Results Of Analysis Research Articles

Network Pharmacology and Molecular Docking Analysis on Mechanisms of Scutellariae Radix in the Treatment of Cerebral Ischemia-reperfusion Injury.

Multiple brain disorders are treated by Scutellaria Radix (SR), including cerebral ischemia-reperfusion (CI/R). However, more studies are needed to clarify the molecular mechanism of SR for CI/R. The active substances and potential targets of SR and CI/R-related genes were obtained through public databases. Overlapping targets of SR and CI/R were analyzed using proteinprotein interaction (PPI) networks. GO and KEGG enrichment analyses were performed to predict the pathways of SR against CI/R, and the key components and targets were screened for molecular docking. The results of network pharmacology analysis were verified using in vitro experiments. 15 components and 64 overlapping targets related to SR and CI/R were obtained. The top targets were AKT1, IL-6, CAS3, TNF, and TP53. These targets have been studied by GO and KEGG to be connected to a number of signaling pathways, including MAPK, PI3K-Akt pathway, and apoptosis. Molecular docking and cell experiments helped to further substantiate the network pharmacology results. The active compound of SR was able to significantly decrease the apoptosis of HT- 22 cells induced by OGD/R. This finding suggests that SR is a potentially effective treatment for CI/R by modulating the MAPK and PI3K-Akt pathways.

Factors related to preoperative anxiety in older patients with sensory impairment: A cross-sectional study.

To identify the factors related to preoperative anxiety in older adults with sensory impairment. Descriptive quantitative research design. Study participants were 120 older adults admitted to a tertiary hospital in Seoul, South Korea to undergo eye or ear surgery. Data were collected from 11 February to 30 April 2023. The collected data were analysed using descriptive statistics, t-tests, ANOVA, and multiple regression analysis using SPSS 28.0. The study was reported following the STROBE checklist. Preoperative anxiety differed significantly according to gender (p = .002), average monthly household income (p < .001), subjective health status (p = .003), subjective vision loss (p = .004), one-sided or both-sided eye discomfort (p = .002), use of glasses (p = .010), subjective hearing loss (p = .022) and anxiety coping mechanisms (p < .001). The results of a multiple regression analysis on the significant variables identified that gender (β = .178, p = .036) and anxiety coping mechanisms (β = .336, p < .001) had the most significant effect on preoperative anxiety, with an explanatory power of 34.5%. The development and practical application of tailored nursing interventions catering to specific genders and anxiety coping mechanisms would contribute to reducing preoperative anxiety and providing efficient nursing care for older adults with sensory impairment.

Application of the Naïve Bayes Classifier Algorithm to Analyze Sentiment for the Covid-19 Vaccine on Twitter in Jakarta

The epidemic of a new disease caused by the coronavirus (2019-nCoV), commonly referred to asCOVID-19, has been declared a global virus epidemic by the World Health Organization (WHO).President Joko Widodo has officially ratified Presidential Decree No. 99 of 2020 concerning theprovision of vaccines and the implementation of vaccination activities. Twitter is a social mediaplatform that allows users to share information and opinions directly with fellow users. Tweets givencan be in any form, either positively or negatively, so one of the methods used is sentiment analysis.Sentiment analysis helps determine an opinion or comment on an issue, whether the response ispositive or negative. The Naïve Bayes algorithm is used in sentiment analysis because it is suitablefor tweets or text data that is not too long or short text. The initial stage of sentiment analysis is textpre-processing which consists of Cleaning, case folding, tokenizing, and stopword removal. Then thedata is labeled manually. The analysis results are visualized as bar charts, pie charts, and word clouds.Then the word weighting is carried out using the term frequency-inverse document (TF-IDF), andclassification is carried out using the Naïve Bayes classifier. From the test results, the accuracy valueof the confusion matrix is 82% from 2600 tweet data with 80% training data composition and 20%test data.

The Project Performance Evaluation of PLBN Project Using Earned Value Management Method

Process of monitoring and controlling projects requires data evaluation activities in the order to findout whether the project is in accordance with the plan to avoid an incompatibility between theplanning and the actual time. The failure of construction project almost reached the figure of 20% -40% which occurred in the project monitoring and controlling process. The purpose of this researchis analysis project performance with earned value management method at PLBN Project. Thisresearch was carried out by several stages: data collection, data processing and analysis. From theresults of the performance index analysis show that the project’s performance on the constructionproject is still not good. At week 16 the CPI and SPI values are 0.94 and 0.85. Both values can beinterpreted that the costs incurred are greater than planning and the implementation time is longerthan planning. The result of EAC is Rp. 2.423.695.294 with an estimated completion time is 21weeks which indicates that there is an additional 3 weeks of completion.

Stochastic Model for Novel Mixture Distribution with Properties and Its Application

Medical research is the one of the most important applications of statistical analysis. A great deal of occasions, specific statistical considerations are needed for cancer research in order to determine the model that best fits the survival data. In this paper, the proposed a new two parameter continuous distribution. It is called a new mixture distribution (NMD). Some statistical properties of the distribution are derived such as, the moments, moments generating function, reliability analysis. Also, the distribution of order statistics is presented and entropies are derived. The application of the maximum likelihood estimate technique to the performance of traditional parameter estimation. Two authentic data sets describing cancer patients' survival are used to empirically demonstrate the potential importance and usability of the proposed distribution. Comparing the new mixture distribution to some other competing distribution, the analysis's results demonstrated that it performed quite well.

A Differential Protein Study on Bronchoalveolar Lavage Fluid at Different Stages of Silicosis.

In this study, by comparing the difference in protein expression in bronchoalveolar lavage fluid between silicosis patients in different stages and healthy controls, the pathogenesis of pneumoconiosis was discussed, and a new idea for the prevention and treatment of pneumoconiosis was provided. The lung lavage fluid was pretreated by 10 K ultrafiltration tube, Agilent 1100 conventional liquid phase separation, strong cation exchange column (SCX) HPLC pre-separation, and C18 reverse phase chromatography desalting purification, and protein was labeled with isotope. GO, KEGG pathway, and PPI analysis of differential proteins were conducted by bioinformatics, and protein types and corresponding signal pathways were obtained. Thermo Q-Exactive mass spectrometry identified 943 proteins. T-test analysis was used to evaluate the different significance of the results, and the different protein of each group was obtained by screening with the Ratio≥1.2 or Ratio≤0.83 and P<0.05. We found that there are 16 kinds of protein throughout the process of silicosis. There are different expressions of protein in stages Ⅲ/control, stages Ⅱ/control, stage Ⅰ/control, stages Ⅲ/ stages Ⅱ, stages Ⅲ/ stage Ⅰ and stages Ⅱ/ stage Ⅰ groups. The results of ontology enrichment analysis of total differential protein genes show that KEGG pathway enrichment analysis of differential protein suggested that there were nine pathways related to silicosis. The main biological changes in the early stage of silicosis are glycolysis or gluconeogenesis, autoimmunity, carbon metabolism, phagocytosis, etc., and microfibril-associated glycoprotein 4 may be involved in the early stage of silicosis. The main biological changes in the late stage of silicosis are autoimmunity, intercellular adhesion, etc. Calcium hippocampus binding protein may participate in the biological changes in the late stage of silicosis. It provides a new idea to understand the pathogenesis of silicosis and also raises new questions for follow-up research.

Knowledge Representation Learning Method Based on Semantic Enhancement of External Information

Background: Knowledge representation learning aims at mapping entity and relational data in knowledge graphs to a low-dimensional space in the form of vectors. The existing work has mainly focused on structured information representation of triples or introducing only one additional kind of information, which has large limitations and reduces the representation efficiency. Objective: This study aims to combine entity description information and textual relationship description information with triadic structure information, and then use the linear mapping method to linearly transform the structure vector and text vector to obtain the joint representation vector. Methods: A knowledge representation learning (DRKRL) model that fuses external information for semantic enhancement is proposed, which combines entity descriptions and textual relations with a triadic structure. For entity descriptions, a vector representation is performed using a bi-directional long- and short-term memory network (Bi-LSTM) model and an attention mechanism. For the textual relations, a convolutional neural network is used to vectorially encode the relations between entities, and then an attention mechanism is used to obtain valuable information as complementary information to the triad. Results: Link prediction and triadic group classification experiments were conducted on the FB15K, FB15K-237, WN18, WN18RR, and NELL-995 datasets. Theoretical analysis and experimental results show that the DRKRL model proposed in this paper has higher accuracy and efficiency compared with existing models. Conclusion: Combining entity description information and textual relationship description information with triadic structure information can make the model have better performance and effectively improve the knowledge representation learning ability.

The Impact of Career Orientation of Beauty Salon Workers on Transfer Motivation: Focused on the Moderating Effect of Organizational Support

Purpose: In this study the impact of beauty shop workers’ Career Orientation on Transfer Motivation was analyzed with organization support as a moderating effect.&#x0D; Method: This study looked at previous studies on each variable based on domestic and foreign research papers and books, and analyzed a total of 310 valid questionnaires using beauty shop workers as the population. Statistical processing was performed using the statistical analysis package SPSS v.23. Frequency analysis was conducted to determine the demographic characteristics of the study subjects, and Cronbach's evaluation of internal consistency was conducted to determine the reliability of the questions on Career Orientation, Transfer Motivation, and organizational support. Reliability analysis was conducted using the Alpha coefficient. The comprehensive influence relationship forming the causal relationship between the variables presented in the research model was conducted through multiple regression analysis and controlled regression analysis.&#x0D; Results: The above analysis results are summarized as follows.&#x0D; First, the impact of Career Orientation on Transfer Motivation is that among the Career Orientation factors, Organizational Orientation (Organizational Orientation) and Professional Orientation (Professional Orientation) are It was found to have a positive (+) direction on Transfer Motivation. In other words, as Organizational Orientation and Professional Orientation among Career Orientation factors increase, Transfer Motivation also increases.&#x0D; Second, among Career Orientation factors, it was verified that Professional Orientation has the greatest influence on Transfer Motivation.&#x0D; Third, organizational support was found to moderate the effect of Career Orientation on Transfer Motivation.&#x0D; Among the Career Orientation factors, organizational support was adjusted to have a lower impact on Transfer Motivation the higher the Organizational Orientation, and the higher the Professional Orientation, the higher the Transfer Motivation. It was verified that the effect on (transition motivation) was adjusted to increase.&#x0D; Conclusion: This study provides basic data to improve the capabilities of employees through the organization's continuous support for organizational members and to facilitate human resource management so that they can directly contribute to organizational performance. As a result, the turnover rate of beauty shop workers can be reduced and individual capabilities can be strengthened. I hope that it can be put to good use.

Distribution Matching for Machine Teaching.

Machine teaching is an inverse problem of machine learning that aims at steering the student toward its target hypothesis, in which the teacher has already known the student's learning parameters. Previous studies on machine teaching focused on balancing the teaching risk and cost to find the best teaching examples deriving from the student model. This optimization solver is in general ineffective when the student does not disclose any cue of the learning parameters. To supervise such a teaching scenario, this article presents a distribution matching-based machine teaching strategy via iteratively shrinking the teaching cost in a smooth surrogate, which eliminates boundary perturbations from the version space. Technically, our strategy could be redefined as a cost-controlled optimization process that finds the optimal teaching examples without further exploring the parameter distribution of the student. Then, given any limited teaching cost, the training examples would have a closed-form expression. Theoretical analysis and experiment results demonstrate the effectiveness of this strategy.

Cross-sectional study of child malnutrition and associated risk factors among children aged under five in West Bengal, India

Using a cross-sectional study of 485 sample households in 2013, the present paper examines the prevalence and risk factors of child malnutrition among children under the age of five in West Bengal, India. As a part of this investigation, children&amp;rsquo;s underweight status, wasting, and stunting were examined in order to determine child nutritional status using the WHO growth standard. We performed bivariate analyses in order to elucidate differentials in nutritional indices and fitted multinomial logistic regression models to examine the net effect of different socio-economic factors on the likelihood of child malnutri-tion. Analysis results revealed stunting (51%) as the most common form of malnutrition among children aged under five, followed by underweight status (41%), and wasting (22%).Gender discrimination among children increases with age, whereby girls are more deprived (as measured by nutritional indic-es) compared to boys later in childhood relative to younger ages. Results from multinomial analyses re-veal age, religion, caste, and birth-order of the child as significant predictors of child&amp;rsquo;s nutritional status.

An Explainable Multichannel Model for COVID-19 Time Series Prediction

Introduction: The COVID-19 pandemic has affected every country and changed people's lives. Accurate prediction of COVID-19 trends can help prevent the further spread of the outbreak. However, the changing environment affects the COVID-19 prediction performance, and previous models are limited in practical applications. Methods: An explainable multichannel deep learning model with spatial, temporal and environmental channels for time series prediction, STE-COVIDNet, was proposed. The time series data of COVID-19 infection, weather, in-state population mobility, and vaccination were collected from May, 2020, to October, 2021, in the USA. In the environmental channel of STE-COVIDNet, an attention mechanism was applied to extract significant environmental factors related to the spread of COVID-19. In addition, the attention weights of these factors were analyzed with the actual situation. Results: STE-COVIDNet was found to be superior to other advanced prediction models of COVID-19 infection cases. The analysis results of attention weight were reported to be consistent with existing studies and reports. It was found that the same environmental factors that influence the spread of COVID-19 can vary across time and region, which explains why findings of previous studies on the relationship between the environment and COVID-19 vary by region and time. Conclusion: STE-COVIDNet is an explainable model that can adapt to environmental changes and thus improve predictive performance.

GREEN PRODUCT PLACEMENT IN TV SERIES: AN EVALUATION ON CONSUMER ATTITUDE AND PURCHASE INTENTION

Purpose: The main purpose of this study is to measure the effect of green product placement strategy on consumer attitude and consumer purchase intention. In addition, the study examined whether attitudes towards green product placement and intention to purchase green products vary according to demographic variables. Material and Method: This study was conducted in Istanbul, Turkey and the main population of the research consists of consumers who are 18 years old and over and watch television series. The sample size was determined to be 402 participants since it would not be possible to reach all participants in the universe in generalizing the analysis result. The data obtained was analyzed with PLS and SPSS programs. In order to test hypotheses; frequency analysis, validity analysis, reliability analysis and confirmatory factor analysis were performed. Findings: As a result of the research, it was concluded that the reality and response to advertising dimensions had a statistically positive effect on the intention to purchase green products. In addition, it was concluded that the consumer attitude variable towards green product placement and the intention to purchase green products differ according to gender. Result: In order to develop a positive attitude towards green products and increase the purchasing tendency towards such products, it would be appropriate for practitioners to focus on realism in product placement strategies, avoid creating a perception of commercial advertising, and increase environmental awareness. Generalization of the subject study would be possible in future studies if comparisons of green product placements in different communication channels are taken in consideration

Climate change extreme events and exposure of local communities to water scarcity: a case study of QwaQwa in South Africa

ABSTRACT The exposure of local communities to multiple climate change-related events is on the rise across the globe. Despite extensive disaster research on climate change, exposure and vulnerability, few studies have focused on the exposure of local communities to multiple climate change-related events. For over a decade QwaQwa, in South Africa, has experienced persistent water crises which have been attributed to climate change and socio-economic drivers. However, there is a lack of understanding of the risk drivers to provide a solid basis for disaster risk reduction and resilience-building. This paper; therefore, aims to conduct analyses of hazards and exposure as part of risk analysis of climate change-related events; notably drought, flooding, wildfires, and water scarcity in QwaQwa. Quantitative and qualitative data sources included key informant interviews, household surveys, and field observations. The study employed the indicator method to analyse both the hazard and exposure. Qualitative data were anlysed using thematic analysis. On a five-point scale, the findings showed high indices of multiple hazards (3.6) and exposure (3.2). Analysis results were validated at a stakeholders’ workshop. The study recommends efficiency in implementing risk-sensitive land use plans, improving early warning systems, and the use of unmanned aerial vehicles to mitigate disaster risks.

Analysis of the Relationship between Rheumatoid Arthritis and Osteoporosis Based on Mendelian Randomization.

As the global population ages, the World Health Organization has found a yearly increase in the incidence of rheumatoid arthritis and osteoporosis. This trend poses a challenge to public health and healthcare and calls for the implementation of more preventive and treatment measures to address these health issues. This study aims to investigate the causal relationship between rheumatoid arthritis (RA) and osteoporosis (OP) using the Mendelian randomization (MR) method. OP diagnosis was based on the gold standard of bone mineral density (BMD). Single nucleotide polymorphisms (SNPs) were identified from the genome-wide association research database formed by RA and BMD, with a parameter setting of P < 5×10-8, chain imbalance r2<0.01, and kb = 10,000. Five complementary MR methods, including inverse variance weighted (IVW), MR-Egger regression, weighted median, simple mode estimation based on mode, and weighted estimation based on mode, were used to evaluate the causal relationship between RA and OP/BMD using odds ratio (OR) values and 95% confidence intervals (CI). Sensitivity analyses were performed using heterogeneity tests, horizontal pleiotropy, and individual rejection tests. A total of 78 instrumental variables were identified that were closely related to both RA and BMD in mixed populations, while 14 instrumental variables were identified in the European population and 38 instrumental variables were identified in the Asian population. Using IVW as the main analysis method, the MR analysis results of RA and BMD showed the following: mixed population OR = 0.96, 95%CI: 0.93-1.00; European population OR = 0.55, 95%CI: 0.27-1.12; and Asian population OR = 0.95, 95%CI: 0.90-1.01. Sensitivity analyses showed that the MR results were robust. The study found insufficient evidence of a causal relationship between RA and OP/BMD, suggesting that RA may not have a direct effect on OP/BMD.

Impact of Sleep Apnea on Nocturnal Parasympathetic Activity in Atrial Fibrillation Patients After Catheter Ablation - Implications for Heart Rate Variability Analysis.

The impact of sleep apnea (SA) on heart rate variability (HRV) in atrial fibrillation (AF) patients has not been investigated. Of 94 patients who underwent AF ablation between January 2021 and September 2022, 76 patients who had a nocturnal Holter electrocardiography and polysomnography conducted simultaneously were included in the analysis. A 15-min duration of HRV, as determined by an electrocardiogram during apnea and non-apnea time, were compared between patients with and without AF recurrence at 12 months' postoperatively. Patients had a mean age of 63.4±11.6 years, 14 were female, and 20 had AF recurrence at 12 months' follow-up. The root mean square of the difference between consecutive normal-to-normal intervals (RMSSD, ms) an indicator of a parasympathetic nervous system, was more highly increased in patients with AF recurrence than those without, during both apnea and non-apnea time (apnea time: 16.7±4.5 vs. 13.5±3.3, P=0.03; non-apnea time: 20.9±9.5 vs. 15.5±5.9, P<0.01). However, RMSSD during an apneic state was decreased more than that in a non-apneic state in both groups of patients with and without AF recurrence (AF recurrence group: 16.7±4.5 vs. 20.9±9.5, P<0.01; non-AF recurrence group; 13.5±3.3 vs. 15.5±5.9, P=0.03). Consequently, the effect of AF recurrence on parasympathetic activity was offset by SA. Similar trends were observed for other parasympathetic activity indices; high frequency (HF), logarithm of HF (lnHF) and the percentage of normal-to-normal intervals >50 ms (pNN50). Without considering the influence of SA, the results of nocturnal HRV analysis might be misinterpreted. Caution should be taken when using nocturnal HRV as a predictor of AF recurrence.

Model based on single-nucleotide polymorphism to discriminate aspirin resistance patients

BackgroundAspirin is widely used for preventing ischaemic events. About 20%–40% of patients have aspirin resistance (ASR), which prevents them from benefiting from aspirin medication. This study aimed to develop and...

An Updated Review on Molecular Biomarkers in Diagnosis and Therapy of Colorectal Cancer.

Colorectal cancer is one of the most common cancer types worldwide. Since colorectal cancer takes time to develop, its incidence and mortality can be treated effectively if it is detected in its early stages. As a result, non-invasive or invasive biomarkers play an essential role in the early diagnosis of colorectal cancer. Many experimental studies have been carried out to assess genetic, epigenetic, or protein markers in feces, serum, and tissue. It may be possible to find biomarkers that will help with the diagnosis of colorectal cancer by identifying the genes, RNAs, and/or proteins indicative of cancer growth. Recent advancements in the molecular subtypes of colorectal cancer, DNA methylation, microRNAs, long noncoding RNAs, exosomes, and their involvement in colorectal cancer have led to the discovery of novel biomarkers. In small-scale investigations, most biomarkers appear promising. However, large-scale clinical trials are required to validate their effectiveness before routine clinical implementation. Hence, this review focuses on small-scale investigations and results of big data analysis that may provide an overview of the biomarkers for the diagnosis, therapy, and prognosis of colorectal cancer.

Effect of demographic factors on attitudes to purchase children’s supplement products

Children's supplements are products widely offered by drug manufacturers. Their benefits and uses are essential for supporting the growth and health of children. Nevertheless, there is a lack of research in Indonesia examining the relationship between parental demographic factors and attitudes towards purchasing children’s supplements. This study employs a mixed-method approach to investigate the impact of demographic factors on attitudes toward purchasing children’s supplement products, aiming to fill the research gap. A total of 124 parents from diverse backgrounds participated as respondents, with 29.8% being male and 70.2% female. Their education levels ranged from high school (7.3%), bachelor’s degree (26.6%), master’s degree (50%), to doctoral degree (16.1%). The results of the regression analysis indicate that only income level plays a positive role in shaping attitudes towards purchasing children’s supplements. Notably, 89.5% of respondents believe in the benefits of these supplements. This study highlights the influence of income level on buying attitudes regarding children’s supplements. Future research should consider a larger sample size to better represent the population.

Bioinformatic Analysis to Identify and Cellular Experiments to Validate Autophagy-related Genes in Psoriasis.

To explore differentially expressed genes (DEGs) associated with autophagy in psoriasis using bioinformatics analysis and verify them in an M5-induced psoriatic cell model. We obtained gene expression microarray data from patients with psoriasis and normal skin tissues from the dataset GSE78097 of the NCBI Gene Expression Omnibus (GEO) database. R software was used to identify DEGs associated with autophagy in psoriasis. Proteinprotein interaction (PPI) and correlation analyses were used to show interactions between certain genes. Their potential biological roles were determined using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Finally, all the DEGs associated with autophagy in psoriasis were validated in a psoriatic cell model by RT-qPCR. 28 DEGs associated with autophagy were identified. These genes were linked to one another, and the most connected hub gene was VEGFA, according to PPI analysis. GO and KEGG enrichment analyses revealed various biological pathways associated with autophagy. The RT-qPCR findings of the expression of 18 genes in the psoriatic cell model confirmed the bioinformatics analysis results. The five genes with the most significant differences were IL24, CCL2, NAMPT, PPP1R15A, and SPHK1. We identified DEGs associated with autophagy in patients with psoriasis. IL24, CCL2, NAMPT, PPP1R15A, and SPHK1 were identified as important genes that may influence psoriasis development through the regulation of autophagy.

Infrared-Visible Synthetic Data from Game Engine for Image Fusion Improvement

Limited by the shooting scenes and angles of fixed cameras, the existing datasets generally lack many detailed pedestrian models in diverse scenarios. Existing deep learning-based image fusion methods, for this reason, bring about overfitting or insufficient information of fusion results in varying degrees. To address this challenge, A new infrared-visible pedestrian synthetic dataset (GIVF) with a synthetic data tagger (GSDT) is constructed and an improved end-to-end image fusion network (FSGAN) is proposed to validate infrared and visible fusion. In the model, the method uses an auxiliary network to extract features that complement the cascade network of the main path, effectively improving the ability to extract pedestrian texture details. Experimental results show that FSGAN can be well applied to GIVF. By conducting extensive comparative experiments with eight state-of-the-art image fusion methods. FSGAN shows better performance than those comparison methods, especially in the two evaluation indexes visual information fidelity (VIF) and structural similarity measurement (SSIM). Besides, by comparing the quantitative analysis results of various methods, and evaluating the fusion results of real images in complex environments on other three datasets, we conclude that FSGAN can be better applied to GIVF datasets than other popular methods, and has outstanding performance in generalization.