Selection Procedure Research Articles

Risk Factors for Invasive Surgical Site Infections Among Adult Single Liver Transplant Recipients at Duke University Hospital in the Period 2015–2020

Background. Invasive primary surgical site infections (IP-SSI) are a severe complication of liver transplant surgery. Identification of risk factors for IP-SSI is critical to IP-SSI prevention. Methods. All adult single liver transplants performed at Duke University Hospital in the period 2015–2020 were reviewed for IP-SSI occurring within 90 d of transplant. Risks for IP-SSI were identified using least absolute shrinkage and selection operator variable selection procedure. A 2-sided P value of <0.05 was considered statistically significant. Results. IP-SSI were identified in 34/470 (7.2%) adult single liver transplants. Repeat transplantation, spilt liver, Roux-en-Y biliary anastomosis, anastomotic leak, and post-transplant renal replacement therapy were positively associated with IP-SSI. IP-SSI were associated with increased length of index transplant hospitalization (24.5 versus 10.0 d, P < 0.01) and 1-y all-cause mortality (14.7% versus 4.1%, P = 0.02). Gram positive bacteria were the main pathogens (51.7%), followed by Gram negative bacteria (24.1%) and Candida (24.1%). Multidrug resistance bacteria increased over time (27.3% in 2015 versus 66.7% in 2020, P = 0.17). Conclusions. In the setting of routine antimicrobial prophylaxis and an overall low rate of IP-SSI, surgical factors were the main determinants of IP-SSI among adult liver transplant recipients. IP-SSI had a negative impact on the length of index transplant hospitalization and 1-y mortality. While the surgical factors associated with an increased risk of IP-SSI are not easily modifiable, their impact may be best contained by close clinical monitoring and tailored antimicrobial therapy.

Abstract A016: Early pancreatic cancer detection using extracellular vesicle DNA methylation signatures in blood

Abstract In 2020, there were 495,773 new cases and 466,003 deaths for pancreatic cancer worldwide. Furthermore, pancreatic cancer is emerging to be the leading cause of cancer related deaths in the U.S. by the year 2030, surpassing lung and colon cancer. The majority of pancreatic cancer patients (∼80%) are diagnosed at the most advanced (metastatic) stage, for which the 5-year survival is 3%. Dramatically, studies show that if it can be detected at an early stage when surgical removal is feasible, the 5-year survival rate significantly increased to 37%. This data highlights the crucial importance of early detection for pancreatic cancer treatment with curative intent. Unfortunately, currently there are no reliable techniques for routine screening to identify pancreatic cancer at early stages. In this Abstract, we developed and validated the performance of those DNA methylation markers to detect pancreatic cancer in a cohort of pancreatic cancer patients. Firstly, we collected a large amount of genome-wide methylation microarray data (targeting over 450,000 methylation sites) of tissue samples from several publicly available databases, including samples of solid pancreatic tumors (n = 384), noncancerous pancreatic tissues (n = 126) and healthy whole blood (n = 453). The raw microarray data were preprocessed using a pipeline implemented in R, which removes inaccurate measurements and corrects for bias caused by technical issues. A two-stage feature selection procedure was designed and implemented in Python, which makes use of various filters and a multivariate embedded method, support vector machine recursive feature elimination (SVM-RFE). The features were evaluated using two metrics, classification performance and stability, based on a tradeoff of which we selected a set of differentially methylated loci for discriminating the samples. The set of prospective markers includes 129 hypermethylated and 142 hypomethylated tumor-specific loci, 8 hypermethylated and 8 hypomethylated pancreatic tissue specific loci. To validate the performance of those pancreatic cancer-specific methylation markers, as a feasibility study, we isolated extracellular vesicles from cancer cells and plasma of cancer patients using lipid nanoprobe (LNP). Extracellular vesicles (EVs) are lipid-bilayer-enclosed vesicles of sub- micrometer size that are secreted by virtually all cell types. In a pilot study encompassing 11 pancreatic cancer patients (Stage I-IV) and age-matched 10 healthy donors (HD), we employed quantitative methylation-specific polymerase chain reaction assays (qMSP) to examine the promoter methylation status of the genes within plasma EV DNA. The receiver operating characteristic (ROC) curves were employed to assess the performance of a combined promoter methylation signature in EV DNA for the detection of pancreatic cancer. Our findings indicate that the promoter methylation of genes can be specific to pancreatic cancer, suggesting the efficient packaging of methylation information from genomic DNA into EVs. Citation Format: Hongzhang He, Siyang Zheng. Early pancreatic cancer detection using extracellular vesicle DNA methylation signatures in blood [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A016.

Advanced Design of Naval Ship Propulsion Systems Utilizing Battery-Diesel Generator Hybrid Electric Propulsion Systems

As advanced sensors and weapons require high power, naval vessels have increasingly adopted electric propulsion systems. This study aims to enhance the efficiency and operability of electric propulsion systems over traditional mechanical propulsion systems by analyzing the operational profiles of modern naval vessels. Consequently, a battery-integrated generator-based electric propulsion system was selected. Considering the purpose of the vessel, a specification selection procedure was developed, leading to the design of a hybrid electric propulsion system (comprising one battery and four generators). The power management control technique of the proposed propulsion system sets the operating modes (depending on the specific fuel oil consumption of the generators) to minimize fuel consumption based on the operating load. Additionally, load distribution control rules for the generators were designed to reduce energy consumption based on the load and battery state of charge. MATLAB/Simulink was used to evaluate the proposed system, with simulation results demonstrating that it maintained the same propulsion performance as existing systems while achieving a 12-ton (22%) reduction in fuel consumption. This improvement results in cost savings and reduced carbon dioxide emissions. These findings suggest that an efficient load-sharing controller can be implemented for various vessels equipped with electric propulsion systems, tailored to their operational profiles.

Strategy Implementation Practices and Performance of Commercial Banks in Nakuru County

Organizational performance entails recurring duties like establishing organizational goals, monitoring progress towards those objectives, and making modifications to enhance their accomplishment in a more efficient manner. Strategy implementation represents a significant phase within an organization; however, existing literature consistently underscores a notable failure rate in the implementation process across numerous organizations. The prevalence of poor performance in banks is attributed to issues such as inadequate coordination, suboptimal structural frameworks, and insufficient personnel proficiency in executing rebranding strategies. As a result, the goal of this study was to look into how Nakuru's commercial banks implemented their strategies and how well they did so. General Systems Theory, Contingency Theory, and Resource-Based Theory served as the study's guiding theories. The study targeted 108 workers from 24 commercial banks in Nakuru County using a descriptive survey research approach. A sample of 54 respondents was chosen using a stratified random selection procedure to assure representation. Structured questionnaires were used to obtain data, and they were chosen for their propensity to do so with little bias and mistake. Pilot research was done in Narok County commercial banks prior to the main investigation. The data was analysed using both descriptive and inferential statistics. Percentages, frequencies, measurements of central tendency (mean), and measures of dispersion (standard deviation) were examples of descriptive statistics. Correlations were used for inferential analysis of qualitative data. To analyse quantitative data, Version 25 of the Statistical Package for the Social Sciences (SPSS) was utilized. The study found that resources, organizational change, structure, and leadership significantly influence the performance of commercial banks in Nakuru CBD. Effective resource allocation, strategic change management, well-defined organizational structures, and strong leadership positively impact operational efficiency and overall performance. The study recommends investing in employee training, optimizing IT services, and aligning organizational structures with strategic goals. Further research should examine how organizational capabilities and market dynamics shape performance outcomes.

Информационная гигиена как фактор успешной профессиональной деятельности сотрудников уголовно-исполнительной системы

The article examines the issues of information hygiene as a factor of successful professional activity of employees of the penal correction system in the context of digital transformation. The insufficiency of research on information hygiene in the psychology of professional activity of specialists is emphasized. Information (digital) hygiene is a certain set of norms and rules that ensure optimal interaction with information of various kinds, which, in turn, ensures the preservation of professional health, personal resources, professional and personal identity and is one of the factors of success of professional activity in the context of digital transformation. Recommendations on the observance of information hygiene in the official activities of employees of the penitentiary system are given. The directions of the official activity of employees of the penal enforcement system in which the use of information hygiene is significant are highlighted: interaction with various information tools and software service products; organization of interdepartmental electronic interaction with law enforcement agencies and other federal executive authorities; information support for the activities of the Federal Penitentiary Service of Russia; information security and protection of information resources of the Federal Penitentiary Service of Russia; implementation of various projects of digital transformation of the Federal Penitentiary Service of Russia. Two approaches are proposed in training to improve the level of development of digital hygiene by employees of the penitentiary system: within the framework of higher and additional professional education. Considering information hygiene as a complex professionally important quality and a factor of successful performance of employees of the penal enforcement system, it is quite natural that these aspects can serve as the basis for taking into account and developing criteria for the success of professional activity in the future, an expanded description and characterization of generalized labor functions of professional standards by position, clarification of occupational and psychograms, optimization of procedures for professional selection and professional certification of employees of the penitentiary system in the dynamics of the professional path.

Detection of hidden drawings using multi-wavelength dynamic speckle, tuneable algorithms, and unsupervised learning

Abstract We implemented an experiment to reveal hidden drawings on papyrus, utilizing an optical technique based on the speckle phenomenon. The goal is to optimize the detection of hidden objects. Our approach proposes using multiple wavelengths for illumination and tuneable algorithms to process the dynamic speckle images. By implementing the suggested method, we generated various results with varying quality, contingent upon the tuneable algorithm parameters. It's feasible to identify the optimal parameter combination to achieve the most effective visualization of the recovered image. To streamline the selection of tuneable algorithms and mitigate reliance on subjective visual judgment, we employed unsupervised machine learning techniques to determine the conditions necessary to achieve optimal results. This approach simplifies the selection procedure and offers an objective and non-invasive method. Furthermore, the proposed procedure holds promise for extending its application to uncover hidden paintings, subsurface archaeological artefacts, and other dynamic speckle experiments.

VS-LTGARCHX: A Flexible Variable Selection in Log-TGARCHX Models

Abstract The log-TGARCHX model is less restrictive in terms of the inclusion of exogenous variables and asymmetry lags compared to the GARCHX model. Nevertheless, adding less (or more) covariates than necessary may lead to under- or overfitting, respectively. In this context, we propose a new algorithm, called VS-LTGARCHX, which incorporates a variable selection procedure into the log-TGARCHX estimation process. Furthermore, the VS-LTGARCHX algorithm is applied to extremely volatile BTC markets using 42 conditioning variables. Interestingly, our results show that the VS-LTGARCHX models outperform benchmark models, namely the log-GARCH(1,1) and log-TGARCHX(1,1) models, in one-step-ahead forecasting.

ANNE: Adaptive Nearest Neighbours and Eigenvector-based sample selection for robust learning with noisy labels

An important stage of most state-of-the-art (SOTA) noisy-label learning methods consists of a sample selection procedure that classifies samples from the noisy-label training set into noisy-label or clean-label subsets. The process of sample selection typically consists of one of the two approaches: loss-based sampling, where high-loss samples are considered to have noisy labels, or feature-based sampling, where samples from the same class tend to cluster together in the feature space and noisy-label samples are identified as anomalies within those clusters. Empirically, loss-based sampling is robust to a wide range of noise rates, while feature-based sampling tends to work effectively in particular scenarios, e.g., the filtering of noisy instances via their eigenvectors (FINE) sampling exhibits greater robustness in scenarios with low noise rates, and the K nearest neighbour (KNN) sampling mitigates better high noise-rate problems. This paper introduces the Adaptive Nearest Neighbours and Eigenvector-based (ANNE) sample selection methodology, a novel approach that integrates loss-based sampling with the feature-based sampling methods FINE and Adaptive KNN to optimize performance across a wide range of noise rate scenarios. ANNE achieves this integration by first partitioning the training set into high-loss and low-loss sub-groups using loss-based sampling. Subsequently, within the low-loss subset, sample selection is performed using FINE, while the high-loss subset employs Adaptive KNN for effective sample selection. We integrate ANNE into the noisy-label learning state of the art (SOTA) method SSR+, and test it on CIFAR-10/-100 (with symmetric, asymmetric and instance-dependent noise), Webvision and ANIMAL-10, where our method shows better accuracy than the SOTA in most experiments, with a competitive training time. The code is available at https://github.com/filipe-research/anne.

Different view of the diagnostics test accuracy measures and optimal cut-off point selection procedure under tree or umbrella ordering

ABSTRACT In the realm of medical diagnostic testing, diagnostic tests can assume either binary forms, distinguishing between diseased and non-diseased states, or ordinal forms, categorizing states from non-diseased to various stages (1 to K). Another significant classification scheme for multi-class scenarios is tree or umbrella ordering, which entails several unordered sub-classes (subtypes) within a biomarker. Within tree or umbrella ordering, the classifier assesses whether the marker measurement for one class surpasses or falls below those for the other classes. Although Receiver Operating Characteristic (ROC) curves and summary measures have been adapted to accommodate tree and umbrella ordering, these approaches often yield cut-off points that generate highly sensitive tests for certain disease subtypes while compromising specificity for others. This may not be ideal for all diseases. Hence, in this investigation, we explore diverse measures of diagnostic test accuracy and optimal cut-off point selection procedures under tree or umbrella ordering to foster more specific tests. We present numerical examples and simulation studies and demonstrate the approach using real data on lung cancer.

Current Practice and Perspectives on Subcutaneous Immunoglobulin Replacement Therapy in Patients with Primary Antibody Deficiency Among Specialized Nurses in Poland

Background/Objectives: Inborn errors of immunity (IEI) encompass various congenital disorders, resulting in immunity defects and recurrent infections. Home-based subcutaneous immunoglobulin replacement therapy (scIgRT) is the best treatment option for those with primary antibody deficiency (PAD). However, the lack of standardized procedures in patient training remains a challenge. Our study investigates nurses’ practice and perspectives, aiming to identify areas for improvement in at-home scIgRT practice. Methods: We prepared a structured survey regarding scIgRT, including needle choice experience and perception of adverse events, and distributed it among qualified nurses involved in patient training and scIgRT supervising. Results: We included 56 nurses with a median age of 50 years. Among them, 67.9% represented adult care providers, while 32.1% supervised IgRT in children. Most respondents (83.9%) used the classic or assisted with hyaluronidase scIgRT preparations. Single-channel needles were administered most commonly (85.7%). The needle length was mostly chosen solely by a nurse (57.1%) or in cooperation with the patient (23.2%). Next, 9 mm and 12 mm needles were used most often (92.9% and 78.6%, respectively). As expected, the 6 mm needle was more frequently applied for children compared to adults (n = 16, 88.9% vs. n = 11, 28.9%, p < 0.001), while 12 mm was primarily used in adults (n = 35, 92.1% vs. n = 9, 50.0%, p < 0.001). Visual skin fold assessment was the basis for the needle selection (58.9%), followed by the injection site rule (26.8%) or a choice between two available needle types for thinner or thicker patients (25.0%). Results of this survey indicate that, according to nurses’ opinions presented in this survey, the needle length could be associated with local scIgRT adverse events, such as side needle leakage or local burning. Yet, it was likely unrelated to general adverse signs, such as headaches or dizziness. Most respondents (66.1%) indicated that, even if local adverse events occur, patients are reluctant to change scIgRT preparation or needle length. Most participants (69.6%) reported that the optimal administration technique needs to be discussed with the patient before and during scIgRT. Conclusions: This study sheds light on scIgRT practice in Poland, emphasizing deficiency in needle selection technique. Future research should focus on standardized training and advanced needle selection procedures on patient outcomes, investigating the correlation between needle strategies and adverse events, as well as the effectiveness of scIgRT.

A Two-Stage Feature Selection Method to Enhance Prediction of Daily PM2.5 Concentration Air Pollution

In recent decades, air pollution has negatively affected human health and the environment. One of the important features contributing to air pollution is called PM2.5. However, daily prediction of PM2.5 is still lacking, especially using feature selection infused into the model. Hence, the main objective of this research is to utilize the feature selection procedures by proposing two stages feature selection methods namely adjusted correlation sharing t-test (adjcorT) and radial basis function neural network (RBFNN) in identifying the important features. This consequently also helps enhance the prediction of daily PM2.5 concentrations. Secondary data were obtained from the Department of Environment Malaysia (DOE) from 2018 until 2022 that consists of 5 years of air pollutant daily data. The results found that adjcorT-RBFNN identified the NO2, PM2.5, PM10, CO, O3, wind speed and SO2 as important features. The finding revealed that the accuracy, sensitivity, specificity, precision, F1 score and AUROC value, for a day-ahead prediction in Shah Alam are 0.756, 0.801, 0.717, 0.717, 0.757, and 0.758 respectively. Additionally, the predicted model may serve as an instrument for an early warning system, providing local authorities with information on air quality for formulation of strategies of air quality improvement.

The Impact of Flipped Learning on Students` Critical Thinking; A Systematic Literature Review

Flipped learning is a revolutionary teaching method that disrupts traditional learning environments by providing instruction outside of the classroom. This strategy enables students to engage with the subject at their convenience and encourages active learning in the classroom. The current study is a comprehensive literature analysis that looks into the impact of flipped learning on student critical thinking skills. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) criteria were utilized to ensure that the article selection was valid and reliable. The articles were selected from some credible databases, such as Eric, Scopus, and ProQuest. After the selection procedure, the systematic review yielded 23 relevant articles to be further reported. Our analysis highlights that flipped learning practices are most commonly observed in natural science disciplines, including physics, mathematics, and chemistry. The majority of the studies employed quantitative methodologies, primarily utilizing quasi-experimental designs, and predominantly focused on university-level students in Asian countries. Notably, the findings indicate a substantial positive effect of online-based flipped learning on the enhancement of students' critical thinking skills, suggesting that this pedagogical approach can significantly benefit student learning outcomes.

Assessment of competitive advantages of IT system integrator companies taking industry factors into account

Purpose. To evaluate competitive advantages of IT system integrators in Ukraine. To investigate the influence of industry factors on the formation of competitive advantages using cases of the mining and fuel & energy industries. Methodology. The empirical basis of the study was presented by performance indicators of the leading system integrator companies of Ukraine. The methodological basis of the research was the scoring method using weight coefficients and the graphic method of creating a radial diagram of competitive advantages. During the research, the following methods were used: analysis and synthesis (to study the components of competitive advantages and to form integral indicators), induction and deduction (to form market trends and to identify enterprise strategies), classification (to form indicators that determine competitive advantages). Findings. In the process of research, a model of competitive advantages for system integrator companies was formed. A methodology was developed and an assessment of competitive advantages of the leading system integrators of IT industry of Ukraine was carried out. A radial diagram of competitive advantages was created. It allows performing a comparative analysis of the factors of company competitive advantages with the aim to form a development strategy to improve the competitive position of the company in the market. Features of influence of the industry factors on the formation of competitive advantages of system integrators were considered and this influence was evaluated using cases of the mining and fuel & energy industries. Originality. A model and methodology for assessing competitive advantages for system integrator companies in IT industry were proposed; the main factors of competitive advantages for system integrators have been identified. The necessity of taking industry factors into account when assessing the competitive advantages of these enterprises has been proposed and confirmed by the research results. Practical value. The applied value of the research lies in the possibility of applying the developed model and methodology to assess the competitive advantages of system integrators. The results of such an assessment can be used by system integrators to develop a competitive strategy and improve their competitive positions in the market. Customers (including enterprises in the mining and fuel & energy industries) can use evaluation elements when conducting procurement (tender) procedures for selection of contractors for the implementation of system integration projects. Also, the results of the study can be used for the analysis of the system integration market.

Sparsified simultaneous confidence intervals for high-dimensional linear models

AbstractStatistical inference of the high-dimensional regression coefficients is challenging because the uncertainty introduced by the model selection procedure is hard to account for. Currently, the inference of the model and the inference of the coefficients are separately sought. A critical question remains unsettled; that is, is it possible to embed the inference of the model into the simultaneous inference of the coefficients? If so, then how to properly design a simultaneous inference tool with desired properties? To this end, we propose a notion of simultaneous confidence intervals called the sparsified simultaneous confidence intervals (SSCI). Our intervals are sparse in the sense that some of the intervals’ upper and lower bounds are shrunken to zero (i.e., [0, 0]), indicating the unimportance of the corresponding covariates. These covariates should be excluded from the final model. The rest of the intervals, either containing zero (e.g., $$[-1,1]$$ [ - 1 , 1 ] or [0, 1]) or not containing zero (e.g., [2, 3]), indicate the plausible and significant covariates, respectively. The SSCI intuitively suggests a lower-bound model with significant covariates only and an upper-bound model with plausible and significant covariates. The proposed method can be coupled with various selection procedures, making it ideal for comparing their uncertainty. For the proposed method, we establish desirable asymptotic properties, develop intuitive graphical tools for visualization, and justify its superior performance through simulation and real data analysis.

Prediction of CRT response using a novel CRT responder score in an Asian patient population

Abstract Introduction Cardiac Resynchronization Therapy (CRT) guidelines, derived from trials among U.S. and European patients, assign weaker recommendations to those with midrange QRS duration (<150ms). Patient height and body size may modulate CRT efficacy. Moreover, these factors may affect CRT utilization and efficacy in Asia. Aim The purpose of this analysis was to determine the prediction scoring system model for CRT responders in a Japanese patient population. Methods We analyzed the database of the Multicenter Prospective Pilot Study to Test LV Intracardiac Conduction Time as a Predictor of CRT Response (BIO|SELECT) which was a single-arm study conducted at 28 hospitals in Japan. This study enrolled patients eligible for a de-novo CRT-defibrillator (CRT-D) based on current guideline indications, who had no recent or planned cardiac surgery and were planned to receive a Sentus ProMRI OTW quadripolar LV lead and a BIOTRONIK CRT-D with multipole pacing feature. Patients were followed for 7 months after implantation. Four accepted parameters to define CRT response [LVESV (cut-off -15%), LVEF (+5%), BNP (-25%), and NYHA class (one class improvement)] were combined into a composite benefit index (CBI) by forming the normalized sum, with CBI = 4.0 meaning response. The CBI was found to be more robust than the individual 4 parameters due to the reduced influence of noise and measurement errors in single parameters. Several factors were studied to create the scoring tool. The CRT responder score was the sum of these individual scores (range 0-100). To construct a predictive scoring system, logistic regression analysis was performed with a backward selection procedure. In addition, the Akaike information criterion (AIC) was applied to determine the best model among those tested. The score was then assigned employing the β coefficient of each variable divided by the reference value. The model's calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. Results A total of 196 patients were enrolled in this study (mean age 69 years, mean LVEF 30%, left bundle-branch block [LBBB] 58%). The Ooccurrence of CRT response during the 7-month follow-up was 54%. Future CRT responders were significantly predicted by low age (< 70 years)(18 points), female (19 points), prior heart failure hospitalization (16 points), body mass index (<20) (17 points), QRS/height (<0.858)(30 points). This score predicted CRT response well (area under curve 0.72; 95% CI 0.643-0.798). When the cut-off of 63 points or more was applied the sensitivity was 0.56, the specificity was 0.72, and the negative predictive value was 0.58. Conclusions The CRT responder score is a novel approach for the prediction of CRT response, but should be validated and further improved to increase its predictive value. This approach highlights the role of preventing the development of severe heart failure and could help to identify individuals who might benefit from CRT.

C-Reactive Protein-to-Serum Chloride Ratio: A Novel Marker of All-Cause Mortality in Maintenance Haemodialysis Patients

Background and Objectives: hypochloremia is an emerging risk factor for mortality in patients with chronic kidney disease. The pathophysiological mechanisms of this finding are not very clear. Some studies suggest the influence of inflammation as a synergistic factor, so we set out to analyse the association of a novel C-reactive protein-to-serum chloride ratio (CRP/Cl−) with the prognosis of maintenance haemodialysis patients and to assess its relationship with fluid status and body composition measured by bioimpedance. Materials and Methods: the present work is a retrospective cohort study of maintenance haemodialysis patients from our chronic outpatient haemodialysis programme between 1 January 2022 and 31 December 2022. (n = 281). Survival time was collected for all patients and analysed using the Kaplan–Meier method. A Cox proportional hazards regression model was used to evaluate survival probabilities. Variables included in the model were selected using a stepwise selection procedure based on the corrected Akaike information criterion (AICc), which balances model fit and complexity. Results: during a median follow-up of 306 days, 34 patients died. Patients in the fourth quartile of the CRP/Cl− (>0.118 mg/mEq) had higher overall mortality (log-rank test, p = 0.0011). In the Cox multivariate analysis, the variables significantly associated with higher mortality were higher modified Charlson index (MCI), lower body surface area (BSA), lower interdialytic weight gain (IDWG), and higher CRP/Cl− ratio. The latter variable was independently associated with higher overall mortality (adjusted hazard ratio = 1.027; 95% confidence interval [CI], 1.000–1.055 p = 0.0469). Conclusions: Higher CRP/Cl− ratio values were associated with higher all-cause mortality in our maintenance haemodialysis patients.

Development of an efficient selection procedure for high imperatorin-yielding hairy root clones of Urena lobata L. using morphological and molecular markers

Development of an efficient selection procedure for high imperatorin-yielding hairy root clones of Urena lobata L. using morphological and molecular markers

Expression of Plasma miRNA-133a is Significantly Lower in Acute Coronary Syndrome (ACS) than in Healthy/Non-ACS Subjects

BACKGROUND: The current biomarker diagnostic modality for acute coronary syndrome (ACS), cardiac troponin, has several limitations. Emerging studies showed that micro-RNA (miR)-133a was released from infarcted heart to circulation, yet the diagnostic value of miR-133a in ACS demonstrated a conflicting result. Therefore, this study was conducted to investigate the potency of plasma miR-133a as a biomarker candidate of ACS.METHODS: This was a case-controlled study, involving ACS and control subjects. The sociodemographic and clinical characteristics were assessed through medical records. A final of 39 ACS and 31 control subjects (consist of healthy and non-ACS subjects) passed the selection procedure by demonstrating a high purity of RNA. miR-133a from ACS and control subjects were detected by quantitative polymerase chain reaction (qPCR). Expression of miR-133a was evaluated for sensitivity and specificity as an ACS biomarker diagnostic using the receiver operating characteristic (ROC) curve.RESULTS: Plasma miR-133a expression was stably found in ACS subjects. The plasma miR-133a level was lower in ACS than in control subjects. miR-133a effectively distinguished ACS subjects from healthy subjects (AUC=0.911) and exhibited high diagnostic performance, with a sensitivity of 87.1% and specificity of 100% at a cut-off value of 44.035. In an extended model including both control subjects (healthy and non-ACS with comorbid conditions), miR-133a maintained diagnostic significance (AUC=0.874), showing sensitivity of 76.9% and specificity of 100% at a cut-off value of 11.69.CONCLUSION: Plasma miR-133a is significantly lower and effectively distinguishes ACS patients from both healthy individuals and non-ACS individuals with comorbid, with a cut-off value of 11.69. Therefore, plasma miR-133a is suggested to be a good candidate for diagnostic biomarkers of ACS.KEYWORDS: circulating miRNA, miRNA-133a, acute coronary syndrome, diagnostic biomarker

US Higher Education's Civic Responsibility to Educate for Informal Political Representation

AbstractIn this paper, Caitlin Murphy Brust and Hannah Widmaier begin with the assumption that highly selective institutions of higher education in the United States have a duty to promote civic equality. They employ Wendy Salkin's theory of informal political representation to examine how highly selective institutions should go about promoting civic equality. According to Salkin's theory, someone serves as an informal political representative (IPR) when they speak or act on behalf of others, without having been selected to do so via a systematized selection procedure. Brust and Widmaier argue that as part of their civic educational missions, highly selective institutions should educate their students for informal political representation, and this includes equipping students to (1) serve as effective IPRs, (2) be responsible audience members to others' informal political representation, and (3) deliberate carefully and with open‐mindedness about when they should or should not take on the role of IPR. Brust and Widmaier examine two types of injustice that students from marginalized communities face at highly selective institutions, and they explore how education for informal political representation might ameliorate those injustices. The authors conclude by offering some practical suggestions to institutional policymakers and educators.

Characterization of the wheat-tetraploid Thinopyrum elongatum 7E(7D) substitution line with Fusarium head blight resistance

BackgroundFusarium head blight (FHB), a devastating disease of wheat production, is predominantly elicited by Fusarium graminearum (Fg). The tetraploid Thinopyrum elongatum is a tertiary gene resource of common wheat that possesses high affinity and displays high resistance traits against multiple biotic and abiotic stress. We aim to employ and utilize the novel FHB resistance resources from the wild germplasm of common wheat for breeding.ResultsDurum wheat-tetraploid Th. elongatum amphiploid 8801 was hybridized with common wheat cultivars SM482 and SM51, and the F5 generation was generated. We conducted cytogenetically in situ hybridization (ISH) technologies to select and confirm a genetically stable 7E(7D) substitution line K17-1069-5, which showed FHB expansion resistance in both field and greenhouse infection experiments and displayed no significant disadvantage in agronomic traits compared to their common wheat parents in the field. The F2 segregation populations (K17-1069-5 × SM830) showed that the 7E chromosome conferred dominant FHB resistance with dosage effect. We developed 19 SSR molecular markers specific to chromosome 7E, which could be conducted for genetic mapping and large breeding populations marker-assisted selection (MAS) during selection procedures in the future. We isolated a novel Fhb7 allele from the tetraploid Th. elongatum chromosome 7E (Chr7E) using homology-based cloning, which was designated as TTE7E-Fhb7.ConclusionsIn summary, our study developed a novel wheat-tetraploid Thinopyrum elongatum 7E(7D) K17-1069-5 substitution line which contains stable FHB resistance.