BACKGROUND: The ability of a nurse to make effective clinical decisions is the most important factor that can affect the treatment quality. However, several factors can affect the ability of nursing and midwifery students to make effective clinical decisions.
OBJECTIVE: This study aims to identify the confounding factors that may affect the clinical decision making of nurses and thus patient outcomes after the COVID-19 pandemic in Jordan.
METHODS: A descriptive cross-sectional design was employed in this study. An online self-administered questionnaire was distributed to 269 nursing and midwifery students selected through purposive sampling, 224 of whom completed the questionnaire. The valid and reliable nursing decision-making instrument, which consisted of 24 items, was employed to gather the data, and descriptive statistics and simple linear regression were employed for the data analysis. Data was collected from November to the end of December 2022.
RESULTS: Among the respondents, 72.8% were female, and the average age was 20.79 years (SD = 1.44). The vast majority of the respondents (94.6%) was unmarried, and 74.1% were pursuing a nursing degree. The simple linear regression analysis showed that clinical decision making had a negative and significant relationship with social media usage of an average of 6 h a day (β=-0.085). Moreover, the male nursing students obtained lower clinical decision-making scores (β= -0.408) compared with the female nursing students.
CONCLUSIONS: Social media usage and gender have a considerable effect on the clinical decision making of the nursing and midwifery students. Therefore, the confounding factors that can affect the clinical decision making of nurses should be discussed further, and strategies to address such factors should be implemented.

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Reconstructing functional gene regulatory networks (GRNs) is a primary prerequisite for understanding pathogenic mechanisms and curing diseases in animals, and it also provides an important foundation for cultivating vegetable and fruit varieties that are resistant to diseases and corrosion in plants. Many computational methods have been developed to infer GRNs, but most of the regulatory relationships between genes obtained by these methods are biased. Eliminating indirect effects in GRNs remains a significant challenge for researchers. In this work, we propose a novel approach for inferring functional GRNs, named EIEPCF (eliminating indirect effects produced by confounding factors), which eliminates indirect effects caused by confounding factors. This method eliminates the influence of confounding factors on regulatory factors and target genes by measuring the similarity between their residuals. The validation results of the EIEPCF method on simulation studies, the gold-standard networks provided by the DREAM3 Challenge and the real gene networks of Escherichia coli demonstrate that it achieves significantly higher accuracy compared to other popular computational methods for inferring GRNs. As a case study, we utilized the EIEPCF method to reconstruct the cold-resistant specific GRN from gene expression data of cold-resistant in Arabidopsis thaliana. The source code and data are available at https://github.com/zhanglab-wbgcas/EIEPCF.

In response to concerns about health due to radiation exposure, the Fukushima Prefecture launched the Thyroid Ultrasound Examination program for residents aged 0-18 years at the time of the earthquake. Herein, we considered the confounding factors involved in the regional differences in the development of thyroid cancer. In this study, the 242 065 individuals who participated in both first- and second-round surveys were classified into four groups by address according to their air radiation dose. The number of participants diagnosed as malignant or suspicious for malignancy by cytological examination were 17, 38, 10 and 4 with detection rates of 53.8, 27.8, 21.7 and 14.5 per 100 000 participants in Regions 1, 2, 3 and 4, respectively. Sex (P = 0.0400), age at the time of the primary examination (P < 0.0001) and interval between the first- and second-round surveys (P < 0.0001) were significantly different among the four regions, and these were suspected to be confounding factors affecting regional differences in malignant nodule detection rates. In addition, significant regional differences were observed in the participation rate in the confirmatory examination (P = 0.0037) and the fine needle aspiration cytology implementation rate (P = 0.0037), which could be potential biases. No significant regional differences in the detection of malignant nodules were found in the multivariate logistic regression analysis after adjusting for the survey interval alone or for sex, age and survey interval. The confounding factors and biases identified in this study that may have important impacts on thyroid cancer detection rate should be fully considered in future studies.

The occurrence and development of oesophageal neoplasia (ON) is closely related to hormone changes. The aim of this study was to investigate the causal relationships between age at menarche (AAMA) or age at menopause (AAMO) and benign oesophageal neoplasia (BON) or malignant oesophageal neoplasia (MON) from a genetic perspective.
Genome-wide association study (GWAS) summary data of exposures (AAMA and AAMO) and outcomes (BON and MON) were obtained from the IEU OpenGWAS database. We performed a two-sample Mendelian randomization (MR) study between them. The inverse variance weighted (IVW) was used as the main analysis method, while the MR Egger, weighted median, simple mode, and weighted mode were supplementary methods. The maximum likelihood, penalized weighted median, and IVW (fixed effects) were validation methods. We used Cochran\'s Q statistic and Rucker\'s Q statistic to detect heterogeneity. The intercept test of the MR Egger and global test of MR pleiotropy residual sum and outlier (MR-PRESSO) were used to detect horizontal pleiotropy, and the distortion test of the MR-PRESSO analysis was used to detect outliers. The leave-one-out analysis was used to detect whether the MR analysis was affected by single nucleotide polymorphisms (SNPs). In addition, the MR robust adjusted profile score (MR-RAPS) method was used to assess the robustness of MR analysis.
The random-effects IVW results showed that AAMA had a negative genetic causal relationship with BON (odds ratio [OR] = 0.285 [95% confidence interval [CI]: 0.130-0.623], P = 0.002). The weighted median, maximum likelihood, penalized weighted median, and IVW (fixed effects) were consistent with random-effects IVW (P < 0.05). The MR Egger, simple mode and weighted mode results showed that AAMA had no genetic causal relationship with BON (P > 0.05). However, there were no causal genetic relationships between AAMA and MON (OR = 1.132 [95%CI: 0.621-2.063], P = 0.685), AAMO and BON (OR = 0.989 [95%CI: 0.755-1.296], P = 0.935), or AAMO and MON (OR = 1.129 [95%CI: 0.938-1.359], P = 0.200). The MR Egger, weighted median, simple mode, weighted mode, maximum likelihood, penalized weighted median, and IVW (fixed effects) were consistent with a random-effects IVW (P > 0.05). MR analysis results showed no heterogeneity, the horizontal pleiotropy and outliers (P > 0.05). They were not driven by a single SNP, and were normally distributed (P > 0.05).
Only AAMA has a negative genetic causal relationship with BON, and no genetic causal relationships exist between AAMA and MON, AAMO and BON, or AAMO and MON. However, it cannot be ruled out that they are related at other levels besides genetics.

BACKGROUND: Latent tuberculosis infection (LTBI) is a mycobacterial infection defined on the basis of cellular immune response to mycobacterial antigens. The tuberculin skin test (TST) and the Interferon-Gamma Release Assay (IGRA) are the two tests currently used to establish the diagnosis of LTB. Literature suggests that a study regarding tuberculosis (TB) infection among women of reproductive age group is limited.
METHODS: Female household contact, married, aged 18-49 years underwent written consent form and are screened for LTBI using the TST and IGRA. Participants are injected with TST [5 tuberculin unit (TU), purified protein derivative (PPD)] and IGRA [QuantiFERON®-TB Gold Plus kit (QFT-Plus)]. All the household contacts were followed-up for one year for incident TB cases. Statistical analysis was done using STATA version 14 (StataCorp., Texas, USA). Cohen\'s kappa test was used to determine the agreement between two tests.
RESULTS: The prevalence of LTBI was found to be 69% (either TST or IGRA positive). Positivity rate of IGRA was higher when compared to that of TST. Out of 139 participants, 68 (49%) tested positive for TST, 80 (57.6%) tested positive for IGRA and 52 (37.4%) tested positive for both. Discordant results were observed in about two fifth of the study population and there was poor agreement between the two tests.
CONCLUSIONS: Longitudinal studies are required to detect incident TB cases to evaluate the usefulness of these tests. The study was found that IGRA is more consistent to diagnosis of latent tuberculosis infection than the TST. Such studies can also be performed in varied settings among different populations which would help us to improve the diagnosis of LTBI and consequently help in TB control.

Multiplex assays that provide simultaneous measurement of multiple analytes in biological samples have now developed into widely used technologies in the study of diseases, drug discovery, and other medical areas. These approaches span multiple assay systems and can provide readouts of specific assay components with similar accuracy as the respective single assay measurements. Multiplexing allows the consumption of lower sample volumes, lower costs, and higher throughput compared with carrying out single assays. A number of recent studies have demonstrated the impact of multiplex assays in the study of the SARS-CoV-2 virus, the infectious agent responsible for the current COVID-19 pandemic. In this respect, machine learning techniques have proven to be highly valuable in capturing complex disease phenotypes and converting these insights into models which can be applied in real-world settings. This chapter gives an overview of opportunities and challenges of multiplexed biomarker analysis, with a focus on the use of machine learning aimed at identification of biological signatures for increasing our understanding of COVID-19 disease, and for improved diagnostics and prediction of disease outcomes.

UNASSIGNED: To investigate the role of smoking as a confounding factor in the association between periodontal pocketing and blood pressure.
UNASSIGNED: After restriction to 45-64-year-old subjects without hypertension, diabetes, rheumatic diseases, obesity and with no history of cardiovascular diseases or ongoing lipid-lowering medications, the study population consisted of 307 subjects of the Health 2000 Survey in Finland. Systolic and diastolic blood pressure and pulse pressure (mmHg) were used as outcome variables. Periodontal condition was measured by the number of teeth with ≥4 mm periodontal pockets. β-estimates and 95% confidence intervals (CI) were obtained from linear regression models. Analyses were made in the whole study population and stratified according to smoking habits/history.
UNASSIGNED: The number of teeth with ≥4 mm periodontal pockets associated statistically significantly with systolic blood pressure and pulse pressure in the whole study population. Among never-smokers or daily smokers, there were no consistent nor statistically significant associations between the number of teeth with ≥4 mm periodontal pockets and systolic/diastolic blood pressure or pulse pressure.
UNASSIGNED: Smoking appeared to confound the association between periodontal condition and blood pressure. Thorough control for the effect of smoking was not obtained using multivariate models.

BACKGROUND: Many confounding factors such as sex, age, and body mass index (BMI) affect pulmonary function parameters, but there are limited data about the direct and/or indirect effects of small airway function on lung function for differences in confounding factors.
OBJECTIVE: This study aimed to use structural equation model (SEM) to explain the influence of the confounding factors (age, sex, and BMI) on the relationship between small airway function and lung function in patients with lung cancer.
METHODS: A cross-sectional observational study was conducted in a single medical center. Subjects were assessed; small airway function was specified by MEF25% and MEF50%; lung function by FVC; pulmonary obstruction by FEV1, FEV1%, and FEV1/FVC; and PEF and PEF% reflected the strength of abdominal muscles. The measurement model was analyzed by confirmatory factor analysis. The SEM was conducted to analyze the structural models of the effects of the confounding factors.
RESULTS: In the measurement model, variables were fit to their domains, the path linking age and sex to pulmonary obstruction was positive and statistically significant, and the path linking sex to muscle strength was also positive and statistically significant. Muscle strength positively and significantly mediates the path between sex and FVC. As a moderator, BMI increased the effects of small airway function on FVC.
CONCLUSIONS: Age and sex were directed to pulmonary obstruction, and muscle strength as a mediator between sex and lung function was novel, and BMI adjusted the effects of small airway function on FVC.

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