ruptured fibrous cap, silent brain infarction, a contralateral transient ischemic attack/stroke episode, male patients <75 years and microembolic signals on transcranial Doppler. There is growing evidence that 80-99% ACS indicate a higher stroke risk than 50-79% stenoses. Conclusions: Although aggressive risk factor control and BMT should be implemented in all ACS patients, several high-risk features that may increase the risk of a future cerebrovascular event are now documented. Consequently, some guidelines recommend a prophylactic carotid intervention in high-risk patients to prevent future cerebrovascular events. Until the results of the much-anticipated randomized controlled trials emerge, the jury is still out regarding the optimal management of ACS patients