Regression Analysis Model Research Articles

Risk stratification models incorporating oxidative stress factors to predict survival and recurrence in patients with gastric cancer after radical gastrectomy: a real-world multicenter study

BackgroundOxidative stress significantly influences the development and progression of gastric cancer (GC). It remains unreported whether incorporating oxidative stress factors into nomograms can improve the predictive accuracy for survival and recurrence risk in GC patients. Methods3498 GC patients who underwent radical gastrectomy between 2009 and 2017 were enrolled and randomly divided into training cohort (TC) and internal validation cohort (IVC). Cox regression analysis model was used to evaluate six preoperative oxidative stress indicators to formulate the Systemic oxidative stress Score (SOSS). Two nomograms based on SOSS was constructed by multivariate Cox regression and validated using 322 patients from another two hospitals. ResultsA total of 3820 patients were included. The SOSS, composed of three preoperative indicators—fibrinogen, albumin, and cholesterol—was an independent prognostic factor for both overall survival (OS) and disease-free survival (DFS). The two nomograms based on SOSS showed a significantly higher AUC than the pTNM stage (OS: 0.830 vs. 0.778, DFS: 0.824 vs. 0.775, all P < 0.001) and were validated in the IVC and EVC (all P<0.001). The local recurrence rate, peritoneal recurrence rate, distant recurrence rate and multiple recurrence rate in high-risk group were significantly higher than those in low-risk group (P < 0.05). ConclusionsThe two novel nomograms based on SOSS which was a combination score of three preoperative blood indicators, demonstrated outstanding predictive abilities for both survival and recurrence in GC patients with different risk groups, which may potentially improve survival through perioperatively active intervention strategies and individualized postoperatively close surveillance.

Phase angle is a useful predicting indicator for protein-energy wasting and cardiovascular risk among maintenance hemodialysis patients.

Protein-energy wasting (PEW) is a major contributor to the high mortality among maintenance hemodialysis (MHD) patients. Cardiovascular disease (CVD) is the leading cause of death in dialysis patients, and PEW can significantly increase cardiovascular mortality in MHD patients. Previous studies have confirmed that PA may be a good objective indicator for determining the nutritional status and prognosis of MHD patients. Our study aimed to determine the predictive value of phase angle (PA) as detected by bioelectrical impedance analysis (BIA) on PEW and cardiovascular (CV) risk among MHD patients. Our retrospective observational study involved 161 adult patients with HD. The Cardiovascular risk score is a risk model based on the Japan Dialysis Outcome and Practice Patterns Study (J-DOPPS). We established LASSO logistic regression analysis model to identify key parameters related to body composition that can predict PEW in MHD patients. The area under the curve (AUC) values for PA, appendicular skeletal muscle mass index (ASMI), body cell mass (BCM), and mid-arm circumference (MAC) in predicting PEW in male MHD patients were relatively large, with 0.708, 0.674, 0.663, and 0.735, respectively. The predicted PEW values of these parameters were slightly lower in female patients than in men. We incorporated PA, ASMI, BCM, and MAC into a model that predicted the incidence of PEW in maintenance hemodialysis patients using LASSO technology. We discovered that the model predicted a greater AUC of PEW occurrence than any single factor, 0.877 for men and 0.76 for women. The results of the univariate logistic regression analysis showed that the low PA tertile array group had a greater incidence of PEW than the high PA group (P < 0.001). Additionally, we also found that lower PA was associated with higher CV risk scores. The PA detected by bioelectrical impedance analysis could predict the risk of PEW and cardiovascular events among patients with MHD. When used in conjunction, PA, ASMI, BCM, and MAC have a high diagnostic efficacy for PEW in patients on maintenance hemodialysis.

Dietary patterns and bone density among school-aged children: a cross-sectional study in China.

Diet is an essential modifiable determinant of bone health, yet the associations between dietary patterns (DPs) and bone mineral density (BMD) in Chinese children remain limited. This study aimed to investigate the relationship between overall diet and low BMD risk among school-aged children in China. A total of 1,099 children aged 9-12 in China were recruited for this cross-sectional study. A semi-quantified food frequency questionnaire (FFQ) was used to assess dietary intake. A priori numerical index, the Chinese Dietary Guidelines Index for Children and Adolescents [CDGI (2021)-C] was utilized to assess dietary quality. Specific DPs were identified by using principal components analysis (PCA). The BMD of the left forearm was assessed using dual-energy X-ray absorptiometry (DXA). Spearman correlation test was conducted to investigate the associations between DPs. Multivariate logistic regression analysis and restricted cubic spline models (RCS) were applied to explore the associations between DPs and BMD. Three distinct DPs were identified: the plant-animal balanced pattern, the grain-tuber-meat pattern, and the bean-dairy pattern. We found a weak but significant positive correlation of the CDGI (2021)-C with the plant-animal balanced pattern (R = 0.318, P < 0.001), and with the bean-dairy pattern (R = 0.266, P < 0.001), respectively. After adjusting for covariates, adherence to the CDGI (2021)-C (Q4 vs. Q1, OR = 0.45, 95% CI: 0.27-0.75), the plant-animal balanced pattern (Q4 vs. Q1, OR = 0.50, 95% CI: 0.31-0.81), and the bean-dairy pattern (Q3 vs. Q1, OR = 0.57, 95% CI: 0.33-0.96) were associated with a lower risk of low BMD. No significant association was observed between the grain-tuber-meat pattern and low BMD (Q4 vs. Q1, OR = 1.09; 95% CI: 0.90-1.31). Adherence to the CDGI (2021)-C and the plant-animal balanced pattern is advantageous for bone health and inversely correlated with the risk of low BMD among school-aged children in China. Additionally, moderate adherence to the bean-dairy pattern may also confer benefits to bone health. A balanced and overall healthy diet should be recommended in our daily life.

The impact of organizational support for human resources in enhancing sustainable competitive advantage

The current study aims to examine the effects of organizational support for human resources and their ability to enhance sustainable competitive advantage. The study also seeks to reveal the level of organizational support and sustainable competitive advantage for the sample studied in private colleges, the research sample. The importance of the current study is derived from the importance of the subject of the current study with its studied variables, and the reflection of their effects on sustainable competitive advantage. The problem of the study was raised through some questions, the most important of which was (to what extent is organizational support available to human resources and to what extent are they able to achieve sustainable competitive advantage?) The current study adopted the descriptive analytical approach as it is the optimal approach to survey the opinions of the sample. The questionnaire was the main tool for collecting data in addition to personal interviews and field visits. A random sample was selected and (160) questionnaires were distributed. (154) questionnaires were collected, of which (137) were valid. Some main and sub-hypotheses were developed, the most important of which was (there is a statistically significant correlation and influence between organizational support on the other hand and sustainable competitive advantage). The results were extracted By using some statistical techniques including correlation analysis, regression analysis and structural modeling to test hypotheses through the statistical program ((SPSS V. 28 to analyze the answers. The study reached a set of conclusions, the most important of which is that the impact of organizational support on sustainable competitive advantage, and when employees feel that they are supported by their organization, their sense of satisfaction and happiness at work is enhanced. This can lead to an increase in performance and productivity, which enhances the competitive advantage of the organization in a sustainable manner.

Liquidity Management Practices and Financial Performance of Microfinance Banks in Nairobi City County, Kenya

Microfinance banks are financial institutions that provide loans, insurance, saving platforms and other financial products to various individuals. Thus, these institutions require sufficient liquid assets to enable them to meet their routine financial obligations. However, in Kenya, microfinance banks have been recording a declining trend regarding their financial performance over time, leading to grave concerns. Thus, this study aimed to assess cash management practices and their effect on Nairobi City County’s MFB's financial performance. The analysis and elucidation of the literature was facilitated by the examination of the Keynesian Liquidity theory. A descriptive research design with a regression analysis model was embraced in determining the predictor variable’s impact on the explained variable. Data review guide was then utilized to gather secondary data, which was obtained from the CBK and covered the years 2017 to 2022. According to the study, operational cash flow management and cash management significantly and favorably impacted the financial performance of Kenyan microfinance institutions. Nonetheless, the financial performance of Kenya's microfinance institutions was significantly impacted negatively by operational efficiency management. As per the study's findings, microfinance banks in Kenya carry out cash budget preparation, bank reconciliation, cash inflow and outflow recording, cash expense payment, and cash surplus investment. As a result, they manage their funds carefully. Furthermore, the stability and functioning of the financial system depend heavily on efficient cash flow management. It entails keeping an eye on money coming in from investments, sales, and outside funding sources as well as tracking money going out to pay for bills, obligations, and loan repayments. Microfinance banks are able to balance inflows and outflows, lower the risk of liquidity crises, and take advantage of growth possibilities by managing cash flow well. This allows microfinance institutions to make well-informed choices regarding debt management, expansion strategies, and investments—all of which are essential for overall financial success. To maximize the beneficial effects on financial performance, the study recommends adhering to the statutory minimum liquidity ratio. Since excessive liquidity and illiquidity reduce the institutions' profitability, microfinance banks should implement effective cash management procedures.

Economic Strain and Recovery Trajectories in Mental Health: The Role of Financial Stability in Mental Health Outcomes

This study explores the intricate relationship between financial stability and recovery from mental health, and clarifies how economic security constitutes an essential feature that enhances mental health results. The study has used SPSS in quantitative analysis for regression analysis, ANOVA, cluster analysis, and structural equation modeling to explore the determinants of influential predictors and patterns in linking financial stability with mental health recovery. The regression outcome showed that financial stability substantially predicts recovery (? = 0.52, p &lt; 0.01), and ANOVA indicated large differences in the scores of recoveries among the financial groups (F = 8.15, p &lt; 0.05). Cluster analysis also indicated a difference such that the average score of recovery for high-stability participants was 8.5, while for low-stability participants it was 4.1. Thus, SEM shows both direct and indirect effects as it manifests that financial stability indeed reduces stress and encourages treatment adherence. The study concludes that economic security indeed plays a multifaceted role ranging from the access of mental healthcare through income support programs to resilience and overall well-being through the promotion of living a financial stability life. Recommendations should include income support programs, financial literacy programs, and integrated financial and mental health support services to form sustainable recovery. Further longitudinal researches, with larger and more diverse samples, should be conducted to study the long-run impact of financial stability on mental health trajectories and tailor policy interventions accordingly.

Bibliometric Analysis of Spline Regression Model for Trend Mapping and Strategy Development Research Using Vosviewer

Bibliometric analysis is a quantitative method used to measure and analyze scientific literature. This technique involves the collection and analysis of publication data, such as journal articles, books, and conferences, to evaluate and understand patterns of scientific communication, research productivity, author collaboration, journal impact, and trends within a scientific field. Bibliometric analysis has a limitation in that it is challenging to visualize effectively, necessitating the use of software for proper visualization. Therefore, this article discusses bibliometric analysis on spline regression for trend mapping and strategy development using VOSviewer software. The research results show that the visualization of spline regression keywords with VOSviewer can help in understanding patterns of relationships between variables, research trends, and network structures in scientific literature. Based on the analysis results, bibliometric analysis on spline regression can be visualized in trend mapping, which can aid in planning further research strategies, including identifying collaboration opportunities and underexplored research areas.

Health-related quality of life and complications of corticosteroid treatment in patients with immune thrombocytopenia in two teaching hospitals in Ethiopia: a cross-sectional study

BackgroundThe treatment of immune thrombocytopenia (ITP) is a major clinical challenge and has a significant impact on health-related quality of life (HRQoL), and prolonged use of corticosteroids may have a negative impact on HRQoL.ObjectivesTo evaluate the impact of ITP on HRQoL and complications of corticosteroid treatment in patients with ITP in two teaching hospitals in Ethiopia.MethodsThe institution-based cross-sectional study was conducted from November 15, 2022, to March 15, 2023, to recruit 214 study participants during the study period (4 months). The ITP Life Quality Index (ILQI) in the Amharic version was used to assess the impact of ITP. Linear regression analysis models were also used, and a p-value of less than 0.05 was generally considered statistically significant.ResultsRegarding treatment, the majority 172 (80.4%) of study participants were taking prednisolone only, and 143 (66.8%) of study participants had at least one side effect of corticosteroids during the entire treatment period. Predictive factors for a higher impact of ITP on HRQoL; all correlated variables explained 36.5% (adjusted R-squared = 0.365, p &amp;lt; 0.0001) of the variance and had a moderate impact on HRQoL. Furthermore, predictive factors for an increasingly higher impact of ITP on HRQoL were the development of emotionally related corticosteroid side effects (β = 0.392, 95% CI: 5.160–9.961, p &amp;lt; 0.001), the presence of fatigue during the assessment (β = 0.326, 95% CI: 4.394–9.475, p &amp;lt; 0.001), patients not taking cotrimoxazole prophylaxis treatment (β = 0.236, 95% CI: 2.236–6.570, p &amp;lt; 0.001), living far from the hematology clinic (outside Addis Ababa) (β = 0166, 95% CI: 1.107–5.114 p = 0.003), having epistaxis and/or wet purpura (mucosal bleeding) (β = 0.191, 95% CI: 0.091–4.259, p = 0.001), and skin symptoms (petechiae and ecchymosis) (β = 0.041, 95% CI: 0.091–4.259 p = 0.041) during diagnosis.ConclusionThe impact of ITP on their energy levels and work life was high compared to the impact of ITP on daily life. The side effects of corticosteroids also affect the HRQoL of ITP patients.

Critical risk factors analysis of infection-associated hemophagocytic lymphohistiocytosis in children.

To analyze the critical factors of infection-associated Hemophagocytic lymphohistiocytosis (HLH) in children, so as to provide theoretical basis for clinicians to evaluate the disease condition, formulate treatment plan and improve prognosis.This study is a retrospective analysis. 60 cases of children with infection-associated HLH were divided into critical and non-critical groups based on the presence of multiple organ dysfunction syndrome (MODS), and the clinical characteristics and laboratory data of the two groups of children were analyzed. A multifactor logistic regression analysis model was used to assess the independent risk factors affecting critical illness in children with infection-associated HLH, and the Receiver Operating Characteristic (ROC) curve analysis was used to evaluate the predictive value of risk factors for critical illness in children with infection-associated HLH. Children in the critical group with HLH had a younger age at onset. Alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), gamma-glutamyl transferase (GGT), D-dimer (DD), and triglycerides (TG) were significantly higher in the critical group, while albumin (ALB) was significantly lower, showing statistical significance (P < 0.05). Multifactorial logistic regression analysis of age, ALB, and TG showed that younger age and lower ALB were associated with a higher risk of MODS in children with infection-associated HLH, with age and ALB being independent risk factors for critical illness. ALB predicted the ROC area under the curve for critical children with infection-associated HLH was 0.765 (95% CI: 0.643-0.888, P = 0.011), with the optimal cut-off value being 32.50g/L (sensitivity = 68.3%, specificity = 84.2%); age predicted the ROC area under the curve for critical children with infection-associated HLH was 0.711 (95% CI: 0.570-0.851, P = 0.009), with the optimal cut-off value being 1.50 years (sensitivity = 70.7%, specificity = 68.4%). This study suggests that younger patients and those with hypoalbuminemia among infection-related HLH patients are more likely to develop MODS. In the future, verification will be required through large-scale, multi-center studies.

Mediation effects of subjective social status and personality traits between childhood nurturing experiences and depressive symptoms in adult volunteers.

Various risk factors, such as childhood nurturing experiences and subjective social status, have been identified to be involved in the onset of depression. However, the mechanism of depression is not yet fully understood. In this study, we hypothesized that nurturing experienced in childhood affects subjective social status and current personality traits, which in turn influence depressive symptoms in adulthood, and verified this hypothesis through structural equation modeling. A questionnaire survey was conducted on 404 adults. Multiple regression analysis and structural equation modeling were conducted using demographic information and scores for the Patient Health Questionnaire-9, Parental Bonding Instrument, and NEO Five-Factor Inventory. Subjective social status was found to mediate the association between nurturing experiences and neuroticism (0.029 for Overprotection and -0.034 for Care). On the other hand, neuroticism was found to mediate the association between subjective social status and depressive symptoms (-0.097 in Care model and -0.103 in Overprotection model), as well as the association between nurturing experiences and depressive symptoms (0.144 for Overprotection and -0.134 for Care). Furthermore, it was also shown that complex paths, in which the association of nurturing experiences with depressive symptoms was mediated by two factors, namely, subjective social status and neuroticism, were statistically significant as indirect effects (0.016 for Overprotection and -0.018 for Care). In this study, we clarified that nurturing experienced in childhood affects neuroticism in adulthood, which is mediated by subjective social status, and furthermore, the effects of nurturing on neuroticism lead to varying levels of depressive symptoms in adulthood. The mediation effects demonstrated in the present study may contribute towards unraveling the causes of depression and developing effective treatments for depressive symptoms.

Pengaruh Strategi Pemasaran dan Inovasi Produk Terhadap Keputusan Pembelian pada Furniture Hj Vera di Kota Karawang

This is a The research aims to determine marketing strategies and product innovation on purchasing decisions for Hj Vera Furniture in Karawang City. The method used in this research is a quantitative method, this research was conducted on Hj Vera Furniture in Karawang City with a total research sample of 96 people with the criteria of consumers who had previously purchased Hj Vera Furniture in Karawang City, using a purpisive sampling method. The multiple linear regression analysis model used in this research uses the f test and t test with a significance threshold of 5%. The findings show that there is a partial contribution between marketing strategies and purchasing decisions at Hj Vera Furniture in Karawang City. The research results obtained are the influence of marketing strategy and product innovation on purchasing decisions at HJ Vera Furniture in Karawang City, there is an influence from all variables.

Assessing the state of cognitive performance of employees and determining the range of thermal comfort of different genders in Ilam hospitals

Introduction: Thermal comfort is essential for a productive work environment and affects the well-being and performance of employees, so maintaining optimal levels of satisfaction, productivity, and efficiency, especially in indoor spaces, is essential. Therefore, the present study's aim is to evaluate the state of cognitive performance. Employees and determination of the thermal comfort range of different genders of employees in the hospitals of Ilam city were done. Methods and Materials: This research was conducted by survey and field method to identify the state of thermal comfort and cognitive performance of people in the real conditions of therapeutic spaces. For this purpose, the responses of 400 employees of five hospitals in Ilam City were recorded by simple sampling from the beginning of August to November 1402. The reliability status of the questionnaires was confirmed using Cronbach's alpha, and the thermal comfort status of the people was determined through the regression model. Linearity and correlation analysis between measured variables were analyzed through SPSS27 software. Results: The analysis of the linear regression model showed that the range of thermal comfort between men and women differs by 1.79 degrees Celsius, and this difference is a tendency for warmer temperatures in men and a tendency for colder temperatures in women, the average scores obtained at Zagros Hospital In the visual-spatial/executive component (7.00), attention (4.34), memory (3.08) has the best performance, Imam Khomeini Hospital (RA) has the best performance in the verbal component (2.95), Hazrat Qaim Hospital (A) In the abstraction component (2.02), it has the best performance and awareness of time and place in all hospitals equal to 6.00. Conclusion: The level of coverage and activity rate among women significantly affects the feeling of thermal comfort, cognitive performance, and productivity of employees at work. The results of this research show the need to revise the current standards of workplace clothing, attendance time, and employee activity rate. Moreover, changes in cognitive performance among hospital staff highlight the need to consider targeted cognitive enhancement strategies to increase staff efficiency.

Development of Particulate Matter Concentration Estimation Models for Road Sections Based on Micro-Data

With increasing global concerns related to global warming, air pollution, and environmental health, South Korea is actively implementing various particulate matter (PM) reduction policies to improve air quality. Accurate data analysis, including the investigation of weather phenomena, monitoring, and integrated prediction, is essential for effective PM reduction. However, the factors influencing the PM generated from domestic road sections have not yet been systematically analyzed, and currently, no predictive models utilize weather and traffic data. This study analyzed the correlations among factors influencing PM to develop models for estimating fine and coarse PM (PM2.5 and PM10, respectively) concentrations in road sections. Regression analysis models were used to assess the sensitivity of PM2.5 and PM10 concentrations to the traffic volume, whereas machine learning-based models, including linear regression, convolutional neural networks, and random forest models, were constructed and compared. The random forest models outperformed the other models, with coefficients of determination of 0.74 and 0.71 and mean absolute errors of 5.78 and 9.60 for PM2.5 and PM10, respectively. These results indicate that the random forest model provides the most accurate PM concentration estimates for road sections. The practical applications of the developed models were considered to inform effective transportation policies aimed at reducing PM. The developed model has practical applications in the formulation of transportation policies aimed at reducing PM. In particular, the model will play an important role in data-driven policymaking for sustainable urban development and environmental protection. By analyzing the correlation between traffic volume and weather conditions, policymakers can formulate more effective and sustainable strategies for reducing air pollution.

Weight Loss and Number of Present Teeth in Community-Dwelling Japanese Older Adults: A Cross-Sectional Study in Niigata.

Weight loss is a critical health issue among the older population. This study aimed to explore the association between weight loss and oral health in older adults. We used cross-sectional data from the Uonuma Chronic Kidney Disease Cohort Study. The binary logistic regression models were fitted to estimate the relationship between weight loss and independent variables including the number of teeth present. The participants included 1656 females and 1625 males. There were 1217 females (73.4%) and 1236 males (76.0%) with normal BMI (18.5 ≤ BMI < 25) in the previous year. Weight loss was determined over a 12-month period. Individuals with normal BMI in previous year, and those with weight loss (≥ 5%) or those whom maintained a stable weight were further analysed. The regression analysis (model 3, fully adjusted) showed that females with 0 and 1-10 teeth present had a higher likelihood of losing weight (OR = 4.07; 95% CI = 1.85-8.93, OR = 2.33; 95% CI = 1.24-4.39) compared with those with 20 or more teeth. Current smokers had a higher likelihood of losing weight (OR = 3.15; 95% CI = 1.01-9.76) than lifelong abstainers. Among males, both current and previous smokers showed a higher likelihood of losing weight (OR = 4.94; 95%CI = 1.76-13.8, OR = 3.35; 95% CI = 1.29-8.68) than those who never smoked. This study indicates that the number of teeth and smoking are both linked to weight loss in females. However, in males, the association between the number of teeth and weight loss may be compromised due to the high prevalence of smoking in this group.

Mediating Effect of Physical Activity on The Relationship between High Dietary Live Microbe Intake and Obesity among U.S Adolescents, Finding from NHANES 1999-2018

Background and AimsThe impact of dietary live microbe intake on adolescent obesity is still not fully understood. This study aims to investigate the potential relationship between dietary live microbe intake and obesity among U.S. adolescents, and to explore the mediating role of physical activity (PA). Methods and ResultsData from NHANES (1999-2018) were analyzed, and dietary live microbe intake was categorized into low, medium, and high groups using a developed framework. Survey-weighted logistic regression and mediation analysis models were used to examine the association between live microbe intake and adolescent obesity, as well as the potential mediating effect of PA. Our study included 8,443 participants aged 6-18, representing the noninstitutionalized U.S population of 184.5 million. We found that participants with a high dietary intake of live microbes had lower odds of developing obesity compared to those with the lowest exposure to live microbes (AOR = 0.900, 95% CI: 0.812, 0.997). Additionally, our mediation analysis revealed a significant indirect effect of live microbes on obesity risk through physical activity (P-value < 0.001), with 39.4% (95% CI: 24.5%, 86.5%) of the effect mediated by PA. ConclusionOur study highlights the association between consuming a higher amount of live microbes in the diet and a decreased risk of obesity among U.S adolescents. It also suggests that PA may act as a mediator in this relationship. Therefore, it is crucial to emphasize the incorporation of both dietary interventions and PA in the development of prevention and therapy policies for managing adolescent obesity.

Analyzing User Reviews on Digital Detox Apps: A Text Mining and Sentiment Analysis Approach

ABSTRACTDue to the growing concerns around problematic smartphone use and its negative impact, there is a rising interest in digital detox. While many digital detox apps have been developed in recent years, there is still limited understanding of the long‐term effectiveness of digital detox applications and the attitude of people towards these apps. This study fills this gap by identifying the topics that people post in their reviews on the Google Play Store about digital detox apps and the emotion‐based sentiment of those reviews. A total of 3500 reviews of 25 digital detox apps were collected from the Google Play Store using a scraping tool called “Parsehub.” Data was analyzed using R studio. Sentiment analysis results suggest that positive sentiments dominated the data frame. “Trust” and “anticipation” were the two most expressed emotions in the reviews. Regression analysis confirmed that sentiment scores could explain the ratings of the apps. Through LDA topic modeling four major topics of the reviews were identified and are discussed in detail in the later section of the research paper. The findings of this study may help app developers and marketers improve digital detox apps so that people can learn and practice mindful smartphone use with the help of these apps. This study fills a gap in digital detox research by adopting a new methodological approach and procedure since it combines text mining, sentiment analysis (NRC Lexicon using Syuzhet package), regression analysis, and LDA topic modeling. To the best of our knowledge, this is the first study which uses this research approach in the context of digital detox apps.

Cumulative Socioeconomic Status Risk is Associated with Greater Increase in Serum Neurofilament Light Chain Levels Among Middle-Aged Black Adults.

This study examined the longitudinal relationship between cumulative socioeconomic status (SES) risk and serum neurofilament light chain (NfL) levels to better understand the association between social factors and a biomarker of neurodegeneration. We used data from the Family and Community Health Study (FACHS), collecting psychosocial and blood data at two waves (2008) and (2019) from 254 Black Americans (43 males and 211 females). Blood samples were analyzed at each wave for serum NfL concentrations. Regression analysis and mixed-effect modeling examined relationships between cumulative SES risk and serum NfL, controlling for covariates and assessing time effects. Utilizing 11-year longitudinal data, serum NfL levels increased with age. Higher cumulative SES risk at baseline correlated with elevated serum NfL at the 11-year follow-up and predicted a greater increase in NfL levels. Clinically, NfL is a sensitive biomarker for axonal injury and neurodegeneration, commonly used to detect early and preclinical stages of conditions such as Alzheimer's disease (AD), multiple sclerosis, and other neurodegenerative disorders. Our results suggest that exposure to cumulative SES risk among Black adults may contribute to elevated levels of NfL, indicating potential early neurodegeneration. Given the established role of NfL in detecting neurodegenerative processes, these findings underscore the importance of interventions that bolster social safety nets and social connectedness to enhance brain health and mitigate neurodegenerative risks.

The Comprehensive Investigation for Covid-19 Trend Prediction Through Machine Learning and Deep Learning

Corona Virus Disease (Covid-19) has surely been a challenging problem to solve for the past few years. Due to the diversity in the form of dataset, it is essential to obtain accurate predictive results of Covid-19 trends. This paper analyzes different artificial intelligence methods used in Covid-19 trend prediction, including several machine learning and deep learning methods. More specifically, this work investigates linear regression, random forest, and decision trees in terms of machine learning and delves into Artificial Neural Network (ANN) as well as Long Short-Term Memory (LSTM) for deep learning. By comparing various past works, the effectiveness of machine learning and deep learning methods is achieved by their hidden algorithms, such as the Multiple Linear Regression (MLR) model for linear regression analysis. Incorporation with other models or methods is applied in deep learning. For example, Ensemble Empirical Mode Decomposition (EEMD) is included in ANN structure to decrease the noises within the Covid-19 datasets. Furthermore, the paper also inquiries into potential improvement of some drawbacks in predictive results for Covid-19 trends by reviewing related works of expert system and transfer learning as well as domain adaptation. The machine learning and deep learning models could provide accurate predictive results as a reference for related organizations to consider or establish insightful policies.

Enhancing Sensing Performance of Capacitive Sensors Using Kirigami Structures.

Capacitive sensors have widespread applications in human-machine interaction, Internet of Things, and smart home systems due to their low cost, high sensitivity, and ease of integration. However, improving the sensitivity and sensing distance of capacitive sensors remains a challenging issue. This study proposes a novel capacitive sensor design method based on Kirigami structures, which enhances sensor performance by introducing specific cutting patterns into the conductive layer to leverage edge effects. Through experimental testing and statistical analysis, we systematically investigated the influence of Kirigami geometric parameters on sensor sensitivity and sensing distance. We designed and fabricated 12 different Kirigami structures, including circular flower patterns, array patterns, layered pointed flower patterns, and circular strip structures, and compared them with traditional non-cut structures. The results show that Kirigami structures significantly improved sensor performance. Compared to traditional sensors without Kirigami structures, optimally designed Kirigami capacitive sensors demonstrated approximately a 3-fold increase in sensitivity and up to 170 percent extension in sensing distance. Multivariate regression analysis and nonlinear models revealed complex relationships between Kirigami structural parameters and sensor performance. Notably, the circular strip (three-layer) structure exhibited the best performance, possibly due to its maximization of edge effects and optimization of electric field distribution. This study provides new design insights for developing high-performance capacitive sensors, with potential applications in improving smart home systems and indoor activity monitoring for solitary elderly individuals.

Vaccination significantly reduced the length of SARS-CoV-2 viral clearance: A story from international healthcare workers.

To investigate the impact of vaccines on sociodemographic characteristics, clinical profiles, and outcomes of SARS-CoV-2 infection among healthcare workers in South China during the period of Omicron variant dominance, a retrospective, analytical cross-sectional study was conducted. The findings revealed that while full vaccination could not prevent Omicron variant infection efficiently (26.51% uninfected vs 14.29% uninfected between vaccinated and unvaccinated participants, p = 0.506), it did substantially reduce the length of viral clearance significantly (p < 0.05), potentially facilitating quicker patient recovery. Unvaccination was found to be an independent risk factor for slow clearance when a linear regression analysis model was used (Coefficient: -3.516; 95% CI: -6.425 to -0.607; p = 0.020). Therefore, all eligible individuals should be fully vaccinated to get prepared for a potential wave of epidemic in the future.