Danieldodd1521
To investigate the complexity of proteomics in cervical cancer tissues, we used isobaric tags for relative and absolute quantitation (iTRAQ)-based mass spectrometry analysis on a panel of normal cervical tissues (N), high-grade squamous intraepithelial lesion tissues (HSIL) and cervical cancer tissues (CC). Total 72 differentially expressed proteins were identified both in CC vs N and CC vs HSIL. selleck inhibitor The expression of HMGB2 was markedly higher in CC than that in HSIL and N. High HMGB2 expression was significantly correlated with primary tumor size, invasion and tumor stage. The up-regulated HMGB2 was discovered to be associated with human cervical cancer. These findings suggest that HMGB2 may be a potentially prognostic biomarker and a target for the therapy of cervical cancer.
Triptans and erenumab are both migraine-specific agents acting on the calcitonin gene-related peptide pathway. Therefore, response to triptans might be associated with response to erenumab.
In our study, consecutive patients referring to the Headache Centers of the Abruzzo region from January 2019 to March 2020 and treated with erenumab were interviewed about past use and efficacy of triptans. Triptan users were classified as 'triptan responders' if they were headache-free 2 h after treating ≥3 migraine attacks with ≥1 triptan. We considered patients as 'erenumab responders', if they had a ≥ 50% mean reduction in monthly migraine days between the 4th and the 6th month from treatment start compared with baseline. Of 91 triptan users, 73 (80.2%) were triptan responders and 58 (63.7%) were erenumab responders. The odds ratio of being erenumab responder was 3.64 (95% CI, 1.25-10.64) for triptan users as compared to non-users. (P = 0.014). Besides, starting erenumab improved triptan response in both erenumab responders and non-responders.
Our data of an association between response to triptans and response to erenumab can be useful for patient advice and to improve the understanding of migraine pathophysiology and treatment.
Our data of an association between response to triptans and response to erenumab can be useful for patient advice and to improve the understanding of migraine pathophysiology and treatment.
Circular RNA (circRNA) is a novel type of RNA with a closed-loop structure. Increasing numbers of circRNAs are being identified in plants and animals, and recent studies have shown that circRNAs play an important role in gene regulation. link2 Therefore, identifying circRNAs from increasing amounts of RNA-seq data is very important. However, traditional circRNA recognition methods have limitations. In recent years, emerging machine learning techniques have provided a good approach for the identification of circRNAs in animals. However, using these features to identify plant circRNAs is infeasible because the characteristics of plant circRNA sequences are different from those of animal circRNAs. For example, plants are extremely rich in splicing signals and transposable elements, and their sequence conservation in rice, for example is far less than that in mammals. To solve these problems and better identify circRNAs in plants, it is urgent to develop circRNA recognition software using machine learning based on thsing random forest algorithm, and the model can also be applied to plant circRNA recognition such as Arabidopsis thaliana and maize. At the same time, after the completion of model construction, the machine learning model constructed and the programming scripts used in this study are packaged into a localized circRNA prediction software Pcirc, which is convenient for plant circRNA researchers to use.
Based on rice circRNA and lncRNA data, a machine learning model for plant circRNA recognition was constructed in this study using random forest algorithm, and the model can also be applied to plant circRNA recognition such as Arabidopsis thaliana and maize. At the same time, after the completion of model construction, the machine learning model constructed and the programming scripts used in this study are packaged into a localized circRNA prediction software Pcirc, which is convenient for plant circRNA researchers to use.
Emergency pediatric care curriculum (EPCC) was developed to address the need for pediatric rapid assessment and resuscitation skills among out-of-hospital emergency providers in Armenia. This study was designed to evaluate the effectiveness of EPCC in increasing physicians' knowledge when instruction transitioned to local instructors. We hypothesize that (1) EPCC will have a positive impact on post-test knowledge, (2) this effect will be maintained when local trainers teach the course, and (3) curriculum will satisfy participants.
This is a quasi-experimental, pre-test/post-test study over a 4-year period from October 2014‑November 2017. Train-the-trainer model was used. Primary outcomes are immediate knowledge acquisition each year and comparison of knowledge acquisition between two cohorts based on North American vs local instructors. Descriptive statistics was used to summarize results. Pre-post change and differences across years were analyzed using repeated measures mixed models.
Test scores improv ill or injured children in the out-of-hospital setting.
EPCC resulted in significant improvement in knowledge and was well received by participants. This is a viable and sustainable model to train providers who have otherwise not had formal education in this field.
EPCC resulted in significant improvement in knowledge and was well received by participants. This is a viable and sustainable model to train providers who have otherwise not had formal education in this field.
Resilient animals can remain productive under different environmental conditions. Rearing in increasingly heterogeneous environmental conditions increases the need of selecting resilient animals. Detection of environmental challenges that affect an entire population can provide a unique opportunity to select animals that are more resilient to these events. The objective of this study was two-fold (1) to present a simple and practical data-driven approach to estimate the probability that, at a given date, an unrecorded environmental challenge occurred; and (2) to evaluate the genetic determinism of resilience to such events.
Our method consists of inferring the existence of highly variable days (indicator of environmental challenges) via mixture models applied to frequently recorded phenotypic measures and then using the inferred probabilities of the occurrence of an environmental challenge in a reaction norm model to evaluate the genetic determinism of resilience to these events. These probabilities are ee text] E interaction and show that the best animals in one environmental condition are not the best in another one.
Although large artery atherosclerosis (LAA) is the most common type of cerebral infarction, non-LAA is not uncommon. The purpose of this paper is to investigate the prognosis of patients with non-LAA and to establish a corresponding nomogram.
Between June 2016 and June 2017, we had 1101 admissions for acute ischemic stroke (AIS). Of these, 848 were LAA and 253 were non-LAA. Patients were followed up every 3months with a minimum of 1year of follow-up. After excluding patients who were lost follow-up and patients who did not meet the inclusion criteria, a total of 152 non-LAA patients were included in this cohort study. After single-factor analysis and multifactor logistic regression analysis, the risk factors associated with prognosis were derived and different nomograms were developed based on these risk factors. After comparison, the best model is derived.
Logistics regression found that the patient's National Institutes of Health Stroke Scale (NIHSS) score, ejection fraction (EF), creatine kinase-MB (CK-MB), age, neutrophil-to-lymphocyte ratio (NLR), aspartate aminotransferase (AST), and serum albumin were independently related to the patient's prognosis. We thus developed three models model 1 single NIHSS score, AUC = 0.8534; model 2, NIHSS + cardiac parameters (CK-MB, EF), AUC = 0.9325; model 3, NIHSS + CK-MB + EF + age + AST + NLR + albumin, AUC = 0.9598. We compare the three models model 1 vs model 2, z = -2.85, p = 0.004; model 2 vs model 3, z = -1.58, p = 0.122. Therefore, model 2 is considered to be the accurate and convenient model.
Predicting the prognosis of patients with non-LAA is important, and our nomogram, built on the NIHSS and cardiac parameters, can predict the prognosis accurately and provide a powerful reference for clinical decision making.
Predicting the prognosis of patients with non-LAA is important, and our nomogram, built on the NIHSS and cardiac parameters, can predict the prognosis accurately and provide a powerful reference for clinical decision making.
The relentless continuing emergence of new genomic sequencing protocols and the resulting generation of ever larger datasets continue to challenge the meaningful summarization and visualization of the underlying signal generated to answer important qualitative and quantitative biological questions. As a result, the need for novel software able to reliably produce quick, comprehensive, and easily repeatable genomic signal visualizations in a user-friendly manner is rapidly re-emerging.
recoup is a Bioconductor package for quick, flexible, versatile, and accurate visualization of genomic coverage profiles generated from Next Generation Sequencing data. Coupled with a database of precalculated genomic regions for multiple organisms, recoup offers processing mechanisms for quick, efficient, and multi-level data interrogation with minimal effort, while at the same time creating publication-quality visualizations. Special focus is given on plot reusability, reproducibility, and real-time exploration and formattcommon computational environment, visualization quality and user friendliness. recoup is a unique package presenting a balanced tradeoff for a combination of assessment criteria while remaining fast and friendly.
Single-cell RNA sequencing (scRNA-seq) enables the possibility of many in-depth transcriptomic analyses at a single-cell resolution. It's already widely used for exploring the dynamic development process of life, studying the gene regulation mechanism, and discovering new cell types. link3 However, the low RNA capture rate, which cause highly sparse expression with dropout, makes it difficult to do downstream analyses.
We propose a new method SCC to impute the dropouts of scRNA-seq data. Experiment results show that SCC gives competitive results compared to two existing methods while showing superiority in reducing the intra-class distance of cells and improving the clustering accuracy in both simulation and real data.
SCC is an effective tool to resolve the dropout noise in scRNA-seq data. The code is freely accessible at https//github.com/nwpuzhengyan/SCC .
SCC is an effective tool to resolve the dropout noise in scRNA-seq data. The code is freely accessible at https//github.com/nwpuzhengyan/SCC .
Nanostructured lipid carriers (NLCs), due to their impressive benefits, have recently been considered in different areas. Besides, NLC loaded with essential oils is attractive for finding more effective antimicrobial products, especially against common bacteria such as Staphylococcus epidermidis (S. epidermidis).
This study aims to prepare and characterize NLCs encapsulated with Punica granatum (P. granatum) seed oil (PGS oil-loaded NLCs) and examine the antimicrobial effect of this combination against S. epidermidis.
PGS oil-loaded NLCs were prepared using a hot melt homogenization method. Later, they were characterized by determining particle size distribution (particle size analyzer), morphology (scanning electron microscopy (SEM)), and zeta potential (surface charge of NLCs). Minimum inhibitory concentrations (MIC) of PGS oil-loaded NLCs were assessed and compared with seed oil emulsion of P. granatum against S. epidermidis.
PGS oil-loaded NLCs were spherical shaped nanoparticles, with a mean size of 102.
